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AI-augmented roles

Software Developers: how AI augments and automates the work

In May 2026 Claude.ai conversations mapped to software developers tasks with a classified collaboration pattern (excluding unclassified conversations), 39.2% augmented the person and 60.8% delegated the work (Anthropic Economic Index), plus O*NET tasks and skills.

Data compiled 2026-10-09 · BLS 2025–35 · O*NET 31.0 · AEI May 2026

Augmentation share, classified patterns only

39.2%

Leans toward automation. Across global Claude.ai conversations with a classified collaboration pattern (excluding unclassified conversations) in May 2026, augmentation was 51.4%.

Anthropic Economic Index (May 2026)

Augmentation vs. automation

Claude.ai conversations mapped to this occupation's tasks, Anthropic Economic Index (May 2026)

39.2%

Augmentation

Share of conversations with a classified collaboration pattern (excluding unclassified conversations) where the person stays in the loop: iterating, learning, or validating.

Anthropic Economic Index (May 2026)

60.8%

Automation

Share of conversations with a classified collaboration pattern (excluding unclassified conversations) where the task is delegated: directive or feedback-loop use.

Anthropic Economic Index (May 2026)

77.2%

Work use

Share of these conversations classified as work rather than personal or coursework use.

Anthropic Economic Index (May 2026)

0.35%

Share of all conversations

This occupation's share of all classified Claude.ai conversations.

Anthropic Economic Index (May 2026)

Collaboration patterns

The six interaction patterns Anthropic classifies

Directive · Automation25.2%

The person hands over the whole task with minimal back-and-forth.

Feedback loop · Automation34.7%

AI completes the task, guided by feedback such as error messages relayed by the person.

Task iteration · Augmentation31.9%

The person and AI refine the work together over several turns.

Learning · Augmentation5.1%

The person uses AI to understand a topic or build a skill.

Validation · Augmentation1.6%

The person asks AI to check or improve work they did.

No clear pattern · Unclassified1.4%

Conversations that fit none of the patterns above.

Anthropic Economic Index (May 2026)

For comparison, 45% of this occupation's tasks are theoretically exposed to LLMs (moderate exposure, β, Eloundou et al., Science 2024).

Core tasks

Highest-importance task statements, O*NET 31.0 Database

  1. Analyze user needs and software requirements to determine feasibility of design within time and cost constraints.
  2. Develop or direct software system testing or validation procedures, programming, or documentation.
  3. Confer with systems analysts, engineers, programmers and others to design systems and to obtain information on project limitations and capabilities, performance requirements and interfaces.
  4. Modify existing software to correct errors, adapt it to new hardware, or upgrade interfaces and improve performance.
  5. Prepare reports or correspondence concerning project specifications, activities, or status.

Most important skills

Importance on a 1–5 scale, O*NET 31.0 Database

  • Programming4.00
  • Critical Thinking3.88
  • Judgment and Decision Making3.62
  • Active Learning3.50
  • Reading Comprehension3.50
  • Systems Analysis3.50

Live openings

Current listings in Metaintro's jobs index

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Sources & methodology

Every figure on this page comes from the public datasets below. We do not edit the source values; derived figures are explained here.

  1. Collaboration shares come from Anthropic Economic Index data for Claude.ai conversations in May 2026, classified to the O*NET tasks they match. "Automation" groups directive and feedback-loop conversations; "augmentation" groups task iteration, learning, and validation. Both buckets are shares of conversations with a classified collaboration pattern, excluding the unclassified "none" pattern; the six individual pattern shares include unclassified conversations. They describe how people use Claude on this occupation's tasks — not what share of workers in the occupation use AI.
  2. Conversation share is this occupation's percentage of all classified Claude.ai conversations. Rows under 0.05% are flagged as low-volume; read their percentages with caution.
  3. Core tasks are the O*NET 31.0 core task statements ranked by O*NET importance; skills are the O*NET importance ratings (1–5).
  4. BLS occupations are matched to O*NET-SOC codes with the BLS O*NET-to-NEM crosswalk; where a BLS occupation spans several O*NET occupations, we average their values.

  • Anthropic Economic Index (May 2026)

    Massenkoff, M., Lyubich, E., Sacher, S., Hitzig, Z., Zhang, S., Heller, R., & McCrory, P. (2026). "Anthropic Economic Index report: Cadences." Anthropic, June 26, 2026. Claude.ai usage metrics by occupation, global, May 2026.

    Vintage: Release of June 26, 2026; Claude.ai conversations from May 2026. License: CC BY (data). Retrieved 2026-10-09.

  • O*NET 31.0 Database

    This page includes information from the O*NET 31.0 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. O*NET® is a trademark of USDOL/ETA. Metaintro has modified all or some of this information. USDOL/ETA has not approved, endorsed, or tested these modifications.

    Vintage: O*NET 31.0, August 2026 release. License: CC BY 4.0. Retrieved 2026-10-09.

  • Eloundou et al., Science 2024

    Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2024). "GPTs are GPTs: Labor market impact potential of LLMs." Science, 384(6702), 1306–1308. doi:10.1126/science.adj0998. Occupation- and task-level exposure data from the authors' public repository (openai/GPTs-are-GPTs).

    Vintage: Published June 21, 2024; human annotations of O*NET tasks collected 2023. License: Data repository: MIT License. Retrieved 2026-10-09.

  • BLS Employment Projections 2025–35

    U.S. Bureau of Labor Statistics, Employment Projections program. Occupational projections, 2025–35, and worker characteristics, 2025 (Table 1.2); fastest growing occupations (Table 1.3); factors affecting occupational utilization (Table 1.12).

    Vintage: 2025–35 projections, released August 27, 2026. License: Public domain (U.S. federal government work). Retrieved 2026-10-09.

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