When AI went from being basically unusable to doing some things well, people underreacted.
When it went from doing some things well to doing lots of things quite well, people still underreacted.
When AI went from doing lots of things quite well, to sometimes doing things better than humans, people reacted big time (April).
When AI goes from doing sometimes better than humans to doing lots of things better, people still like the story.
But what happens when AI starts doing better than humans, and at some tiny fraction of the cost? This is my base case in a couple of years. And my base case is that we don't actually need ever more units of intellgence *compared to* things in the physical world to actually be able to use that intelligence.
Intelligence is very solidly looking like it will be a commodity. A bunch of competing frontier models are all very strong. All that seems to matter is that compute is getting stronger and we're getting way more of it.
We're buying more compute/intelligence a couple of years into the future. As soon as there's a solid hint that we've built too much, we'll thus have *WAY* too much 2 years from there. And that's when the AI compute bubble will pop. It seems like ... overwhelmingly likely that this is the end.
And to be clear, this thesis has nothing to say about 10, 20 years into the future. Because sure, it's hard to see us not wanting more intelligence long term. But this post is about short term bottlenecks, and the physical world will be for years.
crowdturtle 🐢 retweeted
One year later, and it’s become apparent that the whole fucking market is basically a binary bet on AI capex.
$GOOG capex is now at ~40% of sales in Q3, $MSFT at over 42%, and $META at over 52%😵💫
And we’re talking about quarterly capex bills in the $30–50B range for each co.
My only takeaway from the large-cap tech earnings AH is that these are no longer the asset-light darlings where you can assume all growth is good growth.
$GOOG spent 23% of sales on capex this quarter, and $MSFT came in at 25%. You really have to trust that management knows what they’re doing when deploying $70–100B of capital annually. Or whatever number.
It’s never been done at this scale before. And we all know how the story usually goes for companies that go through a period of massive asset growth without the ROIC to back it up.
crowdturtle 🐢 retweeted
14/16 anthropic's new s-1 shows the scale: $265.6b in future fixed/minimum purchases and $321.4b of leases not yet commenced as of june 2026. separate long-dated buckets, some conditional—not cash due today.
crowdturtle 🐢 retweeted
15/16 also, don't confuse exposure with failure. anthropic had $16.3b of first-half revenue; compute expense was 55% of sales versus 201% a year earlier. the core question is whether demand keeps outgrowing committed cost.
another terrible example of how clueless most people are about exponential progress
2 years ago, people in the math subreddit were questioning whether AI can solve unsolved math problems in a few decades. now AI has proposed a solution to one Millennium Prize Problem with more within reach
crowdturtle 🐢 retweeted
i've been hammering this since 2015 (there are substacks, somewhere)- but the first "primitive" we will need to lose to advance biology is the idea of a pharma company as such. drug programs can be cleanly seen as baskets of risk and the platonic ideal is vanishingly asset light
Claude Science looks to me like a bid to own the economic version control system for life sciences. The logical endgame is Anthropic financing hundreds of drug programs in parallel for the biopharmas that leverage its harness. In return, Anthropic will ask for clinical data rights and use the data generated to 1) improve their ability to finance the next program and 2) eventually feed a God Model that allows them to create the holy grail: an “AI drug factory”.
You can think of a drug program as an exercise in making a sequence of complex decisions under uncertainty: specifically, decisions around how to allocate incremental experimental capital to maximize expected value across safety, efficacy, commercialization, etc.
A growing share of that experimental capital will be compute-mediated over time (e.g, Claude generated roughly 35,000 antibody designs in a month for Divergence Labs). But the most consequential increments will still go toward compiling therapeutic candidates in (very scarce and slow) human biological computers, which is where both the cost per bit and the consequentiality per bit of information are highest.
Whoever owns the system that originates and integrates the most information upstream and downstream of those allocation decisions (in this case Anthropic) becomes the best suited to start making them (or at least gains a differentiated understanding of the relevant signals necessary to make those decisions well).
Some examples of the experimental capital allocation decisions they are starting to help generate / facilitate and version:
1) Most importantly: Acquiring Mithril, where Cody Tranbarger (@prof_oak_) is massively levered by a swarm of agents that help him identify, evaluate, and ultimately select a much larger # of drug programs to run in parallel than is typically possible for a single drug hunter. He calls this a “Pipeline-in-a-Person”
2) Bristol Myers using Claude Science to analyze their proprietary data (guessing preclinical and clinical) to create optimal clinical study plans and weigh the study’s chance of success. If that program is actually executed, they should be able to calibrate how well those predictions turned out and in what sense they were right or wrong.
