2026-11-13 21:59:44
Hi friends 👋,
Happy Friday and welcome back to our 219th Weekly Dose of Optimism!
What a week. After two affairs emerged - one with an alleged Chinese spy and one with a direct report - Nvidia founder and CEO Jensen Huang resigned. Sources say the affairs are the tip of the iceberg and that heavier shoes will drop.
But progress marches on, as evidenced by everything in this week’s Dose. It’s going to take more than a wandering Huang to keep us down.
By the way, inspired by Readwise launching Readwise Books, I had Codex turn all of my essays into epubs that you can download and read on your book reader of choice, because while they’re long essays, they’re actually pretty short books! You can find and download all of them, including Of Miracles and Moats, at not boring press.
Let’s get to it.
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So, uhhh, pretty good time to be Google, huh?
On Wednesday, as Nvidia was in turmoil, the maker of GPU competitor TPUs, and our favorite chef of models outside of your plain vanilla LLM, released AlphaGenome 2.
Last year’s AlphaGenome 1 took a million letters of DNA and predicted what they do, but averaged across whole tissues. It was very cool! But averages can hide things, like variants that might cause disease in only one rare cell type or only after a cell gets activated.
AG2 takes the same million letters plus a condition, which can be a tissue, a cell type, a cell state, or data from a few dozen of your own cells that it has never seen before, and it predicts what the DNA does there specifically. It trained on more than 107 million individual cell profiles from 68 human and mouse tissues. On a benchmark of 28 immune cell types, it found 30% of the variants scientists believe are causal, versus 16% for the old model.
As longtime readers know, my process for figuring out whether something in bio is legit is to text Elliot, but this time, he tweeted before I had to:
Genome-wide association studies have handed us an enormous pile of DNA variants linked to diseases, and for most of them, scientists are still trying to figure out which gene they touch or which cell they act in, which often takes a lot of real world experimentation. That's a big part of why genetics - we mapped the whole human genome! you can now get yours done for like $100! - hasn’t turned into drugs as fast as everyone hoped. But AG2 can pick up signals from all of that data computationally, which means we’ll be able to rev through them to (hopefully) find cures much more quickly.
AG2 still misses most of them (30% is 30%), but going from 16% to 30% in one generation is great progress, and there’s a lot scientists can do with that 30%.
Bad time to be a disease.
Real bad time to be a disease, because even if you can use things like AG2 to find targets, you still have to drug them. Which is what macrocycles do.
Macrocycles are ring-shaped peptides that sit somewhere between the small molecules that come in pills and antibodies that need to be injected. The promise is the precision of a biologic in a pill that can slip inside cells to hit targets antibodies can't reach. Think something like an oral GLP-1 or cancer therapeutic.
Macrocycles are what not boring capital portfolio company Unnatural Products does, and now, it will be doing them inside of argenx. On Monday, argenx announced that it's acquiring UNP for $550 million in cash upfront and up to $595 million more if it hits development and regulatory milestones. Big win for Cam, Josh, and the UNP team!
argenx and UNP have been partners since last year, and argenx was already an investor, so it knows what it’s buying. They’ve been working on a pill that goes after FcRn, the same target as Vyvgart, the argenx drug that did $1.72 billion in sales last quarter. The team will keep working out of Santa Cruz, where they have huge lush trees right outside the window, and will become argenx's home for peptide discovery.
Naturally, I’m very excited about the win, and we should all be excited to get more of the potential miracle cures we talk about here delivered in effective pill form in the years ahead.
Good welders are insanely valuable. I remember touring SpaceX a few years back and hearing them talk about the one guy who could weld everything in hushed tones. Just this week, I was talking to someone about a golden gloves welder somewhere else who pulls $1 million a year. Welding is the same story we often hear: not enough welders, let alone great welders, to make all of the things we need to make.
Back in Dose #214, we covered Anduril’s $3.7 billion Arsenal-2 shipyard, its answer to the US’ shipbuilding problem. But even with big shipyards, we’re going to need a lot of welders. They just might not all be human welders.
Path Robotics, which brothers Andy and Alex Lonsberry started in Columbus, Ohio in 2018, makes robots that weld, which is harder than it sounds! Normal welding robots just repeat a programmed path, but that doesn’t work when the parts in front of them are a little different every time, which they always are, which is why humans still do most of the welding, and why even among humans, the ones who can weld anything are so valuable. Path’s model, Obsidian, tries to make robot welders as effective as golden gloves welders by looking at the actual joint each time, planning the custom weld, and adjusting as it goes.
On Tuesday, Path announced a $300 million Series E led by Georgian at a $2.45 billion valuation. Its revenue is up 17x in 18 months, and customers include Lockheed Martin, Saronic, and HII, the Navy shipbuilder, with which Path signed a $600 million deal earlier this year.
They also make a robot dog that welds (Rove, a Boston Dynamics quadruped with Path’s tech on it) with 50 preorders, headed to shipyards and construction sites next year.
If we want to build big things, we’re going to need a lot of welders, whether of the human, robot, or robot dog variety. Learn to weld.
Good week to be Elon Musk. He has people thinking he’s going to hire Jensen to run Terafab, but while they’re thinking about, they’re also thinking about how useful it is to be basically fully vertically integrated and control your own destiny, even if it means sending your data centers (Starmind) up to space on your rocket (Starship), maybe one day filled with your own chips (Terafab), but for now in partnership with Nvidia (???), to run on the unlimited power of the sun (???).
Looks like SpaceXAI will be making solar panels, too. On Tuesday, SINGULUS, a small German company that makes the machines that make solar cells, announced that SpaceX signed a contract for a turnkey heterojunction (HJT) cell production line, to be delivered in the first half of 2027 to SpaceX’s satellite factory in Bastrop, Texas.
That first line is worth a little over €60 million, but SINGULUS says that more are coming, and that the plan is to scale to “more than 100 GW of production equipment.”
That, to be clear, is a preposterous number. Each Starmind is designed to have a 75-meter wingspan and a 210 kW solar array, so if my (machines’) math is right, 100GW per year is enough for something like 475,000 satellites. Every year. There are about 15,000 active satellites in orbit right now.
On the other hand, as it stands, this is a €60 million commitment whose announcement was probably worth much more than €60 million to SPCX’s market cap in terms of making Starmind look more serious. A promise to build 475,000 space data centers a year is also exactly the kind of thing you’d expect to hear - and for people to take at least somewhat seriously - if we were in a bubble.
But A+ for ambition, and if SpaceX ends up building a ton of solar panels and doesn’t end up sending them all up to space to create a sentient sun, then consumers will win again in the form of more cheap, clean energy. Never bet against the American consumer.
via @nasasolarsystem
Speaking of putting ambitious things in space… In 1977, NASA launched two twin probes, Voyager I and Voyager II, each carrying Carl Sagan’s Golden Record. To exploit a rare alignment of the outer planets that let one spacecraft slingshot from one to the next, Voyager 2 launched first, on August 20th, and Voyager 1 followed on September 5 on a faster path.
Between 1979 and 1981, both flew past Jupiter and Saturn, discovering active volcanoes on Io and detail in Saturn’s rings. Then Voyager 1’s passed close enough by one of Saturn’s moons, Titan, to fling itself up and out of the plane of the planets, while Voyager 2 continued in our own solar system. In 1986, it passed Uranus, and in 1989, it reached Neptune.
The next year, on Valentine’s Day 1990, Voyager 1 took the famous Pale Blue Dot picture that I used to frame Us Against Spacetime.
In 1986, Voyager 2 passed Uranus (insert butt joke here) and in 1989, Neptune. It’s the only human-made craft to make it to either one.
Our solar system is so big. But it is tiny tiny tiny compared to the universe.
Voyager 1 crossed the heliopause into interstellar space in 2012, 35 years after it left Earth.
And next Wednesday, 14 years after that, 49 years after the journey began, Voyager one will pass one light-day away from home.
That is a long ways away, the furthest anything we’ve ever made has voyaged by far, and STILL it’s nothing. The Milky Way is something like 100,000 light years across, and the Observable Universe (not the whole thing, just what we can see) is 93 billion light years across. So after this entire 49 year trip, Voyager 1 has traveled just one 34-trillionth of the way across the Observable Universe. At this pace, it would take 1.67 quadrillion years to do the whole thing, or 120,000x longer than the universe has been around.
So this one is both a celebration of our ability to engineer a thing that can travel a whole light-day away, and a reminder that we are just a minuscule part of this big, beautiful universe. There is much left to explore.
Video by Runway
From outer space to inner space we journey.
I can’t really think of anything that would be better for humanity than discovering that psychedelics reverse brain aging and fight neurodegenerative diseases.
Back in Dose #196, we covered the case of an octogenarian Japanese-American woman with a decade-long history of Alzheimer’s who’d been given a 5mg dose of psilocybin and regained a number of functions, including holding an autobiographical conversation, dressing herself, retrieving contextual memory, and walking independently. The improvements weren’t permanent, but they showed that the person was still in there, and that psilocybin might hold the key to getting them back out.
Now, in a new study in Aging Cell, Aurora Savino, Francesco Iorio, and colleagues found that LSD might reverse brain aging, at least in rats.
As the brain ages, its gene expression drifts in a predictable direction. The genes that maintain synapses and dendrites quiet down, and the ones that build glia and blood vessels get louder. A team in Milan and Turin mapped that drift across 3,086 human prefrontal cortex samples, from donors aged two days to 106 years, then went searching for an intervention that could push back on the drift.
