AI and Jobs: What the Data Actually Shows

Opening

Dear reader, an interesting report came out two days ago. A company that builds AI decided to measure, in its own words, “how our technology is affecting jobs.”

It’s a report Anthropic published on March 5, 2026, titled “Labor Market Impacts of AI: A New Measure and Early Evidence.” Where prior research assessed job threats based on “what AI can theoretically do,” this report goes a step further. It combines that with actual Claude usage data to measure which tasks are being replaced by AI in the real world, right now.

The media’s reaction was predictable. Headlines like “74.5% of programmers at risk of replacement!” and “The end of entry-level jobs!” poured out. But when you actually dig into this report, the story the numbers tell is far more complicated — and honestly, more unsettling. Today I want to dissect this report with the eye of a data analyst. It was disappointing to see a few influencers here in Korea just parrot the flashiest numbers as-is. Shocking, threatening… that kind of vibe. The “study hard or take my course or you’ll get fired” kind of framing.

Let’s start with what’s actually new about this report

Most existing research on AI and the labor market has asked only one question: “Can AI do this task?” The 2023 study by OpenAI researchers (Eloundou et al.) is the classic example — they analyzed tasks across roughly 800 U.S. occupations and scored each one 0, 0.5, or 1 based on whether an LLM could theoretically at least double the speed of that task.

The problem is that there’s a massive gap between “can do” and “is actually doing.” Take pharmacy prescription information tasks, for example: Eloundou et al. rated AI as fully capable of performing this (β=1), yet in reality, no instances of Claude actually doing this task were observed. Real-world barriers — legal constraints, verification procedures, software integration — get in the way.

Anthropic’s new report tackles this gap head-on. It introduces a new metric called “Observed Exposure,” which combines three data sources.

First, the task lists by occupation from the O*NET database. Second, the theoretical AI-capability scores from Eloundou et al. Third — and this is the key part — actual Claude usage data collected through the Anthropic Economic Index. On top of that, they applied one more weighting: half-weight for “augmentation” use, where a human is assisted by AI, and full weight for cases where the task is fully automated via API without human involvement.

The result is striking. In theory, 94% of tasks in computer and mathematical occupations could be performed by AI — but the actual observed coverage was just 33%. That’s roughly a 3x gap between theory and reality.