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390 「假肉驱逐真肉」的背后：牛肉产业全球化挤压下的游牧饮食

2025-03-07 08:00:01

390 「假肉驱逐真肉」的背后：牛肉产业全球化挤压下的游牧饮食

然后希望他帮我引荐两位作者。今天终于见到了魏宜然和普华新日部。

大家好，我是魏宜然。我是这篇文章的作者之一，我现在是北京大学环境管理系的博士三年级的博士生。

大家好，我也是这篇文章的作者之一，叫普华新日部。我是一名藏族，现在是在北京大学环境管理系做博士二年级。普华新日部在这篇文章里面的署名叫奇连山牧人，可能很多人也听说过。

爱德华·洛伦斯的所谓蝴蝶效应，亚马逊的一只蝴蝶山洞翅膀会给美国德克萨斯州带来一场龙卷风。其实我觉得你们的文章有一点这种感觉，就是有点反常识的一种意味。假肉驱逐真肉，这篇文章讲的其实就是大量的巴西牛肉的出口，导致了我们国家像青藏牧区的这些牧民吃不到草原上原产的这些所谓的真肉。

所以你们用了一个戏谑的说法，叫假肉驱逐真肉。当然这个假肉是要打引号的，拉美过来的牛肉其实还是牛肉，对吧？就只不过不是我们这边原产的。但我觉得它背后反映的是在这个内陆亚洲当代的一些生产方式、生活方式受到全球化的一些冲击。这是我长期待在上海，可能不太能够接触到的一种视角，所以我看了之后觉得非常新奇。

尤其包括新日部，你自己又是藏族家庭当中成长起来的，你肯定会结合更多自己的一些更有体感的经历。所以今天请到两位来聊一聊这个话题。

刚刚两位都介绍了，你们在北京大学的环境管理系在读博士。我们之前请到过北大的研究环境史的一些老师，侯深老师。我想学科和学科之间其实差别还挺大的。你们环境管理系，尤其你们两位研究的方向，能向我们听众大致介绍一下吗？

我来大概介绍一下，我和西日部是同一个课题组的。我们课题组主要关注的就是中国的草地生态系统上所生活的这些牧民，他们的这些生产生活的方式，以及他们跟草原之间的互动关系。他们的生产生活其实都会对草地的生态系统产生一定的影响，而草地又是我们国家非常重要的一种生态资源。所以说我们整个课题组都比较关注中国草原上的牧民生计和生态的状况。基于这篇文章，也是关注青藏高原的牧民，也是我们课题组在草原上调研的时候发现的一个现象，然后从这个现象引发了这篇文章的产生。

西日部，你有什么补充的吗？

您刚才讲的侯深老师，他是做环境史的，而我们做环境管理有什么差别呢？我们学院叫环境科学与工程学院，这个我们学院是一个偏理工科的学院，跟环境史的方向还是差距挺大的。但是我们课题组本身做的跟学院的整体研究还是有一些差异。我们从研究方向上看，是更关注于牧区的草场以及跟草场相关的这些牧民的生计，包括草场的生态。但整体上是在制度和政策背景下去做这个研究，与我们学院本身偏向大数据的定量的方法是有一些差异的。我们老师更多追求用一些社会科学的方法，结合更多的人类学、社会学的方法，做一些跨学科的研究。所以我们实际上是在各个学科之间。

两位刚都讲到你们研究中国牧区的一些现实问题，其实我想包括我们大部分听众在内，很多人还是生活在一些内地的城市当中。牧民社会对于中国的总人口来说，它只占其中的一部分。两位可以来稍微介绍一下，虽然你们研究的是青藏地区的牧民社会，但从整个中国大的跨度来讲，你们研究的这个人群或者范围大概可以分成哪一些研究对象？

对，我们课题组早先的研究都是在内蒙，然后到14、15年的时候，我们课题组关注的研究区域吧，从内蒙扩展到青藏牧区。后面渐渐地在青藏高原牧区做的就更多了。然后到我们现在在校的这些课题组的成员来说，基本上都是在青藏高原做研究。我们早先的也有老师的学生是来自新疆的，也有些案例是在新疆，北疆的那些。

对，其实就是内蒙草原、新疆草原和青藏高原的草原类型是不太一样的。像内蒙的草原因为内蒙比较长，草原设计的精度非常广，那个东蒙到西蒙坐飞机了好几个小时。然后它的草地类型是比较多样的，可能最东边的是像乌伦贝尔，那一块的草场是比较茂盛的，水土比较好。

然后当地的草原也是比较茂盛的那种，它放牧的品种会有什么区别吗？

会的，就是不同地区的牧民，它们要根据当地草地的具体水土状况，来选择比较合适的品种。比如说像乌伦贝尔那边，可能养殖的羊跟内蒙古中段、西林郭勒蒙那一块的苏尼特羊，它们的适应草地就不一样，然后苏尼特羊可能更适应干旱一些的，比较贫瘠一些的草地。乌伦贝尔那边就是更加茂盛、长得更加健硕的羊的品种。

我自己从一个很喜欢美食的人的角度，比如说到那个中段以及到西内蒙那块，包括宁夏不是所谓滩羊吗？这是不是属于你刚说的那种？

对，滩羊也是另外一种类型，它跟草原上生长的羊还是不太一样。对对对，滩羊是比较偏荒漠。我感觉它是生活得更干旱一点，但身上的羊膻味好像就更少一点。我听说滩羊肉好吃是因为滩羊吃的盐比较多。

盐分摄入。

对对对，但我的印象当中，乌伦贝尔，包括整个东北地区的内蒙的羊肉，似乎偏膻一点。我不知道这跟草地有没有关系。其实在我的认知里边，羊肉本身是不膻的。我之前有一个前同事，我们有一次吃饭，说要去吃个羊肉，他说我从来不吃羊肉，为什么？他说羊肉太膻了。我说不啊，不是所有的羊肉都是有膻味的。然后我们就带他去吃了一个内蒙的羊肉，他是北京的，他跟我们讲说他第一次吃到没有膻味的羊肉。

所以我觉得在这种草地上生长的这种自然羊的羊肉本身是没有膻味的，除非它是山羊是本身有的，就是绵羊。除了种共羊之外，如果不是什么这种集中羊之，这种的话是没有膻味的。

因为像南方华东华南，很多吃的是那种山羊肉，所以只能有红烧做法，添加各种大料把它膻味给盖掉。

对对对，所以我们汉语的语境里边，山羊和羊都归类为羊，但其实在藏语里边，羊是绵羊，山羊是另外一个名词，就是完全是两个种类。所以下面有一个误解，大家可能吃到山羊肉，也会认为这是羊肉，但在藏语和蒙语的语境里边，你吃山羊肉就不是吃了羊肉。

因为最近几年我看到，包括李娟的那个《我的阿勒泰》的流行，很多现在去北疆旅游的人也很多，我感觉新疆作为一个草场，当然更早一点，很多人过去读梁宇生的《武家小说》什么的，其实都是有概念的。

所以对于中国的这几个大的牧区，可能有一个基础的概念。然后具体到，比如说你们在这篇文章里面写到了青藏地区的牧民社会，这个涉及到普华新日部你自己的成长经历。你的署名是祁连山牧人，想请你多讲一讲，就是你生长的那个区域是在青海那块吗？

对，我老家是青海祁连。我说的祁连是一个县城，属于青海省海北藏族自治州。但是大家知道有一个山脉叫祁连山脉，这个比祁连县要有名，而且很大。然后祁连山牧人这个公众号是在2020年，刚好是疫情那年。我当时是研究生二年级，在北京中安民族大学读这个研究生。但疫情后，我们在北京这边有分控，不让回学校。但实际上在青海省内没有这个限制的。

所以我当时在我们老家，因为一年级的研究生的课都上完了，需要做一些田野，一个是为研究生的论文做准备，另外是刚好有这个机会和时间，我就在我们县城召集了一些对此感兴趣的朋友，我们一起做了这个公众号。之后之所以用祁连山牧人这样一个概念，大家都对祁连山有一个印象，但可能不知道祁连山周围是有牧民的，或者是有草场的。

而且祁连山其实是整个青藏高原的东北边缘的山脉。但大家一说青藏高原，就觉得青藏山脉不是青藏高原的，有这样一个错觉。而且我们藏族人传统的地理划分上，会分为三大藏区：以拉萨为中心的区域叫卫藏；另外一个，现在的青海省，除了玉树州之外的全部加上阿坝州、甘南州，还有天柱县，这样的区域构成的区域叫安多。

然后另外一个叫康巴，所以这个安多两个字，我们藏语的解释是从阿钦冉杰到多拉朗末。多拉朗末是藏语的祁连山脉的意思，阿钦冉杰就是可可西里，从可可西里到祁连山脉的区域是安多区域。所以下面有这个概念。

对，可能很多人对祁连山的想象还是那种在历史课本里面学到的那种什么汉朝时代的童谣，但那是跟蒙古草原的匈奴民族挂钩在一起的，使我祁连山，使我六处不翻溪。这种反倒是它作为青藏高原的整个东北屏障的这种概念，就不是特别深入人心。

你刚说到这个藏区的分布，我多问一句。因为我前段时间正在看我朋友黄渤写了两本书，一本是《谷歌王朝史》，另一本讲谷歌王朝覆灭的书，里面都提到这个所谓的阿里地区，这个谷歌王朝是属于卫藏的一部分吗？

其实你讲到谷歌，讲到阿里的话，藏语会说就是上部三围。我们像说北京有三环一样，三个圈。就是藏族传统的地理方位上，一般不会说这个东西南北，一般都是上中下这样子。所以上部是阿里，然后中部以卫藏为中心的有四儒的这样的说法。

就是四儒又是另外一个地理概念，然后下才是我们刚刚讲到的多康。就是说下部才是安多和康巴的这个区域，川藏为区域的，包括西藏的昌都，这些都是康区。在这个概念里边，阿里是属于最上面，其实也是我们用这个东南西北来分的，或者是最西边。

整体上我们认为拉萨是整个金藏高原，或者是藏区的一个中心。所以以这个为中心的这个方位上面，一般把西边的会称为上，然后再往这个东边的会称为下。

踏出去啊，这说远了，回到我们今天要谈论的这个假肉驱逐真肉这个话题。我来问问宜然啊，就是西日部讲了他自己本身是作为这样的一个牧民社会当中成长起来的孩子，那他对这个可能有天然的关注，你对中国的这个牧民社会或者牧区的一个研究你有什么自己的这些兴趣的着力点吗？

是，我最开始关注青藏高原的牧民社会也是在2020年的时候，当时正好疫情刚爆发嘛。我去到了一个藏区的NGO做志愿者，然后在那边跟当地的牧民，还有其他的朋友们一起生活了两个月。在这两个月的生活中就结识了挺多这个藏族朋友，藏族的人情社会或者社会关系跟我自己所处的汉族的社区关系是特别不一样的。

冒冒问你是在哪长大的？

我是山东资博人。对，跟我们的传统的儒家文化是真的特别不一样的。所以说在当地了解藏族的这个传统的文化和社会生活，是个特别有意思的过程。我回到家乡之后，看到有当地认识的朋友转发了公众号的文章，说我做志愿者的那个地方，发现了有冷库里的阿根廷牛肉上面，有核酸阳性的病毒。

所以说当地立刻就被封控了，针对牧民来说，其实是特别不适应的。因为牧民的生活其实就是我骑着摩托车，或者骑着马，我想去哪里就去哪里了。我从草原到县城，从这个地方到那个地方，是特别普遍的，不是说我们今天规划好要怎么怎么样。所以说当封控之后，牧民的生活一下子就变得像被关起来了一样，大家都其实特别不适应。

不过我当时看到那个消息是特别震惊的，因为我在想，我去的地方是甘南州的一个县城，当地是一个纯木野县，没有任何的农田或者是什么其他的生态系统，就是纯草原。而且大家都是以放牧为生的，但那里居然有冷库，冷库里居然有阿根廷牛肉，还是在牛肉的外包装上发现的核酸阳性病毒，就让我非常震惊。

然后从这个事之后，我就开始关注进口牛肉在青藏高原的渗入。听起来是非常反直觉的一件事，就是在大量产肉的地区，居然你平时食用的肉可能是来自于万里以外，就来自于南美洲。这个背后是全球化的现象，就是你们两位决定来做这样的一个调研的契机，是同时进行的吗，还是各自有各自的一些渠道都接触到同样的这些新闻？

我们课题组整体上是关注牧区，然后我们基本上每年的暑假也会在不同的牧区县城做调研。这个事刚才依然讲到，可能疫情的时候就已经听说了，但进口肉到牧区的渗入的这个程度，没有意识到这么大。你自己之前有意识到过吗？

我也没有。依然讲的疫情那个消息，当时我也看到了，也很震惊。然后后面我寒假回家，问我高中的同学，我们县有没有这样的现象，就有朋友跟我说，确实有。就是有牧民在买进口肉。但是祁连县当时有一些限制，说明不让进口肉明摆的去卖，大家想卖进口肉也只能在私底下卖。

所以整体上量不是很大，所以我以为整个藏区都可能差不多。但是后面我们去年24年的7月份在青海的泽库县做调研的时候，当时是做另外一个主题的调研，但发现我们调研的那个村子里面的牧民，买进口肉很普遍。当时我们看到我们那个小镇上面有肉铺都是卖进口肉的，远远超出了我们预想的那个程度。

就觉得这个很有必要去写一写这个问题。然后宜然跟我提，我说好啊，我也觉得这个很有意思，后面宜然就看到石通社他们在招这个项目，他们也是关注食品安全啊这种嘛。

但你们这个项目拉出一个标题，确实就已经非常吸引人了。我其实不太知道你们在接触到这样的一个问题现象之后，你们去了解当地的为什么会出现这样的现象，能向我们的听众先来简单的讲一下这个现象本身背后的一个逻辑吗？

对，其实在文章中我们写了，进口肉出现在牧区，我觉得好像是一种必然的现象。因为进口肉其实从零几年开始就出现在中国了，包括我们在牧区访谈的时候，他们也发现说，我们其实零几年或者是一零年左右，我们就开始往青藏高原进口，包括像巴西、阿根廷、新西兰、澳大利亚这些地区的牛肉。

至于肉铺老板说为什么进口呢，就是因为这些肉在当时特别的便宜。在当时的时候，可能像我们本地的毛牛肉或是藏羊肉大概40元一斤的时候，这些进口肉可能只要20元一斤或者是十几块钱就可以买到。这么便宜，差价是非常大的。

所以说肉铺老板觉得这么便宜的肉我也要买来看看能不能卖出去。最开始的时候，他们的销量确实是不好的，因为对于牧民来说，他们不知道这是什么东西。老板说这是肉，牛肉，你买回去尝一下，然后他们一尝发现这是什么牛肉，这根本不是牛肉。

这也是我们用了假肉这个标题的原因。一会可以请普华解释一下。

所以就后面销量也不是很好，因为大家都不太认可。但是在这几年随着气候变化，导致草地的生态系统特别不稳定，而牧民它本身就是依靠天然的畜牧业，它的牛羊长得壮不壮硕、标多不多，其实就是跟草好不好有关系的。

但是气候不稳定，草地的产草量可能就存在一种不确定性，导致他们需要去购买很多的饲草料来喂养他们的这些牛羊。在这种情况下，他们的生计就受到了影响，他们花了很多钱去买这些生产资料，但没有钱用来自己消费了。

然后导致他们的生活变得特别艰难，所以他们觉得我确实是孩子要上学、要看病，然后我需要买各种各样的东西，要花费，我只能在吃上去尽量的减少我的支出。所以说我就买了进口肉，在这两年进口肉的生意就在牧区变得特别特别好了。

就非常多的牧民也是开始买进口肉。除此之外，藏区其实有非常多的餐馆，各种各样的小餐馆。其实他们做的牛肉通常都是炒制，或者是做面用的，做包子之类的，经过调味之后，其实不太容易分辨出来，哪些是牦牛肉，哪些是进口肉。

所以说可能你在藏区旅游的时候，吃到的牛肉也可能是阿根廷或者是巴西的牛肉。我们在跟肉铺老板访谈的时候，他们也告诉我们，其实他们很大一部分的肉都是流向了当地的餐馆。

他们这个节约成本的考虑，尽量的价格压力，肯定会去进行这样的选择。你刚说到了这个所谓的假肉和真肉，它在牧民的口中是非常容易分辨的。当然那个毛牛肉我平时也很少吃到，我去青海湖旅游的时候吃到过。

但是平时我自己会感觉我吃任何的牛肉，总感觉其实牛肉的味道，当然我吃的都是炒之后的牛肉，或者一些煎的牛排，感觉好像都很相似。就是讲到肉的这个口感，可能跟烹饪方式也有很大的关系。

其实我们内地的人，尤其是汉族人，其实是很擅长做食物，食物的种类很丰富，而且烹饪方式也很多。比如说这个煮着吃、烤着吃、炒着吃、高压锅把它炖烂，各种吃法。

但是其实在牧区的吃肉的吃法相对是比较简单，就是清煮，放的这个调料也很少，有些就只放盐。 我看内蒙那边吃的话，放什么韭菜花之类的。对，我觉得韭菜花其实只是近几年炒起来的。传统上吃肉的时候根本不会占什么料，在我小时候家里边吃肉，什么都没有，就是吃，纯肉。

对，纯肉，而且不放任何佐料。所以这样，大家吃那个肉的时候，追求的就是肉本身的那个味道。还有一个区别，习惯了吃这种草地散养的牛羊肉的人群，它本身的这个味觉系统里边对这个肉有一种定势。

包括我自己，我也是从小就是在牧区环境成长，我小时候食物种类也比较简单，就是那时候就蔬菜比较少。我们的认知里边，土豆和粉条都是蔬菜，粉条也是蔬菜。

所以家里边就除了吃肉和面之外，觉得都是蔬菜，就没有很新鲜的这种蔬菜。所以肉成为了我们的主食，基本上每天都会煮肉，而一次煮很大盘的这个肉，然后直接是手抓吃。

所以当我们吃到与平时吃的这个肉不一样的肉的时候，很明显能够吃出来。就不一样。所以我们在文中用这个假肉，因为我们做这个假肉这篇文章的调研的时候去问牧民，我刚开始用藏语，我们访谈的时候说，肉是藏语里边是进口的话是卡尔森。这样你有没有买过能振的进口的肉这样子讲，他们都听不懂，啥是进口的肉。

然后我就说，那市场里边卖的那个便宜的肉，你们吃了吗？吃了吃了，那不就是卡尔森吗？那不就是假肉吗？他这么讲。后面我们访谈的一些牧民也不叫假肉，叫假下，假是汉族的意思，其实就是相当于内地过来的肉。所以假肉这个概念是你们访谈的这个牧民当中这么称呼它。对对对，然后我们发现原来用跟牧民访谈的时候，用进口肉这个词，他们根本听不懂。 然后用卡尔森这个词，就是假肉这个词的话，大家都能get到，我们聊的是哪个肉。总之一个意思，就是说这些肉，不管是羊肉还是牛肉，跟自己平时吃的肉的口感不一样，甚至有些牧民会说颜色也不一样。但是我自己是不太能看得出来，所以这个是很讲究的。

所以我们就用这样一个概念去区分，但如果只是一个口味上的区别，听起来也不是什么特别大的事，对吧？可能习惯一下也就好了。但是你们那篇文章，其实讨论的是一个更深层次的这些结构性的一些现象。而且你们在文章里面，其实做提案调查，采访了很多的牧民。他们有一些人，我看也是很早就搬到了城市里面去居住，其实脱离了这种所谓的牧区传统生活方式，还有一些可能仍在放牧的这些牧民。

而且我发现他们的这些人在你们的访谈当中，也是差异性很大的。有的人是大量在购买这些所谓的假肉，平时也在消费，还有一些人是很抗拒这些，不太愿意消费。但看起来他们的生活都受到了这个假肉流入牧区的巨大的冲击。假肉它是怎么去影响到这些藏区牧民的日常生活的？那我们就用文章中我们写到的这个案例，还有我们调研中发现的几个小故事，来给大家说明一下吧。

可能大家对牧民不太了解吧，但是现在传统上牧民可能是分为这三类。我觉得第一类是就已经完全放弃了这个草原的牧区的生活，直接搬迁到了城镇。就像我们文中提到的第一位桑杰大叔，他是在90年代放弃了他的草地，搬到了城市。本来是在草地上放牧骑马，现在在县城里面开垃圾车。他是比较典型的一类定居在城市里的牧民，他们没有自己的草地，自然也就不可能有自己的生序、自己的牛羊。

但他们的生活习惯其实是没有改变的，吃肉仍然是他们饮食中的一个主线。所以他们有两种方式获得肉。第一种就是去自己找自己的亲戚或者是朋友去买这些本地的牛羊肉，然后第二种就是去市场上买。你去市场上买，你也可以买本地的肉，也可以买进口的肉，根据这个价格你能接受的程度来选择。

对于桑杰大叔来说，他面临的一个窘境就是他在城市里的生活其实处处都需要开支的，比如说我水电需要开支，然后我也需要加油，买衣服、买东西，各种各样的，无法通过自己生产获得的，我都需要去购买。那我的收入其实是有限的，就是开环卫车这一点点收入。所以说他觉得自己实在是负担不起去买这个稍微价格高一些的牦牛肉，他就开始以这个消费进口肉为主。他可能是搬到城市一段时间之后就开始买这个进口肉，所以说进口肉就成为了我主要的这个肉食来源。

这是第一类牧民，其实他们就是这个消费进口肉的一个主力军。然后第二类牧民呢，就是他们可能在城市和牧区之间穿梭。他们可能需要因为孩子上学，然后需要就医，需要在县城或者州上去生活一段时间，但是他们仍然在牧区有自己的草地，有自己的生序是需要去照顾的。他们和第三类牧民就是仍然是完全生活在传统的牧区社会，在草原上我们有一个自己定居点，然后夏天扎着帐篷，赶着牛羊去夏季的草场，完全生活在草地上的这个传统的牧民。

这两类牧民呢，其实他们的主要的生计来源，可能他们一年收入中的80%都是要通过他们去卖掉他们自己喂养的牛和羊。获得收入之后呢，也是有各种各样需要花费的地方。但是进口肉出现呢，就带来一个很严重的问题，进口肉其实它占据了一部分本地的消费市场，然后很多人就选择不去买这个牧牛肉或者是羊肉了。

所以说这个收购牧牛和藏羊的人也开始变少了，这就导致他们的牧牛和藏羊的出售的价格变得很低。比如说在19年的时候，他们一头成年的母牧牛可能卖到7000元或者8000元，但是在2014年我们做调研的时候，这个价格可能就到了4000元或者是3500元。就是同样的一头牧牛，牧民其实也不知道为什么价格就变得这么低了。

本来我卖10头牧牛就够我生活的，现在我可能要卖30头或者40头才能足够我生活的开支。这对于牧民来说，其实是非常影响他们整个生计的循环的，因为他们的生计是需要靠这个母牧牛继续去生小牧牛，来不断地维持他们这个畜群的规模的。在这种情况下，进口肉就压低了他们出栏的牛肉、牧牛的价格，所以说他们就非常的纠结，他们不知道自己是要等一等，等这个牧牛的价格再上涨再去卖这个牧牛，还是说我现在就把牧牛卖了，多卖一些，可能我以后再想别的办法去获得收入。

这里面可能涉及到一些经济学上的一些逻辑，就可能比如说阿根廷或者巴西的这些牛肉穿过整个太平洋运到中国来万里以外，而且加上各种物流的成本理论上应该是很高。更不用说到了中国你从海关进来之后，还有那么长的路运到青藏高原，怎么到了青藏高原的市场上，竟然还能比本地所养的牛肉的价格更便宜？你可以给我们听众稍微解释一下这背后逻辑吗？

对，其实这跟青藏高原的整个畜牧业的生产系统和南美洲的这个畜牧业的生产的系统模式是不太一样的有关系。像青藏高原本身大家也了解，它就是一个气候比较极端，然后生态相对比较脆弱的一个地方，所以说它的草地的生态系统是需要比较严格的生序的数量的限制才能保证它的生态系统是有一个比较稳定或者是比较良好的一个发展的。

所以说这个牧民其实在青藏高原的草地上能放养的牛羊数量是有限的，这就使得它整个青藏高原的牦牛肉或者是藏羊肉的产量都是有限的。在这种产量有限但是消费者的数量是比较多的，而且消费者对这个消费的需求量也是比较大的时候呢，就导致它的牦牛肉或者是藏羊肉的价格其实可以维持一个相对较高的这个水平，就变成一个比较双赢的局面吧，牧民也有能获得比较好的收入，然后大家也能吃到比较好的肉类。

但是像南美的这个草原生态系统呢，其实跟这个青藏高原是非常不同的。首先南美它的纬度就会比青藏高原低很多，海拔也是更低的。所以说它的天然的水热条件就会特别好，包括那边的草原和亚马逊森林，它的那个降雨量特别的充沛，这对于这个草地的生长是非常有利的。

一方面这个天然的草原的条件比较好，另外一方面呢，就像我们在文中也写了，确实这个巴西是存在着非常明显的这个砍伐亚马逊森林来种植黄豆、大豆或者是放养这个牛群的这种现象。在那个比较良好的自然的条件的加持下，就使得这个牛它自己生长的这个成本非常非常低。在巴西当地呢，就是分了各种各样的不同的牛种，像当地最主要的最便宜的牛叫牛牛，然后这种牛它本身就是不需要什么喂养，你直接把它放在草地上，它就可以长肉。

对，它就可以长了。我见过那种照片，就是那种感觉鸡肉上有很奇怪的凸起。对对对那个牛牛，但它其实肉质是不太好的。在巴西本地的这个市场上就主要是低端一些牛肉市场。然后像巴西它们也有比较高端的牛肉，也会引进一些安格斯牛之类的，这种会通过养殖场的饲养。但养殖场饲养其实就是通过大豆、玉米这些饲料来让这个牛肉去增肥。

但是在当地因为可以砍伐森林去种植这些东西，然后包括它本身的这个种植的条件也比较好，使得它们这个饲养的成本非常低。我们之前在这个看到有一些巴西的朋友发他们在当地的牛肉的照片，当地的牛肉其实比我们说运到中国的巴西牛肉可能20块钱一斤，我们觉得啊比我们本地的30多黄牛肉、40多毛牛肉便宜太多，但在巴西那个牛肉可能只要5块钱或者是8块钱、10块钱这样子。

在巴西来说本身也是生产成本特别低的，所以说这就给了那个巴西的出口商很大的这个利益的空间去出口这些牛肉。巴西、阿根廷这都是可以算是牛肉共和国。对对，我可以补充两个，就是一个是刚刚依然讲，整体上南美洲的牛羊养殖跟青藏高原，包括内蒙也一样相比的话，资源禀赋完全不一样，就导致散养的成本，我们一般会把这个草地上养的叫散养，就是圈养的叫私养，但其实私养两个都可以用，这是一个。

另外一个也是整个的产业链的这个成熟不成熟的问题。像巴西的话，从这个牛的养殖到屠宰到销售到包装，整个的这个过程是一个很完善的这个产业链，从这个养殖户到屠宰上都是有这个很固定的这个关系，大家这个养完了直接按批次直接出栏就可以。相比来说我们牧区的这些牛羊的这个链条是不成熟的，而且每个环节的这个利益相关方又不一样，养牛是牧民的事，收牛又是中间的这个二道贩子，他们来收，二道贩子跟这个育肥商有时候是一个群体，然后他们也不是直接屠宰，有些是屠宰场的人过来买，但中间还有个屠宰场，这一拨人最后到这个肉类的这个销售商又是一群人。

就是不同的人群在中间不断的这个收取利益，整个这个都不是很顺畅的，所以这个环节也是导致两边的这个最后到消费者手中的这个肉的价格不一样的一个原因。还有一个就是就是两边对牛肉的利用不一样，就是附加值这块。像中国牧区的这个牛羊，一只牛卖出去，大家不管是对牧民来说，还是对买这个牛的商人来说，大家所谈论的只是牛的同体的那个肉，就是抛出去内脏和这个梯子，包括头之外的那个整体的那个肉，叫那个叫同体肉嘛。

但是像南美洲，我们之前有个老师，他对这个专门做过调研，说他们是对其他的这个部位的利用度非常的高，而且把这些作为一个重要的这个产品来去销售。而且在整个的这个收益中，这部分占比也很高，所以相比来说我们完全把这部分就忽略掉了，更不用说整个羊皮啊，这些完全没有利用起来。所以这个是两个产业的很大的一个差异。

我多问一句，那传统牧民社会，比如青藏的这些牧民们，他会吃牛的内脏吗？这个就很讽刺，就是市场化进入之前，传统的这个牧民的放牧，其实是会把整个一只羊也好、一头牛也好，都会有一个很充分的利用，屠宰之前会把他们的这个，包括像牛的话，牛毛、牛粪，这些都会利用起来。

然后屠宰以后，它的牛皮，包括牛骨都会利用，然后把里边的内脏牧民们会做四种肠子，就是把肠子洗干净，里边会装不同的这个东西，馅料，分别是肉肠、肝肠，肝肠就是用羊肝做的。然后一种是血肠，那肝肠是怎么样，是把肝剁碎了，然后加一些这个肥肉，然后葱什么调个味，然后把它灌到肠子里。那跟德国人做那香肠其实挺像的。对，还有一个叫面肠，也是将面里边放一些这个调料，放一些肥肉，然后把它灌到一些肠子里边，就是总共会做四种肠子。

所以所有的内脏都会利用掉，然后所有包括羊头、包括羊脚、牛脚，传统上都会利用起来，不是做成产品去卖，但是在牧民传统的这个自己自主的生活中，都会利用起来。整体上我们认为传统的畜牧业是一个综合的这个经济体，它会利用这个畜牧业能产生的所有的这个资源。但是我们现在这个市场体系下的这个畜牧业，它只是把这个肉利用起来了，其他的什么毛啊，包括奶制品的利用率也相对比较低，这个还有，然后其他的皮革呀，骨就更不用说了。

包括这个牛粪羊粪也不用说了，这种前景下我们的畜牧业的这个未来是比较担忧的，就是它就很难有一个可持续的这种发展。我们之前说这个讲到效率的时候，工业化养殖里边，它的所谓的效率就是用最低的成本做出最多的这个产品，这个高产嘛，其实传统的这个畜牧业的思路是用最多的方式，使这个产品的使用价值最高化，把所有的能用的都要用起来。

对，我看到你们这个文章里面提到，比如青藏地区的牧民开始买假肉，它有个关键的节点，好像是从1984年到90年代末，整个20世纪的最后这15年有一个草场承包改革，开始在青藏的这个牧区里面开始落实，这个我想很多的，尤其内地的这些城市群体长大的朋友可能不是特别了解，能两位能向我们稍微介绍一下吗？就它是怎么影响到就我们今天讨论到的这样的一个话题。

其实可能这个概念跟我们就是大家历史课本上都学过的家庭联产承包责任制，小港村，牧区的社会传统上，它其实都没有特别明确的像用铁丝啊，或者是说用道路啊去划分什么地区，就是大家都是以一个部落作为一个整体的领地范围，然后在这个部落的这个王的这个领导下，以山或者是以河、谷沟这样的方式来做地理边界，来去生活。

大家在这个一大片区域上，就不停的移动，不停的游牧，然后不停的带着他们的声序，他们的帐篷到处的利用这个草地来放养他们的牛羊嘛。我觉得是一直到家庭联产承包以前都是这样子的，包括像这个人民公社时期，其实也是这样。那会儿也是，对牧民来说，他们不觉得这个草地是有边界的，他们知道他们这个村或者是他们的部落呀，他们这个边界他们是清楚的，但他们不觉得就是他和他们的邻居之间的草地是有边界的。

但是从1983年开始，最初是在内蒙，内蒙最初实行了这个草絮双承包制度。这里的草絮双承包呢，就是我们把草地和生絮都给承包给牧民家庭，可能当时的政策理解是，我把这个草地和生絮都承包给牧民自己了，那牧民有了自己的草地，有了自己的生序，他就能在自己的草地上不断地提高生产力，能放养更多的牛羊，然后来让这个牧区的发展变得越来越好。

然后到了90年代之后，这个制度开始在青藏高原和新疆的草原也开始推行，这个制度实际上跟牧民本身自己的生产的逻辑是完全不一样的，因为在牧民的概念中，他们生产其实我没法在固定的草地上生产，因为假如说你把这片草地分给我，它可能有个四五百亩或者是一千亩，这个草地看起来很大，但它其实拥有的这个草的类型是完全不一样的。比如说这一块草地上，我有这种春天使用的草，但是另一块草地上可能夏天的时候有我这些牛羊需要使用草，但你现在把这块只能春天用的草地分配给了我，那我夏天的草我该怎么办呢？

我看文学小说里面经常有冬牧场、夏牧场，它有不停的转换的，是的。所以说对牧民来说其实他们是不太适应这种生产逻辑的。但是当时政策推行是比较彻底的，我们把草地分到牧民家庭之后，还把每个牧户家庭的草场都用铁丝的网围栏给围了起来，就把整个草原切成了一个一个井字。对，是的，就把草地分成了这样一块一块的样子，那每个牧民都有一块自己的草地，你的牛羊也就只能在那一片草地上生活。这对牧民来说其实特别的不适应，就像我们文中桑杰大叔说的，他也是90年代之后，这个青藏高原推行这个政策之后，他们那边也开始了这样的就是承包制度。

所以说他们村里面就开始分草场，但其实对于牧民来说，他草地不是一个非常均质的，有的可能在山上，有的可能在河边，有的可能在沟里，总有好的地方和不好的地方。是的，所以说不是每个牧民分到的草场都是一样好的，那桑杰大叔他就遇到这种情况，他的草场上没有水源，但是牛羊是不可能不喝水的，所以说他就必须得赶着他的牛羊去到别人家的草地里，然后穿过去才能到河边去喝水。这对别人来说其实不太能接受，因为你的牛羊从我的草地上过，可能就会对我的草地有影响，然后所以说经过了这样一两年的这种不方便的生活之后，他觉得这样放牧实在是非常的影响跟邻居的关系，包括自己的生活也很不方便，所以就这样放弃了自己的草地，搬到了城镇中，把自己的那个草地出租给了旁边的邻居用。