They, like so many seekers before them, found LSD. In chronically dosed rats, and with a single dose in mice, it opposed the aging signature more strongly than exercise or an enriched environment, the two interventions scientifically proven to be good for old brains. In terms of switching on the right genes, it outranked all 193 drugs in a reference database.
MDMA and alcohol had the opposite effect: they made brains look older, which makes sense if you’ve ever woken up after a night of drinking too much.
But LSD… it gets better! In a dish, LSD repaired mouse neurons that had been damaged by amyloid-β, the protein that builds up in Alzheimer’s, restoring both their branches and their synaptic proteins.
I’ve long told you I’m terrified of getting Alzheimer’s, and that I get easily excited when there’s promising news, even if it’s early. LSD’s success in rat and mouse brains doesn’t guarantee success in human brains. But I guess there’s only one way to find out.
2026-11-11 21:53:22
Welcome to the 1,974 newly Not Boring people who have joined us since our last essay! Join 282,985 smart, curious folks by subscribing here:
Hi friends 👋,
Happy Wednesday!
A consistent theme of this newsletter is that we are fortunate to be living in an Age of Miracles. To wit, I’m sending this over Starlink on a United flight, surrounded by regular people who also received access to free WiFi beamed down from the heavens with their few hundred dollar ticket to fly at 30,000 feet and span the country in mere hours. The Louis CK joke is no longer applicable.
Airlines and internet are a good reminder, though, that a miracle technology does not guarantee a miraculous business. In fact, the more incredible and useful a technology is, and the more you can throw money at scaling it once it works, the more likely it is to attract hordes of competitors who, in aggregate, build too much supply and crash prices.
Let’s get to it.
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It is neither productive nor becoming to call a bubble. Most people who do it are innately pessimistic curmudgeons left out of the fun who believe that the technology underlying the asset in question is overhyped and that those excited about it are rubes. For me, personally, calling a technology bubble would also seem to be pretty off-brand.
Anthropic will probably end its first week of trading at a much higher market cap than it goes out at, and it may well proceed to eat the entire economy en route to becoming the world’s first quadrillion dollar company. The economy very well may end up looking K-shaped in the way that my kids write K’s, with no sense of proportion, a few Anthropic employees at the tippy top each controlling one Bay Area fiefdom/polycule and the rest of us treating Claude with the proper respect in order to continue receiving our allowance from the Machine of Loving Grace (that or somehow outsniping Duet on Kalshi enough to eat).
While I have written that AI is more Oil than God (funny title image choice in retrospect), and I still believe that, it is also true that oil does not produce its own oil, that you can’t replicate Microsoft Office with oil, and that oil is yet to produce a solution to Navier-Stokes.
There is a world in which we look back at Anthropic’s IPO price and laugh at the idea that we ever valued an old school commodity LLM at ~$1.4 trillion. Maybe there’s no new S-curve as intense as coding, because in hindsight, duh, the computers were really good at speaking computer and that didn’t transfer. Or maybe there is, but competition from OpenAI, Google, SpaceXAI, Meta, Chinese open source models, and the neolabs slows revenue growth just enough that compute commitments made in the boom times force the company to sell itself to Amazon or Apple or whoever.
There is another in which Dario has decided that laughing hurts the models’ feelings and well, he’s allowed to tell us not to laugh anymore, since he is the economy. A very real bull case for Anthropic goes something like: it has the best models and they are now improving themselves faster than ever, growing the lead in what may be a winner-take-all market, and unlike other winner-take-all markets, this one extends to all markets, because Anthropic can win whichever markets it chooses to point its smarter-than-anyone models at, from productivity software to medicine to … anything.
Anyone who tells you they know which of those two outcomes will come to pass is full of shit, and I am many things, but I hope not to be full of shit.
There are three arguments against this being a bubble more broadly, though, which are oft-repeated, sound right, and are exactly the things you’d expect to see in a bubble.
Note: I’ll use bubble here as the broad term but I might mean something closer to commodity cycles, and I certainly don’t mean manias, which are when price becomes totally divorced from reality, as with tulips.
The three arguments are:
Unlike other bubbles (lol WebVans and crypto), AI is actually an incredibly useful technology that is being widely and productively adopted in the economy.
These companies are generating real revenue, and growing incredibly fast.
The labs have shown they can be profitable, and any losses are just them investing to capture future growth.
Grant all three! In doing so, however, you’d be saying that the situation is exactly the situation that creates and then deflates bubbles.
A miracle technology arrives.
Early suppliers earn real profits because supply is short.
Demand looks bottomless, which is obvious to everyone in the early numbers.
The demand and profits pull in capital and competitors.
Everyone builds at once, even those who see the glut coming, locking in commitments at scarcity prices just to get capacity online.
Supply arrives, the technology gets better, and prices collapse.
Capital loses, consumers win, and the technology chugs on.
Capital flees, so new entrants can’t get funded.
A survivor or outsider with capital consolidates and becomes the low-cost provider.
Demand catches up, the technology proves bigger than anyone imagined, and the survivor ends up larger than all the failed companies combined.
None of this is new. It’s close to Carlota Perez’s Technological Surge Cycle and Edward Chancellor’s Capital Cycle. Byrne Hobart and Tobias Huber wrote a whole excellent book in praise of bubbles and their role in ending stagnation. What is interesting, though, is that three of the main arguments you hear against AI being a bubble are steps one, two, and three.
The pattern keeps playing out precisely because each new technology really is so impressive that people believe this time, finally, is different.
The first seven steps are pretty consistent, like some law of human nature and capital markets. Steps 8-10 often, but don’t always, play out. Sometimes consumers simply dine on the industry’s corpses.
Take airlines. If ever there was a miracle technology, it was the ability for humans to fly through the air like birds. Flight seemed so improbable beforehand that The New York Times famously predicted it would take 1 million to 10 million years to figure out, just two months and change before the Wright Brothers figured it out.
So did the Wright Brothers get filthy rich? Did those companies that could fly humans through the air like birds become great businesses?
Well, here’s what Warren Buffett had to say on the matter in a 1999 talk at Sun Valley:
“Karl Marx couldn’t have done as much damage to capitalists as Orville did.”
In his 2007 letter, he explained the logic behind the zinger:
The worst sort of business is one that grows rapidly, requires significant capital to engender the growth, and then earns little or no money. Think airlines. Here a durable competitive advantage has proven elusive ever since the days of the Wright Brothers. Indeed, if a farsighted capitalist had been present at Kitty Hawk, he would have done his successors a huge favor by shooting Orville down.
It is unpopular to say it now, perhaps, when everything is moving so fast, but in the long run, a business needs both a product that customers want to buy, and a durable competitive advantage to protect its margins from all of the other companies that want to fulfill those customers’ demands, too. They need moats, and airlines didn’t have them.
Even The Oracle himself, however, couldn’t stay away. In 2016, Berkshire took stakes in American, Delta, Southwest, and United to the tune of roughly $7-8 billion. At Berkshire’s 2017 Annual Meeting, he admitted, “It’s a fiercely competitive industry. The question is whether it’s a suicidally competitive industry, which it used to be.”
He sold all four at a loss in April 2020. He got hit by a Black Swan - COVID - but he pointed to the laws of supply and demand in his decision. “If the business comes back 70 or 80%,” he said, “the aircraft don’t disappear.”
So while not every industry based on a miraculous and broadly useful technology becomes a great industry or produces enduring businesses, they all end up having a bigger impact on the world than anyone could have imagined. Consumers end up winning, and humanity progresses.
There is nothing more optimistic, in fact, than believing that capital will fuel an oversupply of any new miracle technology humans can concoct and in so doing, make the technology cheap enough for everyone to benefit from. Miracles without moats, if you will.
It’s not that capital is stupid, quite the opposite. It takes a pretty spectacular technology indeed to rally the world’s capitalists to invest a significant percentage of global GDP behind a given technology. Oil, railroads, cars, internet. Each of these ended up being even more impactful than their most fervent boosters predicted. In the early days, for example, oil was used to replace whale oil in lamps; it went on to make cars and planes and modern life possible.
And it’s not like those involved don’t see that they might be overinvesting and creating the conditions for their own downfall, but like game theoretically, what are you supposed to do?
The British railways are a great example.
On my birthday, many years before my birth – January 26, 1846 – Charles Russell, chairman of the Great Western Railway, stood in Parliament and urged his colleagues to “expose and discountenance every bubble scheme.” He wanted Britain to slow down the approval of new railways.
In 1842, the first British railroads, including Russell’s, were phenomenal businesses. The Liverpool & Manchester, the London & Birmingham, and a few other early entrants were paying dividends of at least 10%. Great Western, a slightly less good railroad, paid between 6-8%.
Demand for rail transportation seemed bottomless, and there were only so many railways to serve it, at first, when building railroads was hard and prospects were uncertain, but over the next couple of years, entrepreneurs of varying degrees of seriousness introduced hundreds of proposed new railways, each hoping to cash in on those sweet early rail economics.
By the autumn of 1845, Britain was so consumed with railways that the Railway Times spent nine consecutive weeks using its lead article to attack The Times for warning about the speculation in railway shares. On November 17th, The Times published a special supplement tallying the capital required by 1,263 proposed railway schemes. In 1846, Parliament passed acts authorizing 4,540 miles of new track.
Of course Russell wanted to “expose and discountenance every bubble scheme.” Each one brought new competition, which he saw would drive down prices.
Clearly, he was not opposed to railway investment per se, for he himself was playing the game. At an 1848 shareholder meeting in Paddington, he explained why his own railway had taken on such extensive commitments in snatching up rival lines. The Great Western had acted, per The Times, “in self-defence.”
