After three years of immersion in AI, I have come to a relatively simple conclusion: it’s a useful technology that is very likely overhyped to the point of catastrophe.

The best case scenario is that AI is just not as valuable as those who invest in it, make it, and sell it believe. This is a classic bubble scenario. We’ll all take a hit when the air is let out, and given the historic concentration of the market compared to previous bubbles, the hit will really hurt. The worst case scenario is that the people with the most money at stake in AI know it’s not what they say it is. If this is true, we get the bubble and fraud with compound motives. I have an idea about one of them that I’ll get to toward the end of this essay. But first, let’s start with the hype.

As a designer, I’ve found the promise of AI to be seriously overblown. In fact, most of the AI use cases in design tend to feel like straw men to me. I’ve often found myself watching a video about using AI “end to end” in design only to conclude that the process would never work in real work. This is usually because the process depicted assumes total control from end to end — the way it might work when creating, say, a demonstration project for a portfolio, or inventing a brand from scratch with only yourself as a decision-maker. But inserting generative AI in the midst of existing design systems rarely benefits anyone.

It can take enormous amounts of time to replicate existing imagery with prompt engineering, only to have your tool of choice hiccup every now and again or just not get some specific aspect of what a person had created previously. I can think of many examples from my own team’s client work: difficult to replicate custom illustrative styles, impossible to replicate text and image layering, direct connections between images and texts that even the most explicit prompts don’t make. A similar problem happens with layout. Generative AI can help with ideating layout, but fails to deliver efficiently within existing design systems. Yes, there are plenty of AI tools that will generate a layout and offer one-click transport to Figma, where you nearly always have to rebuild it to integrate it properly with whatever was there beforehand. When it comes to layout and UI, every designer I know who is competent will produce a better page or screen faster doing it themselves than involving any AI tool. No caveats.

My experience with AI in the design context tends to reflect what I think is generally true about AI in the workplace: the smaller the use case, the larger the gain. The larger the use case, the larger the expense. Most of the larger use cases that I have observed — where AI is leveraged to automate entire workflows, or capture end to end operational data, or replace an entire function — the outlay of work is equal to or greater than the savings. The time we think we’ll save by using AI tends to be spent on doing something else with AI.

(Before I continue, know also that I am a co-founder of a completely AI-dependent venture, Magnolia. Beyond the design-specific use cases I’ve described, I know what it means to build software that uses AI in a far more complex manner. The investment is enormous, and the maintenance — the effort required to maintain a level of quality and accuracy of output that can compete with general purpose AI tools like ChatGPT or even AI research tools like Perplexity — is even more so. This directly supports my argument because the only reason to even create such a venture is to capitalize on the promise of AI and the normalization of “knowledge work” around it. That may be too steep a hill to climb.)

Much has already been made of the MIT study noting the preponderance of AI initiative failures in corporate environments. Those that expect a uniform application of AI and a uniform, generalized ROI see failure, while those who identify isolated applications with specific targets experience success. The former tends to be a reaction to hype, the latter an outworking of real understanding. There are dozens of small-scale applications that have large-scale effects, most of which I’d categorize as information synthesis — search, summarization, analysis. Magnolia (and any other new, AI-focused venture) fits right in there. But the sweeping, work-wide transformation? That’s the part that doesn’t hold up.

Of course, we should expect AI to increase its usefulness over time as adoption calibrates — this is the pattern with any new technology. But calibration doesn’t mean indefinite growth, and this is where the financial picture becomes troubling. The top seven companies by market value all have mutually dependent investments in AI and one another. The more money that gets injected into this combined venture, the more everyone expects to extract. But there has yet to be a viable model to monetize AI that gets anywhere close to the desired market capitalization. This is Ed Zitron’s whole thing.

This is also the same reckoning that a dot-com inflated market faced twenty-five years ago. It was obvious that we had a useful technology on our hands, but it wasn’t obvious to enough people that it wasn’t a magic money machine.

Looking back, another product hype cycle that came right afterward sums this bubble problem up in a much shorter timescale: The Segway was hyped by venture capitalists as a technology that would change how cities were built. People actually said that. But when everyone saw that it was a scooter, that suddenly sounded awfully silly. Today, we hear that AI will change how all work is done by everyone — a much broader pronouncement than even the design of all cities. I think it’s likely to come closer than the Segway to delivering on its hype, but when the hype is that grand, the delta between scooter and normal technology is, at this point, a trillion dollar gap.

The AI bubble, as measured by the state of the financial market, is much, much bigger than any we’ve seen before. Even Sam Altman has acknowledged we’re likely in a bubble, shrugging it off like a billion-dollar miscalculation on a trillion-dollar balance sheet. The valuation numbers he is immersed in are extraordinarily large — and speculative — so, no wonder, but the market is dangerously imbalanced in its dependence upon them. A sudden burst or even a slower deflation will be a very big deal, and, unfortunately, we should expect it — even if AI doesn’t fail as a venture completely.

