News · 2026-06-25
Anthropic's own data says the best coders gain the most from AI
One of the comforting stories people tell about AI is that it lowers the ladder. If a tool can write code for you, the thinking goes, then a beginner armed with that tool can suddenly do the work of an expert, and the gap between novice and master narrows. Anthropic just published a large study of how people actually use its coding assistant, and the data points the other way. The headline finding is what they call persistent returns to expertise: the more skilled you already are, the more an AI coding agent multiplies your output. The ladder does not flatten. If anything, it tilts further toward the people who were already good.
What makes this worth taking seriously is the scale and the source of the data. Anthropic looked at roughly four hundred thousand coding sessions from about two hundred thirty-five thousand people, gathered over about half a year, from late 2025 into spring 2026. That is not a survey of opinions or a handful of lab volunteers. It is a record of real engineers doing real work with the tool, analyzed in a privacy-preserving way so the company studies patterns across the crowd rather than reading any one person's project. It is, in effect, the largest look anyone has published at how coding agents get used in the wild.
The reason experts pull ahead comes down to what an AI coding agent actually is. It is not a vending machine that spits out finished software when you press a button. It is more like an extremely fast, tireless junior engineer who needs direction. You have to describe the goal precisely, break a big task into the right pieces, notice when the output is subtly wrong, and steer it back on course. Every one of those is a skill, and they are exactly the skills that experience builds. A seasoned engineer knows what to ask for, can smell a bad answer, and can catch the kind of mistake that compiles cleanly but breaks in production. A beginner, handed the same powerful assistant, may not yet know enough to tell good work from plausible-looking garbage, so they get less leverage from it, not more.
Think of it like a power tool. Hand a nail gun to a master carpenter and they frame a house in a fraction of the time. Hand the same nail gun to someone who has never built anything and the speed does not help much, because the bottleneck was never how fast they could drive nails. It was knowing where the walls go. AI coding agents move the bottleneck from typing to judgment, and judgment is precisely what expertise is.
Why this matters: the result cuts against a popular hope and a popular fear at the same time. The hope was that AI would democratize software, letting anyone build. The fear was that AI would make experienced engineers redundant. Anthropic's data suggests both are too simple. Instead of replacing experts, the tools appear to be amplifying them, which has real consequences for hiring, training, and how teams decide where to put their best people. It connects to a thread running through this whole year of AI news, from the finding that AI now writes most of Anthropic's own code to the cautionary tale of a company that burned through its yearly coding budget in four months because powerful agents are powerful spenders too. For more on what these autonomous helpers are, see our explainer on AI agents.
The honest caveat sits right at the center of the study: Anthropic is a company studying how people use Anthropic's own product, and 'expertise' and 'returns' are slippery things to measure from usage logs. The company built the analysis carefully and shared its methods, but a self-interested party measuring its own tool always deserves a second look, ideally an independent one. It is also worth remembering what the finding does not say. 'Experts gain more' is not the same as 'beginners gain nothing,' and the long-run picture, what happens as today's beginners grow into tomorrow's experts using these tools the whole way up, is exactly the part no six-month snapshot can answer yet.