Software development has undergone a massive paradigm shift. Engineering teams are no longer drafting syntax line by line; they are “vibe coding”, using natural language prompts in tools like Cursor, Windsurf, and Claude to orchestrate entire codebases.
This velocity shift has left founders and CTOs asking a critical question: If AI wrote 70% of our code, can we still claim SR&ED tax credits?
The short answer is yes. The CRA does not fund the manual labor of typing syntax; it funds the resolution of Technological Uncertainty. AI speeds up execution, but it does not eliminate architectural risk.
Routine Engineering vs. The “AI Wall”
AI excels at routine engineering by generating basic CRUD APIs, boilerplate UI components, and standard integrations. Crucially, routine work was never eligible for SR&ED in the first place. AI simply stripped away the noise.
Eligibility begins when your team hits the AI Wall. This occurs when an LLM hallucinates, enters infinite error-fix loops, or fails under real-world system constraints (such as high concurrency, low latency, or strict memory bounds). When standard patterns fail, human technical background is required to formulate hypotheses, analyze failure modes, and direct the investigation.
The AI-Era SR&ED Evidence Stack
To defend an AI-assisted claim during a CRA audit, your documentation must highlight human technical intervention:
- AI Failure & Error Logs: Retain traces where the model repeatedly failed or generated broken code under stress tests. This proves to the auditor that the problem could not be solved using generally available knowledge (i.e., the AI’s training set).
- Architectural Constraint Prompts: Document high-level, constraint-driven prompts rather than minor fix requests. For example: “Standard ORM caching violates our row-level security constraints; design a custom server-side buffer.”
- Human Analysis & Decision Records: Log your team’s evaluation of AI outputs. If Claude suggests two approaches that both crash at 10,000 concurrent requests, document why the deadlock occurred and how your engineers chose to pivot the architecture.
- Benchmark & Performance Metrics: Keep empirical data comparing baseline AI-generated code against the refactored solution after human-led experimentation.
The Human-in-the-Loop Defense
CRA auditors will not reject a claim simply because you used Copilot or Claude. Their concern is establishing who directed the research:
“AI Execution (Drafting/Testing)”+”Domain Expertise (Analysis & Direction)”=”Defensible SR&ED”
If AI can solve your problem in a single prompt, you were doing routine engineering. But if AI generated broken edge cases that required your engineers to analyze memory bottlenecks, reinterpret test logs, and redesign the system topology, you have a textbook SR&ED claim.
AI hasn’t killed R&D tax credits. It has simply raised the baseline for what counts as routine. The companies that succeed in 2026 are those documenting the technical failures and human decisions behind the prompt.
Is your team vibe coding through complex technical barriers? Contact our SR&ED specialists today to ensure your R&D tax credits keep pace with your modern dev stack.