We Built The Wrong Future… And AI Is Exposing It

I’ve been thinking a lot about the future lately… not in an abstract, sci-fi sense, but in a much more “what are we really building towards” way.

Maybe it’s time that to question whether we’ve been pointing in the right direction all along? Because for the better part of two decades, we’ve told ourselves a very specific story about progress… better software… better interfaces… better data collection… better systems. We built tools to communicate and connect… transact and optimize. It worked… probably better than most of us could have ever imagined. Entire industries and types of work were created, massive value was unlocked and it felt like we were building the future in real time.

But maybe we built a very narrow version of that future?

That’s what I kept thinking while editing my conversation with Pablos Holman on this week’s episode of Thinking With Mitch Joel. Pablos (author of Deep Future and someone who places big bets on big ideas) made a point that’s hard to shake: the tech layer we’ve been obsessing over represents only a small slice of our real world. The vast majority of the economy is physical (not digital/software/apps)… energy, manufacturing, food, infrastructure… the systems that actually power, feed and sustain all of us. And those are the areas where progress is harder, slower and far more complex (just ask any VC focused on hardware).

So were we focused only of what was easier to move?

That doesn’t mean we built the wrong things… but it might suggest we didn’t build enough in other important (probably critical) areas. While we were busy optimizing filters for selfies and platforms to brag about our work credentials, we largely ignored the deeper, more foundational problems that define long-term progress. It wasn’t a mistake… it was a choice, reinforced by incentives that reward speed, scale and short-term (but massive) returns.

AI is now making that gap impossible to ignore.

Because while most of the conversation is centered on chatbots, vibe coding and agents today, the real breakthroughs are happening somewhere else entirely. AI applied to physics, biology, materials science… accelerating discovery in ways that don’t just make work faster, but fundamentally change what’s possible. And yet, that’s not where most of our attention is going. We’re still focused on outputs, when the real opportunity is outcomes.

If the most important problems in the world are physical, and the tools we’re building can now help solve them, are we still optimizing for convenience?

It starting to feel like we’re using exponential tools to solve incremental problems (if these tools truly can deliver on what they’re promising). So… do currently have a technology limitation… or a priority problem? Pablos pushes this even further. If there’s work that wouldn’t get done unless someone is paid to do it, a machine should probably do it (if it’s able to). Not as a threat, but as a release valve… a way to move human effort up the stack. But that only works if we know what we’re moving toward, and right now, that part feels far less defined.

We talk a lot about what AI might replace… far less about what it should elevate.

Which brings us back to the real tension: It’s not that technology is moving too fast… it’s that our imagination isn’t keeping up. We’ve been incredibly good at building tools, but far less deliberate about deciding what they’re actually for… or could be for. So maybe the problem isn’t that AI is going to take the future away from us. Maybe it’s that it’s exposing how small we’ve been thinking about it? And now, with tools that can genuinely expand what’s possible, we’re being forced to confront that gap. Because this moment isn’t just about building faster (you know how I feel about speed). It’s about deciding to build differently.

Because the next version of the future won’t be defined by what AI can do… it will be defined by what we decide is worth doing with it… and whether we’re willing to aim higher than we have so far.

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