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

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!

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: arxiv.org/abs/2608.28482

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: utm.io/usdRP

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.

Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. thinkingmachines.ai/news/int… Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵

Foresight-V4 is live 🔮 This new model from @lightningrodai sets the Pareto frontier for AI forecasting — whatever your budget, nothing forecasts better. Full reasoning mode is the most accurate model we've ever tested, beating every frontier AI on both accuracy and calibration. Low effort mode also beats every other AI, with much fewer tokens and a much lower cost per forecast. This is critical for forecasting tools or agents operating at scale, making thousands of predictions per day. Full writeup 👇

Our paper "Future-as-Label: Scalable Supervision from Real-World Outcomes" was selected as one of 10 spotlights at the AI Forecasting Workshop at @icmlconf 2026! This paper describes our core methodology, demonstrating that AI can learn directly from real-world outcomes over time at scale — no human annotation required.  Full paper in comments. Excited to be included alongside work from DeepMind, Google, Kalshi, and others! If you'll be at ICML on July 11th, stop by our poster at the lunch session!

🆕 New project added to the prediction markets wiki Lightning Rod Labs @lightningrodai builds infrastructure and research tools for prediction markets ⚡📊 Its work focuses on market intelligence, forecasting systems, and tooling that helps traders and researchers extract signal from prediction markets

On a subset of ForecastBench questions, an LLM has matched superforecaster performance for the first time. A submission from @GoogleDeepMind, named “green tree,” is now #1 on dataset questions on ForecastBench, our AI forecasting benchmark. Superforecasters remain #1 overall.