AI and the six structural unlocks that expand what companies can build
A solo presentation by Nate B Jones arguing that AI's reduction in execution costs creates an expansion of opportunity, not just a reduction in headcount.
Summary
Nate B Jones opens with Whoop's announcement of hiring more than 600 people — nearly doubling its workforce — as a counterpoint to the dominant media narrative that AI primarily destroys jobs. He argues that the more important question is not how many fewer people companies need, but what was previously impossible that is now achievable given that execution costs have dropped by an order of magnitude. Drawing on Jevons' paradox — the historical pattern whereby efficiency gains increase total consumption rather than reduce it — he contends that AI will expand the total demand for human judgment, creativity, and domain expertise rather than shrink it. The bulk of the presentation lays out six concrete "unlocks" available to companies and individuals today, none of which require further technological breakthroughs, covering speed of iteration, the democratization of building, software quality as a default, platform strategy, the economics of ambition, and organizational speed of insight.
Key Takeaways
FULL TRANSCRIPT
The wrong question dominating the AI conversation
Nate B Jones: A few days ago, Whoop announced it is hiring more than 600 people, nearly doubling its 800-person workforce. Will Ahmed, the CEO, said, "Right now, companies are debating whether to hire more people or just invest in AI — and we are doing both." Ahmed is making the most important strategic bet of 2026. And the boards and leadership teams I work with — the ones actually making these decisions, not the ones performing for analysts — increasingly agree with him.
The doom narrative lives in the media. Inside the rooms where it matters, the smartest operators are asking a different question: what would it take for our people to work differently and build what we couldn't build before?
This video is all about how you answer that question. I am going to lay out six different unlocks. All of them are people-focused. I talk about what you need to be as a person, how you change your mindset, how you think differently in the age of AI. And most of all, I'm not going to focus on hope. I know that sounds depressing, but I'll tell you why. Hope is a plan that we don't have validation for. Instead, I'm going to focus on structural unlocks that deliver extraordinary value because AI is compressing the cost of intelligence. We are going to talk about why hope is rational. We're going to talk about why these unlocks enable people to do what we've always dreamed of. And I know that sounds very pie-in-the-sky, but we're going to get super practical with it.
I am not blind to the fact that every conversation about AI right now starts and ends with how many jobs disappear. And that starting point, I think, is blinding us to an opportunity set that is staggeringly large and almost completely unexamined. The companies that break out of the doom frame first are not just going to get a head start here — they're going to get the whole race, because everybody else is still arguing about headcount while the customers are going to move to the companies that dream bigger.
So that's the question getting all the air time: how many fewer people do we need? It can feel sophisticated. You can model it in a spreadsheet. And it's the wrong question. The right question is one I don't see people asking: given that execution cost just dropped by an order of magnitude, what can we do right now that was previously impossible?
The cost-reduction frame assumes a fixed pie of value and optimizes for how efficiently you capture your slice. The ambition frame assumes the pie was artificially constrained by the cost of execution, and that removing that constraint creates a larger opportunity than all of the savings you could get the other way.
Jevons' paradox and the historical pattern of efficiency gains
The history of tech tells us which of those frames wins. When steel got cheap, the industry expanded into skyscrapers, into railroads, into cars. When computing got cheap, it created personal computing and the internet and mobile and cloud. And when distribution got cheap, media companies that played defense got destroyed by companies that built new categories.
The pattern has a name: Jevons' paradox. When efficiency increases, consumption goes up, not down — because cheaper resources make new applications viable. AI is the most dramatic efficiency improvement in the history of work. If Jevons' paradox applies — and every structural indicator says it does — did you know there are more software engineers now than there were a year ago, two years ago? The number of software engineering jobs is up. Just a sidebar: if Jevons' paradox applies, the total demand for insight, judgment, creativity, and domain expertise is about to explode.
The cutters will be pocketing the savings, but the people betting on the paradox are the people who are going to win.
Unlock one: learn how to go fast
So, what I'm going to share with you are six unlocks that give you a picture of what the future looks like. And remember, for each of these, these are illustrative — they're all true, but it's not the full universe of what is true.