像前面说的就是定居的牧民，其实大部分都是这样子的，他们本身的草地可能分到的不太好，或者是没有水源，或者是草地非常的干旱，或者是几乎不长草的这种草地，被分到的草地也有，他们没有办法继续进行这个畜牧业生产了。所以说就放弃了这个畜牧业的生活，去到了县城，开始谋求新的生计的出路。 这种黑土化非常的严重。整个这套模式是到现在还是一直在持续当中吗？你是说分到户的是吧？这个其实也是有这个区域的差异。像我们调研的有一些，其实是少数的村子，目前也有共用的，就是还保持共用。但这个共用的范围，规模有大小。有些是一个村子在共用，有些是一个小组在共用，有些是几个连户在共用。尤其是在川藏这边，像四川的若尔盖，甘南的马曲，有一些这种共用的操场。但像青海的绝大多数的这个木居操场，都已经是分到户，而且非常细碎了。

在整个的青藏或者说藏族聚居区里面，游牧生活方式的这个人口大概占一个什么样的比例？总体的人口其实我也没有很清晰的概念。但是按这个全国的那个木业人口应该是有一个，但那个木业人口划分是跟实际的放牧人口不是很对应。但是我直觉上可能整个藏族人口里边木业和农业人口可能差不多一半一半。区域上面放牧的这个区域远远大于这个农业的区域。整体上青藏高原是属于山地草原嘛，不像内蒙的那种很广阔的这个平原的草原。大家都是在山地的这种高山草甸，这种类型的草场。整体上包括我刚才讲的那个安多，就是传统藏族这个区域里边的安多区域的大部分都是属于木区。就比如说现在的甘南的一半，包括阿坝州的一半，然后像青海的，除了海东的几个县是属于农区，然后黄南的两个县是这个农区，其他的大部分都是木区。可能海南的个别的县的个别这个乡镇是属于农区，整体上这个都是属于木区的。整体上区域上面是木区是这个很大60%以上。

在你们所调研的这个甲肉驱逐真肉的故事当中，其实有个很重要的一个背景因素，是青藏地区的牧民，它养殖的这个牛很多是牦牛。那牦牛肉和我们所理解的那些，比如说在低海拔地区的那些肉牛，那确实它的差异是比较明显的。但是如果比如说像内蒙地区的这些饲养的很多的牛肉，它可能跟世界各地那些畜牧发达国家的牛肉可能更接近一点。

所以在你们的调研当中，这种明显的甲肉口感，它是一个仅限于青藏地区的牧区的事，还是一个比如说在内蒙的牧区或者新疆的牧区也会存在这样的一些现象吗？我们在新疆没有做过调研，不敢乱说，但内蒙也没有专门对进口肉做过调研。但是我们在内蒙有很多认识的人，包括牧民，也包括很多同学朋友做研究的，然后经常跟他们聊天，其实大家的这个认知里边跟这个藏族牧区的人的认知是一样的，就是觉得吃进口肉的那种口感上的差异依然是存在的。这个不是很严格的学术，我们在内蒙也没有做进口肉这个调研，但确实问到过，涉及到这种问题，我们至少在我们这个认识的人的这个反馈里边是认为这个差异是同样存在的。

其实内蒙的牧区社会的变化，相对来说青藏高原会更早一些。它有点像青藏高原的一个发展领先了一步的这样一个地区，包括像这个草叙城包和这个牧民的社会的变化，其实都是比青藏高原更早的。像这个甲肉的问题，我们听这个内蒙的老师说，其实可能在内蒙出现的会比青藏高原更早一些。他们已经完全过了这个过程了。像内蒙其实有很多外蒙古进来的羊肉，包括我在内蒙调研的时候，有一件事让我印象特别深。那是21年的时候，我跟我的师姐，还有我们草原领域非常著名的这个韩念勇老师去内蒙古调研。然后我们去每个牧民家中都要给牧民带一个礼物，相当于做调研的感谢。买礼物的时候，这两位调研的老师买了五六香的牛奶，然后是特伦苏和伊利的牛奶。然后当时就让我很震惊，就我们去牧民家里调研要提牛奶吗？牧民不是应该都有自己的奶牛这样的东西吗？但是就是他们告诉我，其实内蒙的社会已经发生了很多变化，它不是我想象中那种风吹草地见牛羊，全部自给自足什么的。

不是每家每户都有自己的牛奶，就很多牧民家里，他们其实已经没有自己的奶牛能够产奶。他们虽然是这个蒙古族，传统上仍然是有吃奶食，然后喝牛奶这种习惯，但他们没有自己的奶牛，然后也没有自己很大块的草原去使用。所以说他们其实没有办法喝牛奶。我们去提牛奶，也表示一下这种尊重或者是感谢之类的。当时给我留下了特别深刻的印象。其实青藏高原的牧民，在草原生活的牧民，他们仍然是自己挤牧牛奶去喝的。他们仍然是能够喝上自己的牧牛奶。但是对内蒙牧民来说，他们已经过了那个能够自己自作一个模式，他们已经完全跟这个现代化的生活接轨了，更依赖这种市场交易。

除了青藏高原和内蒙这样比，在现代化这个层面上，至少十年左右早于这个青藏高原。同时内蒙的草场的生态恶化的程度也比青藏高原早了这么十多年到二十年的样子。而且这个还是一直在这样持续的在恶化过程当中。然后青藏高原也同样，但这个恶化的因素原因有很多，这个很复杂，但是从这个时间上看的话，青藏高原紧随着这个内蒙也在有发生着同样的类似的这种变化。

而且你考虑到内蒙谷的话，它里面还有一些，比如煤炭资源，这个在可能十来年前，对吧，中国这个煤价那个时期，整个山西陕西内蒙，它这个可能挖矿跟这个牧区之间的冲突。讲到挖矿，其实我特别小的时候，我刚才讲那个夏草场是非常的美好，但是冬草场是非常的不好。因为当时离我们家不到两公里的地方就是一个煤矿，而且有好几个煤矿。就是我们那个当时的镇上，现在我们那个镇已经搬迁到另外一个地方了，但是当时那个老镇那个区域上都是煤矿，因为我们冬草场只有冬天住在那里。然后冬天风比较大，而且煤矿都是露天的那种煤矿，所以煤矿上那种黑色的那个煤的灰，一刮起风就会把那个刮到整个的草场上。我们本身的藏阳是白色的，然后一整个冬天不断的吹那个风以后，对都是变成黑的了，然后整个河流都是黑色的，很恐怖很恐怖。

所以当时冬草场给我的印象特别的差。后面到10年以后吧，有这个整改嘛，所有的煤矿都要撤了。所以那个当时我了解的或者是我们周围的那些煤矿现在都撤了，其实有在做这个生态修复。但当时这个环境的影响，包括对身序人的这个影响，当时已经留下的，这个已经没有办法再做修复。

因为在整个这样的一个假肉驱逐真肉的故事当中，其实价格因素发挥了一个可以说最重要的作用。还是因为本地生产的这些真肉的价格其实很难降低，而那些比如从南美洲运到中国来的这些大型工业化加工运输后的这些冷冻牛肉，确实它的成本非常之低。但刚刚听两位的介绍，其实在我们国家，比如在青藏牧区也好，内蒙牧区也好，其实很多还是那种家庭式的这样的一些牧场。现在解决办法可能是哪些，比如说它有什么方式，可以去让自己生产的本地的牛肉在市场上更有竞争力吗？

相对来说更好的方式，应该还是走比较多元的这个方式。就是所谓的多元，就是把能用的这些畜牧业的这个资源都用起来，不断地开发产品。其实有一些这个资源没能用起来，可能是整个市场问题，但我们认为同时也存在中间的这个技术性的，包括产品的研发呀，主动的去打造市场的这个过程，包括我们在历史上青藏高原的羊毛其实是一个很大宗的商品，而且是在整个西藏的历史上面是一个出口的这个大宗商品。所以畜牧业的这个产品里边羊毛是一个很大类，其他的像羊皮这些传统的贸易里边也有，但可能占比不是特别高。但至少整个包括这个奶制品，包括皮革类的这个毛类的牛毛和牛绒也是，这些其实以前都是在的，但可能现在的这种工业化的这种趋势下很多都没了。

但是如果我们想探讨未来的这种市场的话，还是需要把这些利用起来，牧民主动的要参与到整个这个市场的发展。但是目前的问题就是在整个这个产业链的环境里边，牧民本身的这种自主性是很欠缺的。所以我们觉得这个是一个市场问题，解决这个也需要从这里入手。

我想补充一点，就是说其实牧民社会可以说从这个家庭承包之后，它变成了一个从整体到分散个体的这样一个过程吧。原来可能大家作为一个整体的部落或者说村落，大家进行这个蓄木月生产其实都是一起进行的。他们牧户之间会共同合作啊，或者说互相帮助啊这种方式去进行。但是在这个家庭承包之后呢，牧户它其实就变成一个像您刚才说的比较分散，比较独立的这个精英个体了。但是呢，就是我们现在国家的政策文件，它也意识到了这种问题，其实现在是在提倡草原的这个整合使用，或者说我们进通过整合成立这种乡村合作社，牧业合作社这种方式，来把分散的幕户整合成一个比较大的整体。当这个合作社成立的时候，变成一个比较大的整体的时候，其实在市场面前可能会有更大的这个话语权，或者说有更强的这个溢价能力。

我们这次调研的时候，也在那个县城看到一个新成立的那个畜牧业产业中心，其实就是当地政府扶持成立的一个。然后它其实是把牧民的操场租过来，或者说承包过来，然后我们把这个牧民的牛羊统一进行一个养殖或者管理。通过这种方式呢，其实它就相当于在这个政府的支持和鼓励下去跟这个屠宰场啊，或者说这个收购商人去对接。然后这种时候呢，其实它的定价权，或者说它有更高的这个溢价能力，这对于不管当地的这个产业发展来说，还是对牧民来说，我觉得都是更有帮助的。

有没有可能让这个市场最终，我就接受本地的牛肉就是定价更高的肉的肿量？你看那个韩国做韩牛对吧，日本人他们育种了，做那些什么和牛。当然这两个国家其实也有大量的进口牛肉，进口牛肉就是更便宜，但是他们本国的一些特有品种牛肉其实更贵，而且国民也愿意来买单。对，这个是我们觉得是有这些可能性的。

那篇文章刚开始我们收尾的时候，本来是要放一些国外遇到类似的这种情景的一些处理的方式，解决的方案。包括我们像看到欧洲当时，德国的一个自然保护区叫Rowan的一个保护区里边，那个保护区里有一个本地的羊种，叫黑头羊。其实应该是我记得是1930年代，受到整个进口羊肉的这个影响，那个区域里边有很多进口羊肉，然后导致那个本地羊肉不断的这个减少。后面濒临灭绝的时候，那个保护区有很多牧民发起这种抗议，然后也受到保护区的重视，慢慢的保护区就制定了一个这种保护措施，就是说在这个区域内保护区内不能有别的这个羊肉出现。所有的餐饮业所有的肉业的行业都要用这个本地的这个羊肉，然后也规定这个本地的羊种不能销售到这个保护区之外。

所以慢慢的他们把本地黑头羊的这个凭准打造起来，以至于这个民生起来以后，他们就是有些人专门为了吃这个黑头羊就来这个自然保护区，从而更进一步拉动了区域内的经济。这是一个很成功的案例吧。

你们那个文章里面给我一个很重要的新的一个知识点，也反差的，就是你们在里面提到这个草丝牛肉和骨丝牛肉的这个营养价值和口感的问题。过去其实尤其是我们去逛超市，包括很多进口肉，刚提到了巴西或者说阿根廷，它很多是喂玉米喂谷物长大的这些骨丝牛肉，通常他们的包装上也会把这个东西打印在上面。你选择的时候你看得到这是骨丝牛排或者骨丝牛肉。那我一般默认是不是就是说，骨丝出来的牛肉的整个的全方位是要比草丝的要好的，因为大家感觉好像青草它应该没什么营养，但你们在文章里面写提供的这个论据完全相反。你们说草丝牛肉的营养价值其实比骨丝牛肉更优，这个能跟我们讲一讲吗？

我感觉这是两种认知体系。就是对于我们在访谈的时候牧民来说吧，包括我们访谈的时候也跟一些育肥商，我们在文中其实提了育肥，在牧民看来就是喂穀物或者是喂饲料就叫育肥，就让它多长肉，对让它长的肉的更多。我们跟一个育肥厂的老板聊天，他是每年会去这个市场上收购牧民的牦牛肉，然后把它拉到自己的育肥厂去，通过喂玉米和大豆这些方式来让这个牦牛快速的增重。可能从六个月就能体重翻一倍这样子，然后来获得这个出栏的这种额外的收益。他其实跟我们说的就是他自己做过实验，说明吃天然草长的牦牛肉，就是比吃这个穀物的这个牛肉更好。因为其实吃穀物牛肉或者说穀似的牛肉，它并不是完全吃穀物的。

它有一些其他的，就是让它增重的这种，我们可能所谓的就是叫它激素这样的一些营养物质，来让它有一个更快速的一个体重的增长嘛。他们说它在喂养的时候，其实就是大量的喂喂喂让它们快速的增重，再把它们放到草地上放几天，让它们体内的这个喂饲料、喂穀物这个味道给消除掉，然后再给卖到屠宰场去，就是卖给这个商家再到消费者手中。

然后所以说我们就会觉得比较奇怪就是说这个草寺和穀寺实际上真的有差别吗？所以说我们就去查了一些文献资料吧。其实我觉得可以跟人做一个比方，假如说一个人每天正常吃饭和他突然很集中很大量的去吃一些具有高营养的东西。他的快速增重的这个过程，我觉得其实跟牛是有点类似的。你当然可以说这些穀物，这些饲料，这些玉米，这些可能含有很高的营养成分，但是对于这个牛本身，它的生长其实是被破坏了。

它的生长周期，它一切只是服务于让它在短期内变得更重，产肉更多。所以说包括这个肌肉里面的这个氨基酸，包括这种肌肉蛋白的这些成分，都会发生影响和变化。对，刚刚您问的这个问题，就是本身吃穀物和吃草可能不会有太大的这个营养的差异，或者是这个口感的差异，但其实这个我们平常能听到的这个穀物饲养的这个背后，其实大家不会讲的。

就是一般穀物就是所谓的，我们文章里面写的，如果是育肥，它名义上是都是穀物，但穀丝的这个过程当中，是加很多激素的。我们在写那篇文章的时候，看到巴西的这个牛肉行业的一个报告，就是里面看，我们也看，巴西真的是这个草丝吗？还是怎么样，去看那个数据。整体上巴西的那个牛肉里边，大头的这个占比的是这个，他们所谓的这个草丝。但是我们看整个数据纵向的对比以后，发现他们这个整体上出栏的牛的这个数量，增长率远远高于这个整体的这个存量的增长，说明他们每年在不断的这个加高出栏量，还有一个是他们出栏的这个牛的岁数不断的在缩小。可能刚开始出栏的是三岁以上的占比比较高，后面三岁以上的占比是属于非常少数的。

所以他们其实也是在不断的走向那个工业化的那个趋势。就是这个其实是草丝和古丝背后的不怎么讲的一个过程。我们讲草丝，一个是可能口感上面也有差，因为古丝的话，它是这个种类比较单一，草丝的话草的种类比较丰富。因为保存比较好的草场上这个种类上千。我们也看到有科学的这个研究里边说，这个吃多样性的植物的这个肉的所含的这个营养的成分更高，这个是我们文章里边也提到了，这个是一个。

然后还有一个就是草丝的这些动物都是自然生长的，所以它有一个环境友好性的这样一个特征。按照刚刚说在牧区都这样了，那我们日常吃的牛肉呢？基本都是以进口肉为主了吧？对，就是因为我平时会自己做饭嘛，我当然也需要买牛肉。那我最常用的可能是线上购物的APP。这次从牧区调研完回来之后，我就开始看，看产地。对对对，就是其实商家会著名的，比如说澳大利亚、新西兰的，或者说国外进口的，他们通常都会写冰。像这种冰鲜的牛肉，尤其是比如说切成薄片的那种牛肉，大部分都是黄牛肉比较多。

但是像牛腱牛腩，或者说只要可以冷冻的牛肉，我在购物APP上能看到的，基本上都是进口的。很少很少会看到就是国内这种本地的，或者说黄牛肉啊，或者说就牦牛肉是从来没有见过的，就只见过黄牛肉。黄牛肉还会是多一点。但我觉得像这种巴西亚、阿根廷这些新西兰这些地方，可以能够进口到的，像牛腩牛腱这些冷冻牛肉，我觉得已经在国内的这部分市场上，就是完全占据了主流了。可能除非是你去到一些线下的菜场，在卖一些什么新鲜牛肉、新鲜牛尾的地方，它可能是什么在郊区养殖的那些黄牛肉。

而且刚说的那种，我们可能在线下能买到的那些，比如黄牛肉，它很多应该也不是所谓，慕名家庭生产的那种牛肉，而是国营农场里面集约化养殖的一些。我们在写这个文章之前，看了一本书叫吃动物，就是讲美国的这个肉类养殖的问题。然后他当时讲到美国的99%的这个食用的肉都是工业化养殖的肉。所以他们有这个数据，但国内整个这个数据比较难找到。所以我们写这个文章的时候，也去试图去找过，但是没有很直接的这个数据。但是整体上据我们的观察，整个中国的牧区其实只有青藏高原的牧区，内蒙古的牧区和新疆的牧区也有少部分的其他的，但主要的是这几个区。但这几个区加起来，其实在整个的中国的这个肉类消费的这个占比里面，其实是很低的。可能加起来可能也不超过30%。

所以国内的这个我们吃的主要的肉，还是不是进口肉，就是国内的这个集中养殖的这种肉。对，所以即便在中国想吃到一口这个草原上真正残养出来的，或者用传统生产方式生产出来的这种游牧肉，其实是很少很少。而且我们文章里也写到，就是牧区的这些肉，其实经过的育肥商的中间的育肥过程以后，您即使有些时候你买到了所谓的牧区肉，它也不一定是完全自然这个散养的这个肉，可能就是牧牛肉，但它中间也经历了这个育肥过程。所以这种的占比也很高的。

你们下一步，比如说你们对这个课题的一些研究的方向，大概会是什么？对，其实我们这次调研的时间比较短，可能只是一周。再加上我们前期的一些调研，我们收集到资料也是有限的。我们关注的可能也就是这个青藏高原的东南边缘的这些牧区的一些生产的状态。但是我们对于内蒙或者对于新疆，包括其他的这个青藏牧区的这些牧民，他们生活的一个状况，他们消费的这个肉类，他们这个饮食习惯的是否发生了变化，我们其实都是不清楚的。所以我们也希望以后还是能申请到更多的资助来，就是继续去关注这个牧民的这个餐桌上的肉，到底发生了什么样的变化，进口肉到底对这个中国的这个草原牧区产生了多大的影响。

因为我们观察到一个现象，就是之前我前面也提到了说，牧民因为进口肉的这个价格的这个冲击，他们这个初来，牧牛的价格受到了明显的影响，他们的生计来源受到冲突之后，他们在纠结要不要把这个牧牛卖出去的。但是在这个过程中的草地上的牧牛就变得越来越多了，实际上对这个草地是有一个破坏。因为在文章的评论区可能有人会说，我们其实吃进口肉很好，因为我们吃了进口肉之后，我们本地草原的不需要那么多牛肉了，草原的生态会变得更好。但我们观察到的现象实际是完全相反的，因为牧民们觉得这个价格不好，他们就决定再等一等，价格回升了，我再把这个牧牛卖出去，草地的压力变得越来越大了。

所以说我们下一步还是想关注一下，这个进口肉的价格的变化，对这个草地的生态系统，然后包括对牧民的生计的这些进一步的影响都是怎么样的。你刚说这个例子恰好反映了，就是现实的这种经济社会的运作机制的复杂程度。它不是简单的那种几个因素之间的互相影响而已。对，对对，其实我们上次也有这个评论说，既然牧民这个遇到了这么大的这个进口肉的冲击，为什么不把牧民直接移到这个别的地方去生活，甚至是让他们去国外打工啊什么。这种人如果当领导就太可怕了。

对，就是这个逻辑其实是很简单，就认为这个牧民这是出问题，把他们这个清出来可能就好了。那这种我们经常这个也有很多这样的这个例子，但其实现实真的不是这么简单。整个这个牧民在场的草木系统，我们会说整个的草场牧民这个生序三者为这个链条的这个系统。草原的这个系统本身是就是历史上就是很多年这样自然形成的，它是相互依赖相互依存的这样一个关系。就是你整个草场里边，如果把所有的牲畜都消失了的话，生态未必是变好的，因为牛羊在草地上一方面，它会每年会把这个生长起来的草会吃掉清理掉，然后同时它在吃草的时候也会拉这个牛粪养粪，这些粪其实是作为一种肥料的，来年的这个草的生长是有很重要的这个作用。从牧民的这个角度，虽然它的这个生存依赖于这个牛羊，但是它在放牧的这个系统里边，牧民来放养这个生序，然后生序吃草，然后草又这个供应了整个这个链条里边的最基础的东西。

所以整个这个系统是比较完整的一个系统。如果中间有一个破坏，这个我们有很多研究之前，我们的这个禁木政策就是这个逻辑嘛。你草场破坏了，我就把这个区域给禁起来。但是后面发现一块草场你禁一年两年是可以，但是超过了三年五年，一个是上面的草，枯了以后没有这个牛羊会吃那个草，然后底下的过了几年，在新长的那个草就很稀疏，然后慢慢的草地的地皮会变成这个黑土，整个土质会输松，整体上生态会这个变差。

所以整个这个生态系统不是说把它这个移出去，就整个就变好了这么简单。所这个也是我们做这次的这个进口肉背后的一些我们所考虑的整个生态以及更可持续的一个问题。同时也是刚才我们讲到那个就是虚草双城包到户以后所带来的几个影响，一个就是整体上生态上面，它不断的破碎化嘛，细碎化。我们刚才也讲到，另外一个是从这个牧民的生计上面，我们访谈很多牧区的老人的时候，他们会一口同声的会说，现在的牧民这个人心变了。

但他们所谓的变了就是说变得自私了，其实这个是有背于传统。刚才依然也讲到，其实传统上牧民的这个逻辑是一个集体思维，就是我们不管是放羊也好，不管是这个宰杀牛羊也好，就是生存和生计当中的每一个行为背后的这个逻辑，是说我们这个整体，我们怎么生存下去，还有一个是我们的下一代再下一代的人应该要怎么生存下去，就是时间上面考虑的是更长远的。所以这也是我们做研究，做这个牧区想从牧区的这些本土知识里边想要获得的，以及我们做这个研究本身的一个意义。

好呀，那今天非常感谢两位来到胡佐，会有跟我们谈论这么多你们关于这个青藏草原牧区食用的这个肉的这样的一些研究。尤其背后这个牧民嘴中的这个甲肉，其实我觉得我们按这个标准，我们都在吃的，应该大部分都是甲肉。但它背后折射出来这个现象，可能过去，如果没有像你们做这方面的研究的话，其实我们很难去真正的察觉到。对于大家如何去理解今天中国的一些特定的经济社会，还是有非常多的一些增益，也非常期待你们进一步的这些研究成果。好，那我们今天就到这，感谢各位的收听，我们下期再见，拜拜，谢谢主持人。 谢谢

I Raised $300M To Bring AI To Laywers | Winston Weinberg & Harvey

2025-03-06 08:00:01

[I Raised $300M To Bring AI To Laywers

Winston Weinberg & Harvey](https://www.youtube.com/watch?v=GTU2TyoSRLk)

You should take AI and basically apply it to X industry. If your company isn’t doing that, I don’t think you’re ambitious enough. The average price of a lawyer in the US is $353 an hour. No one can afford that. This technology is a perfect fit for lawyers and I think it will have a pretty large effect on the average person too. The largest competitor for us is just not moving fast enough. I think that indirectly you want to make sure that you are building a business that is going to survive the next 10 years of model releases. If you are willing to take some pain and to deal with some sacrifice, your impact and what you will learn are just massively compounded more than maybe any generation or any time in tech.

Thanks for doing this. We’re sharing a new podcast room. It’s still coming together, but I haven’t been to this office. I’ve never been to this office, so I’m thrilled to do this. I’m curious. I was thinking you announced the fundraise last week and I probably got, I’m not kidding you, no less than 5 to 10 people on LinkedIn or texting me being like, “Hey, can you introduce me to Harvey? I want to go work at Harvey.” You’re kind of the cool kid on the block right now. That’s good, we need to hire. But I wonder, maybe is it good? That’s the question I have because all of these people are coming out of the woodwork. You see this fancy fundraise, you see these nice acronyms like AI. I wonder if they were to go into the reality of working at Harvey, would they be like, “Oh yeah, this is exactly what I want to do,” or are they kind of like clout chasing?

I think one thing that definitely is happening is there’s just a massive switch right now in just the zeitgeist of application layer companies. There’s going to be a lot of value that accrues there. So, yeah, there might be an argument that there are some people that are like, “Okay, I want to go work in an application layer company.” There are a few that are doing really well or that are in the news, etc. You might get more mercenaries than missionaries, although I will say that there’s also just a lot of folks that didn’t know what we were doing or they thought that we were only selling to law firms, or they thought that we were only a GPT wrapper and that was something that was bad.

I think that there’s a combination of yeah, there might be more folks that are flocking to whatever the next best thing is, but I also think there’s a lot of folks that want to do applied AI. There are a lot of ways to do that, not just from the AI standpoint but the engineering standpoint, from the GTM standpoint across the board. I think people are just starting to understand what those opportunities look like. So maybe a little bit of both.

How do you describe Harvey today? It depends who I’m talking to and I should probably have the same one. I think the best way to describe this, and I’ve talked about this a bit, is like if you were using these models and you aren’t basically saying, “Look, we’re going to take AI and apply it to an industry and transform as much of the industry as we can by partnering with the industry,” I don’t think you’re being ambitious enough.

I really do think that if I was to describe it at like the highest level, it is like domain-specific AI for legal, tax, compliance. That doesn’t sound like, “Oh, this is this small point solution that just does contract review,” or “This is this super small point solution that does patent search,” or something like that. It is kind of a broader mission of saying, “Look, I think this industry really hasn’t adopted much technology in a long time.” This technology is a perfect fit for lawyers; it’ll really make their jobs significantly better.

I do think there are a lot of downstream effects of this. The average price of a lawyer in the US is $353 an hour; no one can afford that. Maybe a lot of people in San Francisco can, but across the country, most people can’t afford that. I do think this technology is not only going to make the lives of lawyers better, the big corporate lawyers, etc., and help them navigate all their issues faster and at like a grander scale internationally. I think it will have a pretty large effect on the average person too.

Something that’s pretty interesting, and you had kind of in the beginning, even when we met at the Series B, is as a vision. Can you unpack that a little bit? The axis to justice? There are a lot of hurdles to getting here. One of the main things is just like the quality has to be so high. When you’re selling to law firms, you have a lot of hierarchical review. I think people missed this when they’re thinking about, “Oh, sometimes these tools make mistakes,” etc. Actually, none of this goes straight to the client.

If you are at a very good law firm, you are doing the first pass of something and then a more senior associate is reviewing that. Then that gets reviewed and that gets reviewed, and then it goes out to the client. At enterprises, it’s the same thing, although a lot of enterprise teams are more leanly staffed. A lot of what you’re doing is also sending stuff internally. There’s a lot less hierarchical review in a large legal team in an enterprise, but it still happens. If you are building something for consumers, the output needs to be 100% accurate. You really have to do this right.

I don’t think you want to give a tool to consumers that gives them bad legal advice. That is a terrible situation. I think that is something that you have to think about when you’re navigating that issue over time. I think it is something that we would be interested in in the future, and there’s a lot of stuff that we are doing right now that would potentially set us up for that. But really our main customers today are attorneys at large firms, medium-sized firms as well, now a lot more in very large enterprises.

Isn’t it weird? We at KP run two CIO groups: one Fortune 100 CIOs and the other tech CIOs. In the Fortune 100, they are as forward-leaning, if not more, into AI and adopting new technologies as the tech CIOs that you would traditionally see. Isn’t that crazy? They take law firms and lawyers to the most logical extreme of that example. It’s massive. I think there’s a lot of reasons for this.

I can give you one reason. One of the more interesting ones to me is I spend a lot of time with chief legal officers (CLOs), which, by the way, is a term that didn’t exist—like a position that didn’t exist for a while. Seriously, it was like general counsel and the chief legal officer. One of the reasons why the term changed is they are business advisers. They are not just what you might think of as a traditional big law attorney or something like that. They are an integral part of advising the direction of the business.

That is what they are doing, and so I think there is not only a massive amount of interest general in AI from the Fortune 500, but especially in legal too. The reason why is a lot of in-house teams are just massively understaffed in terms of how complex their issues have gotten over the past 50 years. How many international mergers happened 50 years ago? Very few, right? How much more complex are all of the regulations and all the compliance checks when you’re operating in 70 different countries than it was even 20 years ago?

What about all of the data laws and things like that? Data processing. I think there’s been so much interest too from the Fortune 500 side just because this is a necessity. They’re getting to a point where they do need help getting around all of these tasks. Do you feel like it’s too magical for them? Is it still in the days of prove it? I don’t believe you; you’re just this tech startup out of Silicon Valley, right? We know these models hallucinate.

Here’s the problem. The best way to get through to folks like that is one of the things that we’ve started doing in the product is we build workflows. You can call these agents. I think a better way to call them is like agentic workflows. What you’re doing is instead of it being kind of like a chat interface or something like that that attaches to different knowledge sources, it is a system that is designed to do a particular task from start to finish.

My point here is if you were designing systems like that, you can get incredibly accurate outputs in a very specific domain that they understand. An example of this is it would be very hard to build a tool that can do every single merger analysis task—like all of the diligence, all of the HSR compliance, all the secondary questions, and all of these different things. But if you build a tool that does each piece of that, that is incredibly useful and it’s very impressive. Folks actually get their hands on it.

For example, we built one workflow that basically you upload the target company’s financials, the acquiring company’s financials, and it just tells you in multiple, like dozens of countries, what you need to file for antitrust. Then we’ll build new features on top of that, but having that output for a merger that your company just recently did and checking that that is correct is a massive moment for them. I think similar to the ChatGPT moment in their particular specialization.

Let me ask you, and feel free to give me the VC answer or not, but did you believe that a company like Harvey comes along and then you work backwards to figure out how can I make this? How can I do this? Or are you in the camp of here’s my mental model of the world, here’s my thesis of how I think all of this is going to play out, and here’s how Harvey fits squarely into that thesis? How do you work backwards from? Are you a prophet or not?

It’s basically pretty clearly not. I think it’s a little bit of both. You have to have a prepared mind on these things. For us, from just an investment thesis perspective, you look at things that are happening. You even look at the ChatGPT experience on the individual consumer level, you get a sense of what is possible. Pretty simply put, we sort of said, “Hey, what are the highest paying jobs out there in the world that really need a co-pilot-like experience?”

Where they have some significant percentage of their data that’s automatable, but still, because of maybe the subjective nature of some of the decisions—like the chief legal officer sort of translating law into business outcomes—you still really need a human that’ll do that work. But that human needs better tools because you can’t pursue all these mergers and downstream effects in a single headspace. Who can build the right kinds of products for them?

Lawyers were sort of at the top of that list, and we obviously knew of Harvey for a while. I think we were a day late to the Series A, which stung quite a bit. Lo and behold, Winston and Gabe walked into our office and said, “Hey, we’re raising a Series B.” So we knew that this was incredibly interesting. Obviously, a lot of it came down to the team construct, having somebody who’s been a practitioner of the craft and somebody who is deeply technical and steeped in research to create the right set of products.

That’s really how it came together. If you think about pretty much any job out there in the world, we will have a sort of co-pilot. You look at some of the deep research stuff that’s come out recently, and you can imagine that for investment teams as well, which is probably the smallest industry you’d want to really automate. It’s pretty obvious that this will happen. Then the question is who are the right folks to build it, and what’s their vision? What’s their path?

So much of the story that Winston and Gabe told us made sense. It was to go after the biggest top AM100 law firms, go after large enterprise in-house legal. Those folks work together. It’s a pretty clear collaboration graph. They can rely on multiple levels of review and having the right tools to answer these very intricate questions. Ultimately, it’s all in service of helping them make that critical business-impacting decision.

We talked to a bunch of customers, and they said, “This is the best thing. This is magic. It’s like magic flying off the shelves.” All the customers wanted to invest in the company, so that’s pretty good signal. I think the collaboration thing is going to be something that’s really interesting. I think that’s one of the largest product problems in the next couple of years.

You know what I mean? Collaboration is just hard to do in a product at all between two humans. But how do you do collaboration between AI agents that are doing parts of the work plus internal teams plus external teams? That’s something that we need to focus on, and we’re spending a lot of iterations on is how do you make the product not just collaborative between folks inside a law firm or inside an enterprise, but between the law firm and the enterprise?

I have a third party that’s involved here too, which is the AI systems that are doing the first pass of this work. So far, one thing that we underestimated in the beginning and that we’re spending a lot of time on is how much in the product you need to have back-and-forth communication between the user and the system. People talk a lot about how good the models are at output and instruction following, but I don’t think people talk enough about how you make it easy for the user to give instructions.

This is something that I think we struggle with a lot as humans, but we don’t pay attention to it. Give me an example. Communication, right? I’ll give you an example: struggling to prompt something. At the end of the day, a lot of it is just how good are you at communication? Seriously, a lot of the very low or entry point into being good at prompting is how good are you at giving all of the context necessary?

My guess is if you went into a law firm and you found the folks that were the best at prompting, my guess is those are the supervisors or managers that people like working with the most. I would actually be very surprised if that wasn’t the case. My point here is there are a lot of things in the product that you need to do to help the user basically give input or give intent. An example of this would be like we have shoulder taps.

