Jason Hauer | CEO HauerX Holdings | February 2026
Something is happening inside every knowledge work function right now that most leaders aren't seeing clearly.
Nate Jones wrote a smart breakdown of it recently in his Sunday executive briefing, "The Two-Class System Forming Inside Every Knowledge Work Function." He compiled the evidence that the cost of producing knowledge work is in freefall. Cursor hit $500 million in annual recurring revenue with 150 people. Midjourney hit $500 million with fewer than 170. Lovable crossed $100 million in eight months with 45 employees. A quarter of Y Combinator's latest batch shipped products with codebases that were 95% AI-generated.
Jones calls it a two-class system. Specifiers who define what should happen, and operators who execute with AI. He's right about the pattern. But I think the implications are bigger than a restructuring of roles inside companies. What's forming is an entirely new economy.
I'm calling it the Specification Economy.
And the core thesis is simple: the most valuable skill in every knowledge work function just shifted from producing the work to specifying it with precision. The transition is happening now, the barriers to making it are deeply personal, and the cost of waiting is compounding every month.
That should make you uncomfortable. Here's why.
The Shift That Changes Everything
For decades, knowledge work operated on a simple equation. The person who could produce the artifact was the scarce resource. The engineer who could write clean code. The copywriter who could craft a campaign. The analyst who could build a model. The attorney who could draft an agreement.
Production ability was the job. It's what you were hired for, trained for, evaluated on, and promoted for. Every professional development track reinforced it. Go deep. Get credentialed. Become the best producer in your lane.
AI just broke that equation.
When the cost of production approaches zero, production stops being the value. The value migrates upstream to the person who can define, with precision, what should be produced, why it matters, how to validate it, and where the boundaries are.
That's specification. And it's the new bottleneck in every function.
This isn't theory. Software engineering is 18 months into this transition, and the evidence is already clear. Individual developers report feeling more productive than ever. But Google's 2025 DORA report found that organizational delivery, the thing that actually matters, stayed flat or got worse. More output. Same or lower quality. More rework.
The gap between individual productivity and organizational results isn't a mystery. It's a specification gap. When humans handled production, they were also quietly interpreting vague requirements and filling gaps with domain knowledge. The engineer didn't just write code. She read a loose spec and thought, "they probably mean this, and they haven't considered this edge case, so I'll handle it." That invisible judgment layer disappeared the moment AI took over production. Now every gap in the specification flows straight to the output. And the output looks confident and polished, which makes the problem harder to catch, not easier.
That's the pattern. And here's what most people are missing: it isn't a software engineering story. It's an everything story.
Every Function. Same Arc.
Engineering is the canary. Every other commercial function is on the same curve, just earlier on the timeline.
Think about what's already happening.
In marketing, AI can produce campaigns, write copy, generate creative, and build media plans in minutes. But the CMO who can precisely define the audience, the success criteria, the brand constraints, and the test framework so that AI-generated content actually converts? That person is running circles around the one who's just prompting for "something more engaging."
In finance, AI can build models, run scenarios, and generate reports at a speed that would have been unthinkable two years ago. But the CFO who can define what decision the model needs to inform, what assumptions need stress-testing, and what constitutes signal versus noise? That's where the actual leverage is.
In legal, AI can draft contracts, review documents, and flag risks faster than any associate. But the general counsel who can define the risk framework, the escalation triggers, and the review criteria so that AI-drafted contracts actually protect the business? That's the irreplaceable skill.
In sales, AI can generate outreach, score leads, and personalize follow-ups at scale. But the CRO who can define the ICP precisely enough and the value narrative clearly enough that AI-assisted outreach actually lands? That's the difference between a pipeline that looks full and one that actually converts.
The pattern is identical in every case. The ability to produce becomes abundant. The ability to specify becomes scarce. And the gap between the two is where organizations are bleeding value right now without realizing it.
But here's what makes the specification economy even more demanding than it sounds. It isn't just about defining existing work more precisely. It's about seeing entirely new categories of problems worth solving.
When production costs collapse, the math changes on what's worth building. Use cases that were too expensive to justify suddenly become viable. Data combinations that nobody would have staffed a team to explore become possible in an afternoon. Personalized insights tied to a specific strategy, at a level of depth and relevance that would have taken a research team months, can now be generated in hours.