Unlock number one: learn how to go fast. The iteration rate that we have now with AI is going to change the mechanics of strategy. When you can compress an entire product iteration cycle — as we can today — from months to days, everything is different. You don't get a faster version of old strategy. You have a different relationship between what you understand about the market and what you can do.
Right now, a product bet takes maybe six months, maybe three months if you're lucky. So you have two to four options a year to get it right. The cost of being wrong is a quarter at best. This is why the dominant strategy in corporate America is copy the other guy. The iteration cost is so high that exploration is irrational.
Now, compress that cycle to days. If you can get something in a single working session — which, by the way, you can — Cursor's February 2026 cloud agents update lets developers spin up to 20 parallel agents on isolated cloud VMs, or virtual machines, simultaneously. Every single one working on a project, a separate branch, testing changes, opening pull requests. About a third of Cursor's code and pull requests are written by agents operating autonomously. And that number is going up.
What happens when you can run 200 learning cycles a year? What happens to your people when you can have them run 200 learning cycles a year? Because it's the people conversation I want to focus on here. The tech already enables this. The reason this isn't everywhere is because the people aren't there yet. And the people aren't there because either they don't believe they can go fast, they think they'll get their wrists slapped, or because you haven't given them the infrastructure as a leader, or because they don't dare to dream that big.
When I talk with leaders about what is going on in AI, it's a pretty frank conversation, because so much of this starts with their ability to empower people to go quickly — to recognize that their old paradigms will shift fundamentally if they have the courage to let their people be entrepreneurial. And it's such an unlock, because if you go fast, you start to think creatively about other things you can go fast with, like internal processes that need to change.
Right now, startups die because they exhaust their funding before they exhaust their hypotheses. If you drop the cost of testing each hypothesis by a couple of orders of magnitude, the runway equation changes. It becomes rational to test so much more.
The human role in this new world is not smaller — it's bigger. You need people who can generate good hypotheses, who have deep customer intuition, who have contrarian market insight, who have creative vision. Today, those people spend 80% of their energy shepherding a single bet through the organization. Tomorrow, they're generating and evaluating ten bets a week. The bottleneck shifts from "can we build it?" to "should we build it?" And that's a human question.
This is the story I see in so many AI-native organizations. They are changing their idea of what is possible, and it is unlocking for them a future that they can only get to with speed.
Unlock two: the equation for builders has fundamentally changed
Unlock number two is recognizing that the equation for builders has fundamentally changed. This is an unlock that will reshape the entire economy. It's bigger than anything else I'm going to talk about here today. It will change our civilization.
It's very simple. Right now, we have something like 35 million developers — maybe a few more, maybe 40 million. And we have hundreds and hundreds of millions of people who are legitimate domain experts. The doctor who knows what software her patient panel needs. The logistics manager who can draw the warehouse routing algorithm on a whiteboard. The teacher who knows exactly what adaptive learning her students need. All of these domain experts have been blocked by overloaded software teams — whether those are overloaded software teams selling them bad software, or overloaded internal software teams that have a backlog. They're blocked.
Fundamentally, these domain experts have been locked out of building by the translation layer — the gap between knowing what should exist and making it exist as a piece of software. That translation is super lossy. It's slow. It's expensive. And that translation layer is going away. It's gone.
When a doctor can describe what she needs and an agent can build it in an afternoon, you're unlocking an entirely new class of builder. Platforms like Lovable, Bolt, and Replit are already putting production-quality development in the hands of non-coders. And if you sit there and think, "Well, I work at a company — is this relevant to me?" I tell you it is. Because the bigger the company, the more internal ideas like this people have and do not act on. They are the domain experts in their corners of the business, and they can't solve things because they've been locked out of building. They don't have to be locked out of building anymore. They can just build.
The scale of this is massive. We are about to go to hundreds of millions of builders. The total surface area of human problems addressed by custom tools is going to increase by an order of magnitude, if not two or three. And so the challenge for you, on a people scale, is: how can you start to build against the problems that you see? Understand what you're an expert in. If you're a leader, how can you unlock your people to do that? Give them the ambition. Give them the tools. Take it seriously. This is not pie-in-the-sky thinking. People are already doing this.
