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Articles about GPUs from podcasts, articles and papers, with links to the original sources.
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 Moonshots #288, a 4 a.m. chart about DeepSeek's new V4.1-Flash model sent the panel from cache statistics to the shopping list for an AI data center. DeepSeek says the model's lookup memory needs a quarter of the expensive high-bandwidth memory and an eighth of the SSD cache storage of its previous generation. The panel's argument was about what that does to a buildout in which, by one panelist's estimate, 40% of American capital spending goes to that one component.
Nvidia's chief executive declared AGI achieved on September 6 while announcing more GPU capacity, and the Moonshots panel split between calling the label meaningless and calling the underlying capability the most important moment in history. A second claim on the same show — that OpenAI's agents now do 3.1 days of research work per human day — comes from an internal report that measures how long agents ran, not how much research they finished.
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, the panel works through a report that Anthropic is designing its IPO to keep its founders in control. Untangling economic ownership, voting power and the Long-Term Benefit Trust's board-selection rights leads to a sharper disagreement: whether anyone outside a founding group can realistically hold the steering wheel of a frontier AI lab.
On Moonshots EP #284, the panel read out the performance figures OpenAI published for Jalapeño, the inference chip it built with Broadcom, and argued that inference is moving off NVIDIA. One panelist went further, imagining an "OpenAI Compute" cloud that rents capacity to competitors — possibly even hosting an Anthropic model. Dave Blundin explained why CUDA no longer locks buyers in at inference time, and why NVIDIA's next defense is the networking between chips.
NVIDIA reported $96.2 billion in quarterly revenue and guided to $108 billion for the current quarter. On Moonshots with Peter Diamandis, the panel split over what the number proves: Dave Blundin sees a company nobody can avoid, Alex wants to know how much of the demand NVIDIA itself financed, and Salim Ismail would prefer slower growth that markets have time to correct.
On Moonshots EP #283, Peter Diamandis introduced a reported $6 billion NVIDIA arrangement with the coding startup Poolside as America's answer to Chinese open models. Emad Mostaque argued the real driver is selling more GPUs, while Alex and Dave disagreed about whether licensing-and-hiring deals exist to dodge antitrust review or simply to hire fast — and what happens to the half of Poolside that stays behind.
On The Diary of a CEO, critic Ed Zitron praises a chatbot for reading a troubleshooting log and for helping fix his son's Minecraft mod, then argues that neither is worth a trillion dollars. The host counters with his fiancée's one-woman business and his chief of staff's inbox. The argument turns on tokens, subscription rate limits and who is paying the real bill.
On The Diary of a CEO, critic Ed Zitron laid out a sequence he expects to start with OpenAI failing to raise its next round and end in ordinary retirement accounts. He traces the chain from a delayed stock-market listing to SoftBank's paper holdings, cloud growth forecasts and the concentrated US indexes — while Amazon's own filings and Andy Jassy's shareholder letter offer a different account of why the spending is happening.
Alvin Graylin argues that China is competing to spread useful AI through industry and overseas developer communities, rather than betting everything on reaching general intelligence first. Provincial competition and open-weight models help explain his account, though the policy contrast is not absolute: America’s AI Action Plan also explicitly promotes adoption.
Alvin Graylin argues that AI can become more useful while earning less for the companies financing its infrastructure. His warning centers on cheaper models and local computing weakening cloud revenues, just as NVIDIA proposes financing platforms intended to mobilize more than $500 billion of outside capital.