Daniel LemireModify

2026-07-22 22:32:18

“`In 1945, Vannevar Bush published a report entitled Science: The Endless Frontier. His thesis was that prosperity follows from basic research. The report was highly influential in the United States and elsewhere. It led to the creation of an entirely new government bureaucracy.

With this report, Bush popularized the linear model of innovation: innovation (such as medical cures) flows sequentially from basic research to applied research to development to production and diffusion. Grow basic research, and the rest will follow.

When Bush wrote his report, basic research was not usually supported directly by the state. We did not have a large basic research infrastructure. And yet, the West had just lived through an unprecedented period of rapid scientific progress: the theory of evolution, electromagnetism, radio communication, special and general relativity, quantum mechanics, nuclear technology, rockets, the combustion engine, and more. We would get the invention of the transistor only two years after Bush’s report. We also did not have today’s peer-review mechanism.

Even though Bush’s report has been viewed as a piece of genius that unlocked a golden era of scientific prosperity, I believe that the linear model of innovation is hopelessly naïve. I believe the thesis that a large bureaucracy delivering funding to other bureaucracies (such as universities) is how we get innovation is absurd. Except perhaps in the domain of computing (“bits”), we have been largely stagnant technologically since about the 1970s. So Bush’s model failed over time. To be clear, it might have worked for a while by encouraging more young people to study engineering and science. It may also have shone a favorable light on a few enterprising professors who got to promote useful ideas.

If you visit a research lab today in a leading university, what you are most likely to see is a boring bureaucracy that caters to whatever is politically favorable at the moment—a bureaucracy that plays it safe and avoids controversy. You see young people seeking well-paid jobs, going through the motions with often little genuine interest in, say, curing cancer. We have never published so many research papers—the volume has been growing exponentially ever since Bush wrote his report—but it is doubtful that this is how technological breakthroughs are achieved.

The evidence is overwhelming that shoddy science is widespread. We have a severe reproducibility crisis: if you redo an experiment (even a highly cited one), you are likely to fail to reproduce the results. This affects psychology, medicine, and many other fields. The system does not particularly care because the incentives to get things right are not there. As long as the work is politically aligned, solidity of the results seems secondary.

There was a TV show (The Big Bang Theory) where the main character, Sheldon Cooper—an awkward genius—gets to work on crazy ideas. That is how Bush imagined it: fund young people like Sheldon Cooper, and you will get extraordinary breakthroughs. In the real world, Sheldon would not get very far on campus. I have met misfits like him. When they are incapable of playing the political game, the system crushes them. But even if that were not the case, extraordinary intelligence needs to be applied to the right problems to be of value. You could have a ChatGPT that is brighter than any of us in every possible way, and it could still be deployed simply to fill out forms faster and better than we do—it may not cure cancer.

The American government has just released what might be considered an update to Bush’s report. Michael Kratsios wrote a report entitled Science, A New Golden Age. The report states outright that the linear model no longer holds. It states what I have argued for vehemently: innovation is not a linear process. Take large-language models, for example, which can be used by engineers and scientists to further their research. I have also argued that the success of large-language models today has as much to do with the users as with the researchers.

At this point, some people engage in the following type of rhetoric: if we had not invented calculus, we would not have AI today; therefore, calculus caused AI. But you could also say that the subsidized nail factory in the Soviet Union, which made overpriced and bad nails, was necessary to hold Landau’s house together, and that without those nails we would not have the theory of Landau levels. The causality argument goes in all directions.

Innovation is the result of a complex system. We see that the United States and, more recently, China are innovative countries. In 2026, you do not go to France for the latest advances. The evidence is overwhelming that scientific and technological progress depends as much on culture as on anything else. It is not something to be managed by bureaucrats.

One of the cultural ingredients that seems essential is meritocracy. You must put the people who are good at building on top of your hierarchy. This does not happen magically. You need a set of incentives in which rewarding the wrong people is costly.

What does Kratsios propose? Many interesting ideas that, I expect, could renew our culture. He proposes to break out of the Cold War–era funding model. Today, the research funding mechanism is centered around the government giving money to the university bureaucracy. The grant might be in the name of one professor, but the recipient is still the university. In the new model, instead of funding universities, the government would assign money directly to individuals in various ways (short grants, prizes, and so forth). This would shift power away from administrators toward individuals who know how to get things done. It would also neutralize some of the political power of the current mandarin class of scientists who control access to the top positions.

