Source podcast
Nathan Labenz explores AI through expert interviews and discussions of its uses and consequences.
Thomas Sohmers, co-founder of AI chip company Positron, says GPT-6 Astra and Opus 5.5 agents now run its chip tests on emulators in a closed loop. He says the company's token spend briefly exceeded its human salaries and peaked above $100,000 a day, and the agents have not yet had a novel design idea.
Positron CTO Thomas Sohmers said AI agents are multiplying demand for memory. He claimed Positron's first-generation product sustained 93% of theoretical memory bandwidth, against an average of 30–40% for NVIDIA GPUs when decoding with transformer models. Its next chip, Asimov, is planned for 2027.
Nathan Labenz argues an AI-assembled music track shows models generalizing beyond tasks with checkable answers. Mercor's Edward Hu is more conditional: progress depends on how clearly and cheaply 'better' can be measured.
Nathan Labenz says frontier-lab insiders at The Curve described models feeding their own training data, rivals kept close by distillation, and timelines shorter than 2028. He notes the room leaned toward short timelines.
Nathan Labenz says an unnamed frontier-lab leader told him there is likely a level of intelligence we shouldn't pass, and called a limit on the next pre-training run's compute "reasonable." Labenz himself adds that counting compute is hard.
OutSystems CEO Woodson Martin argues that as AI software platforms converge on the same building blocks, trust earned in regulated industries and industry specialization will decide who wins. It is a vendor's view; OutSystems sponsored the episode.
OutSystems CEO Woodson Martin expects to still have 100-plus phone apps a year from now. He sees a real chance that much software becomes widgets inside AI conversations, and cited a June demo that moved a loan request from Claude into a bank app.
OutSystems CEO Woodson Martin says AI makes feared legacy rewrites feasible, while self-built AI dashboards create cleanup work. He says customer gains take time to reach profits and that deciding what to build is still the hardest part, even though building capability has improved.
OutSystems CEO Woodson Martin says the company's AI token spending peaked in June and July, then fell below forecast after it built its own harness and model router. He argues most enterprise work doesn't need frontier models.
Keerthana Gopalakrishnan of Google DeepMind said robot training will probably blend teleoperation, sensor-equipped handheld rigs and human video, each trading scale for precision. On world models, she said the jury is still out.
Keerthana Gopalakrishnan, research lead for Gemini Robotics at Google DeepMind, argues that people won't use unsafe robots. She splits robot safety into avoiding accidents, following intent within guardrails and handling sensor failures.
Google DeepMind's Keerthana Gopalakrishnan said humanoid sprint records show locomotion that trains well in simulation. In her view, contact-heavy hand work is the real frontier, where hands have improved but still need reliability and lower cost.
