AI Adoption Is Leading to Greater Efficiency, Not Innovation

There is a quiet assumption running underneath the loudest investment of our age, and Sunday is a good day to bring it into the light.

We are building compute. Enormous, almost unimaginable quantities of it. We are miniaturizing computers at one end — fabricating features measured in handfuls of atoms — and scaling them up at the other into hyperscale clouds that span continents. The capital is real, the engineering is genuine, and the people doing it are among the most capable we have. I do not doubt any of that.

What I have been turning over is a smaller, more stubborn question:

Are we inventing, or are we accelerating?

Some say we must build this capacity ahead of demand, the way the railways were laid before the freight existed, the way fibre was buried dark in 2000 and waited years for the traffic that finally lit it. There is wisdom in that. You cannot discover what you have no room to experiment in, and slack capacity is how an era leaves space for the unimagined. I accept the argument. And yet I notice what the capacity is being measured in. Tokens per second. GPU-hours. Throughput, utilization, cost per unit. These are the metrics of a mature commodity, not a young invention. We are provisioning ahead of a demand denominated entirely in the units of the world we already have.

That is the first thing worth naming plainly. AI adoption is delivering efficiency, not innovation — and we have begun to mistake the one for the other. 

Adoption and innovation are not the same move.

Adoption bolts the new capability onto the work we already do and makes it faster and cheaper. Innovation changes which things are possible at all. Almost everything the AI economy sells today is the first kind — summarize the document, draft the email, deflect the support ticket, take the cost out of a task we already knew how to do. 

That is efficiency, and efficiency is what you reach for when you have accepted the problem as given and are only negotiating its price. It is the safest possible use of a general-purpose technology, because the return is legible to a finance committee this quarter. It is also, almost by definition, not innovation. Innovation asks a different question: what was previously unsolvable — not merely expensive — that now becomes possible? What problem was on no one's roadmap until the capability arrived?

The rule we forgot to change

Eliyahu Goldratt gave us the clearest lens for seeing this clearly, and he gave it to us a generation ago. He argued that every new technology must answer four questions. What is its power? What limitation does it diminish? What rules did we adopt to live with that limitation, back when it was binding? And what rules should we use now that it no longer is?

Everything hangs on his iron law: technology is necessary but not sufficient. A new technology delivers its benefit only if it diminishes a real limitation and we change the old rules that grew up around it. Install the technology, keep the rules, and you get nothing — or worse, you automate the constraint and call the automation progress.

He wrote this, pointedly, about ERP — an industry that spent fortunes on infrastructure and reaped disappointment, because companies bought the new system and preserved their old rules. They paved the cow paths. Adoption, in other words, is precisely the act of keeping the rules — which is why adoption can only ever return efficiency. I have come to believe AI is having its ERP moment, now, at civilizational scale and three orders of magnitude more capital. We are installing extraordinary new technology on top of rules we have not stopped to question.

And what are those rules? Every rule of the Information Age was a rule for accommodating a single limitation: the human being as the processor of last resort. Dashboards, reports, business intelligence, the whole apparatus of moving and shaping data into information — all of it exists because, at the end of every pipeline, a person had to read the output and decide. And to decide well, they needed one coherent picture — a single version of the truth. The truth, however, was scattered across systems and inconsistent with itself. So we built an architecture to manufacture coherence: extract the truth from where it lay, assemble it into one authoritative version, and bring it to the centre where a person could trust it and act on it.

That is the rule. Assemble the truth and bring it to the centre, because a human must be able to act on one version of it.

The doorman's fallacy

From here, the conclusion looks obvious. It is also wrong.

The tempting reading is simple: the human is the bottleneck, so remove the human. Take them out of the loop, out of the decision, out of the act — all of it slow, all of it friction, all of it in the way. Let intelligence reach the data directly, at speed and at scale, with the capacity to act. It sounds like the future. And it is, of course, the wrong conclusion to the wrong question — the doorman's fallacy wearing the language of intelligence.

