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Articles about NVIDIA 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, the panel played a launch video for Redwood, an accelerator that Palo Alto startup Architect Labs says its AI designed end to end from a specification written by two architects. The company's paper reports two weeks to verified design and FPGA deployment, with a small language model running in a third week — while the headline 3.4-times efficiency figure comes from a projected Samsung 8-nanometer chip that has not been built. The panel, who disclosed they are investors and an advisor, argued the real story is a "designless" company and recursive self-improvement at the chip layer.
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 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 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.
Naam predicts that AI-assisted code conversion could weaken NVIDIA’s software lock-in in 2026–2027, making rival chips easier to use. The panel’s counterargument: fast connections between chips still matter, even when the work shifts from training models to answering users.
Expensive AI chips can sit idle while data centers wait for grid connections. On Moonshots, energy investor Ramez Naam argued that accepting less grid power during peak demand could shorten that wait. Workload scheduling and batteries offer two ways to do it, but national estimates of spare capacity are not promises of power at a particular site.
NVIDIA has signed memorandums with six financial institutions aiming to mobilize more than $500 billion in outside capital for customers’ AI infrastructure. On Moonshots, the panel debated whether rapidly changing chips can support long-term investments: Salim Ismail warned of stranded assets, Alex argued for financial hedges, and Emad Mostaque explained why older, paid-off GPUs can keep earning.