The report recommends restoring permissionless innovation. It is sometimes poorly understood how limited the system has become. I once had a graduate student undertake interviews with practitioners. This required an ethics approval which, in her case, took a few months to obtain. Again, the system has built up political structures that seek to block innovation it does not like. They need to be torn down, the sooner the better.

The report has many other interesting recommendations. One that I particularly like is an AI-guided agenda. We need to hook up our brand-new AIs to experimental devices. We are not going to cure aging with chatbots. We need experiments on a massive scale.

Will Kratsios’s vision move from report to reality? History shows that cultural and institutional change is never easy. Yet the stakes could not be higher. By embracing meritocracy, permissionless innovation, and ambitious AI-augmented experimentation, we have a genuine chance to escape decades of stagnation and rekindle the spirit of discovery that once defined the West. The opportunity is before us. It must not be squandered.

2026-07-17 04:00:21

A few years ago, a friend of mine was stuck. He needed to quickly process over a hundred high-quality images according to a complicated sequence. He was using Photoshop, but it was going to take him days. Initially, he asked for my help, could I do the manual labor? I spent 15 minutes writing a script with ImageMagick that processed all the images in seconds, but in a completely automated way.

When my kids were young, instead of helping them study algebra and grammar, I wrote small JavaScript apps for them to use. I built a small collection of educational tools.

The great success story of AI for me is exactly this: AI helps you write your own tools, faster and better.

Last night, I was struggling with videos I had to process. I wanted to add nice subtitles to them. There are software applications for that, but they require manual labor and don’t always work the way I want them to. After a long night, I had an insight: why don’t I ask my AI to help build the automated tool I need? So I did—and it worked really well, very quickly. So what’s the lesson here? Maybe that we should spend more time building our own software for our own personal use than we used to.

2026-07-12 03:53:17

I have been on X for a long time. Like most people who post regularly, I have a gut feeling for what might interest people. I post in the morning. Longer posts seem to do better.

But gut feelings are not measurements. And until recently, digging into your own posting data meant either clicking around the web UI or writing custom scripts. Neither is particularly friendly when you want to ask ad hoc questions with an AI assistant.

X recently launched hosted MCP servers: official endpoints that AI tools can connect to. MCP is a protocol for plugging tools into language models: the model can search posts, manage bookmarks, fetch trends, and so on. In practice, I connected an AI coding agent to the X MCP server and simply started asking questions about my account.

I spent a session exploring about two months of my own activity. Here is what I found interesting.

Over roughly sixty days (mid-May through mid-July 2026), I published on the order of 435 posts that were not pure retweets of other people—mostly a mix of original posts, replies, and a few X Articles. The agent pulled them through the MCP tools, kept the public metrics (likes, views, reposts), and ran simple analyses.

I asked for every post to be binned by local hour of day (America/Toronto, Eastern time), and for each hour: how many posts, and the min / median / max view count.

My posting is heavily skewed toward the morning:

The 9 a.m. hour is both my busiest and, among busy hours, my strongest by median views. The overall median across all hours was only about 188 views, so most of what I write is quiet. The distribution is heavy-tailed: a few posts get tens or hundreds of thousands of impressions; the rest are background noise.

I then binned posts by character length in steps of 25 characters (using the text as returned by the API, including short t.co URLs).

The bulk of my writing is short, often a reply of a few dozen characters:

Under about 175 characters, the median stays at zero or one like. Around full-length posts (roughly the old 280-character regime and a bit beyond), engagement jumps. The 300–325 character band is where a large fraction of my “serious” posts live, and the median likes there are an order of magnitude higher than for short replies.

I also asked the AI to identify the posts that had the most likes, the following types of posts were liked:

• AI vs. “experts” claiming models are nowhere near human intelligence
• Go adding SIMD-style data-parallelism to the standard library
• SIMD-accelerated data processing talks and library notes (JSON, string→integer maps, vulnerability-report fatigue)
• Nvidia hardware, university AI-cheating, C++ contracts

The interesting part is the workflow. I did not export a CSV by hand and open Excel. I asked an agent, connected to X’s MCP server. If AI agents can do this for one account’s metrics, they can do it for bug trackers, logs, paper drafts, and codebases.

