Your paper reminds me of a passage, likely one of the last things T.S. Eliot wrote, from `Little Gidding` in which one stanza describes a moment in history when Germany bombed England long before the end of the war:
> "A people without history
Is not redeemed from time, for history is a pattern
Of timeless moments. So, while the light fails
On a winter's afternoon, in a secluded chapel
History is now and England."
Asking an LLM about this verse, it seems to understand history is a pattern and that history is used to predict the next event in a sequence but it really doesn't understand the significance of the author writing "History is now and England."
I agree with this output:
> In essence, the stanza argues that history—composed of key, enduring moments—is vital for redemption and identity. Without it, a people are lost in time. This concept parallels how LLMs work: by analyzing and learning from historical (past) data, they identify patterns that allow them to generate future text. While LLMs don’t “predict the future” in a prophetic sense, understanding and leveraging patterns—much like those in history—enables them to produce output that reflects continuity, context, and nuance.
Thus, while the poem and LLMs operate in very different realms (human experience vs. statistical computation), both rely on the idea that recognizing patterns from the past is crucial to shaping or anticipating what comes next.
Assuming LLMs eventually get really really good at this.
Do you see this destroying prediction-based markets (i.e. the stock market and Polymarket)?
Markets exist because there's uncertainty about the future. If LLMs can predict with extremely high accuracy, would there no longer be a need for markets?
If your oracle can tell me (and everyone else) the prevailing price of copper in 6 months in a manner which accounts for the reflexivity of everyone suddenly learning what will be the precise prevailing price of copper in 6 months, you've got yourself a perfect universe simulator and I'm not sure what the point is of worrying about any hypotheticals (or copper) at that point.
LLMs might get better at making predictions than humans but there are fundamental mathematical laws that limit how accurate they can get. A key result of chaos theory is that many processes take exponentially more work to simulate linearly further into the future, so accurately predicting them far enough in the future quickly grows in hardware requirements to the point where it would take more compute than is available in the known universe. So there's a hard limit on how accurately any phenomena that's a result of chaotic processes (in the mathematical sense) could be predicted in the future.
I don't forsee this destroying prediction-based markets in the near-term. It might make them more efficient, but you could have different LLMs competing in the same way humans do now. Its also interesting how this could create markets for more things that aren't considered on as much now because they are too difficult to estimate. At the end of the day though, LLMs are limited by the information provided to them.
Wouldn't predicting the future at that scale automatically change the future and make it unpredictable again?
It is one thing to predict the future and have everyone not know about the predictions, but in a world where many people will be able to use LLMs to predict the future, the lower the quality of the predictions will be because they won't take into account that there are other agents predicting the future, which would influence the action of those agents, so you end up in a game theory scenario not that dissimilar from what we have now
I think you could simply shift the market 6 months in the future. no prediction system will be perfect for arbitrarily long horizons at reasonable cost.
We are currently focused on our plans for the next phase of this but cleaning things up and open sourcing is something we could consider in the future!
