Loading Runway...
Loading Runway...
Evidence-backed analysis across 20 specific tasks. Capability claims sourced from peer-reviewed research, independent benchmarks, and industry data. Adoption rates tracked by industry and company size.
AI Exposure
Defensibility
Avg Capability
20/20 tasks with evidence
Avg Deployment
202 evidence sources
Market Context
41% of all code written in 2025 is AI-generated (Stack Overflow, 49K developers). Traditional programmer employment declined 27.5% between 2023–2025 per US BLS data. Mid-level coding tasks (boilerplate, unit tests, bug fixes) are now largely AI-assisted. Demand is bifurcating: AI-fluent engineers commanding premiums; traditional stack developers seeing reduced demand.
Source: Based on Stack Overflow Developer Survey 2025, BLS Occupational Outlook 2025, GitHub Copilot adoption data, and METR July 2025 RCT findings on AI-assisted development.
Role Defensibility Profile
Higher = harder to automate
Task-Level Analysis — 20 Tasks
Write production code to implement new features and functionality — translating requirements and designs into working, tested, and deployable software.
Capability Evidence
OpenAI's shift to focus on enterprise coding tools suggests improved AI capabilities for implementing coding features and tools
Highest Exposure Areas
Hands-On Technical Execution
41% of code written in 2025 is AI-generated. The defensible technical work is system architecture, novel problem-solving, and integration of AI tools — not execution of known patterns. Standard technical execution is being absorbed at an accelerating rate.
Meetings / Coordination / Scheduling
Calendar AI and agentic scheduling tools already handle meeting coordination. The coordination value that remains human is the nuanced political navigation — and that erodes as AI gains organisational context.
Writing / Summarising / Documentation
GPT-5 Deep Research and Claude already produce publication-quality reports, emails, and documentation. By 2027, AI writing assistants will handle first-draft creation for virtually all standard business documents with minimal human input.
Strongest Defenses
Hands-On Technical Execution
41% of code written in 2025 is AI-generated. The defensible technical work is system architecture, novel problem-solving, and integration of AI tools — not execution of known patterns. Standard technical execution is being absorbed at an accelerating rate.
Decision-Making Under Uncertainty
This remains one of the most defensible task categories — AI struggles with genuine novelty and accountability. The erosion condition: as AI decision-support tools become standard, the bar for what counts as 'genuine uncertainty' rises, and roles that mostly execute defined playbooks lose this protection.
Domain Specialist Judgement
Deep domain expertise is the most durable protection — but it degrades when AI is trained on sufficient domain-specific data to match pattern recognition. The erosion condition: the more codifiable your expertise, the faster this protection erodes. Truly novel, context-dependent judgement remains human-critical.
Live signals
Real-time AI signals affecting this role
Compare roles
See how other roles compare
This is the average. What about you?
The average Software Engineer scores 42/100 risk. But your specific role, environment, and task allocation could be higher or lower. Get your personalised score in ~4 minutes.
Code writing for feature implementation is becoming commoditized by AI, reducing the human effort needed for basic coding tasks
Claude Code auto mode can execute development tasks with fewer human approvals, increasing AI autonomy in coding workflows
Deployment by Industry
Diagnose and fix bugs in existing code by reproducing issues, analysing logs and stack traces, isolating root causes, and implementing targeted fixes.
Capability Evidence
Can detect and repair security vulnerabilities in code, which is a specific type of debugging focused on security issues
AI can execute development tasks autonomously including debugging with reduced human intervention
Claude Code and Cowork can use development tools autonomously to debug and troubleshoot code
Deployment by Industry
Review pull requests from teammates for correctness, readability, performance, security, and adherence to codebase conventions — providing constructive feedback and approving changes.
Capability Evidence
Automatically generate code that can be formally verified
Fine-tuned large language model can automate systematic review screening by reviewing titles and abstracts for inclusion decisions
Can identify and fix security vulnerabilities in code during review process, contributing to overall code quality assessment
Deployment by Industry
Write and maintain automated tests — unit tests, integration tests, and end-to-end tests — to verify correctness and prevent regressions.