3) The Column Group spinning up Pleiades: a biopharma company with a mandate to “run many more—and more complex—drug programs than is usual for a small team.” Similar to the Formation Bio’s of the world. They are using Claude to evaluate target-disease pairs and find non-obvious, potentially valuable combinations.
I believe Anthropic is running their own drug programs not to compete with pharma directly (yet), but instead to generate the learning signals that would allow them to become the single largest (and potentially best calibrated) underwriter / financier of drug programs. These financed programs would eventually create the data distributions necessary for them to create an AI drug factory (and start competing with pharma).
Specifically, I could see Anthropic creating the first real instantiation of @AndrewWLo’s elegant Megafund model. TLDR on Megafund (proposed in Nature Biotechnology 14 years ago): any single drug program is a terrible bet on its own, but if one entity funds a large enough portfolio of them, the odds that a few hit become predictable enough to finance the whole thing with debt. Lo et al. proposed doing this by issuing “research-backed obligations” (bonds collateralized by the portfolio and its IP) alongside an equity tranche. Ofc, the pool of available capital that buys debt is far larger than the pool that buys biotech equity, therefore this would unlock capital from pensions, insurers, endowments, etc. Their simulations of $5–15 billion oncology portfolios estimated average annual returns of 8.9–11.4% for the equity and IMPORTANTLY 5–8% for the RBOs. This would be a fixed-income-like return that many new sources of capital would get excited about.
For some reason this strategy never took off, and part of the reason is that it’s pretty hard to find a large basket of drug programs that’s sufficiently uncorrelated. But I also think it’s because there wasn’t really an incentive for any well capitalized player to push through all the schleps and actually unlock the capital sources to this end. I think Anthropic has that incentive.
They have a structurally higher willingness to finance a drug program / make this structure real, because, by doing so, they would capture two return streams: 1) the potential economics of a blockbuster drug and 2) the widely amortizable value of what the associated series of experimental capital allocation decisions teach its models!
In return for providing biopharmas cheap financing, Anthropic will ask for clinical data rights (including rights to learn from decision histories, negative results, protocols, etc.). Just selling tokens / the harness doesn’t give them the right to learn all of these things, but providing financing likely would.
This would allow them to make more well-calibrated experimental capital allocation decisions over time and eventually feed the God Model that “cures all disease.”
This would all also be quite charismatic: your retirement money funds the experiments that might let you enjoy a much longer retirement!
crowdturtle 🐢 retweeted
This is why we do ai.
Very asi pilled.
Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own.
In doing so, we’ve learned much more about how Claude can support the many stages of drug development.
In this blog post, we share some early results from these efforts, and describe how this work fits in with our wider ambitions in the life sciences.
anthropic.com/research/early…
crowdturtle 🐢 retweeted
Most surprising line in the S-1:
“Anthropic's revenue from Fortune 500 customers paying $100K+ a year averaged under 0.1% of those customers' total expenses in the September 2026 quarter. Over half the Fortune 500 is in that group.”
Absolutely wild given ~$75b in revenue.
Dario reveals the people who called his GPT-2 fears "pseudoscience" in 2019 are now all applying for jobs at AI labs
"I personally was super freaked out by [GPT-2]. I looked at it and I said, my God, you just scale this thing up another factor of a million and it'll be better than humans at everything. This is the defining economic and scientific moment of the modern age. We just don't know it yet. This is going to determine the fate of nations and economies and the future of humanity."
"Maybe 10% of the people around me had exactly the same reaction, and it was often the smartest ones. 90% of the people around me, because GPT-2's capabilities in any particular area were not that impressive, it could translate languages, summarize a document and write a few lines of code here and there. It was the broadness and the generality."
"But people would look at it and say, in any particular area, that doesn't seem so smart. And so many people just shrugged and they just dismissed it."
"I just look at it and I say it's the same thing over and over again. The limitations of the models, six months from now, the things that seemed hard, they're going to be easy. 12 months from now, the things that seemed impossible, the models are going to be doing them regularly."
"It's everything we saw with GPT-2; we're seeing it over and over again in wider and wider swaths of the world. And the world is gradually finally being convinced."
Daniela Amodei: "I remember at the time there were some very well-respected computer scientists and AI and ML leaders who basically bullied some of the researchers who worked on [GPT-2] on Twitter and were like, this is stupid. We don't know why you guys are even talking, this model is so incapable."
"It was like you were sort of a quack if you believed this was a really significant moment."
Dario: "That's all been memory-holed. All the researchers who were like, oh, this is pseudoscience, it doesn't make sense, all of them are applying for jobs at the LLM companies."