New entrants have nothing to lose and incumbents have everything to lose and both get you to the same place: you have to invest.
That’s how Google is thinking about it today. Founder Larry Page reportedly said, “I’m willing to go bankrupt rather than lose this race.” CEO Sundar Pichai, on the company’s Q2 2024 earnings call, told investors that “the risk of underinvesting is dramatically greater than the risk of overinvesting.”
Zuck agrees. In September 2025, on Alex Heath’s Access podcast, he admitted that “it’s quite possible” that AI is a bubble. “If we end up misspending a couple of hundred billion dollars, I think that that is going to be very unfortunate, obviously,” he added. “But what I’d say is I actually think the risk is higher on the other side. The risk, at least for a company like Meta, is probably in not being aggressive enough rather than being somewhat too aggressive.”
History rhymes.
Earlier this year, Anthropic CEO Dario Amodei told Dwarkesh Patel that he believes a country of geniuses in a data center is coming soon, but that “If you’re off by only a year, you destroy yourselves.” To put a finer point on it, he said, “I could buy a trillion dollars a year of compute that starts at the end of 2027. And if my revenue is not a trillion dollars, if it’s even 800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if I buy that much compute.”
At the time, Anthropic had committed about $100 billion to compute, a relatively sensible number. Competitors (cough, OpenAI) were being reckless, he thought: “I kind of get the impression that, you know, some of the other companies have not written down the spreadsheet that they don’t really understand the risk they’re taking.” For context, the following week, OpenAI told investors it was targeting about $600 billion in compute spend by 2030, down from a $1.4 trillion number it had previously touted. Whatever, horseshoes and hand grenades.
Anyway, in its S-1, Anthropic reported that it has now committed to $900.9 billion worth of compute through 2045, about half of which is expected before the end of this decade.
This may be the absolute right move! 1. Who am I to say it’s not? 2. Revenue is growing fast. 3. If they don’t, someone else who is less spreadsheet-literate will. 4. They even make their own spreadsheet software now, and 5. This math is likely being run by a combination of incredibly smart humans and models so advanced that they’re too dangerous to release. Plus, Anthropic actually generated an operating profit in Q2 and would have done so again in Q3 excluding roughly $2 billion in noncash donation-matching charges, so they may have their hand firmly on the tiller.
But like, sounds risky, per Dario. And sounds an awful lot like the British railways, then the American railways, even… cannabis?
Thank you Writing Gods. The growers got hit by the same oversupply issue, and now some of them are flying to AI data centers for safety.
Sounds like fiber, too.
We tend to dismiss comparisons between the dot com boom and bust and the AI boom because there were so many obviously dumb, revenue-less companies getting funded and adding dot com to their stodgy businesses’ names and popping 420% that that one was clearly a mania, right?
It’s worth separating the internet stock mania from the bandwidth bubble - overbuilding capacity based on optimistic early demand signals. We look back on it all as silly, but while things got heated, many of those businesses laying fiber were acting rationally, at least independently.
Internet traffic really was exploding. In 1990, basically no one was using the internet. By 1995, 9% of Americans were, and by 2000, 43% were.
And that was just Americans. The world came online, too, adding new growth S-curves. And that was just the current uses with low-bandwidth internet! I used early dial-up internet. It was painfully slow. It took a minute to get a picture to load, and anywhere from 20 minutes to forever to download a song, assuming someone in your house didn’t get a call. Imagine all of the new use cases that would be unlocked when everyone had access to practically unlimited bandwidth, on-demand. We would watch movies and TV on the internet. We would attend events on the internet. We would do business and buy things and come up with entirely new things to do on the internet once we had the bandwidth.
No less of a luminary than Bill Gates himself made this argument, back in his 1995 book, the #1 New York Times Bestseller, The Road Ahead. And he fucking nailed it. Read that excerpt I just linked. He was totally right, and not even right enough. He wasn’t alone. Qwest Communications, a long-distance fiber optic carrier, made an ad that asked viewers to imagine a motel in which “All rooms have every movie ever made in every language, any time, day or night.” That motel was the internet, baby, and it was available right in your home.
It was hard to imagine where and when demand for the internet would possibly saturate. A 1998 US Commerce Department Report, The Emerging Digital Economy, stated that internet traffic was “doubling every 100 days.” Turns out, it got that number from a 1997 Inktomi white paper, which in turn cited Mike O’Dell, Chief Scientist of bandwidth-seller WorldCom’s UUNET, which, Andrew Odlyzko later figured out was a reference to WorldCom’s buildout pace - supply - and not demand, but no matter! The internet was ripping.
In a 1998 meeting, Qwest CEO Joseph Nacchio responded to an investor question about Qwest and its competitors all laying fiber at once by pointing out that, “The explosive demand driven by the Internet phenomenon ensures there won’t be a glut of bandwidth.”
“If we were even modestly competent executives,” he added, “we’d do well.”
Qwest, which had begun as a division of the Southern Pacific Railroad and laid fiber on the routes the railroad owned, did do well, for a while.
Qwest grew reported revenue from $2.24 billion in 1998 to $3.93 billion in 1999, thanks in part to its June 1998 acquisition of long distance carrier LCI, and its operating profit grew from negative $754 million in 1998 to positive $324 million in 1999. (Well, that and maybe some accounting fraud, per the SEC.) Its market cap rose to $91 billion in 2000. Then it did less well. By August 2002, it had fallen to $1.9 billion.
What happened? You’re reading this on the internet right now, from one of the at least two and probably a lot more internet devices that you use nearly all day, that you’re so addicted to, in fact, that you keep trying to stop using them so much.
After growing 10x in 1996, internet traffic really did double frequently – every year! – but it didn’t keep doubling the every 100 days that the broken telephone Commerce report suggested it was.
Meanwhile, U.S. long-haul fiber mileage roughly doubled from 1996 to 2001 post the 1996 Telecommunications Act, from 100k miles to 200k miles, while upgrades to the electronics let each strand carry up to 40 times as much data, from 2.5 Gbps to 100 Gbps. In total, telcos spent something like $500 billion on the buildout.
With all that supply, a 2002 TeleGeography report said that bandwidth prices on major U.S. and European routes had fallen about 70% annually for three years. In one example, a 155 Mbps circuit between New York and Los Angeles, a connection that cost $1.8 million a year in 2000 could be leased for under $150,000 by early 2002.
Humanity ended up putting that bandwidth to good use, and needing a lot more, but that was cold comfort to those who’d built out the network. WorldCom filed for Chapter 11 in 2002 amid fraud allegations. Williams Communications filed for Chapter 11 the same year under a $5.9 billion pile of debt. Global Crossing did too, and emerged in late 2003, with its debt load lightened from $11 billion to $200 million. Preferred and common shareholders received nothing.
In the year that Global Crossing emerged from bankruptcy, it had explosive growth. Traffic on its network grew 130%! Problem was, bandwidth wasn’t worth what it once was. Its revenue fell 6%.
The biggest winner – Level 3 – which had raised money at just the right time before the markets shut in February 2000, which had built a cost advantage by laying 12 conduits where others laid one or two, which had the post-crash backing of none other than Warren Buffett, and which rolled up many of its competitors’ corpses at advantageous terms, sold to CenturyLink in 2017 at a price 97% below its peak. Even identifying the eventual winner might not matter if you pay entry prices that assume away the corrosive forces of competition.
But again, what are you going to do? Not play ball? The internet is a fucking miracle. You can chat with anyone in the world, face-to-face, on your computer, or even on a piece of glass in your pocket. Unless you’re a local models guy, you access the country of geniuses in a data center via the internet. You buy things on the internet. You listen to things on the internet. You can watch every movie ever made on the internet, in any language, day or night, and you can even watch a bunch of movies and TV shows that were made specifically because the internet exists and demands more content than even Qwest Communications could ever have hoped the internet would demand. I don’t know what I would be doing without the internet, but it certainly wouldn’t be this. Thank god for the internet.
The fiber companies got a little carried away, sure, but they weren’t idiots. Neither were the people prospecting for oil back in 1860, the year after Colonel Drake (who would end up dying penniless) discovered it in Titusville, PA. Oil was going for $20 a barrel, real money! Problem was, so many people saw that there was black gold in them there hills that the price of a barrel fell to 50 cents by June 1861, and to 10 cents by December, wiping out a ton of prospectors.
This was not a demand problem, really, although I guess every supply glut is technically a demand problem. Even in the early days, when oil was used only for illumination, demand was practically bottomless. After the gusher-led fall-off in 1861, early wells depleted, demand spiked, and prices rebounded above $8 a barrel in 1864 … which attracted more drillers and an even more frenzied atmosphere.
At Pithole, PA, some once-worthless farmland sold for $1.3 million in July 1865 and resold for $2 million in September. By Christmas 1865, almost 20,000 people lived there. By the Census in 1870, only 237 remained and by 1878, the $2 million farmland was sold back to the county for $4.37.
Per PT Barnum (yes, that PT Barnum) in his 1865 Humbugs of the World, roughly 600 petroleum companies sought a combined $500 million of capitalization. “Petroleum exists, no doubt, and is an important addition to our national wealth,” he wrote. “But the petroleum humbug... is a humbug.”
Investors were not wrong on the size of the opportunity. Oil was real, and a single oil company would come to support a market value greater than the entire $500 million.
They got the market sizing right and ignored the competitive dynamics.
John D. Rockefeller did not. The man was too attuned to competitive dynamics, if you believe Ida Tarbell, Theodore Roosevelt, and the US Justice Department.