Capability Evidence
Mistral Small 4's coding capabilities can assist with writing unit and integration tests
Holotron-12B can automate high throughput testing workflows and repetitive testing interactions
The Claude system card reports near-expert performance on graduate-level reasoning (GPQA), professional coding (SWE-bench), and document analysis tasks. For Unit & Integration Testing, Claude demonstr...
Deployment by Industry
Design system architecture for new services, features, or infrastructure — making decisions about data models, service boundaries, API contracts, and scalability patterns.
Capability Evidence
Multi-agent AI system can automatically convert process sketches into executable simulation models
LLMs can analyze software architecture models to identify security vulnerabilities and generate test cases
Architecture design remains a critical human skill that is not commoditized, requiring higher-level thinking and systems design capabilities
Deployment by Industry
Design REST, GraphQL, or RPC API interfaces — defining endpoints, request/response schemas, error handling patterns, versioning strategies, and authentication flows.
Capability Evidence
AI can perform automated coding approaches for web design
HacxGPT implements configurable API endpoints for multi-provider AI model access
GPT-5.4 mini and nano are specifically optimized for API workloads, indicating capability in API design tasks
Deployment by Industry
Write and maintain technical documentation — architecture decision records, API docs, runbooks, README files, and inline code documentation.
Capability Evidence
Technical documentation and writing tasks are among the highest-exposure categories in the Anthropic Economic Index analysis. Writing-related tasks represent the single largest category of professiona...
AI systems can gather and process regulatory documentation
AI can handle documentation tasks for knowledge workers
Deployment by Industry
Profile, benchmark, and optimise application performance — identifying bottlenecks in code, queries, network calls, and rendering paths, then implementing targeted improvements.
Capability Evidence
Multi-agent system framework can optimize swarm performance through explainable AI techniques
LLMs can generate optimized Triton operators for GPU kernels, though still requiring trial and error according to the research
AI model M2.7 can optimize performance for 30-50% of reinforcement learning research tasks
Deployment by Industry
Review code and infrastructure for security vulnerabilities — injection attacks, authentication flaws, data exposure risks — and implement mitigations and security best practices.
Capability Evidence
Perform autonomous security testing tasks traditionally done by penetration testers
Autonomous security tools can augment SOC analyst workflows for incident response
Research identifies critical vulnerability in AI agents that perform GUI understanding and mobile task automation through instruction injection attacks that exploit over-privileged static permissions
Deployment by Industry
Work with product managers and designers to clarify ambiguous requirements, identify edge cases, surface technical constraints, and refine specifications before implementation begins.
Capability Evidence
AI systems can filter content based on regional requirements
Local AI deployment could help with understanding and clarifying requirements without relying on cloud services
The Anthropic Economic Index found that tasks requiring stakeholder negotiation, ambiguity resolution through dialogue, and understanding unstated assumptions are among the least automated professiona...
Deployment by Industry
Collaborate with engineers on other teams to align on shared interfaces, resolve integration issues, coordinate migrations, and ensure consistent technical standards.
Capability Evidence
The Anthropic Economic Index identifies cross-functional coordination, negotiation, and relationship management as the professional task categories with the lowest LLM automation potential. Cross-team...
AI coding agents can automate part of advanced technical job functions in AI development roles
Multi-agent AI workflows enable better coordination and collaboration between development teams within repository environments
Deployment by Industry
Onboard new team members to the codebase, conduct pairing sessions, explain system context and historical decisions, and ensure institutional knowledge is shared effectively.
Capability Evidence
AI systems can perform knowledge retrieval tasks for customer support
AI can handle documentation tasks for knowledge workers
AI systems can automate industrial operations tasks that currently require specialized knowledge of industrial protocols
Deployment by Industry
Configure, maintain, and improve continuous integration and deployment pipelines — build scripts, test automation, deployment workflows, and environment management.
Capability Evidence
Automate building-grid co-simulation for energy management
Coordinated AI agents can assist with managing CI/CD processes within repository-based development workflows
Codex reduced mean time to recovery by 50% through automated CI/CD processes
Deployment by Industry
Respond to production incidents — triaging alerts, diagnosing issues under time pressure, implementing hotfixes, coordinating with affected teams, and writing post-mortems.