The fallacy, you will remember, is this: a shop sees a doorman and decides he merely opens doors — a function a sensor performs for a fraction of the cost. So they remove him. And they discover, too late, that the doorman was never only opening doors. He was greeting regulars by name, watching the street, turning away trouble, holding something the throughput metric could never see. Define a role by its most visible mechanical function, optimize that function away, and you destroy the invisible value you never knew you were buying.

The human at the end of the Information Age pipeline was never only processing. They were judging what mattered. They held the intent the processing served. They were the accountable agent — the one who answered for the result. The Information Age had fused three things into a single human seat: intelligence, intent, and accountability. They lived together only because they had to. To apply judgment at the point of decision, a person also had to do the processing there, because only a person could do either.

What AI actually diminishes is not the human. It is the necessity of that fusion. Processing no longer requires a person to be present. And the moment you see that, Goldratt's last question changes its answer entirely.

The rule that must die is not "humans in the loop." It is the belief that the human in the loop was only a processor — that the seat held a function and nothing more. Take the processing away and what remains is not an empty chair. It is the part that was always the actual work: deciding what matters, holding the intent the work served, standing answerable for the result. The machine can take the processing. It cannot take that. And a corporation that reads the whole seat as overhead — that optimizes away the function and assumes the value came with it for free — has made the doorman's mistake with its own judgment.

What efficiency cannot provide

And here's the thing. Efficiency is something you can buy. Value is something you need to create.

The capacity to act — the speed, the scale, the throughput — is a supply problem, and we are solving it magnificently with fabs and clouds. But the things that make an act worth doing were never in the supply. Intent. The judgment that weighs what matters against what merely counts. The answerability of someone who can be held to the result. None of these arrive with the compute.

They were carried by the very person we were so eager to optimize away, and removing the person does not hand them to the machine. It simply leaves them behind. The machine inherits the doing and none of the answering.

That is the danger I keep returning to. The efficient future, pursued without care, gives us systems that act at speed and scale with no one answerable for the acting — enormous capability in the service of no particular value, because value was the expensive, human, unmeasurable thing we had quietly decided we could do without.

What we are really choosing

Which brings me, on this Sunday, to the pivot underneath all of it.

We are being asked, quietly and without a vote, to become technology-driven corporations rather than corporations driven by mission, values, and judgment. The shift is presented as inevitability — the technology is here, the capacity is built, adoption is the only rational response — and in that framing the corporation reorganizes itself around what the technology can do, rather than around what the institution is for.

This is the doorman's fallacy raised to the level of the enterprise, and then to the level of the age. We mistake the door-opening for the doorman. We see in mission, values, and judgment a kind of friction — slow, expensive, human — and we imagine that removing the friction is the same as keeping the value. It is not. Mission is intent. Values are the standing promises an institution makes about what it will and will not do. Judgment is the moral agent exercising accountable choice. These are precisely the things that do not arrive free with the compute. They are the things being orphaned while we celebrate the buildout.

So here is where the threads connect. The infrastructure is necessary and is not sufficient. The efficiency is real, and it is not innovation, and it is not value. And the corporation that lets the technology drive will have optimized everything it could measure and surrendered everything it could not — fast, scaled, capable, and answerable to no one for anything that matters.

The technology cannot supply intent. It cannot hold a mission. It cannot bear accountability, because it cannot be answered to. Those remain, stubbornly and permanently, the work of moral agents — of people who decide what the whole apparatus is for and stand answerable for the answer. We can provision the substrate. We can buy capacity ahead of demand. What we cannot buy, ahead of demand or at any price, is the judgment that decides what the capacity is for.

Technology is meant to serve the mission — to help an enterprise do what it exists to do, and become what it is trying to become. 

The danger of this moment is the inversion: the mission quietly bending to serve the technology instead, until the enterprise organizes itself around what the tools can do rather than what it is for. And for most enterprises, what AI adoption actually delivers is narrower than the promise — the same things done faster, the familiar work made cheaper. It does not, on its own, invent what did not exist, imagine a different way of working, or transform a business into something new. Those are acts of mission, not of adoption. Adoption with no mission to serve only carries the enterprise faster toward wherever it was already heading.

Necessary, but not sufficient. The sufficient part — knowing what it is all for, and answering for it — was never going to come from the machine. It comes from us, or it comes from nowhere.