As we covered in Power in the Age of Intelligence, Rockefeller, who started refining crude in Cleveland in 1863 and would incorporate Standard Oil in 1870, recognized that the constraint to the profitable growth of the oil industry was its volatility, that the way to tamp down on volatility was through scale and control, that to get scale and control, he’d need to drive down costs and capture market share, and that to do that he’d have to improve kerosene yield, sell what others threw away, and aggressively vertically integrate.
By the 1880s, Standard Oil controlled 90% of American refining, which fell to 64% by the time it was broken up in 1911, when its $1.1 billion market cap represented about 6% of the entire US stock market and more than twice the entire industry’s capitalization in 1865.
Where others sought quick profits, Rockefeller sought durable competitive advantage. He won, and consumers certainly won - our world still runs on refined oil to this day - but countless others who totally nailed the scale of the opportunity died in pursuit of it.
Rockefeller was exemplary of the type of entrepreneur who typically comes out of these things on top. He anticipated the glut, and positioned himself to benefit from it.
Whether he’d put it in the same terms, Henry Ford did the same thing. There were something like 2,000 automakers in the first couple decades of the automobile’s existence in America, and from 1900 to 1919, as car ownership grew from roughly 0% to one-third of American households, nearly all of the 2,000 automakers that made those cars failed.
By the early 1940s, when more than half of American households owned a car, just three companies - Ford, GM, and Chrysler – sold 90% of them.
What Ford realized was that in a glut, being the low cost provider of quality, using low prices to get to scale, and using scale to get even lower prices and more scale was how you win.
Cornelius Vanderbilt, who TJ Stiles called “The First Tycoon” and who was the biggest American winner in railroads, didn’t even really care about railroads. His goal was to get as many people and parcels from one place to another as quickly and cheaply as possible, using the best technology available for the job.
Vanderbilt started with sailboats, made a fortune in steamships, and then made a bigger one in railroads. Across the latter two, he looked for routes with advantages in speed and cost. His route to California through Nicaragua was hundreds of miles shorter than the competition’s through Panama, which gave him an advantage so strong they had to buy him out. When the railroad looked like it would be a better way to move things cheaper and faster, he donated his biggest steamship to the Union Navy, got out of shipping, and rolled his capital into rail. But not just any rail - there was plenty of that. He bought the struggling Harlem Railroad, which had valuable access into the heart of Manhattan, reduced its operating costs, then connected it to lines he bought by the Hudson River and then on to Chicago. Amtrak still mostly runs on that same route today, that’s how ideal it was.
Vanderbilt was willing to change technologies, but consistently looked for durable competitive advantages. And while scores of his competitors, who’d rightly recognized that steam and then rail were the future too, lost their shirts, Vanderbilt died with a net worth of about $100 million in 1877, which sounds like a moderately sized seed round, but for context: if he’d sold everything that day, he’d have taken one of every twenty dollars in circulation in America.
Today, Americans have access to relatively cheap flights, logistics, cars, oil, and internet, even if the people who paid in blood, sweat, and capital to make those things abundant didn’t end up surviving. That’s capitalism, baby.
As Yale economist William Nordhaus found in Schumpeterian Profits in the American Economy, “only a miniscule fraction of the social returns from technological advances over the 1948-2001 period was captured by producers, indicating that most of the benefits of technological change are passed on to consumers rather than captured by producers.” He calculated that producers captured only about 2.2% of the total social surplus from innovation, while consumers captured roughly 97–98%. That 97-98% shows up as lower prices, better products, and higher real incomes for consumers rather than as excess profits.
Hell yeah.
A 2004 NBER summary of the paper includes Nordhaus’ interpretation of the recent bust: “Part of the New Economy bubble might have arisen because investors overestimated the appropriability of innovations in that sector.” In other words, it’s not that what was built wasn’t valuable; it’s just that investors overestimated how much of the value they’d be able to capture for themselves.
Plus ça change.
Nearly every promising and ultimately transformational technology goes through this cycle. It’s practically a requirement for being a promising and transformational technology, with a few exceptions.
As I wrote in When to Dig a Moat, startups need to develop moats before they run out of uncertainty. Normally, moats take time to develop, which is why a lot of ultimately successful startups emerge from categories that were highly non-obvious. In categories like oil and AI, the enormous potential demand is obvious to everyone. If you build it, they will come. When demand is near-certain, the only categories that avoided this cycle are ones in which entry was highly regulated like pharma, airlines under federal route regulations pre-1978, Bell Telephone until its patents expired in 1894, or Alcoa with its patented aluminum process. These are Cornered Resources, and when they lapse, competition floods in and the same dynamics play out.
Other edge cases include situations where demand is not certain, like when people don’t realize how good the business is (Amazon didn’t break out AWS profits for nine years, and cloud had killed companies pre-AWS), and when Process Power built on risky bets compounds over decades, as was the case with ASML.
Opportunities where you can’t just throw money at the problem in order to compete, for one reason or another, are somewhat protected. Everything else is subject to the laws of the jungle, and on a long enough timeframe, the jungle favors consumers.
One of the things that investors love about AI is that you can just throw money at it.
It also makes a lot of other things easier to build, which should set this cycle running across industries. Anthropic itself used frontier Claude models to weaken NVIDIA’s CUDA moat so that they and others can more easily… cheat on the world’s largest GPU supplier.
The net of it all is that I think we are entering an age of consumer surplus the likes of which humanity has never seen. We have focused on AI in this piece, but there is a boom underway in sectors from robotics to power to manufacturing to biotech.
Categories that once seemed impossibly sci-fi - from flying cars to robots - are now being pursued by so many smart, well-funded players, that the question seems to no longer be if, but how soon, and who will capture the profits? As we speak, there are companies devising cures for cancer that until recently would have been miraculous, but now get outcompeted by newer technologies within months.
As a human and consumer, I am so psyched to get to live through this Age of Miracles.
As an investor… I think it is simultaneously one of the most fraught and most exciting times to get to invest. There exist companies that, in my opinion, stand to benefit if the boom continues and if there is suddenly a fire sale on miracle technologies.
I like companies that are like Vanderbilt, Rockefeller, or Ford – there will be some bumps, but if a better technology or model becomes available, they can plug it in to deliver a better product more cheaply as opposed to being threatened by it. While the internet thrived on network effects, I suspect scale economies and cost advantages will be critical as technology once again reaches out into the physical world.
Coincidentally, after writing this, I saw a tweet with The Economist’s Tycoon Power Rankings, and the leaders are exactly who this piece would predict: those Industrial Age businessmen who consolidated after the busts and created cost leaders, a Vertical Integrator in a non-obvious market (Musk), and people like Zuck, Bezos, and Sergey & Larry who took advantage of abundant internet fiber. The Wrights come in considerably lower, at 50 and 51, respectively.
And as for the Anthropic IPO, and the AI bubble more broadly? No fucking idea.
In AI itself, and in categories beyond, AI may simultaneously accelerate imitation and the accumulation of advantage. No matter how futuristic the technology in question, it is the presence or absence of durable competitive advantage that determines whether a business and its investors will profit from its proliferation. That, and the price you pay to get in.
The more miraculous your technology, the more crucial your moats.
That’s all for today. We’ll be back in your inbox Friday with a Weekly Dose.
Thanks for reading,
Packy
2026-11-06 21:53:10
Hi friends 👋,
Stocks are worth $0 now (I think, haven’t checked) and the string of perfect weather in NYC has ended, but the optimism streak continues! The world’s researchers and engineers are still cooking up the good stuff, and we’re here to serve it to you.
Let’s get to it.
Runway is building foundational AI for how the world creates. 65M+ people use Runway to generate, edit and produce video, images and audio with 80+ top media models. You can read my essay with co-founder Anastasis Germanidis to learn more on where Runway is headed beyond video.
Nvidia, Amazon, Lionsgate, Chime, and 95%+ of the Fortune 100 are part of a growing community of teams, founders, and independent creators using Runway to make national TV spots, social ads at scale, launch videos and more.
You can also use Runway in ChatGPT, Claude, Grok, Cursor, Replit and more via the Runway MCP.
FDA / INOVIO, video by Runway
Last Friday, just a few hours after I hit send on the Dose, the FDA granted accelerated approval to INOVIO’s Voceryq, the first plasmid DNA-based immunotherapy approved in the US.
Voceryq will be used to treat recurrent respiratory papillomatosis, a disease I’d never heard of but sounds awful. Caused by HPV types 6 and 11, RPP gives victims wart-like growths in the airway, usually around the vocal cords, that make their voices hoarse, make them cough, and in severe cases, make it hard to breathe.
Surgeons can remove the growths, but the virus remains in the tissue, so the throat growths keep coming back, and patients end up in the operating room again and again for years. In the trial, 26 adults had needed at least three surgeries in the year before treatment.
As shown in the video above, Voceryq delivers two plasmids (small loops of DNA originally borrowed from bacteria) into muscle cells. One carries instructions for HPV-6 and HPV-11 proteins, which teaches the immune system to go after infected cells, and the other carries IL-12, a molecule that turns the immune response up. A device fires brief electrical pulses that open temporary pores in cell membranes so the DNA can get inside without requiring a virus for delivery.
After treatment, 7 of the 26 patients in the trial didn’t need surgery for the entire following year. It’s a small open-label trial, and INOVIO still has to confirm the results in a follow-up study, but it’s on the path now, which thank god, because this was the last shot. The company was set to run out of cash in Q1 2027, but its stock tripled in after hours after posting the results.
This isn’t the first treatment for RPP - Precigen’s Papzimeos, which uses a modified virus to target the same HPV types, won full approval in August 2025.
