Software ate the world. AI ate software. Hardware is eating AI.

The software industry is in the midst of a panic attack, fifteen years after Marc Andreessen declared that software was eating the world. Software ETFs are down 30% since January, wiping out all the gains since the launch of ChatGPT. They’re calling it the SaaSpocalypse. Everyone is asking if it’s a bubble. What matters is whether the thing being bought can be switched on.

The argument is not that AI is overhyped. AI is a physical industry priced like a software industry and the correction is not going to be a sentiment crash. It arrives in a queue. So…It’s fair to say that hardware, for the time being is eating AI.

Look at the pricing of the model layer and what is striking is not that it is expensive. That the diffusion in it has nothing to do with quality of model. At $852 billion, or about 34 times its $25 billion run rate, OpenAI is losing about $1.22 for every dollar it earns. Anthropic beat it in May at $965 billion on a bigger revenue base, a lower multiple and a first operating profit of $559 million. Train frontier models. One makes money. The difference between them is not intelligence, but the cost of servicing it. Anthropic’s gross margin improved from about negative 94 percent in 2024 to about 60 percent on inference efficiency, not pricing. The physical layer already determines who is profitable at the model layer. This is not a bubble. It is a category error about where the scarcity is.

Not all atoms are the same. And the discipline here is not to treat them as one thing. Chips, memory, packaging, steel and electricity are not all tied at the same time. The task is to determine which is the tightest.

Why is this happening now?

Not because hardware became easier, cause everything else ran out.

The software abstractions got so cheap, and so universal, that they were no longer the constraint. Frontier-level intelligence cost about $60 per million tokens at the launch of the GPT-3 API in 2020. That’s about $0.10 today: a 600x increase in six years and still climbing.

That number did not disappear. It moved to whatever the scarcity left in plenty. The Residual Ratio is how we track migration – complements over intelligence, set to 1.00 in 2020 and now near 987. The finding is the decomposition. Intelligence collapsing is about 600-fold of it. Complements appreciating is only 1.65-fold.

The physical side didn’t move. Bain expects about 200 GW of incremental compute demand by 2030, the IEA has data-center electricity rising from 415 TWh to 945, and hyperscaler capex guidance is near $725B for 2026 alone. Against that, Berkeley Lab’s Queued Up data shows more than 2,060 GW seeking interconnection at the end of 2025, with a median wait from request to switch-on of more than five years.

In February 2026, Elon Musk put it bluntly: those who have lived in software land are about to get “a hard lesson in hardware.” His example was not the chip. It was the interconnection. You go to a utility at scale, and a year later they come back to you with a study. No link. A study.

^{Musk, Elon. Interview by Dwarkesh Patel. Dwarkesh Podcast, 5 February 2026.}

So here’s the arithmetic. Intelligence falls ten-fold a year. But the median interconnection needs more than five. Electrical output outside China is flat, but accelerator output is rising almost exponentially. The conclusion is obvious and the industry’s hardest sentence: chips pile up and nobody can turn ‘em on.

^{Intelligence falls five orders of magnitude before the median interconnection is energized.}

Andreessen walked through industries, pointing out that each had quietly become a software company. This works the same way, in reverse.

The frontier lab thinks it’s on top. It leases. It calculates what it does not own, in buildings it has not built, using power it has not bought. Amazon led the March deal with a $50 billion stake, and Nvidia and SoftBank each pledged $30 billion. When your compute vendor capitalizes you, you are not the customer. You are the conduit.

The neocloud believes it owns compute. It has a depreciation schedule. It leases a sold out for two years TSMC wafer in CoWoS packaging, stacked with memory from SK Hynix, Samsung or Micron.

The hyperscaler sees itself as a tech company. It’s a utility customer with a nine-figure interconnection problem and a transformer book measured in years. It can pay any sum for electricity and still not get any in 2027 because what it needs is not electrons. It’s permission.

The chip designer believes he has the chokepoint. Nvidia has a blueprint. That design is produced by TSMC, on machines that only ASML produces, in packaging already sold, stacked with memory produced by three companies that control virtually the whole market. “Each of them is a veto. Nvidia has none of them.

The data center believes it is real estate. It’s a thermal problem attached to a substation, waiting for a transformer, waiting for electrical steel, waiting for a mill nobody built because for twenty years nobody needed one.

^{Each layer and the thing it cannot proceed without. The rust spine marks a veto capital cannot buy through.}

Everyone thinks it is the peak and everyone is waiting for someone who does not respond quickly to email. Imagine you’re a hardware company and you haven’t hit your constraint yet.

Liebig, not Schumpeter

Schumpeter was Andreessen’s call. Creative destruction was perfect for software, because the destroyer was faster than the incumbent. Not here. It’s the slower thing than AI. It’s the thing that eats AI .