2026-06-22 01:51:16

When I was a kid, most people did not know how to type. We took typing class. The final exam was a speed test: words per minute. Today, you will not impress anyone by saying you can type. In fact, cursive writing is fading. Kids increasingly cannot read or write it. We type constantly. We forget how many skills are learned, and how often some of these skills have faded.

But not everything fades. Socrates would be immensely popular today as a teacher. I still buy and recommend paper books.

Is reading and writing code more like Socrates, or more like cursive writing? There are clear signs that code could become like cursive writing. This year, I have met more than one student who could use AI to build an application but could not read or write code. It is not new. Software has long had non-technical people who describe what they built or designed. In fact, in much of the industry, the standard view was that once you had a university degree, you no longer coded. Coding was for monkeys or low-status employees. Top engineers paid a million dollars a year at Google or Meta know how to write code. They often read and write assembly and TypeScript. They know it all.

Why the discrepancy?

We pay an engineer a million dollars because he understands concepts few others grasp. He outruns others because he sees the problems more deeply. Reading and writing large amounts of code is part of how you gain those insights. Chatting with an AI will not make you a top 1% programmer. In the future, top engineers might read more code than anyone could in the past. These engineers will not be everywhere, but they will pack a punch. “But Daniel, people say programming is solved. Why read or write code?” Be careful with your models. When television arrived, some predicted it would replace the university lecturer. In some respects the model was correct, yet it did not happen. The lecturer’s job was never to deliver a TV show. The Google engineer paid a million dollars was never a machine that produces code. Nobody actually wants code, any more than they want raw text.

In fact, I predict a bifurcation in the tooling. The best engineers will work with tools that maximize their understanding of the code. I believe that reading and writing code, at a high level, is more like studying Socrates than like cursive writing. It is a necessary mental labor that does not become obsolete just because we have better tools for generating output.

2026-06-14 22:59:44

Suppose that you have a configuration file in JSON. Something like this:

{ "width": 1920, "height": 1080, "fullscreen": true,
  "title": "My Game", "volume": 0.8 }

Normally you ship this file alongside your program, open it at startup, read it, and parse it. That is a lot of work for data that never changes. What if the file is fixed at build time? Could the compiler read it, parse it, and bake the result directly into the executable as a constant?

With C++26, the answer is yes. We need two new ingredients, all of which are usable right now with the latest version of the GCC compiler (16).

1. #embed to pull the file into the program at compile time,
2. A software library supporting static reflection like simdjson.

Let me show you how far we can take this.

The new #embed directive reads a file and expands it into a comma-separated list of byte values. To read the file data.json at compile time and keep it around as a constant, we write:

constexpr const char json_data[] = {
#embed "data.json"
    , 0
};

I use constexpr because I want the compiler to be allowed to inspect these bytes during constant evaluation. The trailing , 0 simply appends a null terminator, so the array can be treated as an ordinary C string.

There is no run-time input/output of any kind. The bytes are part of the program.

But embedded bytes are not yet useful by themselves. What I really want is a typed C++ object. In my example, the target type is this configuration struct:

struct Window {
  int         width;
  int         height;
  bool        fullscreen;
  std::string title;
  double      volume;
};

The traditional way to populate such a struct from JSON is to write, by hand, one line per field: read "width", store it into width, read "height", store it into height, and so on. It is tedious. And because it runs at startup, a malformed file becomes a run-time error, discovered by your users rather than by you.

Recent versions of simdjson can parse JSON at compile time using C++26 static reflection. The entry point is simdjson::compile_time::parse_json, and it does something I still find slightly magical: it reads the JSON and, from the keys it finds, and synthesises the struct type for you.

#define SIMDJSON_STATIC_REFLECTION 1
#include "simdjson.h"
constexpr const char json_data[] = {
#embed "data.json"
    , 0
};
constexpr auto window = simdjson::compile_time::parse_json<json_data>();

The variable window is a value computed entirely by the compiler. Its type is generated from the document: it has a width and a height (both 64-bit integers), a bool fullscreen, a double volume, and a title. From here on I write window.width and it behaves like any ordinary field.

How do I know the parsing really happened at compile time? Because I can assert things about the result that the compiler must check before the program even exists:

static_assert(window.width      == 1920);
static_assert(window.height     == 1080);
static_assert(window.fullscreen == true);

If I corrupt the JSON — delete a brace, misspell true, leave a trailing comma — the program no longer compiles, and the error points at the parse_json line. The broken file is caught at build time, on my machine, instead of at startup on someone else’s.

