If the forecasting models were so good that people were actually consistently beating prediction markets, they wouldn't be starting startups to be selling it.

And even if it is good enough, once you're shelling out thousands of dollars a year in research costs, does that give you any remaining alpha?

> And even if it is good enough, once you're shelling out thousands of dollars a year in research costs, does that give you any remaining alpha?

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.

You got it (I was going to say "nailed it" but that's becoming an LLM marker).

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.

Agree, to an extent. The link certainly smells like an ad. If the predictions are on lower volume markets (i.e. no institutional investors with actual quants behind) the compounding power stops. Also that means it can't beat institutional investors. So the reasonable option is to monetize what you can.

If you had a tool that would be capable of writing all software better than a human, why would you price it by the word (token)? Why wouldn’t you take a percentage?

(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)

The reason is, customer might care a lot more about the prediction than you do, or anyone else does.

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.

If a model demonstrates being good by winning on prediction markets I may want to ask the same model questions which are too niche to have markets. That said I don't know if niche questions are enough of a revenue stream to be interesting for model providers.

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.

Doesn't this argument prove too much? Why does AlphaSense sell their company research instead of using it to trade themselves? Why do people work on open source time series forecasting packages instead of quietly using them to trade?

They do. One guy with at least 7 figure PnL is a well known forecaster, SemioticRivalry.

The market of trading is smaller than the market of business. I don't think most people have ever actually tried trading for real. The frequency with which you can put in a buy/sell order and nothing happens. Then you raise/lower the price several times and nothing still happens because nobody wants to buy or sell at a price that makes you money is much higher than you would imagine. If you've never tried it yourself.

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

Yes, but have you considered the performance of a few lucky outliers? Catch 'em fast before they regress to the mean!