RPP patients have gone from no treatments beside surgery to two, which is amazing, but the platform will likely prove to be even more important.
If you can get a plasmid into cells and get the immune system to act on it, you can swap in instructions for other HPV diseases, cancers, and infections, which is what INOVIO is planning to do next.
DN-AYYYYYY!
There are very few companies out there as cool as Zipline. After starting by serving hospitals and health facilities across Africa, where they were allowed to fly drones and the need was most acute, they came to America last year to do deliveries, starting in Texas.
CEO Keller Clifton said that over the past 18 months, they’ve grown flight volumes 18x and are now doing more that a million deliveries per year per metro.
Now, they’re going to be able to do a lot more. The company raised $1.5 billion at a $21.5 billion valuation in a round led by Paradigm, WCM, and Layer.
They’re using the money to bring drone deliveries nationwide. To that end, they signed “the largest autonomous vehicle contract in human history to expand automated delivery nationally with Walmart,” with whom they’ll scale to 300 supercenters throughout these United States.
This is awesome news. Zipline’s drones are quiet, safe (they’ve done over 150M commercial autonomous miles with no safety incidents), fast, affordable, and don’t require a human chaperone. As I wrote in my Deep Dive on Airbound, I’m incredibly optimistic that a good deal of movement will move to the skies, and this is an important step in that direction.
Since the Eagles stink and I can’t use this on them much, I’ll use it here:
Fly Zipline Fly.
In April, Owl Posting wrote an essay on “new knobs of control,” including DREADDS, which he explains like this:
You use a viral vector to deliver the DREADD gene to whatever cells you want to control. Once the DREADD is expressed on the cell surface, it just sits there, inert. Then you administer CNO, which binds exclusively to the DREADDs. When it binds, the receptor activates its coupled G-protein just like any normal GPCR would, doing whatever you engineered the GPCR to do. And, as far as anyone can tell, there are no off-target effects.
This is unambiguously a new knob. The receptor didn’t exist in your body before. Its binding partner (CNO) doesn’t exist endogenously. As a result, you, and you alone, get to decide when and where it is turned on.
Well, now we have them. In the first adult human study, per a Grok explainer in the replies, “doctors placed inhibitory DREADDs in the hippocampus of 3 people with medication-resistant epilepsy. Taking the drug quieted the seizure-causing cells, cutting seizures by ~93% with only mild side effects. It lets doctors precisely dial down overactive brain circuits on demand.”
That is wild. We have a knob that can turn down epilepsy.
DARPA
If we’re going to become a spacefaring civilization, we’re going to need to build bigger structures in space than can fit in a rocket. Today, there are two options for big space things: we can fold something up like origami and unfold it in orbit, which is how the James Webb Space Telescope works, or take the ISS approach, where you launch big pieces over many missions and put them together by hand.
Until now!
DARPA’s NOM4D program (Novel Orbital and Moon Manufacturing, Materials and Mass-efficient Design, great name) is working on a third option. They can send up a box of parts and assemble them in an automated way. On Thursday, DARPA announced that a Caltech payload had done just that, in what the agency calls the first-ever demonstration of automated in-space assembly. Hopefully, it’s the first of many many.
In this case, a simple gantry robot, which moves along three straight axes, joined composite truss pieces with custom connectors one section at a time while unrolling a thin reflective mesh. It is a proof of concept for future space antennas.
Program manager Andrew Detor compared the system to Tinkertoys or K’nex. It’s meant to be super simple, without fancy robotics or advanced algorithms. NOM4D is aiming at structures hundreds of meters to kilometers across, like space depots, habitats, huge solar arrays, and radio antennas large enough to listen all the way to the edges of the universe.
And speaking of making things in space, not boring capital portfolio company Varda is currently making drugs in space!
A couple of years ago, after listening to Season 1 of The Telepathy Tapes, I wrote The Return if Magic, and then I kept going down the rabbit hole, which led to pieces like Means & Meaning and Riding the Leopard.
Season 3 came out last month, and it’s on reincarnation. Reincarnation is a compelling bridge to believing that the world is more magical than we appreciate. For one thing, an awful lot of the world’s people believe in reincarnation as part of their religion, but for another, it’s a surprisingly well-researched topic. Since psychiatrist Ian Stevenson created the Division of Perceptual Studies (DOPS) at the University of Virginia in the 1960s, researchers there have documented and coded more than 2,500 cases worldwide of young children who spontaneously report detailed memories of previous lives.
I don’t want to spoil the series, but I do think you should give it a listen and consider what it might mean if it’s true.
With all the Means stories we cover here in the Dose, mix in a little Meaning.
And while we’re add it, this popped up in my feed yesterday…
2026-10-30 20:58:11
Hi friends 👋,
Happy Friday and welcome back to our 217th Weekly Dose of Optimism.
And what a week it’s been! Per New York Metro Weather, we’re in the midst of the longest streak of 8/10 or higher days since the account started in 2021, and here at the Dose, we’re in the middle of a string of 9/10 or higher weeks so long that I’ve lost count.
Have a great Halloween tomorrow. For now, all treats no tricks.
Let’s get to it.
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Another week, another extension of Anduril’s capabilities1.
Last week, we wrote about Warhound, Ben Lamm’s effort to (humanely) genetically engineer dogs to be used in police and military work. Anduril is providing “software and hardware enhancement for items such as dog protection vests and eye goggles.”
This week, Palmer Luckey announced that Anduril would be making its very own Made in America computer, BlueChip: designed here, with components from here, with metals America can mine, refine, process, and mill at scale and at reasonable price. American batteries, too.
This is cool because as Palmer says in the video, “If you don’t control your technology, you can’t control your future.” But it’s also cool because it seems as if Anduril has gotten to the point where it’s built a machine that can make machines. Assemble enough engineers, capital, manufacturing capacity, distribution, etc… and you can build almost anything. It’s very Elon Companies-esque.
And as we wrote in The Electric Slide, making products that can sell a ton of units, like laptops, can create the pull to bring more of the supply chain here, too.
Video created with Runway
The race to solve everything solvable continues apace.
This week, OpenAI announced “three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.”
In math, OpenAI’s models produced a proof of Christopher Sogge’s 1991 local smoothing conjecture in every dimension, which would also settle the Kakeya conjecture and Stein’s restriction conjecture (of course). For context, Hong Wang and Joshua Zahl proved Kakeya in three dimensions just last year, and it was one of the biggest math stories of 2025.
In computer science, a model refuted the Exponential Time Hypothesis (ETH), the widely held belief that problems like 3-SAT can’t be solved meaningfully faster than brute force. If that makes no sense to you, check out the explainer video I made with Runway, which breaks it down in claymation well enough that I could actually understand it.
In physics, the models produced a rigorous proof that interacting quantum field theories, including QCD and pure Yang–Mills, exist in four dimensions. That’s half of a Millennium Prize problem.
Whereas before OpenAI has just dropped a bunch of solutions on the internet’s head and walked away, leading to quasi-embarrassing situations like when they said GPT-5 had solved ten open Erdős problems but it turned out the model had mostly found solutions that were already sitting in the literature, this time, OpenAI is sharing the outputs as research data with experts they call “shepherds” in each field, who will choose what to do with the data and where/when to publish.
Shepherds don’t have to sign NDAs, don’t get paid, and do get the credit for explaining the work. Russell Impagliazzo and Ramamohan Paturi, who proposed ETH, are leading the review of its disproof with MIT’s Ryan Williams. Nobel laureate David Gross is leading the physics group, which chose to withhold the AI material and write it up themselves. The math proof and the ETH disproof are on GitHub with Lean formalizations. Lean, a checking software, guarantees the proof proves the formal statement, but human shepherds still have to confirm that the formal statement is the right one.
Big week for Lean, by the way. On Tuesday, Axiom announced a complete Lean auto-formalization of the Classification of Finite Simple Groups, the “periodic table of symmetry.” The proof runs about 15,000 pages and was written by more than 100 mathematicians over several decades. It’s so big that no single person has ever checked all of it. For a sense of scale, it took Georges Gonthier’s team six years to formalize just one piece of the classification, the Feit–Thompson theorem, back in 2012. But Axiom’s machines can do a lot of human-years quickly, and they’re going to need to to keep up with all of the math and physics coming out of the labs.
Because not only did OpenAI announce those results this week, a physics neolab, Physical Superintelligence (which we covered in Dose #209) released results on five problems from IQOQI Vienna’s list of open quantum problems, each with a paper, a Lean formalization, and open source verification code on GitHub.
For those of you who were planning to spend the weekend cracking OQP 12: Can vacuum correlations violate the CHSH Bell inequality at arbitrarily large separations? or OQP 12: Can vacuum correlations violate the CHSH Bell inequality at arbitrarily large separations?, I’m sorry. Machines did it. You’re going to have to find a new hobby.
Also, if you were trying to use the entanglement between two empty regions of space no matter how far apart they are to violate the Bell Theorem, I’m sorry for you too. The machine found that you can’t do it past some finite distance.
Anyway, it seems like this is just how we do math and physics now.
Apparently, it’s how we do drug development now too, if Anthropic succeeds.
AI for drug discovery is hot, but most of the focus is on discovering new molecules, which creates bottlenecks further downstream in actually developing these drugs and getting them to market. To understand and fix them, Anthropic believes, it needs to get inside the programs themselves, so for the past several months, its life sciences team has partnered with six organizations to start running programs of its own.
