The AI Party Isn’t Over. But the Bill Will Show Up Soon
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I live in Utah. Right now there’s a fight going on out by the Great Salt Lake over a data center that, depending on who you ask, was supposed to be the largest in the world before the project was scaled back. If it goes through, it still might be one of the largest in the world.
The community pushback has been enormous, and the whole thing still feels like it got railroaded through with basically no public consent. And then you come to find out that some of the politicians who run the state happen to own real estate conveniently close to where this thing is supposed to go. Great look, guys.
That’s just not a Utah story. It’s playing out all over the country. Data centers are one of the few bipartisan issues that’s united people in an otherwise hyperpolarized nation. And it’s a good place to start, because it’s a good bellwether of where we are with AI right now.
It’s been a wild summer for AI. The NASDAQ is sliding, partly on the AI story. Semiconductors are in a bear market. IBM just took a historic one-day hit. After urging employees to tokenmaxx, now companies are quietly cutting back on tokens. Rewind to the start of this whole boom and all anyone talked about was possibility. The enthusiasm. As in prior cycles, much of it was just hysteria.
In this hype cycle, we’ve moved from the possibility of AI to the accountability of AI.
Let’s call it the reconciliation phase. The capabilities are real, and continuously improving. Anyone who uses this stuff every day knows exactly how effective the latest crop of AI tools can be. But the economics, the infrastructure, and the organizational reality all have to reconcile with the promises we made getting here.
The last few years have reminded me of a small happy hour with a few friends (ChatGPT wasn’t expected to be popular when it launched) that suddenly morphed into a massive party. The exuberant party isn’t over just yet, but the vibe is changing. The bartender is going to set the bill on the counter at some point. And the bill is going to be very expensive.
A Chip Selloff Isn’t an AI Funeral
Every time semiconductor stocks wobble, the takes split into two dumb camps: the boom is fine, or the bubble just popped. Both are wrong, mostly because they’re answering the wrong question.
Here are the numbers. The Philadelphia Semiconductor Index fell roughly 24% off its June peak and is in a bear market. But right before the sell-off, it had run up about 83% on the year. That second number is the whole story. This isn’t “AI hardware demand vanished.” It’s the market having priced these names for perfection and then getting a little queasy about it. This happens all the time.
Priced for perfection means they don’t just have to be good. They have to be flawless. TSMC can post strong results, and the sector can still fall, because the question isn’t just “is demand strong?” It became “will demand stay strong enough, concentrated enough, and profitable enough to justify these valuations and this insane capacity buildout?” That’s a bit shaky right now.
Then there’s the efficiency paradox, which matters more than any single quarter. Better models, cheaper inference, custom accelerators, smarter routing - all of it drops the compute you need per unit of output. So AI usage can go straight up while the economic rent captured by the priciest chips comes down. The old hypothesis was simple - more AI use means more premium chips means more semiconductor profits. That chain is no longer guaranteed.
Chips have always been a turbulent, cyclical business. They’re used to this. Doesn’t make the repricing any less real.
Everyone’s Still Spending, Cuz Prisoner’s Dilemma
Here’s the strongest argument that the boom is alive: the hyperscalers are still behaving like it is.
Alphabet raised its 2026 capex guidance to about $180–190 billion and said 2027 capex will increase significantly from there, describing demand for AI compute as “unprecedented.” Here’s the part that shocks me: that’s roughly double the $91 billion it spent in 2025. Its planned build doubled in a single year.
Companies are putting their money where their mouths are. Capex is evidence of conviction. It is not proof of return. Google, Microsoft, Amazon, and Meta literally cannot afford to be caught short on compute if AI becomes the front door to software and information. So they build. Each one is making a rational, defensive bet, and the industry can still collectively overbuild. That’s the Prisoner’s Dilemma driving the whole infrastructure arms race. You can’t afford to build, even when you can’t yet prove the build pays off. So you do it anyway.
