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 Moonshots with Peter Diamandis, a panelist interrupted an argument about AI regulation to read a headline off his feed: Anthropic had formalized Fermat's Last Theorem. Anthropic's report describes dozens of agents working eleven days, about six billion output tokens and 30,300 intermediate theorems — plus a piece of bookkeeping software that stopped runs from losing track of their own work. The panel's takeaway was about how to narrow enormous machine output into one result you can build on.
On the Moonshots panel, Peter Diamandis runs a Grok Bot chief of staff called Skippy and Emad Mostaque runs 18 of them across his own machines, installing models and making art. Salim Ismail calls it the move from asking an AI to assigning work; Alex argues the messaging-app interface cannot possibly scale.
On Training Data, Parallel Web Systems founder Parag Agrawal traces how agents multiply web searches — from a weekly credit-risk review across 10,000 small businesses to the meeting-prep agents that run hundreds of searches before his own calls — and forecasts a web that, in a couple of years, tells agents when something worth acting on has changed.
Meta’s Muse Glimmer is a 30-billion-parameter model designed to run agents on personal computers. Alongside Mark Zuckerberg’s vision of personal superintelligence, it prompted a Moonshots debate about whether open models put users in charge—or strengthen the company that already owns their favorite apps.