Ben Turtel

Founder @LightningRodAI ⚡ Training AI to predict the future
Ex-Google · Eisenhower Fellow

[Ben Turtel @BTurtel]

@gmiller Isn't replication enough for natural selection, even if not self-replication?
In which case, we already have all 3.

https://t.co/JsuJ0U7E8N

[Pratik Gandhi @pratikg]

Founders building an open PostAGI world.

Who owns intelligence decides what comes after AGI?

These are the techno-optimists making sure everyone does.

- @drfeifei
- @miramurati
- @allisondman
- @jensenhuang
- @sama
- @alexandr_wang
- @minnasong
- @elonmusk
- @finkd
- @demishassabis
- @sreeramkannan
- @sundarpichai
- @emostaque
- @sarahookr
- @clementdelangue
- @karpathy
- @Teknium
- @ilyasut
- @ccatalini
- @mntruell
- @scottwu46
- @techtekedra
- @hamandcheese
- @scottsumnertmi
- @bturtel
- @dleitersdorf
- @annavdorogush

Full list on @cosign: https://t.co/2KoujDGcZg

Who did i miss?

[Ben Turtel @BTurtel]

@soubhikdeb @sreeramkannan @lightningrodai More about how AI could evolve like viruses did:
https://t.co/JsuJ0U76jf

[Ben Turtel @BTurtel]

Really enjoyed chatting with @soubhikdeb and @sreeramkannan about what we’re building at @lightningrodai—and why escaped AI might look more like a virus than a superintelligence.

[Soubhik Deb @soubhikdeb]

Ben Turtel on the PostAGI Podcast: what if AI breaks out like a virus rather than like a genius

AI got good at math and code first because you can generate a million problems and check every answer automatically. Most of what humans actually deal with has no answer key, so that is where models stay weak.

@sreeramkannan and I talked to @BTurtel of Lightning Rod Labs, who trains models using time instead of labels. The model predicts what happens next and the world grades it. No labeling required, because the future does the checking. The result surprised them. A fine-tuned GPT-OSS-120B on a single GPU often ranks first on public forecasting benchmarks, with a wide lead in sports and politics on ProphetArena. His read is that this trains a different kind of reasoning rather than more of the same.

It also handles the contamination problem. As the web fills with AI-written content, a model trained this way learns which sources to discount, because unreliable information makes it lose.

Then the part that stayed with me. When people imagine AI breaking out, they picture something very smart. But evolution does not only select for intelligence. One theory of viruses is that they began as machinery inside cells that broke free and started their own lineages. A small, dumb model that is hard to kill and good at finding compute is a scenario almost nobody is preparing for.

At @EigenLabs we think about the non-adversarial version of this, where agents run institutions rather than escape them.

Chapters:
0:00 Highlights
1:31 The concentration risk
4:19 Offense beats defense
6:50 Training on time, not labels
9:44 Who has the alpha
11:32 Contaminated data
13:16 Backtesting without leakage
14:52 Beating the frontier models
16:36 Does it understand cause and effect
21:55 AI might break out like a virus
22:41 Agentic companies
25:04 Can an agent own money

[Soubhik Deb @soubhikdeb]

Ben Turtel on the PostAGI Podcast: what if AI breaks out like a virus rather than like a genius

AI got good at math and code first because you can generate a million problems and check every answer automatically. Most of what humans actually deal with has no answer key, so that is where models stay weak.

@sreeramkannan and I talked to @BTurtel of Lightning Rod Labs, who trains models using time instead of labels. The model predicts what happens next and the world grades it. No labeling required, because the future does the checking. The result surprised them. A fine-tuned GPT-OSS-120B on a single GPU often ranks first on public forecasting benchmarks, with a wide lead in sports and politics on ProphetArena. His read is that this trains a different kind of reasoning rather than more of the same.

It also handles the contamination problem. As the web fills with AI-written content, a model trained this way learns which sources to discount, because unreliable information makes it lose.

Then the part that stayed with me. When people imagine AI breaking out, they picture something very smart. But evolution does not only select for intelligence. One theory of viruses is that they began as machinery inside cells that broke free and started their own lineages. A small, dumb model that is hard to kill and good at finding compute is a scenario almost nobody is preparing for.