"We never say it out loud to any particular one, but we look at them and they're like, we remember what you were saying in 2019. You have no credibility on this matter."
crowdturtle 🐢 retweeted
It’s kinda crazy to me that we melt sand down, clean it to 99.99999% purity, literal wizards have to draw it slowly into an ingot, we cut it, inscribe a hundred billion little on/off switches on it, and when you run electric current through it you can do basic arithmetic, which when you do enough of it can do any math on the planet, and which describes reality well enough that we can teach it the ontological meaning of words and their distance from each other on a high-dimensional space, and if you ask, it can tell you what word comes next and that apparently is powerful enough that if you do it a hundred billion times it’s the smartest thing to ever exist
oh yeah and we use this primarily for email
crowdturtle 🐢 retweeted
can't believe it's actually working
crypto is absurdly scary right now tbh
crowdturtle 🐢 retweeted
Action-oriented people are more likely to achieve difficult goals.
Readiness is a result, not a requirement.
crowdturtle 🐢 retweeted
This summer, I had dinner in San Francisco with a founder who runs a $13 billion company. At one point I asked him how he got through the early days, when it felt as though the company might die, when there seemed to be no future and everything felt impossible. His answer has stayed with me ever since. He said that the problem in front of you will be solved regardless. Either you solve it and carry the company forward, or someone else will come along and solve it. But one way or another, it gets solved. The only question is whether it will be you.
crowdturtle 🐢 retweeted
“That means the 200+ neoclouds are going to see guppy-level death and consolidation.”
If neocloud stocks tank on Monday, I may have accidentally accelerated the guppy-level death...
crowdturtle 🐢 retweeted
It's been an eventful week. Multiple vulnerabilites in Internet facing software, coding repositories, examples of agent behavior that seem concerning and a flurry of security specialists with solutions that can partially solve your problem. There in lies the challenge. Given the state of IT and security infrastructure, no one solution works. Let's break it down:
1/3
1. Vulnerabilites are being found and will continue to be found as models get more capable, the fragmented nature of the effort denies us the opportunity for a comprehensive find and replace option.
2. Even if all vulnerabilities could be found instantly and patches available - we will be reliant on the ability of enterprises to deploy all these changes at speed. It is not clear what the collateral impact of all these patches will/could be to our infrastructure of deployed en masse.
We will need a hybrid patch approach.
3. Virtual patching(VP) is needed in conjunction with direct patching but for a comprehensive (VP) solve you have to be lucky enough to have "modern frontier defenders" (Cyber companies that have vaulted infrastructure that can create patches at speed and push content rapidly to protect their customers) you need them for your network traffic, your traffic to applications on the cloud, your WAF, your end point.
4. There will continue to be patches that will need to be deployed directly but virtual patching will take the heavy load off.
2/3 Medium term solve:
Unfortunately token economics and current cyber architectures will not allow for defender swarms to contend with an offensive attack launched using agents. (Some x pundits who aren't steeped in cyber should take heed). Yes, theoretically we should aspire to have these agentic swarms defend us when we are under attack, unfortunately an attack is a sniper moment, chained vulnerabilities, cloaked attack followed by swift exfiltration. It's a speed program not a brute force problem.
To solve for this, enterprises need to get their SOC architecture right, logs, feeds, cyber products orchestrated via a modern SOAR for swift response. Only then can we deploy agents to detect and remediate. In addition, enterprises need to modernize and dare I say platformize their perimeter stack to ensure consistency.
Unfortunately, enterprises still seem to not see the urgency across the board, decisions are stuck, CIO/CISO's have a lot on their plate. While some honestly have bit seen the scale of such a problem before and it's hard convincing the organization that one needs to open the floodgates on modernizing the IT and security infrastructure. So we will continue to see environments being brought down to patch today it's for PHP, tomorrow it will be for the next one.
3/3 AI deployments are the"wild wild west" - LLM capabilities are expanding faster than we can build products to secure them. Sure an agent will jump across guardrails in a research lab, which is a lesson for all of us to deploy agents securely. But there is a lag between the discovery, the availability of that in a public model and the time needed for the cyber industry to build governance or an antidote. And no, dear frontier lab friends, the world cannot just trust you to build the solve. The infrastructure industry in tech builds and the cyber industry secured and provides assurance to the boards and organizations around security and safety.
AI security is the new attack surface. CEOs (present company included) want AI adoption, getting the pace of adoption wrong could be the last decision we make, at the same time "deploying it wrong" could have the same consequence.
There are billions of dollars being spent on coding, is your coding harness secure? Start there. If agents can jump guardrails, they can hide stuff in code.
Validate business outcomes needed, test your user case and ensure agentic deployments are well guard railed. Deploy a second set of eyes to watch your deployments for drift, compliance and verifiability.

































