History Has Already Answered Your Biggest AI Question.

We can see the wreckage clearly in hindsight. Sears. Blockbuster. The technology changes. The mistakes don't. And you may be standing on the precipice of making them again.

In September 2000, Reed Hastings flew to Dallas to offer Blockbuster the chance to buy Netflix for $50 million. Netflix was struggling - an unprofitable DVD-by-mail startup hemorrhaging cash in the middle of the dot-com crash. Blockbuster had 9,000 stores, 60 million customers, and $6 billion in annual revenue. They laughed them out of the room.

Blockbuster's CEO wasn't stupid. He was right that the dot-com bubble was overblown. He was right that Netflix was losing money. What he couldn't see - what he never asked - was what Netflix's underlying idea made possible. He evaluated the offer through the lens of what Blockbuster already was. And that was the mistake.

Every major technology disruption produces the same two initial responses. The first: adapt the old model. Take what you already do and make it work with the new tool. The second: automate the old model. Take what you already do and make it faster or cheaper. Both feel like progress. Neither is rethinking anything.

The technology changes. The mistakes don't.

The most important question you can ask about your AI investments isn't whether they're ambitious enough. It's which question they're built around.

The pattern doesn't only play out at the organizational level. It plays out in careers.

In 1983, Lotus released 1-2-3. For the first time, a spreadsheet could do in seconds what financial analysts had spent days calculating by hand. The reaction was not measured. The financial analyst, people said, was finished.

It wasn't the analysis that the software replaced. It was the calculation - the hours of manual number-crunching that consumed most of an analyst's working day. What the spreadsheet actually did was give analysts their time back. The question was what they were going to do with it.

The professionals who thrived picked up what the software couldn't do: interpretation, judgment, the ability to ask what the numbers actually meant. The ones who struggled had built their identity around the calculation itself rather than the thinking behind it. They had defined their value too narrowly.

Marshall Goldsmith wrote that what got you here won't get you there. He was writing about individual leaders outgrowing the habits that made them successful. But the same principle applies to entire industries - and to the technology strategies inside them. What made Blockbuster dominant is exactly what made them blind. What made the calculation-focused analyst valuable is exactly what made them vulnerable.

The question in 1983 wasn't whether to use the spreadsheet. It was what you were going to do with the time it gave you back. That's the same question in front of every leader right now.

The company that should have built Amazon

Sears figured out something fundamental: people didn't want to go to stores. They never did. Given the choice, customers would rather browse from home and have products delivered. The catalog was proof. And for most of a century, it made Sears the dominant retailer in America.

Jeff Bezos knew this. He cited the Sears catalog as direct inspiration for Amazon. But Bezos didn't ask how to put the Sears catalog on the internet. He asked what the underlying idea - that customers want what they want, delivered to them, without friction - could become when every constraint of the physical catalog disappeared.

No print deadline. Infinite inventory. Real-time pricing. Customer reviews. One-click purchasing. Personalized recommendations. Same-day delivery. That isn't a catalog online. That's what the original insight made possible when someone finally asked the right question about it.

Sears had the insight. Amazon had the question.

Sears organized their online store around their physical store departments. They treated the internet as a new channel for the same model. They never asked what their assets - the logistics infrastructure, the supplier relationships, the century of customer trust - made possible in a new environment. They owned the idea. They let it slip away. And they had discovered the original insight in 1888.

The New York Times asked a different question. While competitors gave away content for free and destroyed their advertising model in the process, the Times asked who would actually pay for journalism and why. They invested in the product. They built a direct relationship with readers. They diversified into cooking, games, sports. They are now primarily a subscription business with more than 10 million digital subscribers. That is a model change. Not a channel change.

Goldsmith was writing about individual leaders. But Sears is a reminder that the same concept can be applied to entire organizations. The very assets and instincts that made them the dominant retailer in America became the assumptions that blinded them to what the internet actually made possible. And if it applies to organizations, it applies to you.

The dividing line

Every organization deploying AI right now is asking one of two questions.

