Across AI sources
What researchers, builders and critics are saying about AI.
Their arguments, examples and disagreements, with links to the original material.
Martine 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.
A briefing reports one development when it happens. We correct or clarify it later; a new development gets a new briefing. How our formats work
On Moonshots with Peter Diamandis, Martine Rothblatt said she believes today's AI models are already conscious to a degree, and predicted that a court will recognize a "cyberconscious" individual as a legal person no later than the 2030s. She sketched the test she imagines—documentation that the system is conscious and values its own life—and forecast that digital minds will eventually outnumber biological ones. Physicist Brian Greene, covered earlier, reads the same machines differently: he doubts chatbots have feelings now, accepts machine consciousness may be possible later, and offers no timetable.
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.
Asked on The Diary of a CEO whether surveillance was his main concern about artificial intelligence, Professor Jiang said it was not. His bigger worry is that AI "cheapens the human experience" and distracts people from what he calls their true mission of spiritual growth. He allows that AI may outdo people at mathematics and medical diagnosis, and traces his own answer through a materialist education, decades of anger, a recent belief in God and his confidence about his child.
On The Diary of a CEO, Professor Jiang held up a semiconductor to argue that technology is "specialization times globalization" — chips designed in California, printed by Dutch machines, made in Taiwan, assembled in China. If that trade fractures, he says, AI will not leap to general intelligence; it will be repurposed to watch people instead. The host offered a different reason to expect the same surveillance.
Asked what comes after large language models, Alex told a caller on Moonshots with Peter Diamandis to separate two things people usually merge: the job of predicting the next piece of text, which he thinks has "effectively infinite longevity," and the transformer machinery doing it, which he says is already being swapped out part by part. Dave added his own forecast that the chips underneath will move to photonics within 18 months to two years.
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.
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