Most developers hate to admit this, but the uncomfortable truth is simple, a huge majority of developers produce predictable output. Yes, implementations vary. Yes, styles differ. Yes, everyone has opinions. But when you zoom out far enough, the inputs and outputs are mostly the same.
Given a well-defined problem, the “best” solution can often be derived by:
- Defining efficiency constraints
- Modeling trade-offs
- Optimizing toward a measurable goal
In other words: it’s computable.
So Why Does “AI” Still Fail So Often?
Because the real bottleneck was never intelligence. It was instrumentation.
We’ve been very good at writing code. We’ve been terrible at clearly defining what the system should actually do. LLMs don’t lack reasoning. They lack precise, structured intent.
And that’s changing fast.
Once instrumentation improves, once goals, constraints, and feedback loops are clearly defined - the rest becomes trivial. The execution layer collapses into a commodity. This is why the most dangerous misconception today is that:
“Being a specialist will save me.”
It won’t.
Jack-of-all-trades roles were mocked for years. Now they’re quietly becoming the most valuable ones. Why? Because width beats depth when the problem space itself is fluid.
The future belongs to people who can:
- Frame problems clearly
- Understand systems end-to-end
- Translate ambiguity into structure
- Decide what should be built before worrying about how
Or, as Star Trek already figured out decades ago:
“Computer, design a program capable of analyzing the situation and providing the optimal solution.”
And the computer does it. Instantly. Without asking about frameworks, languages, or code style.
The irony?
We laughed at that as sci-fi fantasy.
Now we’re slowly realizing that the hard part was never the execution — it was knowing what to ask for in the first place.
So if you’re investing in yourself:
Don’t memorize syntax.
Syntax is the first thing to become obsolete.
Don’t chase frameworks.
Frameworks are implementation details.
Don’t optimize for today’s tooling.
Today’s tools are tomorrow’s legacy code.
Instead:
- Learn how to think in systems. Understand feedback loops, emergent behavior, and second-order effects.
- Learn how to define problems precisely. The better you can articulate constraints and goals, the more valuable you become.
- Learn how to instrument reality. Measurement, observability, and clear success criteria are what separate good systems from chaos.
Because once that part is solved…
The computer does the rest.
We’re already seeing this play out:
- DevOps engineers became more valuable than pure sysadmins
- Product managers who understand tech became more valuable than pure project managers
- Full-stack developers became more valuable than narrow specialists
The pattern is clear: breadth + context beats pure depth.
Not because depth doesn’t matter, but because the value of depth collapses when:
- The execution layer becomes commoditized
- Problems span multiple domains
- The bottleneck shifts from “how” to “what”
If you’re betting your career on being the best at one narrow thing, you’re making the same mistake buggy whip manufacturers made.
It’s not that your skill is worthless.
It’s that the market for that skill is about to collapse.
The future doesn’t need more people who can execute well.
It needs people who can define what should be executed in the first place.
Because once we crack the instrumentation problem — once we can clearly specify what systems should do — the GAC (Glorified Auto-Complete) that I talked about before will handle the rest.
And it will do it faster, cheaper, and more consistently than any human ever could.
So choose wisely:
Learn to define reality. Or become part of the execution layer that gets automated.
The clock is ticking.