The European Commission has announced delays in the implementation of the EU’s Artificial Intelligence Act.
Grace periods, exemptions, postponements. Pressure from corporations, lobbying from the U.S., political calculations.
It all feels familiar — like the GDPR, a regulation that began with noble intentions and ended up as another bureaucratic checkbox exercise with little real impact.
Europe’s recurring pattern: regulation before understanding
Europe has a habit of trying to regulate technologies it doesn’t fully understand — and entrusting that task to people who have never built a system themselves.
The result? Rules that look good on paper but collapse in practice.
GDPR didn’t fail because it was unnecessary.
It failed because it treated compliance as paperwork instead of architecture.
Organizations learned to hire “GDPR consultants” and fill forms, not to build secure, transparent systems.
And now, the same mindset is creeping into the AI Act.
AI without infrastructure = another compliance theatre
Artificial Intelligence isn’t just another technology.
It’s an ecosystem of data, infrastructure, and accountability.
If transparency isn’t built directly into the code, the data, and the workflows, no law will ever guarantee “trustworthy AI.”
While committees are still drafting PDFs, builders, engineers, and founders are already creating the next generation of systems — without regulation, but with direction.
The wrong people, solving the wrong problem
Delay is not the real issue.
The issue is who decides the delay.
When Europe’s most critical tech policies are designed by legal experts instead of system architects, we end up with policies instead of solutions.
AI regulation needs engineers, data architects, and people who understand how systems behave under pressure — not compliance boards detached from technical reality.
What we need: compliance as engineering, not paperwork
The answer isn’t more rules.
It’s better engineering.
Compliance that’s built by design, not after the fact.
The real AI regulation will come when code and data themselves become the instruments of governance — with audit trails, versioning, integrity checks, and verifiable trust.
