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I’ve spent most of my career in the business of "making digital things". At my gaming company Sticky Studios, that meant building games around theatrical IP and interactive worlds; each a new production, handcrafted and fought for.. Later, at Talespin and now Cornerstone, it meant building software: platforms, systems, and IP designed to scale. I’ve lived on both sides of that spectrum; the craft of creative production and the repeatability of productization, each with its own beauty, tension, and fragility.

But AI has upended that logic. The strange thing about the AI boom is that it’s erasing the very premise that defined the last one: that making something has intrinsic value. For two decades, founders and product builders were rewarded for creating: software, content, systems. Each artifact had an implied scarcity; each piece of code or media could stand as an asset. But that logic is breaking down.

Why individuality feels performative in an era that monetizes sameness

I started thinking seriously about this idea after watching an interview with comic-book legend Frank Miller. At one point, he described our time as an age of “pathological conformity”. The phrase stuck with me. It sounded exaggerated at first, but the more I thought about it, the more accurate it felt. We live in a society that celebrates self-expression, yet produces uniformity on a mass scale.

Everywhere you look, individuality is aestheticized but standardized. The same opinions, wrapped in the same language of wellness, hustle, and virtue, echo endlessly across screens. Everyone seems unique in exactly the same way.

There is no censor in this story, no authoritarian government telling us what to think.
The mechanism is subtler, softer, and far more efficient. Algorithms have replaced priests; the feed has replaced the town square. Our behavior is shaped not by command, but by invisible incentives. We have entered a new stage of conformity that feels like freedom, yet quietly rewires how we think and speak.

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Every product launch, especially from a company like Apple, offers a masterclass in strategy, sometimes in success, and sometimes, in the kind of strategic miscalculation that turns a marvel of engineering into a cautionary tale. The Vision Pro is now that tale.

This isn't just about calling a product a failure; it’s about extracting a powerful lesson in business strategy: what happens when a product is launched without a clear, compelling market need. The rumored $600 million misfire and the subsequent reports of a scaled-back commitment, like the Vision Pro 2 being little more than a chip upgrade to M5 rather than a fundamental design overhaul, serve as Apple's quiet acknowledgement that they overspent and under-delivered on utility.

It’s a strategic pivot away from the initial grand vision, and it reminds us that technical excellence is never enough.

When the "Late to the Party" Play Doesn't Work

Apple built a legend on the strategy of being late to the category but winning on execution. They didn’t invent the MP3 player, the smartphone, or the smartwatch, but they completely reinterpreted and perfected them. This approach relies on one key assumption: that the market is already waiting for the category to reach its inflection point. With the Vision Pro, Apple was late to an Extended Reality (XR) party where Meta had already captured the consumer mindshare with an excellent, affordable strategy (the $499 Quest 3 and a high-quality gaming portfolio). This forced Apple to strategize after the fact on "how do we avoid being compared with that?" rather than defining the category on their own terms. Even flawless execution on bad timing is still bad timing.

They dropped a $3,500 headset that was heavy, required an external battery pack, and offered no single, immediate use case that would make a customer's daily life substantially better. It became the Apple Newton of the XR age: a technological marvel, an astounding first-generation product, that was utterly disposable from a consumer utility perspective.

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I’ve spent the past few years deep inside the generative AI space, building platforms, running experiments, integrating models into real products. I’ve seen firsthand how transformative the technology can be when it’s applied to actual problems. But lately, I’ve also become increasingly uncomfortable with how AI is shaping the broader economy, or rather, how it seems to be creating its own parallel one.