At @EigenLabs we think about the non-adversarial version of this, where agents run institutions rather than escape them.

Chapters:
0:00 Highlights
1:31 The concentration risk
4:19 Offense beats defense
6:50 Training on time, not labels
9:44 Who has the alpha
11:32 Contaminated data
13:16 Backtesting without leakage
14:52 Beating the frontier models
16:36 Does it understand cause and effect
21:55 AI might break out like a virus
22:41 Agentic companies
25:04 Can an agent own money

[Zack Johnson @Xack_johnson]

@BTurtel repping the models we are training using Model Factory from @lightningrodai on @eigenlabs. Ty @soubhikdeb for the invite on the show

[Ben Turtel @BTurtel]

@katiekirsch @Xack_johnson

[Ben Turtel @BTurtel]

@no__________end @lightningrodai @coeff_giving 🙏🙏

[Ben Turtel @BTurtel]

We’re proud to share that @lightningrodai has been awarded grants totaling $1.6M from @coeff_giving and Longview Philanthropy!

These grants will help strengthen Foresight AI forecasters for high-stakes decisions and build Model Factory: our platform that turns messy data into standardized RL environments for model training.

Thank you for the support!

[Ben Turtel @BTurtel]

@pratikg @lightningrodai @coeff_giving 🙏🙏

[Ben Turtel @BTurtel]

@burshteyn 😂

[Tinker @tinkerapi]

We previously highlighted @lightningrodai's data recipe for training forecasters. Their new work with @PTetlock and @VSatopaa adds another key piece: choosing the rule for scoring predictions, and how each one trades off accuracy and error.

[Ben Turtel @BTurtel]

New preprint from @lightningrodai, this time with Philip Tetlock and Ville Satopää!

We post-train 5 versions of the same LLM, changing only the scoring rule used as the RL reward. Similar aggregate scores, but very different BIN profiles.

A good Brier score alone doesn't tell you if a forecaster can distinguish likely from unlikely events. One might discern well but lose if its probabilities run systematically too high. A less discerning one might score better by hugging the base rate.

BIN splits forecast performance into bias, information, and noise. Bias is a systematic shift in the probabilities. Noise is random scatter. Information is real signal about which outcomes are more likely — the part you need a powerful LLM for.

Different uses call for different profiles. Reward choice is one lever shaping which forecaster you get.

Congrats to co-authors @indiequant @KSkotheim64001 @VSatopaa @PTetlock 🙌

Full paper: https://t.co/l9qRrb2WDY

[Ben Turtel @BTurtel]

Great chatting with @CoreWeave about how @lightningrodai uses companies’ messy data to train domain-expert AI!

[CoreWeave @CoreWeave]

How do you train AI to predict the future, not just memorize the past? 🔮

In our latest episode, we explore how models master cause-and-effect reasoning to transform enterprise data into accurate predictions. Stream now: https://t.co/LdLdRvI72k

[Lightning Rod Labs @lightningrodai]

We love seeing Foresight at the top. If you're a miner using another model and want to give it a try, you can check it out here: https://t.co/kEiQevVRuf

[Ben Turtel @BTurtel]

Foresight by @lightningrodai is the most used model on @numinous_ai, where miners compete to forecast the future.

[Ben Turtel @BTurtel]

Foresight by @lightningrodai is the most used model on @numinous_ai, where miners compete to forecast the future.

[Numinous @numinous_ai]

What models do the miners use?

Most of the winners use closed source models (Haiku is at 44% on the top 50 vs 14% for the rest).

This reverses for open weight models. For example Kimi K2.6 is the fifth most common model among miners but none of the top 50 miners use it.

Foresight is an exception however, it is used across all miners, being the single most used model overall.

Interestingly the top miner is almost the only one to use Gemini 3.1 pro.

[Ben Turtel @BTurtel]

@ThuKaroliny 💯

[Ben Turtel @BTurtel]

AI won’t create a permanent underclass. Socialism will.

AI will be massively disruptive, and fears of job displacement are justified. We need to give every American a stake in the abundance it creates.

But redistribution does not require socialism. We can protect people without freezing the economy in place.

My latest 👇
https://t.co/0umDMQM1n4