Hasn't this been true for like 40 years

The Economist has actually published other human forecasts many times, e.g. Metaculus or Good Judgment forecasts. They do year-end forecasts too.

Whether they draw on AI or other humans seems immaterial to the quality of their reporting.

AI won't replace Ann Wroe at The Economist. It is difficult to appreciate until you've read a few, but Ann Wroe's approach transformed The Economist's obituary section into one of the most widely read features in international journalism.

Cramer is infamous for being a terrible forecaster, and still has a large audience. Which tells you there is more at play than being good at forecasting, you also have to sell a good story

You were obviously within the blast radius.

I was under the impression that front-running was something that happened in the span of seconds (or milliseconds), not a timeframe compatible with LLM inference time.

I know this is tongue-in-cheek, but I think your idea could actually work, but not in financial markets. (The "keynesian beauty contest" of trying to predict what others think been played out to death there.)

You could train a model to anticipating scientific trends. Or policy trends. Others will definitely use mainline LLMs to make decisions there, so they may be more predictable now!

Be sure to sound excited when they call you at 3AM for your award. It helps to say : DYNAMITE! As a term of excitement.

Relatedly: I suspect LLMs are influencing baby names. If you ask Claude or ChatGPT for its favorite baby names, you'll get baby names that right now are skyrocketing in terms of popularity.

Please let me know where I should pick up my Nobel prize.

Maybe you could settle for the FIFA Economics Prize.

Stealing?

I think it's widely recognized to be a game. Humans love games so it's OK

benefit to society

[[richmenlaughing.gif]]

I'll give you 100 Robux how's that

They make their money by peeking at what everyone else's LLM's are doing and trading off that info.

I don't think popularity is a goal for Anthropic or OpenAI. They merely want to have a product that they control and you depend on, and don't care about anything else.

Nobody really *likes* their drug dealer.

Looking good, Billy Ray.

Any better-than-market prediction system won't work after it becomes public knowledge.

Ok like Jim Cramer but for bots

"...but I repeat myself." /rimshot-noise

nsider.ai

"Roundup (YC F28): We combat malicious actors posioning training data, so your market forecasts stay where you want them to."

Given the training data isn’t that more a win for the wisdom of the crowd?

Paywall

Forecasting = modeling + intelligence

How about: forecasting is something you can do by modeling, AI is just modeling with a computer.

I think also a lot of it is intuition.

Yes, and if the things I learned in my university class on the subject still holds, forecasts are incredibly sensitive to modeling decisions such as what independent variables you choose and how you believe they might mathematically relate to the outcome variable. It’s not a zero skill thing, but if anyone’s found a way to consistently mitigate the luck factor then I’d expect them to be wealthier than Elon Musk by now.

And there’s always a huge amount of variation that you simply can’t model, for whatever reason, and is therefore functionally a random factor.

I don’t want to say too much because this isn’t something I went on to actually do after school so I’m way out of my lane here, but I can see room for this to be more akin to “AI wins parcheesi tournament” than it is to “AI wins chess tournament.”

Right. What's really surprising is how much better the best are. Human superforecasters, and prediction markets are surprisingly accurate too.

We could live in a world where things are much more chaotic, and the best humans (or AIs) would only be slightly better than chance. Evidently the world we live in is pretty darn predictable.

The test is when reflexivity kicks in and the prediction itself changes market behavior. LLMs usually melt there

It still doesn't work for me

Doesn't work.

The quant firms are heavy AI investors I believe.

I'm no quant/hft/wall st person, but iiuc a lot of those trades happen in dark pools or by other means to make the positions they take hard to track. meaning you can't go get the receipts of every trade made by medallion fund nor some competitor

It’s actually really easy to make models that can predict “will the market move up or down in the next X microseconds” that score above 50% accuracy. It’s just that there are so many ways to do it that overfitting is practically guaranteed and most models don’t work when actually trading against the market, which reacts to you. Doing those trades well requires more understanding of the underlying mechanisms, not to mention access to data sources that the public simply doesn’t have.

You mean neural networks?

As someone who started working on AI forecasting 3 years ago, I can confidently say that most people did not expect AI to beat Tetlock's superforecasters, Metaculus pros, or prediction markets as quickly as it did.

This is probably the most important concept for "normies" to understand about AI, IMO. It's the stochastic brother of the deterministic Church-Turing thesis. Any function that can be computed can be computed on any computer. And that function can be approximated to an arbitrary degree of precision with a DNN.

The real kicker is DNNs are much easier to program than CPUs because they don't require a closed-form description ("a program") of the function to be approximated; you just throw a bunch of input/output pairs at the model, compute loss, backprop and update weights, repeat.

Hence the unslakeable thirst for input/output pairs, i.e. data.

In the field of machine learning, the universal approximation theorems (UATs) state that

neural networks with a certain structure can, in principle, approximate any continuous

function to any desired degree of accuracy. These theorems provide a mathematical

justification for using neural networks, assuring researchers that a sufficiently large or

deep network can model the complex, non-linear relationships often found in real-world data.[1][2]

The best-known version of the theorem applies to feedforward networks with a single hidden

layer. It states that if the layer's activation function is non-polynomial (which is true

for common choices like the sigmoid function or ReLU), then the network can act as a

"universal approximator." Universality is achieved by increasing the number of neurons in

the hidden layer, making the network "wider." Other versions of the theorem show that

universality can also be achieved by keeping the network's width fixed but increasing its

number of layers, making it "deeper."

https://en.wikipedia.org/wiki/Universal_approximation_theore...