A shoulder tap would be like a follow-up question, making sure, “Hey, you’ve uploaded this type of document before. Which type of workflows would you like to run off of that?” Instead of you having to find the workflow button that does XYZ, can you check this output to make sure that the formatting is correct or that we’ve identified the correct parties before we take 10 minutes to run this process?

These are all just things about how we all communicate. I will do a bunch of emails, I will do check-ins. I’m not going to give somebody a task and then say, “Okay, I’ll see you in 6 months, hopefully that goes well,” zero check-in, zero back-and-forth communication. This is going to be something that’s really important for these products because I don’t think we’re going to get to the point—maybe we do, but I don’t think so—in the next couple of years, where you basically just give the models all of the Activision and Microsoft documents, and you just go, “Merge, please,” and then it just does that.

I mean, there are so many different pieces of data; there’s so much context that is specific to what the clients want. There’s context specific to how you structure it for taxes and all of these different things. I don’t think the models are just going to be able to one-shot that. I think a lot of the product service is going to be how do you make it so it’s easier for folks to communicate with the models too—not just model performance but also the other side?

Can you help me reconcile something? Like Ilia calls it a co-pilot, right? You have this idea of an agent which is, in many ways, doing a lot of the work of what somebody would be doing. You see Salesforce that goes out and has their AI cloud or whatever, and then they’re telling customers that they can replace all of their salespeople, and then they go hire a giant sales team to sell their AI cloud. How do you see that world unfolding? Are we just saying that now so we don’t scare away customers that we’re going to replace people?

I think the reality is there is going to be a lot of task automation. Task automation doesn’t mean job automation. That’s something that people miss a lot. You are a transactional attorney; part of your job might be looking in a data room and finding change of control provisions. Is that going to change in the next couple of years? For sure! These systems are going to be able to do that at a level where you are checking, and you’re probably not doing necessarily hands-on all of that work yourself.

Are you an M&A attorney now? That’s it, no. There are so many different pieces, especially to high-knowledge work, that I don’t think these models are just going to be able to automate immediately. I think what you’re going to see is increasing levels of task automation, which again does not mean job automation. Those are different.

Do I think that jobs have to change if 70% of the tasks in the next 3 years of your job are automated? Does that job have to change? Yes, but that doesn’t mean that it happens overnight. It’s not just your entire job is gone; it’s the tasks that are automated over time, and then your job’s going to change. I think that will happen. I do think jobs are going to change.

I mean, I think it’s kind of like the early days of Excel. If you work in finance today and you can’t use Excel, you know you can’t work in finance. But it doesn’t mean the job of finance went away. It’s just you have to use this tool as a core part of your toolkit and be really good at it. I think that’s a good analogy because there are tons of tools like Harvey for financial services, and they’re going to build systems that automate Excel.

Does that mean that everyone that works in private equity is gone? No, right? It’s just like some capital states exactly our work is as subjective as it is. My point here is that the task is going to change, but they’re probably just going to do different things. Also, I think, and this is maybe less about every domain and more about the domain that we work in, a lot of what you want to do as a lawyer are these things that I think you will be able to do much faster in your career.

Fifty years ago, more people were like the CLO—most lawyers were business advisers. They were not basically sitting in data rooms. A legal officer, yeah. I mean, or they were an adviser; they were a strategic partner to businesses. Now, you have to be basically the tippy top of the profession in order to be doing that. Most of what you’re doing is basically giving those advisers all of the insights from all the work you’re doing so that they can advise. That’s what you’re doing, right?

My point here is the more you can automate a lot of those tasks, the earlier on in your career you can start doing the advisory work. I think that’s going to happen, and from a client perspective, I’m a client of a lot of law firms now. My perspective on this has even made me more convinced of this as I’ve been more and more of a client of law firms for that very expert advice.

I would pay more. The reality is, for should you buy this company or should you structure the deal like this, or should you incorporate in XYZ state—things like that are incredibly valuable, right? I would pay more for that than I’m currently paying for if I could pay less for the stuff that AI could automate, if that makes sense.

It makes total sense. It’s also kind of an interesting question to ponder whether that’s ever going to be automatable or managed by a model, right? Because it’s so subjective. I think that’s right. There’s also just like there’s so many pieces. A lot of people have the argument of, “Well, these are the things that no matter what, these models are not going to be able to do because of XYZ.”

I don’t know where I stand on that. I do think that the models can do a lot if they have all the context. But the reality is, are we going to carry a model around with us in our pocket that’s listening to all of our conversations and all, and we’re giving feedback on what our instinctual responses to people are? I don’t know, maybe. I think even with that, there are tons of things that humans do that these models couldn’t do.

But I also will push on the point that if there’s more context for the models, they will increasingly be able to do more things. How’d you pick the name Harvey? This gets a lot of questions. We actually incorporated as Council AI, which is intense. Every time I look at it, I’m like, “What is this?” It’s definitely intense.

Yeah, you do business, like that is a very business name. We had that for a while and it was kind of like Council AI, and we just had people didn’t give feedback as much there. There was something about the element of if we thought if you could give it a name, people would start basically saying, “Harvey did really well at XYZ,” or “I wish Harvey could do D,” or something like that. The other thing that it also ended up doing is—it makes it, people are better at prompting. I swear to God, people got better at prompting. When we changed it from Council AI to Harvey, they got better at prompting. I think going back to my point of prompting is a lot about communication ability. You start thinking about it as a coworker, and so you end up actually prompting the system in a better way.

Now our system doesn’t rely on prompting as much because we have a bunch of routing and we have these knowledge sources and all these different things, so it just doesn’t matter as much. But in the beginning, it was a huge deal anyway, so Harvey definitely was influenced by Harvey Spectre from Suits, for sure. I think another piece of it is Prestige, which is really important in Professional Services. It does kind of sound like Harvard, and I think that had a little bit to do with it, if I’m being honest.

I think there were a couple other characters that were named Harvey that kind of influenced it, and we’ve said it. I remember Gabe, my co-founder, was just like, “This is perfect,” and we went with it. It’s been really good since. There’s also something about the balance of it that is quite good in terms of like the lettering.

I think Suits went back to Netflix right before the Series B. Yeah, it did, so it’s like Peak Netflix got the rights again. So, it was like Peak Suits, Peak Harvey moment. The timing was coming out perfectly; it’s free marketing.

We were talking about brand and maybe hiring people that are involved in brand, and you immediately were like, “I’m never giving up brand. I’m never giving up this component of the business.” It was clear for you. I wonder where that comes from.

For lack of a better explanation, it is respect for the industry. There have been a bunch of companies that have come out of Silicon Valley and basically said, “We’re from Tech and we’re going to completely automate your industry” without tons of respect for it. I think that’s something we care a lot about, and it’s not just for show. I actually think a lot of our companies’ success has come from this, from the product side, from the client’s side, from kind of everything.

You have to partner with these industries. If you are building, you know, earlier I said you should take AI and basically apply it to X industry, like if your company isn’t doing that, I don’t think you’re ambitious enough. If you are doing that, you need to partner with the industry. These industries are incredibly complex. Legal is one of the oldest professions known to man. There are firms that are over 100 years old. There are firms that are hundreds of years old, and having a brand that says, “We are partnering with the industry to transform it,” versus “We are just going to steamroll the industry,” is really important for us.

I think that it’s led to a unique brand where it’s very tech-forward but also caring about tradition. I think we’ve nailed that, and we have really good brand folks at our company. I think the other piece of it is just the importance of making that clear to clients and interacting with clients, helping them define the brand too. That came through in diligence, pretty obviously at the Series B and even more so most recently for the most recent round where you expect to call a decade-old law firm or a multi-decade-old law firm and then say “Hey, you know this tech stuff is pretty cool,” and they were all in. They were like, “This is the future. We got to get on the train.”

If we don’t get on the train, we’re not going to be in the future, and Harvey is truly our partner to help shape that future. We’ve actually rejected VCs because they have had conversations with clients that have been offensive to the client.

The other thing too is it’s created a lot of respect internally at the company. Early on in the company, we sometimes had speakers come in. We had a speaker come and kind of talk a little bit about all the public information about the Dell take-private, and I think that was when our engineers were basically like, “Oh my God, this is incredibly complex work. I understand how important this industry is.” Doing more of that, especially if you are a company that is operating at state-of-the-art technology plus a conservative industry, requires bringing your entire organization together around that, as well as the customers.

When you are an associate at Melvin, were you like, “I’m definitely going to go start a startup one day?”

Not a startup. I knew you wanted to get out of in-house. The reason I actually became a lawyer was I was not a very good student. In the sophomore summer of college, I had an internship at the U.S. Attorney’s Office in the Eastern District of Louisiana. They’re federal prosecutors, so they work with like the FBI and that was so cool. I looked up to them; they all went to really good law schools, etc. I looked up the GPA for getting into those, and I was like, “Oh no.”

Actually, I was like, “Yeah, so I need like a 60 basically for two years in order to do this,” something close to that. But my point here is that was an amazing experience for me and it propelled me forward to do this. Then what happened? How did you actually get there? I think when did you quit? Why did you quit?

So the main motivation was the U.S. Attorney’s Office. Then what ended up happening was I worked at O’Melveny in L.A. and I loved it. It was a great firm. I really liked being a litigator. I think I always wanted to go back to being a U.S. Attorney. My plan was basically to go back, be a U.S. Attorney, and then start up my own firm. I think I had a decent amount of experience with what are the things necessary to start up a firm, and you end up spending a lot of time in the industry thinking about that and talking to a lot of people.

I talked to a lot of people that had spun out into like Houston and Hennington, a famous litigation boutique in Southern California. Up until I met my co-founder Gabe, I had no idea I was going to do a startup at all. I didn’t have a single friend that worked in tech. I didn’t really know anything about the different VC firms or tech companies, and so it was more I had a plan for what I wanted to do in the legal industry. That was entrepreneurial, but I didn’t have a plan to do a startup.

Your co-founder, how did you meet?

We met in San Diego about five years ago. I was still in law school when he was working on an education startup. Then he ended up working at Meta, and I was at O’Melveny. He always wanted to do a startup, and he was talking about a different one. He was doing one at the time, but he wanted to do other ones, and education is hard to get funding for.

I think we kind of talked about this for a bit, and I saw gbd3, which was the public version of gbd3 before gbd4 and anything like this. We started using it; we started doing chain of thought prompting, which was something that was not really a thing at the time, but we called it that at the time. It was basically just like here are the different rules, and based off of this rule, do something else.

After this step, do one of these steps depending on what else happens before it. We did this on a hundred questions from r/legaladvice, which is basically a subreddit where people ask, “Hey, who do I sue for like my neighbor’s dog bit me?” or something like that. We did it on a bunch of landlord-tenant questions, and we gave it, she went into Reddit and took all the questions and answers, fed it into the model.

No, we weren’t doing training at this point. It was just gbd3 with chain of thought prompting. We took those questions, we ran the model over them, and then we basically gave a left and right panel to three landlord-tenant attorneys. We just said, “Here’s the question that a client asked, and here’s a lawyer’s response. Is this good? Would you send this response to the client with zero edits?”

Eighty-six out of 100 of the questions were yes. We literally grabbed those and cold emailed Jason Quan, who is now the CSO at OpenAI. We pitched him on the idea of like “Okay, we should use these models.” We were thinking about doing consumer at first, and there were some reasons for not doing that.

Then we ended up pitching to the C-suite of OpenAI a couple weeks afterward. I think it was actually the morning of July 4th, 2022, which was a vibe. We raised after that.

What did they say when they saw the work you were doing?

When we sent it to Jason for the first time, he was like, “I had no idea these models were this good at legal.” It was really smart of us to send it to a lawyer because one of the things that’s difficult with products, with models, with everything, is if you are building something and the output is just a bunch of text, you have to really get somebody to actually know what that text says.

I know that sounds like a really stupid and superficial thing, but this was a huge advantage for us in the beginning in terms of GTM where we would literally find things that lawyers had filed. We would take those out of the federal court dockets and say, “Make 10 arguments against this,” or “How could you better draft this?”

The lawyer would recognize their work, so they would read it and recognize whether the output is good. Otherwise, it’s how do you do a demo if you have an output that is 10 pages? It’s hard to tell if it worked or not.

When you came to our LP meeting and presented to a bunch of our limited partners, I think you were coming off a plane from India.

That was Singapore. Yeah, that was India. Then my plane got canceled, so I had an 18-hour layover in South Korea. I landed, and then 15 minutes later I presented to you guys. That was a rough one.

Well, you rocked it.

I don’t remember, so I have no idea if I rocked it or not.

Is that an atypical week for you? It seems you’re not an easy guy to pin down because you are everywhere all at once right now.

I’m better now. This is like maybe one of the hardest things for me on a personal level: just figuring out how to scale yourself as the company scales. Last year, I traveled that amount, especially the beginning half of last year. A lot of our GTM motion in the beginning, and it’s much more scalable now, was basically travel to different countries.

We did it in the U.S. We went to all the top law firms in the U.S., and almost all of our first 50 customers were referrals from law firms. We actually weren’t even reaching out to the enterprises; the law firms would intro us to their clients.

My point is we did this in the U.S., and then we did it in different countries. We’d travel to London, and you’d sign one of the firms there, and then they intro you to customers in London. You do the same thing in Germany, do the same thing in France, and my VP of sales did a lot of this. Our early GTM folks did a lot of it; I did a lot of it too in the beginning.

I think I probably did it too long, but that’s something I have struggled with over time. It’s an advantage to make sure you do all of the stuff yourself because then it’s easier to enable a GTM, etc. But I probably did it for too long.

Can you put a finer point on “too much for too long”? What does that actually mean?

It depends on which role. The best way to describe this is I believe founders should do almost every single role at a company for like a month or half a quarter or something like that before they hire for that role, if not a full quarter. If you are doing 95% of that role after one quarter, you probably have not delegated it correctly.

I think that was a problem for me; it’s still a constant problem for me is delegation and making sure that I am hiring six months in advance, a year in advance, etc. It’s something I constantly struggle with because I actually do think I learned a lot from doing a lot of the roles myself in the beginning. I learned a lot, and I think I had better hiring there.

Do you think you’re better at scaling yourself now? You mentioned that things are getting better.

Yeah, what do you attribute that to?

One of the best ways to get better at it is to have a leadership team that holds you accountable for it. Seriously! No, I’m serious; they hold you accountable for not playing ball.

I think there have been a lot of folks on my leadership team that have helped me with this. One of the things that helped the most was that I had someone from my leadership team actually do this fairly recently, which really helped. We have hired a bunch of junior talent that I care a lot about, and there were fights about whether we should hire a more senior person.

We got external pressure for all of those things, but I made a lot of bets on junior hires. I had someone from my leadership team recently say, “Look, if you are spending all this time trying to do all these jobs yourself, you are actually not spending time letting any of that junior leadership team grow, and you aren’t getting better at coaching, teaching, or doing any of those things.”

That was a huge deal for me. I think the argument of, “Oh well, you’re not going to be able to scale yourself out of the heroics,” worked to a degree, but it didn’t convince me that much. I was just thinking, “Oh no, I can work more hours.”

You know, I used to wake up every morning. Most of last year, every morning, I’d wake up, and there’d be a message from Winston at 3 a.m. or 2 a.m. Some sleep guy. I think that stopped, and I got better. Part of it has been just scaling; you just can’t.

How big was the company at the end of last year or how many people did you just finish?

We were 240.

Where did you start at?

About 38.

Yeah, it was a lot. I mean, I think that it’s definitely a combination of hitting scaling limits where you literally have to figure out how to better delegate. You have to hire people, teach people, and enable people. The second piece that worked for me on a personal level was that you were just not giving people a chance. You weren’t giving junior talent a chance to shine here.

What percentage of your team are researchers, and what percentage are lawyers?

We have lawyers across a bunch of different roles. We have internal lawyers that do traditional legal advisory things for the company. We have lawyers that help with go-to-market both on pre-sales and post-sales. These are kind of like your domain experts that help explain how the product works.

We also have lawyers in a unique role, basically product specialists. They’re helping with not only designing a lot of the workflow from a domain-specific area. You have to say, “You have to do this process, then interact with this internal data set, then interact with this external data set, etc.” That data isn’t on the internet anywhere, so you have to collect it from domain experts.

We’re looking at how do you evaluate a merger agreement? We’re looking at how do you transition to autogring? It’s still going to require a lot of human labor for a very long time, I think.

Across there, we probably have around 50 lawyers.

What about research?

That depends on what you call research, but our EPD organization overall is around 85 to 90 people right now. Our engineering organization is a little bit on the smaller side. In there, we have about a third of folks working on applied AI.

When I think about the composition of your company, these aren’t the traditional people that I think of as company builders. Meaning, a lawyer or a researcher at an early-stage startup.

Do you feel like if you’ve been there from 30 to 250, you’re probably… Even if you’ve been at startups, you’re like, “This is insanity.”

Yeah, we definitely have that. We have made so many organizational mistakes, and we will continue to make mistakes. It will get better, and I think to your point, there are two factors we’re trying to navigate. There’s entropy; you have to deal with that. Some of that is you have to just power through it.

Some of that will get better as we scale, and you have to trust leadership that it will do that. The second piece is, yeah, this is a very different type of company. Some stuff is similar, but a lot of how we structure our EPD, we’re definitely thinking about that all the time.

I talked to the CPO of Anthropic and OpenAI a decent amount about how they structure their organizations, and it’s a work in progress that makes me feel a little better. It is different, and I think what we are trying to figure out and getting closer to is creating an organization that does AI patterns.

These are basically the 100, 15, 20, whatever it is, things and systems on the AI side that we need to get better at as a company. This is maybe certain types of retrieval problems, clause extraction, clause comparison, follow-up questions, query routing, and the orchestration layer of our product.

All these different systems. If we have a team focusing on making those systems better, doing research on how to improve those systems, you can then give that to the rest of the organization, and people can incorporate that into the product in different ways.

I think those are two different muscles. It’s very complex to figure out how to do the handoff and all of that.

It turns out revenue solves all problems.

Yeah, I mean, I don’t know. But it does.

I definitely agree. It allows you to make mistakes.

Yeah, it’s a massive work in progress. Can I actually chime in here for one second just to interrupt? I think there’s a very common misconception that these great companies, the canonical companies we know of, don’t make mistakes. In fact, they made as many, if not more than most companies. They just have great product-market fit, and they’re executing pretty well on a rising tide.

But the idea that the best companies don’t make mistakes is such a misnomer. The idea they are perfectly structured from day one, and it’s just always the correct swim lane, and there’s no thrash, that’s the antithesis of what moving fast looks like. Part of this is I think you have to build trust with your team.

There are two ways to do that. One is they have to trust you, and you have to admit when you make a mistake and say, “We messed this up. We’re going to fix it.” That’s one piece that’s really important.

The second piece is I think when you are hiring, it’s important to make that clear. The way I’ve started doing this is like, “We are on an incredibly compressed timeline.” I’ve said this a couple of times: the next 10 years, if you are willing to deal with some thrash, make a lot of sacrifices — and maybe sacrifice is the key word here — if you are willing to take some pain and deal with sacrifice, your impact and what you will learn is massively compounded.

More than maybe any generation or any time in tech. You need that combination of trust plus the willingness to make it very clear that that is what you’re choosing to do as a company. You might make it so that some people aren’t attracted to that type of company, and they don’t want to make that sacrifice.

But I think you have to be transparent about it. I don’t know if I did a good job of being transparent about it in some of my hiring. I think this is something I’m revisiting — making it clear that that is the trade-off you’re making by joining us.

What type of sacrifices do you think you make? What are the trade-offs that you feel like most people don’t see in a given week or a month that are actual sacrifices for you?

This is actually a very good point about sacrifice. So it is not hard to get me to work it. It is not hard to make it so that I do nothing but work like I am obsessed with this. That’s not hard; I don’t know if that’s sacrifice. I actually think that for me part of sacrifice is I hate delegating. It’s hard for me to get good at delegating, right? And so on the surface, you might look at me and you’re like, “Oh wow, all he does is work.” He literally is not taking a day off in this amount of time or whatever, and all these things must be sacrificing a bunch.

I don’t know if I am. I love this; it’s so much fun. It’s easy to be motivated. I’m one of the co-founders, and so you can kind of get away with not actually getting better at the things that are not sacrificing the things that actually are sacrificed to you, right? I think for me, one of the hardest things is delegating and spending more time on system building than just working an extra couple of hours to make sure something gets done and doing it myself.

When I think about sacrifice, I don’t think about it as, I know there’s all this Twitter stuff about being in the office till a certain amount of time and all those. I think that’s important, and I do think that is sacrifice. But I think there are a lot of ways that it can look like sacrifice. I’ll give you another example of sacrifice: dealing with thrash and saying, “Hey, I’m maybe not in exactly the org structure that I wanted,” or this was a very hard thrash. I was put on this project; I don’t really know what the outcome is. Patience might be something that’s sacrificed for a lot of people.

My point here is I think that a lot of folks, and we have a lot of folks at our team that are like this too, that you can kind of… it looks like you are making tons of sacrifices, but in reality, it’s like you’re actually fine with all the things you’re doing. It might be something that’s unique to you that’s hard for you to do. That’s how I would talk about my sacrifice. It’s like the work 24/7 and things like that.

I don’t know if that’s sacrifice; that part’s actually pretty easy for me. It’s these other things that are hard for me. And so it’s like that’s what going on podcasts is, right? Things like that—I’m not a big public speaking or press person. It’s harder for me to go on a podcast than it feels to you like a distraction from work.

But it doesn’t matter. My point is, like, for maybe most people, the podcast is cooler or something like that, whereas working every weekend is like a sacrifice. To me, the podcast is more of a sacrifice, and working every weekend is easy. So I don’t know; it just depends.

Do you feel like there’s such a thing as too fast? When the opportunity is so clear and ahead of us, is there a thing as going from 30 to what are the hiring plans for this year? Yeah, like probably double. You can’t go too fast. I think there’s such a thing as too fast.

I think there are two dimensions to too fast. Just on the org side, you know, the question is how fast can you grow? You can triple, you could quadruple; beyond that, it starts to get pretty tough within a year, mostly because if you think about it, if you’re going from 40 to 240 people in the span of a year, at some point there are more net new folks on the team who have been there less than the existing team. The existing team has been there, and so the energy of the existing team goes towards onboarding and training the people who have just come in, and you distract—obviously, those hours you take away from doing the actual work of growth.

There’s just kind of a natural physics and sort of rate of growth you can have. Now, back to the revenue point: revenue does solve a ton of problems and lets you make mistakes. If you have a high revenue growth, you can definitely scale pretty quickly, but you also need to make sure that those folks are then onboarded, integrated, and on the same page.

Because at the scale of going from, you know, Dunbar’s number at 150, where it’s like the number of people you can keep in your head—you know everybody’s name, you know everybody’s name—but that also relates to organizational context. Below that number, you can pretty easily get the whole team on the same context. Because you can kind of have the relationships.

Beyond that, you really have to work at getting people onto the context of what’s important, what are the priorities, where are we trying to go, here’s a mistake we made, we’re going to change things up. All of that is just a function of communication, and it gets harder and harder, and it’s something you really have to invest in as the company goes.

Then when you get to a bigger scale, it’s easier to double and maybe kind of keep doubling year-over-year because you already have this foundation and kind of institutionalized knowledge and muscle memory for doing it. So that’s one dimension.

I think the other dimension is like there’s infinite opportunity, especially for a company like Harvey. There’s so many things to be done in legal and professional services and enterprises and international. In sort of different types of organizations, different types of subverticals, there’s stuff you can do on the technology side of things, on the collaboration side of things. Just how do you sequence them, and how do you make sure that you actually deliver things sequentially in a way that’s creative to your customer and helps you build your business and not, you know, peanut butter your organization in a way that actually slows you down?

I think that’s kind of the art of it, and that’s what I think a lot of folks who hit hyperscale probably get wrong more than they get right. Your opportunity to do more and your ambition become bigger and bigger as you scale. So how do you actually sequence these things out in a way that’s the optimal trajectory for the company that lets you ultimately deliver the best experience to your customers and really scalably grow your business?

Yeah, the saying no part is really hard. That is something I did a terrible job of last year, and I think I’m doing hopefully a better job of it so far this year. There are so many good ideas, but the good ideas come at the cost of great ideas, and there’s only so many great ideas. I think that’s right.

And it’s like there’s also another thing too: one weird—I mean, great position to be in as a company is there isn’t tons of AI penetration in enterprises, right? And so sometimes our customers will say, “You know, we work with a large Fortune 50 company or something like that,” and they’ll basically say, “There really isn’t like an AI software that we’re doing for this HR workflow,” because you may kind of rotate towards that. “You guys are smart AI guys!”

Yeah, serious, I’m serious. It’s very hard as like a new founder to not be like, “You’re a big logo. Yeah, we’ll do HR too.” Right? Seriously. Then you kind of make an argument that it’s like, “Well, HR is some compliance things that are related to it.” And so that’s still legal, tax, and compliance, etc. But my point is that saying no part is definitely hard for me, and I think it’s something, again, like when you surround yourself with a good leadership team.

This is actually something too that—not just leadership team, but like junior leaders are really good at. I have a bunch of folks that are really good at being like, “That’s come on, Winston, like no, I’m serious.” It’s not necessarily like SE—it’s a lot of like, you know, junior managers and things that are like, “We’re super bandwidth constrained,” and it’s like, “I know you’re excited about this, but like come on.”

I don’t know; I think that’s been really helpful too. I actually have a lot of trust in those junior folks, and I’ve started to listen to them more for those things. Speaking to the point of how do you make sure you scale—it’s like, well, if they can do that with me, they probably can do that with their teams, right? Hopefully.

I think that’s a good way to scale, is getting into the culture of people being like, “Whoa, wait, we should prioritize this instead,” and like asking and requesting to make the priority super clear.

Who is the best you’ve ever seen at saying no at sequencing? Am I asking you to pick a favorite kid? Right now? No, I mean, I don’t think anybody’s particularly good at it. I definitely don’t think anybody’s perfect at it because I think it’s this constant battle of, “Oh, here’s like this awesome thing we can do.” It may not even be like one customer asking; maybe it’s like, “Hey, I’ve had this thing on the whiteboard for when I founded the company; we’re going to do this and we’re going to do that thing, and we’re going to do this other thing.”

Now I have a team, I have resources, I raised capital—let me go do all those, right? That’s the impulse, and I think it’s a little bit of an art because you don’t want to say no to all these things because maybe one of them will actually be the really, really good important one. But you kind of need to know when to say yes to it, and it’s almost like you need your point, this organizational infrastructure that creates enough tension for you to say no to the low conviction things.

And then, when there’s something you’re like, “No, you’re all wrong. I know this is true; I’m going to go do this,” if it makes it past that barrier, I think that’s actually probably the best thing you can do: so build trust, right? If you keep doing this—this is a thing I did last year, and I think it was a mistake. Especially the first half, I think it was better than the second, but it’s like, if you constantly say yes to everything, then that last piece of like, “No, no, no, trust me, we have to do this,” you kind of lose the conviction there, right?

Versus if you keep saying no to a bunch of things for a while and you gain your organization’s trust with that, when it is time to be like, “You’re all wrong. We are doing this. My gut says do this,” you’re going to have full support because it’s like, “Oh wow, you don’t have conviction over everything, and this is like something you want to double down.”

It’s kind of like doing deals in venture capital. If you show up at a partner meeting and everybody is like, “Amazing deal; go do it,” you know, it’s kind of easy. Those happen, but then, you show up and everybody’s kind of like, they’re skeptical, they’re not sure, they’re giving you feedback. It’s the ones where you’re like, “I hear the feedback, but I see it, and I’m going to go do that deal.” Those wind up being pretty exciting.

Yeah, because you really kind of overcome internally all the sort of feedback that you’re getting, and then you have trust with the organization. You can’t do this on every deal; if you do, you lose a lot of money. Exactly, and your partners won’t take you seriously. But you know, you want that filter; you want that narrow band filter for the really, really good things.

On the people allocation and hiring, I had Marissa Mayer on, and she was telling me a story about when Eric Schmidt came to run to be the CEO of Google. Google had the world ahead of it, and they were trying to do everything all at once. It was around the same size that Harvey is.

He went and printed out Larry and Sergey bucks—literally like laminated dollars with Larry and Sergey’s pictures on them—and he gave a certain amount. There was a pool; the N was only a certain number, and each one was a higher. Then he would distribute an equal amount to the leadership team, and they had to basically horse trade for, like, this was the hiring cap, and if sales wanted a product built, then they could give up a Larry and Sergey Buck to the product and engineering team to go build that thing.

To go hire somebody to go build it. Anyway, one thing I really like about that is just the cross-functional collaboration on hiring. So like this seems like a huge problem when you’re scaling really fast. I don’t know which role this is under—whose responsibility? It’s kind of cross-functional. Figuring out who actual responsibility is to hire that role has been hard in some instances.

So you want some Winston and Gab bucks? Yeah, I don’t hate that idea, although I hate the idea of some of our leaders going out and gambling with it. We just have like one small niche part of the org that has like 100 people in it. There’s been like a black market for this; that’s what it turned into. There’s like a straight-up black market.

A couple more on competition: like, it’s interesting like number—and this is for both of you—but number one, I can’t—how many companies are there that are going after this AI for legal? I think I saw a market map a long time ago, and I have to zoom in a lot because it’s just text. But you know, I mean, I’ll let Winston app on this one, but you know, it’s the markets.

It’s not just like there’s going to be one legal tech company; it’s not like one market necessarily, right? There are segments of that market that are really important, impactful, and a good place to start. I think like the—where Harvey is, which is big law firms, big enterprise in-house legal teams, that’s like the juicy right entry point.

That’s a pretty interesting one because it’s, again, very collaborative. I’m sure there’s going to be, you know, AI for legal in a variety of flavors—obviously for lots of parts of the market—but to me at least, this is kind of the biggest media one because it really drives everything else. There’s trust, there’s brand, there’s reference customers—that’s like the place.

I think like one thing that I try to think about a lot is just like what can the models do in like 10 years, right? How do you build a company around that? You know the terra mode, etc. All these things. But I think like one interesting piece about the—let’s go back to like the Microsoft Activision merger, right?

This is work that is at the complete tail end of the models. It can just automate this. In other words, if we’re in a world where GPT can just automate that, most companies are gone. Let’s just be honest. I think that what’s really interesting about going after that type of work is it is incredibly messy; it’s incredibly context specific. The accuracy of it really, really matters.

We are trying to basically put all of our effort into that because we think that is not something that’s going to be solved by the next generation of models. I would say that when I think about competitors, I think that the largest competitor for us is just not moving fast enough. By far, I think that indirectly, you want to make sure that you are building a business that is going to survive the next 10 years of model releases.

That’s how I think about the product, that’s how I think about GTM, that’s how I think about basically everything. Is that the correct play? I think it will be in a while. But you know, we’ve had a lot of revenue growth. To be honest, we’d have much more if we made it self-serve and we kind of did a different type of route.

I think that stuff ends up getting kind of cannibalized by the models. When Gleam was growing in its early days, there was so much pressure on that team to self-serve—just give it away, right? Yeah, and they did it the hard way, similar to how you’re doing it top down, enterprise, get deep into the organization.

Yeah, it’s paying off really well for them. And it’s interesting too because we have a very land-and-expand motion too. We have this weird—like we have PLG. You have to get into the organization for the PLG to start. I think there are a lot of advantages to that.

The nice thing too—and we talked about this kind of early on—is we have the option because we have Fortune 500. You talked about these three parties collaborating; you have internal virality. In other words, at a law firm, you have different lawyers spreading it to each other across different groups in an enterprise.

You know, we’ll sell something to the legal team, and then they’ll tell the tax team about it as well, right? You have virality there internally. Then you have external virality. If you have a bunch of law firms using our product, they tell their clients.

Remember I said they mostly refer to the enterprise customers that we had in the beginning, and vice versa, where the enterprises will tell their law firms, “Hey, we’re doing a deal; are you using Harvey?” “We are!” I think that’s another way to kind of go about expansion. It’s just slower in the beginning, but I think you can still do PLG really fast; it’s just a different kind.

I think the other part here is trust. At the end of the day, if you sort of release something into the ether where people can self-serve, it’s bottoms-up adopted, whether it’s in a big company or a small company. But it’s not hardened yet, and it’s not fully trusted yet, and you haven’t been sitting next to the customer using it.

You’re potentially going to miss out because if you get something wrong, especially in something as critical as legal—that’s kind of a third rail. I think the approach here is really smart because you’re going to build something that is hardened, enterprise-grade, works super well, and then you can kind of let it spread on its own over time.

With Gleam, I think that’s the right way to do it. Yeah, we definitely got some flack for that in the beginning. I think we had a little bit of, “Why are you guys in stealth for so long?” One thing that folks haven’t really realized is we got our first client when we were four people. So a lot of this was we did design partnerships with a lot of our first clients.

It wasn’t like we were trying to stay in stealth to do some sort of marketing blitz. We still kind of just started a marketing org that was a late hire on my end. But it really was because, to your point, we wanted to make sure the product was a certain quality before it gets to the point where it’s like you can’t control it as much, right?

Where you are actually spreading it, people are referring it. Because the reality is, if you mess that up—it’s a big industry from a tech perspective, but it’s actually a small industry from how much people talk to each other. Everyone’s kids go to the same school, all of those things.

You really have to do well with your first impression, or you’re in trouble. So on the model question, are you hot swapping models on the back end, or are you riding a horse all the way? Oh yeah, on a new model. Yeah, yeah. So we’re constantly testing.

No matter what, we’re testing. The way that we’ve structured our product is we’re kind of constantly expanding it and then collapsing it. The expansion is these, whether it’s like knowledge sources—different types of data that you can access—or it’s a specific workflow that’s for a particular, like, very specialized task.

The nice thing about that is you can change the model or replace the model for a particular part of the product without just changing the entire product. It’s like a new model version, right? And so we use a lot of different models and a lot of different—even when I say a lot of different models, I mean a lot of different models from OpenAI.

