crowdturtle 🐢 (@crowdturtle) header
crowdturtle 🐢 (@crowdturtle) profile picture

Data scientist in big tech. Investor in cash-flowing real estate, special situations in public securities, and blockchain.

NYC
Joined September 2011
Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)Photo by crowdturtle 🐢 (@crowdturtle)
crowdturtle 🐢 retweeted
The real risk with the Binance hack is not insolvency but the chilling effect it will have on the asset class. Past hacks were viewed as isolated incidents. But in this case, the market will (imo correctly) assume that this is the first of many LLM-enabled exploits. My non-crypto friends are already talking about Binance - it’s the biggest mainstream news coverage we've gotten in months. I don't see how instis will be able to justify additional investments into crypto under these conditions.
I am worried that this week's cybersecurity crisis is the beginning of a new bearish narrative for crypto. The early version of this narrative was "AI breaks crypto" - a concern that cryptographic primitives used in major protocols would be broken by automated research. At the time, I thought this fear was mostly overblown, and so far the market seems to have agreed. However, a slightly different version now seems plausible: "AI breaks crypto exchanges." There are hundreds of billions in digital assets sitting in the centralized custody of major exchanges. This is the juiciest target for hackers anywhere on the internet. I think there is a high probability that in the next few months, a top ten exchange loses billions of dollars of customer assets. If this happens, there will be a small immediate selloff as we've seen after past hacks. But it will also likely kick off a new narrative: that further hacks are only a matter of time, and that all assets on centralized exchanges are fundamentally insecure. Crypto's best path through the coming months is to define itself *positively* with respect to AI - for example, "crypto is the financial layer for agents." But if the main AI-related narrative that sticks to crypto is *negative* - like "crypto exchanges are the biggest losers in the cybersecurity apocalypse" - then we're in for a rough winter.
7
6
1
65
13,330
Google had to cannibalize a business where they have 90% market share (Search) to build a business where they have 25% market share (Gemini) If you think 30% decline in the stock from the top is bad, buckle up because it should be 50% at least
51
73
4
866
64,703
crowdturtle 🐢 retweeted
many such cases
I’m dropping all the unit test from our CI. The ratio of the value they bring to tokens (& GitHub actions) you pay for their development and maintenance rushes to 0, if it’s not already there. It’s only e2e tests & evals for me now
1
1
1
22
5,253
crowdturtle 🐢 retweeted
this is a fantastic conversation with sam altman with a bunch of easter eggs: - the major leap in recent models is due to GPT's specialization in mathematics. the same model that solved navier stokes is inherently better at self-improving. - the biggest bottleneck is asking the right questions to the AI model. he implies the models are so smart that humans are basically the chokepoint right now. - openai's internal RSI model (gpt-7) is good enough at solving basic tasks but can't "build a new architecture of the same leap as the transformer" - sam expects the $10M cost to solve the hardest math problems to cost $100K 12 months from now. - he expects to have GPT-controlled robots building more robots to eventually construct self-improving data centers (yes i just said that) by end of 2027 so effectively an army of robots that recursively self-improve. everytime i think the timeline is already insane enough - one of these conversations drops.
Tomorrow on Cheeky Pint: @sama of @OpenAI
4
8
137
16,763
crowdturtle 🐢 retweeted
This is not at all what you see in a world that is compute constrained.
In the last month: • Anthropic giving free API credits as part of their higher plans • OpenAI giving free API credits equal to your subscription as part of their higher plans • OpenAI doubling the usage available in their plans It's an amazing time to be a token consumer.
1
1
14
2,070
crowdturtle 🐢 retweeted
you're seeing a consistent pattern: - Fortinet: people bought bc of AI cybersecurity 'emergency'. turns out 4q decelerating, management price sensitive. stock -25% - ServiceNow: AI SAAS transformation requires forward deployed engineers, turns out 4q is decelerating - stock -25% - Google: spent entire time on TPUs and AI, going FCF negative while its core business (ads) is - you guessed it, decelerating in 4Q. stock -13% this was after youtube was supposed to be killing Netflix (stock -44% over past year) - Robinhood: big agentic push, Monthly active users sequentially decelerating. meanwhile the consumer sector (big ad buyer) is in beast mode. Cheesecake factory 15% comp, Chili's 10%. Norwegian cruise lifts full year guide - passengers up 22% year over year (983k vs 900k expected). which begs the question: if you have RSI, and AI is the future why are you underperforming fried mac n cheese sales or cruise billings in the middle of a war. like what are you actually investing against so company after company is going full tilt into AI, and not really getting a return - just as everyone is 1] locking in to multi year memory contracts (which is part of the price sensitivity fortinet noted) 2] going FCF negative 3] ready to absorb $2-4T of public equity issuance from the labs meanwhile market rejoices when Oracle *cuts back* investment guidance in data centers. Labs on fund raising roadshows are saying the new demand story is that software and hardware are insecure and need to be completely refactored for AI era. Which is just an expense, with no revenue acceleration - which brings to the macro question of 'is productivity going to accelerate' right now sub 2.5% - needs to be above 4% sustainably for debt dynamics to work out. if everyone has to rewrite their software to address cyber threat environment, and banks get hacked constantly (which is apparently the bull case for labs?) - you enter unustainable fiscal situation and decelerating growth which was buttressed by AI infra investment which (per Oracle) is likely to decel even at macro valuation level, China is far ahead of Europe in AI (non controversial). Hang Seng Tech index trades at 10.5x forward PE while French stock market (CAC Index) trades at 14x while Chinese 5 year yield is 1.3% and France is 4.3% so there's a completely believable world in stocks where you get a lot more inference, and a lot more AI, and multiples decline or costs accelerate. which seems like Blasphemy in SF but if you just listen to public companies seems closer to a base case
65
62
19
1,046
111,462
crowdturtle 🐢 retweeted
Instinct supposedly is raising at a valuation worth more than the market cap of Snapchat ($10.7B), American Airlines ($8.6B), and Etsy ($8.3B) COMBINED $30B. Just this week, I've spoken to 5 other AI agent companies that are raising their Series A... This space is so damn competitive, what a time to be building in consumer AI!!
Instinct is currently raising @ $30b. $100M → $500M → $2.5B → $10B → ~$30B in sub 1y. If you're still not inside the private market this should be a good signal to pivot aggressively. Wealth creation is going to be heavily skewed toward private investors over the coming decade.
12
2
61
9,940
crowdturtle 🐢 retweeted
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.
39
22
7
313
75,024
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.
3
1
60
5,512
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.
1
3
140
62,223
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.
1
9
2
187
68,442
crowdturtle 🐢 retweeted
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
30
44
3
896
29,309
crowdturtle 🐢 retweeted
They all told you that inference was not a good business It was wrong
20
27
3
319
26,763
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!
13
10
1
300
19,418
crowdturtle 🐢 retweeted
ngl this is getting out of hands.
115
222
57
2,234
159,029
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…
12
42
3
832
34,105
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.
173
186
59
2,911
312,415
crowdturtle 🐢 retweeted
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."
61
106
74
2,211
440,185
crowdturtle 🐢 retweeted
Taps sign
I dont know anything about theoretical compsci or math so I asked chat to rank all of the AI discoveries to date by importance and it said 3 of the top 5 ever came out today.
8
8
241
14,198
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
218
973
107
12,346
445,999