AI resilience estimate
Your role appears to have useful advantages alongside tasks that may change. Start with one practical experiment.
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
AI tools can now automate 30-40% of routine coding tasks (boilerplate, simple functions, unit tests, documentation). However, system design, architecture decisions, debugging complex distributed systems, code review requiring business context, and cross-team integration remain largely human-driven. Your 5 years of experience means you likely spend more time on these complex tasks than junior engineers.
20% of the total
AI coding tools are advancing rapidly (GPT-4, Claude 3.5 Sonnet, specialized models like AlphaCode) with significant improvements in code generation quality. However, progress on autonomous software engineering (full project completion without human oversight) remains limited. The field is evolving toward human-AI collaboration rather than full replacement, but the pace of change requires continuous adaptation.
15% of the total
Large enterprises (5000+ employees) adopt AI tools more cautiously due to security, compliance, and integration challenges. While 92% of developers now use AI coding assistants, enterprise deployment of fully autonomous coding systems is slow. Your company size suggests structured processes and legacy systems that create natural barriers to rapid AI displacement.
15% of the total
Software engineering requires significant human judgment: understanding ambiguous business requirements, making architectural tradeoffs, collaborating across teams, mentoring others, and navigating organizational politics. Code review, incident response, and technical decision-making in uncertain environments remain deeply human activities. Large organizations amplify these human-centric aspects.
10% of the total
Software engineering skills are highly transferable across industries and roles. With 5 years of experience, you can pivot to product management, technical program management, DevOps/SRE, data engineering, security engineering, or solutions architecture. Your problem-solving, systems thinking, and technical communication skills apply broadly. The tech industry's talent mobility further enhances transferability.
5% of the total
Software engineering shows exceptionally strong demand signals: BLS projects 25% job growth (2022-2032), median salary remains high ($130k+), and tech unemployment stays below 2%. The rise of AI is creating new engineering roles (ML engineers, AI integration specialists, prompt engineers) faster than it eliminates traditional ones. Enterprise digital transformation continues to drive sustained demand.
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
AI tools can now automate 30-40% of routine coding tasks (boilerplate, simple functions, unit tests, documentation). However, system design, architecture decisions, debugging complex distributed systems, code review requiring business context, and cross-team integration remain largely human-driven. Your 5 years of experience means you likely spend more time on these complex tasks than junior engineers.
20% of the total
AI coding tools are advancing rapidly (GPT-4, Claude 3.5 Sonnet, specialized models like AlphaCode) with significant improvements in code generation quality. However, progress on autonomous software engineering (full project completion without human oversight) remains limited. The field is evolving toward human-AI collaboration rather than full replacement, but the pace of change requires continuous adaptation.
15% of the total
Large enterprises (5000+ employees) adopt AI tools more cautiously due to security, compliance, and integration challenges. While 92% of developers now use AI coding assistants, enterprise deployment of fully autonomous coding systems is slow. Your company size suggests structured processes and legacy systems that create natural barriers to rapid AI displacement.
15% of the total
Software engineering requires significant human judgment: understanding ambiguous business requirements, making architectural tradeoffs, collaborating across teams, mentoring others, and navigating organizational politics. Code review, incident response, and technical decision-making in uncertain environments remain deeply human activities. Large organizations amplify these human-centric aspects.
10% of the total
Software engineering skills are highly transferable across industries and roles. With 5 years of experience, you can pivot to product management, technical program management, DevOps/SRE, data engineering, security engineering, or solutions architecture. Your problem-solving, systems thinking, and technical communication skills apply broadly. The tech industry's talent mobility further enhances transferability.
5% of the total
Software engineering shows exceptionally strong demand signals: BLS projects 25% job growth (2022-2032), median salary remains high ($130k+), and tech unemployment stays below 2%. The rise of AI is creating new engineering roles (ML engineers, AI integration specialists, prompt engineers) faster than it eliminates traditional ones. Enterprise digital transformation continues to drive sustained demand.
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