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On September 20, 2024, Microsoft signed a $1.6 billion power purchase agreement. The electricity was coming from the Three Mile Island nuclear plant.
Yes, that Three Mile Island from 1979. The unit that melted down back then was Unit 2. Unit 1 had actually operated safely for decades and was only shut down in 2019 because it wasn't turning a profit. Microsoft paid to have Constellation Energy restart this 835-megawatt reactor, rename it the Crane Clean Energy Center, and feed 100% of its output into Microsoft's data centers. They signed a 20-year deal.
By January 2026, Constellation reported they were ahead of schedule. Originally set to connect to the grid in 2028, it’s now slated for 2027.
A power plant infamous for a nuclear disaster was resurrected by a software company. Its purpose isn't to heat homes; it's to power GPUs for AI.
The Shattered Illusion
For the past two decades, the entire IT industry believed in one core tenet: digital is light. Once code is deployed globally, its marginal cost is near zero. If an article is published online, whether one person reads it or a hundred million do, the extra electricity cost is negligible. Venture capitalists called this "zero-marginal-cost scalability."
Bitcoin was the first to puncture this illusion. In 2025, the Bitcoin network consumed about 173 TWh of electricity, with Cambridge estimates exceeding 211 TWh. That’s roughly the annual power consumption of the entire country of Ukraine.
AI followed up and tore the hole wide open. On April 20, Fortune reported a staggering figure: data centers devoured half of all new electricity demand in the US last year. The IEA estimates that total global data center power consumption will break 1,050 TWh by the end of the year—ranking fifth globally, wedged right between Japan and Russia.
It's time to throw away the notion of an "asset-light" digital world.
What Does the Same Kilowatt-Hour Produce?
Both Bitcoin and AI are converting electricity into digital outputs at a massive scale. But once the power is burned, what comes out the other end is entirely different.
Bitcoin burns electricity to produce trust. Miners repeatedly run SHA-256 hash operations, and the resulting outputs carry no semantic meaning—nobody cares what that specific string of numbers is. What miners are proving is simply: "I genuinely spent this much electricity." This proof makes tampering with the ledger incredibly expensive, so expensive that it's economically irrational. Gold operates on the exact same logic: it’s valuable not just because it’s shiny, but because digging it out of the ground is tremendously difficult. Bitcoin transplanted this logic into the digital realm—using physical cost to anchor trust, bypassing the need for banks or government stamps.
AI burns electricity to produce replicas of cognitive ability. GPT-5.5 running an inference, Claude Opus 4.6 writing a script, Gemini 3.1 analyzing a CT scan—every single invocation is a GPU cluster grinding through floating-point operations. Unlike Bitcoin, these computations yield specific semantic outputs: an analysis report, a bug fix, a contract review.
Bitcoin manufactures scarcity; AI manufactures abundance. One turns electricity into "you can trust me," while the other turns it into "I can do work for you." Both put the cost on the power meter, yet they seem to point toward entirely different civilizations.
But beneath the surface lies a commonality that very few people mention.
Two Probability Machines
Bitcoin's consensus is probabilistic. Once a transaction is packed into a block, it isn't "irreversible forever." More accurately, as subsequent blocks are added, the probability of reversing that transaction drops exponentially. The industry standard is to wait for 6 confirmations—not because it's absolutely mathematically safe after 6, but because by then, the cost of an override is so astronomical that no rational attacker would attempt it. Satoshi Nakamoto calculated this probability decay curve in the whitepaper. The so-called "immutability" was never a mathematical impossibility; it was always an economic impracticality.
AI's output is also probabilistic. The process of an LLM generating each token is essentially sampling from a probability distribution. Ask the same question twice, and you might get different answers. The model doesn't "know" what is correct—it is statistically inclined to produce a plausible result, but no mechanism guarantees that any single output is flawless. The so-called "hallucination" is not a bug; it is an inherent property of this sampling mechanism.
This leads to a highly underestimated commonality: both systems use energy to buy probabilistic guarantees, not deterministic ones.
Bitcoin burns electricity to buy the guarantee that "this transaction will probably not be reversed." After six blocks, the probability is low enough that you can safely accept the money, but theoretically, it is never zero. AI burns electricity to buy the guarantee that "this output is probably reasonable." It’s good enough for most daily scenarios, but nobody can promise that every inference is correct.
This shared trait goes much deeper than just "they both use a lot of power." It implies that both systems inherently require additional mechanisms to bridge the gap between "probably right" and "definitely right." Bitcoin bridges it by waiting for more blocks, relying on exchange risk controls, and leaning on the credit enhancements of clearing networks. What does AI rely on? We still lack a solid engineering answer for that.
Software Companies Are Becoming Infrastructure Companies
The Three Mile Island story isn't an isolated incident; it's a microcosm of a structural shift.
In 2026, the combined capital expenditures of Amazon, Alphabet, Microsoft, Meta, and Oracle are hurtling toward 660−660−690 billion. A Goldman Sachs report put it bluntly: the capex intensity of these companies has reached 45% to 57% of their revenues.
What do these numbers mean? The capex intensity of traditional software companies usually hovers around 5% to 10%. Automakers sit roughly at 15% to 25%. Oil companies might hit 30% at the peak of their industry cycle. Right now, the capex intensity of Microsoft and Google surpasses that of Toyota and ExxonMobil.
What are they buying? GPU clusters, liquid cooling systems, fiber optics, electrical substations, and long-term power purchase agreements. Google signed the world's first corporate PPA for an SMR (Small Modular Reactor). Amazon bought the entire substation capacity next to a nuclear plant in Virginia.
Harvard economist Jason Furman shared a figure that made me read it twice: in the first half of 2025, 92% of US GDP growth came from AI infrastructure investments. Strip those investments away, and the annualized growth rate of the US economy was a mere 0.1%.
This doesn't look like a tech industry expansion. This looks like a nation's economic growth is tethered to the construction of data centers.
In the internet era, the core assets of tech companies were code, users, and network effects. Today, that list includes a few new items: power contracts, chip fab capacities, water cooling rights, and substation access. Software companies are morphing into infrastructure companies. This transition is happening so quietly that almost no one discusses it, yet it is far more profound than any new model release.
The End of the Replication Economy
When physical forms change, business logic must follow suit.
For the past twenty years, the two most lucrative businesses on the internet were online advertising and online gaming. The defining trait of both models is that the marginal cost of delivery is practically zero. When Google shows you one more ad, or Tencent lets you play one more match, the extra electricity and bandwidth cost to the servers is negligible. Therefore, the internet plays a traffic game—corral the users, and indirectly monetize them via ads or virtual items. Users don't pay directly for the product's core value; advertisers and whales foot the bill.
I call this the "replication economy." A product is created once and replicated infinitely, with each replication costing almost nothing. Software, music, video, social networks—nearly all tech giants of the past two decades were built on this logic.
AI has overturned this logic completely.
Every single API call burns GPU time. Every token carries real electricity and compute costs. The more users you have and the more frequently they prompt, the higher your costs go. This is the exact opposite of the internet's "scale equals profitability" logic. OpenAI lost over 5billionin2024,burningmassiveamountsofcashoninferencecompute.Anthropicisalsolosingmoney.A5billionin2024,burningmassiveamountsofcashoninferencecompute.Anthropicisalsolosingmoney.A20 monthly subscription fee simply cannot sustain a heavy AI user. A programmer coding all day with Claude might burn through their monthly subscription's worth of compute in a single day.
This is a "production economy," not a "replication economy." Every single delivery incurs a tangible production cost.
So where does the money come from?
The advertising model doesn't work here. Shoving ads into an AI dialogue ruins the experience, and fundamentally, advertising is a traffic game of "trading free content for attention," which directly contradicts the physical reality that every AI invocation burns money. The subscription model has a ceiling, too. A fixed monthly fee can't cover the compute consumption of power users; you either cap their usage (frustrating users) or subsidize the compute (bankrupting the company).
The way out lies in the B2B sector, in direct value creation.
If a white-collar worker earns 15,000amonth,andanAIcanreplaceoraugment1015,000amonth,andanAIcanreplaceoraugment101,500 a month. How much is the enterprise willing to pay for this? Definitely more than a 20subscription.WhenenterprisesbuyAI,theyaren′tbuyingachatbox;theyarebuyingcostreplacement.Ifaparalegal′shumancosttoreviewacontractis20subscription.WhenenterprisesbuyAI,theyaren′tbuyingachatbox;theyarebuyingcostreplacement.Ifaparalegal′shumancosttoreviewacontractis300 an hour, and an AI takes ten seconds to do an initial screening at an inference cost of 20 cents—even if the AI is only 60% accurate and still requires human review later, the enterprise's ROI is solidly positive.
