ddp26

FutureSearch | Multiple senior roles | REMOTE (London, SF) | Full-time | $130k–$250k + equity | https://futuresearch.ai

FutureSearch builds AI that predicts the future. You can see our public track record on our site. We launched the FutureSearch product this August. We also publish benchmarks and datasets, and do work for frontier AI labs.

I'm the CEO, previously CTO of Metaculus and creator of Google's current internal prediction market. FutureSearch is about ten people, remote-first and concentrated in London.

We're hiring (US pay, adjusted to the local market elsewhere):

- Chief Operating Officer, $180–250k - Director of Partnerships, $160–240k - Director of Strategic Projects, $140–220k - Head of Product, $160–240k - Program Manager, Research, $130–190k - Research Scientist, $160–250k

See the jobs and apply at https://futuresearch.ai/careers/

Isn't the important takeaway here that Gemini 4 is not released and has no planned release date?

This is marketing from Google, not a competitive offering

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!

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.

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.

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.

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!

I spent a summer in PNG in 2010, on the island of Karkar. It was wild.

One of my most formative memories was finding out that few of the people who live there, even those who are literate, had books and some DVDs, were high school students, even had occasional access to computers with internet, knew that humanity had landed on the moon!

There must be a deeper read on why Google can rapidly ship better small models while being delayed months on the bigger model.

What's the simplest explanation?

Even though these predictions turned out mostly wrong, we should not castigate people for publicly forecasting! That is virtuous, and more people should do it.

Thank you Ed!

What are we to infer from no release of gemini-3.5-pro, but frequent releases of smaller flash models (presumably from the same large pre-training run?)

Not forecasting though. You can't goodhart predicting real-world events

This seems bad for AI safety/risk. Does DeepMind have any checks on model alignment now? What's stopping them from using AI for military/surveillance purposes?

Would it though? Meta and Microsoft have had very scandalous AI things happen, and their shares didn't tank (or quickly recovered)

I would have thought Jeff Dean would never ever leave Google. What on earth is going on?

Isn't this the same as saying "utility regulators delaying connecting new power to the grid hiked electricity prices on the public by $23B?"

When my apples are expensive, I don't generally grumble about all the demand from pie makers. If they demand more apples, new suppliers should come in to restore the price, right?

> I can ask an agent to add OAuth, you can ask one to add caching, and somebody else can ask one to rebuild the database from first principles and make the UI pink. Each change can be reasonable in isolation.

But this is just bad vibecoding? This would be bad if humans did it too. With agents or humans, you need to coordinate.

When I know something is (primarily) AI generated, I lose interest.

The exception is when it's about a niche I care about, e.g. an analysis of opening trends of early world chess champions. I'll read AI on that for an hour.

My sense is that, for most writing, it's fundamentally interpersonal, the information is about the author as much as it is about the world.

Maybe this flood of slop will cause people to care more about the substance of the writing, not the perspectives of the writing.

Is it possible GPT-5.6 is not a very aligned model?

People have been making claims about the commoditization of llms since chatGPT, and they've been wrong every time as quality and prices and differentiation have increased.

But Scott's point is more: why even have markets? Once you have the superforecasting available on the questions you care about, why do you need to publish it for everyone to also react to?

Almost by definition, once AI forecasters are in the market, they won't (all) be beating the market.

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.