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