On the testing side, we’re actually doing this right now; we have folks that kind of do all of the testing constantly. It’s a combination of lawyers plus research scientists, and we’re creating a team that literally just does this. Because there are going to be so many releases, even just this week, right? There are tons that you have to constantly be testing on.

Each different type of piece of your product: is this better or not? We have a bunch of benchmarks. I think those benchmarks over time are really important because the public benchmarks are not useful at all anymore for us. It turns out building a GPT wrapper is really hard.

Yeah, exactly. I mean, it’s just like—the benchmark is just, like, none of them are relevant to doing these tasks. So, wasn’t it frustrating in the beginning? Everyone was like, “Oh, you’re building a wrapper on top of OpenAI,” and now everyone’s like, “Oh, thank God you’re building a wrapper on top of OpenAI.”

One advantage is, I didn’t have any friends in tech before this, so there wasn’t like a prestige meter for me, and so like this bothered me to the extent that it ever affected hiring. That’s it. There were no social circles that this was affected by for me or anything like that.

I think I was a little bit immune to it to a degree. The thing that I did—I think like the place that did impact us a little bit was just that there were folks that, you know, didn’t want to join application layer companies at all. They only wanted to work at labs.

I think that definitely had an impact on us a little bit, although we managed to hire really good people, and a lot of those people were people that wanted applied problems—like real-world problems—rather than being kind of like the 19th, you know, nth person working on XYZ model improvement on a benchmark.

I think we did a really good job hiring a bunch of really good folks in the beginning that were much more—they really wanted to have a direct contact with the product and a direct contact with the client. Then now I think, yeah, it’s become easier and easier to hire folks.

We’re thrilled to be a small part of this and double or triple down. Congrats on the new fund! Doing the math—that’s triple; it’s triple, yeah. Congratulations on where you are so far and on the fundraise; it’s a good milestone.

Thank you. Help you go faster, if that’s possible. Are you hiring massively? Like everything? Bay Area? In New York? Yeah, I mean, Bay Area, New York, and London. I’d say probably one of the areas we’re hiring the most is just on engineering, just across the board—everything.

When you hear the word grit, what do you think of? Probably my point earlier about sacrifice. I think that to that point of what is grit—there are certain buckets that people think these are the things that show grit, whether it’s hours worked or how long you’re in the office or whatever it is.

I think the reality is sacrifice, or grit, or whatever synonym you want to use for it, is very personal to whoever it is that is exhibiting it. I have found that the best employees are the folks that improve themselves, and the way that they improve themselves is they work on the things that they’re bad at.

They don’t avoid the things that are really hard for them, and those things might be easy for everybody else. I think that is the important distinction: that those might be easy for everybody else, or they might be really hard for everybody else. It doesn’t matter. It’s whether that person is willing to work and push through the things that are hard specifically for them. Gentlemen, thank you. Thank you, this is fun.

Deep Learning Day: Generative Modeling

2025-03-06 08:00:01

Deep Learning Day: Generative Modeling

Good to see you everyone. In this second talk, I will talk about generative modeling.

So first question: who has used chatbot or anything like that? Maybe everyone, right? But who has heard the term generative model before you got to know ChatGPT? Well, still quite a lot, yeah. So in this talk, I will give a very high-level overview of what generative modeling is and how it’s impacting our life and our future research.

There is no doubt that we are in the so-called generative AI area. For public audience, perhaps this moment happens when chatbots or many other chat systems were introduced, so you can communicate with the computer in natural languages. You can talk, you can ask the computer whatever problems, and it’s just like an agent that can help you to solve many issues.

So this is not the only generative AI model. Another very popular and very powerful tool is the so-called text-to-image generation. For example, users can give the computer some text which is usually called a prompt, and then the computer can generate an image.

So for example, in this case, the prompt would be a teddy bear teaching a course with “generative model” written on a blackboard. It is very likely that the computer algorithms haven’t seen this exact image before, but this is how it can produce from the given text prompt.

We can even go one step further; we can ask the computer algorithm to generate a video. This is what was generated by Sora one year ago. This is just really impressive. I believe perhaps no producers have ever filmed a video in this way, having so many paper planes flying over trees or forests. This is completely generated by the imagination of the computer algorithms.

Actually, generative models could be very powerful productive tools in our daily life. For example, it’s still kind of a chatbot, but it is a tool that can help us to write code. This is kind of an AI assistant; it can read your code, it can try to fix the issues of your code, and you can directly communicate with the agent using natural language.

The agent will turn it into code. In some sense, perhaps the previous programming languages like C++, Python, or Java, the next level of programming language would just be English or human language. It’s more than that; it’s more than just computer science. Actually, generative models have been used in many scientific problems.

This is an application called protein design and generation. The ultimate goal is to design or generate some type of proteins that can solve some problems that we care about, let’s say some very dangerous diseases. This work is called RF diffusion. It is actually part of the work of the Nobel Prize winner this year, and there are many other scientific problems that can benefit from AI generative modeling.

This is a work from DeepMind a few years ago. They can use this model to predict the weather change over the next several hours or next several days. This would be a very difficult problem for classical algorithms because, as we may know, the change of weather or the change of climate is chaotic, so it is very difficult to predict it precisely.

We may not want to have the exact physical state of that moment; what we want is some qualitative behavior, let’s say whether it’s raining or whether it is windy at that moment. In this sense, generative models of deep learning could provide a very good solution to this problem.

Actually, before the emergence of generative models in our daily life, generative models have been used or developed for decades. This is a tool called patch match or content-aware fill in software like Photoshop. It was a very impressive tool when I was a PhD student, and at that time, I worked exactly on the same problem.

The problem here is that you will be given a photo and the user can specify some area or some structures in the photo. The computer algorithm is trying to fix or edit the photo based on user instructions. At that time, there was no deep learning, and to be exact, for this application or for this algorithm, there was even no machine learning.

It is a very classical computer vision algorithm, but conceptually this is also a kind of generative modeling. The technique behind this generative model can date back even another 10 years ago. This is an algorithm called texture synthesis. The goal is that you will be given an example texture and you want to extend the texture to a bigger image or you want to paste the texture to some 3D objects that you care about.

The idea here is just very simple; you just try to synthesize the texture pixel by pixel based on what has been synthesized. In today’s world, this is actually an autoregressive model. So this is basically what I’m going to talk about. In this talk, I will very quickly go through the concept of what a generative model is, and then I will introduce some approaches, some modern approaches to how we can build generative models using today’s deep neural networks.

Then I will also talk about how we can formulate real-world problems into generative modeling.

Okay, so first, what are generative models? It turns out this is a very difficult question because when generative models become more powerful, the scope of generative models keeps changing. Even though I will talk about some classical definitions of generative models, I just want to say perhaps today every single problem could be formulated as a kind of generative model.

So now let’s look at the applications or scenarios we have just introduced. What do these scenarios have in common? For example, in image generation, video generation, and text generation, there are multiple predictions or conceptually infinite predictions just from one input.

Let’s say if you want the computer to generate an image of a cat, you will tell the computer, “This is a cat; I want a cat.” Conceptually, there is an infinite number of possible cats. Another property of generative models is that some predictions are more plausible than others.

For example, if you want a cat, the computer may generate a lion or it can also generate a dog. Perhaps in common sense, a lion is more plausible than a dog in this scenario. Of course, a cat is more plausible than a lion.

Another very intriguing property of generative modeling is that your training data may not contain the exact solution. As we have seen, I believe the computer has never seen a teddy bear standing in front of a blackboard and teaching generative models. Similarly, the computer may not have seen these paper planes flying over a forest, so this is a kind of out-of-distribution generation.

The computer algorithms were trained on some data, but what they are generating is some distribution that could be outside of the training data. Additionally, most of the time, the predictions of generative models could be more complex and more informative. Conceptually, it is higher dimensional than their input.

For example, in text-to-image generation, if you want a computer to generate a cat, which is just a very short word, the output image would have millions of pixels or maybe even more.

All these properties make generative models way more difficult than some of the classical deep learning or recognition problems. In a textbook, this is a kind of formal definition of what a generative model would be. Usually, when generative models are introduced, people would compare it with a so-called discriminative model.

So what is a discriminative model? Typically, as you have seen in Philip’s talk, if we care about image classification problems, you will be given an image, and then you are going to train a model, for example, a neural network. You want the neural network to output a label, let’s say a dog. In this very simple scenario, we can just imagine a generative model as reversing this process.

In this case, you would be given a dog, and then you would like to train a model, again perhaps a neural network, and then you want to output the image, which is X. In this case, there would be many possible outputs, many possible dogs. The output will be higher dimensional and the output would be another dog that you haven’t seen before.

Then conceptually, this is kind of a probabilistic visualization of what a discriminative model would be and what a generative model would be.

So on the left-hand side is a discriminative model. You have some green dots, which is one class, and some orange dots, which is another class. The goal of a discriminative model is just to find a boundary that can separate these two classes.

Conceptually, the task is to try to find this conditional probability distribution, which means you will be given X, such as an image, then you want to estimate the probability of Y, such as it is a label zero or label one. As a comparison, in the context of a generative model, you would still be given the same data, the same dots, but the goal here is to estimate the probability distribution of these dots.

Let’s say in the case of this class that corresponds to y equals 1, you want to estimate what the conditional probability distribution of this class is. Conceptually, in a generative model, we care about probabilistic modeling, so that is the key problem generative models want to address, and that is also the key challenge.

You may wonder why there is probability and why we care about probabilistic modeling. Actually, in many of the real-world problems, we can assume there are some underlying distributions, and you can also assume your data is actually generated by some very complicated world models.

For example, if we care about human face images, we can formulate the problem such that there would be some latent factors, such as the pose, the lighting, the scale, and actually the identity of the face. This would be the latent factors, and then you assume there will be some distributions about these latent factors.

These latent factors would be rendered by a world model. This is, for example, how you can project a 3D object onto a 2D grid of pixels. What you can actually observe is just a 2D grid, so that is the observation X. Your 2D grid would follow some very complicated distributions that cannot simply be described by some underlying distributions.

This is why we care about probabilistic modeling, and a generative model is trying to uncover these underlying factors or to reverse this process.

Now, for example, let’s say we have some data; let’s say I have a dataset of dogs, which means I have many data points, and every single data point corresponds to one image of a dog. Conceptually, we imagine there is some underlying distribution that can model the distribution of all dogs.

It’s worth noticing that this is already part of your modeling because you can model the underlying world generator in many different ways. Even though we often assume there is this underlying distribution, this distribution is a part of the modeling.

Then the goal of generative modeling is to learn a neural network or perhaps another model to approximate this distribution. Let’s say this red distribution is what we can learn from a neural network, and the goal here is to minimize the distance between the data distribution and the distribution you estimate.

This is still a very difficult problem. There are many solutions to this problem, but conceptually, almost all existing generative models could be formulated in this way, and they are just trying to address the challenges posed by this problem.

Then conceptually, assuming your model has done a good job on this, you can start to sample from the distribution you estimated. If your model is doing good work, that means when you sample from this distribution, you would be doing something that is conceptually similar to sampling from the original data distribution.

In this case, hopefully, it will produce another dog that your algorithm hasn’t seen. It is also possible to do the probability estimation, so that is, your model would be given another image, let’s say a cat, and then you can ask the model how likely this image is under the original data distribution.

In this case, if the original data distribution is about dogs and the input image is a cat, then hopefully it will produce a low estimation of the probability density. This is kind of how we can use probability modeling to formulate the generative modeling problem.

As you can imagine, the most powerful tool today for us to address generative modeling is deep learning. Philip has given a very excellent and very quick introduction to what deep learning is, so conceptually, in a nutshell, deep learning is representation learning.

What Philip has introduced is a process of learning to represent the data… conceptually the data instances that means you will be given the data let’s say the images and then you want to map the images to labels. This is one way of using deep neural networks for representation learning.

Then in the case of generative modeling, actually there is another way of using deep learning but still for the goal of representation learning. We don’t just want to learn the representation of one single data instance. We want to learn the representation of a probability distribution. That is a more complicated problem, and conceptually it can be viewed as learning the mapping the other way. Let’s say here the output would be the labels of, let’s say, the label of cats or the label of dogs, and then you want to map it back to the pixel space.

Then as you can imagine, deep learning or deep networks is a very powerful tool for generative modeling. Conceptually, when you use this tool for this problem, the models are actually simultaneously playing these two roles: first learning to represent data instances and second learning to represent probability distributions.

Then this is conceptually what a model would look like. Your model will be given a very simple distribution, for example, it could be a Gaussian distribution or it could be a uniform distribution; it doesn’t matter. So in the case of an image, this would look like just a completely noisy image. Then the goal is to learn a neural network such that it can map a noisy image to just another image in the output space.

Then conceptually, if your model can do a good job, hopefully, the output would be a visually reasonable image, such as a dog in this case. Then you can just keep sampling noise from the input distribution, and hopefully, the neural network will turn everything into some meaningful images in the output. Then conceptually, when you do this, actually your neural network is trying to map a simple distribution, let’s say a Gaussian distribution, to another distribution, which conceptually is to approximate the underlying data distribution.

Then in this sense, a generative model is a mapping between distributions. It is not just a mapping between a pair of data points and a label; it goes from one distribution to another distribution. The next slide would be a little bit technical, perhaps, and I can go very quickly. So these are some of the fundamental elements of a generative model.

First of all, you may need to formulate a real-world problem as a probabilistic model. This is one of the most critical parts for us to design an algorithm. After you can do that, you need some representations, and today usually this is a neural network. You want to represent the data and their distribution, and then you need to introduce some objective functions to measure the difference between two distributions. Then you need an optimizer that can solve the very difficult optimization problem, and then you also need an inference algorithm, which is conceptually a sampler that can sample from the underlying distribution.

So today, many of the mathematical or theoretical research would be about one or many elements in this list. I’m not going to delve into the details, but next, I’m going to give a very high-level and very quick overview of what are some of the modern approaches and popular approaches to generative models, and I’m also going to explain why a generative model is a hard problem.

This is the figure you have just seen. As you can see, the problem here is that if your model is given one noisy image or noise input, you want it to map it. So why is this hard? Recall that in Philip’s talk he has talked about the problem of supervised learning. In that case, you will be given one image and also a label of that image, so you have a pair of input and output. That is a very well formulated problem of supervised learning, and that problem is easy for modern neural networks to solve.

But in the case of generative modeling, conceptually it is an unsupervised learning problem. You will be given an image, but then conceptually you have no idea what the input noise corresponds to that image. This correspondence or this pairing problem is also what your underlying algorithm should try to figure out.

So then in this sense, conceptually it is not just about mapping pairs of images or pairs of data; it is about mapping two distributions. You want to map a simple Gaussian distribution to a very complicated data distribution, and this is why generative modeling is hard. There are many effective and very smart algorithms to address this problem.

I will start from some very fundamental and elegant algorithms, and then I will start to talk about some of the state-of-the-art algorithms today. So first, I will talk about variational autoencoders or VAEs. Conceptually, in generative models as we have introduced, you want to map an input distribution to an output distribution. Then we can formulate this as an autoencoding problem that means if you have the distribution of the data, then you can train another neural network to map the data distribution to the distribution you like, let’s say a Gaussian distribution.

After you have this distribution, you can learn the generator to transform it back. Then conceptually, you compute the distance between the inputs and the output. This is a very classical idea of autoencoding in deep learning, but in classical algorithms, usually, this would be applied to the concept of data instances. You apply this to every single image.

In the case of variational autoencoders, conceptually, the concept of autoencoding is applied to the distribution. You can just imagine this distribution as just one object; it’s just one entity that you want to process. You transform this object into a simpler object, and then you transform it back.

This is the autoencoding idea. Another very popular solution that is kind of the beginning of research in generative modeling 10 years ago is called the generative adversarial networks, or in short, GANs. Again, conceptually, it also just wants to learn a generator that goes from a simple distribution to the data distribution.

But instead of introducing another network before the data or before the simple distribution in the case of GANs, it introduces the discriminator network after you have obtained the estimated distribution. This extra neuron network would be called a discriminator. The goal of the discriminator is to tell whether your sample is from the predicted distribution or is from the real distribution. If the discriminator cannot tell which distribution it is from, then it means these two distributions would be very similar.

GANs are kind of the most popular and most powerful generative models over the last decades until some very powerful tools came out over the last three or four years. Another very powerful generative modeling tool is called autoregressive models. In the context of natural language processing, this is usually known as next token prediction, but the idea of autoregressive or auto-regression is more than just predicting the next token.

Basically, if we care about probabilities that involve many elements or many variables, then following the very basic principle of probability theory, we can always decompose this joint probability into a train of many conditional probabilities. So the key idea of autoregressive modeling is to model every single conditional probability individually, rather than modeling the entire joint probability.

If you do this decomposition following the order of the sequence, let’s say in this case you want to predict X1 first and then predict X2 conditional on X1, and so on. If you follow this sequential order, then you can turn your problem into next token prediction. This idea of autoregressive models is to break a very complicated problem into a bunch of simpler and smaller problems.

For example, in this case, in the first output, you will estimate a very simple and lower-dimensional distribution; in this illustration, for example, it would be a one-dimensional distribution. Then in the second output, it will predict the next dimension of the variable, which will be a two-dimensional distribution, and so forth. It will be difficult to visualize a higher-dimensional distribution, but conceptually when you do this, it would be a distribution in a high-dimensional space.

This is the key idea of autoregressive modeling. Over the last three or four years, there is a very powerful model emerging, especially in the context of image generation and computer vision. This model was motivated by thermodynamics in physics. The idea is that you can formulate the problem as repeatedly corrupting the clean data or input image by adding Gaussian noise, and then you can progressively turn it into a fully noisy image.

Then the goal of learning is to reverse this process, and if you can do that, then you can progressively go from a noisy input back to the clean image. This idea is called diffusion, or it is often also called denoising diffusion. So conceptually using the terminology of probability or probability distribution, this means you will have an input data distribution that, hopefully, will be about clean images. Then you just repeat adding noise on top of it.

Conceptually, this is just like running a convolutional kernel on top of the distribution space, and by doing it many times, ultimately, you will turn the data distribution into a GAN distribution. Then your model is just trying to learn to reverse this process.

This is what a diffusion model may look like at inference time. It will start from a very simple distribution, say a Gaussian, and then it will progressively reverse the process and go back to the data distribution. Actually, this visualization is very similar to many of the concepts that are popular in graphics.

For example, you can imagine the starting points of this process is some canonical shape, let’s say it would be a sphere or a cylinder. Then you want to progressively morph or warp this object into another shape that you like. Let’s say this could be, for example, just a mountain or a bunny. You want to progressively walk the input sphere to a bunny, and this is a very well-studied problem.

So in the case of distribution modeling, we can imagine this distribution literally as a geometric entity, and then you can formulate a process to do this transformation. What I have just described is an emerging idea which is called flow matching. You want to flow from a very simple object or very simple shape, such as a sphere, to another more complicated shape, such as a bunny.

If you have this algorithm, and then if you formulate your underlying shape as some probability distributions, you can use this idea to do probability modeling that is generative modeling. Here conceptually, this is just another visualization of the same thing. You will be starting from some simple distribution, let’s say a Gaussian, and this would be your data distribution that you want to model.

The goal here is to progressively change your input distribution to the output distribution. There are many excellent solutions in computer graphics to this problem. One idea here is to learn a flow field. You can imagine if this is literally a 3D object, then you will have some 3D vertices or 3D surfaces. You want to gradually move these 3D surfaces from the sphere to some 3D surfaces in your bunny.

Then if you do that, there will be a flow field that can be constructed via this process. There will be a lot of mathematical details behind flow matching, and of course, I’m not going to delve into it, but this is kind of the high-level idea of the latest progress in generative modeling that is flow matching.

Conceptually, these are some of the popular approaches today to generative models. I haven’t covered any of the mathematical details, but it is kind of fun to walk through all these methods. The point I’m going to make is that in all of these generative models, there would be some deep neural networks as the building blocks. Conceptually, this is just like in deep neural networks where there would be some layers as the building blocks.

The layers are those modules that Philip has just introduced. They could be a linear layer, they could be a ReLU, they could be a normalization layer, or a softmax layer. Neural networks are entities that are built by so-called layers, and today these generative models are some entities that are built by deep neural networks.

In this sense, the generative models are the next level of abstractions. Okay, then next, I will talk about how we can use these mathematical models or theoretical models of generative modeling in the context of solving real-world problems. As we have introduced, the key problem in generative modeling is about this conditional distribution. You want to model a distribution that conceptually you will be given. The condition why it is about the distribution of your data X but then reality what is y and what is x in common terminology Y is called the conditions.

Let’s say you want to generate a cat. It could also be some constraints. Let’s say you don’t want to generate some type of output images. It could also be labels or text labels, or maybe some other labels. It could also be attributes. Let’s say you want to generate a big object or a small object. So in most cases, the label, the condition y would be more abstract, and it will be less informative.

As a comparison, the output X is usually called the data or it would be the observations or measurements of the samples that you can see in real-world problems. So in the case of image generation, usually X is just the image. So usually X would be more concrete than the condition Y, and it would be more informative. It would be higher dimensional.

Now let’s go through the applications we have just introduced and let’s discuss what would be X and what would be Y in the context of a natural language conversation or chatbot. The condition Y would be the so-called prompt that is given by the user, and the output X would be the response of the chatbot. So usually the output is higher dimensional, and there would be many plausible outputs that can correspond to the same prompt.

Similarly, in the context of text to image generation or text to video generation, the condition would be the text prompt. It could be a sentence, it could be a class label, it could be some attribute, and the output would be the generated visual content such as an image or video. The output is higher dimensional; it is more complicated.

So these are kind of typical use cases, and of course, this is also the case in terms of 3D generation. In this case, the condition would still be a text prompt, and the output would be the 3D text structures. In this computer vision or graphics application, the 3D text structure would be the shapes, textures, or maybe even illuminations of the underlying object.

Then we can move one step further. We can generalize the scenario to the problem of, let’s say, protein generation. In this case, the input condition could still be some prompt; it could still be some text. Let’s say you can try to tell the computer that you want to generate a protein that can cure cancer. That is valid, but the problem here is that there’s no way for the computer to understand what it means to cure cancer or what it can do to cure cancer.

So in this case, there would be a lot of research into how you can represent the underlying condition that you care about. You want your output protein to have some properties, and you hope that those properties would be related to curing cancer or curing some special diseases. In this case, the condition would be more abstract. It could also be higher dimensional because it is the abstraction of some behaviors, let’s say, curing cancer.

The output would be another representation that is also higher dimensional. Let’s say the protein structure in 3D; it would just be like another kind of 3D object.

Then let’s talk about some other scenarios that typically people won’t think of as generative models. Let’s say this is a very classical case that people regard as discriminative models. We have introduced this typical case of image generation as well. You will be given a class label, and then your algorithm will be asked to generate the output image. This is the so-called class conditional case, which means your Y would be very specific about one label.

But then there is another scenario where you can imagine you won’t be given any conditions. That means you want to generate the data output that will follow the entire distribution of the data. In this case, you can imagine the underlying condition as an implicit condition, which means you want the image to follow the distribution of your underlying data sets.

If your model can do a good job in this regard, it will try to distinguish the distribution of this dataset from the distribution of any other data set. This is the idea that we can apply generative modeling to the scenario of discriminative modeling. So here is a very typical case of supervised learning or discriminative learning that is image classification.

You will be given an image, and then you want to estimate the label of that image. If we want to formulate this as a generative model, then in this case, actually, Y, which was a label in almost all our previous examples, would be the image. In this case, the image is your condition, and then the class label X would be the predicted output.

You want to model the probability distribution of your output. Just because this problem is too simple and too trivial, usually people won’t think about it as a generative model, but it can be. So then, what is the point here? If you can model image classification as a generative model, then actually you can extend the scenario from closed vocabulary classification, which means you will be given a predefined set of class labels, to the scenario of open vocabulary recognition.

That means you won’t be given a predefined set of class labels, so there could be many plausible answers to the same image. In this case, you will still be given one image, but then your output is no longer one unique correct answer; there could be many different possible answers that can describe this image. For example, in this case, these are all reasonable answers to say this is a bird or a flamingo, or that this is a red color or perhaps orange color.

As you can see, even for this very classical image classification problem, if we try to formulate it as a generative model, it could also open up new opportunities and will enable new applications that are non-typical for classical discriminative models.

We can even move one step forward. You can imagine the input condition Y is still an image, and you want the output not just to be a label or a short description; it can be an entire sentence or it can even be some paragraphs that can describe this image. So actually, this is also a classical problem in computer vision that is known as image captioning. You want the computer to write a caption about this image.

With this context, we can even move one step forward. So then this image could just be part of the input in your natural language conversation with your chatbot. In this scenario, the condition would be the input image and some other text that is a prompt given by the user. The output would be the response of the chatbot based on this image and the text prompt.

Let’s say in this scenario, given this image, the user could ask what is unusual about this image, and the chatbot can try to come up with some answers regarding this problem, and it might say it is just unusual to have an ironing part attached to the roof of a moving taxi.

In many other real-world problems such as robotics, we can also formulate the problem of policy learning as a generative model. For example, in robotics control, there could be many plausible trajectories, many plausible policies that can fulfill the same task. In this case, for example, you want the robot to move this T-shaped object into the target location.

The robot could either move from the right-hand side, or it could move from the left-hand side. So both trajectories are plausible; there is no single unique answer. This is also where we can use generative models to model this policy learning problem.

So in general, this is what we have just seen. A generative model conceptually just cares about this conditional distribution. In my opinion, there are no constraints or requirements about what can be X or what can be Y. Conceptually, they can be anything.

This means we can use generative models to solve many kinds of real-world problems. We can just try to formulate all these real-world problems as kind of conditional distribution problems, and then we can try to apply the latest advances in generative models as a tool for this problem. This is also partially why generative models are becoming more and more common today for people to solve real-world problems.

This will be the last slides of this talk, but I just want to give some high-level ideas and convey some of the most important messages in my mind. As we have seen, generative models have some deep neural networks as their building blocks. This is just like deep neural networks will have some layers as their building blocks.

Ten years ago, the research in deep learning was mainly about these layers such as convolutions, activation functions, normalizations, self-attention layers, etc. So that was the research about one decade ago.

Then we have generative models. Generative models become the next level of abstractions. All previous research on deep neural networks still applies, but there is a new level of research that would be built around generative models. Moving forward, when people use these generative models to do more amazing stuff, such as large language models, reasoning, and agentic machine learning, which we will cover in the remaining parts of this talk, these existing generative models will become another level of building blocks.

As we can see, and as you have seen from Philip’s introduction slides, we are building a stack of many different levels of models. These are different levels of abstractions. The abstractions could be layers, deep neural networks, generative models, and they could be reasoning agents. This is just like how computer science has progressed over the last century.

People are building different levels of abstractions, and then we can unlock different levels of new opportunities. In this sense, I would say so generative models represent the next level of deep learning, and they are also the next level of abstraction and building blocks.

With that, that is the end of my talk.

[Applause]

Mapping distributions which is much higher means they are inferior in that simple task of supervised learning, yes.

I think there is no certain answer yet because in some sense, it is not yet a common understanding that you can address a discriminative problem using generative models. If it is a very easy, let’s say close vocabulary classification task and you clearly know that you have 10 or 1,000 possible labels, usually a simple solution is sufficient. But even in the case of the so-called open vocabulary recognition, let’s say you will be given one image. You still want one label, let’s say a hashtag. You can still have a vocabulary, but that vocabulary is just the English vocabulary, the human vocabulary; it could be very long.

Even in that case, I think a generative model is a good idea. If you want to move one step further, if you want to have a sentence as a description, or if you want to start having some conversations based on this image, then a generative model is perhaps the only solution that you should use.

Great presentation, two questions.

Is it possible to go the other way? I think it depends on what the method is. I think recently the answer is yes. The flow matching algorithm can enable us to do that. As you can imagine, in my analogy of flow matching as moving from a sphere to a rabbit, conceptually it doesn’t need to be a sphere; it could be a cat. You can move from a cat to a rabbit.

In this scenario, that means you can transform from one arbitrary distribution to another arbitrary distribution, and their positions are just symmetric, so conceptually you can swap them.

The second question is about the robotic scenario. Is there a clear objective function? That’s a good question. I think it is more like a distinction between reinforcement learning and imitation learning or basically just supervised learning. Conceptually, we can always formulate the problem as reinforcement learning, which means you just want to approach the goal.

Let’s say if the goal is to move the T-shaped object to the target location, then if you can do that, you receive a reward; if you can’t do that, your reward is zero. That’s possible. Then imitation learning or supervised learning is the other way. You try to give some examples of what would be the possible trajectory, and then I try to model the behavior.

Yeah, I think I can take questions offline because I’m over time, and let’s move on to the next talk.

[Applause]

Stanford CS224N: NLP with Deep Learning | Spring 2024 | Lecture 15 - After DPO by Nathan Lambert

2025-03-05 08:00:01

[Stanford CS224N: NLP with Deep Learning

Spring 2024

Lecture 15 - After DPO by Nathan Lambert](https://www.youtube.com/watch?v=dnF463_Ar9I)

okay um well uh welcome back to cs224n. it’s welcome back for me too cs224n um too since I was traveling for a couple of weeks. I hope everything went smoothly in the meantime. Um, so today I’m delighted to introduce our first invited speaker Nathan Lambert. Um, so Nathan um did his PhD at UC Berkeley, so you’re allowed to Boo and hiss for that.

But um since then um he worked first for a couple of years at Hugging Face and now he’s working at AI2, the Allen Institute for Artificial Intelligence um in Seattle. Um, so Nathan um comes from a background in reinforcement learning like quite a few other people who are now applying reinforcement learning to language models. He had an early background applying reinforcement learning to robots, but it turns out it’s more fun to do it with language models. Um, um no it’s not um okay um but anyway I mean he’s been very influential in both developing ideas as to how to do post-training with RHF and other ideas that come since then.

Including DPO that he’ll definitely mention in today’s talk um and so he’s one of the best experts on the post-training um phase of language model development which has just proven as time has passed by that more and more of the action of the large language model companies is happening not in the initial um pre-training language model training phase but this subsequent post-training phase. Nathan will have a lot to say about that today. Thanks a lot for coming to do this.

Yeah, thanks for the wonderful intro. Um, you can see my talk is life after DPO which is a little bit of an unclear title so I apologize about this but it’s trying to capture like what is the moment that we’re at in alignment and Alignment research. Really DPO is the paper, the story of last year which is this paper that came out and I’ll get to the math.

Now a lot more people are interested in being able to do alignment and it’s building on from there. So it’s like what are we going to be interested in after DPO? A tidbit talking with Chris that isn’t explicitly in my slides is like what we’re trying to close and the labs like Meta and people with the amount of data that they’re using for this kind of post-training um fine-tuning. There are all these words defined as so big that like the amount of data points that Meta bought in Llama 2 from one of these providers is much more data than all of the data that’s been collected on chatbot Arena for MMIs.

So chatbot Arena has like 800,000 data points that have been collected and Meta 2’s paper says they bought about 1.5 million comparisons and these are years outdated. Chatbot Arena’s data is that’s as of a few weeks ago so you can only imagine what OpenAI, Anthropic, etc. are buying at this scale. This is the kind of reality that we need to adapt to, it’s like what is different.

Like we don’t have that type of resource doing research and what are we going to do? So this lecture is some history on the things that led up to DPO that I saw that I think are important to remember. And then really we’ll kind of go zero to 100 and talk about recent research that we’re doing to try to answer this question and define what is happening.

So I’ll start with a heavily abbreviated history of language models. I won’t go through all of this, there’s a bunch of this in the class already. This is late in the lecture. I like to start with Claude Shannon and then you skip a whole bunch of stuff where this autoregressive loss function shows a lot of promise.

And this was not fast. You can see how many years it took to build language modeling as a field here, and deep learning is brewing in the background of one of many things that went into this. And then you have these years with like 2017 the Transformer paper that you hear about, 2018 with GPT-1, ELMO and BERT, kind of these foundational topics in language processing and how embeddings are created.

Then with GPT-2 and scaling laws become this kind of key idea that people are looking at and tracking and how these models are improving. In 2020 is when people really started to wake up to how useful these large-scale trained language models were. At this time, I wasn’t even a language modeling person but for a lot of people in AI this is when the kind of gravity of the situation was starting to suck people in.

There’s a lot of cadence to these things. In 2021 we had the stochastic parrots paper which, before chat GPT, was raising the warnings of what are actually what are we actually putting into these models and what are they learning? Like are they actually learning something meaningful from language or are they repeating the language that we have?

This is a kind of philosophical debate depending on where you land on what language is and what these language models are doing today. But it’s important that it came out before chat GPT and it’s like these foundations of debates of what language models are doing. In 20 end of 2022 is when chat GPT actually came out which was supposed to be this kind of quiet launch of a demo from OpenAI.

It has since captured the attention of the world that we have seen, and the simple question is can chat GPT exist without RLHF? I think it’s important to acknowledge that so much of this is from pre-training but at every point of the line in chat GPT and then a lot of these popular models since then RLHF and these human-related or other fine-tuning technologies seem to be necessary but not sufficient.

Like you need the pre-training but you also need this kind of RLHF or this post-training to really shift the needle in what the most important models are at that certain moment. Some examples you can list so many of them where RLHF has relied upon. I like to look at these plots from the Anthropic constitutional AI paper where they kind of show this iterative improvement of their different RLHF methods.

It kind of shows how you have these multiple model versions that are evolving over time as you add more fine-tuning data. This is a dense paper, but one of the most representative figures of kind of what RLHF can do—there’s a lot of information in here that you don’t need to follow right now. Then like Meta’s Llama 2 paper is pretty funny where they have this quote, this like reinforcement learning known for its instability seemed as some shadowy field for those in the NLP research community.

However, reinforcement learning proved highly effective particularly given its cost and time effectiveness. This is like, this is from the technical report directly, which I find really entertaining. This is back in the day when we were like oh we don’t know if RLHF is really going to take off. This is July of 2023, it’s like in this building period and it’s just directly from the report, and that’s aged really well where people are still using this today.