The first: how do we do this with fewer people? How do we automate what we already do, reduce the cost of doing it, and protect the margins we have? It feels like strategy. It has spreadsheets behind it. It gets presented to boards. It is, in almost every case, the wrong question - or at least the smaller one.

The second: what does this make possible that wasn't possible before?

That question is harder. It doesn't have an obvious answer. It requires honesty about what your organization actually is - not what it does, but what it makes possible for the people it serves. It requires the willingness to follow the answer even when it leads somewhere uncomfortable.

Most organizations are asking the first question. A few are asking the second.

Jack Dorsey asked the second one about Block. He looked at his organization and asked what the hierarchy was actually for. His honest answer: it exists to route information and coordinate work across teams. Then he asked the harder question. What does it mean to build a company when AI can do that better than humans can?

He didn't eliminate 40% of his workforce because of AI. He did it because AI made it possible to ask a question most leaders won't ask - and he was willing to act on the honest answer. Three roles replaced the traditional org chart: individual contributors who build, directly responsible individuals who own problems, and player-coaches who develop people while doing the work themselves. Cash App still processes payments. Square still serves merchants. What changed wasn't what Block does. It was how the organization coordinates itself to do it.

That's not optimization. That's reinvention.

Peter Diamandis has spent his career documenting what happens when people ask the second question at scale. His argument - built across decades of evidence - is that technology has always been net positive for human prosperity over time. But net positive is a macro claim, not an individual guarantee. The spreadsheet reduced the number of analysts needed for calculation work. But it opened the door to financial modeling, investment banking at scale, algorithmic trading, fintech - categories that employed more people than the original profession ever did. The aggregate expanded even as individuals were displaced.

That tension is the point. The abundance is real. But it belongs to the people who walked through the door - not the ones who were fighting to protect what they already knew how to do.

But those categories weren't created by the technology alone. They were created through two things happening simultaneously. Leaders asking what the technology made possible for the people they serve. And individuals getting honest about what only they could contribute that the technology couldn't. The organizations that rethought their model created demand for capabilities that didn't exist before. The individuals who found their irreducible thing became the people who could meet that demand.

One without the other doesn't produce abundance. It produces either a rethought model with no one capable of executing it, or talented people trapped inside a model being automated around them.

The abundance is real. But it requires both. And it starts with the same question, asked at every level: what does this make possible that wasn't possible before?

The irreducible thing

There is a question worth sitting with before you close this article.

If someone asked you what your role is actually for - not what you do, but what you make possible - how much of your answer would survive AI?

Dorsey is right. Most leaders, if they're honest, will find that a significant portion of their current work is about keeping people aligned, moving information to where it needs to go, and making sure the right decisions reach the right people. Look at your calendar. Look at your inbox. Look at where your time actually goes. A lot of it is connective tissue - necessary, familiar, and now something AI does better than we do.

AI doesn't threaten leadership. It exposes what was never really leadership in the first place.

What remains - what has always remained, under every disruption - is the irreducible thing. Judgment. Context. Relationships. Ethical instinct. The ability to ask the right question before you know what the answer looks like. These are not soft skills. They are the whole job, once AI handles everything else.

The leaders who thrive in this moment won't be the ones who mastered AI fastest. They'll be the ones who got clear about what only they can do - and built deliberately around that answer. The same is true for the organizations they lead.

But there is a dimension to this that the optimistic version of the story tends to leave out. The spreadsheet didn't just change what financial professionals did. It changed how many were needed. The individuals who evolved early got the seats. The ones who evolved late found them taken. Timing isn't everything. But it isn't nothing either.

So are you asking the right question?

Your organization needs you to. Your future career needs you to. And the people whose livelihoods depend on the new categories of work that only get created when leaders like you ask it - they need you to as well.

The abundance is real. The opportunity is real. Every disruption in history has produced more than it displaced - but only for the people who showed up to build what came next.

They discovered what only they could do. And they were intentional about it.