The right lens is Liebig’s Law of the Minimum: yield is limited by the scarcest input, not the average. Twice the accelerators . Three times the parameters . Half the price of inference . If the substation is not energized you have generated zero additional tokens .

Many writings combine two questions. What’s stopping you from deploying? Power. Where’s the spread (margin)? Memory and advanced packaging, the layers that capital is unable to replicate in its own horizon. Power limits capacity. Memory collects the rent. Treat them as one and you will buy the constraint that is loosening as the one that is printing money tightens.

This is why “we will just build more” is not a rebuttal. Capital is fast; permits are not. A fab takes three to five years, the median interconnection more than five, a transformer order years, and verified data does not regenerate once the public web is exhausted. Money arrives in a quarter. None of these do.

And here is the part that stings. A software career trains one reflex: hit a wall, route around it. Add a layer, cache it, shard it, ship Friday. Against a physical constraint that reflex is not merely useless. It is harmful, because it produces the confident belief that a solution exists at your current velocity, and it burns the eighteen months you should have spent standing in the queue.

The hard lesson

The constraint does not negotiate. You cannot ship past a five-year queue and you cannot hire past it. Everyone faces the same physics, so the differentiator is not cleverness but position: who stood in which line, how early. Most positions that matter in 2029 are being taken this quarter, by people not posting about it.

Most of “hardware” gets eaten too. One question sorts every layer: can capital overbuild it inside its own investment horizon? Merchant generation, GPU rental and undifferentiated silicon, yes, and they commoditize. Leading-edge fabrication, advanced packaging, high-bandwidth memory, interconnection, firm power and verified data, no. “Long infrastructure” is now as coarse and as dangerous as “long software” was in 2021.

The margins already say which is which. SK Hynix reported a 58.4 percent operating margin in Q4 2025, passing TSMC for the first time in seven years, with Samsung on the same path and gross margins forecast at 63 to 67 percent against TSMC’s 59 to 61. Memory went above the leading foundry in the same quarter the consensus called compute a commodity. Commodities do not do that. And SK Hynix has said HBM4 demand exceeds its capacity for the next three years. That is what a layer capital cannot overbuild sounds like when the supplier says it aloud.

Owning the physical layer is not the end state. In 1982 Alan Kay said that “People who are really serious about software should make their own hardware.” Jobs quoted him introducing the iPhone. Run it backwards and you get the operative sentence for the next ten years: anyone serious about hardware must make their own software. A memory maker that ships parts is a supplier; one that co-designs the hierarchy against the architecture running on it owns a design space. A power developer that sells electrons is a merchant; one that co-designs against its own grid position owns the interconnection.

Call the clock on that loop loop time: from identifying a binding constraint to shipping a co-designed system that relaxes it. It runs in years, dominated by permitting rather than tapeout, which is the real reason the physical layer is winning. It also dates the expiry, and the gate is already moving: FERC's 2023 reforms landed the following year and the queue fell 10 percent in 2025. Owning a bottleneck is not buy and hold. It is buy and watch.

The bill

Building an AI company in 2026 is difficult in an older, less forgiving way: the way a refinery is difficult, or a rail line. Feedback takes years. Mistakes are not reversible in a sprint. The people who are good at it get no attention, because nobody makes a thumbnail out of a substation.

So the right posture is neither optimism nor pessimism. It’s inventory. Find the physical constraint of your business. Know the lead time. Know who you have in front of you in line, and when you would have had to start to be in operation in 2029. If you can’t answer those four questions, you have no strategy. There is the burn rate.

Andreessen concluded by saying he knew where he was putting his money. These are the stakes, in fact, that are written to be graded, not admired.

1. The binding constraint on incremental frontier-scale capacity in the United States through 31 December 2027 continues to be power availability and grid interconnection, not accelerator supply. Disproved if a published quarter cites delivered accelerator supply, instead of energized capacity, as the reason for a significant buildout delay. Probability .75.

2. High-bandwidth memory, or advanced packaging, margins hold or expand. At least 1 merchant power or GPU-rental category has a visible margin compression cycle from overbuild by 31 December 2028. This tests the second half of the hard lesson and is the claim that is most likely to make me look foolish. If both compress together, then falsified. Probability 0.65

Both enter the register today and will be graded in public whichever way they go.

The hard lesson is not that hardware is valuable. That’s why it’s almost done being an “edge”. Everybody’s figured that out. The hard lesson is that the physical world is the one that sets the clock, it has never once sped up to accommodate a roadmap, it does not care if you have taken the lesson yet.

One disclosure, kept in view: I build and invest in some of the markets I write about, and I say so wherever it is relevant.

Mind Meets Matter. Where intelligence meets its physical limits.