Six Unlocks: Why AI's Biggest Opportunity Is Building, Not Cutting

Everyone is cutting. AI made it possible to do more with fewer people, so naturally the spreadsheet brains went to work: fewer heads, same output, better margins. Done. Next slide.

And then there's Whoop. Hiring 600 people. In 2026. While everyone else sharpens the axe.

I first came across this through Nate B. Jones' video on the topic, and it crystallized something I've been thinking about for a while. The dominant AI narrative right now is about subtraction. Fewer developers, fewer marketers, fewer humans in the loop. But the most interesting companies are doing the opposite. They're using AI as an expansion engine — not to do the same things cheaper, but to do things that were previously out of reach.

Nate lays out a frame I find compelling: instead of talking about hope (which is just a plan without validation), focus on structural unlocks — concrete shifts that create extraordinary value. I want to walk through six of them and share my take on what they mean for leaders, consultancies, and anyone trying to figure out where to point their energy right now.

1. Go Fast

The average enterprise runs maybe four meaningful learning cycles per year. Quarterly planning, execution, retrospective, repeat. AI collapses that to something closer to 200 cycles. Not because people work harder, but because the bottleneck shifts entirely.

In my experience, the constraint used to be can we build it? — do we have the engineering capacity, the design bandwidth, the QA pipeline? Now the constraint is should we build it? That's a fundamentally different muscle, and most organizations haven't trained it yet. They're still structured around managing scarcity of execution. In a world where execution is cheap, the scarce resource is judgment.

I think this is where leaders earn their keep. Not in approving roadmaps, but in the speed and quality of their decisions about what to pursue and what to kill. The organizations that learn to cycle fast — hypothesis, build, validate, pivot — will outrun everyone still doing quarterly planning like it's 2019.

2. Everyone's a Builder

I've seen this movie before. In the early 90s, digitalization triggered the exact same fear. "Am I supposed to become a computer nerd now? I like my binders. I don't want to be part of the geek club." People genuinely resisted. And then, quietly, everyone just... adapted. The accountant learned Excel. The manager started using email. The entire workforce became digitally fluent, not because they transformed into IT specialists, but because the tools met them where they were. Everything got digitalized. The fear turned out to be irrelevant.

We're watching the same pattern unfold right now with AI. Tomorrow's professionals — the logistics manager, the doctor, the teacher, the project lead — will navigate agents and AI the way we all navigate spreadsheets and browsers today. They'll prototype working tools, automate their own workflows, and create what they need without filing a ticket to the engineering team. We just need to free our minds from the assumption that "building software" requires a computer science degree. It doesn't. Not anymore.

But here's a nuance that gets lost in the excitement: we will still need engineers. In fact, they become more important. The plumbing underneath — the infrastructure, the security, the architecture that makes all of this agent-driven building reliable and scalable — that's engineering work. The role changes, absolutely. Less ticket-driven feature factory, more platform thinking and systems design. But the craft itself? More critical than ever.

My take: this is the single biggest unlock on the list. The world's best product ideas have always lived inside the heads of people who aren't professional engineers. AI is the bridge that finally lets those ideas cross over into reality. Just like digitalization made every worker computer-literate, AI will make every domain expert a builder.

3. Quality Is Default

Here's a counterintuitive one. AI doesn't just make development faster — it makes the baseline quality dramatically higher. Agent-driven testing, automated documentation, security scanning, accessibility checks, consistent code patterns. The boring stuff that separates professional engineering from hobby projects.

The gap between the top 5% of engineering organizations and everyone else is collapsing. I've watched companies go from zero automated tests to comprehensive coverage in weeks, not because they suddenly hired amazing QA engineers, but because the AI handles it as a default part of the development loop.

Quality used to be expensive. Now it's becoming atmospheric — just the pressure level at which everything operates. If you're a leader who's been competing on engineering excellence, that advantage is eroding. The new differentiator is what you choose to build and how well you understand your users, not how cleanly your team writes code.

4. Every Company Becomes a Platform

I think we're in a plumbing phase right now. Unglamorous, essential infrastructure work. The AI equivalent of laying fiber optic cables in the 90s — nobody wrote magazine covers about it, but the companies that had broadband infrastructure when the web took off dominated the next decade.

That's where we are with AI. Agents are going to interact with your systems whether you plan for it or not. Your customers' AI assistants will try to navigate your website, query your APIs, and transact on your platform. The question is whether that happens chaotically or by design.

In my CMS work, I see this every day. Structured content, clean APIs, headless architectures, well-documented data models — this is the plumbing. It's not sexy. Nobody's getting a standing ovation at the all-hands for migrating to a headless CMS. But the organizations doing this work right now are building the foundation that everything else sits on. When the infrastructure matures and agents become reliable consumers of your digital presence, the companies that prepared will be operating at a completely different level. The ones that didn't will be scrambling to retrofit — and retrofitting is always more expensive and slower than building it right from the start.

If your digital presence was built for human eyeballs only, you're about to discover that half your future "users" don't have eyes. The plumbing you lay today determines whether you're ready for them.

5. A Market for Ambition

When execution cost drops 10–100x, markets that were previously invisible become viable. A $10 million opportunity that would have cost $8 million to pursue wasn't worth the risk. At $800K? Suddenly the math works. At $80K? You'd be foolish not to try.

This is Jevons Paradox playing out in real time. William Stanley Jevons observed in 1865 that making coal more efficient didn't reduce coal consumption — it exploded it, because cheaper energy made entirely new applications viable. The same thing is happening with cognitive labor. Making thinking cheaper doesn't mean we'll think less. It means we'll think about things we never had the resources to consider before.

In my work as a tech director, I see this shift clearly. The consultancy model used to be about selling hours of expert labor. Increasingly, the value we provide is strategic clarity — helping clients see the $10M opportunities they're sitting on and showing them that the cost to pursue those opportunities just dropped through the floor. The clients who get this are dreaming bigger than ever. The ones who don't are still just asking how to cut 20% of their engineering budget.

6. Speed of Insight (Strategic Velocity)

The final unlock ties everything together. I wrote about this in The Coming Era of Strategic Velocity — when production stops being the bottleneck, the constraint shifts to strategic clarity and the courage to act fast. When building is cheap, fast, and high-quality by default, the natural move is to get ideas into code immediately. Skip the spec. Skip the meeting about the spec. Build a prototype and then decide if it has legs.

The organization moves at the speed of insight, not the speed of process.

I think this is the unlock that will separate the winners from the losers over the next three years. Not AI capability — everyone will have roughly the same tools. The gap will be in organizational culture. Can your company act on a good idea the same week it surfaces? Or does it take six weeks to get through the approval chain, by which time the market has already moved?

What This Means for Leaders

When I step back and look at these six unlocks together, a pattern emerges. Every single one of them increases the value of human judgment, domain expertise, creative vision, and strategic clarity. And every single one of them decreases the value of pure execution skill, process management, and gatekeeping.

The AI cost curve is doing exactly what Jevons predicted. It's not shrinking the pie — it's exploding it. But only for those who have the ambition to claim their share.

In the consulting world I live in, this creates a clean dividing line. Some clients come to us saying, "AI should help us run leaner." Others say, "AI should help us do things that were previously impossible." Both are valid. But the second group is building the future, and the first is just optimizing the present.

Nate B. Jones puts it well in his video: hope isn't a strategy, but structural unlocks are. These six shifts aren't predictions or wishes. They're things happening right now, in companies like Whoop, that you can build on today.

The question for every leader reading this: are you using AI to do less with less? Or are you using it to do more than you ever thought possible?

I know which side I'm betting on.