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?
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.
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.
Ok… assuming you can’t use that $2mil for some reason, simply take out a $10k line of credit and you’ll have $571 million in 7 months.
If you have the 2mil, congrats you’re 7 months away from $114B - you’re now one of the top 20 richest people in the world.
If this was truely the money printing machine they are saying it is, they would not be talking about it.
A particular trade that can 2x $20k won't be able to do the same for $20 billion.
It's why RenTech capped their Medallion Fund and closed it to outside investment.
If there's only a billion dollars sloshing around on Kalshi, you can't expect to put $1 trillion into bets and take $2 trillion out.
This doesn't pass the smell test.
I asked the guy who turned $35 into $2 million in seven months on Kalshi whether, in another seven months, he would be able to 100,000x his money a second time to $200 billion. Unsurprisingly, he said no - there’s only so much easy money on Kalshi, and his AI had already taken it all (also, other people with similar AIs are starting to fight him for it!)2. The alpha dries up with more players, even in the year or whatever since that founder started.
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.
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.
(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)
That said, the very concept of selling such information means that it would eliminate any edge and become zero profit anyway.
Futures are the only thing for me this has never happened on. However no retail broker I'm aware of allows trading futures though API.
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.
As soon as the prediction market says “this path here has a 25% chance of curing cancer” all sorts of money is moved away from other things.
It will absolutely cause political outcomes not just predict them.
And then of course there’s the cheating element. Anything that’s feasible to change the outcome.
Maybe this just contributes to efficient markets? Or maybe the continued quests towards utopia have dystopic externalities.
"This, then, is my prediction for the AI superforecaster future: for basic questions, your off-the-shelf AI chatbot will be able to offer opinionated probabilities superior to those of any human. For more controversial or bias-laden questions, a new era of prediction markets will smooth over differences in brand and model and efficiently aggregate all AIs’ opinions."
But one reason that you would publish it for everyone else to react was exactly what I said, because if you can show people that a superhuman AI believes a certain outcome is more likely, you impact whether or not that certain outcome is more likely. Which feels like a flaw in this version of the future. That in fact you could overcorrect and make the markets less efficient.
Play if you want, and call yourself a genius if you win, but only with money you can afford to lose.
Eg. Is corp ABC's stock price going to go down by 10% in 30 days?
Orchestrate a disinformation campaign on the 29th day to tank ABC's stock price.
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.
I am embarrassed this “rationalist, I’m so much smarter than you, so I know better” asshole Scott Alexander hasn't been skewered yet for promotion of gambling (prediction markets) that people like Trumps son are making millions of on
https://www.theguardian.com/us-news/ng-interactive/2026/may/...
As prediction markets already show, forecasts can influence the outcomes they are trying to predict.
What happens when these models become extremely accurate and widely trusted? A forecast like “Will there be a war between countries A and B?” may itself affect whether the war happens.
If the model says there is a 1% chance of war, little changes. But if it says 90%, governments, markets, militaries, and the public may react: capital flees, troops mobilize, diplomatic trust collapses, and each side starts preparing for the other side’s preparation. The prediction helps make itself true.
The same feedback loop could apply to bank runs, market crashes, civil unrest, elections, and corporate failures.
At some point, the most accurate forecaster may become less like an observer and more like an actor with enormous power over the system it predicts.