Unlock three: quality software is now the default
Unlock number three: quality software is the default today. It's not at a premium. This is going to break so many developers' minds — I will apologize in advance.
So much of our software has been mediocre. And we all know why. It's not because engineers are bad. It's because we lack the execution capacity to do great testing, great documentation, great security review, great performance optimization, great accessibility, and great visual polish — all on time, all under budget. These problems are all agent-verifiable. We can get them done. They're just very, very labor-intensive, and for most of history we have chosen not to prioritize them.
Now it's just table stakes. When agent harnesses run testing and security review and documentation — everything else — not as expensive add-ons but as standard procedure, the baseline quality of all software goes up dramatically. Shipping a new feature is not going to be a big deal anymore. Shipping a feature with polish is not going to be a big deal. Every shortcut that we take today that we think is rational is predicated on the idea that building is hard and complicated — and that's going away.
This is a massive mindset shift. It takes actually experiencing it. You need to find someone on your team — or, if you have a team, get them together — and work on this as a project. Find a way to use agents in an eval-driven development loop, a loop that forces the agent to work until it has tested a complete, finished, working product. Do that entire process until you have a working product out the door. And then you're going to realize — even if the first one isn't perfect, even if you get a little bit better over time — what is possible.
For so long, the gap between the top 5% of engineering teams and everyone else has been polish — what they can spend money on that we can't. Not anymore. The quality of software in our world is just going to be incredible. And that is going to push differentiation to product: what is the amazing customer experience we bring? That's a challenge for us. But I think it's a challenge we should be excited about.
Unlock four: every company is now a platform
Unlock number four: every company is going to be a platform. Right now, building and maintaining integrations is a nightmare for anyone in product, anyone in engineering. We all hate integrations. That's been the way we've done it for so long.
Open protocols — like the Model Context Protocol from Anthropic — remind us that that world has shifted beneath our feet. Instead of thinking of the world as our system is closed and we have to build bridges, we need to think of our system as fundamentally open, because agents will get into it. And we can choose to do that reactively and let them figure out a way — maybe by using a browser — or we can choose to proactively build integrations very, very cheaply.
And what this means, by the way, is every company is now a platform. You don't have to say, "All right, we're going to spend lots and lots of money on becoming a platform" — that's the big product strategy the CPO rolled out. Every company's a platform. The question for you is whether your platform is sticky, whether you deliver something that's valuable. That's what matters.
And from a human perspective, the implicit lesson here — this is high-level platform strategy stuff that typically operates at the VP level and doesn't get talked about with ICs very much unless it's at an all-hands — is that everybody needs to get fluent in this kind of thinking. The challenge is to recognize that you need to socialize that thinking down and really take the time to talk about strategy in a way that matters, because people are going to have this kind of impact. You can have someone roll out two or three integrations in an afternoon. If that's the power they have, they should probably know more about corporate strategy.
Unlock five: the market for ambition is through the roof
Unlock number five: the market for ambition is through the roof right now. Companies throw so much money on the floor. Companies will look at a $10 million market and won't touch it because the engineering team costs $3 million a year. They'll look at an R&D project with a 20% shot at success and say, "I don't think it's worth pursuing — failure would cost us two quarters of roadmap. We're just not going to do it."
But remember the structural changes. Execution cost is going down by ten times or a hundred times. All of those calculations flip. Now the $10 million market is viable. The experiment — you could do five of them. This is not a conversation about "I can't justify the investment" anymore.
CFOs need to change their mindset here. And capturing those expanded opportunities — guess what it takes? It takes people. People doing different work. People with vision and domain expertise and creative insight who can see the opportunities and go after them aggressively.
When you are looking to tell people to raise their sights and dream big, you are tapping into something that's a little bit like the childlike wonder we were sold when we watched Disney and Pixar films. You are looking for people who can dream that kind of big, because for the first time in history, the cost of execution is dropping that far. And that's a huge deal.