Not sure that is enough for forecasting as the function to be estimated could change over time in random ways.

Climate as a pattern of weather over a long period of time. If the climate is increasingly unpredictable, I would think that it wouldn't really effect our ability to make short-term predictions, like a few days out.

But our ability to forecast weather on a longer timeline, like for industrial forecasting, is calibrated on historical weather patterns. But with weather being more erratic and unusual, I don't understand how AI will be forecasting with the models they have now.

Yes, I heard from one first-rate forecaster that he thinks AI forecasters are especially weak in predicting big disruptive changes to the world.

Hard to study this, obviously!

Of course model predictions will be acted upon, which will invalidate the predictions.

See also The AI Superforecasters Are Here https://www.astralcodexten.com/p/the-ai-superforecasters-are... and discussion https://news.ycombinator.com/item?id=48806296

I'm only 80% sure you're joking

That's just a guess.

Do you and your cat make better predictions than your friend without a cat?

I don't understand your analogy. Are you just suggesting that luck plays too large a role in this contest? Clearly there is some "skill" or ability factor because AI's have been scoring higher and higher each year. Also, they make reference to superforecaster humans, who are presumably consistently better at forecasting than their peers.

They aren't guessing heads or tails, they give odds for each event. It's more like eyeballing a thousand coins to guess how fair they are, and then flipping each one just once.

Some are weighted to be 99% heads, others are 10% heads etc.

You could have 1,000,000 people guess random percentages for each coin, but suppose 10 of the coins are weighted 100% heads. To guess within 25% of the true value for all 10 of those coins would be roughly 1 in a million.

So a lucky guy guesses within 25% for all 10, he'd have another 990 coins he's being judged on.

The markets are a highly complex dynamic system. There are many instances of it exhibiting disastrous behavior, especially in response to changes and shocks.

AI trading and investment advice meaningfully changes the system and its dynamics. It seems highly probable that this will result in it failing in new ways.

This is a pretty good article on Mercer and Renaissance: https://www.newyorker.com/magazine/2017/03/27/the-reclusive-... https://archive.ph/8Iuyh

Magerman told me, “Bob believes that human beings have no inherent value other than how much money they make. A cat has value, he’s said, because it provides pleasure to humans. But if someone is on welfare they have negative value. If he earns a thousand times more than a schoolteacher, then he’s a thousand times more valuable.” Magerman added, “He thinks society is upside down—that government helps the weak people get strong, and makes the strong people weak by taking their money away, through taxes.” … Another former high-level Renaissance employee said, “Bob thinks the less government the better. He’s happy if people don’t trust the government. And if the President’s a bozo? He’s fine with that. He wants it to all fall down.”

I don't think analyzing just the directional correctness is enough. Magnitudes shouldn't just be ignored. A trader can still make money even if more than half of their trades were wrong directionally, and can still lose money (or even go bankrupt) even if more than half of their trades were correct directionally.

We measure skill differentiation between frontier / last-gen LLMs across our environments, and one of our curated coding environments is a closed-system market simulator, containing only other agents and some system participants (a market maker and a liquidity provider via issuance / buybacks) whose behavior is fully defined for all of the agents.

This has the least measured skill differentiation of all of our environments, and not because forecasting/markets don't require skill or intelligence. Even the best models are so far from anticipating the behavior of the other agents and understanding the emergent effects that a 2025 model with a naive strategy can often outperform over the timeframes of the simulation simply because some other models in the simulation chose a similar self-reinforcing strategy. This likely happens to some degree in real markets.

You can watch these simulations here https://gertlabs.com/spectate?game=market

Anyone who believes this news story should create their LLM slop bot to trade on prediction markets like Polymarket and Kalshi. These acceletards provide a great influx of money to many human traders on these platforms.

Investing used to be a resource-weighted signal of human economic projections, where resources flow to better projections. Public markets had social value for their resource allocation and signalling/coordination benefits. Now? How could they avoid hallucination contagions?

I don't at all understand this perspective.

It seems to me that LLMs excel at a few things, and synthesizing data is a big one, which is very much the domain of forecasting. The challenge is understanding which signals are relevant for a forecast, but with enough historical context and structured data, LLMs appear to be almost perfectly designed for the task.

For example, I let Google AI see my fantasy football team on Sleeper and make recommendations. It is helpful because it sees everything about my team, the league settings, player rankings, etc. and can make relevant recommendations. But the recommendations are only as good as the source data allows. If there was a massive repository of data about WRs who went through Nebraska's program and how that translates to NFL performance in year 1, or how rainy weather is likely to affect Josh Allen's performance on the road, or the impact of playing Thursday night games on a short week in relation to defense performance. If those billions of data points were embedded in a model, imagine how much better recommendations/predictions could get.

Since I can't read the article I believe they are referencing to https://www.metaculus.com/tournament/metaculus-cup-summer-20...

It seems to be that they were trying beat a guy named Dylan Mathews. And seems like the community beat him in making predictions for 58 questions about the future