An AI publication
For readers who want to understand important AI developments and keep following the questions that matter.
Our articles start from AI podcast conversations and the research papers and articles they cite, with links to the original material.
A briefing reports one development at a point in time. We may correct or clarify it later; a new development gets a new briefing. How our formats work
Evan Conrad, CEO of SF Compute, says delivering traded GPU time made his firm do a cloud's work, yet it owns no chips because that would let it manipulate prices. He argues NVIDIA and big clouds couldn't run such a market neutrally.
SF Compute CEO Evan Conrad says only about 5% of his company's capacity is on its order book, but customers recoup $25 per $100 on average by reselling. In his account, interruption terms, lead time and expected demand shape GPU prices.
SF Compute CEO Evan Conrad argues that GPU time is too varied to trade as one uniform commodity. He says cash-settled contracts and price indexes should be built on markets that actually deliver compute, or they risk manipulation and poor hedges.
Biohub's Alex Rives expects pretraining, elicitation and RLVR to carry over from language models to biology, but says verification must happen in the lab. He is unsure whether biology and language models will share one latent space.
Biohub science head Alex Rives argues that no lab animal can ever be a perfect stand-in for people. He says an AI model trained across many species, as protein models were trained across evolution, could predict human biology far better.
Biohub's head of science Alex Rives argues that cell models are held back by what biology can measure. His examples are whole-zebrafish imaging and a laser phase plate for electron microscopes, so far tested only on isolated proteins.
Biohub's head of science, Alex Rives, says virtual cell models fall short above all because biology can't yet measure what matters. He expects an information-level cell model within about five years.
Josh Gao, CEO of robot-data supplier Mecka AI, says labs train on about a million hours of human data, tens of thousands of gripper hours and a few teleop hours. He argues scaling that alone won't yield a robot that learns any task by watching.
Mecka CEO Josh Gao says robotics is still orders of magnitude short of Tesla's driving data. He expects constrained, goal-oriented jobs such as industrial cleaning to come first. He says homes are where the field must take the most care, and that data collected on site can close the reliability gap.
Mecka CEO Josh Gao described how his company makes robot training data, with camera caps, force-sensing gloves and checks against fraud. He said contributor pay matters less to its margin than verifiably high-quality data at scale.
Peter Diamandis said racing and engagement-driven business models push AI toward misalignment. He proposed insurer-backed standards like 1894's Underwriters Laboratories and alignment prizes. Salim Ismail asked whose values AI should follow.
XPRIZE CEO Anousheh Ansari said a prize to store donor organs for months, tested in pigs with a six-month target, won this year's Visioneering. She and Peter Diamandis also described a funded telescope-data prize and less advanced ideas.
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