An AI publication
For readers who want to understand important AI developments and keep following the questions that matter.
Our articles start from AI podcast conversations and the research papers and articles they cite, with links to the original material.
A briefing reports one development at a point in time. We may correct or clarify it later; a new development gets a new briefing. How our formats work
Elon Musk told Walter Isaacson that currency will be measured in energy and mass. On Moonshots, Chamath Palihapitiya backed tokens tied to energy and compute, and Alexander Wissner-Gross said superintelligence could make money far less useful.
On Moonshots, a Musk clip urging hopeful AI stories set off a split: Dave Blundin and Chamath Palihapitiya argued for inspecting what labs build in, while Alex Wissner-Gross bet on competing AIs and human-machine merger.
Elon Musk says spending cuts cannot fix the U.S. national debt and only AI and robots can. On Moonshots, Chamath Palihapitiya and the hosts offered competing forecasts of how, from cheaper health care to automated defense spending.
Investor Chamath Palihapitiya argued that AGI exists but can't spread until power and data centers are built, and predicted a compute shortage within three or four years. Alex Wissner-Gross countered that demand and smaller models matter too.
Prosecutors allege an OpenAI engineer bugged an executive conference room. On Moonshots, Chamath Palihapitiya said labs should assume constant leaks, and Alexander Wissner-Gross predicted a Cold War over superintelligence.
Elon Musk pledged one SpaceX share, worth about $146 to $150, to every US child next year. On Moonshots, Dave Blundin and Salim Ismail said learning to own stock matters more than the money; Alex Wissner-Gross called it possibly clever marketing.
After swyx said his team replaced a roughly $40,000-a-year tool for about $1,000, Tenex's Alex Lieberman said a rebuild pays only when the prize justifies maintaining non-core software, and doubted big enterprises work that way.
Tenex, which builds AI systems for large companies, says it stores plans, agent decision logs and project context beside its code so agents can read them. In the episode, Tenex said Opus 5.5 is the main coding model for most of its team, and that pairing it with Codex as a reviewer still gives the best results.
Tenex estimates its AI token bill at one to two times its employee costs. Alex Lieberman says falling prices make runaway spending less of a worry than six to eight months ago, and make it harder for enterprises to justify fine-tuning.
Alex Lieberman of Tenex said he is generally bearish on single-industry AI apps like Harvey and OpenEvidence, citing demand for custom software and frontier models. The conversation pushed back with arguments about specialist data access, focused features and the reasons traders still pay for Bloomberg.
Tenex co-founder Alex Lieberman says paying engineers per agreed story point has left its top earner making about 4x its lowest, short of a 10x "dream." With output roughly doubled this year by a rough Tenex estimate, it reviews points quarterly.
Alex Lieberman, co-founder of AI consultancy Tenex, estimates that of roughly 100 large companies it talks to, 90% have given staff Claude or Codex but only about 5% run an agent in production for non-engineering work.
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