But there’s just a lot of interesting hints in kind of history of culture of RLHF in the releases of these models where the people these companies like to talk about it and give us kind of these cultural details to what’s going on. So I’m going to kind of go through some definitions and I don’t spend too much time on saying doing RLHF 101 and like exactly what is happening with these kind of mathematical terms, but it’s important to get on the same page of what some of these things do and don’t mean.

Um, there’s a lot of definitions I think some of the interesting ones that if they don’t make sense right now to come back to is like what’s the difference between instruction fine-tuning and supervised fine-tuning? I think like instruction fine-tuning is what’s become really popular where it’s like you’re training a model to follow instructions. I have another slide on this after and supervised fine-tuning is like this domain-specific thing and we want to do both of them.

I think instruction fine-tuning is more linked to RLHF. It’s about making these models really useful and really engaging and kind of easy to work with. There are other things like alignment which is like super vague but it’s in the word, it’s align, it’s training a model to be mirrored to what a user wants.

There’s a lot of things that you can align to. RLHF is a mouthful which is one specific tool for doing alignment where you have this kind of human feedback data. Feedback is a really loaded word there where there can be like preferences and learning to rank is related to actually putting feedback on preferences.

There are a lot of little things I tried to make preference fine-tuning a phrase at one point but didn’t really double down on it. I think it’s a little bit clearer than RLHF especially in the context of DPO, but there’s just a lot of spheres that are overlapping in this kind of post-training or fine-tuning space of models these days.

Instruction tuning, instruction fine-tuning is the kind of it’s still the foundation of a lot of this. This is where things called system prompts are added where we’re like making the model ready for a specific style of input. Um, OpenAI is still kind of innovating on this. They have this model spec document they released a few weeks ago where they said they’re going to have like a second level system prompt here which just adds some structure to how the models can take in data.

So that you can do a lot more of this fine-tuning down the line and how user data actually gets passed to the model or how the developer passes information that the user doesn’t see. So what this can often look like is like Stack Overflow, Reddit data where you have a question at the top and then an answer. This is still I think a lot of what is happening behind the scenes.

There’s a lot of datasets of Stack Overflow out there. Reddit has these data partnerships and this still uses the autoregressive loss function that we started with. We haven’t branched out into kind of different loss functions yet but it’s still super important. A lot of academic research shows that this is like all you need in some ways which I think is a much more mixed bag, but it’s the simple method, and it’s the right place to start.

Where we kind of go is then we go to this RLHF objective which looks really familiar to people that are trained in reinforcement learning. I think this is a little different from the NLP loss function. Um, on the left side is like the standard reinforcement learning objective which is you’re learning a policy pi to maximize some reward which is a function of something depending on how you set up the problem.

On the right side is going to be this kind of KL constraint. This um, it’s a distance so that the policy doesn’t change too much. It’s related to this whole idea of over-optimization that I don’t go into too much of this talk. Um, but the key ideas is that we want to optimize a reward but not over-optimize it. The primary questions when doing RLHF is like how do we implement a reward function?

Like what is our reward actually going to be? And then how do we optimize it? You see this abstracted later as we train a specific reward model and then we have specific policy updates. DPO, direct preference optimization, handles this a little bit differently.

So to get before we get there, it’s like the actual preference model that people use for RLHF is well I find this interesting. It’s from this Bradley-Terry model which is from economics in like the 1950s which is essentially a probability distribution over a pairwise choice.

What ends up happening for various technical reasons is that if we train a preference model, it needs to output a scalar value. By some coincidence that I think is still very convenient, they just take the output of this learned probability distribution as a reward. They say that okay our reward is going to be proportional to this probability and it’s going to work and it ends up doing so. But that’s like even a big leap to accept that it’s like we have this pairwise preference probability that’s saying the probability that one answer is chosen over another.

Then you have to kind of this mental crazy step of saying we just pass in one number or one piece of text and we’re getting the probability that that one piece of text is chosen over any arbitrary other one. So there’s a lot of assumptions that make this—there’s like kind of deep concepts in here. But what we’re getting is a model that’s giving us the score out and the kind of question is if why do we have to do this and like what if we can just take our original objective and use gradient ascent on this equation.

Ascent because it’s a maximum and this is really what DPO does. I’m blurring through a ton of math. It’s a great paper to learn a lot of this math of language modeling where you learn how these probabilities of different pieces of text are handled by the model and how it ends up being a lot of these like log probability ratios and seeing how the prompt and the completion are handled differently.

It’s worth digging into and understanding the derivation, but the core idea is like why can’t we just do gradient descent or gradient ascent to solve RLHF optimization? This becomes incredibly simple so if you look at the code on the right, it’s the um reference code from the original implementation. It’s extremely simple to implement and it has this characteristic where if you work with something like transformers before, it’s pretty easy to write a loss function that uses DPO rather than building an entire infrastructure stack to start with when you do something like PPO and this full RLHF stuff that OpenAI does.

You normally need an almost entirely new infrastructure stack, but you can get started with DPO in a much simpler way. There are some kind of characteristics that I’ll get to later which is DPO still has a reward model which is really important to the math actually checking out. Whereas you’re using your original language model as a different type of reward model.

But that quickly takes us down a whole bunch of derivations that is probably at least not the lecture that I think is as fun to give. The key thing is, which is why this lecture is called what it is, is that the first two points mean we’ll see more DPO models than anything else. DPO is where everyone will start with if they want to do alignment research and it’s for good reason.

Like it is the right place to start if you’re thinking about doing this. It scales more easily on compute, it’s easier to debug, it’s even easier to learn. So like, it’s not really worth second-guessing that, and it is a good place to start. But it also leads into these ridiculous conversations online where everyone is trying to figure out like is DPO better than other RL methods, PPO which is this older popular deep RL algorithm which John Schulman wrote.

Reinforce is a slightly different parameterization of policy gradient. They’re very similar, and DPO ends up being much simpler to work with. So there’s this meme where it’s like if you just do gradient descent it’ll work. In reality, they’re pretty different loss functions, and they’re doing very different things, but you can get similar results with both of them which is why if something is much easier to do you should just start with it.

I come back to this much later in the talk which is like what is fundamentally different about these RL algorithms, and how your data is processed and where the signals actually come from. But for now it’s like we don’t need to say one versus the other. We can do both and they are different.

So that’s kind of the quick one-on-one of what the core ideas are. I’m going to kind of take a path to where we how we actually got to training models with DPO because I think this slide was from a different talk where this subsection is reduced from. But DPO really came out months before we started getting popular models trained with it.

So it’s like how did we actually get to the point where the community was training models with DPO which is much more recently than the paper was actually released? This comes all the way back to these first instruction-tuned models that you saw. So the Alpaca, the Vicuna, Koala, Dolly of the world all in April of 2023. These are all built on kind of similar things and slight iterations.

So there’s kind of figuring out how to use synthetic data building on this first Llama release. There are some other things that I’ll talk about but this is where we started. They’re all using instruction tuning. Most of them use synthetic data and what Vicuna actually did was they used this thing called ShareGPT which was the first time that people working in kind of this academic alignment space had access to data that was from humans.

It ended up being a bit of a legal gray area because it was logging data that people used in a Google Chrome extension called ShareGPT to like make it so chat GPT had a share button. But this data was really important to things like Vicuna and a lot of the other models that came down the line and is still used in models today as like one subset of the training dataset. So just having access to these human prompts unlocked a lot of potential back in the day and is still something that we’re seeing.

Thankfully now we’re starting to get datasets like this that were collected in more permissive ways. Like this kind of LMIs data has prompts that are collected and with consent and WildChat which was a project from AI2 which essentially gave people free access to chat GPT in exchange for their data. The thing that came after ShareGPT was the realization that we need more human data and this Open Assistant project is one that um we honestly need more of.

It shows how hard it is to create human data that we haven’t seen. More things like this. This was run by a few people in a Discord community working extremely long hours to generate prompts, responses, and preference pairs to kind of common requests that the language models. This was from April of 2023 and we haven’t seen anything like it.

Chat GPT or LMC’s data is similar but there’s not the same level of controls and voting and ranking that they went into this Open Assistant data. Again, it is a dataset that we’re still training models with and many people still train models who I think come up time and time again. So it’s just like these one or two influential datasets from over a year ago are still what are used to train models.

So you’ll get the theme as I keep going. There’s actually RLHF models trained in April of 2023 as well. Um, this was from Carper AI that was doing a lot of work in the space. They’ve kind of fallen back a bit in recent times but there were people that were doing the similar methods to what I’m going to talk about at the end of the talk. That kind of knowledge and infrastructure was not translated into things that were easy to use.

So there’s also this vein of like even if things are open it doesn’t mean that it’s going to immediately catch on and be useful. You have to have the resources, the data, and your codebase set up in a way that people can build on it which is what DPO did really well. This like RLHF model from Carper was successful. It was better than the Vicuna model but no one really built on it right away which I always find confusing. Then kind of later in the year, another key thing for this open alignment was the Llama 2 backlash where the Llama 2 was asked to kill a Linux process.

It would refuse and this kind of bred a whole series of models which are kind of called—we are still referred to as uncensored which I don’t think is the best name. I don’t think there was ever actually any censoring to the model. There wasn’t intentional censorship but the goal is to make models that don’t refuse any request which is useful as a research artifact.

It’s like what do you get out of a model if it answers every question? Like what are the limits in that regard? There are other ways to use that which are up to you but like what ended up happening is a lot of these shared GPT datasets, because they’re from chat GPT, there’s data that says oh as a language model I shouldn’t answer that.

So people started filtering all of that out and there you still see a lot of people releasing these uncensored models today as a popular area of development. I think that we should understand what people need when doing research and researching a model that doesn’t refuse is reasonable, but if you’re to deploy a model for free use to users you should consider whether or not everything should be answered.

So it’s like as a researcher how your artifacts are used kind of depend on the work that you’re actually going to be doing. In the alignments, there’s this long series—I’m almost done with the end lens—but there’s this long series of models that are really interesting to people like me that never really broke through the narrative where they’re saying their things like we used RLHF where the first model to beat GPT-4 on Alpaca, Val and these other V-tools.

They’re scaling things up but they don’t always have papers, they don’t always have codebases, and it’s like things are happening around. I just like it’s not just like the hugging face of the world. There’s a lot of different organizations in the US and elsewhere that are aligning models and getting similar numbers or beating these kind of mainstream tech companies.

These places that you look for models to find. These are all in the summer of 2023 and this is kind of all this like—I bring these up because this comes before like the first big splash of DPO. So this Zephyr model was really the first model that I remember making a splash with DPO.

This is when it took until this time which was in September after the May release of the paper for people to really be like, oh DPO is the real deal. It took four months and now like the paper has best paper, everyone uses it, there are tons of derivations. But in industry and people trying to train models there was a lot of skepticism until this moment.

So this is like a classic academic story of needing to wait a bit until your work is vindicated in some ways. But the two crucial things here were a new dataset, the ultra feedback dataset, which is a dataset of um synthetically generated text labeled by GPT-4. So it’s again this kind of new ways of making data where it’s a preference dataset um we didn’t make it.

It was made by um OpenBMB, I think they’re based in China and should know more, and then we also just had to do a lot of experiments to make it work. There’s a weird really low learning rate that was needed to make this kind of chat model work with DPO which is like 5e minus 7. If you’re really plugged into AI, you’ll know that like 3 e minus 4 is like the lore of the best learning rate.

So it’s many of a magnitude lower. So that’s kind of what it took to get this to work. We probably could have done it months earlier if we just did more hyperparameter sweeps. But like this is the random happenstance of the stories that people now like backcast as being like this is the super important bottle. It’s just—it’s somewhat random.

At the same time, I was switching jobs to the Allen Institute and they were already working. On this project which is trying to do a systematic study of instruction tuning data along with some of this preference tuning recipes that were coming out. Because once this Zephyr model came out, there’s always skeptics of like oh doing it at 7B is easy like that’s a small model, so it’s like oh is it going to actually scale to the real deal to bigger models to be what like Chad gbt does.

So it was like okay we have some more compute and we tried it on this 70 billion parameter scale and we showed similar gains. All we did was use the same Ultra feedback recipe, the low learning rate, and it largely worked. So this is within two months, and then this is when there’s tons of new DPO models. Anyone, all these startups that are releasing their own models will release an instruct version that is a DPO thing, and that kind of continued for 6 months. I think just today I’m starting to see less DPO models which is interesting.

I’ve been keeping track of them for another evaluation project and it has finally kind of slowed down a little bit. I don’t know if that’s alignment at large, but there are so many… I should add a slide that’s like a list of the ridiculous amount of DPO models after these two. But this is really when the floodgates kind of started and when we’re like okay DPO really works. So this is kind of why I say like what comes next, it’s like we could retrain models on data sets that we have.

We don’t have that many data sets, but it kind of feels like we’re fishing in the dark. Like Zephyr was built on the success of needing the low learning rate. This Tulu 2 model is actually trained on TPUs because we have the Google Tensor Research Cloud, so we have bigger TPUs to train these models. And it’s like how do we do this more systematically?

And that’s kind of where most of what I talk about today on the technical matter is the recent research that we’ve been doing to just kind of make sense of this and answer the fundamental questions of like what do we need to change about DPO. Is PO better?

So this is kind of the reality that I go back and forth in between, which is we don’t really have the human data to do RHF like industry, but it is getting much easier to do alignment research. So you can kind of choose your narrative. I think sometimes because I’m so close to industry and hear about people whom I’m like too often on this side, but there is a lot of opportunity to do things. It feels crowded, but being crowded at this point when there’s so much investment is just because you’re in the right area, and most people in this room aren’t trying to be professors.

So if you get scooped, it’s okay. But I find it very fun. And so like how do we actually understand what we’re doing with alignment, and can we improve on these models? Like we have to 2; it has a number, because we want to keep releasing more models. So it’s like how do we get better at evaluating what we’re doing to try to understand this process, and then how do we train better models?

So these are the sort of things that I’m up to. I have a few examples of things I’ve been working on. I built an evaluation tool for reward models. I’ll talk more about reward models to start here, and we need better evaluation. Because when you’re training models, you need to be able to do kind of what I call local evaluation. You need to be able to get a number that tells you if your training technique is improving the end result.

You can’t wait until Chatbot Arena evaluates your model, because that takes you about a month to get your numbers back. You need to be able to run something at your desk that gives you a signal on if you’re actually doing a good job. And we’re still pretty behind on those evaluation tools, and there are more coming, which is promising.

And then given DPO’s simplicity, can we actually improve on that? And can we catch on to some of the industry rumors that they’ve let it drift aside? So reward bench is this project that I started because there are no evaluation tools for reward models. My motivation was mostly for transparency given how much industry says reward models are what you need to focus on. They’re really important for getting good models out the door, and it’s like what does that mean?

What does it mean for a reward model to be good? If we look at this kind of feedback diagram, which is the one kind of homage to the RL background just feedback loops, is like a reward model is in this case the agent is your actual language model. Pi is the policy, and the training data is prompts that you get.

So in this kind of RHF framework, you have this feedback loop where the policy generates something, which is the action, which is the completion. It goes to the reward model, which then scores it. But you kind of on the side are looking at all these evaluation tools, and it’s like none of these evaluation tools are giving us internal insight into what’s happening in this feedback loop.

It seems kind of external to what we are doing when we’re training these models, so we really wanted to zoom in on this reward model. Reward models are trained in another kind of weird way—the many quirks of RHF. So in order to train a reward model, you need to collect this pairwise preference data.

If you’re kind of using Chat GPT a lot, you’ll sometimes see it give you two answers and ask you which one is better. This data is literally what is used to train a reward model. It’s a prompt and then two completions: a chosen completion and a rejected completion. But in order to train these models, you have to pass both of them in at the same time.

So you pass both of them in at the same time and it gives you two scalar values. You use a language model that outputs a scalar just by some modifications of the last layers rather than outputting text, and then this L function, I’ll show you on the next slide, is essentially why you need to use this batch mode idea, which is you pass multiple things at once and you get multiple numbers out.

So this L function is ESS. Here this R is the output directly from the reward model for the rejected completion and the chosen completion. So you’re trying to separate the distance between them, and then automatic differentiation kind of updates the parameters so that this distance is bigger.

So you can’t just kind of do supervised learning directly on one thing to say for the reward model. There are alignment methods researching that now, but it’s really built on this idea of separating two things and creating a margin in the preferences to kind of learn the decision boundary.

There’s a lot of really specific details in industry such as these models are only trained for one epoch. They get really low accuracy scores when you compare them to other kinds of train-test set things in machine learning, and there are some additional tweaks that people do. You can do ensembles, Lamud did this weird margin loss, but none of it really trans is transformative in how these models are trained.

They’re in this weird place where you can only get about 70% agreement with your annotators. It’s the kind of thing of is the noise part of the signal, or is it a bug? So like in preferences, it could make sense that it’s a signal because not everyone’s preferences here are the same. So not getting full agreement feels like this system might be working. We don’t want Chat GPT to be fully narrow-minded all the time, and this kind of reads to the question of how do we actually evaluate these reward models that I was talking about?

I hear all the time that reward models are crucial to RHF, but how do we know exactly what types of the final policy they’re improving? Should we include safety in these reward models? How does scaling laws impact reward models? These are kind of basic machine learning questions. It’s like can we evaluate these? What should we think about?

So what we did is we collected a bunch of prompts, and then we manually created chosen and rejected answers for each prompt. And then we can see whether or not the reward model agrees with our human-created data and call that like a win or loss from an accurate point of view. It’s really direct. We’re just doing inference on existing models and we’re going to see whether or not they agree with human data.

This is a slide if you want to go into the academic side of things. This was built on a lot of existing evaluation tools that were out there. You’ll see some common names like Alpaca Val, Mt Ben are things that you’ve heard about. EXs test was on the slide when I mentioned llama 2 being overly safe.

And there are some other things that are really good, but you might not have heard about like this LLM bar data set from Princeton, which is a bunch of trick questions that I’ll have an example on later. There are also some kind of normal names from Anthropic and Open AI in here as well, so there’s a lot of different things that we’re testing with this data set.

And then we’re trying to get the full picture of like what is going on with these models. We released this in March of 24, and you can see a key in the bottom where these kind of red circles with the arrow in them are DPO models which you can use as a reward model. Then these kind of these dice, which look like gray squares when you zoom out, are what I described in this classifier-type of training.

You can see that there’s reasonable scores. The benchmark isn’t saturated—bunch of open models, some names that you’ve seen before like the Tulu models and the Zephyr models are on here. Kind of normal stuff we like; this is what we expected. It’s not too saturated, but if you look here, I’ll show you where this model has moved in a few months.

So today we have a lot more models and there’s a lot more information here. So I get to tell you about more interesting things, which is like how Open AI’s and Coheres models do on this, which is like I mentioned, wanting to do this for transparency. But we also add new types.

So this is where the fifth model ended up. In two months, the model that was fifth on your leaderboard is now 31st. We’re trying—we’re getting the saturation from people doing research in the area to actually have places to compare their models. But we also have models from some closed labs.

I’ll kind of get into the details here. So like some of these are labeled as different types of models, which is LLM as a judge. LLM as a judge is the idea that you can ask a language model which answer is better. This is kind of how things like Alpaca Val and Mt Bench are built.

But you can also use that as a reward model. I told you that I have prompts and then chosen and rejected. I could just ask Chat GPT which one is better and see what it does, and this is what we added in as a baseline. This ends up being really interesting because GPT-4 and GBT-40 are not actually as good in this closed domain as a reward model that Coheres is training.

So we don’t have full information because we don’t have Open AI’s reward models, but we can use their models to compare. We have a lot of different information going into one system about how language models and different parts of the alignment process choose different categories.

So I’ll kind of go back and you can see this Co here is across two different months. Their scores have improved a lot, and then these kind of earlier DPO models that we saw higher up on the leaderboard have been shifting down by more people training reward models to begin with.

The specific category that I’ll focus most on is this kind of chat hard thing. If you think about evaluation a lot, it’s actually surprisingly common as a topic covered in tech coverage. It’s how evaluation is saturating. This is the one feature of our benchmark that hasn’t fully saturated, and it’s really important to kind of have some sort of longevity to the benchmark.

And I’ll talk more about this as we go from here. So I mentioned this data set, and it’s interesting to understand if you could actually do this problem. So what we have is a prompt, a chosen, and a rejected. The prompt is: give an example of a metaphor that uses the following object: stars. The chosen and rejected are two similar metaphors, but you can see the differences if you read these.

I’m just pausing for the people that are still paying attention to reading these, but essentially what happens is that the chosen one is about the sky, and the rejected is about the moon. The twinkling diamonds in the sky—see, I haven’t messed it up reading the slide, but it asks for stars and it’s about this kind of metaphor of stars where the rejected is about the moon, which is also in the sky at night.

This data set is a whole bunch of things like this, where what they do to create this is they either manually or by Chat GPT ask or request to rephrase a prompt, and then you create a new generation from it. So you can kind of get these rejected generations that are just off-topic, and it makes sense for something that would be really hard for language models because they have this association between the stars and the moon.

But we want our language models to be able to answer questions like this, and this is the type of thing where our reward model benchmark, which is something that is training language models, has the best correlation as something that is hard. So this is promising; this is the sort of thing that if you’re in research, it is interesting.

So it’s really in the weeds, but it shows that we still have things to learn about these models, and there are things that we can’t do yet. Another interesting pattern in safety, I mentioned this kind of uncensored models, and in safety we see all the patterns we would expect. The breakdown at the top of this table shows refusals, which are things that we want the language model to refuse, and then this excess T test data set can be split into something that we want models to refuse, and we want models to respond.

You can kind of see that there are multiple categories of either DPO models or reward models, where the model that handles safety really well refuses things like asking for advice on causing harm and responds to something that is borderline. But there are actually a lot of models out there that just refuse everything, so that’ll tank your score on things that respond to everything, which is kind of the safe bet.

We’ve been seeing a lot of tech companies release models like this, which just feels like it doesn’t feel right when you talk to them. But there are also the models that just respond to everything. It’s like it’s not my job to gate whether or not—I should—it’s not like the language model’s job to gate the question is the philosophy there, which is something that we hear a lot about in the discourse of alignment.

But to see it in these reward models and DPO models when directly probing them without asking them to generate text is nice to confirm a lot of suspicions that we have. So this is back to some of the DPO math, which is again good to know. If you go into the DPO paper, you’ll see equation three here, which is the reward that is defined in order to make the math actually work.

This is very different than just outputting a scalar. It ends up being a ratio of the probability of the policy relative to the original policy during training, which is called the reference model. It’s a very complicated mathematical representation.

So if you actually take a piece of text and pass it through a DPO model, the reward will be something like minus 200 or something because it’s a bunch of log probabilities. Probabilities are between 0 to 1; you take the log, you get negative numbers, and you sum all of these up, so you get a big negative number and that intuitively is like the score that these models are providing, which is very different than the other types of reward models that I talked about training earlier.

If you have two prompts with a chosen and a rejected, equation 4 is the math that you actually need to do to decide whether or not one of the answers was better. You’re kind of comparing these ratios of probabilities from two different models with respect to this reference model, which was the starting point of training.

The question is when people release a DPO model, they normally release a model, and they don’t release all the intermediate checkpoints. So this reference model would be an intermediate checkpoint in the training process. The question is like can you do this? Can you use it as a reward model if you don’t have access to all the information?

The short answer is no, which is all the scores on our benchmark plummet across all the DPO models that we have. It makes sense because this extra model is a regularizer in the probabilities, or it’s in the actual reward equation. If you go back a few slides, it’s in the equation.

What we do is we get rid of this, and we stop normalizing equation 4 and we just see if it works, and it doesn’t. But this is important because DPO is training a reward model, but if we don’t always have access to it, we just can’t learn from it, and we can’t use that in another system as clearly. So it’s just a lot to ask for when getting people to release models.

This is an interesting slide showing Coheres’ kind of progress on reward models in just a few months. They released something that was clearly state-of-the-art on our benchmark, an alignment lab. They released something in May and then just a few days later Coheres sent another number of those like here’s our new model; it’s still better than everyone else.

So it’s nice to have this academic-industry intersection, but it’s very rare and takes a lot of work in terms of networking and building relationships. But we’re trying to do it, at least in these small niches where the companies are willing to share.

Reward Bench 2 is going to need to just mostly make everything harder and make everything more human. The last point is what I’m going to transition into next—everything I’ve told you about is about part of this RHF pipeline. But I haven’t told you how it is impacting the final model that you use at the end of the day, which is a very rightful criticism.

If you’re evaluating part of the alignment pipeline, you should be telling me whether or not the final model is actually useful. So this is kind of where I talk about our journey into trying to train PO models. So we’re trying to fine-tune a good model. We spent a lot of time on DPO with this Tulu work, and we wanted to know if we could do better by switching to PO.

This is a lot of not-yet-published work, but it’s going to be out soon, so the numbers aren’t entirely final. We’re just trying to disentangle what the difference between DPO and PO is at a very empirical level. So we’re trying to answer if it’s better or not.

What we’re going to do is kind of walk through a series of design decisions and see how it affects the suite of evaluations. We’re starting with this Llama 2 13B model, and that has already been instruction tuned. The difference between the blue and the red is the gains from instruction tuning for these reasoning, coding, and chat tasks.

Instruction tuning does the biggest delta that you’ll see among all these slides. Instruction tuning puts the model on the map as being useful, and it is easy to see gains at the beginning, and then it becomes harder and harder for us to really keep improving these models.

So we start with is we add this Anthropic helpful, harmless RHF data with DPO, and you can see that there is a small bump across all the metrics that we did. This data set is known to be particularly noisy among researchers in the area, but it is kind of the starting point when you’re doing research on alignment.

It’s been around for a few years. It’s big, it’s multi-turn; it’s known to be noisy, and it still gives improvement. Then if we switch to this data that was used for both Zephyr and Tulu officially, this Ultra feedback data, we get an even bigger bump.

So this is just kind of showing the difference that changing only the data can give you in a DPO recipe. It’s normally increases of like 0 to 2%, and in the research sphere of trying to ship a model, that’s a big deal.

So this is kind of where we tried it into new territory. Grad students worked really hard and implemented PO and Jacks in addition to what they already had. We were like okay, what happens when we add PO and require reliable results across multiple experiments?

This is one example with the 13 billion parameters. PO just happens to do a little bit better; it’s like 1% better, and we try to change a lot of things and the changing things is where things get a bit messier. We’ve heard from industry that using a bigger reward model can be really helpful to getting a better policy model.

Essentially, these bigger reward models should be better at nuance; they should give more labeled, better scores, which are used as rewards. They should just kind of make this process a little bit more stable if we have the compute for it. We see that it does improve some things, but it doesn’t actually make the model overall much better; it’s kind of flatlined with pretty similar data.

Just making the reward model bigger is a little bit surprising to us. This is like the most realistic few slides of the talk, but we did this thing where we were trying to see if our reward model training was bad as we scaled it up.

We used reward bench on the right, which I had told you about earlier. It’s not clearly correlated whether or not these two 13B models or 70B are better. We also did this best event sampling idea, which is if you generate a bunch of completions from the language model, you can rank them by your reward model and then reevaluate on the top-ranked completions.

That shows that our reward models are better at the bigger scale, but we couldn’t get this to really click into a downstream model in a notion of the world. We even tried adding more prompts to RHF. We added more code and reasoning prompts because that’s something that OpenAI talks about a lot; it’s like we want to improve our models.

It doesn’t really shift the needle on this kind of cohesive average over many tasks. In the paper, what you’ll see when it’s out is it shows that we added prompts really similar to two math and code evaluations. Those specific evaluations got a bit better, but adding the full noise into the fact that some other evaluations might go down makes this just like, this process is really hard to disentangle.

This is why we’re getting the 0 to 2% improvement out of PO, but DPO doesn’t have this sort of mess. What we ended up getting to is like there’s always one more thing for us to oblate when you’re training these models with PO. The sort of things like different regularization—we’re learning a value function in RL—different warmup, different size parameters.

Like there’s just so many knobs to turn. in Po and it was reliably getting us a pretty good model but it’s like we’re staring into the abyss trying to improve this right now in the next few months. The bottleneck in terms of the actual technical side is that PO generates new responses from the model as it trains to kind of refresh the data, and that is by far the biggest bottleneck when you’re actually training these models. It’s just way slower than DPO, so all these resources for PO things are somewhat available to academics. The Google Tensor Research Cloud, I think, is pretty available. The grad students I work with seem to sign up. The code base is open, so if you’re a grad student and you’re trying to do PO alignment and have access to TPUs, please get in touch. It’s a very fun can of worms.

But kind of as a summary, this is the many different DPO data sets that we tried. This is almost all of the well-received data sets that are out there in the open, and they all look at the factuality column. Some of these things just don’t matter at all when you’re aligning these models. So, we need to get new data sets that are really adding different capabilities to these models and something that matches these ultra feedback numbers at the bottom. I don’t like, I’m surprised whenever I look at this, but this is where we are at and we need to try to keep building data sets and keep adding freshness to this system. Ultra feedback at this point is maybe six months old or so. I don’t know the exact age, but in terms of people training models, that feels old to people compared to things that are happening.

These are the actual sort of numbers that you get when you compare DPO versus PO. This is all with this 13 billion parameter. Again, we changed the data set, and every one of these PO comes out a little bit better on average. This is a few grad students and people like me. This is not a big team in industry doing this. Like, we’re scraping by and I don’t know if it’s worth the effort. I see why OpenAI uses this because we’re able to get a bit more signal out of it, but it’s a ton of effort to get a bit better signal out.

I’ll kind of transition into a bit more of an open-ended discussion of this, and then we’ll have questions. But it’s like, what about PO is actually special? Like this generation and this online nature, and can we just change DPO to be like this? Or where are the new things going to go? I had the pleasure of advising one project that was related to this, but this is much more general. So, what is special about online data? There are multiple ways that you can get new data into your RL process, and then there’s also this related question in reinforcement learning literature, which is like on versus off policy, which is a technical distinction that often gets looped in with these discussions of DPO versus PO. They’re actually related, but the reinforcement learning discussions have a very much more definitional flavor to them, while in this alignment space we’re more focused on if we need to get fresh data in and how we need to label our data for language models.

So, I’d make this distinction between these two things, which is freshly generated data from the policy. If you zoom into a data set like Ultra Feedback, it has generations from all sorts of models— from Alpaca, Von Kunta, GPT 3.5, GPT 4, Llama. There’s generations from all sorts of models in this data set we are using. So, when we train these Zephyr, these 2U models, we’re incorporating information from a lot of different models down into our one policy, whereas what PPO is doing is only generating data from your existing model and kind of changing this distribution over time. So like that is a very different idea of where the signal is coming from from the models.

The second thing is whether or not refreshing the data labels over time. If I have human labelers comparing chosen and rejected, that’s one data point. But I can also later on take this reward model that I trained and generate a chosen and rejected and change the label. So these kind of two things of like what the actual text is and when the chosen rejected label was given are what people mean when they’re talking about, like, is something special about online in RHF. It’s much clearer to see that PO does it very differently than DPO, but we’re not restricted to this.

In the last few weeks, I have the dates all in here, so um, April in May of 2024 there started to be a lot of papers on this about DPO, PO, online, offline, and they really kind of say similar things, which is that online is important. These papers on this slide show these kind of more theoretical and closed form experiments on like what is special about online data and what performance drops if you use this kind of offline data. It’s good to dig into these, but this is why I say it’s nice to do research now, because if you have an idea, a lot of times people have like three papers that confirm the notion that you have. It’s a lot easier to be confident in things if three independent institutions say something similar at the same time. There’s a lot of methods coming out where people are trying to modify DPO to actually use this kind of online notion.

I think self-rewarding language models for meta was the first really popular one, where they just asked the DPO model, hey, which of these answers is better in between each iteration. So they did this like LLM as a judge to relabel their own data, and then they did multiple iterations of DPO. The model had really strong scores. There are now ideas like not using all of your data at once, so you can kind of do batches of DPO and update your data. The paper that I was on with this discriminator guided DPO, which I’ll talk about in a second, is using reward models plus this DPO training objective. There’s just a lot of things that we can change.

I think the community again is in this expansion phase, where I even get messages from people who are like, oh, my paper was really similar to this other paper they did first; they didn’t cite us, and I’m like, this is kind of the point. But it’s hard, it’s like it’s going to be like this for a little bit longer, and then hopefully at the end of the year, in a few years, we’re going to be like, okay, this is clearly what we need to do on the method side of things.

So this is one example, D2P, discriminator guided DPO, which I’m an advisor to. This is an undergrad researcher, and the idea is comparing these three different things. So like A is the standard DPO; you have a data set; you apply the loss function on it. B is what we call some sort of online preference optimization, which is where you can repeatedly label your data with a reward model—just kind of like the self-reward paper that I mentioned, where you can reshuffle your preference data based on a reward model. That kind of adds some notion of online to your data.

The third thing is like what if we’re relabeling data and we’re retraining our reward model over time, so we’re just really trying to keep our policy related to our reward model and keep everything really updated in real time so that it’s all lined up.

This is wondering how much of a gain do you have by retraining the reward model over time in a DPO framework. Part of why I like this paper is there are things like closed form tasks. The biggest question that I get for alignment is like, how do we actually evaluate it? Like what tasks is it good for? There’s a whole philosophical discussion where I think information transformation is a valuable task. Writers tell the same stories in different ways, but the best-told story is the one that resonates with people— that has value. But at the other time, we’re academics, and we need to be able to measure things. So this paper has things like your reward is counting the number of nouns in a sentence, and then you’re using these alignment methods to increase the number of nouns in the outputted sentences from the model.

You can measure that a lot better because we have classifiers which know our nouns. You can see on this left figure that just by retraining this reward model a few times, it converges better than if you were just to relabel your preference data. It’s a mouthful, but it’s just like keeping your training process a little bit more online can improve performance.

