Micron

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The Atlantic 2026-05-02-2

So, About That AI Bubble

Anthropic's run rate doubled from $14B to $30B in two months, the METR study reversed from -20% to +20% developer productivity with current tooling, and some firms are now spending 10% of total engineering labor cost on AI subscriptions: the revenue story is no longer contested. The load-bearing extension claim, MIT's projection that AI completes 80-95% of white-collar tasks by 2029, rests on a linear extrapolation from two data points and an s-curve that doesn't bend. That's the overshoot zone: coding gains are real and documented; legal, marketing, and consulting at the same velocity is a 2027-2028 question, and the piece elides gross margins entirely, which remains the actual bear thesis.

Wall Street Journal — Heard on the Street 2026-04-30-1

The Clock Is Ticking for Big Tech to Make AI Pay

The market split the hyperscalers 14 percentage points apart on April 29 — Google up 7, Meta down 7 — on essentially the same balance sheet shape, which means investors stopped pricing Big Tech capex as a single risk factor. The new metric is AI revenue per depreciation dollar, and Google's 16 billion tokens per minute disclosure is the template every other CFO copies by Q3. With $430B in annual depreciation projected within five years against $372B in combined net income last year, the companies that can't show that attachment quality will face structural margin compression, not a narrative problem.

The Economist 2026-04-29-1

AI is confronting a supply-chain crunch

Hyperscaler capex grew 190% from 2024 to 2026; their hardware suppliers grew 45%. That gap is why every throttling notice, plan change, and Sora shutdown traces back to the same constraint. The less-discussed dimension: agentic systems need 1 CPU per GPU versus 1:12 for chatbots, which is why Intel has doubled in six months and why every agent platform deck needs a CPU supply slide.

Financial Times 2026-03-28-3

Memory chip stocks shed $100bn as AI-driven shortage trade unwinds

A single Google Research paper on model compression wiped $100 billion from memory chip stocks in five days. Micron dropped 15%; SanDisk, the best S&P 500 performer in 2025, shed $15 billion in market cap. Morgan Stanley's defense was textbook Jevons: efficiency expands demand. But the market just revealed a new risk class: AI efficiency research as a first-order investment catalyst. The next compression paper is already being written; the question is whether you see it before or after the sell-off.