The best specifiers aren't just precise. They're imaginative. They're asking: what's now possible that wasn't before? What problems can we solve that we couldn't even afford to think about last year? What happens when we layer these three data sources together in a way nobody has tried? What new business models emerge when the cost of building the thing is essentially zero?
And here's what makes this really demanding: that kind of imagination doesn't come from going deeper in your lane. It comes from going wider. It requires a functional understanding that most specialists have never been asked to develop. You need to know how marketing actually works to specify an AI system that connects cultural signals to product development. You need to understand how finance evaluates risk to specify an AI workflow that connects sales data to pricing strategy. You need to see across functions, across industries, across business models.
The person who's studied how a DTC brand structures its supply chain, how a SaaS company thinks about retention, how a CPG company approaches retail execution, and can synthesize those patterns into something new for the problem in front of them? That person is going to be extraordinarily valuable. Because they can see connections and possibilities that a specialist in any single domain can't.
The specification economy rewards learning speed. The faster you can absorb how different businesses work, the faster you can identify new problems worth solving and new ways to solve them. That's a fundamentally different skill than mastering one domain over twenty years. And it puts a premium on curiosity, cross-functional fluency, and the willingness to operate outside your comfort zone.
That's thinking in an entirely new dimension. And it's a muscle most organizations haven't started developing, because they're still focused on using AI to do the old work faster instead of imagining the new work that only becomes possible now.
When you prompt AI with vague direction, you get polished, confident output that looks shippable and often isn't. The more AI produces, the more the specification gap compounds. More output, same quality thinking, no improvement in outcomes.
That's not an AI problem. That's a leadership problem.
Why Almost Nobody Is Ready
If specification is the new bottleneck, the obvious move is to start building that muscle. So why aren't more people doing it?
Because the barriers aren't technical. They're structural, cultural, and deeply personal. And they compound on each other in ways that make this one of the hardest transitions most professionals will face.
Thirty Years of Training in the Wrong Direction
The professional development system for the last three decades has optimized for one thing: specialist production. Go deep. Get credentialed. Master your craft within your lane.
Specification requires the opposite. You need to understand the full workflow, not just your piece. What happens upstream, where inputs come from. What happens downstream, what decisions your output feeds. You need strategic context that spans functions.
Most people haven't been asked to think this way. Many have been told not to. Stay in your lane. That's above your pay grade. Focus on your deliverable.
Now the most valuable thing you can do is understand enough about every function your work touches to specify across all of them. And beyond that, to learn from entirely different industries, different business models, different approaches to growth, and bring those patterns back to the problem in front of you. The specification economy doesn't just reward depth. It rewards the speed at which you can develop breadth and synthesize what you learn into something new.
Thirty years of "go deep" didn't prepare anyone for that.
Organizations Designed to Prevent This
In most enterprises, work is sliced so thin that no single person holds the full context of any meaningful workflow. Strategy defines goals. Operations translates. Functional teams execute fragments. Each handoff loses context.
That fragmentation was manageable when humans filled gaps at every step. Inefficient, but functional.
Now you need someone who can specify an entire workflow with enough precision for AI to execute across those handoffs. That person barely exists in most organizations, because the org was specifically designed to make sure nobody needed to hold that much context.
The division of labor was a feature. Now it's a constraint.
The Leadership Identity Crisis
This is the hardest one, and the one nobody talks about.
Most senior leaders built their careers on pattern recognition and intuition. Seeing the answer faster. Making the call before everyone else. Being the smartest person in the room.
That was incredibly valuable when the production layer was staffed by humans who could take a vague directive and figure out what you meant.
Specification requires the opposite. It requires you to externalize everything you know. Make your intuition explicit. Write down the assumptions you've been carrying for twenty years. Define "good" in terms a system can evaluate, not something you know when you see it.
For a lot of leaders, that feels like a demotion. You're used to saying "make it better" and having a team figure out what that means. Now you need to say exactly what "better" means. In measurable terms. With clear boundaries. Or the AI will produce something that looks great and misses the point entirely.
This isn't a skills gap. It's an identity shift. And it's the reason most AI training programs fail. They teach prompt syntax when the real problem is that people have never been asked to articulate their own thinking with that level of precision.
