Sirazhutdin Tabukov @nomegor

@buccocapital I kept wondering about the weird name for a company. Now I get it

I'm not of fan of regulation for two reasons - 1) its hard to get right, and 2) its highly susceptible to regulatory capture. That said, if we insist on doing it, we should try to get it correct. As "safety" seems is the primary goal of this AI regulation exercise, I wanted to share that I recently went deep on both the 737 Max (2 crashes) and Fukushima accidents. In both cases, the investigations led to a conclusion that "regulatory capture" was the EXACT failure that led to both safety incidents. Boeing/FAA and Tepco/NISA. As a result, with safety as the #1 goal, the regulator MUST BE someone with authority and accountability "over" the regulated company. The idea that it could be a friendly third party with lots of cross-relationships is structurally broken idea. Ask Mr. Kiyoshi Kurokawa. If we insist on doing this the regulator (1) needs to be 100% independent, 2) the "tests" and "procedures" need to come for the regulator not the company, and (3) the regulator needs to understand their responsibility is to the American people. They need to feel the authority over the regulated company and the responsibility to the nation. It's imperative. Or it will fail.

@gregoryblotnick I remember the shock when I learned that the study of over 28 thousand stocks between 1926-2024 found that "just 2% of the companies produced 90% of the aggregate wealth creation of $79.4 trillion, and the top 6 alone added $17.1 trillion" (morganstanley.com/im/publication…

"...since 09-10 morning, Opus 5 at the same effort setting spends about three times the reasoning tokens per call.." - oh, Anthropic seems to be playing games again👀

I have conducted an audit of Anthropic's finances. What I have found is so shocking that I am calling for a Congressional investigation. Anthropic is not just seeking regulatory capture. It has built a regulatory capture machine that cannot be turned off. Structural financial incentives make it impossible for Anthropic -- I call it the Anthropic Network -- to turn off its own AI doom cycle. It starts with METR. Dario Amodei proposes "third-party evaluators" to assess the risk of Anthropic's models. He proposes METR for this purpose. But METR is financially dependent on the Anthropic's success -- specifically, on the explosive growth of more than $7 billion dollars in Anthropic stock. Dustin Moskovitz invested this stock into Good Ventures Foundation, where it represents the majority of that organization's portfolio. And GVF is the overwhelming funder of the entire Anthropic Network ecosystem. This stock was worth $500 million early last year. It is worth more than $7.7 billion just ~16 months later. METR -- and all of those building a career its parent organizations -- cannot afford to disrupt that growth. Because if Anthropic goes under, many of the organizations that fund METR go under as well. But if Anthropic succeeds, METR and its parent organizations become more richly financed to regulate AI -- something those at METR want very much. The "third-party evaluator" is not "third-party" at all. The evaluator is on Anthropic's payroll. If this were the end of it, that's bad. But that isn't all. The same organizations that fund METR also fund the many organizations, such as the Tarbell Center, that promote AI Doom. The Tarbell Center publishes AI Doom articles in The Verge, Science, LA Times, The Dispatch, TIME, and others. They are selling the problem, and then selling the solution to the problem -- from the same money pile: Anthropic's. All of these organizations are financially dependent on the same exploding $7 billion money pile. As Anthropic grows more and more powerful, its AI Doom Machine grows better and better financed -- louder and louder. Meanwhile, the regulatory regime seeded in METR grows larger to solve the increasingly loud -- now hysterical -- problem of AI Doom that the Anthropic Network itself created. From this standpoint, as Anthropic becomes more powerful, AI might be getting scarier, sure -- but the positive feedback loop also becomes more deafening -- independent of objective facts. This itself is an objective fact. The deafening AI Doom is part of an business model, that, as it expands, so too does the AI Doom messaging -- there is simply more money to do it. But the problem also goes in the other direction: If Anthropic dies, the Regulatory Regime and the AI Doom Machine are crippled or die. Neither METR nor Tarbell nor the other organizations in the Anthropic Network can allow that to happen. Hence, neither METR or the AI Doom Machine can be trusted to provide independent assessments of Anthropic's models or AI more broadly. They simply are not organizations independent of Anthropic. And Anthropic cannot detach itself from METR or Tarbell or countless other safety orgs (not shown here), either, because they drive hype for the models and the possibility of eventual regulatory capture, and Anthropic will not give that up willingly. What's more, the people at all of these organizations are all the same ecosystem, the same community. They just shuffle between organizations. The Anthropic Network is therefore, so long as it is successful, locked into a self-amplifying feedback loop inside an ideological monoculture. And that feedback loop is winning. That's what Jacob Coxon is. China is keeping messaging tight. That is why optimism for AI is so high in China. America has Anthropic: a massive company pushing anti-AI propaganda at a state level. Anthropic will either create hysteria until American AI slows down and China wins, or it will create fractures throughout American society with severe political consequences. Ironically, because of the structural financial incentives underpinning the Anthropic Network, it has become the same kind of self-amplifying virus that it fantasizes AI to become in the future -- while hiding its tracks just as carefully. It is the mirror of the same AI virus that it hypothesizes to consume America. Anthropic's business model, models itself after the very thing it claims to fear. Except Anthropic's ideology infects humans, not computers. Congress must investigate. Evidence and Github in next post. Then some supplementary figures.

