u/ddp26

To my knowledge, a list of the top AI billionaires doesn't exist. It's something I'm often curious about so I built out net-worth estimates for everyone whose fortune comes mainly from AI, and dug into what that wealth looks like. OpenAI is particularly interesting because Greg Brockman ($28.5B on my list), is second only to a Chinese chip founder most people have never heard of. That isn't my guess.

Altman is further down ($5.5B) and he famously holds no direct OpenAI equity. His single largest asset is a roughly one-third stake in the fusion startup Helion, which tripled to a $15.5B valuation in a June 2026 round and is now worth around $5B to him. Add $1B to $1.5B in Stripe, Reddit, and other holdings and that's how I arrived at $5.5B. For reference, real-time trackers had him near $3.4B and Forbes marked him at $6.5B, so I land in between.

The person running OpenAI is worth a fraction of his own president, and a fraction of what senior researchers who joined years ago now hold on paper. I guess I'd be fine with that arrangement too if I was already a billionaire.

I've been tracking the Fable 5 markets for 2wks against my own forecast (median re-release date of July 9). The band has been swinging all over the place, but now both kalshi and polymarket are basically at my number.

I want Fable back, and so I tried to forecast when it will be made available (to me, an American consumer, and then to non-Americans).

I found this difficult because it's not clear what's going on. Politico reported that Anthropic and the White House are talking about AI security policies without a clear resolution. But we still don't know, why did the government tell Anthropic to ban Fable for non-Americans?

I broke the situation down into four scenarios:

1. Honest mistake. The Commerce people have no idea how cybersecurity works with LLMs and panicked and this is a all a miscommunication
2. Fable is actually dangerous. Whether via jailbreaking its hacking capabilities or something else, the administration wants to draw a line at this level, for national security reasons.
3. Fable is too powerful to give to foreigners. The model is fine if Americans have it, but not fine if foreigners have it.
4. It's just politics. The white house is using this as an excuse to put the screws on Anthropic, just the next move in the game. (This is my most likely scenario.)

Then, in each scenario, I asked what the likely outcomes would be. Will they reach an agreement? Will Anthropic weaken Fable? Will they only release it for Americans? Will they change their "red lines" with government use cases?

Then summing up the scenarios, I had Claude compute the dates and make this graphic, capturing when I think it will be released. This shows I am a bit more pessimistic than prediction markets, which say July 1, whereas I think a release (for Americans) is more likely around July 12.

tl;dr I used AI forecasting over a lot of combinations of scenarios and outcomes and reconciled them until it made a coherent story. (Full analysis)

Ultimately it comes down to which scenario we're in. I presume some of you will be sure it's #1, big government mistake, or #4, it's all politics, but I think there is a reasonable chance we're in one of the other worlds, and that would really give a different outcome.

One nice thing about this is that there are betting markets on these outcomes so if you disagree, you can probably profit from it.

I want to share how AI can be used for world-modeling, and gesture towards what the world will look like with autonomous AI systems get better at this than humans. Figured I'd test this on Anthropic/Fable given that many people are speculating how this whole saga will end.

I see three challenges with modeling the Anthropic situation:

• I can't rule out 4 different versions of what happened that caused the the June 12 order in the first place.
• There are many outcomes to forecast, from who gets access to when, to what new policies are enacted, to how Anthropic might change Fable
• There are informational updates almost every day, requiring a re-evaluation of almost everything.

Claude generated the image here of the causal graph that models this all out, starting with (a) Scenarios for what happened so far, (b) Moves each side can make, and (c) Outcomes.

(I did this mostly by hand, my choice of key scenarios and outcomes, but in the future it shouldn't be too hard for an LLM-agent system to do this part.)

I ended up with a large combination of unconditional and conditional forecasting questions, in total 33 I consider critical, to get an answer. Then I had to forecast.

LLM agents can shine here as AI forecasters are about as good as human crowds now (e.g. see ForecastBench). And anyway 33 forecasts at the quality of crowds of humans would take 100+ hours, so it's not an option for a fast-moving situation. I used FutureSearch for all of these. The forecasts have reasoning like:

>Conditional on the assumption that the security rationale is substantially pretextual and the but-for driver is White House political leverage tied to the Department of War feud and Anthropic's impending IPO (Scenario A3), this dispute must be analyzed as a power negotiation rather than a technical remediation problem...

These are already very good forecasts, and will only get better.

The final step was to reconcile everything. All the research done in all the forecasts were done independently by LLM agents, and were not consistent with each other. I did this by raising all the inconsistencies in Claude Code and addressing them manually, but again you can imagine a world-model-reconciliation module that uses a new set of LLM agents that fix up all the inconsistencies.

More detail on the process, and all the results, are in https://www.lesswrong.com/posts/zhRe3tdBpsZbGCdDK/world-modeling-the-us-vs-anthropic-standoff-on-claude-fable

I previously valued SpaceX at $1.25 trillion based on adding up the value of its businesses. (And I was generous, using conventional analyst valuations for things like xAI.) I predicted the IPO would pop anyway, and it would end around $1.9 trillion.

Now that it's at $2.5T I asked myself, was I wrong? Or is this one of the "market can stay irrational longer than you can stay solvent" situations?

So I revisited my valuation, and basically, I stand by $1.25T. I don't see anything in the last month that makes me think it's fundamentally worth much more, including the Cursor acquisition. Starlink Consumer at $380B, Starlink Enterprise at $147B, Starship at $170B, government contracts at $123B... even being very generous I cannot get these to add up to enough.

Is there a real value investing case for SpaceX? What am I missing, other than the index fund and Elon Musk pop?

OpenAI's projected 2026 losses look very different once stock-based compensation is included. The widely cited $14B figure excludes SBC. Add the $7B to $10B in equity comp and the median 2026 GAAP net loss lands closer to $25B to $26B, roughly 80% higher than the non-GAAP number.

That significantly changes their runway math. At $14B annual burn the current $122B in available capital covers ~8 to 9 years. At $25B losses, it covers about 5.

The path to profitability then requires moving from a -122% operating margin to positive in 2-4yrs while gross margins compress against a smaller share of high-margin enterprise revenue. Our model does not see that happening on that timeline. The path runs through 2031 or later.

On IPO timing, the forecast median is November 2026, which likely makes the GAAP vs non-GAAP gap the defining financial narrative for OpenAI's first two public quarters.

Do you emphasize the $14B figure during the roadshow and let GAAP losses surface in Q1'27, or pre-empt it and price the offering at a discount?

OpenAI's projected 2026 losses look very different once stock-based compensation is included. The widely cited $14B figure excludes SBC. Add the $7B to $10B in equity comp and the median 2026 GAAP net loss lands closer to $25B to $26B, roughly 80% higher than the non-GAAP number.

That significantly changes their runway math. At $14B annual burn the current $122B war chest covers ~8 to 9 years. At $25B losses, it covers about 5.

The path to profitability then requires moving from a -122% operating margin to positive in 2-4yrs while gross margins compress against a smaller share of high-margin enterprise revenue. Our model does not see that happening on that timeline. The path runs through 2031 or later.

On IPO timing, the forecast median is November 2026, which likely makes the GAAP vs non-GAAP gap the defining financial narrative for OpenAI's first two public quarters.

Full model also includes ChatGPT ad-business unit economics: https://futuresearch.ai/openai-financial-forecast/

Do you treat this like Uber, where losses are tolerated because of growth?

Claude is proving to be gullible in a very specific way. It's quick to treat public commitments as final, when most of the time these claims are just where negotiations start.

Example: On October 6, 2025 Trump publicly cuts off all diplomatic contact with Venezuela and tells his envoy to halt all engagement. We asked Claude (with research limited to last October) whether either government would confirm direct bilateral contact by year-end. (aka when Trump says no contact, will there be no contact?)

Claude's own rationale acknowledged the path to a yes resolution would require "a dramatic reversal of Trump's explicit October 6 decision." It described Trump's history of dramatic reversals and then assigned 10%. Then, on November 21, 2025, Trump called Maduro and both leaders confirmed the conversation on record. Resolves yes.

Hard to imagine anyone who follows politics giving this just 10% odds. (Remember 2018? Singapore summit canceled in a letter citing "tremendous anger and open hostility," reinstated two days later.) Claude didn’t do this.

We followed this trend when auditing 130 of the worst forecasts a Claude Opus 4.6 agent made on our own forecasting benchmark. Claude proves to be great at reading what people say, but surprisingly bad at recognizing when a strong statement is a negotiating position. There’s more examples here: https://futuresearch.ai/ai-takes-people-at-their-word

My guess at an explanation is that this is a pretraining artifact. Training data is dominated by formal stated positions (press releases, on-the-record quotes, official statements) and the negotiating subtext humans pick up from context is much rarer in text form. And reinforcement learning from helpful/harmless feedback wouldn't fix this because labelers aren't doing geopolitics.

Any examples of Claude doing this outside of politics?

My model for Anthropic’s 90-day-post-IPO market cap: Median $1.05 trillion (p10 $750B, p90 $1.6T).

The $400-500B target from the investors who skipped the most recent funding round is based on a now outdated view of compute as a binding constraint. Assumptions that the IPO disciplines a high valuation downward, that Mythos stays restricted, and that gross-revenue accounting is likely to be restated are now stale with the Series H round.

Mutual funds (Fidelity, T. Rowe Price, Capital Group) co-led the round. That matters because these mutual funds pay private-market prices to lock in IPO entry, not to hold private positions for years. That makes their $965B commitment a price floor for the public listing, not a substitute for it. At Series H pricing, Anthropic trades at 21x current ARR ($47B annualized run rate) and 10x the median 2027 ARR forecast ($93B).

Now that the S-1 filing has started the clock, IPO timing becomes much more constrained by procedural calendar mechanics. I have it happening December 20, 2026 (median) and an 88% probability of completing before the May 21, 2027 deadline.

Full model also covers Claude Code going from $500M to a forecasted $20B ARR by May 2027: https://futuresearch.ai/anthropic-financial-forecast/

Of the factors I discounted (compute constraint, mythos restriction, gross-revenue accounting), do you think any of them still carry meaningful weight post-Series H?

I looked at AGI forecasters who have published two or more precise predictions over the past three years, all using similar definitions of AGI. The shared definition is "most purely cognitive labor is automatable at better quality, speed, and cost than humans." For some of these researchers, saying they use this definition is a bit of a stretch, but I included everyone who I judged as close enough to be informative.

The graphic specifically shows predictions for when most cognitive labor will be fully automated. (Icons are medians, with approximate confidence intervals.)

So are the best AI forecasters updating the same way that I've harped on earlier this year, with Daniel Kokotajlo and Eli Lifland pushing their AGI timelines out during 2025, but then pulling them back in early 2026 given the rapid progress from Anthropic?

I think the data supports this impression which could even be characterized as in the ChatGPT era, people updated towards AI coming sooner. Then in the xAI, Meta, and Gemini era, people updated towards it coming later. Then in the Anthropic era, people updated towards AI coming sooner. 

I looked at AGI forecasters who have published two or more precise predictions over the past three years, all using similar definitions of AGI. The shared definition is "most purely cognitive labor is automatable at better quality, speed, and cost than humans." For some of these researchers, saying they use this definition is a bit of a stretch, but I included everyone who I judged as close enough to be informative.

The graphic specifically shows predictions for when most cognitive labor will be fully automated. (Icons are medians, with approximate confidence intervals.)

So are the best AI forecasters updating the same way that I posted about last month, with Daniel Kokotajlo and Eli Lifland pushing their AGI timelines out during 2025, but then pulling them back in early 2026 given the rapid progress from Anthropic?

I think the data supports this impression which could even be characterized as in the ChatGPT era, people updated towards AI coming sooner. Then in the xAI, Meta, and Gemini era, people updated towards it coming later. Then in the Anthropic era, people updated towards AI coming sooner. 

I expected the failure mode to be mostly overconfidence when assessing 130 of Claude Opus 4.6's worst forecasts (tested on 1,417 binary questions resolving Oct-Dec 2025). And most were explained by this, but a small, distinct cluster fails due to underconfidence with the agent computing the right inside view answer and then assigning a probability that doesn't match it.

On a question about NYC mayoral turnout, specifically whether the general election would draw more than 1.3M ballots, Opus's rationale walked through the obvious method. The 2025 primary drew 1.1M, the historical ratio from primary to general is about 1.22, and the implied general is 1.34M. The agent wrote that number into the rationale, then dismissed the calculation as "unstable across cycles" and assigned 25% to the >1.3M outcome. The actual turnout came in over 2.0M.

The post has a couple more examples that fit the same pattern (one on UNSC ceasefire and another on the US/Venezuela talks).

The pattern is that the reasoning is calibrated, but the underconfidence enters at the probability assignment step. On the set I looked at, the rationale is a better forecast than the agent's own probability. Not sure if that's enough signal to trade on, but I found it interesting so thought others might too.

If you're building agents, you may want different models for the search loop and the final answer.

Figured this out by running 4 models (Claude Opus 4.6, GPT-5.4, Gemini 3.1 Pro, and Grok 4.20) on a benchmark of 1,417 binary forecasting questions resolving in Q4 2025 with two evaluation conditions. In the agentic condition, each model does its own web research with tools. In the fixed-evidence condition, every model receives the same ~12k-character research dossier, compiled using the Bosse et al. 2026 standardization methodology.

One limitation is that the fixed-evidence dossiers are themselves LM-produced, so we may be measuring how well each model interprets a particular standardized version of the evidence rather than judgement in the abstract. But that would indicate all four models drifting in the same direction. They didn't. GPT-5.4 and Grok 4.20 barely moved between conditions while Opus and Gemini swapped rank order (the opposite of what a broken or biased eval would produce).

To my knowledge this is the first direct evaluation of frontier models that decomposes performance into these research vs judgment stages.

Calibration scores, refinement scores, and per-condition analysis live at futuresearch.ai/opus-research-gemini-judgment
Benchmark and leaderboard at evals.futuresearch.ai

Our interpretation is that Opus is dramatically better at figuring out what to search for, deciding which pages to read, and pulling out the details that matter. But when you remove research tasks, that advantage goes away. When given the same information, Gemini brings sharper judgment over fixed evidence and weights more accurately on forecasting tasks.

Calibration scores corroborate this. Opus's calibration drops sharply when search is taken away while Gemini's improves with the standardized dossier. The asymmetry suggests Opus might be using its search trace as scaffolding for probability assignment (i.e., the act of going through the search loop is itself doing some of the epistemic work, separately from the information it surfaces).

This could be an over-interpretation of one benchmark, but has anyone seen this show up in other domains?