Meanwhile, generative AI presents a few other broader challenges to the integrity of our society. First is to truth. We’ve already seen how internet technologies can be used to manipulate a population’s understanding of reality. The last ten years have practically been defined by filter bubbles, alternative facts, and weaponized social media — without AI. AI can do all of that better, faster, and with more precision. With a culture-wide degradation of trust in our major global networks, it leaves us vulnerable to lies of all kinds from all kinds of sources and no standard by which to vet the things we see, hear, or read.

I really don’t like this, and to my mind, it represents, on its own, a good reason to back off from AI. Society is more than just a market. It’s a fabric of minds, all of which are vulnerable to losing coherence in the midst of AI output. Given the stated purpose of AI, such a thing would be a collateral damage, you know, like testing a nuclear bomb in the town square.

But then I wonder about the true purpose of AI. As in, is it really for what they say it’s for?

There is a vast chasm between what we, the users, and them, the investors, are “sold” in AI. We are told that AI will do our tasks faster and better than we can — that there is no future of work without AI. And that is a huge sell, one I’ve spent the majority of this post deconstructing from my, albeit limited, perspective. But they — the people who commit billions toward AI — are sold something entirely different. They are sold AGI, the idea of a transformative artificial intelligence, an idea so big that it can accommodate any hope or fear a billionaire might have. Their billions buy them ownership over what they are told will remake a future world nearly entirely monetized for them. And if not them, someone else. That’s where the fear comes in. It leads to Manhattan Project rationale, where any lingering doubt over the prudence of pursuing this technology is overpowered by the conviction of its inexorability. Someone will make it, so it should be them, because they can trust them.

And yet, as much as I doubt what we are sold in AI, I feel the same about what they — the billionaire investors in an AI future — are sold as well. I doubt the AGI promise, not just because we keep moving the goal posts by redefining what we mean by AGI, but because it was always an abstract science fiction fantasy rather than a coherent, precise and measurable pursuit. Rather than previous audacious scientific goals like mapping the human genome, achieving AGI has never been precise enough to achieve. To think that with enough compute we can code consciousness is like thinking that with enough rainbows one of them will have a pot of gold at its end.

Again, I think that AI is probably just a normal technology, riding a normal hype wave.

And here’s where I nurse a particular conspiracy theory: I think the makers of AI know that.

I think that what is really behind the AI bubble is the same thing behind most money, power, and influence: land and resources. The AI future that is promised, whether to you and me or to the billionaires, requires the same thing: lots of energy, lots of land, and lots of water. Datacenters that outburn cities to keep the data churning are big, expensive, and have to be built somewhere. The deals made to develop this kind of property are political — they affect cities and states more than just about any other business run within their borders.

AI companies say they need datacenters to deliver on their ground-level, day-to-day user promises while simultaneously claiming they’re nearly at AGI. That’s quite a contradiction. A datacenter takes years to construct. How will today’s plans ever enable a company like OpenAI to catch up with what they already claim is a computational deficit that demands more datacenters? And yet, these deals are made. There’s a logic hole here that’s easily filled by the possibility that AI is a fitting front for consolidation of resources and power. The value of AI can drop to nothing, but owning the land and the flow of water through it won’t.

When the list of people who own this property is as short as it is, you have a very peculiar imbalance of power that almost creates an independent nation within a nation. Globalism eroded borders by crossing them, this new thing — this Privatism — erodes them from within. Remember, datacenters are built on large pieces of land, drawing more heavily from existing infrastructure and natural resources than they give back to the immediately surrounding community, so much so that they often measure up to municipal statuses without having the populace or governance that connects actual cities and towns to the systems that comprise our country.

When a private company can construct what is essentially a new energy city with no people and no elected representation, and do this dozens of times a year across a nation to the point that half a century of national energy policy suddenly gets turned on its head and nuclear reactors are back in style, you have a sudden imbalance of power that looks like a cancer spreading within a national body.

The scale has already been tipped. I don’t worry about the end of work so much as I worry about what comes after — when the infrastructure that powers AI becomes more valuable than the AI itself, when the people who control that infrastructure hold more sway over policy and resources than elected governments. I know, you can picture me wildly gesticulating at my crazy board of pins and string, but I’m really just following the money and the power to their logical conclusion.

Maybe AI will do everything humans do. Maybe it will usher in a new society defined by something other than the balancing of labor units and wealth units. Maybe AGI — these days defined as a general intelligence that exceeds human kind in all contexts — will emerge and “justify” all of this. Maybe.

I’m more than open to being wrong; I’d prefer it. But I’ve been watching technology long enough to know that when something requires this much money, this much hype, and this many contradictions to explain itself, it’s worth asking what else might be going on. The market concentration and incestuous investment shell game is real. The infrastructure is real. The land deals are real. The resulting shifts in power are real. Whether the AI lives up to its promise or not, those things won’t go away and sooner than later, we will find ourselves citizens of a very new kind of place and we may not like it.