The Pyramid Is Dead: How AI Changes What It Means to Build Software
Software development used to be pyramid construction. With AI tools, building is cheap. Rebuilding is cheap. So what replaces the old model?
Software development used to be pyramid construction.
You picked your stack. Laid the foundation. Built one function at a time with testing as you went. CI/CD scripts made sure new code didn’t break old code. You worked your way up, carefully, brick by brick.
This made sense when building was expensive.
With AI tools, building is cheap. Rebuilding is cheap. I rewrote an entire client workflow system in 3 hours last month. When you can reconstruct the foundation in an afternoon, the pyramid metaphor stops working.
So what replaces it?
The New Model: Scientific Research
The shift looks something like this:
Old paradigm: Construction. Build layer by layer. Don’t break what’s below. Most time spent in execution.
New paradigm: Research. Hypothesis -> experiment -> observe -> analyze -> refine -> repeat.
Here’s what that looks like in practice:
Deep planning upfront. Generate a working prototype. Define what “good enough” actually means for your use case. Test against that standard. Watch the failure patterns. Ask what those failures tell you. Diagnose. Fix. Loop back.
The counterintuitive part: planning matters more with AI, not less. Good planning means you can often get a near-complete working version of your product quickly. But that’s where the real work begins.
Your job becomes pattern recognition and quality judgment.
The Discriminator Problem
Here’s something I’ve noticed training 200+ professionals on AI workflows.
Almost everyone can learn to prompt well enough. The real skill gap is evaluation speed. How fast can you tell good output from bad? That determines your throughput.
Think about it this way. AI can generate infinite variations. The human bottleneck is how fast you can evaluate them. This is literally the GAN architecture—you’re the discriminator.
If your judgment is slow or inconsistent, AI speed doesn’t help you.
The people who get results fastest have the sharpest judgment about what matters in their domain. They know what “good” looks like before they start generating anything.
Why Most Developers Struggle (Hint: It’s Not Prompting)
Most “prompt engineering” advice focuses on the wrong thing.
If rebuilding is cheap, first-try quality matters less. What matters: speed and accuracy of your judgment loop. Can you quickly tell good from bad? Can you identify why something failed? Can you form a hypothesis about what to change?
Those skills have names. Problem decomposition. Quality control. Failure analysis. Hypothesis formation. Experimental design.
Notice anything? These sound like management skills. Or scientific research skills. They don’t sound like traditional coding skills.
Your Spec Is Your Rubric
Here’s a reframe that’s helped my clients.
Think of your specification as a rubric for judging output quality. Write that before you write your first prompt.
AI handles ambiguity fine. Humans don’t. Your spec isn’t instructions for the machine. It’s your tool for evaluating whether what came back is acceptable.
Without a clear rubric, you either accept garbage or reject everything. Documentation matters more than ever—as a judgment tool.
Domain Expertise Gets More Valuable
This is the part that surprises people.
AI democratizes execution. Anyone can generate code, copy, designs, analysis. The barrier to producing something drops to zero.
But judgment remains human. Only domain experts know what “good” looks like in context. Only they can spot which failure patterns actually matter. Only they know which edge cases will blow up in production.
The gap between “domain expert + AI” and “generalist + AI” is widening, not shrinking.
If you’re a biotech researcher, a healthcare administrator, a real estate professional with years of pattern recognition built up—that expertise just became more valuable. You can now execute at 10x speed while your judgment stays sharp.
If you’re a generalist who planned to use AI to skip the domain learning curve… that’s getting harder, not easier.
The Hollowing Out Risk
There’s a trap here.
If you become a “prompt router” who accepts whatever AI outputs without judgment, you’ve outsourced the thing that makes you valuable. The loop requires human judgment at every step. Skip it and you’re not developing—you’re copy-pasting with extra steps.
I’ve seen this happen. Professionals who use AI to “save time” by skipping the evaluation step. Six months later they can’t tell good work from bad. They’ve hollowed out their own expertise.
The solution: stay in the loop. Every output gets evaluated. Every failure gets analyzed. The AI handles execution. You handle judgment. That division has to hold.
The Real Optimization Target
“One-shotting” a prototype sounds impressive. It’s the wrong goal.
The real goal: compress your judgment loop.
If you can prototype + evaluate in 10 minutes, you iterate 6x while someone else is still refining their first prompt. Speed of iteration beats quality of initial generation.
This changes what you should practice. Instead of “how do I write the perfect prompt,” ask “how do I evaluate output faster” and “how do I diagnose failures more accurately.”
Build the quality control loop first. Perfect prompts are overrated.
What This Means for AI Adoption
Here’s what I keep telling teams.
AI adoption is a management problem, not a technical one. The skills that matter in AI-assisted work—problem decomposition, quality standards, failure pattern analysis, diagnostic thinking—these are management skills dressed up as technical work.
Organizations that treat AI adoption as “teach everyone to prompt better” miss the point. The bottleneck is judgment infrastructure. Do your people know what good looks like? Can they evaluate quickly? Do they have rubrics? Can they diagnose failures systematically?
Those are organizational capabilities. They require management attention. Tools are the easy part.
The Bottom Line
The pyramid is dead. Software development now looks more like running experiments than laying bricks.
Planning matters more. Execution matters less. Judgment is the bottleneck.
Your spec is your rubric. Your domain expertise is your edge. Your evaluation speed determines your throughput.
Build the quality control loop first. Everything else follows.
I help teams in biotech, healthcare, and professional services build AI workflows that don’t break compliance or quality standards. If your organization is figuring out how to adopt AI without sacrificing the judgment that makes your work valuable, let’s talk.