Watch the debt, too. Companies like Oracle have taken on enormous piles of it to play this game, and I’m very curious what kind of bag they end up holding. Meanwhile, OpenAI and Anthropic are designing their own chips while renting hyperscaler capacity. Every week is a new decade around here.
We’ve seen this movie. Railroads remade the world and torched a mountain of investor capital in the process. The fiber boom wired the internet and buried half the companies that laid the cable. A technology can be foundational, and the buildout can still wreck the people funding it. This is the norm in infrastructure cycles.
Poor Big Blue. IBM and the Substitution Problem
IBM is the one that should make people sit up. Roughly a 25% one-day drop last week. That’s insane for a company that had quietly become part of the “safe enterprise AI winner” bucket. Nobody got fired for buying IBM in their heyday back in the 20th century. Now, I’m guessing you’ll get a lot of weird looks (at a minimum) if you suggest IBM instead of the bajillion other modern options out there.
The weird part is the actual revenue miss was much smaller than the move in the stock. Investors weren’t reacting to one quarter. They were repricing the assumption that IBM is a predictable beneficiary of enterprise AI spending. And that assumption is shaky. A big chunk of IBM’s business is selling mainframes to the same customers they’ve sold to for decades. What happens when those customers start delaying purchases and renewals because (just maybe) AI helps them finally migrate off the mainframe?
This is the SaaS apocalypse argument in a different blue or gray suit (IBM used to force employees to wear those). Vendors keep pitching AI as a clean, additive revenue layer stacked neatly on top of what they already do. The market is starting to recognize that AI is also a substitute. It creates new winners, sure, but it can destroy existing revenue pools faster than the incumbents can capture new ones. Sometimes AI is a layer. Sometimes it dissolves things.
The AI Rollout is Murky
Here’s the tell. In public, every data leader is killing it - agents everywhere, deploying like crazy. Off the record, over a beer or a Long Island Iced Tea if we’re getting real drunk on a budget at the party, it’s a much quieter story. It’s nowhere near as entrenched as the LinkedIn posts suggest.
The Fed estimated about 18% of US firms had adopted AI by the end of 2025. A recent look at the S&P 500 found only 11% had deeply integrated AI into their business processes, with another 10% using it in production or service delivery. So adoption is climbing, but deep operational integration is still very much the minority.
The contradiction is that individuals are getting immediate value. That’s why Claude Code went vertical straight out of the gate. I’m sure you remember your “Claude Code moment”. You try it, it’s amazing and useful, you keep using it. Barring a ban on AI or a solar flare that knocks humanity back to the Stone Age, I don’t think we’re going back to the Before-Times of non-AI programming.
But enterprise-wide financial transformation? The actual ROI? That’s still TBD. Most companies are stuck in POC purgatory, shoving AI into the process they already had instead of redesigning the process around what AI can now do. Agents are stuck in an antiquated org chart made for humans, not machines. And that’s a people problem, not a model problem. Organizations are political. Humans have every incentive to keep things exactly as they are. Especially when people are hearing from business and political leaders that AI is coming for their job, against a backdrop of rising costs, fewer opportunities, and massive uncertainty across the board, how else shall people respond? Nobody volunteers to automate themselves out of a paycheck. You wouldn’t either.
You want the transformation and promise of AI in your company? Then you have to do the unglamorous work around it: data quality, modeling, governance, killing obsolete workflows, reorganizing teams against Conway’s Law, and thinking of work charts and org charts. Skip that, and you get incremental assistance from Copilot, not transformational economics or new ways of working. This is exactly what most people are getting.
AI’s Pitchfork Moment
Back to the Great Salt Lake data center.
AI got sold to the public like every other app. What makes this different is heavy industry involved. Land. Power plants. Transmission lines. Substations. Water. Cooling. Concrete that sits in the dirt for thirty years. And people are pissed, because they can see the bill coming and nobody asked them. As the Washington Post writes in a headline, “Data centers have united Americans of both parties in a shared hatred.”
