The Conceptual Last Mile

There is an assumption embedded in nearly every conversation about AI adoption that the challenge is distribution—getting the tools in people’s hands and training users on the interface. This assumption is wrong. Anybody in a modern enterprise can open an AI assistant right now. The technology has been distributed, but the adoption won’t always follow.

The real bottleneck isn’t access; it’s something much harder to solve. The last mile of AI adoption is conceptual—the gap between having a transformative tool and reconceiving what your job becomes with that tool.

The Diffusion Gap

In a recent conversation with Ross Douthat, Anthropic CEO Dario Amodei made an observation that deserves more attention than it got. He noted that software engineers have adopted AI tooling faster than nearly any other profession. The models aren’t inherently better at code, but because developers are “socially adjacent to the AI world.” They pay attention to what’s happening. They’re comfortable with rapid technological change. The distance between their existing identity and their AI-augmented identity is relatively short.

Now consider the inverse. A compliance analyst at a regional bank, commercial loan officer, or paralegal reviewing documents. These professionals may have access to the same tools, but the distance they need to travel is enormous—not technically, but conceptually. This requires a new understanding of what their expertise means when a machine can do the procedural parts of their job in seconds.

This is the diffusion gap. When we imagine an “AI future,” we tend to picture a uniform transformation—society shifting together into a new era. What actually happens is something more like tectonic plates moving at different speeds, and the friction shows up at the boundaries. Inside a single organization, you’ll have teams operating in 2028 alongside teams still operating in 2024. That unevenness is itself an underappreciated risk.

What the Last Mile Actually Looks Like

I’ve taught employees at a large financial institution to work with AI tools, and the pattern I see most often isn’t resistance or fear. It’s something more stubborn and less obvious.

It’s the person who can demonstrate competence with the tool—and then returns to their desk and works exactly the way they did before because they haven’t yet reconciled what the tool implies about the nature of their work. This is the conceptual last mile. It’s the gap between capability and self-concept. And it’s where most AI adoption strategies fail.

The Centaur Phase Is Closing

Amodei used an analogy in the interview that I think is more urgent than it sounds. After Deep Blue defeated Garry Kasparov, there was an era in chess—roughly fifteen to twenty years—where a human working alongside an AI could defeat any human or any AI playing alone. Human judgment plus machine calculation was the strongest combination. That era is coming to an end. Now it’s just the machine.

Amodei sees the same pattern in knowledge work, but compressed. We are in the centaur phase right now—the window where human expertise combined with AI produces the best outcomes. But the window is a transition state, and it may last only a few years.

The people who never cross the conceptual last mile will miss this window entirely. They’ll go from doing the work the old way to being unable to compete with the machine doing it alone. This narrow centaur phase is our opportunity to redefine our roles while we still have leverage. But it requires something most organizations aren’t providing: identity management.

Asking someone to move from performing cognitive work to directing AI that performs cognitive work is not a “skills upgrade.” It’s a loss. It means releasing the thing that made you valuable and replacing it with something less tangible. Supervision. Judgment. Knowing what to ask for and whether the output is right.

Build Over Buy

This is where a strategic question becomes existential: does your organization build its AI capability or buy it?

The buy approach looks clean on a slide deck. License an enterprise platform, deploy it across the organization, measure ROI. But if the real bottleneck is conceptual and existential—if the actual barrier to adoption is people reconceiving their relationship to their own expertise—then procurement can’t solve it. An enterprise license doesn’t help someone cross the last mile. It just puts a powerful tool on the desktop of someone who doesn’t yet understand the new relationship with their own expertise.

Build organizations are different, and not because they have better technology. They’re different because the process of building forces people to internalize the transition. When you build AI capability internally, you develop people who’ve wrestled with what these systems can and can’t do, who understand both the tool and the institutional context it’s entering, who embrace the discomfort of redefining their own expertise in the presence of new technology. That understanding doesn’t come from a vendor. It accumulates through years of building, failing, iterating, and thinking deeply inside the specific environment where the change needs to happen.

There’s also an incentive problem worth naming. The vendor selling you the enterprise license has no structural incentive to help your people cross the conceptual last mile. Your dependency serves them. They want you to need the next upgrade, the next tier, the next feature. A build organization develops internal capacity that compounds. A buy organization develops external dependency that extracts.

This doesn’t mean building everything from scratch. It means recognizing that the most important thing you’re building isn’t the system—that's a byproduct of people who understand the system deeply enough to bring everyone else across.

The Real Infrastructure

The companies that navigate this transition won’t be the ones that spent the most on licenses. They’ll be the ones that understood, early enough, that the bottleneck was identity. They invested accordingly—in people who could sit at the boundary between the technology and the workforce and translate not the tool, but the meaning of the tool. People who had crossed the conceptual last mile themselves and could guide others through it. The important infrastructure of AI adoption is human, it’s slow, and you can’t buy it.