The radiologist, the dynamo, and the dangerous allure of the "retrofit"

Two weeks ago I wrote about the bottleneck cascade and how every breakthrough shifts scarcity somewhere else. This piece zooms in on one of the most seductive dead ends that cascades produce: the retrofit. 

In 2016, AI pioneer Geoffrey Hinton famously declared that we should "stop training radiologists now," as it was obvious that deep learning would soon outperform them. The prediction made perfect sense. AI excels at pattern recognition, and a radiologist’s job, it seemed, was to spot patterns in images.

And yet today, not only are radiologists still employed, but the demand for them is growing.

Why was Hinton so wrong? For a complete telling of the story, read this excellent piece from Works in Progress. But the short version is that the attempt to replace radiologists was a classic retrofit: inserting a new technology into a single step of a complex workflow. And the effort quickly slammed into a cascade of deeper bottlenecks that technology alone couldn’t solve, from messy real-world data to the thorny issues of legal liability and the simple fact that a radiologist’s job is more about judgment and consultation than just spotting shadows.

This is the bottleneck cascade in action. The attempt to solve a single problem (that a technology is well-suited to solve) reveals deeper, systemic ones (that said technology may or may not be relevant for).

And this dynamic isn’t unique to healthcare. It is the central challenge of the AI transition for every industry. How many of today's celebrated startups raising gobs of capital at sky-high valuations are selling sophisticated retrofits? How many of these products accelerate one piece of an old process, without attempting to meaningfully change the process itself? 

As investors and entrepreneurs, this is a particularly important line of inquiry because the long-term prospects of the retrofit strategy are decidedly uncertain. Retrofits are, by their very nature, destined to be ripped out and replaced once businesses begin the far more difficult, and valuable, work of completely re-architecting their operations. Some retrofits may catalyze that shift (which is why FDEs are the startup go-to-market strategy du jour, and VC/PE roll-ups are so hot), but most will not. Understanding the difference between these two approaches is the single most important strategic challenge for founders and investors today. To navigate it, we need a map. Thankfully, history provides one.

The dynamo’s lesson

In 1990, economist Paul A. David wrote a landmark paper to address a puzzle that vexed his peers: with powerful computers appearing everywhere, why weren't they showing up in the productivity statistics? This "productivity paradox" felt like a deep contradiction.

To answer this question, David looked back in time to the diffusion of electricity and the rise of the electric dynamo (the precursor to the electric motor) at the turn of the 20th century. In 1900, factories were installing dynamos, cities were lit with electric lamps, and streetcars were powered by electricity, yet the massive economic boom everyone expected had not materialized. That boom, he pointed out, only arrived in the 1920s, a full forty years after the dynamo's introduction.

The delay, David explained, was caused by a critical lack of imagination about the unique capabilities of the dynamo. Factory owners initially treated the new technology as a substitute for the old one. They would replace a single, massive steam engine with a single, massive electric motor, but keep the old, inefficient factory design, which relied on power coming from a central source. 

This is what happens in the early days of any new revolutionary technology. It’s not that the factory owners weren’t trying to employ electricity to make themselves more productive. They just thought they were re-architecting, when they were actually retrofitting.

The revolution began when engineers realized the dynamo's true potential wasn't just generating power, but distributing it. This insight led to the “unit drive” (that is, a small motor dedicated to each machine). Freed from a single, central power source, factories could be completely re-architected. Production was now organized in logical assembly lines, making factories lighter, safer, and radically more efficient. And the payoff was enormous. Between 1915 and 1929, U.S. manufacturing productivity jumped from ~1.4% to ~5% annual growth.

This reveals an undeniable truth. Truly revolutionary technology doesn't just speed up the old way of doing things, it demands we invent an entirely new one. The productivity gains don't arrive until we stop retrofitting and start re-architecting.

What is AI’s “unit drive”?

We find ourselves in a similar moment right now. AI is the next great general-purpose technology. But to unlock the next wave of productivity, we must identify the "unit drive" of our time. What is the fundamental, granular building block that will allow us to fully re-architect our world?

I don’t know. I don’t think anybody knows. But here are three hypotheses. If intelligence is best understood as a unit of labor, then we’ll need autonomous agents that can reason, plan, and execute tasks. If it’s best understood as a standardized utility, like electricity itself, then we’ll need a shared intelligence layer that any AI “appliance” can plug into. And if it’s best understood as a trustworthy answer, then we’ll need verified components that can provide auditable and insurable responses ready to be built into critical systems.

The form this “unit drive” takes is still an open question. The real test will be whether it enables true re-architecture or whether it leaves us stuck in ever more sophisticated retrofits.

So what’s the opportunity for entrepreneurs and investors today? Retrofits will be ripped out. The models will eat most of the margin from the application layer (as Jerry Neumann persuasively argued in his fantastic essay “AI Will Not Make You Rich”). So the only lasting winners (other than the labs themselves) will be the re-architectors. The firms that reorganize themselves to capture the downstream productivity boom. That leaves a narrow but critical band of opportunity today: the tooling that makes re-architecture possible. In other words, the unit drive. Or at least the standardized wiring and plugs that made the unit drive possible. Unglamorous, yes, but necessary.

Neumann is right that most of today’s AI profits will be competed away at the model and application layer. But that doesn’t mean there isn’t real opportunity right now. When microprocessors first appeared, Intel imagined calculators. It took outsiders to realize the personal computer was the real prize. The same is true now. Somewhere in the crowded field of seeming retrofits lies AI’s “unit drive. For entrepreneurs and investors, the work today is not in betting on the shiniest retrofit, but in backing the toolmakers, experimenters, and tinkerers who are laying the wiring for what comes next.