I wouldn't have founded @ValsAI without first working at Palantir and Vals wouldn't be what it is without 8VC investing in our seed. @JTLonsdale was key to both. Got to sit down with Joe and @vvkgopalan to talk about the story behind Vals, where the industry is going, and why I'm broadly optimistic while the world is incredibly high-variance.

The diffusion of personal agents (Grok bot, Muse, Dots, Instinct, etc) may cause a rapid increase in socioeconomic inequality that we are not prepared for. This follows from a rough first-principles argument: 1. Life is a set of negotiations. 2. As personal agents get better, we will delegate more of our negotiations to them. 3. Negotiations will increasingly happen directly agent-to-agent. 4. In general, agent performance scales with model intelligence and inference-time compute. 5. Personal agents that consume more frontier intelligence will win more negotiations, in expectation. 6. People with more resources will pay for more expensive personal agents. 7. Winning more negotiations begets more resources. Therefore - people with more resources will use better agents, win more negotiations, and acquire more resources. Consider a situation where my roommate and I are deciding how to split our rent. If we delegate the decision to our agents, with mine running Fable 5.1 and his running Luna 6, I should expect a more favorable split than if our agents were equally intelligent. The most important junctures in life are negotiations (jobs, promotions, purchases, housing, lawsuits, etc.), so people who can afford the best agents should expect to do better in these situations, compounding their initial material advantage. This already happens to some extent (people with more resources hire better lawyers), but it may be amplified if 1) the gap between frontier and free intelligence is wider than the status quo gap (Big Law attorney vs. public defender), or 2) delegation to agents means more negotiations occur in total, so differences compound. Personal agents may instead reduce inequality if 1) the gap between everyday and frontier intelligence is smaller than the human equivalent, 2) agents are more neutral/rational than humans and converge on an independent truth rather than acting at the behest of their operators, or 3) superintelligence frees everyone from resource constraints entirely. If this form of inequality emerges, possible solutions include 1) taxing tokens and 2) subsidizing intelligence for the masses.

Joined @EdLudlow to talk about AI progress, independent evaluation, and why I’m optimistic. Some quick takes: - We’re better at building AI than understanding it. Attention towards testing/evaluation matters more than slowing down. - Our RSI Index projects models could match human researchers on the tasks we test by August 2027. Embedded evaluators can produce more accurate estimates based on internal systems. - Public conflict masks cooperation. The labs, policymakers, and enterprises we work with want better evidence. I’ve seen enough to believe coordination is possible. - Independence comes at a cost. We’ve rejected contracts that would compromise ours. The same group doing the testing shouldn’t also sell the solution. - Evaluation should scale through better technology. If it becomes a bureaucratic moat for incumbent labs, we’ve failed. - Market-based evaluation has a role with or without regulation. Competition pushes us to build better technology and keep up with the frontier.

With AI safety topics going mainstream, the public seems anxious and disconnected from the reality of the problem. From what I’ve seen at Vals AI, I’m optimistic we’ll coordinate toward an optimal future for AI. Our study on RSI shows that, at their current pace, Anthropic’s models could match human researchers by August 2027. That creates urgency but gives us time to prepare. It’s hard for the public to know whom to trust when everyone debating has their own incentives. This was the concern I had when I started Vals AI: that a multipolar paradox would emerge, where the actions of self-interested parties lead to a non-optimal outcome for the system. To overcome this, we independently evaluate models for their real-world impact. This mirrors the role of auditing firms. I’m optimistic because we have found rational and willing partners across the industry. Every major lab has been a great collaborator, providing us with early access to models for testing on our public benchmarks. Every member of Congress and government agency we’ve briefed has been eager to learn. Enterprises are becoming more sophisticated about adopting models based on evaluated capabilities/risks. It hasn't been easy. We have had to earn the trust of competing groups and reject significant contracts that would have compromised our independence. But done right, evaluation can scale with the frontier through automated infrastructure. Embedded evaluators can understand systems during development while maintaining independence. This makes it easier for new entrants to compete and promotes transparency that builds trust with the public. We’re eager to see a diverse ecosystem emerge. We’ve open-sourced our core infrastructure and published our methods, supporting peers. This is a time of high variance. The decisions we make now will have an outsized impact on the future we arrive at. I remain optimistic we will get this right in the year ahead.

Meta made a “minor” release to Muse Spark, there’s nothing minor about it. Lots to parse here: - This model is so fucking cheap I almost don’t believe it. In practice we see it’s 1/10 the cost of both Fable and GPT 5.5. If you thought OS models would compete away margins, just wait till you see this. It’s somehow cheaper to use MS 1.1 than host your own OS model… - Coding improvements are significant. This was a real shortcoming in 1.0. But 1.1 sees a ~50% improvement in VibeCodeBench and ~10% improvement in SWE Bench. Not quite SOTA, but at this cost/latency it is still incredibly compelling. - Speaking of latency, wow this model is fast. Across our benchmarks, we find it to be 1/4 the latency of Opus 4.8 and 1/2 the latency of GPT 5.5. I would expect Meta to have incredible web infra, but really don’t know what witchcraft they’re pulling to host the model for such fast inference at high rate limits. - There is a public API. This is the first time Meta has released a model through a hosted API. I’m expecting lots of AI natives to hot-swap and rapidly test this model as a replacement. We’ll soon see if it's performant enough for those production uses. - Speaking of AI natives, it’s been a wild week for Harvey’s legal benchmark. Grok 4.5 held the SOTA position for ~24 hours at 12% before MS 1.1 unseated it with a big jump up to ~20%. I suspect many internal evals will see surprising results like this. Glad to collab with @harvey @gabepereyra @nikogrupen @ItsJulioPereyra on this eval. - Intelligence is more jagged than ever, even within individual domains like legal and coding. Every application and user benefits from staying dynamic. There an edge in picking the right model/system for each task.