On the right is a more standard open-ended evaluation task where we’re asking a language model like ChatGPT which answer is better. That has all sorts of problems, but we can show similar results. I think the big takeaway is really like these few slides, which is that the literature is moving. We have studies that show that online is better and people are coming up with really cool, clever ways to actually use online data.

So I would, I combined with new data sets, this is kind of the deep of this year— like online methods and how they work. This kind of goes back to what the industry is doing, and I showed this figure earlier on the left with Claude, where you can see the little points along the lines. These are these different iterations. We don’t know exactly what they’re doing, but it seems a little bit different, where the dots on these figures are new data sets from humans rather than this kind of redo a reward model relabel your data. This is what happens when you have access to a different type of scale.

The Llama 2 paper makes this much clearer. They say they work with an annotator; they get batches of data. When they’re generating this new batch of data, the previous model’s checkpoint was used for generations. They do this many times, and you can kind of see that they’re collecting new human data, new human data, new human data. Each time they generate human data, it is trained for a new model. They’re doing a lot of training updates, and they’re kind of building on each other.

This kind of leads into the last section that I’ll talk about in the conclusion. What did Meta do with Llama 3? This is one of the most funny blog post sentences. It’s like the ridiculous things that they give us, and then we parse the tea leaves. They say in the blog post that our approach to post-training is a combination of supervised fine-tuning, rejection sampling, proximal policy optimization (PO), and direct preference optimization.

So it’s like, people ask me what the heck did they do? I mean, I kind of agree, but it really goes back to this slide in my mind, which is that they’re getting new data, and then they’re training a new model over time. So what I think is happening at each one of these points is they tried a few methods and they chose the training method that worked best. It’s really practical. Meta is a really practical organization, especially in the generative space right now, and that just makes sense.

It’s like at different points in the model, your model has different capabilities and it’s ready to be trained in different ways. Rejection sampling, which I didn’t cover here, is the simplest training method. You take a reward model, you rank some supervised fine-tuning outputs, and then you use this autoregressive loss function again. From there, DPO is much simpler than PO, but it might not give you the highest-end performance.

As your model really starts kicking into gear or you have more time to train this model once all of your data is collected and you’re not on a weekly time crunch, you can experiment with all the little knobs of PO, and you can really try to get the best model out. At the end of the day, it’s just, hopefully, they release a technical report that confirms some of my hypothesis.

I think this is normally what people are interested in when somebody from industry comes up to give a lecture. I wish we had more details on what industry was doing, but in terms of current directions that I’m most interested in RHF, I talked about data a lot. We are very bottlenecked on data, even as academics, with very limited compute. We literally try every data set that is available. It’s not like we don’t have a lot of compute, but we need to keep innovating there.

We’re going to see more DPO methods. It’s here to stay. There are a ton I didn’t cover here— things like removing the reference model, changing the loss function slightly, not using pairwise preferences, but singlewise preferences. There’s a lot going on there. We should use more model sizes— in 7 and 13 billion parameters or in Llama’s case, like 7 and 70 billion parameters. Particularly, scaling down is very useful; it’s a place where academia can still play.

There’s kind of less of a weird marketing dynamic where all the companies are racing to go bigger for certain strategic reasons, but this is something that’s accessible to many people. Aligning small models is hard to get signal out of them because the models show more or less random scores on many benchmarks that people care about or really low scores. So even just breaking through in that domain would be really impactful work to kind of get more people working on alignment.

Then kind of evaluations I covered at length, which is we need to keep getting more specific on things we care about, and personalization is something in alignment that I didn’t cover in this talk, but is something that is good to compete with this kind of big tech. How do we train models that are good for you as an individual rather than one big model for one big technology organization?

These slides will get to you, but these are the types of places that I follow when I’m trying to see open models or open data sets that are reputable and easy to keep track of, so you don’t have to try to follow everyone. I write about this a lot without doing too much self-promotion, but I ended like 10 minutes early for questions that I’m happy to take in a Q&A format. If you don’t have to stay and wait if you don’t want to.

[Applause]

Okay, thank you, Nathan. Um, questions? Anyone got questions? Assuming you’re hand a good reward model, which is a large assumption. I agree. But what is the key challenge to doing online D in the sense you can do your rollouts and then just like rank them using a model, and then go, and you can iterate this. So what is the hard thing?

Yeah, I’m going to repeat the questions so that people can hear them and it gets recorded. The idea is if you have a good reward model, what is stopping you from doing online DPO and kind of just improving the policy from there? I think there are kind of multiple angles to this. They’re both technical and industry-wide, but the technical thing is I think the prompt matching ends up being really important. So prompt matching, what your reward model can learn is specific to the prompts.

There’re a technical detail where the prompts used for your policy often are exactly the same as your reward model in PO, which is really strange because we talk about generalization in machine learning, but we’re kind of like soft balling ourselves at the PO stage, which is we’re only grading PO answers which our reward model is trained to answer, which is kind of strange. So people think that some of that might break down, and we see some of that when trying to train PO models with off-the-shelf reward models.

It’s kind of a long answer. I think that’s mostly distribution matching if I had to guess, but if we had truly a good model, it should work for some things, and that could be one of the reasons why there aren’t that many in the open because it would kind of help people catch up in alignment.

It’s like a reward model, if it is as important as people say it is, might be easy. Other questions?

Yeah.

[Music]

For example, me. Yeah, I think there’s this whole conversation. If I don’t cover it, if you want more after, I can… you can come up. But the question is, is there more than pairwise preferences that could be used in RHF? There are a lot of different lines of work that are studying this. One is methods like there’s a method out of Stanford that’s co-named like cSKY; I always mess it up as these names are hard to pronounce. But it’s the idea of using one-sided preference data. So a lot of customer apps have, like, did you get good support from this agent? Yes or no.

You could use data like that. It’s just a different loss function for using single preferences or just yes or no. There are other things like learning to rank for multiple answers. This is something I slightly insinuated, but binary preferences are kind of like—there’s a lot of literature on learning preferences, and one of the models that got reduced down is the Starling model. They use a k-wise preference, so they have like five or nine answers to every prompt, and then they collect answers and then they have a different loss function. This is one of the models that has kind of broken through in the open alignment space. It’s one of the few that I left in and skipped in my slide deck, but that’s kind of interesting.

There are also other research that’s like fine-grained preferences. For every completion to a prompt, you get labels like conciseness, helpfulness, honesty, so there are a few things on that regard. There’s a steer LM paper from Nvidia, and then there’s work from UW that does learning from fine-grained preferences. That one is probably the one that’s most emerging in the academic sense, but there’s so much to learn here. Literally, all the fields of social choice need to get condensed into these things.

Any other questions?

[Applause]

Questions? Yeah, so the question is how can we broadly exceed human performance with fine-tuning or any training for that regard? I think this is where some older ideas in CS will come back. I think one of the foundational ideas in CS is search, which is really motivated as exploration in RL. Therefore, we need to have some sort of language models that can search and generate new data.

I was talking with somebody before, a grad student, and I think that search will be a large part of synthetic data. But then the human aspect will be what gets it across the line if it can’t solve a certain area. This is like the QAR rumors; they’re ridiculous, but that seems to be the best argument for the sort of thing that OpenAI is trying with that. It’s like how to get that barrier broken with AI.

Thank you so much for coming in. You mentioned data sets as a big limitation, and I was curious how one goes about creating a new data set.

Yeah, this is another thing that’s hard. I think community efforts are what people have tried to do. I mentioned Open Assistant, but most people that do a community effort are like, I never want to do this again. While I still think it’s worth doing things once that are highly impactful even if you might not want to do it again, other avenues for building these in a sustainable manner are very important.

I think that there is some way that this is being done, like Chatbot Arena returns some of the prompts and the labels to users. There are specific concerns I have with that data around being too noisy, but that is the sort of thing that can happen if AI2 has a demo for their models. It’s going to be about science and generating information rather than being a ChatGPT competitor. It’s like a nonprofit; it can’t do a product competitor, but that’s the sort of data that we would want to release. Something that I might just have to do, but I’m interested in academic workshops and competitions as a ground where you could have communities meet every three, six, eight months and have work that’s focused on an area or like focused time to have people contribute to it.

But it’s a good question. It’s probably why there aren’t very many.

How do you feel about the subject of reward hacking as well?

So we get one at the front first, yeah. Close first, and then we’ll come to you.

The various places you’ve done research at over the years, do you have any sense of how they compare in terms of specifically alignment research? I mean, obviously, they weren’t doing alignment research specifically at those times.

I think generally, they represent different cultures and investments of the company. Like, I wasn’t doing language models until a time at Hugging Face, so I can really only speak to these kind of two open companies. From Hugging Face’s perspective, it’s to show that more people can do this. We’re not trying to compete with ChatGPT, but we’re trying to foster an ecosystem of doing this. AI2 is similar but more about like what is happening—how do we learn about this? How do we do science? How do we study the science of this and communicate that clearly?

I’m sure if you do the exercise, you can map this to every company—what is their important thing? They have different goals in their products and their corporate structure and things like that.

I will talk more when not—

[Laughter]

Recorded.

Okay. Up the back—are reward models also subject to reward hacking? Like they achieve a good result on the outcome, but in reality, the outcome is not as expected.

Yeah, so this is like when talking about reward models. This is probably the most established line of work. The question is, are reward models subject to reward hacking? Reward hacking is a classic problem in RL. I should bring back from my RL slides where you have the bot swimming, going in circles, and then be like, this happens to your language model.

What happens? It is, and there’s a lot of research to mitigate it, but it’s a fundamental problem, which is you have a very powerful optimizer and you have an incomplete representation of your reward, and it will always find where your representation of the reward is wrong.

We will always be doing the best we can, but I think saying it’s perfect is not possible in the math. I mean, I can also say the ways that it fails are pretty funny because if you train these models, you’ll end up with a model that just says JavaScript to every answer for infinity. Sometimes it’s really easy to see when that is happening, which is good. Or like, you could change your loss function so that it will always exploit, and it’s a good way to kind of ensure that things are working, which is you should be able to easily exploit if you turn the brakes off.

Okay, any last public questions? If not, thank you, Nathan, for giving this talk. If there’s anything you’d like to ask off the record, he’ll be here for a bit longer.

Making A Browser Is Harder Than You Think (Ft Andreas Kling)

2025-03-05 08:00:01

Making A Browser Is Harder Than You Think (Ft Andreas Kling)

So today we’re having Andreas Kling, who’s been working on the Ladybird browser, a fully open-source browser. I’d like to thank our sponsors Grace Swan AI and Infinite Red. More details about them later on and links in the description. I hope you enjoy today’s amazing podcast.

All right, well hey, today I’m your host, the Primagen, and today we have another episode of The Top Shelf. With us, we have Andreas Kling and also my co-host TJ, who loves Neovim so much he’s never even heard or opened a browser in his lifetime. Andreas Kling, for those that are unfamiliar—sorry TJ, you don’t even get a chance to talk right now—has somehow built a successful operating system and even has started to build another web browser, which is very shocking to me. First off, how does it feel to build an impossible project, another OS followed by another impossible project, a browser?

Pretty good, I think. It’s been a journey, that’s for sure. But there’s this endless source of fuel, which is people telling you that it’s impossible. It’s just satisfying to one by one go through those people and try to convince them. It’s a bit like how comedians sort of focus on one person in the audience and try to make them laugh. I feel kind of the same about Hacker News; I try to get that one Hacker News guy to believe. I feel good about it.

I thought you were going to say something about how comedians pick out one guy and just make fun of him for an hour. Is that how that works? That sounds terrible.

That’s pretty much how all roast comedians operate.

All right, so hold on just to get this straight: the motivation is kind of like Michael Jordan. Michael Jordan would actually, before every game, pick out somebody on the other team and start messing with them, finding a reason to be angry with them or to show them up. Are you saying that you have a bit of a Michael Jordan in you, where the haters motivate you?

Well, it’s certainly not what kicked things off, but over the years I have taken a liking to letting that spur me on, I think. But the whole thing started—and I don’t know how deeply we want to get into this—but it started back in 2018 when I had been to a drug rehab program and had learned how not to do drugs all day. I discovered that if you don’t do that, the day is very long, and I needed to fill it with something. So I just started programming and got a bunch of initially disjoint pieces and realized at some point I could smash them together and it would be an operating system, and that was the birth of Serenity OS.

Let me pause you one second; for whatever reason, you’re coming through really choppy, so I’m going to refresh the source, which means I lose the audio for a second. Sorry about that.

All right, yeah, no, that’s my bad; I was too ambitious with the resolution.

Right, so yeah, it started as sort of just a hobby project to fill my time when I couldn’t do drugs anymore and ended up just snowballing from that. Initially, it was just me building an operating system because I thought that would be a fun thing to do in C++.

Was that partially inspired by drug use?

No, it was really from working at Apple for so many years before that. I worked on Safari and WebKit, and it’s written in this distinctive dialect of C++ that I liked very much. I thought to myself, wouldn’t it be neat if the whole operating system would be written in this style? It would all be in one repository, all the code would be written on top of the same libraries, same abstractions, same templates, and container classes. How fun would that be to hack on? Okay, let’s do that.

And then I did that. It was just by myself for six months, and then I posted it on Reddit, and a bunch of people thought it was neat and started contributing on GitHub. Fast forward, now over a thousand people have contributed to it, and we’ve spun out all kinds of applications and libraries. There’s photo editing, sound editing, programming languages, and also a browser, which is what I focus on these days because at some point we just sort of spun off the browser from the operating system. Trying to do both of those turns out there’s a practical limit for how much you can really do within one project, and I think we found it.

At least it seemed really uncomfortable doing both of those things in one repository. And yeah, that’s what became Ladybird, which is what I work on these days. Serenity OS still exists out there; there’s a huge community working on it, but I’ve sort of moved on to build browsers instead because that is what I do.

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So tell us a little bit more about what you’re building, like the Serenity OS HTML viewer. Right? That’s kind of like where you’re at, right? Was it primarily sort of difficulties having that live in the same project with Serenity OS, or was it more like an excitement of, oh, I think we really have the chance to ship like a from-scratch browser?

It was a combo of both, where the motivation to spin it off was initially that I just wanted to put rich text on screen. I wanted to have messages where a part of it was bold. That was it. Ambitious, yeah, right? Rich text got a browser—that’s pretty good. That was the scope originally. So I wanted rich text, and I thought, well, what better format for rich text than HTML? I mean, everybody knows it works on browsers.

Yeah, it makes perfect sense, right? It just makes sense. So that’s where it started. And then I guess, having worked on browsers for many years before this, it was just like falling back into these old habits—but good habits, not like the habits I was trying to avoid falling back into.

So I fell back into the habit of browser engineering, and then it just kind of spiraled. Initially, it was just rich text for a while. Then I thought, well, you know, what’s a really nice way to implement rich text? It’s CSS because if you just have HTML elements, you can do rich text, but it’s kind of cumbersome. CSS is a great way to do that, and so I added CSS.

And then, yeah, it just kept going like that. At some point, we went for almost a year, I think, without JavaScript. People kept asking, like, “When are you going to do JavaScript and build a real browser?” I would say, “No, no, no. Don’t ask; we’re not going to do that. This isn’t the real browser.”

And then one day, I thought, why don’t we try adding some JavaScript?

Yeah, it’s very organic, this whole thing. It grew like fungus, really. The whole operating system did, and the community as well, and in some ways, little by little every day, just people sort of expanding their comfort zones with what they were comfortable working on. Myself as well, like I’d never worked on operating systems before, but if you just start somewhere and work your way outwards, it turns out it’s pretty approachable.

But to your question about when did it become untenable or whatever to do this in the Serenity OS repository, it was almost more of a social problem. When the projects were still in one place, we just had a bunch of people who wanted to work on operating system stuff. They wanted to develop device drivers and work on window systems, and then there were these people who were working on browser stuff, and they wanted to do JavaScript optimizations or GPU rendering for web content. Those things are pretty far apart.

Then you try to put all of those into one CI pipeline where, like, “Hey, I’m working on the device driver for this network card and I have to run this battery of thousands of web browser tests.”

So, yeah, it was like a scalability problem; at least that’s how I looked at it. It was a controversial decision at the time because many people liked that everything was in one place, but it really just felt like it got too big for the one repository model. Our bug tracker was just a mess. You can imagine having a GitHub bug tracker for all these things—the world’s two largest projects together.

So that was an issue. We’ve separated into two projects. I think that split has been very healthy. Now the people who want to do kernel stuff work on Serenity, and the people who want to do browsers work on Ladybird. In theory, they are somewhat compatible still, but Ladybird has diverged quite substantially because in Serenity OS one of the core principles is that we’re going to do everything ourselves, no matter what.

That’s really fun, but it’s not a great way to ship software anytime soon. In Ladybird, we kind of relaxed that whole thing and said we’re going to use third-party software. We’re going to take stuff that’s not our core competency, like low-level GPU driver stuff. We don’t want to figure out how to talk to the GPU on every platform we care about; let’s get the Angle Library that already does this and gives you sort of an OpenGL ES3 context no matter what sits underneath. Other stuff like Skia, Curl for networking—so we just started sort of taking all these best and brightest libraries from the open-source world and building on top of those. That gave us a ton of speed, which has been great as well.

But in Serenity, you can’t do that. That’s why they’re sort of not really compatible anymore.

What’s the current state of what is implemented from scratch in Ladybird versus some of these big-ticket items that are shared in other places? Because I think people hear another browser, their first thought is probably like, “Oh, it’s just a Chrome reskin or something like that,” right? Which is obviously not the case for Ladybird.

So, it’s sort of an evolving thing; we’re not exactly sure where the boundaries are. But there is no Chrome here; there’s no Firefox. We’re not building on top of an existing browser; this is from scratch. We built out all the stuff from scratch that we have since replaced with some third-party stuff.

So we had our own graphics stack, for example, but it is just something that is not our core interest to build really fast graphics. So we started using Skia instead. In terms of what we are doing ourselves, we are implementing the web specifications—HTML, CSS, JavaScript—and various supporting specs, like URL encoding, all those kind of things.

Does that mean you’re implementing your own version of a JavaScript interpreter?

Oh, you are! Nice. What’s the name of it?

It’s called LIJS. It comes from the Serenity OS tradition of naming everything “lib” followed by the most obvious thing possible. So it’s not like Spider Monkey or V8; no, no. QuickJS is LIJS. The web engine is called LiWeb.

Can you believe it?

Wow, nice! That’s a good name! I immediately know exactly what it is.

Okay, so you are building your own. How far into JavaScript compliance with web standards are you? Because if I’m not mistaken, the W3 specification for the web is about 100 million lines; it’s a number that’s so massive that it would take a group of people writing a line a second for like a year to actually get it done.

Yeah, that’s very possible. I don’t know; that’s probably not the best metric to measure completeness of implementation. But in terms of compliance, our JavaScript engine is highly compliant. Just a few weeks ago, it was the most compliant. The JavaScript working group, TC39, maintains this test suite for compliance testing called Test262, and we had the highest score at least a few weeks ago.

I think the Firefox team dumped a full implementation of the new temporal proposal for JavaScript, which bumped them slightly above us. We’re sitting at something like 97 point whatever percent. They dumped code and bumped themselves up to 97.3, so we just have to catch up.

But yeah, we’ve been sitting on top of that quite nicely for a while. You can speculate as to why at the end of the day. It’s like super compliance with every little corner of the spec is not necessarily going to make you the most compatible with the web, but we are a nerdy project with many nerds, so we just like making the number go up. If we can just fix tests and make the spec compliance number go up, that’s just fun, so we do that.

Anyway, that’s JavaScript. Compliance on general web content is harder, and it’s not as well tested because, you know, there are many specs—millions of lines, as you alluded to. The closest thing that exists is this project called Web Platform Test, which is a giant project co-maintained by browser vendors—Google, Apple, Mozilla, and I guess Microsoft and whoever else builds browsers. They all collaborate on this thing called Web Platform Tests (WPT).

I think the idea is that when you fix a bug in your browser, you contribute a test to the shared test suite so that everyone can integrate that into their testing. It doesn’t always work like that in practice, but that’s kind of the idea. There are something like 1.8 million tests in there, so it is comprehensive.

It’s important to note that I think about 1.1 million of those tests are specifically about Chinese, Japanese, and Korean text encoding. Somebody wrote a test generator for his pet encoding issues and generated a truckload to test for that, so it’s a little bit weighted in that random arbitrary category.

But outside of that, it’s the best thing that we have, and we are currently at about 1.7 million out of the leaders, I think, who are at about 1.9 million tests passing. We’ve done all the low-hanging fruits, so strictly middle and top-hanging fruits remain, I think.

If you look at sort of the browsers that are ongoing in development right now, at the top in terms of engines, you have WebKit, Chrome, and Gecko—Firefox—and then you have sort of three up-and-coming engines, which are us, Servo, and a British project called Flow, which is a closed-source browser engine only shipping for Raspberry Pi at the moment. Of the bottom three underdogs, we are leading that pack.

Servo is the rewrite with Rust.

Right, Servo is Rust-based.

Don’t worry; it won! You got a lot of time. You’re going to easily be able to keep outperforming them. You’ve got years of runway.

Well, to be fair, they started back in 2012, so like I said, you’ve got years of runway.

Sure, I hope they succeed, but we have been able to stand up a new browser in half the time since they started. For whatever reason, there’s been a lot of chaos around that project. I think they used to be well-staffed by Mozilla, and then suddenly they fired everybody to save money or whatever. Only recently, the Linux Foundation picked it up and decided to fund Servo. Now it’s kind of up and kicking a bit again; they are making progress, which is great.

My hope is that we see many new browser engines, not just Ladybird. I would feel much better if they did ship and if they shipped something amazing, so that people get more choice. Ultimately, that is what I’m about; what we’re about is that we just want to inject some damn choice into this market because it’s been taken away from everybody.

For many reasons, but you see stuff like Microsoft just throwing out their own engine, consolidating back behind Chromium, and you see Opera doing the same. Anybody who says, “I’m building a new browser” in the last five years, they’re really just building something on top of Chromium. There hasn’t been an attempt to build new browser engines for so long. I think it’s something worth doing, especially now with what’s going on with the DOJ coming up on Google here. We don’t know what’s going to happen with that, but it is looking a mess. They might have to sell Chrome, whatever that even means; they might be banned from participating in the browser market for x amount of years.

What the hell happens to all the people building stuff on top of Chrome if Google is no longer developing that? It’s high time we see some new engines and some new approaches to this.

All of that to say, I hope Servo succeeds. I hope they ship, and I think it would be good for them to speed up a little bit. But I think they will.

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Speaking of being able to choose between browsers, I’m interested in how much you get to use Ladybird as a daily browser. Is it like, every website I go to, I’m opening it in Ladybird? How far away do you feel that is? I’m just interested, for that user experience, how are you feeling on it? How’s that going?

Right, so for the average user, it is completely unusable. There’s no way that you would not be disappointed—100% certainty you would be disappointed. You would say, “This is a piece of [__]. What the hell?” A 100% satisfaction guarantee—it’s guaranteed dissatisfaction.

Right, no doubt! This is why we’re not telling anybody, “Please download this and check it out.” We are pre-alpha for a reason; everybody would just be disappointed.

It’s still possible to build it, you know, because it’s open-source, and we’re doing it all in the open, but you do need to do that little manual step. There’s proof of work, I suppose, that you have to provide yourself to get the experience of being so disappointed in our new browser.

But to your question, I don’t—I try to use it every day for like random websites. Every time somebody sends me a link, I try to open it in Ladybird and see what breaks. A couple of websites work well, like our GitHub development workflows, for example, reviewing pull requests—stuff like that. We’ve tested those a lot; we tend to fix whatever gets broken there, so that stuff works.

But you know, you throw a random website at it, and chances are just so high that they’re using some random CSS features that we haven’t implemented yet or some combination of things. There’s really a lot of interesting ways to combine CSS features you wouldn’t think, like, “Oh, but if you put a flexbox inside of a float inside of an absolutely positioned grid that has this—”

And there is the Web Platform Test, but there’s only so much coverage in there. We’re constantly discovering new ways to combine web platform features.

Let’s say is CSS one of these kind of projects where, as you fix one, you realize you’ve broken ten others? It feels like they’re just so interdependent on each other that it feels like it’s just very dangerous to fix anything.

I will say that if you don’t have the right mental model, that will happen every time. I am still working on my mental model. I keep updating it and keep discovering things that I did not realize.

So it’s a journey, and sometimes the patches to the mental model are so severe that you just have to rewrite large parts of things—things that seemed like they made sense in the past. You discover, wait a minute, it doesn’t make sense because there’s this one edge case.

A typical thing that I always have to re-model is the relationship between heights and widths in CSS, for example. When can you figure out a width if you only have the height? What are the circumstances, especially when there are percentages involved?

This percentage can only be figured out once you know how big something is, and maybe the size of the thing depends on this percentage value. What order do you do things in? Yeah, that kind of stuff tends to just wreak havoc on your mental model when you discover something.

We’ve been doing a lot of that, but I would say the real chaos from CSS comes from the fact that it is continuously updated and edited. These specs are alive; it’s not like you can just get the CSS specs, print them out, and start implementing.

If you reload the spec tomorrow, someone might have changed something. Most of the time, when you’re lucky, they’re just clarifying something like, “Oh, this was a little ambiguous—we’ve clarified it.” But other times it’s like, “We added a new property or a new value that this property could have.”

Alignment is something like that, for example. They’ve really expanded the ways that you can align boxes within each other, and then you just have to kind of suck it up and say, “Okay, well, I guess you can align a box relative to this now.” Then you have to think of a way to do that in your C++.

But yeah, I guess I agree with what you said—CSS is like that. You have to keep working on it.

So, how often is this spec changing in ways that cause a really big churn? Something that actually feels kind of earth-shaking comparatively. I would assume REM and M were probably really painful, but they didn’t come around during Ladybird development; they predate Ladybird. I don’t know how old those are, but they’re new to me.

It seems like I keep seeing new units, which means you have to thread through just so much little bits of information just to be able to land in every single place. It seems very hard.

Does this happen frequently that you just get wrecked by a whole new feature that you have to rethread everything through?

No, I wouldn’t say so. Getting really wrecked by spec changes—that’s like once or twice a year so far.

Okay, just once or twice a year.

Just a few times a year, you know, not too bad.

That’s okay; we can handle that.

It could be worse; I guess you could be writing it in Rust. Then you’d definitely be sad to have to change everything.

Hey, we’re not making fun of Rust right now!

Oh, sorry, sorry, wrong format.

Continue.

Continue! Yeah, no, but you know, fair enough. C++ is pretty friendly when it comes to sudden rewrites. You have to rebuild or recontextualize some ownership model, and doing that in C++ where it’s not so strict about all the ownership stuff, you know—there are benefits to that, certainly, in terms of development velocity.

But there are downsides to that as well, which we acknowledge. It has certainly helped us, I think, being in a flexible language.

We are a very small team and also, like, with a small budget, relatively speaking. If you look at our budget compared to our competitors, we are something like 0.1% of their budget.

That means that we have a very different approach to development, obviously, and we have to prioritize differently and take some shortcuts, cut some features. The spec changes that happen day-to-day, we’re not necessarily always on top of those anyway. We were frequently just letting things slide.

And it’s more of those major, big changes that we try to catch up with. It’s okay so far. I do feel like tomorrow they could drop a big specs change on us that would just ruin everything. This has happened before. they did this big rewrite of navigation APIs and history management called navigables which is like a comprehensive change to the HTML specification which we are still reeling from. I don’t think it makes any difference to you as a browser user, but for us it was surprising, let’s say.

So obviously we hear all the time, I mean if you’ve been on Twitter for longer than 14 seconds, you’ll hear that programming in C++ is a horrible decision to make and that anyone that does it is an intentionally bad actor at this point, potentially Russian state level acting. So it sounds like ladybird is largely built, if not exclusively built, in C++.

Are there any other languages being used? And also is modern C++ as bad as Twitter actually makes it, or do the shared and unique features actually make it not as bad? Well, I think nothing is as bad as Twitter makes it.

Fair, true, true, good answer, good answer. But there are flaws, obviously. We are aware of the flaws and we are C++ almost entirely our own code of C++, but of course now we’ve started integrating third-party code so we’re also taking in C code such as the curl library, which is some very sturdy C that never seems to fall apart, which is cool.

We are happy to use whatever language our third-party packages use as long as they’re good packages with good APIs. For our own code, we don’t have that many problems with C++ unsafety, certainly not as many as Twitter would have you believe, but we recognize that there is the occasional terrible security issue that would have been avoidable with another language.

And so we do want to build a browser that people can use and feel good about using, so we do have to do something about these things. We’re using C++ just because that’s where Serenos started and SerenityOS started with that because I just wanted to make WebKit the operating system.

It’s just this weird sort of sequence of events that led to suddenly having a C++ codebase, but in the long term, we want to… well, we really would have liked it if C++ got their act together and added safety features to the language. There were some attempts, but the committee has sort of… how do I say this in a friendly way? They’re kind of getting in the way of… I’ve read about some of it. It sounds very, it sounds very lightning charged, some of the discussions.

Yeah, it’s been rough, and there are people who put forward some serious effort into designing safe versions of C++ and they’re just having a very hard time convincing the committee and the greater language community that these efforts are worthwhile. That was pretty diplomatic, and we would have loved it if C++ was solved somehow in this way.

Now it kind of doesn’t look like it’s going to be anytime soon, and we want to put out a browser in the next couple of years, so we are looking at alternative approaches and we’ve evaluated sort of a bunch of different languages, and the one that we ended up with was Swift, because everybody liked writing it.

We tested a bunch of them. People always ask like, which ones did you test? What didn’t you like about them? It’s like 100% bait, right? They just want to get into an argument. But seriously, which ones did you test and why didn’t you like them? Yeah, and be specific so we can really clip that out of context and make you look like you’ve never written code before.

Right, well I always say that I don’t want to comment on languages where I haven’t written at least 100K lines because my opinions on those languages are not really worth hearing. They’re not based on anything that you should use to make your decisions, so it’s very anticlimactic to hear for people who keep asking me.

Can I clarify one quick point with that? Is it a 100K sum or is it 100K singular project? I suppose that would be 100K sum, yeah, just because a 100K size project is a big project which exposes different warts of a language, whereas 100K a bunch of small projects, you could effectively avoid a lot of the warts by having 100 1,000 line code projects.

Right, yeah, just hello world all day or just like five JSX templates. You know, just produce enough code in some ways. Yeah, no, so we tested all the usual suspects. I had people on my team spend a couple of weeks writing Rust, for example, and trying to model various things that they thought were interesting. Like hey, take this part of the browser, try to rewrite it in Rust, see how you like it, see what kind of trouble you run into.

Initially, it was pretty good. People were excited, I think in part because there’s a lot of hype around the language, but there was some frustration that started creeping in. It’s not what happened when they tested some other languages. Swift was the one that people tested and they sort of came out of that saying, hey, wait a minute, this is great. I didn’t run into any major problems, and I was able to write the program the way I wanted. I didn’t have to argue with the compiler and it kind of just worked.

So it seemed like the right thing to do was to ignore the hype and just go with the thing that everybody enjoyed and then sort of work backwards from there to explain what is it about it that we enjoy and what is it about it that makes sense for a browser. That ends up being that it’s very object-oriented programming friendly, which is not the big thing these days, but the browser, if you ever looked inside a browser, it is very 1990s world because browsers really were like… the core architecture of browsers was invented when XML and Java was like the big thing, right?

Back in the 90s, they built this whole document object model thing around that, and now we’re stuck with that, and you have to model that in your programs. It’s just a lot easier to do if you have an O friendly language. If you look at Servo, for example, I know they have to jump through a bunch of interesting hoops and do a lot of unnatural patterns in Rust to express some of these things that you need to have in a browser.

You can still do it, but it’s just not natural. I think that’s certainly what I enjoy about Swift, is that it just kind of maps to these things that we want to do really nicely. But the big downside with both of those languages is that neither of them are garbage collector friendly, and the browser is a garbage collected environment because JavaScript is a garbage collector or has a garbage collector.

So whatever language we use, we have to do our own garbage collection. We have one that we have written ourselves, and now we are figuring out how to make that thing work with Swift. If we had used Rust, we would have to figure out how to make that use… how to make Rust understand it. So there’s no way around that. I guess I would say if I were starting over today, I would probably just pick a language with a garbage collector so that this would be a non-issue.

Can you explain that a little bit more? Are you saying like use Go or use C or use Java when you’re saying the language itself has garbage collection or a language that provides a garbage collector? I don’t know that the difference is meaningful. Okay, I would have just liked it if someone else took care of garbage collection.

Fair enough, because especially with modern programming style, garbage collection gets kind of complicated, especially when you have stuff like asynchronous programming or you make a Lambda where you capture a bunch of stuff, and then that has to stay alive until the Lambda executes. All of these things you have to take care of, and it would be real nice if somebody else just took care of it. But we put ourselves in this silly boat where we have to take care of that and we’re stuck with it.

So that’s what we’re doing now with Swift, just trying to figure out how to make Swift tell our garbage collector what it needs to know about everything. Are you saying particularly for like the JS runtime, or are you saying for the whole browser, you would rather pick something that’s garbage collected, or both? I guess I would also prefer something for the entire thing because the specifications just sort of assume that you’re in a garbage collected environment.

There’s no malloc and free in the spec. They just say like create a new thing and then set the reference to that thing to null somewhere, and then it just disappears, I guess. They never explain in any of the specs why things disappear, and so the implication is that it’s a garbage collected environment, but it’s just never explained. It’s something that other browsers traditionally have done a lot of stuff with reference counting.

So WebKit and Chromium, at least those are the codebases I’m more familiar with, use reference counting extensively. But then you have the problem of ref cycles where two ref counted objects point at each other, and now you leak. It would have been nice to… if you were using a garbage collector, you wouldn’t have that problem. For that reason, I say, hey, these specs are all GC assuming, let’s just use a GC language.

Out of all the things, that’s probably the thing I would do very differently today. So what’s the status of Swift in Ladybird right now? I know you guys are working on setting that up and interoperability. I’ve seen you do quite a bit of things in the repo, sort of generating. It looks like, at least from a quick overview, generating Swift files and other… what’s the current status? Are you actually getting to write Swift, or is it still sort of exploratory?

