LEARNING TO SEE

There are two ways to become an AI Native leader:

1. Be born in 2012 or later and grow up using AI to learn, build things, and grow professionally.
2. Train yourself to see the opportunities that the first group will (eventually) take for granted.

Almost all of you are in the second category. So this will be a book that teaches you how to see the opportunities being made possible by the widespread availability of powerful AI assistants. Whether you're using Gemini, Claude, Claude Code, Codex, products inspired by them, or future products and models that haven't even been envisioned yet, we want you to be tuned in to what's possible… and primed to capture value that can't even be imagined today.

We're particularly excited about AI because we've been working with it for the bulk of our careers (over 70 years total between us!) We've spent most of that time using AI and machine learning (ML) to solve real business problems. And while interest has waxed and waned throughout that time, AI assistants are the game changer. They make it possible to write code, build mathematical models, automate workflows, and solve problems just by talking about them in natural language. Work that once required specialized expertise and extensive effort is now possible for far more people, because the gap between having an idea and acting on it just got a lot smaller.

While the promise is palpable, so is the hype. Is this a bubble? Will there be a market correction? Sure there will. In every industrial and technological revolution, the true winners are the hype-resistant, the ones who focus on first principles to solve real problems. We will help you become that leader.

We're here to help build your immunity to the inevitable, and create value beyond the hype.

To do that, we can look to the past to find lessons that help us navigate the future. In the USA in the 1970s and 1980s, for example, computers were all the rage.

Businesses excitedly poured money into the expanding field of information technology, seeking to equip the masses with the productivity magic of the personal computer. The first spreadsheets, Apple's Visicalc in 1979 and IBM's Lotus 1-2-3 in 1982, were much appreciated: no longer would office workers have to struggle with piles of pencils and erasers when a single cell on their 11”x17” paper spreadsheet required recalculation. When Microsoft released Excel in 1985, removing even more friction around using spreadsheets, adoption surged even more (Weaver 1985).

But despite these individual wins, national productivity statistics failed to reflect the anticipated boom… for a long time. Companies were buying computers, but economic output per worker wasn't improving as expected. In 1987, economist Robert Solow was one of the first to notice that "you can see the computer age everywhere but in the productivity statistics." Others (like Panko, 1991) didn't believe a productivity gap existed at all. They thought it was a panic-inducing myth that researchers couldn't stop chasing.

A few years later, Erik Brynjolfsson (who, in 2026, leads the Digital Economy Lab at the Stanford Institute for Human-Centered AI) studied this "Productivity Paradox" and found two possible reasons for it. Perhaps the research was faulty, he said, and the inputs or outputs just couldn't be measured properly, or outcomes were lagging. Or, more likely: "[businesses] have systematically mismanaged IT: there is something in its nature that leads firms or industries to invest in it when they shouldn't, to misallocate it, or to use it to create slack instead of productivity" (Brynjolfsson, 1993).

Brynjolfsson suggested we might be focusing on the wrong thing.

The real value of computers wasn't in making existing work faster, he proposed, but in enabling entirely new capabilities and ways of working that hadn't existed before. But that's hard, because our sense of what work can look like comes from the examples we've already seen.

If we've only ever seen jobs structured one way, that's not a failure of imagination. It's just that we've never had the chance to picture the alternatives. Without exposure to different models of how work could be done, those possibilities are genuinely hard to see at all.

We made the same collective mistake in the late 90s, when the internet became a business imperative. Today, as organizations rush to deploy AI with similar expectations of immediate wins, we risk repeating this mistake. Even the most magical technologies fade into invisibility, becoming part of the backdrop of life as we grow accustomed to their capabilities.

The transformative power of AI may not show up where we'd look first, in traditional efficiency and productivity metrics, but rather in our ability to tackle problems and create solutions that were previously impossible… together.

The AI Native leader doesn't get caught up in the hype. To them, AI is an ordinary tool, to be used everywhere it makes economic sense, in ways that support human growth and flourishing. Value creation will always be a team sport.

We've written this book to help you transcend the hype.

References

Brynjolfsson, E. (1993). The productivity paradox of information technology. Communications of the ACM, 36(12), 66-77. Retrieved from http://ccs.mit.edu/papers/CCSWP130/ccswp130.html

Panko, R.R. (1991, June). Is Office Productivity Stagnant? MIS Quarterly, 190-203. Retrieved from https://misq.umn.edu/misq/article-pdf/15/2/191/4092/3_panko.pdf

Weaver, K. R. (1985, May). Lotus 1-2-3 for mainframes (bringing a product to market). In Proceedings of the international conference on APL: APL and the future (pp. 207-214). Retrieved from https://dl.acm.org/doi/pdf/10.1145/17701.255659