These are my key takeaways distilled from talks, side discussions, and patterns that kept repeating.

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Software engineering is shifting from code to systems thinking

  • Code is no longer the main asset, solutions are, “Code → test → refactor” still matters, but now maintenance > writing
  • Great engineers delete more than they write
  • Specification is the source of truth before, during, after coding
  • Reverse engineering from code alone is no longer viable
  • The job is evolving from writing code to designing systems that survive AI

AI doesn’t replace engineering, it amplifies it

  • AI makes good practices better — and bad practices catastrophic
  • “Vibe coding” ≠ software engineering
  • AI-generated code often not maintainable, poorly understood, creates hidden technical debt
  • Better to be 2–3× faster sustainably, not 10× faster and unmaintainable

Documentation became critical infrastructure

  • “The code is the documentation” → no longer true
  • AI cannot see invisible context, if you don’t document, future engineers (and AI) will fail
  • Keep docs close to code (plain text), document decisions, not just code
  • Good docs today → better AI outputs tomorrow

The real bottleneck is no longer coding — it’s maintenance

  • Writing code is cheap
  • Maintaining systems is expensive
  • New reality is that code is an output, not an input
  • Systems decay faster due to AI speed
  • Companies must track technical debt and track AI-generated debt
  • We can build anything” → now replaced with → “What should we NOT build?”

Fundamentals matter more than ever

  • Despite all the AI you still need infrastructure, understanding, system design thinking, debugging skills
  • Hiring is shifting - less focus on output, more on process and reasoning
  • Abstraction is rising — but fundamentals are the only anchor

AI is a social shift, not just a technical one

  • Adoption is uneven: juniors → heavy usage, seniors → cautious, but reviewing AI output
  • Companies now define AI budgets, AI strategies
  • Some trends: middle management questioned, smaller teams by default, more remote work
  • AI is behaving more like electricity than a tool

Risk, security, and regulation are catching up (slowly)

  • Key risks: data leakage (GDPR), automation bias (“death by GPS”), overconfident AI outputs
  • Best practices: never use production data → use Faker
  • Sandbox everything
  • Implement guardrails before/after LLM use
  • Inform users instead of silently blocking
  • Important idea: technology is not forbidden — its usage is

Agents are the new computing model

  • The focus is shifting to memory management, tool orchestration
  • Constraints are that context windows are limited
  • More context ≠ better results
  • Patterns emerging: multi-agent systems, model-as-a-tool (LLMs calling LLMs), disposable sub-agents
  • We’re moving toward software factories run by agents

The uncomfortable truth about AI

  • Productivity gains don’t equal production impact
  • Many AI-built features never ship
  • Marketing > reality (for now)
  • And the biggest insight “The future isn’t what it used to be.”

Key takeaway

Code is cheap, decisions are expensive, maintenance is everything

  • The winners won’t be the fastest coders
  • They’ll be the best system thinkers