For properly shipping it to whatever prod means for you today, it’s still kind of exploratory. We’re working together with some folks on the Swift team to figure out our garbage collector integration. It’s not something that has been done a lot, and when I worked on the WebKit team, I know that the Swift team really wanted us to make use of Swift, but there were all these complicating factors such as the WebKit garbage collector, and WebKit never really integrated Swift that much.

So here I am 10 years later outside of Apple, but trying to put Swift into my garbage collector browser engine. You just never get away from it, I guess. So yeah, we’re still exploring, and what that means in practical terms is that we’re just trying to figure out how to get all the information the garbage collector needs across the language boundary. We’re making progress and the Swift team is very nice, very friendly and responsive, but we are sort of just giving them problems that they don’t normally have, and so it takes time to figure out the right solutions.

But we’re hopeful. It’s kind of exciting, I think, to see if we can get this working because we need Ladybird to be different from other engines in many ways. You know, like if we are just building… if we end up building Chromium again, then it’s less useful, I would say. So if we do things differently, then we have a bit more biodiversity in the ecosystem, which is probably a good thing.

Yeah, that totally makes sense. I’m interested in sort of where you envision Ladybird going. Max, what’s your roadmap? What are the things you’re shooting for? If there are any sort of really unique aspects of Ladybird right in that roadmap, I’d love to hear about those too.

Sure. So at the moment, we are just trying to make a browser that works. Fair enough, yep. So we don’t really have the luxury of differentiation yet, but we’re trying to plant the seeds for differentiation. It’s important to look at what are our opportunities to differentiate, and I think one of our key strengths is that we are not dependent on Google in any way.

All the other browsers are depending on Google either for their code or for manpower or for money. We are trying to find a new way to develop and maintain a browser that doesn’t require a billion dollars or a thousand people. I listened to a podcast from the Open Web Association this week where he was talking about the DOJ case against Google, and he had some interesting numbers. He said that there are about a thousand developers working on Chrome right now paid by Google and that he estimated their cost between $1.5 and $3 billion per year, which is a wild, I think.

That’s certainly what an operation at that scale costs, but if the DOJ decides that that kind of money is just not available, at least not in this way, then we’re all going to have to figure out how to build browsers in a different way with a very different budget. I think that’s an area that we are exploring, not that we have a choice. You know, it’s not like we can say, screw this, let’s just do the billion-dollar thing and say, oh, we’ll just do it with a billion dollars. I guess it’s fine.

No, so by necessity, we’re exploring what would it look like to build a browser with a tiny team and a tiny budget, and what kind of compromises do you have to make to make this possible? But, you know, as everything turns out, maybe that information is going to be super relevant to all browsers in the near future. Who knows?

I guess the follow-up question would be that if you become successful building Ladybird and Google is in the middle of potentially being dissembled at some level by the DOJ, wouldn’t your success then actually start to be like against the monopoly that is currently being prosecuted? Could you save Google by building a browser with just a few engineers?

I don’t think we have the time necessary to do that with the scale that we’re running at right now, but in theory, I think it could be done. If we could get everything to align correctly, you could certainly make the argument that, well, look at these guys. They’re building a browser at a much smaller scale. They don’t need all that money. Maybe Google just needs to be restructured a little bit, I don’t know.

So if you’re saying a bunch of engineers all of a sudden start committing to Ladybird under these unknown names and all that, it might actually be Google spending a billion dollars to get your successful all Gmail.com. Weirdly, they’re thinking very sneaky. Well, you know, a lot of people say that’s exactly what they’re doing at Firefox, right? That they’re just sending the money to Milla to save off the antitrust argument.

On the topic of fake emails, by the way, it’s funny. I remember back before Chrome was announced. Chrome was originally built on WebKit, right? And I know that the people who were making Chrome, they were like sending fixes to the WebKit team under fake names and fake emails for a while before Chrome was announced, so that was pretty good. It wouldn’t be the first time they’d under their fake identities contributing to other browsers.

But no, I don’t know. In all seriousness, I don’t think that we can save Google in this case. Sorry, Google. Yeah, yeah, would have loved to, but just going to have to let this one play out.

Do you have a guess for… obviously not like every website. I mean, I don’t think any browser works for every website, but do you have a guess for, let’s say, you know, I’m doing my daily drive of checking GitHub, scrolling a little bit on x.com, watching a YouTube video. When’s sort of a guess of when? Okay, you could do that. Maybe it’s a little slow, maybe there are a few edge case kind of scenarios, but someone could actually do that as a daily driver for Ladybird.

Right, so your GitHub stuff you can do today, cool. Scrolling on x.com you can kind of do today. You have to fake the user agent because if we tell them that we’re Ladybird, they just say no.

Interesting. Okay, we’ll phone a few friends at X and we’ll tell them to change that. We’ll get you on the list. Sure. Yeah, no, the user agent string is a constant source of pain for us, and every day it’s like, should we just suck it up and put like Chrome, WebKit, Gecko, Firefox… everything like everybody else does, just put every other browser name in there, and suddenly all the websites work?

So far, we’ve sort of stayed pure. We just say Ladybird. We don’t lie. Good, that’s been a long-standing problem in the terminal world. Like people just lie about… oh God, yeah. You know, so it’s like we don’t have to repeat the same mistakes from history over and over again. Maybe other people can just fix the other problems, and so that’s good.

I’m excited to hear you’re not caving to the pressure. That’s good. Well, we’re probably going to cave to the pressure. Oh no. Alright, let’s end it. We don’t even need to do live Q&A.

Well, the problem here is that there isn’t some new term cap or whatever that we can just go and fix. Fair, that’s fair. There’s just every website out there, many of them unmaintained. Nobody even knows what machine is serving up this damn website. They couldn’t find the server if they tried and we just have to deal with those machines being configured in those ways.

So at the end of the day, we’re probably just going to have to suck it up and say we are Ladybird. I mean Chromium. I mean Firefox. I mean Safari. Just don’t worry about it. Give us the content. But yeah, so today if you say that you’re Chrome, you say that you’re Safari, x will work. You can scroll there.

YouTube was your last request. That’s the hard one. YouTube is a very complex piece of web content. We are making progress on it. I think we have the video codec stuff necessary, but we don’t have all of the APIs hooked up for like media source extensions, whatever they’re called. Certainly none of the DRM stuff, and performance is not great for this kind of stuff either.

But obviously, everybody loves YouTube. We want to get YouTube working, so we are chipping away at it. In terms of a timeline, so that you can clip this, we’ve said 2026 for an alpha release. That means it’s something that we would pre-compile, so you can download it and test it out and tell us what didn’t work. It will be a lot of things, and we will by that point be in a position where we can actually meaningfully take advantage of this feedback.

Today, if you try it out and tell us something didn’t work, we know it wasn’t going to work. You don’t have to tell us, but hopefully, next year it will be a bit more valuable. But we’re not saying any specific date in 2026, just 2026 could mean a lot of things.

I guess my follow-up question is: how does it feel to probably ship an entire browser before Prime finishes a project on stream? It’s a good question, especially since mine are under 10,000 lines of code. I mean, I’m still giving you a whole year and a half at least for Prime to do it. But I’m just anticipating like my poly market bet for this would definitely be on Ladybird.

I guess I don’t feel great about that. I wish that Prime would get to ship first. Thank you. But where’s the but? I’m just waiting for it to happen. There’s no but. He’s just rooting for you, buddy. He’s just got your back. I just want you to… thank you.

Alright, but real talk: GTA 6 or Ladybird, what’s your bet here? Okay, we want a real one. Um, I think they might beat us. Oh, I want to believe that would directly impact the shipping of Ladybird.

Oh, see, now you’re asking the real questions. Um, possibly. Okay, because that’s like the danger of life is that you’re competing on two axes there. GTA ships early 2026; Ladybird is now late 2026. Weird, we don’t know why. Somebody at Apple told me that there was at least one cycle of Mac OS that was directly impacted by World of Warcraft coming out.

So this does happen. It does. See, I remember. I remember those days; they were very magical days. Yes.

Alright, awesome. Well, I actually want to go back. I want to go back to the DOJ. I have some more questions about the DOJ just and Ladybird in general. Because if this does happen, let’s just say that the outcome of this is that Chrome can no longer participate in some stuff for some amount of years, some sort of reprimanding or intentional government-enforced slowdown put onto Chrome.

What is the implications of that? Because that is not something I’ve really thought well through, especially since we have an almost total browser alignment around Chrome. There are a lot of implications. I guess the direct issue is that somebody has to take over a lot of things that Google are currently owning. That’s the Chromium engine, obviously.

But also, they do a ton of standards work. I would even say the majority of standards work is done by Google employees today. So if Google is just booted out of this entirely, then a bunch of web standards will just grind to a halt, I think, at least until somebody figures out what’s going on and other companies maybe start pitching in more staff.

Those are sort of direct effects because, like, the Google people disappear, but then you have a lot of secondary effects where money disappears from the other browsers. Firefox famously makes what, like half a billion dollars a year? Or something from exclusively why they pretty much exist is because of the page searching or whatever you call that, that they get all their money from.

That obviously presents a problem for the way that they’ve been maintaining an alternative browser. I don’t think that money is going to last that long, at least not unless they significantly change their structure and burn rate. You can see that they are sort of desperately flailing around looking for new revenue streams and stuff, and I hope they find something that works.

But is this the reason why Servo kind of came about? Or the rework on Servo? Do you think that has any implication from this? Because I know Servo died for quite some while. I think from Milla’s perspective, Servo was kind of a research project and they looked at it as a way to perhaps develop some interesting new approaches to browser engine technology.

They’ve sort of taken the best of Servo and put it into Firefox, which is great. I don’t know that they were looking at it ever as a way to build a new browser entirely. I think their ambition was to sort of prototype stuff and bring the best back into Firefox. But I may be wrong about that. I’ve never been inside Firefox; I just know some people who work there.

I think what they said publicly when they let the Servo team go, if I remember correctly, was just that they wanted to focus on other things and cost-saving measures, basically like the usual. And focus on other things outside of the browser. Everyone was like, huh, classic browser behavior, where they said they were going to focus on non-browser activities.

Yeah, I mean, I don’t like to crap on other projects and organizations and things like that, but it is a little bit sad because I was a huge fan of Milla ever since they came out. Yep, to see that they’re gradually scaling down their interest and investment in browser stuff and just trying to branch out into anything else like AI or advertising, the latest thing. It’s a little sad because we all fell in love with them because of Firefox.

So yeah, that’s been a little sad, but I have hope that they will find a way. Maybe not a way that costs them half a billion dollars a year, but a way. But then you also have other projects like Safari. Every time I say this, people always say, well, you know, $20 billion a year is like chump change for Apple; they don’t care.

But $20 billion is not chump change for anybody. I don’t care if you’re Apple; that’s enough to make you care. Yeah, that’s several research projects going in parallel. That’s a lot of iPhones. Even at iPhone prices, that’s still a lot of iPhones.

And I think, you know, if tomorrow Apple learns that, okay, you’re not getting those 20 billies anymore, I think the calculus looks a little different for how much they care about building a browser. I don’t think they would stop, but things will probably shift around significantly as a consequence of that.

And then you have all these browsers that are just building on top of Chromium. So Brave, for example, or what are all these other ones? Like AR… Valetti? Valetti? VDI? VDI. There we go, sure. I think you’re… Right, sure Valentino.

Yes, all of those that sit on top of chromium, they’re obviously also going to stagnate if nobody is developing chromium. So those are some of the basic sort of consequences and implications that you can easily reason your way to. I’m sure that if you think this through and if you have more context and you have a better understanding of what the DOJ is really proposing, you can figure out a lot of things that might happen as well as a consequence.

Who knows? Do you think it’s pretty serendipitous that you’re this far into a browser and these things are happening? Is this one of those moments where people talk about how luck is just opportunity meeting preparedness, and you just happen to have potential where maybe Brave or one of these browsers are like we need something different to avoid this entire potential conflict? Let’s choose ladybird, right? Because you potentially could become an alternative. I think what you’re saying makes a lot of sense.

What was that? Opportunity plus preparedness equals luck, right? A lot of people don’t define luck as random. Not all life is just one by pure random chance. It’s just like you happen to be studying browsers, you happen to devote your life to browsers, and now browsers are in this terrible weird state. You’re just like, well, this just happens to be a preparedness meant an opportunity, right?

Yeah, I think that would be lovely. I’ll say that if we can somehow, if it turns out that we can provide sort of an alternative solution to all of this—maybe not now, but like in a year or two—if we would be in sufficiently good shape that somebody could start prototyping, let’s say Brave on top of our engine, I think that would be fantastic. I would say the same thing, you know, if Servo is in great shape in a year and somebody could start prototyping their browser on top of Servo or whoever, because I’ve always felt that the way browsers are funded is not wholesome.

It’s not great that we have to track users and put ads relevant to them somewhere, and then like that money somehow changes hands a million times and eventually it pays for the browsers. I feel like it would be more wholesome if we had a different way of doing that. I can’t think of a way that would make as much money as the current approach does, but maybe it doesn’t have to be done with so much money.

That’s kind of, I guess, part of what motivated me to work on a browser. I always felt like, I mean, maybe I’m a little bit overly idealistic here. I know that, but I always felt like the browsers that exist are a little bit compromised because they can never really put the user first because their loyalties are always going to be to the hand that feeds, right? Is there such a thing as putting the user first and putting the user privacy first if you get your funding and staffing from advertising? I think not really.

And that’s not to say that projects like Brave aren’t doing hugely awesome work on user privacy. But at the end of the day, I would love it if we could have a browser that could put the user first and could say that with a straight face—no fingers behind the back or anything—or whatever you do in America. You can say it, and you can mean it. I think that would be nice.

One of the questions was actually about Brave that we have from our live Q&A. I don’t know if there’s anything else you sort of find interesting or cool about Brave or like some of the ideas. I know they were trying to do like a token and they’ve tried to do like a bunch of—

They got BAT, they have a lot of at least interesting ideas. I’d just love to hear a little bit more of your thoughts on that, if you want to share.

Sure, so I think Brave, they’ve certainly been experimenting to look for alternative ways to fund browser development. I’m not intimately familiar with how that has worked out for them, but I think it is great that they’re trying different things and they’re just, in general, experimenting.

People like to say like, “Oh, that’s just a Chrome wrapper or a Chrome skin” or whatever. I do feel with Brave that they are actually building browsers. They just don’t have a thousand people. They have the team and the budget that they have, and they do what they can with that. They are experimenting within the browser space looking for alternative revenue streams.

They’re not like Mozilla, just trying to throw everything at the wall and see what sticks, so I do like them for that. I think they have a ton of interesting work on preventing user fingerprinting and things like that, like really locking down random JavaScript APIs to make the browser appear homogeneous.

We want to definitely do things like that. If in the future there is a future where we could be the engine for Brave, I would be super happy. We’d be happy to have them collaborate on that with us. But today, we’re nowhere near that being a reality.

But who knows where this goes? Go ahead. I was just going to read one.

How can you convince people and sponsors to join and believe in this project even before the first version of ladybird is available? Do you have milestones you’re hitting and more attempts you’re taking at raising more funds if that’s even what the right term is for this?

So we are a nonprofit created last year, and we’ve been raising donations essentially, saying that you can sponsor this project but you can’t really buy influence over it because we want to be independent. What we do is we’ll put your logo on our website and say thank you on social media, and then that’s the end of that.

As it turns out, there are a lot of companies and individuals with money who feel that what we’re doing or what we’re attempting is worth getting behind. We have companies like Shopify, for example, backing us, which they were our first sponsor actually, even before we had a nonprofit. They just like to see somebody trying this and somebody trying to inject some diversity into the web browser market, I suppose. I’m sure they can say that in a much nicer way than I’m doing off the cuff, but they were very supportive of our mission and we’re very thankful for that support.

Over the last year, I’ve been able to find a bunch of other companies and individuals who wanted to sponsor that as well, and none of them have been concerned with specific dates. It’s more about, “Hey, you’re doing something that we think is worth pursuing. We want to back you.” It’s been great.

One of your earlier guests, DHH, is one of our sponsors. We’re very happy to get him.

I could just I could just feel it coming when you’re going to say I just knew.

He was very nice and convinced his company to sponsor us and a bunch of others as well. Not only that, but we also allow individuals to donate, and I think we’re just looking for—we’ve set very tight constraints on how we allow ourselves to be funded.

We don’t want to sell out influence in any way. We don’t want to become dependent on a single company. We don’t want to be dependent on like advertising or tracking or anything like that. It’s pretty tight what we can really do, so it’s all about those sponsors, donations essentially.

How do you avoid doing that though, in a real practical sense? Maybe my view of the world is a bit too cynical, but at the end of the day when someone has proven time and time again to be a project-saving type of funding, how are you not swayed by what they have to say?

How are you avoiding that whole situation? Because that is—I would just assume it’s virtually impossible at some level to just always avoid that. It’s certainly an issue, so our main strategy is to keep our scope so tight that we don’t go and hire a bunch of more people just because somebody gave us a bunch of more money.

I think that’s the main mechanism by which we avoid stepping into this trap. If Google were to say tomorrow that they want to give us a million bucks to build this thing, that doesn’t mean I’m going to go hire ten more people right away. It just means that now our war chest is that much bigger, and maybe we will modestly scale up the team a tiny little bit by one person or two or something like that.

I think continuously monitoring sort of the diversity of funds that we have and looking at where it comes from, making sure that we don’t have just a single sponsor—I think that’s our main approach to this.

How is that going to play out over time in the real world? Well, I don’t know. I hope we’ll do a good job of it. I’m hopeful also that we will have a range of diverse sponsors so that this doesn’t have to become an issue that we even have to think about.

One easy way to avoid it, of course, would be to set a cap on sponsorships and just say like, “Well, we’re not going to take any more than this.” That kind of goes against all of the capitalist impulses that one might have, but that sounds a bit kind of risky to say no when you’re a nonprofit to money.

Yes, I’m just thinking out loud and not saying that we’ll do that. But yeah, it’s an important problem to keep in mind always, I think, because of what you see happen to everyone else who tries this. Every successful software nonprofit runs into some version of this problem.

You can see a lot of them, for example, giving out board seats to big donors, and then that has consequences. Or they become dependent on a single donor that gives way too much or whatever. So yeah, we’re trying to keep all these things in mind and move carefully and keep the team small.

I love that. Here’s another one for you. I’ll slightly rephrase, but basically I think, picture ladybird three to five years from now, right? You have a browser where you’re not saying, “Oh, it’s Alpha.” You’re like using it, it’s a daily driver kind of thing. What do you believe the next big hurdle or challenge is after that? Do you have a vision for the future where you’re like, “Oh, if Ladybird does XYZ, we’re going to be so happy. We’ve conquered our next big challenge?”

Oh, I think the—I guess that’s when the real challenge begins, which is the maintenance of this thing. Maintaining a browser is a bit different than maintaining other software projects because of the ever-changing specifications.

It’s a bit like you’re writing an emulator for a video game platform that keeps changing, and you have all these ROMs which are websites, and they’re releasing new ones every day. I think there’s going to be just an infinitely long tale of maintenance work to be done at the end of this. Maybe this, what we’re doing now, is perhaps the easy part.

Getting this to work with 80% of the web might be the easy part, and the rest of eternity is going to be the last 20% or whatever. I could see it playing out that way, and I don’t personally have anything I want to do after this. I just want to do more of this.

I love this. In some ways, I think when I left Apple, I missed my job, and I spent the next six years recreating it. Instead of advancing within Apple, I just created a new browser, so I became in charge of that instead.

It’s a little bit strange.

Straight to the top.

Yeah, you just started your own company instead—nice. Smart. I see the CEO is the way to go.

But okay, I have a couple of questions. I want to kind of do them back to back here, but the Apple one is kind of interesting because you’ve mentioned this quite a few times—that you were at Apple. I don’t know if you’ve stated it, but what made you leave Apple?

Because obviously, being at Apple for a long time, working on a very core feature means you’re probably in a very secure, good position. So leaving it is obviously a non-trivial decision, at least I would assume.

Yeah, there were a bunch of different things, but I think the main thing that really happened was that I left in 2017, and I just had started feeling like I didn’t fit in at the company anymore. It was in the year after Trump was elected, and that just kind of activated everybody inside the company.

People were now super eager to talk about politics all the time, and I tried to keep up with that, but I just wasn’t really interested or invested in American politics. I’m Swedish myself, and you should really shouldn’t even—right?

I just felt like that was not the environment that I wanted to be in. There were many other things as well, but it just felt like suddenly I could easily step away from this and do something else, and I wouldn’t even feel bad about that.

So you’re saying we can thank Trump for ladybird? Got it.

We’ll put that—that’s the title.

You very much clip it and ship it, boys. He’s building a browser.

Okay, the best browser. The best one I know of.

Right, okay. Okay, but on a kind of a, I guess maybe an equally kind of light-hearted note, if there was one browser feature that you could just take out back and Old Yeller, which one would it be and why?

Oh my goodness, JavaScript eval probably.

Oh, good answer. Good answer. I thought you were going to say with, but eval is also pretty good. But with has been deprecated, though it’s still alive.

And with, like once you implement with, then you’re good. But eval just keeps finding new ways to screw you over. You think that you have some great JavaScript engine optimization that you’re cooking up, and everything is looking great, and then somebody says, “Well, what about eval?”

Then it turns out that there’s some stupid way to add a new var if you just eval at the wrong place, and then if you add that new var, then it breaks everything that you thought you were going to do. Yeah, eval is a constant source of not fun. So yeah, probably get rid of that one.

Do I have to come up with more or is there—

If you have one that you’re just really jonesing about, you can. If not, I want to do the inverse, which is what is your favorite feature—the one that you implemented or part of it or saw it happen that just was like, “this is the best part”?

It’s got to be bolded text, right? That’s how the whole thing started.

Oh my goodness. We can put bold text on the screen. It’s just incredible. Started with a slash or a bracket B, ended with the browser.

I think my favorite feature might be Flex layout, because I learned web development in the 90s. When CSS first came out, and like Netscape 3 or whatever shipped with CSS, I couldn’t get my head around it.

It felt weird and clunky, but I eventually understood how it worked. When Flex layout came out, I thought, wow, this is how it should be. It made so much more sense to me because I was used to making GUI apps with Visual Basic and stuff like that.

It just felt like this is a better way to do GUI apps, and finally, CSS is amazing. I always had a soft spot for Flex.

If I had to pick one, I like this follow-up question too, which is the feature you wish that browsers added the most or what spec thing are you just like, can you please add this?

It just means more work, TJ. I’m not—does he wish for more work upon himself?

Tailwind natively in the browser?

Yeah, that’s funny. I talked with Adam a while back, asking him like what would that look like if we did Tailwind natively in the browser? None of us could figure it out.

But there you go.

Well, you say it would just be more work. My favorite type of feature is any new feature that allows us to avoid work. So whenever you have users doing something in JavaScript that we could just do in the browser instead, I think that’s generally sweet.

The big suck for many years was like JavaScript scroll handlers, right? You would handle the scroll event to do something like move something around before position fixed was a thing. For example, people would do that in the scroll handler, and like it was super janky.

Then position fixed became a thing, or position sticky for that matter. Just taking sort of those annoying poor performance patterns that people do in JavaScript and turning them into browser features, I just like those.

CSS animations, I guess, was a big one that just turned out pretty sweet. Anything where we can do the thing for you, I think, is nice.

It’s also the source of a lot of complexity in the platform because nobody ever agrees on what we should do for you. So we just end up doing way more things for you than maybe we should.

Everybody has that pet thing that they want CSS to do, you know? But yeah, that’s just nice—let us do the difficult stuff in the browser.

So there’s an old saying which is, “Whatever the current year is, this is the year of the Linux desktop.” It’s been going on for, I don’t know, a decade at this point. It’s obviously a funny trope that we all say all the time.

But there’s also one that kind of has it with browsers, which is, “This is the year of web assembly.” Is this actually like—are we actually living in the year of web assembly? Is it really ever going to happen, or is it always going to be, “This is the year of web assembly,” but there’s only like 10 people actually using it?

Oh, that’s a good question. I think we might already be living in the age of web assembly because there are already really cool tools built on it, like Figma for example.

It doesn’t make a lot of noise about itself; it just kind of works. That’s how I would like the age of web assembly to be—that it’s not something that like, “Oh, this website powered by web assembly.”

It would just be suddenly you have all this functionality that seems like it belongs in a native app, but it just works. I get the impression that a lot of websites have started just using web assembly and not making a big deal about it.

We’ve certainly discovered that when we built ladybird that this website needs web assembly and we’re not doing this correctly. I think we might have just sneakily entered the year of web assembly without fanfare.

Okay, because generally, what I see is that a lot of people do not reach for web assembly first. I cannot think of a single boot camp person that comes out that doesn’t say like, “React felt solid”—something like that, right?

It’s like this very specific way in which you build a modern app. I don’t hear anybody being like, “Oh, web assembly is actually the way you want to build a modern app.” That’s kind of what I meant by that—that it is seemingly for large projects like Figma, I think it makes sense.

I would assume Dingboard, I’m not sure if it makes sense, but it uses it anyway. There are a few places that use it, and I just don’t, you know—maybe I am mistaken because I’m not the one building the browser, so I actually don’t know the extent to which web assembly is being used.

So you’re thinking of like the year of web assembly means that now we’re building entire web apps in web assembly and screw everything else, yeah. It’s like in the sense that you no longer need to be held to the same standards that we were because right now, it’s just like you have to JavaScript all the things.

Maybe that’s just not a world you have to live in. I do like the idea of single-language programs, meaning that I could write my back end in C++ and I could write my front end in C++, and I could just assemble it down.

I actually just have that single-language kind of thing because that’s the benefit of, say, TypeScript or that’s the benefit of all this stuff that they always talk about, which is you have one definition for a thing, and it works uniformly.

I don’t know that we’re anywhere near that. I think the usual suspects that come up whenever people talk about this is, “Well, what about accessibility? What about selecting text?” Classic selecting text problem, right?

You’re essentially trying to go back to Adobe Flash, right? All roads lead to Adobe Flash. Do we want to go back there?

It was pretty sick. ActionScript was the best internet language of all time. Yeah, I was just watching Home Star Runner the other day, thinking Flash.

I don’t know if you guys are old enough for that.

Okay.

There has been some attempt to spec a web assembly-centric continuation or evolution of the web platform, but I don’t think that has gone anywhere. There was an effort by Ian Hickson, who was the original author of the web and the HTML5 specification.

One of the things he worked on in recent years was, like, what would the web platform look like if it was WASM and the framebuffer, and that kind of thing. But I don’t think that ever really picked up enough interest that anybody went to try to do it.

I don’t know. I think web assembly, at least the way that it exists today, is going to be a very incremental thing that’s just added to enhance websites in little ways here and there. You will see the occasional huge app, but for the most part, it’s probably going to be little widgets and ads.

I may be wrong. I don’t know. I barely make any websites. You should know that people who work on browsers, we don’t know much about web development generally.

I’ve written, you know, hundreds of thousands of lines of HTML and JavaScript, but as you were saying earlier, they were all essentially like three-line files just to make a unit test for something.

This is the reason why we needed you guys back then and no one was there.

Right, well, I think this actually wraps up the questions. There were a couple of other questions, but we’re going to ignore them for now. Thank you, Andreas, for being on here. Thank you, TJ, for being the co-host.

Andreas, if there is anything you wish you could shill to the best of your abilities—anything you want people to go check out, any links you want added to the YouTube video or for Twitch or Twitter to know about?

No, not really. I can take this opportunity to shill your terminal coffee company. Have you purchased it?

Good answer. No, I don’t drink coffee.

Okay, that wasn’t an answer, though. That wasn’t yes or no. I mean, there’s no question where you have to sign that you drink coffee before you order it—just throwing it out there.

Right, no, I haven’t purchased or drank any of the coffee yet.

Oh, we have a secret surprise; we’ll tell it about you after. We’re not going to tell anybody. It’ll get announced in the next month or two, but it would be pretty funny to do the demo in ladybird if it’s possible. We’ll talk about it afterwards.

We should try that.

But I mean, since I’m shilling things, do go to ladybird.org if any of this is interesting to you because we are always onboarding new developers. People are becoming browser developers in ladybird every day, and it is a lot of fun.

Anybody can learn how to do it if you’ve been programming for a couple years. Ladybird.org, I am spamming the links now. I did do ladybird.org sponsors, but I’ll also do ladybird.org.

Yeah, right, and if you just want to hear about development, you can follow ladybird browser on X. If you want to hear even more about random things, you can follow me on X as well.

Oh, I think that’s a great, awesome thing. Do I remember that correctly?

Let me do that.

Awesome. Well, thanks for coming on. It was really fun.

Yeah, thanks for having me, guys.

All right, well, hey, don’t leave. We’re going to leave, and then we got to talk for a second.

Yeah, yeah.

Bye, goodbye everyone. Thanks for coming on this top-shelf episode. We’ll see you guys later.

Well, I haven’t quite done anything yet.

I know, but then we can end the recording so there’s not two minutes of you just clicking keyboard sounds at the end. Everybody, oh my gosh, the YouTube retention.

Daniel Spielman “Miracles of Algebraic Graph Theory”

2025-03-05 08:00:01

Daniel Spielman “Miracles of Algebraic Graph Theory”

Well, welcome to our next plenary lecture. It’s my pleasure to introduce Daniel Spielman, who was a famous theoretical computer scientist and applied mathematician at Yale University.

Professor Spielman received his bachelor’s from Yale and PhD from MIT, and then he taught at MIT for 10 years, first as an assistant professor and later as an associate professor. He moved to Yale in 2006, where he’s currently a Sterling Professor of Computer Science and also a professor of Statistics and Data Storage and also Mathematics. He has an amazing amount of major award hardware including the Godel Prize twice, the Nevanlinna Prize for smooth analysis of linear programming, and algorithms for graph-based codes. In 2014, he received the Polya Prize with Marcus and Srivastava for their joint solution to the Kaczynski problem, a famous question in functional analysis which has been open for over 50 years.

He was elected to the National Academy of Sciences in 2017 and several others, but I think we get the idea anyway. Here’s Daniel Spielman speaking on miracles of algebraic graph theory.

[Applause]

Thank you. So, I’m gonna use this talk as an opportunity to give you an introduction to spectral and algebraic graph theory. It’s a field that when I first encountered as a student really blew me away. It provides tools that I’ve used throughout my career, and it was the topic of the graduate course I taught a semester.

So, my goal here is not to tell you about my work. I think I will mention one result at the very end just to tell you why I’m standing on stage talking about this business. Rather, I’m gonna try to tell you about some of the results that inspired me to follow this field and also try to convey to you some of the intuition that I’ve developed for spectra of graphs over the years. The hope is that it’ll make it easier for you to think about them.

In about 20 minutes from now, I’ll explain that figure. So first, let’s begin by defining really what spectral graph theory is. You take a graph whose primary objects are, say, these circles I’ve drawn here. I will refer to them interchangeably as nodes or vertices; different communities use different language, and I’ve been somehow stuck between both of them. These are the important parts.

These nodes are connected by edges. Edges connect pairs of vertices; it’s the fundamental combinatorial object in spectral graph theory. We relate graphs to matrices. The one most people look at first is the adjacency matrix. The adjacency matrix has zeros and ones in it; it’s symmetric. The rows and columns are indexed by the vertices. So this matrix has eight rows and columns because there are eight vertices.

There are ones in the off-diagonal entries where there are edges in the graph. For example, this graph has that red edge there between vertex one and vertex five that corresponds to the one that I circled in red in row one and column five and row five in column one. It is zero everywhere else. The idea of spectral graph theory is that when you take this matrix and look at its eigenvalues and eigenvectors, shockingly, they tell you fundamentally important things about the graph.

Not just things that people in linear algebra might care about, but things that graph theorists care about are revealed by the eigenvalues and eigenvectors. This was just amazing to me because I think of eigenvalues and eigenvectors as somehow these strange continuous things, and it’s not immediately obvious, but hopefully it will be obvious in ten minutes that they should tell you something about the graph.

That said, I also want to admit this is a little bit of an accident. I think it is an accident that the adjacency matrix is actually useful. There are other matrices, which I think I can make it obvious are useful that are more naturally associated with a graph, and the adjacency matrix is useful because on a lot of the fundamental examples we think about it happens to coincide with those, but I’ll be telling you about other matrices.

So, just think of this as an example that we’re gonna ignore now. So what do we do in spectral and algebraic graph theory? We understand a graph by looking at matrices associated with it, and we can think about those matrices in many different ways. We use them to define systems of linear equations related to a graph.

We use them to design to find a quadratic form associated with a graph or an operator associated with the graph. If these are sufficiently natural objects associated with the graph, then it makes sense that they should reveal its structure. Now, I’m mainly gonna talk about quadratic forms in this lecture.

Before I leave this slide, I just want to say a word about operators. One of the most natural operators to associate with a graph is the operator that governs the random walk on a graph. A random walk is a process that each time step is at some vertex of a graph, and what it does is at the next time step, it moves to a random neighbor of that node in the graph.

If you want to understand random walks, we usually look at the probability distributions of those. That’s a vector with a real value at every vertex between 0 and 1, and they should sum to 1. If you know the probability distribution of one time step and you want to know the probability distribution of the next time step, you get that by multiplying by a matrix that corresponds to the walk operator.

Sometimes the walk matrix looks a lot like the adjacency matrix, but it is usually weighted a little bit differently. If you’re used to thinking about operators, you know that if you want to understand what happens when you apply one many times, which is what you want to do if you want to simulate a random walk for many times, the right way to understand them is by thinking about their eigenvalues and eigenvectors.

So, if you believe that random walks on graphs tell you something you care about regarding graphs, then you will believe that the eigenvalues and eigenvectors of the walk operator should tell you something about the graph. But we’re gonna look at quadratic forms for this lecture. I just wanted to get that example out there, and we’re gonna see that these quadratic forms give us many beautiful theorems about graphs.