This isn’t just my backyard. Community opposition has reportedly blocked about $18 billion in US data-center projects and delayed another $46 billion since the middle of 2024. That’s not a fringe of cranks. That’s serious money.
In Utah, the move is to place a military-development designation on the land so the project sails through with little public say. They pulled the same trick up at Deer Valley, Utah. It short-circuits the accountability and railroads the thing through.
The public isn’t stupid. They watch this happen and ask the obvious question: who gets to pay for this? Oh…we do. And nobody even told us what it was we’re paying for. AI for the good of humanity? To beat the Chinese? To surveil us? To take our jobs?
Do the math on who’s on each side of that deal:
- The tech company books the revenue.
- The utility builds more generation and transmission.
- Ratepayers are expected to bear a share of the infrastructure costs.
- The community gets the noise, the construction, and the land-use consequences.
- The government hands over the tax breaks.
- Permanent local jobs? Modest, if any. From what I’ve seen and heard in Utah, they’re bringing in mostly out-of-state workers to build these things.
Where I live, we already have a water problem. So where’s this water coming from? The Great Salt Lake? The aquifer underneath it that might not survive? Somebody has to pay, and it’s usually the public.
Then, when people object, they get gaslit. If they protest, they’re told they’re basically paid protestors or Chinese operatives for asking about their own water, air, and their own tax dollars. Come on.
I don’t think the public is anti-technology. Every fight over every technology gets framed this way: anyone who pushes back is a Luddite standing in front of progress. To the folks who talk like that, I’d say, kindly, shut the hell up. What people are actually against is the sense that a small group of elites is using this technology, and these data centers, to prop up their profits at the public’s expense. And honestly? The AI industry has done the worst possible job of selling this. The pitch, boiled down, is: we’re going to take your job, put a data center next to your town, and hand you the tab. Good luck paying, because you’re broke, because AI took your job. How are people supposed to react to that?
My friend nailed the deeper version of this in a piece this week, “The mutual blindness behind the AI backlash.” She’s been reporting on AI full-time for four years (Venturebeat, Fortune, etc), and she describes two camps living in flat-out different realities: the people building and using this stuff every day, who feel the progress in their bones, and the people watching data centers roll into their towns to eat their land, power, and water, wondering why any of it has to exist at their expense. Each side finds the other impossible to understand. Sharon’s fair about it. The skeptics genuinely underrate how fast the capabilities are moving. But the builders are so high on what AI can do that they’ve gone deaf to what it costs everyone else. Her When AI Comes to Town series is worth your time (subscribe to her newsletter Ground Level AI too. It’s great).
The real question of this whole era isn’t the cost of inference tokens. It’s generation capacity, grids, water, land, and depreciation. And the fact our economic fate is tied to the equities and bond markets, heavily dependent upon this AI narrative working out. Who pays for AI? We all do.
The Tide in the Sea of Sameness Will Recede
“Only when the tide goes out do you discover who’s been swimming naked” - Warren Buffett
I suspect there are lots of companies swimming naked right now. We’ll soon find out.
The tech vendor market is absurdly overcrowded with feature wrappers and companies doing temporary arbitrage around this month’s model limitations. That’s not a business with any sustainable advantage. I see a lot of sameness with vendors, particularly in the data industry. Everyone has an agent. Everyone is trying to make an “AI data engineer.” This is incremental and boring to me. The next two years (or put whatever timestamp you want on it) are going to be brutal if you’re an undifferentiated vendor or a narrative-driven equity.
We’ve watched this before. In the dotcom bubble, if you had a website, you could raise money. For the last few years, if you had “AI,” you could raise money. If you’re building a data center in space, you could IPO and still see your stock drop below its opening price. The market is losing its appetite for fantasy stories.
The market is about to get very good at distinguishing between companies that use AI and companies that actually have a business. That was easy to ignore when money was free, and every chart went up and to the right. It’s about to get very hard to ignore.