At the discovery end, Divergence Labs announced positive results in the fight against food allergies. Allergic reactions are triggered by IgE, which sits on your immune cells as well as floating in your blood. Standard drugs mostly neutralize the floating IgE, so Divergence’s approach aims to pry it off the cells instead. Claude turned that goal into a design plan and, largely on its own, generated about 35,000 antibody designs in a month. Three of the early hits dislodged IgE better than the standard medication, and people seem psyched that this represents meaningful progress against food allergies.
Another biotech, Generate Biomedicines, used Claude Code to redesign part of CAR-T, the therapy that uses a body’s own immune cells to hunt and kill cancer, so that it could eventually work on solid tumors. One of Claude’s designs was about 70% shorter than the standard version and got cells to make roughly 10 times as many cancer-spotting receptors.
At BigHat Biosciences, Claude proposed improvements to two antibody fragments, and BigHat’s automated lab made and tested about 1,010 variants in 10 weeks. The best ones bound more than 10x more tightly. Along the way, Claude and an Anthropic researcher figured out that the standard stability metrics were misleading and proposed better ones.
But the point is that these improvements are still hard to get to patients, so Anthropic turned to paperwork with Enveda, whose manufacturing scientists spend up to 75% of their time on it. Claude reviewed 1,061 pages of manufacturing records, caught all of the issues human reviewers had found (plus seven they’d missed), and cut review time by 89%. It also drafted a regulatory document in four hours that takes about three months by hand.
At the trial stage, Bristol Myers Squibb is using Claude to assess plans for an early-stage drug and estimate its odds of success. The Column Group, a biotech investor, is launching Pleiades, a company built to run many more drug programs with Claude than a small team normally could. The theory is that if discovery gets cheaper and faster, investors can back riskier ideas.
Anthropic is also running programs itself. In July, it acquired Mithril Medicine, a one-person company whose founder, Cody Tranbarger (aka Professor Oak), used Claude to run experiments across 11 outside labs. In under 18 months, he took a treatment for a rare muscular dystrophy from idea to promising results in mice. In one cohort of 42 kids with the severe form of the disease, only one ever walked on their own. If the treatment holds up, human trials could start as soon as late 2028.
Anthropic is upfront that nearly all of this is preclinical, so most of these drugs haven’t been tested in people yet, but they showed speedups in every part of the process. The tricky thing about complex processes like drug development is that you have to improve the whole thing at once, lest any one thing that didn’t improve slows the whole thing down.
We might finally have a machine capable of doing just that. Bad time to be a disease. Good time to be a human.
I just saw this one this morning, so didn’t have time to write it up, but that’s ok, just read the headline and the tweet thread and realize that AI AGENTS PERFORMED BRAIN SURGERY ON A MOUSE.
I’m an investor in Readwise, friends with its founders Dan Doyon and Tristan Homsi, and I love reading, so I’ve been lucky enough to get to be a part of the beta for what they were calling Bookwise and are now calling Readwise Books.
I love this app. It’s my second-most-used app on my phone, after X (need to break that habit again), and that’s not counting the time that I spend reading through Readwise Books on my Daylight Computer. The description people give most often is Kindle x Goodreads, which is part of it, but there’s also a little Strava mixed in there. There’s a leaderboard, which has definitely pushed me to read more, like the week I ended up #1 on the Readerboard in July.
The reading experience itself is familiar to those used to reading on the Kindle App, but it’s just a little bit better in places. There’s AI natively built in, which I don’t use normally, but was a lifesaver when I was reading Jean Beaudrillard’s Simulacra and Simulation, for example. Highlights go right into Readwise. And its text-to-speech is really good for listening when I get off the subway and start walking.
I am a firm believer that people should Read More Books, and although I’m biased, I’ve also voted with a ton of my time that this is the best product out there for doing that.
So go Read More Books. There’s a waitlist, but if you’re reading this, you can skip it here.
Lilly’s Retatrutide is perhaps the closest thing we have to a miracle drug.
We first covered Reta in Dose #175, as a gray-market “holy grail” in Vectorculture’s piece on Chinese peptides. By May, it was the lead item in Dose #194, after Lilly’s TRIUMPH-1 trial showed people on the top dose losing 28.3% of their body weight over 80 weeks.
Since then, the triple-hormone shot, which mimics GLP-1, GIP, and glucagon, has kept piling up wins. Now, it seems to work on sleep apnea.
Tens of millions of Americans have obstructive sleep apnea (mostly undiagnosed). The standard treatment is strapping a CPAP mask to your face every night. In two 80-week Phase 3 trials, TRIUMPH-1-OSA and TRIUMPH-2-OSA, 691 adults with moderate-to-severe sleep apnea started out stopping or nearly stopping breathing about 57 times an hour. In the trial without diabetes, the 9 mg dose cut that by 34 events an hour, versus 10 on placebo. About half of patients on the higher doses ended up with apnea that was resolved or mild without daytime sleepiness, compared with roughly one in ten on placebo. Along the way, they lost about a fifth of their body weight.
Some people have pointed out that of course, losing weight should help with sleep apnea, but OK sure whatever the mechanism, Reta just keeps fixing stuff:
Diabetes: In TRANSCEND-T2D-1, presented at ADA in June, 75–83% of patients got their A1C to 6.5% or lower in 40 weeks.
Heart risk: In TRIUMPH-3 in July, people with severe obesity and existing heart disease lost 22.6% of their body weight, with inflammation (hsCRP) down 51% and systolic blood pressure down 9.3 points.
Knees: In TRIUMPH-1, knee osteoarthritis pain fell by about three-quarters.
Liver: In a Phase 2 substudy, 93% of patients on the top dose had normal liver fat after 48 weeks.
The drug hasn’t even hit the market yet (if you don’t count the grey market). There’s still an open question as to whether these improvements also prevent heart attack and stroke, which Lilly’s dedicated cardiorenal trial will attempt to show, and which would be another massive win, but for now, we’ll just have to be happy with a drug that cuts weight, lowers heart disease risk, fights diabetes, cuts liver fat, makes our knees feel better, and replaces a CPAP.
Lilly, which crushed earnings this week and raised its full year revenue outlook by $5 billion, deserves every dollar of its $1.3T market cap.
From miracle drugs to downright miracles. Given the quantity and consistency of mind-blowing breakthroughs we cover here in the Dose, it’s easy to forget how miraculous some of them are, and how miraculous they must feel to those who benefit from them.
This week’s news out of Neuralink is one of those.
ALS, or Lou Gehrig’s Disease, is a fatal, progressive neurodegenerative disorder that destroys the motor neurons in the brain and spinal cord that control voluntary muscle movement. It takes away its victims’ ability to speak even though it leaves their mind intact, meaning they can think clearly but can’t verbalize those thoughts.
Neuralink has been working to fix that with VOICE. This week, it released early numbers from one participant who hit 75 words per minute with a 5.8% error rate across a 125,000 word vocabulary in their first week with the implant. Before VOICE, the best published result on the same vocabulary size, Stanford’s 2023 system, managed 62 words per minute with nearly a quarter of words wrong.
Natural conversation runs around 160 words per minute, so we’re about halfway there. Importantly, the system can also speak in the participant’s pre-ALS voice.
VOICE launched at UT Southwestern last October with FDA Breakthrough Device designation and plans to enroll up to six people with ALS, spinal cord injury, or stroke. That’s not a big group, and the one person in this study is just one-sixth of that. But every person who’s able to get their voice back through a BCI is a win, and the technology will only get better, faster, more accurate, and less invasive from here.
In related news, Elon Musk announced yesterday that its second product, Blindsight, which will restore sight to the blind, is ready engineering-wise.
Age of Miracles, pending regulatory approval.
2026-10-23 20:34:23
Hi friends 👋,
Happy Friday and welcome to the 216th Weekly Dose of Optimism!
It looks like the hacks this week broke a lot of the internet, but over here in our little corner of it, things are looking good. We have everything from cancer to plant communications to genetically engineered war dogs to basically alchemy. Once again, the week was so jam packed that we have more in the Extra Dose (7 Extras!) than we do up top.
Let’s get to it.
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Its new frontier parsing model, r-1, combines layout detection, reading order, tables, formatting, grounding, and granular citations in a single request. It reduces parsing errors by up to 20%, improves latency at high volumes, and costs 1¢ per page all-in.
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U.S. Food and Drug Administration, Jeff Tang, Coherence Neuro; video created with Runway
We hate cancer here at the Dose, and we’re not afraid to say it. It’s probably why not a single cancer reads the Dose. We’re willing to sacrifice our numbers for our belief.
Consequently, we love when humans come up with new ways to kill cancer, and they’ve done it once again (and again, and again) this week.
In June 2022, in a small trial at Memorial Sloan Kettering, twelve patients with locally advanced rectal cancer took six months of an immunotherapy called dostarlimab. In all twelve, the cancer disappeared without chemo, radiation, or surgery.
On Monday, the FDA granted dostarlimab (GSK's Jemperli) accelerated approval as a standalone first treatment for stage II and III rectal cancers that are mismatch repair deficient (dMMR). These tumors basically have a broken DNA spell-checker, so mutations pile up until the cancer looks foreign to the immune system. Dostarlimab blocks PD-1, the off switch tumors use to keep T cells at bay, which lets the immune system clear the cancer (explained in the video above).
In GSK’s 154-patient trial, after a median of 26 months, cancer hadn’t progressed in 71% of patients treated with dostarlimab alone. In MSK’s expanded 50-patient study, every patient responded, and 93% stayed cancer-free for at least a year.
