Jobs › Computer and Mathematical

AI exposure report

Will AI replace Software developer?

Many of your tasks are within reach of AI. The role will change, so it pays to act now.

Task exposure to today's AI

55/100

High exposure

This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.

Get your personal score

Will AI replace Software developer?

Not entirely. Today's AI tools can take on about 55% of the typical work in this occupation, mostly: writing routine code (crud, boilerplate, integrations) The parts that stay human include: Understanding the business problem and building the right thing; Architectural trade-offs and accountability in production; Critical review and security of AI-generated code.

People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.

Which tasks AI can take on

The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.

TaskAutomatable
Writing routine code (CRUD, boilerplate, integrations) 80%
Documentation 80%
Writing tests 70%
Debugging and code review 55%
System design and architecture 30%
Clarifying requirements and talking to stakeholders 20%

+ 1 more tasks (10% of the week) in the full test.

Your human edge

  • Understanding the business problem and building the right thing
  • Architectural trade-offs and accountability in production
  • Critical review and security of AI-generated code

AI tools to try

Claude CodeGitHub CopilotCursorAI code review

Related roles

  • AI/LLM application developer
  • Platform / DevEx engineer
  • Technical architect

A 3-phase AI learning plan

Phase 1

Pair programming with AI

  • Use an agentic coding assistant (Claude Code, Cursor) on real tasks – not just autocomplete.
  • Learn to give context: CLAUDE.md / rules files, tests before code, small steps.
  • Let AI write tests and documentation while you review them.

Project

Take an old project of yours and have an AI agent fix 5 bugs or add 1 feature – time it.

Phase 2

LLMs in your own applications

  • LLM APIs (Anthropic / OpenAI): structured output, tool use, streaming.
  • RAG: embeddings, a vector database (pgvector), chunking.
  • Spring AI or LangChain4j in the Java world.

Project

Add an AI feature to an existing Spring Boot app (e.g. answering questions about a document).

Phase 3

Production-ready AI

  • Evals and regression tests for prompts.
  • Cost, latency, rate limits, caching.
  • Security: prompt injection, data leaks, MCP servers.

Project

Build an MCP server for your company's or project's API and connect it to an AI assistant.
Take the free 11-question test