They've always had the luxury of being vague. Of letting other humans interpret and clean up after them.
That luxury is over.
The Jobs Question
Now the part everyone wants to talk about. Is AI going to take my job?
Wrong question. Here's the right one: where is your job moving, and are you moving with it?
The historical evidence on productivity revolutions is remarkably consistent. They create turbulence. Old roles contract. And then the market expands in ways almost nobody predicted from inside the transition.
Jones makes this point well with the telephone operator example. When operators were automated in the early 20th century, the telephone system didn't shrink. It exploded. It reached orders of magnitude more users. The industry got massively bigger. The roles inside it looked completely different.
The same thing is happening with software right now. When the cost of building software approaches zero, the total amount of software the world needs doesn't stay flat. It expands dramatically. Every business problem that wasn't worth building a custom solution for suddenly becomes viable. Every workflow too niche to justify a dev team gets automated. The market for software grows, and with it, the roles required to specify, validate, manage, and improve all of it.
Economists like Erik Brynjolfsson have studied this pattern across previous technology waves. Steam engines, electrification, early computing. The pattern holds: there's a trough where productivity gains are invisible because organizations are still restructuring. Then the curve turns up.
Whether AI produces a net increase or decrease in total jobs is still genuinely unknown. That's the honest answer. But what we can see clearly is that where value is created will move. The roles that emerge on the other side of this transition will be higher-value, more strategic, and focused on specification, oversight, and judgment rather than production.
The people building that capability now will be positioned for those roles.
The people waiting won't.
And that's the part that should create urgency. Because the specification economy isn't arriving in five years. It's arriving now. Engineering is already there. The other functions are months behind, not years.
What This Demands of Us
I'm not writing this to add to the fear. There's enough of that. And I'm not writing it to sell you on AI adoption. You don't need another pitch.
I'm writing it because the shift from the production economy to the specification economy is real, it's here, and the window to get ahead of it is closing.
If you're still investing primarily in production capacity, more tools, more output, faster execution, you're optimizing the part that's getting cheaper while ignoring the part that's becoming scarce.
If you're still evaluating people on production speed, you're rewarding the wrong skill and watching your best specifiers leave for organizations that value what they do.
If you're still running AI training focused on prompt syntax, you're solving the surface problem while the structural one compounds underneath.
And there's one more layer that I don't think enough people are reckoning with.
The specification economy doesn't just change what's valuable. It changes the clock.
When production costs collapse, the entire cycle compresses. Idea to working prototype to market test to business result. What used to take quarters now takes weeks. What took weeks takes days. The distance between "I have an idea" and "here are the results" is shrinking to almost nothing.
That sounds exciting until you realize what it demands. Faster decisions with less room for error. More bets running simultaneously. Shorter feedback loops that require real-time judgment. The ability to kill something that isn't working on Tuesday and redirect by Wednesday.
Most people aren't ready for that level of pressure, urgency, and execution speed. Most organizations aren't structured for it. Most leadership teams still operate on quarterly planning cycles and monthly review cadences that were built for a world where production took months, not minutes.
The specification economy compresses everything. Including the time you have to adapt to it.
The specification economy rewards a different kind of expertise. People who hold strategic context across functions. People who can make their judgment explicit, not just exercise it intuitively. People who can define precisely what "good" looks like before anyone starts producing. And people who can operate at a pace that matches what the technology now makes possible.
But here's the part I keep coming back to. This isn't just an individual challenge. It's a collective one.
It's upon all of us to see this shift and make it. And to help the people around us make it too.
The leaders who hoard context instead of distributing it. The organizations that keep people in specialist lanes instead of building cross-functional fluency. The managers who evaluate production speed instead of specification quality. They're not just holding themselves back. They're holding everyone around them back.
The best thing you can do right now isn't just develop your own specification capability. It's make it visible, valued, and accessible to the people you work with. Share context. Break down silos. Start rewarding the clearest thinkers who drive value to market at record speed.
The specification economy is here. The roles are shifting. The market is expanding.
The companies that figure this out first won't just win. They'll make the gap permanent.
Jason Hauer is Founder and CEO of HauerX Holdings, a portfolio of AI-native companies driving growth for the biggest brands in the world, including Coca-Cola, Nike, Walmart, and Allianz.
