On Moonshots, Ramez Naam pointed to the brain’s modest power needs and children’s ability to learn from relatively little data. Co-host Alex countered with a rack of chips producing text thousands of times faster than one writer. Their disagreement connects AI’s energy bill to a larger question: how much improvement can more computation buy?
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.
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.
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.
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.