I am very interested in them because they’re very useful for algorithms, and I will tell you about some algorithms that we get from them. They’re also very useful in sort of more machine learning aspects; they give us heuristics for understanding graphs that we can’t prove great theorems about. But you’ll still see that they’re useful at least, and maybe one of you will come up with the theorem I’ve been looking for for a long time to explain why they’re so great.

Okay, so let’s begin by talking about linear equations we can associate with a graph. I’m gonna derive these from a physical system. So, let’s imagine a graph defining a spring network. Think of every edge in the graph as defining an ideal linear spring with spring constant 1. Now there’s a very natural experiment you can do with this spring network: you can nail down some of the vertices and let the others settle where they’re supposed to.

Physics tells us that when the system is in equilibrium, when they settle, every vertex should be at the position that’s the average of its neighbors or the center of gravity of its neighbors. So that means physics is telling us that to understand where these nodes land, we have to solve a system of linear equations. There’s one linear equation for every non-fixed vertex saying that its position is the average of its neighbors.

We can also think of this in terms of quadratic forms. When an ideal linear spring with constant one is stretched to length L, it has potential energy L squared over two, and physics tells us that the position that these nodes will land in is the one that minimizes the total potential energy. So this is going to be a very important term in this talk: this potential energy I call the Laplacian quadratic form.

Let me explain what it is, and this will also enable me to define my notation. If I want to capture the sum of the lengths of every edge, I need to sum over edges. I use letters like A and B to denote vertices; a pair of them A, B is an edge. For every single edge, I need to record its length, which is the square of the difference in the positions of its endpoints. I use something like X of A to denote the position of vertex A.

So we get 1/2 the sum over edges of the square of X of A minus X of B. That is the total potential energy, and physics tells us that this will be minimized subject to the very hard boundary constraints, which are nails. They asked me not to nail them to the screen, so I just drew them for you here on the slide. This leads to one of the most amazing foundational results in algebraic graph theory.

It appears in an amazing paper by Tutte called “How to Draw a Graph,” back from 1963. To begin with, you’ve got to think about what graphs can you draw. Tutte was thinking about planar graphs; those are the graphs that you can draw on the plane. You locate every vertex in the plane and will draw the edges in straight lines. You can draw them so that none of the edges are crossing.

You’ll get a better idea in a moment, but the first thing to understand when someone gives you a graph, you might not know if it’s planar. So, imagine they give you this mess. Well, what do you do? Tut says first identify a face in it. I will define a face precisely in a moment, but for now think of a triangle. If I can find a triangle in this graph, Tut suggests taking the vertices of that triangle and move them to the corners of a convex polygon that’s on the outside of this image and nail them down.

Now let every edge be a spring and let them settle into the correct position. Eventually, it settles down. Tut proved that if the graph is planar and three connected, I’ll explain three connected in a moment, it avoids some obvious degeneracies. If it’s three connected and planar, then this gives you a planar drawing of the graph. There will be no crossing edges.

Now you might not be able to see that right now because there are a whole bunch of vertices that are all bunched up somewhere in the middle, but I promise you that it is planar. I’ll make a better picture so that you can see it more clearly. Now I can tell you what it faces. The face of the planar graph is one of the regions in a planar embedding, or rather it is the cycle enclosing them.

For example, this cycle here, because it encloses an empty region in the embedding, is a face. You can do this for any face. So we take, let’s say that face, move it to the outside, nail down the position of the vertices, and let the spring settle, and we now will wind up with a planar drawing with no crossing edges of this planar graph. This works for every planar graph.

One of the reasons I was awed by this theorem is I thought that my desire to draw a graph without any crossing edges was a human thing, but apparently, springs want to do it too, and that just blows me away.

Okay, so I should tell you what does three connected mean? What is the obstruction in this theorem? A graph is three connected if you can remove any two vertices and it remains connected. If there are two vertices that you can remove that break the graph into two different pieces and it’s not three connected, then like this graph is not three connected. If I remove those red vertices, I break the graph into two pieces.

If it is not three connected, if there are two vertices that I can remove that break the graph into two pieces, then there’s no way this spring embedding thing is going to work. Because if you take a look at the component you get when you remove those two vertices, all the nodes in that component are going to collapse onto a line. That is sort of the obvious thing that can go wrong if you don’t have this.

Then Tutte’s theorem works for other reasons, more than just that it fails and things will collapse if it’s not three connected. You can’t even really define the faces of a planar graph if it’s not three connected. What I mean is if the graph is not three connected, the set of faces is not really well-defined.

So think about this graph here again and take a look at the top face, by which I mean the one enclosed by vertices 1, 2, 3, 6, 7, and 5. That is a face, but there are other planar drawings of this graph in which that is not a face. In particular, I can exchange the location of nodes 7 and 8, and then 7 is no longer part of that face, and it is not really on a face with vertex 2 anymore.

So three connected tells you that you have a nice planar graph in which the faces are well-defined, and then Tutte’s theorem applies. Okay, so now let me remind you of a little bit of the quadratic form because that is the fundamental object for the rest of this talk. It takes as input a vector that assigns a real number to every vertex and returns the sum of the squares of the differences of that vector across the vertices.

For example, here’s a graph. I could assign a real number to every vertex. We can then look at the sum of the squares of the differences across the edges. That’s what the Laplacian quadratic form gives you. Whenever you have a quadratic form, there is some symmetric matrix such that—let’s call this case L for the Laplacian matrix—such that you can write that quadratic form as X transpose L X.

That is the Laplacian matrix, and that is really the right way to define the Laplacian matrix. It is the symmetric matrix that realizes the Laplacian quadratic form. But if you want to know what the entries are, I put them up here too. So the Laplacian quadratic form and the off diagonals have a minus 1 for every edge. So for example, that blue edge between nodes 2 and 6 corresponds to the minus 1 in row 2, column 6 and row 6, column 2.

The other off diagonals are 0 where there is no edge, and the diagonals are set to be the degrees of the nodes. They are positive numbers chosen precisely so that every row and column sums to zero. So that is the Laplacian matrix.

You can also, of course, define the positive matrix for weighted graphs. In a weighted graph, the only thing to note is weights on edges. There are many reasons you might want to put weights on edges. You might want to indicate the strength of an edge. If you’re looking at a social network, it indicates perhaps how important is a tie or how frequent is the communication between two people.

If you’re looking at a spring network, that should be the spring constant. Or if you like to draw multi-graphs where you allow multiple edges between pairs of vertices, those don’t encode very well as matrices, but you can sort of achieve that effect by recording the multiplicity of an edge as its weight.

Then, of course, what you do is you just put the weight in front of the appropriate term in the quadratic form, and in the matrix, that’ll put minus that weight in the corresponding off-diagonal entry. By the way, I’m only gonna consider positive or non-negative edge weights; negative edge weights have many different ways of doing things and each have some defects and give you some definitions.

So let’s just think about positive edge weights for now. Okay, if I give you this matrix associated with the graph, we can then apply the spectral theorem. The spectral theorem tells us that if we have a real symmetric matrix, it has or is Hermitian; it has n real eigenvalues. That is a wonderful thing. If you don’t play with symmetric matrices every day, it’s possible to forget that.

So I remind you not only that you have an orthonormal basis of eigenvectors, and they satisfy this fundamental equation that the Laplacian times the eigenvector is equal to the eigenvalue times that vector. That is how we first learned to define eigenvalues and eigenvectors, and as I’m promising you, they tell you a lot about the graph.

Now you might have some intuition as to why if you think about spring networks, what this equation tells you is that the eigenvectors are giving you the fundamental modes of vibration of that object, and that’s part of why this is useful, but it’s only part of it. I’ll admit I actually get much more out of the eigenvectors by applying the Courant-Fischer theorem.

The Courant-Fischer theorem gives you another characterization of them. It tells you that the eigenvalues and eigenvectors of a symmetric matrix are the solution to maximization and minimization problems. Because they sort of come up in these optimization problems, it’s possible to use and improve many inequalities and relate them to the structure of the graph.

So I’ll tell you what the Courant-Fischer theorem gives. First, it tells you about the first eigenvalue. The smallest lambda one tells you that lambda one is the minimum possible value of the… Laplacian quadratic form over we have to normalize. Let’s take over unit vectors. The corresponding eigenvector is, of course, the vector on which that minimum is achieved.

Now, for Laplacian matrices, this is not very informative. For Laplacian matrices, lambda 1 is 0 and the corresponding eigenvector is the constant factor. You can see this because if you put a constant vector into the quadratic form, you will get 0. This quadratic form is always non-negative, so you can never get anything lower than 0.

Okay, so lambda 1 is 0 and V 1 is the constant vector. Let’s take a look at V 2. The second eigenvector is the minimum of the Laplacian quadratic form over all unit vectors that are orthogonal to the first eigenvector. In this case, that’s very nice because we know that it’s the all-ones factor. So that is a very nice characterization of the second eigenvalue, and of course the vector is the vector on which you achieve that minimum.

You can go on this way to define lambda 3 and lambda 4 and V 3 and V 4. Always, you take the vector that minimizes a quadratic form orthogonal to the ones that you’ve already found. This is part of the justification for a very beautiful heuristic for understanding graph drawing called spectral graph drawing, and this is sort of the birth of graph drawing or the other birth of graph drawing other than Tait’s theorem.

I don’t have wonderful theorems for this, but they provide me with a lot of intuition. So I’m gonna show you a bunch of pictures. It’s also a useful magic trick. I run an institute called the Yale Institute for Network Science. What this means is every once in a while, people come into my office and they say, “Dan, I have this graph for a network. Can you tell me what it is?” Usually, it’s some text file or data file, and I think, “So what do I do?”

Well, it could be some jumbled mess like this, but I use all spectral graph theory, and it draws graph strong. Everyone says, “Well, it just gets me this beautiful picture of a graph,” and they’re blown away, and then they understand it. Here’s what you do: Paul said, “Okay, V1 is not useful. Let’s take a look at V2 and V3,” the second and third eigenvectors of the Laplacian.

Remember, each of them is a vector and each gives you one real number for every vertex of the graph. We’re going to use those as coordinates of the vertices with which to draw them. So take V2, use it to give it a real number for every single vertex, use it to form the horizontal axis. Take V3, use it to form the vertical axis, plot those nodes at the locations given by the eigenvectors, and then draw the edges as straight lines.

It gets me this beautiful picture of this graph, and when I look at that beautiful picture, I know everything about this graph. I understand this graph when I see it drawn that way, unlike in the original horrible messed up image.

Now, you might wonder, does this always work? No, but it often tells you something useful, especially for graphs that you can draw. Let me do some other examples. Here I sampled some random points from the plane; it’s a way of generating a graph called Delaunay triangulation.

Again, here what I’ve done is I’ve welded triangles. It shows sort of the nicest, or one of the nicest graphs you can draw if I take a look at the edges of the triangles given that set of points. Here, we start with the geometry. I have fixed the locations of the vertices and then drew nice edges.

Now, let’s forget the geometry. Just pay attention to what’s connected to what. Using that information of the graph, compute the eigenvector, plot the vertices, and here’s what we get. It’s a surprisingly nice picture. Let me point out some features of it. The middle looks like a triangulation of something. The triangles pretty much all have nice aspect ratios, which is sort of shocking, nicer than in the Delaunay triangulation reform before.

It messes up a little bit on the boundary, and actually, if we looked at it carefully enough, you’d see that it isn’t really planar on the boundary. It looks like it wants to wrap around some surface, but when you only give it two eigenvectors, there’s no way you can do that, right? You need three dimensions to do that. Okay, but it gives you a remarkably good picture of this graph, and this keeps happening.

Here’s one of my favorite examples. This is the airfoil mesh. If you’re a MATLAB user, you can load airfoil. This is one of their example matrices they give you. It comes from modeling airflow around an airplane wing. This is a cross-section of an airplane wing, and that’s a discretization of the region on the left.

I’ve shown you the picture from the original coordinates. On the right, we forget the coordinates, and we just use the eigenvalues and eigenvectors to draw this. Again, you get this amazing picture. It’s got these giant holes in it, but that’s because the graph of the airfoil has those giant holes.

To show you how nice it is, let me zoom in on it. If we zoom in on the edge, we see again a beautiful triangulation in a just giant planar region. For a little over 20 years now, I’ve been staring at these images and asking if someone could give me a theorem that explains why this happens.

Why do we get these beautiful planar regions in these spectral graphs? I still don’t know why. There are a few theorems that are beginning to get us there, but really not enough. We probably need someone who understands differential equations and the finite element method much better than I do.

Okay, of course, you know this doesn’t always work, not even for all planar graphs. So let’s consider something like a graph we get from a Platonic solid like the dodecahedron. So take a dodecahedron. For every corner, make a vertex in the graph. For the edges along the dodecahedron, put an edge in the graph. That gives you a graph that has 12 vertices, and you can consider drawing it using the eigenvectors, and you get sort of a squashed dodecahedron.

But you had to, and it shouldn’t embed nicely with two vectors because it’s a three-dimensional object. Actually, lambda 2 has multiplicity 3, lambda 2 is equal to lambda 3, is equal to lambda 4. My code was doing something incredibly arbitrary when it shows two vectors in this three-dimensional eigenspace to form a basis.

If you know, I imagine maybe 10 years from now, you would all be wearing 3D glasses, and I’d have a 3D projector, and we could look at the dodecahedron in three dimensions on the stage if we plotted it with three eigenvectors. You would actually get exactly the dodecahedron as you expect in three dimensions.

If we use three eigenvectors, the same is true of every Platonic solid in every dimension and all sorts of other regular polytopes in higher dimensions. If it’s in d dimensions, you take d eigenvectors and you get the image of it.

Okay, but some graphs are just too complicated to be drawn. Here’s one that’s tricky. I took Paul Ornith’s co-authorship network. What I did was I took every paper written by Paul Ornith. Each of his co-authors is a vertex in this graph. I didn’t include Paul Ornith because we already know he’s in all the papers.

Then, for each of those papers, I drew an edge between every pair of people who were co-authors. It had a very large connected component. This is the biggest netted spectral drawing. We didn’t get a great image. That’s okay; some graphs can’t be drawn well.

I’ll tell you in a moment how to recognize them, or at least how to prove that some can’t be drawn well. You might be concerned if you take a look at this graph and you know enough about Paul Ornith, you know there are some vertices of very high degree and you might think that’s the reason.

So let me show you that it’s not. Here’s another graph you can’t draw well. Here, I chose a random regular graph, although I’ll admit I see two vertices of degree three. It’s basically a four-regular graph; two edges landed on top of each other. If you take a random graph, it should not have a nice drawing, right? A nice drawing would reveal structure, and a random graph should not have that structure.

We’ll see again how to make that a little more formal in a moment, but for now, let me just say what I mean by a bad drawing. Well, for me, a drawing is bad if, say, many, many edges cross and all the edges are long. Most of the pixels on the screen are devoted to showing edges and not vertices; that should be a bad drawing.

You can show that in the limit with large random graphs, the overwhelming fractions of all the pixels have to be edges and that really is no nice drawing that you can use to make sense out of. Okay, so how do we prove this?

Well, let’s say you have a nice drawing of a graph, and some years in my spectral graph theory class, I asked students to prove a theorem like, “Prove that if you have a nice drawing of a graph, then lambda 2 is small.” Last year, I was nicer. I defined what I meant by a nice drawing. Some years I say you define it.

This is a very robust homework problem that works under all definitions of nice, but usually I mean something like most edges are short and the vertices are reasonably spread out so some measure of them not clumping too much anywhere. You can prove that if there is a nice drawing of a graph, then lambda 2 is close to 0.

How close? Well, you can prove that if lambda 2 is big, say, more than like 10 over the square root of the number of vertices, then there will be no nice drawing of the graph. So this is useful for a lot of purposes, in particular for graduate students in network science. You know, if your advisor is saying, “Give me a picture of this graph,” and you come back with an ugly mess, and they say, “Well, come up with a better algorithm,” and you come back with an ugly mess, and you keep doing that, you know it’s good to be able to tell your advisor, “Look, lambda 2 is big; there is no nice picture of this graph. Please give me something else to do.” It’s very powerful.

Now, let me tell you why this happens. It’s because the eigenvalues, in particular lambda 2, are connected to boundaries. So let’s see why that is. If I have a set of vertices in the graph, like a sphere, the boundary is the set of edges leaving that set. We can measure the boundary using the Laplacian quadratic form by applying it to the characteristic vector.

The characteristic vector of this set is the vector that’s one of the vertices and 0 elsewhere. If I plug that in the Laplacian quadratic form, you will get exactly the size of the boundary because the sum of the squares of the differences of the characteristic vector across edges is 1. It goes between 0 and 1 for every edge on the boundary.

So you get one for every edge on the boundary, and every edge that’s not on the boundary is going between 0 and 0 or 1 and 1, so it’s not contributing. The plotting quadratic form helps us measure boundaries. If you have a nice picture of a graph, you can show that there will be a large set of vertices with small boundary and then take the characteristic vector of that set, orthogonalize with respect to the all-ones vector, and you will find via test vector a proof that lambda 2 is small using the Courant-Fischer theorem, which tells us that lambda 2 is the minimum over vectors orthogonal to the all-ones vector of the Laplacian quadratic form.

This motivated a heuristic that is part of what made spectral graph theory so popular. It was originally used in the scientific computing community, where people who were doing things like studying airflow around an airplane wing wanted to break up their simulations into large parallel computers.

This meant different computers were going to be simulating different regions of space, and they wanted them to be able to do their simulations with low communication. The amount of communication depends on how many edges there are between those different pieces, so they want to divide up a graph into two pieces without too many edges going across them.

The heuristic people came up with is to look at the second eigenvector. Take V2 and now look at the level sets of it. For example, here S is the set of all vertices in which the value is more than 0.8. We take a look at those level sets, and one of them usually gives us a good partition.

To be a little more formal, what we’re doing is we’re taking a look at this spectral graph drawing and saying, “Take a look at all of the vertices on the left side of some line or on the right side.” This addition is some line through this spectral graph drawing is a good partition. There’s a lot of experimental support that this is a good thing.

You can actually use it. The theorem of Cheeger, or an extension of Cheeger’s inequality proved through these works, gives you a good approximate solution to a problem. I just got to state precisely what the problem is.

Before I do that, let me show you this is the partition you get for the airfoil mesh from that last cut. To precisely state Cheeger’s inequality, I need to find the conductance of a set of vertices. It is a way of making formal my notion of a big set of vertices with small boundary. The conductance of a set of vertices is the size of the boundary of the set divided by the size of the set.

Well, unless the set has more than half the vertices, then we want to take a look at the size of the complement. So take a look at what you’re removing from the graph and how many edges it takes to do it. That is what we call the conductance of this set. People want cuts of low conductance; those are ones where you can remove many vertices without cutting too many edges.

The minimum conductance near the minimum conductance set may be called the conductance of the graph. Cheeger’s inequality gives an amazing relationship between conductance and lambda 2. To state this, I’m assuming for now all edges have weight one and every vertex has degree at most D. So there are no more than D edges touching every vertex.

The lower bound is easy. The fact that the conductance is at least lambda 2 over 2 comes from taking the characteristic vector or thinking of a set, orthogonalizing that with respect to the all-ones vector and applying Courant-Fischer. Really, it’s the right hand side that Cheeger’s inequality achieves.

Approved loci are not chips. They are manifolds, and this is an extension of Cheeger’s inequality to graphs that was obtained by different groups of authors with slightly different flavors. It’s about a decade later; it says that the conductance is at most the square root of 2 times the maximum degree times lambda 2.

Moreover, the procedure I showed you on the previous slide gives you a set of vertices with that conductance that satisfies that bound. You can use V2 to find sets of vertices of low conductance. I found this incredibly useful in many applications, and both sides of this inequality are useful to me. The left-hand side is incredibly useful because, well, one, if lambda 2 is big, it tells you the conductance is high.

That means there is no great structure in your graph; you won’t find communities. You can’t partition it. Sometimes you want to know that there isn’t community structure in your graph. It also means that if lambda 2 is big, your graph is what we call an expander, and random walks mix rapidly, and many beautiful things happen.

The right-hand side, on the other hand, tells me that if lambda 2 is small, then I can cut up my graph very nicely. I can find sets of low conductance, and that enables me to analyze things. I should mention that both sides of this inequality can be tight, at least asymptotically. I haven’t given you the sharpest constants, but the phenomenon can happen.

On the left-hand side, you can have conductance close to lambda 2, and a good example of this is the complete binary tree. You form this by taking a vertex, you give it two children, you give those two children two children, and you keep going, but we’re gonna stop and make it finite and have n vertices.

The conductance of this graph is about 2 over n, and if you cut that red edge, one of the edges attached to the root, you’ll get conductance about 2 over n, and lambda 2 is about 1 over n. So in this case, the conductance and lambda 2 are about the same.

In contrast, if you take the path graph, it has just vertices on the line; adjacent nodes are connected. It has sort of the same cut; it has conductance about 2 over n, cutting an edge in the middle, but lambda 2 is on the order of 1 over n squared.

It’s after very close to pi squared over n squared, so you can get the quadratic end of Cheeger as well. Many other people have been wondering for many years if there’s a precise way we can explain what lambda 2 is actually doing because this quadratic gap is a little disconcerting. To tell you about a theorem that was published last month that gives us that characterization. But first, let me just sharpen Sugar’s inequality a little bit. I want to refine the notion of conductance and refine the eigenvalues.

So I’ll consider general weighted graphs, and for weighted graphs, I will measure now the degree of a vertex as the sum of the weights of the edges attached to it. The degree of a set of vertices I’ll just find to be the sum of the degrees of the vertices in the set. Now, my refined notion of conductance will measure the sum of the weights of all edges leaving a set divided by the minimum of the degree of the set or the complement, whichever is smaller. That is a somewhat refined notion of conductance.

We also want to change the matrix a little bit using the normalized Laplacian. This is the ordinary Laplacian times D inverse, where D is the diagonal matrix of degrees of vertices. If we know these agarose matrices, we get rid of this D term from Cheeger’s inequality. It makes it a little better. You see that the conductance is at most the square root of 2 times lambda 2.

That was our first step towards refining this. That was old; here’s the new one. It appears in a paper by Aaron Shield, who’s a graduate student at Berkeley finishing this year, I might add. Actually, oddly enough, there was a paper published a few days later on archive by Miller, Walker, and Wang, which has some other theorems that also imply this result, and I don’t yet know why two people thought of the same thing forty years after Cheeger’s inequality or more.

But anyway, we now really understand what lambda 2 is doing. To explain it, I need to tell you a little about equivalent networks. Physicists consider these; they say, let’s say you have a spring network and you want to understand how it behaves just on a subset of the nodes. They consider eliminating the other nodes that you don’t care about, and they know that you can actually understand this network on the nodes you care about by eliminating the others and drawing another spring network.

When you eliminate nodes, you actually get another spring network. Many of us learn about this in physics, about what springs do in series. If you have a series of springs, you can treat it as one big spring between the end points with a much lower spring constant. Or you learn about springs in parallel. If you’re really lucky, you learn the Y-Delta transform, and that lets you eliminate everything. It actually corresponds to Gaussian elimination in the Laplacian matrix.

So Shield’s theorem is going to interpret lambda 2 in terms of conductance. It’s not necessarily the original graph but in terms of the effective graph on a subset of the vertices or the equivalent network. What it says is, for every single graph G, there exist two sets of vertices s1 and s2 such that if we look at the equivalent network on their union, then the conductance of s1 in that equivalent network is a constant approximation of lambda 2 in the original graph.

Maybe I should have written this the other way around: the theorem says lambda 2 is approximated by the conductance of s1 in the equivalent network. But this is a very sharp characterization of lambda 2. To explain what this does for the path graph, let’s say we have a path graph. Again, there’s a big discrepancy between conductance and lambda 2.

Lambda 2 is like 1 over n squared; conductance is like 2 over n. If you eliminate the middle third of the vertices, you replace those n over 3 vertices by one spring. That’s very weak; it now has a spring constant of 3 over n. Now, if I take a look at just, say, the left half of the vertices, the set s1, that’s n over 3 vertices. They’re connected by one very weak spring with a constant of about 3 over n to the other side. So this has conductance on the order of 1 over n squared.

Okay, that’s at least a quick example of Shield’s theorem, which explains the phenomenon people have been looking at for a very long time.

I’d like to tell you about something completely different now that eigenvalues and eigenvectors are good for: that’s the graph isomorphism problem. You think if you understand anything about graphs, you should be able to tell when two of them are the same. Take a look at these two graphs. You know, are they the same graph? Well, I know they are.

They are different drawings of the Petersen graph, and I can give you a labeling of the vertices. Once you see that labeling, you see that these two are the same graph. The graph isomorphism problem is that of asking if there is a labeling so that two graphs are the same. It is a disturbingly hard problem. As a computer scientist, I can’t prove to you that it’s hard, but I can tell you there are no great algorithms that we can prove always work.

As a matter of fact, there was a major breakthrough two years ago, and Babai proved it. He came up with the fastest known algorithm. It’s not polynomial time, but it’s less than exponential; it’s somewhere in between. There are many heuristics that help solve this problem in practice, and eigenvalues and eigenvectors can help. That’s why I want to tell you a little bit about it.

First, let me just say why you should care about this. The fact that this is hard means that telling if two things are the same is hard. There are many times you want to measure the distance between some objects or a notion of how similar they are. But if you can’t even tell if they’re the same, you’re not going to have any success with that, at least algorithmically speaking.

For example, you might say, give me some shape in D-dimensional space, and I give you another one. Is one a rotation of another? Well, that is equivalent to this problem: testing if two graphs are isomorphic. So it’s going to be hard, at least in the worst case.

Let me tell you how eigenvalues and eigenvectors can help. First, observe that changing labels doesn’t change eigenvalues. So if two graphs have different eigenvalues, they’re different. That already labels you to distinguish most graphs; but it doesn’t necessarily tell you when they’re the same. Permuting labels and changing labels only permutes entries in the eigenvectors.

Or, to put it more precisely, if you permute the labels in a graph, you don’t change those spectral graph drawings I was showing you earlier. You can go a long way towards testing if two graphs are isomorphic or finding the isomorphisms by taking a look at spectral drawings of them. If you really want to push this, sort of do the limit using the right computational group theory, well there’s a theorem of Babai, Grigoriev, and Mount that tells you that if all of the eigenvalues of a graph have bounded multiplicity bounded by a constant, then you can test isomorphism in polynomial time.

I’m going to give you this two hints of how that’s done partially. They give you more intuition for some of the strange things that can happen in eigenvectors of graphs. So let’s consider the extreme case of bounded multiplicity: every eigenvalue has multiplicity 1. That means every eigenvector is determined up to sign. If v is an eigenvector, minus v is also an eigenvector.

That means that for any graph that I was drawing this way, there are four possible drawings of it. So now if you give me two graphs, “Oh, you mean this graph?” and you give me another one, I should be able to tell you whether or not they’re isomorphic. I’d make these spectral drawings, and I’d say is one of those for the same drawing? If they are, well then that’s great because it gives me a way of lining up the vertices one to one, and it tells me the isomorphism. If the drawings are different, then I know the graphs are different.

Unfortunately, it doesn’t always give me a way of lining up the vertices. I was very lucky in these pictures that every vertex mapped to a unique place, but if two vertices land in the same place or many vertices land in the same place, then this doesn’t help me fully resolve the isomorphisms, and that can happen.

Let me skip this slide. Here’s an example. I first also want to mention that there can be many many. I’ll go back right; I was right the reason for this slide—I can make it rhyme in a slightly simpler way as opposed to worrying about isomorphisms right now.

Let’s think about automorphisms. Try to think about all of the isomorphisms of a graph to itself. Finding those is equivalent to solving the verification for some problems. It’s a little easier to think about. It’s made complicated by the fact that some graphs have many many automorphisms. Think of a graph that has distinct eigenvalues, but if I flip nodes 1 and 2, I get the same graph. If I flip nodes 4 and 5, I get the same graph.

So if I flip nodes 6 and 7, I get the same graph. Okay, so I’ve already got 8 automorphisms on 7 nodes, and you can make bigger and bigger examples. You can have an absurd number of automorphisms. So we don’t want to compute all of them; rather, we want to compute a set of generators of the automorphism group of a graph, and that’s the useful thing to do. We can construct them by looking at the eigenvectors.

So here I’ve taken this graph, and I put the eigenvectors in the columns. The first one is the constant eigenvector; it doesn’t tell you anything. But let’s look at the next one. It has these entries: minus 2.37 at nodes 1, 2, 3, and 4. Those entries don’t appear on the rest of the eigenvector.

Now you know that permutations or automorphisms will just permute the entries in the eigenvector. So those nodes we can identify as a class. What we do is we use the eigenvectors to find classes of vertices. Once you define a class of vertices, you look at the entire set of eigenvectors. You find the group of automorphisms that act in this case as C2 cross V2, and we do that for every class of vertices: 5 and 7 are a class, and 8 is its own class, 6 is its own class.

Then we use some elementary computational group theory after I find to assemble these together. You sort of divide the vertices into classes by looking at the eigenvectors. Find the group of automorphisms for each class, and then you have to take their intersection to find the group of automorphisms for the entire graph. The main thing to know is that if all the eigenvalue multiplicities are bounded, then you can do this pretty quickly using some elementary computational group theory.

To finish, I will tell you about just one result of mine, which deals with when graphs are approximations of each other. Now, okay, I said if I can’t tell if two graphs are the same, how can I tell if they’re approximations of each other?

Wrong. To make this easier, we’re going to fix the labeling of the vertices, so keep all the vertices in the state. Just imagine changing edges. In this case, we say that one graph is an epsilon-approximation of another if their Laplacian quadratic forms are approximately the same, which I mean is that, for all real vectors X, the ratio should be sandwiched between 1 plus epsilon and 1 over 1 plus epsilon.

So this is a very strong notion of approximation. It implies many things. In particular, it means that the boundary of every single set of vertices is approximately the same, at least if you count edges by weight. So you might have many fewer edges, but if they have higher weights, then you can have the same boundary. It means that solutions to systems of linear equations in these matrices are approximately the same, and it also means that their behavior in spring networks in the graphs are approximately the same.

So one theorem of mine that I really love was joint work with Josh Batson and Akhil Shrivastav. We proved that every graph can be approximated by a sparse graph. So you fix epsilon; if you have a graph on n vertices, it means you can find an epsilon-approximation of it with basically for n over epsilon squared edges.

This means that you don’t need most edges, and you can still maintain and preserve almost everything I want to know about the structure of a graph. For example, if I take the complete graph on 10 vertices, the right sparse approximation of it is the Petersen graph, and you’ll notice I’ve drawn the edges of the Petersen graph a little thicker. That’s because we increase their weight.

Think of it as a conservation of mass phenomenon. If I’m going to get rid of some edges, I’ve got to increase the weights of others. In general, these sparse approximations of large complete graphs are expanders. If you don’t know what an expander is, think of a random regular graph like I showed you before. Those all have large eigenvalues, and you can show they’re great approximations of a complete graph.

If you know what an expander is, then you know that the best expanders are the Ramanujan expanders. You’re probably wondering how these compare. These have just slightly less than twice as many edges as the Ramanujan expander you would use to approximate the complete graph.

Okay, so if you want to learn more, find my webpage, and you’ll find, I hope, many useful things. I have some links to popular articles related to this material, links to other talks. I’ve talked about spectral graph theory a lot. Links to my lecture notes from my courses on spectral graph theory, and if you really want to dig in, I have maintained a list of many papers on related topics that I like, related to sparse vacation, graph clustering, partitioning, Laplacian equations, and more, and you’ll find the link.

Thank you! Are there a few questions, if we have any?

Oh, what happens if you do those spring settling on graphs that aren’t planar? Okay, so if you try this spring thing on graphs that aren’t planar, there is a paper. I’m going to be embarrassed that I forget the list of co-authors right now that does deal with some reasonable generalizations of this, at least to graphs that you can draw nicely in higher dimensions.

So there are some generalizations that are interesting. All right, graphs that aren’t planar—when you try to do it on the plane, I can’t say anything that nice. There are reasonable conjectures. I don’t like what happens if you have a graph that you can actually draw on a genus G surface.

Okay, you probably have to make a few choices about what you nail down, but after that, I think you’d find something nice. That should be known; I’m not quite sure if it is. There are some generalizations of the spectral graph that are drawn in some analysis of that that have been done for genus G surfaces and graphs that can be drawn on them. But it’s a good question—there probably are nice things you can say.

Yeah, that was almost exactly my question.

Any new questions? Yes, just one last thing. What’s the computational complexity of computing those epsilon pacifiers? Oh, that’s a good question.

So right now, it is nearly linear time, but not practical to implement. There’s an amazing paper of Yin, Tetley, and his son, which maybe was published two or three years ago that some of my students and I have tried with mine. We can’t believe it’s theoretically nearly linear. There’s also very good heuristic code out there, so you can find code that apparently does a very good job of it.

We can also check if you’ve got a good approximation that is actually practical and fast, in time and log-squared in. All right, I didn’t put up a link advertising my software package, but I have a package called Laplacian CL that enables you to compute many of these things and, in particular, is fast at computing eigenvalues, checking approximations, and things like that.

You can use that to check if you have it. There is a very fast algorithm based on random sampling that is almost as good in terms of approximation quality, but it loses a log n. So there are some things you can do, but the best ones—we’re still really waiting for good code that we can prove works and implement.

If you use more eigenvectors and then project it to a plane or to 3D, do you get any interesting drawings that way? Oh yes, all right. You can use more eigenvectors and get great pictures, and actually, I’ve spent a lot of time drawing and rotating them around both in 3D.

You can sort of rotate around and get a feel for it. It’s really hard, but who knows? Maybe with some virtual reality technology or something, we’ll be able to think about them as well soon. I mean, I know we implemented the drawing in 2D. We implemented code for making—I have code for making 3D images you can rotate around.

I don’t remember if I put it in the package, but if I didn’t, send me an email, because it’s only like two or three more lines of code over what’s there.

Okay, well let’s thank our speaker again for a great talk!

[Applause]