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Anthropic grew from $1B to $30B in 15 months. So why does it trade at a discount to public comparables?

High-growth companies trade on forward (NTM) revenue. Anthropic’s $30B run rate implies $20B in actual TTM revenue. If they exit 2026 at an $80B run rate¹, we can estimate NTM revenue of around $50B. The EV/NTM multiple is 17x.

Anthropic commands a 65% discount to Palantir while growing nearly 3x faster. Four factors explain the gap.

What happens when a startup employee leaves on a Monday?

In a twenty-person engineering team, one resignation is a 5% headcount loss. The remaining nineteen absorb the work.

In an AI-pilled three-person team running twenty autonomous agents, one resignation is a 33% headcount loss.

The agents do not resign. They keep generating, reviewing, testing, and deploying. But one-third of the institutional memory that trains, prompts, validates, and debugs the agent fleet walks out the door.

One search ad every 40 minutes pays for a trillion-parameter model. One content ad every 3 minutes does the same. The math on ad-supported AI is better than you think.

When Anthropic pulled Claude Code from the $20 plan last month, it signaled an industry assumption : frontier intelligence requires frontier pricing. For open models, the economics flip.

A B200 GPU costs $4.50/hour on spot markets.¹ Google Search ads generate $38.40 CPM (cost per thousand impressions),² while Google Display runs $3.12.³

I remember growing up reading Sports Illustrated. There was a small column called “This Week’s Sign the Apocalypse Is Upon Us.”

With all the dire predictions about AI, it’s important to also spend time recognizing the tremendous pace of innovation & the impact AI is having broadly. Here’s what it fixed, taught, and discovered in the last two weeks.

In medicine

• Mayo Clinic validated an AI that detects pancreatic cancer up to 3 years before diagnosis on routine CT scans.¹
• J&J reported AI is halving the time to generate new drug development leads and cut clinical trial report preparation from 700 hours to about 15 minutes.²

In classrooms

Google Cloud grew 63% year-over-year in Q1 2026. Amazon Web Services posted 28%. Microsoft Azure hit 40%. All three are exceptional. Only one hit 63%.

The divergence is striking. AWS & Azure resell compute. Google bundles compute with its own models. Whether that explains the full gap is unclear, but the structural advantage is not : Google owns Gemini & TPUs top to bottom, with no licensing fees to OpenAI or Anthropic. Its growth may be more profitable too.

The inference market is the fastest growing market in the world & it’s splitting up. Each modality is developing its own inference stack.

NVIDIA’s data center revenue was flat through 2022. Then ChatGPT launched. Three years later : 17x growth.¹

Databases did the same thing. What started as one market fragmented into relational, document, key-value, graph, time series, vector, & others. Each category reflects different workload requirements : real-time transactions vs batch analytics, ACID compliance vs eventual consistency.

The old sales motion asked : what’s your software budget for this category?

The new motion asks customers three questions :

• What’s your software budget?
• What’s your total labor budget?
• What do you want that ratio to be in three years?

That third question shifts a software sale into a strategic planning conversation, the same conversation every board is having right now.

The higher the ratio today, the larger the opportunity. Sales runs 10:1. Support runs 4:1. Engineering runs as high as 25:1. If AI collapses the labor side, the software budget isn’t the ceiling. It’s the floor.