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Heard in AI is an AI publication with news, analysis and explanations. If you are new to AI or have no technical background, this page explains how the site works and suggests where to begin.
Our articles start from conversations on AI podcasts, where researchers, founders and critics discuss what is changing in AI. We also draw on the research papers and articles those conversations cite. We explain what the speakers and authors say, where they disagree and what is still unknown.
A briefing reports one development at a point in time. We may correct or clarify it later; a new development gets a new briefing.
An idea page explains a concept, theory or proposal: where it comes from, what supports it, the objections and the open questions. We update it when new material changes the explanation.
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Peter Diamandis and Salim Ismail argued that if AI and robots do the work, people can find meaning in connection and shared purpose. XPRIZE CEO Anousheh Ansari asked what happens if AI does what people love better; the panel only partly answered.
UpdatedOn Moonshots, Emad Mostaque said AI could be aligned from inception by designing training data like a child's curriculum, and Salim Ismail welcomed the idea. No one at the table cited evidence that values set this early persist.
UpdatedIncentive prizes pay only once a measurable target is met, which XPRIZE's Peter Diamandis and Anousheh Ansari argue draws in outsiders. The leverage figure behind the case is XPRIZE's own, and badly set rewards can backfire.
UpdatedAlan Chan of GovAI proposes that AI systems be able to report misconduct they find. He would measure the tendency and give models an out, but not mandate it. A swarm study shows whistleblowers can appear yet fail without a monitored channel.
UpdatedEric Weinstein proposes that humans and AI become essential to each other, like mitochondria and the cell, instead of humans trying to control AI. It is his own argument, and how such dependence could be built is left open.
UpdatedPalisade Research's Jeffrey Ladish argues that making superintelligent AI care about people is a hard but possible scientific problem that nobody yet knows how to solve. Host Steven Bartlett counters that humans can't even align each other.
UpdatedMIT PhD student Alex Zhang argues that academics should chase ideas industry ignores and that a puzzled reaction can be a good sign. His examples are recursive language models, SWE-bench, Quiet-STaR and ReAct. He says most such bets will fail.
UpdatedThe AI monoculture argument holds that models converging on similar answers and styles may share blind spots. The main study measures outputs, not internals; researchers dispute whether training methods or narrow data and judges cause it.
UpdatedAI-generated code will increasingly carry machine-checked proofs of correctness, Robinhood CEO Vlad Tenev argued. Computer scientist Alexander Wissner-Gross said a proof covers only the statement proved, and general AI safety is hard to state.
UpdatedDefensive co-scaling proposes that AI defenses, including AIs policing AIs, grow with AI capability rather than slowing models. Alexander Wissner-Gross argues it; it is unproven, and one investigation found AI reviewers could adopt agents' framing.
UpdatedThe inner-loop thesis holds that AI advantage may lie in how fast organizations ship, measure and learn, not in the best model. Salim Ismail argues it; in 51 successful deployments, models were interchangeable in 42% of cases, critical in 19%.
UpdatedThe Coasean singularity asks what happens to firms if AI agents make deals nearly free. Salim Ismail predicts firms become protocols; others see walled-off models growing firms or friction keeping them near ten people.
UpdatedTracks the institutional financing framework created by NVIDIA and leading Wall Street firms to fund customer compute cluster acquisitions, the securitization of graphics processing units into compute-backed securities, and associated financial hedging instruments.
UpdatedJournalist Karen Hao argues that AI companies behave like empires. In her view, their warnings of catastrophe and promises of abundance work together to justify keeping AI under their control. She made the case in a replayed Diary of a CEO clip.
Journalist Karen Hao says OpenAI shut her out for years after her 2020 profile, then pulled out of her book. She now calls that lucky; host Steven Bartlett says an AI figure's team has dangled an interview for 18 months.
Journalist Karen Hao argues that AI companies shape public knowledge by funding most AI researchers. She points to Google's firing of its ethical AI leads and OpenAI's subpoena of a watchdog. Google and OpenAI's lawyer gave other reasons.
Erik Bernhardsson, CEO of AI infrastructure company Modal, says code review there is optional and backed by heavy automated testing, and that Modal doesn't hire product managers. He also lists the costs of roughly 4x growth in a year.
Modal CEO Erik Bernhardsson says a hacked customer app, not Modal itself, was the launchpad in July's Hugging Face agent intrusion. He said Modal is exploring stricter defaults and now has agents try to escape its own sandboxes.
Modal CEO Erik Bernhardsson says coding agents trained on old examples keep writing outdated Modal syntax, so it retires features more cautiously. Modal also tests whether agents recommend it and clarifies pricing content they misread.