AI‑assisted development is the mobile camera moment – powerful, not a pro replacement
by Michał Ordon, Founder
The mobile camera moment for software
AI‑assisted development is having its iPhone‑camera moment. It’s a game‑changer for the masses: anyone can take a good photo, and anyone can now ship something that works. That’s progress.
But just as mobile cameras can’t match a DSLR in the hands of a pro for low‑light, dynamic range, or fast action, AI doesn’t replace skilled developers for complex, custom, or performance‑critical work. It augments. It accelerates. It does not substitute judgment.
Even ruined a recipe despite having all ingredients and a step‑by‑step with pictures? Precisely. Execution still matters.
Where AI shines today
- Scaffolding and boilerplate: CRUD endpoints, typed clients, test harnesses.
- Refactors and migrations: repetitive transformations with high consistency.
- Glue code and integrations: SDK setup, common patterns, infra scripts.
- Docs and tests: first drafts, coverage scaffolds, usage examples.
These are thin slices where speed compounds. Teams move faster when repetitive work is handled in seconds.
Where pros are still essential
- Complexity and constraints: trade‑offs across performance, security, privacy, cost.
- Architecture and shaping: thin‑slice plans, interfaces, contracts, boundaries.
- Optimization: performance bottlenecks, memory, concurrency, network behavior.
- Debugging the unknown: ambiguous failures, flaky systems, emergent behavior.
This is where seasoned engineers earn their keep – not for typing faster, but for deciding better.
Our simple operating model (TEH*IDEA, 2025)
This is TEH*IDEA’s operating model – simple, pragmatic, and battle‑tested.
- Listen & Diagnose: align on outcomes, constraints, and risks. Sample real signals (analytics, support, sales). Summarize with AI to surface themes, not vibes.
- Shape & Build: sketch options, decide trade‑offs, write acceptance criteria. Use AI to generate variants, code thin slices behind flags with tests and telemetry.
- Ship & Learn: roll out gradually, review HEART and business metrics, capture feedback, and iterate.
Unit tests in the age of LLMs
- Lock in critical behavior at the function level to catch subtle regressions from AI‑assisted refactors.
- Use golden tests for prompts/tools and deterministic helpers to detect drift.
- Keep integration tests for end‑to‑end confidence; unit tests provide fast feedback and clearer failure signals.
Guardrails so speed doesn’t break things
- Feature flags and staged rollout
- CI/CD
- Unit + integration tests; smoke checks
- Observability by default
- Privacy and security as requirements
A practical checklist for teams adopting AI
- Start with scaffolding and migrations, not critical paths.
- Keep humans in the loop for code reviews and decisions.
- Capture and share prompts as living documentation.
- Measure impact in cycle time, defect rates, and user outcomes.
- Keep a “what we don’t automate yet” list to avoid overreach.
AI is a force multiplier. With the right process and guardrails, it compounds speed without sacrificing quality. Pros aren’t going anywhere – they’re just getting better tools to do higher‑leverage work.