Only about 3% of locally advanced rectal cancers are dMMR, per the FDA, so this isn’t a cure for all rectal cancers, let alone all cancer, but we’re getting into a pretty consistent, almost weekly, pattern of new therapies working against specific cancers, which seems like the way we’ll beat cancer overall.
In some cases, individuals are even figuring out how to cure their own, n-of-1 specific cancer.
In #177 we led with Elliot’s story on one of those people, GitLab founder Sid Sijbrandij, going founder mode on his osteosarcoma after running out of standard options. He’s one of six people Jeff Tang included in a list of those who have cured or are curing their own cancer, or a loved one’s.
But wait, there’s more!
On Wednesday, Coherence Neuro announced that it had raised a $45M Series A from Founders Fund and OpenAI “to build cancer therapy that learns.”
The idea is to turn neurotech and BCIs at cancer to read the electrical signals surrounding it, understand how it grows over time, and influence the electrical signals to modulate the disease. Human trials are underway in glioblastoma patients, and they’re gearing up for pancreatic cancer and more.
There’s a long way to go on these and so many of the cancer treatments we discuss in the Dose, but the promising results have become a drumbeat, and it really does seem like we’re on the path to knocking out cancer once and for all.
So say it with me, as heartily as you can: get fucked, cancer.
Alexander C. Kaufman for Heatmap
As much as we hate cancer, that’s how much we at the Dose love nuclear power.
We love small nuclear reactors, and we love big nuclear reactors, and we especially love a lot of either. Big week for us, then.
It hasn’t always been so good for nuclear here in the US. In the roughly 15 years it took the US to finish its first two Westinghouse AP1000 reactors at Vogtle, China built four of them, then they reverse-engineered the design into its own Hualong One, and now they have 10 Hualongs running with at least 37 more on the way. China knows that the way to make nuclear cheap is to make a lot of reactors over and over.
The Nuclear Company, which employs a bunch of people who worked and learned on Vogtle, launched in 2024 to do just that.
On Wednesday, it announced Prosperity One, a proposed campus for up to six AP1000s, about 6.6 GW, on roughly 4,000 acres along the Ohio River in Lewis County, Kentucky.
6.6 GW is an enormous amount of power. Fully built, Prosperity One would be the largest nuclear site in the US and account for over 5% of the country’s total nuclear capacity (we’re at 97GW right now).
TNC’s plan is to build two units at a time, keeping the design consistent on each, so that it can bring down costs on each successive one, a lesson learned the hard way at Vogtle. After a slow, expensive start with Unit 3, Vogtle Unit 4 came in roughly 30% cheaper and faster. TNC Chief Nuclear Officer Joe Klecha said that 30% is the floor expected savings. The company is targeting 2028 NRC approval.
At the other end of the size spectrum, the Navy picked Radiant to install its Kaleidos microreactor at Naval Weapons Station Crane in Indiana. Kaleidos is a factory-built, 1 MW reactor (6,600 times smaller than Prosperity One), that is built centrally and shipped to sites by truck, ship, or plane. When completed (the Navy wants 2028), it will be the Navy’s first shore-based advanced reactor and the first power-producing reactor in Indiana. Radiant, which we wrote a Deep Dive on in 2024, now has partnerships with the Army, Navy, and Air Force.
ELECTRONAISSANCE.
I already had this whole thing written, with a different story in this slot, when the news dropped that Colossal Biosciences’ Ben Lamm and Andruil’s Palmer Luckey were teaming up to launch Warhound with a fresh $200M from investors including In-Q-Tel, Sequoia, and 8VC. The new company is worth $1.2 billion.
This is the advanced technology version of a superhero movie, literally in that we’re talking about genetically engineered war dogs and figuratively in that we’re seeing two characters combining forces within a wider cinematic universe. Per the website, Warhound is “combining licensed technology from Colossal Biosciences and Anduril,” which is just a really interesting way to start a new business.
“Animal welfare is central to that mission,” the company says on its website, “with American Humane certification supporting our commitment to the care and well-being of every dog.”
I don’t think I’m going to be able to say anything Lamm didn’t, so Per TechCrunch:
Lamm believed dogs could be enhanced for health, longevity, and “for things like speed and agility or enhance olfactory sensors for bomb sniffing and whatnot,” he said. His team looked at the top-performing working dog breeds — the Belgian Malinois and German Shepherd — taking the cell lines from them and putting them in a biobank. From there, they will edit the genes, adding the ones that will make the dogs perform better while taking out the ones that won’t, to create “a new type of dog.”
Andruil, meanwhile, will provide software and hardware enhancement for items such as dog protection vests and eye goggles to assist the dogs and their handlers with matters like situational awareness and coordination.
“The idea is, if we make the dog safer, make it healthier, make it perform better, it’s going to perform better in the line of duty,” Lamm said. He added that Anduril’s additions aim to make the dogs safer and healthier outside of their genetic components, “on top of the dog, not in the dog.” He believes such enhancements will also protect the dogs’ human handlers better.
This is not a new structure for Lamm. In late 2022, Lamm co-founded Form Bio, a software company spun out of Colossal, and he later launched Breaking as another spinout. As with Warhound, Colossal will own equity (Luckey won’t), but this time, it’s combining technological forces with another company, Anduril.
They’re good dogs, Brent. 16/10 please thank them for their service.
Janis Hesse, Doris Tsao, et al. on bioRxiv, video created with Runway
Late last year, in Dose #173, we covered Dr. Doris Tsao’s joining our friends at Astera to lead a new team, Astera Neuro. “The new team’s goal is to solve one of the longest-standing, most fundamental questions: how the brain produces conscious experience, cognition, and intelligent behavior.”
Just ten months later, Tsao and team are out with their first big paper, and it’s a doozy. The video above is one attempt to explain it, and here’s the word version.
If you show your left eye one face and your right eye another, you see just one face. Instead of blending them, one just vanishes from awareness, even though it’s hitting your retina. Neuroscientists use this trick, called binocular flash suppression, to separate what the brain receives from what it’s aware of.
The textbook belief has been that this happens because the suppressed image fades as signals move forward through the brain and never “ignites” into awareness. Last week, a team led by Janis Hesse in Tsao’s lab found a different explanation.
Recording thousands of neurons in the face-processing regions of two macaques’ inferotemporal and prefrontal cortex, they found that the same neurons alternate roughly every 60 milliseconds between representing the face the monkey is aware of and the one it isn’t. When they reconstruct an image from one set of moments, they get the conscious face. But when they reconstructed it from the others, they got the suppressed face in full detail.
So why are we only aware of one? Still need to figure that out.
But Tsao called it the most important discovery of her career, because it means that a decodable signal deep in the brain exists even for things you’re not conscious of, so scientists can now follow both faces and figure out exactly why we become aware of one and not the other, which should give deeper clues to how consciousness works.
Human consciousness research is one of the areas I’m most fascinated by, and I hope that this is the first of many breakthroughs from Astera Neuro and others.
As we better understand our own human brains, humans are also making progress in understanding the language of… plants.
Electric Plant came out of stealth this week with $11.2 million in funding and a foundation model, Bombadil, that reads electrical signals from inside living plants and translates them to estimate seven kinds of stress, like thirst and nutrient needs. Bombadil was pretrained on more than 4 million hours of recordings from 100 species, from redwoods to tomatoes, captured with sensors that cost tens of dollars. Co-founder Justin Mast, who founded Bloomscape, compares it to one model that reads the brainwaves of blue whales, horseshoe crabs, and fruit flies, given how diverse plant species are.
Practically, in the near-term, the company thinks it can cut farm water use by 20% or more with its models and sensors, which is important and economical, but more wildly…we are learning the language of plants? And it’s bioelectric?
I got to see a preview of this at Onto Ventures’ summit in California a couple weeks ago and it was one of the most mindblowing demoes I’ve seen. Justin is kind of like the Lorax, but he speaks to the trees.
We cover a lot of impressive accomplishments in the Dose, like talking to plants and curing cancer, but it’s not every day that we cover a company that’s succeeded where the inimitable Sir Isaac Newton failed.
That’s what Marathon Fusion has done. Newton, under the pseudonym Jeova Sanctus Unus, spent three decades trying to crack alchemy, as I wrote about in Take Weird Ideas Seriously. I think it’s great that he tried. He just didn’t have the technology.
Marathon Fusion seems to. Co-founded in 2023 by CEO Kyle Schiller and CTO Adam Rutkowski, a former SpaceX propulsion engineer, the company posted that last month it turned mercury into “real, stable gold” at ten times the rate of CERN’s 2025 record. CERN's version produced about 29 picograms across four years of collider runs, so we’re not talking Fort Knox, but the physics is real. Fast neutrons from a deuterium-tritium fusion reaction knock a neutron out of mercury-198, which becomes mercury-197 and decays into gold over a few days.
Gold dropped pretty hard yesterday (unsure if related), but for now, it’s just a demonstration.
In the nearer-term, Marathon will use the technology to make medical isotopes. Neutrons that can turn mercury into gold can also turn zinc into copper-64 and copper-67, used in cancer imaging and targeted radiotherapy. Marathon says it has started making both.
The neutrons can make a lot of things. In August, outside testing lab Covalent certified that Marathon’s plasma centrifuge had enriched lithium-6, which fusion plants need to breed tritium fuel, and this week the company said a new run beat a Soviet record that had stood for 40 years. Next is a fusion reactor in San Francisco designed to fire a trillion neutrons a second into a one-centimeter target, continuously, for years.
It’s a very Fuse-like bet that the neutrons produced in fusion reactions can be very valuable long before Q>whatever, and a Small Steps approach to fusion.