Because window is a genuine compile-time constant, any computation over it is a constant too. Consider this function:

int  screen_area()   { return window.width * window.height; }

Compiled with -O3, there is no multiplication, no field access, and certainly no parsing left — only the answers, as immediate values (here on my macBook):

screen_area:    mov  w0, #0xa400        // 0x1fa400 = 2073600
                movk w0, #0x1f, lsl #16
                ret

The JSON has vanished from the binary. It was read and parsed exactly once, by the compiler, and all that survives is the number 2073600.

Because static reflection is so new, when building with GCC 16, you need to pass the flags -std=c++26 -freflection: the -freflection flag is necessary to activate compile-time reflection You must also set the simdjson macro SIMDJSON_STATIC_REFLECTION=1 before importing the simdjson.h. It is a temporary safeguard.

The source code to reproduce these examples is available.

Reference: P2996 — Reflection for C++26 and the simdjson library.

Credit: The simdjson implementation is joint work with Francisco Geiman Thiesen.

2026-06-10 02:39:32

The keyword in politics these days is ‘sovereign’.

What few will admit is that it is effectively the adoption of the American strategy: Make America Great Again. In other words, reindustrialization of key sectors of the economy. The UK used to be a computing champion. Our chip designs (ARM) originated from the UK. Canada had BlackBerry, everyone was using Canadian phones.

Like Canada, many countries have progressively slid into financialization. Huge banks and bank-related businesses, surrounded by emptied factories.

Part of it was the doing of economists who promoted globalization. We are going to make our best CPUs in Taiwan, because they have a comparative advantage (whatever that means).

Another part is the rise of the managerial class, or our version of the technocracy: the summum of the status game is to make PowerPoint presentations in a nice office. Everyone has 2 or 3 university degrees. And if you don’t have many degrees, what is wrong with you?

I think that this is breaking apart for a few reasons.

One of them is Trump. And I don’t mean bombastic statements, bad hair color or overly long ties… But rather the realization that globalization might leave your people economically better off for a time because they have cheap stuff… But it also leaves your people with few skills. You can fund a robotics factory near Montreal, and you’ll find 2000 people with robotics PhDs, but nobody actually knows how to build robots. The analogy is thus: salary is not everything (to the great chagrin of economists). I have left a much better paying job. My previous job meant that I had to sit in an office and do little concrete. I would have had a great and early retirement… but I would never have developed my skills nor would I have built anything. And that’s what we did at the country level. Great total compensation, but a dead-end skill-wise.

Another factor, I believe, is the COVID era (2020-2023) and its final outcome: empty offices, closed coffee shops. What happened at work was illegible. Lots of people in offices. Certainly, something important was happening. Entire businesses and government organizations have now migrated partially or entirely to a pajama party of some kind. Netflix in the middle of the workday is no longer a dream, but a reality.

Another element is educational misalignment. A country like Canada has the most schooled population in history. You cannot throw a rock without hitting someone with a PhD. Meanwhile, we are not making robots or microchips. You can’t even pay with your phone in the Montreal subway. It is a project for another decade, maybe. We are using a push strategy: push more people with degrees into the economy and you are going to get a fancier economy. Won’t work.

Finally, the AI breakthrough of 2022 is the final nail in the proverbial coffin. My country (Canada) claimed for decades that we were the AI powerhouse. All these PDFs online can’t lie, can they? Canada basically invited modern AI, didn’t it? We did. On paper. On paper we did a great many things. In practice? Few know how to build anything.

In a country like Canada, the population has not yet caught up. They blame the orange man for whatever trouble they see. And the politicians promise to do what they must: shower money to build tech sovereignty. It won’t work. They will try again. It won’t work again. The cycle makes things worse because it sustains a managerial class that is great at politics but terrible at building.

Meanwhile, you can’t escape preference falsification. People will use ChatGPT, Claude, Grok, Gemini. And if Elon produces robots, they’ll want them.

Trump will leave office in two years… Canada and the UK will still be flat-lined economically. The USA will still surge ahead.

Countries like Canada and the UK will have to realize that it is industry and know-how first. Build stuff and the wealth will follow. Stop the virtue signaling. Stop the credentialism. Build.