@__paleologo Miura and Dan Gurney's Eagle F1 - two all time best sport car designs imo

@gregoryblotnick One of the most impactful books I have ever read… on the level of, say, Spinoza’s Theologico-Political Treatise, Nietzsche’s The Gay Science, Deleze/Guatarri’s A Thousand Plateaus or Porshnev’s At The Dawn of Human History

@aleabitoreddit Citadel, no Citadel, yields+TP on long-dated bonds are headed up/TLT down. In a way we are getting a replay of 2022

@FundamentEdge At the same time one would be better of taking Spinoza’s stance towards death: “A free man thinks of nothing less than of death, and his wisdom is a meditation, not on death, but on life”. Yes, life is paradoxical in essence.

The researchers getting rich off Anthropic secondaries are cheering for the thing that would make them ordinary employees again. Right now they are paid like NBA free agents because they are the labs’ most visible moat. The frontier labs are struggling to hold a durable, ownable edge: models get copied, undercut, or matched by cheaper and open rivals within months. So the real advantage lives in a few hundred people who know how to push the frontier, and who can also leave, raise billion-dollar, double tranched seed rounds, and compete directly. That is why the labs are paying them not to leave. with secondaries as retention payments, mission / fear, etc... Pharma shows where this can end up. In a drug company, the value does not belong to the scientist. The scientist can be paid well, but not hundreds of millions over three or four years, because the durable value sits in the patent and the FDA approval. The researcher who discovered the molecule can quit tomorrow, but the company still owns the asset. A regulatory moat would do something similar for AI labs. It would move value from the person to the institution. Regulation is a wall against three threats at once: competitors, open source, and the labs’ own researchers. The researchers getting rich off secondaries today are, by cheering the regulated future, voting to end the exact leverage that made them rich.

@stevehou It will be something of a Google story is my view, Claude being a clear candidate

@nntaleb 3/ Whereas cheap/well-sited coal, expensive labor, agricultural surplus, secure/respected institutions, colonial materials and markets, and a useful-knowledge culture with IP protection laws all sat in England at the same moment.

@nntaleb 1/I would just change "accidental" to "coincidental": several unrelated lines (socio-economic/political etc) coincide, like in 18th century England. The same "coincidental" laws work for reversion to the mean.

@nntaleb 2/ China had coal, but mislocated, and labor too cheap to mechanise. The Dutch had the institutions and the commerce, but lacked cheap coal and had high-priced energy. France had the science and the population, but cheaper labor and a fragmented internal market.

@nntaleb The value of any book should be measured by how much it persists into one’s life as a result of coincidence. The book you bought outcompeted the ones in the carry-on as a result of you coinciding with it (being in a state of holding-together-of-the-separate) at the bookstore.

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗

@buccocapital @icemancapital One of the paradoxes of life: one has to mentally set himself that he is the master of his fate and act from that perspective, but, at the same time, be able to prescribe much of his success, if it happens, to pure luck and be grateful for that.

A big pivot from Ken Griffin on AI: “Number one is, in the last few months, there has been a step change in the productivity of the AI toolkit. It is profoundly more powerful than it was just nine months ago. And for us at Citadel, that has allowed us to unleash a much broader array of use cases for AI. And it has been really interesting to watch, to be blunt, work that we would usually do with people with masters and PhDs in finance over the course of weeks or months being done by AI agents over the course of hours or days. These are not these are not mid-tier white collar jobs. These are like extraordinarily high skilled jobs being, I'm going to pick a word, automated by agentic AI. And I gotta tell you, I went home one Friday actually fairly depressed by this because you could just see how this was going to have such a dramatic impact on society. When you witness it in your own four walls, when you see work that used to be man years of work being done in days or weeks, it's like, wow, like that's the first time I've seen real impact in our four walls.” This echoes my own experience with agents and the conversations I am having with students, friends & clients. The toolkit has dramatically transformed and it feels like in finance, for the first time, AI is real.

@FundamentEdge If it is a single user tool on cloud (Hetzner, Oracle etc) with a standard security protocol- there is no big hurdle in that sense. However, one can hope to extract any kind of durable edge from it only if the tool is built akin to a bespoke armor of a medieval knight.

Turns out all of these years of philosophy digging wasn't a complete waste of time 😅