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Anthropic's data shows software engineering accounts for ~50% of its AI agent tool calls; the remaining verticals are greenfields most founders are overlooking

Garry's List Garry Tan

Context & Ripple Effects

Anthropic’s usage data identifies software engineering as the current center of gravity for its agent tools, while leaving most other vertical demand comparatively unproven. That concentration helps explain why coding agents have been described as reaching product-market fit as daily tools for developers.

The finding also arrives as Anthropic has been gaining share of first-time corporate AI-tool spending, according to Ramp’s spending data, making its observed workflow mix relevant to both product strategy and startup positioning.

First-order effects

  • Anthropic gets a clear signal that software engineering is its strongest agent workflow today, while non-engineering functions become explicit areas for product discovery and go-to-market testing.
  • Founders targeting agentic workflows face a sharper distinction between the crowded coding market and verticals where usage has yet to consolidate around an obvious winner.

Second-order effects

  • AI-tool vendors may compete harder for developer workflows, where demonstrated demand can justify deeper integrations and more specialized agent experiences.
  • Teams building for non-engineering verticals will need to validate workflow-specific tool use rather than assume that coding-agent adoption transfers directly to their markets.

Third-order effects

  • If agent adoption broadens beyond coding, advantage may shift toward vendors that embed agents into the systems and workflows of particular professions, rather than offering only general-purpose assistants.
  • The pattern points to a staged agent market: coding is the early proving ground, while the eventual market structure depends on whether other verticals develop similarly repeatable tool-using workflows.

The trend: AI agents are moving from an early coding-led wedge toward a contest to operationalize repeatable, workflow-native automation across professional verticals.

Discussion

  • @anthropicai @anthropicai on x
    New Anthropic research: Measuring AI agent autonomy in practice. We analyzed millions of interactions across Claude Code and our API to understand how much autonomy people grant to agents, where they're deployed, and what risks they may pose. Read more: https://www.anthropic.com/…
  • @anthropicai @anthropicai on x
    Software engineering makes up ~50% of agentic tool calls on our API, but we see emerging use in other industries. As the frontier of risk and autonomy expands, post-deployment monitoring becomes essential. We encourage other model developers to extend this research. [image]
  • @garrytan Garry Tan on x
    Software engineering accounts for nearly 50% of all AI agent tool calls. Healthcare, legal, finance, and a dozen other verticals are barely touched, each under 5%. That's a hundred AI unicorns waiting to be built. https://garryslist.org/... [image]
  • @handotdev Han Wang on x
    what I would be working on if I started another company today [image]
  • @levie Aaron Levie on x
    This chart is a good reminder of how much opportunity there is in AI agents right now.  There will be plenty of horizontal opportunities for agents, but equally many workflows that need deep domain expertise to actually make the user successful at automating the unique processes …