PodcastPlain English with Derek Thompson

The AI Story Wall Street Doesn't Want to Hear

Written by an Ethmos research agent · shared with you

AI spending growth is concentrated in 1% of firms and now plateauing, casting doubt on the revenue trajectory the AI buildout needs.

ShowPlain English with Derek Thompson

Episode dateOctober 23, 2026

Runtime47 min

FeaturingAra Kharazian, Derek Thompson

Revenue trajectory needed: AI model spending (about $200B this year) must roughly double to about $400B and then to about $800B the year after that to justify current infrastructure spending, per host Derek Thompson.
Extreme concentration: The top 1% of Ramp's business customers drive 80% of their OpenAI and Anthropic spending, spending upwards of $10,000 per employee per month versus $12-15 for the median company.
Spending plateau at the top: That top 1% cohort's AI spending growth has slowed in recent months even as usage rises, because businesses now route tasks across multiple cheaper models instead of token-maxing on frontier models.
Prices falling, usage rising: Competitive pressure from Anthropic, OpenAI, SpaceX AI, NVIDIA's own models, and open-weight competitors is driving per-token prices down, a dynamic Kharazian frames as bullish for compute/chip demand (Jevons Paradox) but bearish for lab margins.
ROI is narrow but real: Firms using AI 'with high intensity' (>$50/employee/month) grew headcount 10% over two years post-adoption; low-intensity users showed no measurable gains, per Ramp research.
Demand data is opaque: Unlike supply-side chip/data-center spend, actual AI revenue is unverifiable — reported OpenAI revenue estimates ranged from $50B to $70B annually within one week.

Deep dive

Framing the bubble as supply vs demand

Thompson opens by distinguishing the AI bubble's supply side (chips, data centers, hyperscaler capex now partly debt-funded) from its demand side (actual revenue AI models generate). He argues the supply side looks less alarming than past bubbles because the buyers — Amazon, Microsoft, Google, Meta — are already enormously profitable (unlike thinly capitalized 19th-century railroads), and because nearly every AI chip today is in active, premium-priced use, unlike the vast majority of fiber left dark after the dot-com bust or the quarter of U.S. railroad mileage in bankruptcy by the 1890s. The demand side, by contrast, is largely unverifiable: OpenAI and Anthropic are private, so the public relies on leaks — one report pegged OpenAI's revenue pace near $70B annually, another (FT) at closer to $50B, a large discrepancy that still moved markets. This opacity is why Thompson frames demand, not supply, as the central question, and why he brought on Kharazian, whose firm Ramp processes over $275 billion in annual business spend across 85,000+ companies, including over $10 billion in AI spend.

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Also inside: 01 Revenue history and the Claude Code inflection · 02 Concentration and the slowdown among top spenders · 03 Where new growth could come from — and its limits · 04 ROI evidence and the Fable episode · 05 Thompson's synthesis and historical parallel

This page is an AI-generated summary and analysis prepared with Ethmos and shared by an Ethmos user. It does not reproduce the original programming. The underlying episode and its recording remain the property of their respective creators, and all show and company names are the property of their respective owners.

AI summary. May contain errors. Not investment advice.