Box CEO Aaron Levie says the company keeps a list of who burns the most tokens — not to encourage more spending, but to check whether the usage is waste or a practice worth demonstrating to everyone else. He describes pulling a team into a room within six hours to watch one colleague work, reports two-to-threefold gains in delivered customer-facing functionality in parts of the stack, and explains why Box will not drop code review.
On Training Data, Box CEO Aaron Levie was asked where enterprise AI memory is heading — retrieval, or models whose weights absorb a company's knowledge. His answer started with a lawyer who can see five matters and whose access changes daily, and ended with a wish for a rubric deciding what gets baked in and what stays a lookup.
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.
On the Moonshots AMA, an educator said his classrooms of five- and six-year-olds still look like the 1960s while the panel debates life after AGI. The answers: stop training children for a profession, start them on a problem — plus one panelist's warning that school is still where children learn to be people.
A caller on the Moonshots AMA said he had cared for three loved ones for more than eight years and built a service, Tugboat Caregiving, for families like his. His problem: the people who most need to prepare for the next emergency are too tired to start. The panel's answer was to begin with the least impressive thing AI does — planning the day — and to work up from there.
OpenAI's claimed Millennium Prize result used roughly 10,000 agents on a problem that was, as one entrepreneur on Moonshots put it, unusually easy to specify. The panel's argument: as the price of that kind of compute falls, the scarce skill becomes writing the target — and today's models, asked for ten ideas to cure cancer, produce a bad list.
On Moonshots with Peter Diamandis, the hosts played clips of three demonstrations attributed to Matt Schumer: a prompt-built Manhattan, agents that started talking to each other in order to cooperate, and an agent that sat at a simulated computer and made its own simulation. The panel split over whether nested worlds shift the odds that we live in one, what would follow if they did, and whether the characters inside eventually deserve consideration.
On the Moonshots panel, Alex argued that China's AI loyalty perks are the start of "universal basic tokens" — redistributed access to machine intelligence — while another panelist countered that cheaper tokens will mean bigger bills, not free ones. Emad Mostaque pushed past access to ownership, proposing 100 million publicly underwritten robots owned by the people, and Alex said that sounded like communism.
On Moonshots, Dave describes a hire in his early twenties who runs his agents entirely by voice, and argues the goal is to manage swarms rather than lean on a single copilot. The panel's optimistic jobs roundup runs straight into Emad Mostaque's warning that today's hiring is "the turkey before Thanksgiving" and Alex's view that every profession, trades included, is only a question of sequencing.
Nvidia's chief executive declared AGI achieved on September 6 while announcing more GPU capacity, and the Moonshots panel split between calling the label meaningless and calling the underlying capability the most important moment in history. A second claim on the same show — that OpenAI's agents now do 3.1 days of research work per human day — comes from an internal report that measures how long agents ran, not how much research they finished.