That's precisely why you would want to make a startup to get investment now rather than self-fund and bootstrap. That alpha isn't going to last forever, especially because everyone has access to the frontier LLMs, which keep getting better, and will eventually beat your fancy harness or specialized finetune.

And also, perhaps more importantly, so you can start developing an alternative to prediction markets and become the new PM; as Scott notes, with superforecaster AI, it's unclear why you really need Kalshi or Manifold or anyone else, with all their fees and overhead. Leave them to the degens, and carve off the socially useful part to do much more efficiently - tokens are cheaper than transactions! This is the big prize, but you need to start now before someone else does it better or commoditizes it.

This is exactly what I feel about a lot of the paid investment advice out there. Compounding can make any decent alpha worth a ton, to the point that these people would be investment bankers and not advisors if they knew what they were talking about.

(This is one of the more interesting questions that came out of Alex Karp’s televised borderline psychotic break rant the other day and it has stuck in my mind even though he is clearly unstable)

For example, your customer might really care a lot about some niche prediction like the number of car break-ins in Walmart parking lots. In practice you won’t have sufficient liquidity in a prediction market to actually profit off of that prediction. But a security company might really want to know the answer to it.

Perhaps they may enforce a knowledge cut-off for information retrieval and price the service based on how recent the cut-off is, and also use the cut-off as a way to guard their advantage on the markets.

That said, the very concept of selling such information means that it would eliminate any edge and become zero profit anyway.

But why evaluate AI forecasters by beating the market? Do we evaluate deep learning by whether hedge funds make money from it in the markets? These things have far, far more utility outside of finance.

In general you should judge different prediction markets differently and read the fulfilment criteria though - some are very ripe for abuse, AI or no, while others are much more ironclad.