AI-Native Engineering: 11 Hard Truths for 2025
A founder note on building AI-native organizations and systems.
I build AI-native organizations and products. These are the principles I keep coming back to:
11 Hard Truths
- If a knowledge, methodology, or tech stack is older than 3 years, it’s obsolete until proven otherwise.
- Your codebase, your docs, your meeting notes — none of these are your backbone. Your only reliable, compounding asset is your test case library.
- To truly understand AI, you must understand its nature: AI is completely, fundamentally stateless.
- We can accept AI making mistakes — as long as those mistakes don’t kill the team before the next model upgrade.
- Software complexity must flatten. We’re moving from deep, vertical stacks to wide, horizontal systems.
- The context window is the most critical computational resource every engineer must master.
- Plan–Act, Test–Code, and Doc–Code–Doc are the new working loops of engineering.
- The future of code isn’t abstraction — it’s tiny, isolated, AI-readable units that stand on their own.
- AI will never solve the first mile or the last mile. Those remain stubbornly, unavoidably human.
- AI-generated Artifacts are not side effects. They are a new software modality — and they become part of your engineering assets.
- The real power of AI IDEs and Agents is not generation — it’s ruthless, intelligent context selection.
If you want the full manifesto and playbook:
LinkedIn post:
