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Articles about Language models from podcasts, articles and papers, with links to the original sources.
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.
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 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.
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.
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.
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.
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 a replayed Diary of a CEO conversation, one guest argues that an AI trained to be a good teacher could give every child the one-to-one attention a class of 30 makes impossible. Host Steven Bartlett interrupts to ask who supplies the tutor's morals, and a second guest warns that children who stop using their brains will have weaker ones. The exchange ends with a call to study children in comparison groups before the consequences arrive.
On Moonshots, the panel revisits Elon Musk's January prediction that models were "off by two orders of magnitude" in intelligence per gigabyte, after Tim Sweeney tweeted that it had come true and Musk replied that specialist AIs add another 100x. Dave calls 100x a lower bound and asks what anyone would actually do with 10,000 brilliant agents; Emad Mostaque describes running specialized agent teams, while Alex argues Musk's "specialist models" are really sparsification inside generalist models.
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.