A few weeks ago I wrote that boards are treating AI intelligence as a permanently scarce, permanently expensive input, and that this assumption is already cracking. A few people asked me a fair follow up question. If intelligence stops being the scarce thing, where does the scarcity actually go. It doesn’t just vanish. Someone always ends up holding it.
I have watched this happen up close twice in my own career, and I know a third example only from history, but it is the cleanest one to start with because you can actually see the migration happen.
The first time was containers. Malcolm McLean did not invent a faster ship. He invented a standard box, and that box made loading and unloading cargo dramatically cheaper. Everyone assumed the story was over once ships stopped idling in port for a week at a time. It wasn’t. Ports suddenly needed acres of land to stack containers. Rail lines had to sync up with ship schedules. Crane operators and terminal planners became more valuable than the stevedores whose jobs the container had just eliminated. The scarcity did not disappear when loading got cheap. It walked a few hundred yards down the dock and set up shop in land, rail and coordination.
The second time was the shift from FTE pricing to outcome pricing in IT and BPO, which is a conversation I have had more times than I can count over the last two years. For a long time, the constraint looked like headcount. You needed bodies to run processes, so you priced by the body. As automation and now agentic AI made raw execution cheaper, everyone assumed the constraint would just dissolve along with the headcount. It didn’t, because the bottleneck was never just labor. It was the entire system built around buying, measuring and governing labor. Procurement teams that are set up to negotiate FTE contracts are, frankly, not set up to negotiate outcome contracts, and it isn’t only because procurement is slow. When a workflow runs through a mix of human teams, vendor agents and enterprise software, agreeing on who actually caused a given outcome is a genuinely messy problem, not just a paperwork one. Finance teams that know how to forecast a headcount ramp don’t automatically know how to forecast a variable outcome fee they can’t cleanly attribute in the first place. That system, not the labor it was built to manage, is the thing that is actually scarce right now. I said back in February that this is exactly why Khosla’s five year timeline for IT and BPO extinction won’t hold. Enterprises are slow to redesign the muscle that buys and governs work, and rebuilding that muscle takes a lot longer than swapping the underlying technology.
The third time is AI, and we are living through the early innings of it.
The public conversation is entirely about model capability. Whose benchmark is better this month, whose inference is cheaper, whose context window is longer. That is the visible layer, and it is genuinely moving fast. But if you sit in enough steering committee meetings, as I do, you notice the real conversation has already shifted somewhere else. Nobody is asking whether the model is good enough anymore. They are asking who is accountable when an agent acts on its own, how you audit a decision a model made six tool calls deep, and whether legal and risk can sign off before the business quarter ends. Legal, risk and compliance are quietly becoming the functions that decide how fast AI actually ships, not engineering. And this isn’t only a soft, organizational story either. I wrote back in February that Jevons paradox is still very much alive in AI, cheap intelligence doesn’t shrink total demand, it multiplies the number of things people try to do with it. That multiplication is what’s straining power grids and chip supply right now, and it will keep straining them. The bottleneck isn’t only moving into legal’s inbox. It’s splitting, part of it lands on governance, part of it lands on the physical world’s ability to keep up.
That is the pattern, and it holds across all three examples. When something that used to be the bottleneck becomes cheap, the constraint does not evaporate. It relocates to whatever has to absorb the new abundance. Land and rail after containers. Contracts and procurement after outcome based pricing. Governance and organizational readiness after intelligence.
I want to be honest about where this framework is weaker than it sounds. It is easy to find three examples that fit a pattern after the fact. The real test is whether it predicts anything, and whether there are cases where it breaks. It does break sometimes. The cloud is actually the interesting counterexample here, not the confirming one. When compute got cheap, the constraint should have moved to independent architecture and security specialists. Instead, the hyperscalers largely built and sold that layer themselves. AWS didn’t watch a market of third party cloud governance firms spring up and capture the value, it built Control Tower and Security Hub and kept the margin in house. The incumbents who already controlled the abundant layer often reach up and grab the scarce layer too, they just do it slower than a scrappy new entrant would. Call it the adjacent ownership problem if you want a name for it.
But I don’t think AI plays out quite the same way, and it’s worth being precise about why. AWS could absorb cloud security because cloud security is still fundamentally tooling, dashboards, policies, audit logs, things a vendor can build and sell. Frontier labs can absorb model guardrails the same way, and several of them are trying to. What they cannot absorb is the thing sitting underneath the tooling: whose name is on the regulatory filing, who eats the liability when an agent makes a bad call, who signs the indemnity clause. That layer doesn’t move to the vendor no matter how good their safety tooling gets. It stays inside the enterprise. So the AI version of this migration may actually be more durable than the cloud version, the technical guardrails can be commoditized by whoever owns the model, but the accountability cannot be outsourced the same way. Though I’d bet even that has a shelf life. The moment someone figures out how to price and package agentic risk the way insurers price everything else, that liability becomes securitizable too, and the scarce resource quietly becomes actuarial expertise instead. Scarcity doesn’t stop migrating just because it hit an enterprise’s balance sheet. And I should be honest that this bottleneck doesn’t always slow things down the tidy way a land shortage slows down a port. Sometimes a business unit just routes around legal entirely, the way shadow IT always found a way around IT, and what looks like delay from the boardroom is actually unowned risk quietly accumulating somewhere nobody is tracking it yet.
So if you are trying to figure out where value is actually migrating in your own AI strategy, the technology roadmap is the least useful thing to stare at. Watch where the friction is showing up instead. Watch which meetings in your company have gotten longer, not shorter, since AI arrived. Watch which job titles didn’t exist eighteen months ago and are now impossible to hire for fast enough. Watch whether your procurement team can even write a contract for an outcome nobody has priced before.
The technology tells you what just became possible. The friction tells you where value is about to accumulate. And the companies that win the next few years will not be the ones who called the breakthrough early. They will be the ones who noticed where the scarcity went after it left.
And Vijay Says... 