This Isn’t an AI Winter. But the Vibe Is Changing
My base case is not an AI winter. The capabilities are too real, adoption’s too broad, and the strategic money is too enormous for the whole thing to freeze. I’m not calling a top. Is the bubble bursting? Not yet, though Ed Zitron will tell you OpenAI is the catalyst that brings the whole thing down, and given the wars, the energy shocks, the inflation, and the general state of the world, I can’t fully rule it out.
Here’s what I keep coming back to: all of it can be true at once.
A genuine technological revolution. A capex bubble. A real productivity improvement. A vendor graveyard. An infrastructure bottleneck. A lousy investment at the wrong price and the wrong time. None of those contradict each other, because they’re all happening simultaneously, right now.
AI’s capabilities are real and improving. The infrastructure market has massive demand but valuations that may have run ahead of it. The vendor market is drowning in wrappers. The enterprise transformation market is as slow as ever because organizations can’t adopt at the rate models improve. Same as it ever was. And the investment market is losing patience with stories that don’t come with visible revenue, margins, or defensibility.
Meanwhile, the AI acceleration crowd keeps insisting superintelligence is right around the corner, and I can’t fully discount them either. If you look at the charts of AI progress, it’s exponential. Both things can be true: the technology gets more important at the exact moment a pile of nominal “AI companies” get less valuable.
We started the party with a few friends at the bar for a happy hour. Then more people showed up, and it became a wild party. People got drunk and started making outlandish claims and promises. The vibe is changing. People are now sloshed, some are picking fights, and this party (there might be others) looks like it’s coming to an end. It’s not over yet, but the bill will showed up. Everyone is too drunk to know who’s paying. And either the party moves to a new venue, or people go home and deal with the hangover.
In this Freestyle Friday episode, I chat about the various craziness and trends of AI right now.
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Where I’m At
My fall calendar is shaping up, and here’s an idea of what I’ll be doing.
- Dataversity’s Data Architecture Online. Me, Bill Inmon, and several others talking about the future of data architecture. July 22. Register here.
- Big Data London. September 22-24. Register here.
Got a very busy fall travel schedule. More to be announced very soon.
Cool Videos and Reads
In this episode, I chat with Steve Brown, an AI futurist, independent consultant, and former professional at Intel and Google DeepMind. Steve shares his insights on helping global leadership teams decode the future of artificial intelligence.
We dive into the common pitfalls of corporate AI adoption, including why so many AI initiatives fail due to poor communication and a lack of cultural integration.
Steve outlines a definitive three-step path to AI transformation, emphasizing the critical difference between 20th-century cost-cutting and 21st-century labor amplification. The conversation also explores how AI is reshaping middle management, the distinct differences between "AI-first" and "AI-native" businesses, and the staggering acceleration of physical AI and humanoid robotics.
Here are some things I read this week that you might enjoy
The mutual blindness behind the AI backlash
Mentioned above. This explores the skepticism surrounding the AI backlash, discussing the mutual blindness between those hyping AI capabilities and those overly critical of its potential. (Ground Level AI)
‘They Don’t Need People’: The Workers Left Behind by China’s Robot Drive
Amidst China's push for automation on its workforce, low-skilled workers displaced by industrial robots face a grim future. Might be a preview of what job displacement looks like in the future with higher-skilled workers? (NY Times)
[UNVRS] Owner Yann Pissenem Tells VF How He Became Ibiza's King of Nightlife
An interview with Yann Pissenem, founder of Ushuaïa and Hï Ibiza, discussing his journey to becoming the kind of Ibiza and the launch of the new hyperclub [UNVRS]. (Vanity Fair)
The Inside Story of IBM’s Shocking Profit Warning
The AI infrastructure boom is diverting corporate IT budgets away from IBM's traditional mainframe and software businesses. What do you think happens next? (WSJ)
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