From Matt Levine’s always excellent Money Stuff newsletter today:
This is a naturally long-term trade. Aschenbrenner’s famous June 2024 essay series is titled “Situational Awareness: The Decade Ahead.” The point is not, like, “SK Hynix will beat earnings expectations next quarter”; the point is stuff like “by the end of the decade, we are headed to $1T+ individual training clusters, requiring power equivalent to >20% of US electricity production.” A lot of people believe this, and a lot of money has been mobilized to bet on it. Tech companies and private credit firms are raising tens of billions of dollars of bonds to build data centers. Frontier AI labs and public AI companies like Alphabet and SpaceX are selling tens of billions of dollars of stock. The way those bonds work is that investors give the data-center builders their money and get paid back over decades as the data centers make money; the money doesn’t have to be repaid for years. The way those stocks work is that investors give the AI companies their money and hope that their stocks become more valuable; they never have to be paid back.
If you are a hedge fund borrowing money from banks to buy AI stocks, the way that borrowing works is, uh, if the AI stocks go down you get margin calls? Like, that day? Your money is not locked up for the long term, and you might have to repay it at any time. There is a mismatch between your thesis, which is measured in decades, and your funding, which is kind of overnight. The AI thesis is up a ton over the past two years, but it is down quite a bit over the past two weeks...
A crude but useful characterization is that Situational Awareness is really really good at thinking about the long-term implications of AI, and Citadel is really really good at thinking about funding risk.[1] So now Citadel owns Situational Awareness’s long-term AI bets.
A crude but useful characterization is that Situational Awareness is really really good at thinking about the long-term implications of AI
If SA had been good at thinking about AI implications, it would have been about the short-term implications: a rapid, monotonic death of SaaS and growth in AI hardware stocks. Their thesis itself demanded the factors that destroyed the fund: urgency, ultraconfident theoretician AI insider managers inexperienced with financial plumbing, the lack of risk management.
Endpoint bets are more common in forecasting than continuous path questions. And I doubt forecasting expertise implies expertise in posing the questions most relevant to a given agenda. Co-manager Carl Shulman is a forecasting expert. It makes me wonder if part of the conversation around whether forecasting is overrated needs to include the possibility it doesn’t just fail to produce benefit, but that forecasting ability misleads when taken as a qualification and is overrated as an “alternative credential.”
But being constructive, it also makes me think that insofar as rationalists/EA are going to keep supporting forecasting, it might be worth putting the emphasis on these more neglected areas of handling continuous, path-dependent forecasts, technique for crafting questions genuinely relevant to specific agendas instead of their convenient-to-resolve and fun-to-think-about proxies, and the reflexivity issues that ensue when there are other reasons to make a public bet or confident public prediction than being right.
From Matt Levine’s always excellent Money Stuff newsletter today:
If SA had been good at thinking about AI implications, it would have been about the short-term implications: a rapid, monotonic death of SaaS and growth in AI hardware stocks. Their thesis itself demanded the factors that destroyed the fund: urgency, ultraconfident theoretician AI insider managers inexperienced with financial plumbing, the lack of risk management.
Endpoint bets are more common in forecasting than continuous path questions. And I doubt forecasting expertise implies expertise in posing the questions most relevant to a given agenda. Co-manager Carl Shulman is a forecasting expert. It makes me wonder if part of the conversation around whether forecasting is overrated needs to include the possibility it doesn’t just fail to produce benefit, but that forecasting ability misleads when taken as a qualification and is overrated as an “alternative credential.”
But being constructive, it also makes me think that insofar as rationalists/EA are going to keep supporting forecasting, it might be worth putting the emphasis on these more neglected areas of handling continuous, path-dependent forecasts, technique for crafting questions genuinely relevant to specific agendas instead of their convenient-to-resolve and fun-to-think-about proxies, and the reflexivity issues that ensue when there are other reasons to make a public bet or confident public prediction than being right.