Market Predictions, Rates & Inflation, DOGE, CES, AI Compute | BG2 w/ Bill Gurley & Brad Gerstner

Summary

• The boogeyman is rates, not AI: Gerstner’s team-wide year-end exercise landed on inflation plus higher rates colliding with elevated valuations. The 10-year has gone up almost 100bps to ~4.7% while the market repriced from many cuts to one in the first half of next year; if the 10-year goes to 5.25–5.5%, “it’s going to be an anchor on the stock market.” Altimeter started 2023 at 95% net long, last year ~80% — fewer chips on the table because “the price of entry to play is higher.”
• Not a bubble, but no cushion: S&P at 23x vs. the recent 22x peak and 16x trough; Meta 23x, Google 21x, Nvidia down from 66x to 36x consensus (28–30x on Altimeter’s numbers) while growing at unprecedented rates. The catch: Mag-5 earnings decelerate from 44% to 21% while consensus needs the other 495 to accelerate from 2% to 11% — healthcare +4→+20%, industrials -4→+16%, materials -10→+17% — and tax cuts explain only ~30% of that bump. If non-tech earnings miss, “it’s hard for me to see how you can have a big year.”
• Capex is real and pre-committed: combined large-tech capex stepped from ~$160B to ~$260B, Microsoft alone guiding $80B, consuming roughly 100% of incremental free cash flow at Meta/Microsoft. The debate over whether the 2025 spend materializes “we can put to bed” — it shows up, partly prisoner’s dilemma, but mostly because if Elon is right that AI does every cognitive task within 3–4 years, the labor being replaced “is measured in trillions.”
• Compute stays scarce all year: Microsoft was compute-constrained all 2024; OpenAI couldn’t ship Sora or voice widely for lack of compute; Jensen says inference — already ~half his revenue — goes up “a million x, maybe even a billion times” as reasoning models and agents devour tokens. Brad: “I think we’re going to end this year compute constrained.”
• The tradeable recipients: consensus Nvidia data-center revenue of $61B (2023) → ~$200B (2025) implies share loss Brad doesn’t believe, since the $60B step-up equals the hyperscalers’ incremental spend almost wall-to-wall. Memory is the contrarian leg — SK Hynix at ~6x forward because the market prices commodity boom-bust while Altimeter sees “secular not cyclical” plus a re-rating. Power is the binding constraint: likely Gavin Newsom still hasn’t signed Diablo Canyon past 2029 (10% of California’s clean power) while “China is building 100x as much as we are.”
• DOGE is the swing factor for the 10-year: 2024 federal spending of $6.7T vs. a 2019 baseline grown at 2.5%/yr of $5T — a $1.7T differential that explains the $2T DOGE target. Trump tax cuts add ~$400B/yr of stimulus; the market wants $300–400B of offsetting cuts and “I’ll believe it when I see it.” Brad’s out-of-consensus forecast: this administration actually cuts, the 10-year comes in, and “it could be off to the races” — resolved in a single reconciliation package by April/May.
• State AI regulation is the stupidity risk: the post-1047 regulation push “moved underground” into as many as 25 state initiatives, with the Texas bill (risk assessments, backward-looking liability) the most ironic given Texas’ pro-business brand — Bill calls a state-by-state gauntlet “mind-numbingly stupid” versus federal preemption under the commerce clause. Brad expects Trump to rescind the Biden EO, and 2025 to bring much more open-sourcing across major labs.
• Researchers think they’re “looking into AGI” and are surprised by outside skepticism; reasoning-model scaling is only at a “ChatGPT 2.0 level” on the curve, with dramatic 2025 breakthroughs expected in coding agents from Google and OpenAI. Google faces “the largest innovator’s dilemma in the history of Silicon Valley” but the breadcrumbs (Gemini, deep reasoning, NotebookLM) have Apple worried for the first time; Elon’s xAI is effectively the hyperscaler for a robotics-autonomy-Grok “keiretsu” with a very low cost of capital.

Deep dive

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