Capability Evidence
Anthropic's study of real-world Claude usage across millions of professional conversations found that tasks related to Incident Response & On-Call represent a significant category of AI-augmented work...
The Anthropic Economic Index found that real-time decision-making tasks requiring organisational context, live system state awareness, and cross-team coordination are among the least automated by curr...
Autonomous security tools can augment SOC analyst workflows for incident response
Deployment by Industry
Design and evolve database schemas — table structures, indexes, constraints, migrations, and query optimisation — balancing normalisation, performance, and maintainability.
Capability Evidence
Anthropic's study of real-world Claude usage across millions of professional conversations found that tasks related to Database Schema Design represent a significant category of AI-augmented work. The...
The Anthropic Economic Index found that data-related tasks including schema design and query optimization are common professional uses of LLMs. Models perform well on standard schema patterns and can ...
Cognizant and Oxford Economics analysed 18,000+ tasks across industries and found that Gen AI will impact 90% of jobs but fully displace very few. For tasks like Database Schema Design, the study esti...
Deployment by Industry
Estimate development effort, complexity, and risk for proposed features and projects — breaking down work into tasks and providing time and resource estimates to product and leadership.
Capability Evidence
The IMF finds that approximately 40% of global employment is exposed to AI, with up to 60% in advanced economies. For knowledge work tasks like Technical Estimation, the study estimates 24% of task co...
AI coding agents can automate part of advanced technical job functions in AI development roles
The Anthropic Economic Index analysis of professional LLM usage found that planning, estimation, and project scoping tasks had low representation among effective use cases. Tasks requiring calibration...
Deployment by Industry
Evaluate, update, and manage third-party dependencies — assessing security advisories, compatibility, licensing, and upgrade paths for libraries and frameworks.
Capability Evidence
Automate building-grid co-simulation for energy management
MIT Sloan Management Review's annual survey of 3,000+ managers found that only 10% of organizations report significant financial value from AI deployment, despite widespread experimentation. For tasks...
A systematic literature review of LLMs for code review found that AI detects 30-60% of code defects identified by human reviewers. For tasks like Dependency Management, AI-assisted review achieves app...
Deployment by Industry
Identify, document, and systematically address technical debt — refactoring legacy code, improving test coverage, and modernising outdated patterns without breaking existing functionality.
Capability Evidence
GitHub's updated impact study shows 46% of all code is now AI-generated among Copilot users, with 82% developer satisfaction. For tasks like Technical Debt Management, AI coding assistants demonstrate...
LLMs can identify code smells, duplicated logic, overly complex functions, and common anti-patterns through static analysis augmentation. The SWE-bench evaluations demonstrate that models can successf...
OpenAI's o1 system card demonstrates significant advancement in complex reasoning tasks, achieving 83rd percentile on Codeforces and 93rd percentile on AMC math competitions. For analytical aspects of...
Deployment by Industry
Instrument applications with logging, metrics, and tracing — configuring dashboards, alerts, and health checks to ensure production systems are observable and issues are detected early.
Capability Evidence
Explainable AI component provides transparency and interpretability for monitoring multi-agent swarm systems
Autonomous optical network system provides monitoring and observability across distributed AI training lifecycle with ~98% task completion rate
Local AI agents can provide enhanced monitoring and observability capabilities for systems and processes running on the same hardware
Deployment by Industry
Coordinate and execute software releases — managing feature flags, rollout strategies, canary deployments, rollback procedures, and post-deployment verification.
Capability Evidence
GitHub's updated impact study shows 46% of all code is now AI-generated among Copilot users, with 82% developer satisfaction. For tasks like Release & Deployment Management, AI coding assistants demon...
MIT Sloan Management Review's annual survey of 3,000+ managers found that only 10% of organizations report significant financial value from AI deployment, despite widespread experimentation. For tasks...
The McKinsey generative AI analysis identifies release management as a task with moderate automation potential. AI can automate changelog generation, coordinate deployment steps, and manage feature fl...
Deployment by Industry