Reader · no content
No body text on file.
Open the original to read the full piece.
Inside the AI power wars
Brief
The conversation moved to frontier AI labs and the market consequences. OpenAI and Anthropic are hiring small but growing energy teams and demanding massive, certain power — Anthropic’s roadmap was cited as ~1.5 GW by 2025 and >10 GW by 2027. That scale and the labs’ non‑investment‑grade profiles drive the turn to BTM generation and to hyperscalers’ financial backstops. Jeremy described Google’s aggressive approach — providing balance‑sheet support and TPU economics — as creating roughly $50 billion of obligations tied to Anthropic buildouts (translating to ~4–5 GW), a dynamic that parallels how Nvidia and others have supported neoclouds but is even more direct. Shayle and Jeremy debated constraints: Jeremy stressed both generation and T&D shortages given data‑center growth at high double‑digit rates, while Shayle emphasized T&D upgrades and argued more grid generation will arrive. Practical responses include fast‑deploy aero‑derivative turbines, large fleets of reciprocating engines (multi‑MW units), modular fuel‑cell rolls (Bloom Energy) for fast permitting, and massive West Texas solar+storage campuses. Financing, equipment lead times, and contractual structure — not just technology choice — now determine who can scale: hyperscalers can post large deposits and place orders, whereas neoclouds and labs rely on vendor/backstop support (and, Jeremy predicts, more vendor financing from Nvidia in H2 2026). Overall, the episode framed power as a strategic battleground shaping chip economics, vendor relationships, and where AI workloads will ultimately run.
Why it matters
Jeremy Eliahu Ontiveros (SemiAnalysis) says hyperscalers have meaningfully different power strategies: Google is the most sophisticated with the largest energy trading desk and long track record (24/7 clean commitments), Meta has aggressively deployed behind‑the‑meter sites (notably Columbus, Ohio and a Louisiana site) using fast 'tent' designs, and Amazon has stepped up large PPAs including deals with gas and nuclear.
Key details
- Frontier AI labs (OpenAI, Anthropic) are driving demand and building or contracting huge capacity: Anthropic targeted ~1.5 GW by 2025 and >10 GW by 2027, prompting hyperscalers and third‑party developers to provide financing/backstops; Jeremy estimates Google has placed ~ $50 billion of obligations backing Anthropic‑related buildouts (roughly 4–5 GW equivalence).
- Supply constraints are acute: Jeremy says data‑center buildouts are running in the 'tens of gigawatts per year' and growing ~50%/yr; he estimated 2027–28 additions of ~10–15 GW gas plus ~20 GW ELCC‑adjusted solar+battery (~30–35 GW total), but still short vs. data‑center demand. Shayle pushed back that transmission/distribution (T&D) — not just generation — will be the binding constraint.
- Behind‑the‑meter generation is being used as a bridge to grid connection: large deployments favor aero‑derivative gas turbines (IGTs), reciprocating engines (many 4 MW units — e.g., a reported 2.3 GW site), and quick‑deploy modular solutions; hyperscalers differ (Google/Amazon favor utility deposits and on‑grid procurement; Meta leans into BTM with less redundant backup).
- Fuel cells (Bloom Energy) and modular options are competitive on speed-to‑deploy: Bloom benefitted from long lead times for turbines and secured major deals (e.g., Oracle projects), but fuel cells are poor as short‑term backup because of slow start times and high CAPEX; Jeremy sees them as 'islanded for life' or long‑term on‑site capacity.
- Financing and equipment lead times are reshaping competitive advantage: neoclouds (CoreWeave, etc.) face higher financing and deposit requirements versus hyperscalers; Jeremy expects Nvidia to provide more budget/support to neoclouds in H2 2026 to unlock growth, while Google’s TPU/backstop strategy is already shifting market share dynamics.