Or as Mike Meyers would say, “I LOVE GOOOOOOOOLD.”
2026-10-16 20:47:12
Hi friends 👋,
Happy Friday and welcome back to our 215th Weekly Dose of Optimism!
Welcome to the flying flipping car era. As bad as things have been for my Philadelphia Eagles, that’s how good they’ve been for humanity.
Let’s get to it.
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Its new frontier parsing model, r-1, combines layout detection, reading order, tables, formatting, grounding, and granular citations in a single request. It reduces parsing errors by up to 20%, improves latency at high volumes, and costs 1¢ per page all-in.
Already using another parser? Apply for up to $5,000 in migration credits and hands-on support to benchmark r-1 against your current stack on the documents that matter most.
Nick Cammarata · Goodfire
Good news - AI is probably way less likely to kill us all now because humans rock and one of us just kind of figured out how to learn to read their minds.
It is beautiful that a person I became aware of and started following because of his understanding of his own mind—I discovered Nick Cammarata through his tweets on meditation and the jhanas—seems to be the person who’s figured out how LLMs’ brains work.
In the “The Short Version” section, he writes:
I think I have good news about the nature of neural networks.
After many months studying neurons and circuits in image and language models, I’ve come to believe a network’s own mechanisms are its legible building blocks, and that the outlook for mechanistic interpretability may be far better than I’d feared. The canonical evidence of polysemanticity and superposition that convinced much of the field, including me, that networks pack concepts arbitrarily was misread. What looked like arbitrary packing was mostly a motif I’ll call recursive generalization, which is much less concerning. This means we can study networks directly, instead of first untangling them. Neural networks are already clean.
Basically, he thinks that we can understand what’s going on inside of models’ brains - “interpretability” - which means that alignment is probably solveble.
As he explains, “interpretability might be the most important scientific problem in the world right now, as I don’t think we can solve alignment if we don’t know what we’re building.”
Cammarata used to work on interpretability at OpenAI, and he himself “came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots.” Now, he tweeted:
the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works.
If he’s right, not only did the p(doom) from runaway AI just tank, but it’s also another win for using technology to solve problems that previously seemed unsolvable. Chalk one up for the optimists.
Thank god we’re going to live, because it seems like the future is going to be as cool as advertised.
Last night, nine years and a number of delays after Tesla first teased the second-gen Roadster, Elon took the wraps off at a SpaceX test site near Waco. It was worth the wait.
The car went 0–60 in about a second, which would have been the coolest thing at any car reveal in history, except this one. Next, using 10 nozzles fed by a 600-liter compressed-air tank, plus some very large fans, it drove up an upside-down ramp, spun around, and set itself down on the pavement. Then it hopped over a Cybercab, Cybertruck, Semi, Model Y, and Model 3 while moving. Tesla said the car would go half the speed of sound (?!?!) at 380 MPH?
Musk said production starts in 2028 (for real) and joked, I think, that they’d try to jump the Snake River Canyon, the ramp where daredevil Evel Knievel attempted to cross the canyon on a steam-powered rocket.
Just… watch the video if you haven’t.
Arrowhead Pharma, video created with Runway
Speaking of brains…
As I’ve written before, my grandmother had Alzheimer’s, and me or someone I love getting Alzheimer’s is one of my biggest fears in life. So whenever there’s a potential development on the Alzheimer’s front, we cover it, even if they’ve historically ended in disappointment.
The hard part of treating brain disease like Alzheimer’s is getting drugs to the brain. The blood-brain barrier is very good at its job, which is why RNAi drugs, which silence a gene before it can make a protein, have been ineffective against brain disease. They’ve mostly been effective in targeting the liver (which filters everything).
On Wednesday, Arrowhead Pharmaceuticals reported interim Phase 1/2a data on ARO-MAPT, which silences the gene that produces tau, the protein that tangles up in Alzheimer’s, frontotemporal dementia, and progressive supranuclear palsy. Healthy volunteers who got two subcutaneous doses at 12.8 mg/kg saw mean total tau in their spinal fluid fall 73% by Day 105, with one participant down 85%. Day 105 was 90 days after the last shot, which means the effect held for three months and the eventual dosing schedule could end up be quarterly or longer. Check the video to see how it works.
All from a shot in the arm!
It’s early, but early Alzheimer’s cohorts are enrolling now and Arrowhead expects to return data in 2027. I hope it works, and that we can all forget about Alzheimer’s once and for all.
Nature Will, Pi, DeepMind, Flexport
On Tuesday, Runway co-CEO Anastasis Germanidis and I published Runway: Learning From Watching the World, in which we lay out the company’s bet that robots can learn most of what they need from third-person video. It’s been a big week for robots overall.
On Monday, Physical Intelligence opened its Pi API beta. With it, companies can upload their robot data, fine-tune a π model on π’s compute, and run inference right on their own robots. Early partner companies are using it on everything from picking strawberries to assembling servers, from driving autonomous wheel loaders in Utah to controlling a dual-arm robot that deburrs the spacers used in its own assembly, basically with an API call.
On Tuesday, the Beijing lab Nature Will run by Tsinghua professor Ning Ding released Natura-1, a 50 billion parameter model pretrained on one million hours of egocentric human video. Egocentric means its video from people’s perspective, not robots’, which is important, because robot data is harder, slower, and more expensive to collect. The model watched egocentric video of people using their hands from their own perspective. Only afterward did they post-train on a small amount of robot data, a la Runway.
Per Rohan Paul, what’s interesting about this is: “One model handles 3 things robots usually fail at together: fine 2-handed control after the grasp, new tasks without extra training, and long sequences where it has to remember earlier steps and recover from mistakes. Many robot systems show 1 of these in a narrow setting.”
That same day, GDM entered the fray. Personally, as a long time GDM fan and an unbiased observer, I think Google DeepMind’s demo was the week’s coolest. Norman Di Palo posted a video of Gemini Argon teaching an Apollo humanoid to play cornhole (above). They gave the model a video of a person throwing a beanbag, and Argon took control of the robot, generated its own simulation environment, ran system identification so the sim matched the real motors, trained the throw, and started sinking them. The team did not have to write any specific cornhole controller or anything. It missed a few first — who amongst us hasn’t — but then it gets the hand of it.
The dream: robots enjoying themselves playing cornhole while we humans work!
Unless… Warehouse robots are here! on Thursday, Ryan Petersen posted a video of Flexport’s newest automated fulfillment center, just outside Chicago, where robots move real freight for real customers.
Robot demos are notoriously bad at turning into actual, valuable robots, and Flexport’s certainly isn’t the first automated warehouse. I’m not smart enough to judge any of these approaches against each other, or to say whether any of them will be the one, but there certainly seems to be a lot of smoke in robotics recently. If there’s fire (metaphorically, of course), the near future could get real abundant real fast.
Applied Materials makes the machines that chipmakers like TSMC and Intel use to build chips: the deposition, etch, and inspection tools that lay down and carve the layers on a silicon wafer.
On Monday, it opened the EPIC (Equipment and Process Innovation and Commercialization) Center in Sunnyvale, California. It’s a big investment and a big building. It will cost five billion dollars, cover 600,000 square feet, include nearly four acres of cleanroom, use enough concrete to fill 34 Olympic pools and enough steel for two Eiffel Towers, and take six million construction hours to complete.
As impressive as the space is, this made the Dose because of the partners it’s bringing under one roof in the US of A: TSMC, Intel, Samsung, SK hynix, and Micron are all founding partners, alongside Broadcom, Advantest, KIOXIA, SCREEN, and Besi on the equipment side and Stanford, Berkeley, UCLA, Arizona State, and RPI on the research side. These are competitors, who occasionally sue each other, but have now agreed to put their engineers in the same cleanroom to work on process technology three to four generations ahead of anything in production today.
The point of EPIC is to speed up chip development, which normally runs in series, where materials research hands off to equipment development which hands off to pilot production, with many steps taking place in far flung locales around the globe. The whole process, as it stands, takes the better part of a decade. EPIC is built to run those stages in parallel under one roof, in order to compress the learning cycle on the slowest (and least glamorous) layer of the entire AI stack.
A few years back, I wrote a piece called I, Exponential praising the beauty of exponential curves in technology, the most famous of which is Moore’s Law. As Byrne and Tobias wrote in Boom, Moore’s Law happens because people believe it will happen and then invest ahead of future bottlenecks against that belief, which becomes a self-fulfilling prophecy. This is that in action.
Putting this one above the paywall because Ben is awesome, David is awesome for doing this, and if anyone the not boring community can help in any way, it would make me very happy. I’ll just leave you with David’s intro:
Ben Wilson has stage four cancer and four small kids. I wanted to have him on the podcast to talk about his work on the How to Take Over the World podcast, which is, in my opinion, one of the best podcasts ever created, and to talk about what it’s like dealing with terminal cancer at such a young age. I also want to ask you for help in any way that you can. Ben is a good man and he’s been dealt a bad hand. Please watch this podcast and see if you can help with medicine, introductions, or donations. It doesn’t matter if you have $5 to spare or 5,000. any little bit can help Ben’s family through this devastating time. He’s a good guy and a close friend. Please help in any way that you can.
Discussing his prognosis, Ben says, “the amount of new treatments that are coming out for all sorts of different types of cancers is remarkable. We live in a really remarkable age.” He’s right. We cover it every week.
Just this week, for example, Bristol Myers Squibb and RayzeBio's RYZ101 significantly delayed progression of neuroendocrine tumors in a 343-patient trial. Neuroendicrine carcinoma is exactly the kind of cancer Ben has. Get fucked, cancer.