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Reporting and discussion about AI & Work, with links to the original sources.
A 1937 question about why firms exist at all came back to the Moonshots panel by way of an economics workshop framework on AI agents as market participants. Salim Ismail argues the...
Updated 15 Sep 2026 IdeaThe inner-loop thesis says AI advantage may come from how well an organization turns experience into better systems, rather than from owning the best model. A study of 51 successfu...
Updated 14 Sep 2026On Sequoia's Training Data podcast, Box's Aaron Levie explains why generated code feels acceptable while a generated board deck does not: a presentation is still read as evidence of what its author knows and can execute. He admits the double standard — he uses AI for his own brainstorms and decisions — and describes reading posts twice, once for the substance and once to guess who wrote them.
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 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 a Moonshots listener call, Matt from Terre Haute, Indiana asked what people with families, mortgages and a fixed location should do, since so much AI advice seems aimed at 22-year-olds working 80-hour weeks. The panel's answers: describe your actual obligations to a language model and brainstorm at the margin, practice on the tools at night, build a few online peer relationships, offer your domain knowledge to a marketplace like Mercor, and schedule slack time rather than waiting for spare time. The bigger forecasts about franchise-style openings from AI labs remain forecasts.
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, 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.
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's Ben Rudolph described integration agents that run for hours, inspect a customer's databases and split work among sub-agents, writing roughly 90% of the Python notebooks the company uses to connect public-safety records, under the deployment team's oversight. The conversation put the share of effort that happens before a user types a question at 95% — the preparation that let a Florida county ask why it had suddenly run more than a hundred water rescues.
On the Training Data podcast, Peregrine co-founder Ben Rudolph describes building the company's first operational AI agent with a police customer that had worked a case ending in the exoneration of a wrongly convicted man, then asked whether an agent could reproduce the same findings. He says the agent, which runs for 30 to 60 minutes over hundreds of gigabytes of case evidence, is now used in a few US departments, including a Wisconsin county where a handful of phone records helped place a suspect. Co-founder Nick Noone says the company deliberately lets customers take the credit.
On The Diary of a CEO, investor David Friedberg argued that AI grows companies rather than shrinking payrolls, using a painter commanding five robots as his image. Pressed on entry-level hiring, Klarna's staffing numbers and a Stanford payroll study, he named the assumption he thinks could be wrong: that displaced people will see an opportunity and take it.
On the Moonshots podcast, Salim Ismail put up a slide from a Principal Financial Group employer survey to argue that the jobs apocalypse is not showing up in the data: 1.4% of surveyed businesses reported a staffing decrease they attributed to AI or automation. The survey's numbers are narrower than the headlines they were used against, and the panel spent the rest of the segment on what changing work actually looks like — including a book that took three years the first time and six months the third.