AI resilience estimate
Some parts of the work may change more than others. Start by finding where AI can help and where your judgment matters.
Higher means more resilience in this estimate. This is not a percentage chance of keeping or losing your job.
Start with one repeatable task. The review below helps you separate routine steps from decisions that need your attention.
Look for moments that need context, trust, or a careful decision. Describe one, then consider what a good outcome would look like.
Based on a typical version of your role. Your actual tasks may differ. Your employer’s plans and local job market are not known.
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Read the reasoning and the six factors.
This is the saved reasoning from an earlier version of the assessment. Its claims were not independently verified. New assessments use the revised method linked below.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
Current AI coding assistants can already automate 30-50% of routine coding tasks including boilerplate generation, basic CRUD operations, unit test creation, and documentation. For entry-level engineers, this percentage is higher since junior tasks tend to be more routine. However, complex debugging, system integration, architectural decisions, and understanding nuanced business requirements remain challenging for AI.
20% of the total
Software engineering is experiencing extremely rapid AI advancement. Major breakthroughs occur monthly, with models like GPT-4, Claude, and specialized coding models (AlphaCode, Codex) improving dramatically. Devin AI and similar autonomous coding agents represent the next wave. This is one of the fastest-moving AI application areas, with massive investment from tech giants and startups alike.
15% of the total
The tech industry is aggressively adopting AI coding tools, with GitHub Copilot having millions of users and most major tech companies integrating AI assistants into development workflows. Startups (like your 1-10 person company) often adopt these tools even faster due to resource constraints. Adoption is accelerating rapidly with minimal regulatory barriers in this space.
15% of the total
Software engineering retains significant human advantages in areas like understanding ambiguous business requirements, making architectural trade-off decisions, collaborating with cross-functional teams, mentoring others, and applying ethical judgment to technical choices. Creative problem-solving for novel challenges and debugging complex system interactions remain distinctly human strengths. However, at the entry level, you may not yet be exercising these advantages fully.
10% of the total
Software engineering skills are highly transferable across industries and roles. Logical thinking, problem-solving, technical communication, and understanding of systems apply to product management, technical consulting, data engineering, DevOps, and many other paths. However, with 0 years of experience, you haven't yet built the deep expertise and professional network that make transitions easier for senior engineers.
5% of the total
Despite AI disruption, software engineering remains one of the highest-demand professions with strong salary growth and persistent talent shortages. The Bureau of Labor Statistics projects 25% growth through 2032. However, demand is shifting toward engineers who can work effectively with AI tools and focus on higher-level responsibilities rather than pure code production.
This is the saved reasoning from an earlier version of the assessment. Its claims were not independently verified. New assessments use the revised method at https://www.ai-proof.me/methodology.
Higher means more resilience in this estimate. The weights show each factor’s share of the total. A score of 50 can mean information is unknown; read the note alongside it.
35% of the total
Current AI coding assistants can already automate 30-50% of routine coding tasks including boilerplate generation, basic CRUD operations, unit test creation, and documentation. For entry-level engineers, this percentage is higher since junior tasks tend to be more routine. However, complex debugging, system integration, architectural decisions, and understanding nuanced business requirements remain challenging for AI.
20% of the total
Software engineering is experiencing extremely rapid AI advancement. Major breakthroughs occur monthly, with models like GPT-4, Claude, and specialized coding models (AlphaCode, Codex) improving dramatically. Devin AI and similar autonomous coding agents represent the next wave. This is one of the fastest-moving AI application areas, with massive investment from tech giants and startups alike.
15% of the total
The tech industry is aggressively adopting AI coding tools, with GitHub Copilot having millions of users and most major tech companies integrating AI assistants into development workflows. Startups (like your 1-10 person company) often adopt these tools even faster due to resource constraints. Adoption is accelerating rapidly with minimal regulatory barriers in this space.
15% of the total
Software engineering retains significant human advantages in areas like understanding ambiguous business requirements, making architectural trade-off decisions, collaborating with cross-functional teams, mentoring others, and applying ethical judgment to technical choices. Creative problem-solving for novel challenges and debugging complex system interactions remain distinctly human strengths. However, at the entry level, you may not yet be exercising these advantages fully.
10% of the total
Software engineering skills are highly transferable across industries and roles. Logical thinking, problem-solving, technical communication, and understanding of systems apply to product management, technical consulting, data engineering, DevOps, and many other paths. However, with 0 years of experience, you haven't yet built the deep expertise and professional network that make transitions easier for senior engineers.
5% of the total
Despite AI disruption, software engineering remains one of the highest-demand professions with strong salary growth and persistent talent shortages. The Bureau of Labor Statistics projects 25% growth through 2032. However, demand is shifting toward engineers who can work effectively with AI tools and focus on higher-level responsibilities rather than pure code production.
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