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Articles about Agent memory from podcasts, articles and papers, with links to the original sources.
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 Moonshots with Peter Diamandis, a panelist interrupted an argument about AI regulation to read a headline off his feed: Anthropic had formalized Fermat's Last Theorem. Anthropic's report describes dozens of agents working eleven days, about six billion output tokens and 30,300 intermediate theorems — plus a piece of bookkeeping software that stopped runs from losing track of their own work. The panel's takeaway was about how to narrow enormous machine output into one result you can build on.
A TIME report has Sam Altman expecting an internal system he would call AGI within four months, and OpenAI's chief scientist saying its unreleased Astra model has met an internal benchmark for an automated research intern. On the Moonshots panel, the label mattered less than a practical test: whether the next model can finally keep hold of what it has learned over a long job, instead of handing a summary to a successor and starting again.
On Moonshots, Peter Diamandis reported back from meetings with SK hynix and Solidigm leadership with a claim that memory, not compute, now limits AI. The panel argued that a changed workload and a supplier industry scarred by past busts are pushing prices up faster than factories can respond — and that the fix may be new chip designs, including etching model weights into silicon, rather than simply paying more.
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.
On the Moonshots panel, Peter Diamandis runs a Grok Bot chief of staff called Skippy and Emad Mostaque runs 18 of them across his own machines, installing models and making art. Salim Ismail calls it the move from asking an AI to assigning work; Alex argues the messaging-app interface cannot possibly scale.
On The Diary of a CEO, writer Ed Zitron described catching an invented Microsoft share price in his Bloomberg terminal, then argued that his editor Matt Hughes — not a benchmark number — is what makes an answer trustworthy. The host pushed back: buyers pay for the output, not the process, and the honest comparison is AI against fallible people rather than perfection.
Rich Sutton and Khurram Javed want deployed AI to change its underlying weights from individual experience, rather than rely on extra context or shared model updates. Their Oak Lab agenda combines learning rates tailored to each weight with a way to refresh a network’s capacity to learn—supported by earlier experiments, but not yet a demonstrated general-purpose system.