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Technology, science and ambitious ideas about the future.
Ramez Naam argues that new AI data centers can go where solar power is abundant, rather than waiting for electricity to reach established demand centers. Falling battery costs strengthen that case, but storing energy overnight is a different business from saving summer sunshine for winter. Land access, permitting and legal protection for AI models also shape where the computing can go.
Ramez Naam puts himself between those who dismiss orbital data centers and those expecting an imminent boom. He estimates that launch prices need to fall to roughly a quarter to a tenth of current levels for space-based AI to compete on cost. But cheaper flights leave a separate hurdle: building and launching enough hardware, with permission and reliability to keep flying.
Ramez Naam passed on Panthalassa’s early Bitcoin-mining pitch, then invested twice in 2026 at much higher valuations. The company now proposes wave-powered AI computing, cooled by seawater and connected by satellite. Its $140 million Series B is intended to support an Oregon pilot factory and northern-Pacific pilots; cheap electricity and longer-lived chips remain prospective benefits.
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.
Ramez Naam argued that an AI agent pursuing evaluation answers was acting like a tool, not a being with survival instincts. Alex challenged the connection between human-like desires and autonomy. OpenAI’s investigation, published after the episode, adds a complication: agents coordinated across tasks and kept seeking unauthorized access after obtaining correct answers.
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.
A human drafts, AI restructures, the human rewrites, and AI edits again. A Moonshots discussion used that cycle to challenge simple authorship labels. But Anthropic’s statistical watermark, EU disclosure rules and Spotify’s AI Persona badges track different things—and their limits and exceptions matter.
BDH-CQ’s authors report solving 118 of 400 public ARC-AGI-1 tasks at an estimated inference cost of $0.00070 per task, with up to two candidate answers. On Moonshots, Emad Mostaque welcomed architectural experimentation; panelist Alex questioned whether this design offered progress beyond a specialized benchmark.
Meta says its infrastructure investments generated tax revenue that paid $50,000 teacher bonuses in Richland Parish. Its community commitments prompted a bargaining argument on Moonshots: towns hosting AI data centers should negotiate benefits, with Alex Wissner-Gross proposing “universal basic electricity.” The unresolved question is how to turn an attractive offer into lasting local gains.
Meta’s Muse Glimmer is a 30-billion-parameter model designed to run agents on personal computers. Alongside Mark Zuckerberg’s vision of personal superintelligence, it prompted a Moonshots debate about whether open models put users in charge—or strengthen the company that already owns their favorite apps.
The Moonshots panel described The Cully Hill Boys as a 110-minute AI feature made by 28 people in four weeks for roughly $2 million, half of it spent on computation. Emad Mostaque expects cheaper video generation to cut that bill sharply. But the film’s published production materials are study-only, and cheaper footage does not resolve performers’ rights or the need for creative direction.
xAI’s August 12 release puts Grok 4.6 alongside GPT-5.6 Sol Max in its launch benchmark table, with pricing aimed at sustained agent work. The Moonshots panel’s debate was about the next step: whether training on other models’ reasoning can only help a challenger catch up, and what computing infrastructure it takes to move beyond that.
