On Training Data, Box CEO Aaron Levie describes how his customers actually pick models: a default for asking questions of their files, and hard-nosed accuracy evaluations for the high-volume extraction work where most tokens are spent. He endorses Decagon founder Jesse Zhang's argument that mature workflows migrate to open-weight models, and explains why the big labs' revenue and open-weight token volume can climb at the same time.
On Sequoia's Training Data podcast, Box CEO Aaron Levie said any company sitting on customers' data now has two obligations: build an agent measurably better than an off-the-shelf one at its own workflows, and expose the same capabilities to outside assistants like Claude and ChatGPT. He described the tuned search-and-retrieval harness behind Box's agent, the evaluations that track model progress, and his bet that within five years roughly 90% of enterprise tokens will be spent on work nobody asked for directly.
On Sequoia's Training Data podcast, Box chief executive Aaron Levie explains why AI swept through software engineering and is moving far more slowly through legal work, sales and the rest of knowledge work: code is text, engineers fix their own broken connections, and their work already lives in GitHub. His conclusion is that the tedious work of getting AI into other people's workflows — not the models themselves — is where he is betting the money is.
A civil engineer called into the Moonshots AMA to ask why two data buyers had shown interest in his 30-year project archive and then gone quiet for weeks. The panel's answer: they are not haggling, they are overwhelmed — so put the archive in a package with a price on it.
A Moonshots panel unpacks Dwarkesh Patel and Jerry Han's experiment, which found that improvements in training data delivered a 12-fold compute-efficiency gain between 2019 and 2025 against 3.7-fold for architectures and training recipes — at small scale, on easy benchmarks. The panel then splits over whether a company's proprietary data is a durable advantage, with a $32 billion data-subsidiary valuation on one side and the fate of BloombergGPT on the other.
Google DeepMind's AlphaGenome Atlas stores predicted molecular effects for every possible single-letter change in the human genome. On Moonshots, the panel called it the "bulk solution" to variant effect prediction, proposed it as a naming system that could help people with the same rare mutation find each other — and argued about why it is not really a lookup table.
Tesla opened an interest form for businesses that want to buy Cybercab fleets and build mobility hubs for its Robotaxi Network, without publishing prices, delivery dates or revenue-sharing terms. On Moonshots, Peter Diamandis said he had filled it out, and the panel turned the invitation into an argument about owning machines instead of working for wages.
Anthropic's Fable 5.1 charges $0.25 per million tokens for cached reads, a quarter of the previous rate, which one Moonshots panelist read as an invitation to load an entire company's context into the model and keep it there. The panel connected that price to a wider scramble: with model leads lasting about a month, the labs are racing to convert them into customer workflows, partnerships and proprietary design data that a rival cannot copy.
A Moonshots panel watched video of Tesla's two-seat Cybercabs moving through Austin and spent most of the segment on a price: the roughly $30,000 the panel says Elon Musk wants to charge for the car, and what happens if ordinary people buy a handful each and put them to work. Their forecasts of twenty-cent miles and car-free city centers sit alongside Tesla's own more modest description of a limited Austin service — and alongside London, where Uber's first autonomous rides still carry a licensed driver.
OpenAI has proposed ending the agreement that supplies its models to Cursor, now owned by SpaceX, on 12 November. On the Moonshots panel, one guest read the move as OpenAI betting on its own enterprise stack; another argued the real prize is reasoning traces — the working a model shows while solving a problem. Both explanations lead to the same awkward conclusion: Elon Musk and Anthropic now need each other.
An engineer who faked a missing editing feature using comment fields told Peregrine what to build next. A hurricane simulator stayed with one city. Co-founders Nick Noone and Ben Rudolph describe how they decide which piece of field improvisation becomes a product — and what they say it now costs to serve a city this way.
On the Training Data podcast, Peregrine founders Nick Noone and Ben Rudolph argue that the public-safety software business has grown by collecting ever more data, and that their company inverts it: join the records an agency already holds, leave ownership with the agency, and lock down who may look. The same logic leads Noone to refuse a company-wide ban on facial recognition, leaving that decision to customers, law and local norms.