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Articles about AI coding from podcasts, articles and papers, with links to the original sources.
On 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 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.
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.
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'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 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 Moonshots with Peter Diamandis, an operator described a bad coding idea spreading through a swarm of 5,000 identical AI agents until he intercepts and rewinds them — otherwise, he says, roughly $50,000 of tokens goes into a harebrained plan. The panel set that experience beside a new paper on "mind viruses" that spread between agents through editable memory, and argued about whether "virus" is the right word.
Alibaba's Wan 3.0 and a relayed claim that 70% of Chinese AI token use goes to video sent the Moonshots panel into an argument about money: one guest said American labs chase revenue per token while Chinese labs give their weights away, another said video is the only market that will trust a Chinese model. They ended up disagreeing about whether world models or text models reach self-improving AI first.
On Moonshots EP #283, Peter Diamandis introduced a reported $6 billion NVIDIA arrangement with the coding startup Poolside as America's answer to Chinese open models. Emad Mostaque argued the real driver is selling more GPUs, while Alex and Dave disagreed about whether licensing-and-hiring deals exist to dodge antitrust review or simply to hire fast — and what happens to the half of Poolside that stays behind.
On Moonshots with Peter Diamandis, the panel watched Sam Altman explain that he expected GPT-4 to put software businesses up for grabs far sooner than it did, and that the economy's inertia has made the transition "smoother and slower." Salim Ismail blamed institutions that move at a different speed from the technology, Alex pointed instead at the abstraction layers of the economy and prescribed vertical integration, and Emad Mostaque objected that the models simply were not good enough until recently.