If you're working with people, that dreaming part — that's actually the part you need to get them to buy. And I know that's hard because so many of us are jaded. I could hear the engineers screaming back at the unlock about software quality: "It's not that easy. You don't get it." I've been in a lot of standups. I get it. I know that the world we live in is real — just like I know that the world that Anthropic lives in is also real. And guess what? They ship software every couple of days. And it's not because it took a quarter to build it.
The world is already here. The AI-native world I'm talking about is not some pie-in-the-sky future. There are companies that operate this way today. There are workers that operate this way today. If you want to move to that world, you can. But it's going to take some massive changes in mindset. It's a people-change problem more than a tech-change problem.
Unlock six: the org needs to move at the speed of insight
Unlock number six: the org needs to move at the speed of insight. I talked about speed of execution and how we can move fast. This is about speed of insight. When we get a reliable insight into something the customer wants, we need to be acting on it. We need to not be stuck in process. We need to not be stuck in writing up the documentation. We need to not be stuck in raising it to leadership. We need to go and default to getting it into code. Default to getting it into code.
And that is such a strange instinct for so many of us, because we've been told that code is scary for so long. But that is the direction we're going. That is where we are headed.
And so if you want to figure out how to thrive in this AI world, it is going to be around this ability to take those childlike instincts — the dreaming big, the insights — and recognize that the cost of thinking has gone down so much structurally that you now have the ability to execute on those instincts in a way you never had before. Maybe you're a leader and you have to convey that to your team. Maybe you're an individual and you have to believe in it for the first time. Maybe you're a team lead and you have to convince your team to give it a shot. These are the real conversations I see happening right now. And they are much, much more interesting than the boring layoff conversations I see in the newspaper.
Why none of this is speculative
Here's what strikes me most about the six unlocks I just described: none of them are speculative. None require a tech breakthrough. None depend on AGI. They're all available today — and still we don't talk about them.
And I want to call out: these are not the only ones. I'm giving you six that seem like low-hanging fruit. But the nature of this moment is that these new applications are inherently unpredictable. Nobody in 1995 looked at cheap internet bandwidth and said, "Ah — ride sharing, social media, gig economy." The applications that mattered most were the ones nobody could envision from inside the old world. That's where we are now. We're in the old world, and the unlocks I laid out are the obvious ones, the low-hanging fruit to help us move forward.
I'm not claiming that there will never be displacement. I'm not claiming that we will not have hard human consequences. What I am saying is that the idea that we are living in a fixed-pie world is incorrect. The pie was artificially constrained by execution cost. We weren't limited by our ideas. We weren't limited by our ambition. We were limited by the cost of turning ideas into products.
If you remove that bottleneck, the constraint shifts to our good ideas, to our deep domain knowledge, to our customer empathy, to our creative vision. Well, those are all human capacities, and they are in drastically short supply right now relative to what we need to build.
Think about what the world needs and doesn't have. Think about how much bad software people tolerate. Think about how many niches there are that we could never serve — personalized education, clinical decision support for individual patients, financial planning for the two billion adults with a bank account and no adviser. These are unsolved economic problems, not unsolved technical problems. The cost of building software has just been too high. Well, not anymore.
And the thing that concerns me most is that if you can't see this opportunity, you cannot begin to plan the path from here to there. The hardest work ahead isn't a technical challenge. It's figuring out what upskilling looks like when the job isn't "do the same thing faster," but "do something you've never been asked to do before." That is a different world. That is something we need to get engaged on very seriously — as individuals, as team leads, as directors, as VPs, as C-suite. This is the defining challenge of our generation.
And I want to challenge us to recognize that what I am proposing here is not dramatic. Even if it sounds dramatic, it's simply the logical outcome of execution cost for intelligence dropping ten or a hundred times — which is what is going on. This is just happening. And you can either engage with this proactively, wrestle with the people challenges, recognize we need to upskill proactively — or it can happen around you. I prefer to be proactive. Build something cool. Raise your ambitions.