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Articles about Enterprise AI from podcasts, articles and papers, 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 IdeaOn Moonshots EP #285, the panel worked through a proposed shift in how AI is sold: not by tokens consumed but by results delivered. This explainer sets out what outcome pricing mea...
Updated 14 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 2026Martine Rothblatt told Moonshots that her digital double, the Marvatar, is available to all 2,000 United Therapeutics employees, "any question 24-7, 365." She uses it to argue that videos, audio, documents and conversations, fed to today's language models, can reconstruct a person without copying every neuron.
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 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.
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.
On Moonshots, the panel picked apart a rental index showing H100 prices rising 22% in a single month to $3.28 per GPU-hour. Dave called it a reversal of a lifetime of chip depreciation; Emad Mostaque explained why better models make the same old Hopper worth more; and the warning for companies was that the compute they assume will be there later is already sold out.
On the Moonshots panel, Emad Mostaque presented what he calls a Champion: a locally owned intelligence utility, sold to residents at a symbolic $1 pre-money valuation before outside investors arrive, with 10% of the equity set aside in perpetuity for every child under 20. He borrows the structure from his account of how TSMC was capitalized, and argues that as the cost of intelligence falls, the money will sit in robots, deployment engineers and citizen agents. He calls it an idea, not an offering — and it leaves governance, dilution and distribution unsettled.
OpenAI's September 1 announcement lets healthcare organizations connect authorized Epic patient records to ChatGPT, so clinicians can ask what changed since a visit, review labs and medications, and find referrals that were never closed, with summaries pointing back to the chart. On Moonshots with Peter Diamandis, Emad Mostaque called for a sprint to have every health decision double-checked by an AI within a year or two, and Diamandis predicted it would become malpractice to diagnose without AI in the loop — proposals, not current clinical practice.