This is a fundamental paradigm shift. The internet era relied on indirect monetization (ads, traffic, attention economy); the AI era must rely on direct monetization (charging for value created). Not because direct monetization is nobler, but because every delivery has a cost, and you have to ensure the revenue covers it.
Taking it a step further: this is actually a good thing. The byproducts of the traffic game are information pollution, attention wars, and the rampant spread of fake content. Because the marginal cost is zero, producing and distributing garbage content is also practically free. AI's "production economy" logic naturally repels low-value output because every meaningless token is a real financial loss. Economic pressure will force AI toward high-value scenarios, rather than incentivizing it to generate more noise like the internet did.
Of course, this filtering mechanism isn't automatic. If someone is willing to burn cash using AI to batch-generate garbage for SEO, being economically irrational doesn't mean it's technically impossible. But the overall direction is clear: the center of gravity for AI commercialization will be in B2B, in scenarios with quantifiable ROI, not in B2C subscriptions.
Agents Don't Sleep
The biggest change hasn't even arrived yet.
The mainstream use of AI today is still "human asks a question, machine gives an answer." The energy consumption of this usage pattern is pulsed: you burn power when you use it, and it sits idle when you don't. But Agentic AI is altering this model.
Deloitte pointed out an easily overlooked trend in this year's report: enterprises are deploying "always-on" monitoring agents—scanning emails, logs, market data, and operational metrics 24/7. These backend agents don't wait for a prompt to start; they continuously consume compute, even at 3 AM.
From chatbots to reasoning models to autonomous agents, the compute required for a single inference session has grown roughly 10,000 times.
10,000 times.
In 2024, the average enterprise AI budget was 1.2million.By2026,itsurgedto1.2million.By2026,itsurgedto7 million. Inference accounts for 85% of enterprise AI budgets. And these numbers were recorded before the large-scale deployment of agents.
When millions of agents are running simultaneously—managing supply chains, inspecting codebases, monitoring compliance risks, coordinating cross-timezone teams—inference demand will no longer be a "peak load." It will become "baseload," much like the foundational layer of demand on a power grid that never fluctuates, doesn't care about human sleep schedules, and doesn't shut off at 5 PM.
This is AI's true energy challenge. Training massive models is a one-time capital expenditure; once it's burned, it's done. The continuous power draw from millions of always-on agents will be the lion's share. Baseload demand requires baseload power. Wind and solar cannot serve as baseload; only nuclear and natural gas can.
Microsoft resurrecting Three Mile Island, Google betting on SMRs, Amazon buying up nuclear substations—these moves don't seem so absurd anymore. They aren't buying power for today's chatbots; they are stockpiling rations for tomorrow's fleet of always-on agents.
The $25 Billion Hidden Bill
On April 21, Fortune published a report highlighting a number that rarely gets discussed: US data centers cause an estimated $25 billion in hidden damages annually. The air pollution from coal and gas plants, the freshwater consumed by cooling systems, the public resources squeezed by grid expansions—these costs don't appear on any tech company's balance sheet.
Google's 2025 environmental report is a prime example. Its data center electricity use surged by 27% in 2024, and its carbon emissions grew by 51% cumulatively since 2019, hitting 11.5 million tons. The share of clean energy rose from 64% to 66%. A two-percentage-point progress in decarbonization simply cannot keep pace with a 30% surge in power demand.
The pushback has evolved from numbers on a page to real people. In 2025, at least 16 data centers in the US were voted down or delayed by local communities, affecting $64 billion in investments. A resident in a Virginia town put it bluntly:
"I pay my power bill to run my air conditioner, not to calculate tokens for your chatbots."
Pew's polling corroborates this sentiment: the public has positive views on the jobs and tax revenues data centers bring, but their resentment toward the energy consumption and environmental impact is far stronger. Once AI's physical footprint spreads from the server farms of Silicon Valley to the farmlands of Ohio, it ceases to be just a technical issue.
The Cost of Probability
As mentioned earlier, both Bitcoin and AI are probability machines. On the Bitcoin side, the methods for bridging the probability gap are quite mature—wait for more block confirmations, employ exchange risk controls, and use clearing networks as a safety net. Burn more electricity, wait a bit longer, and you crush the probability of failure down to near-zero.
The problem on the AI side is much messier.
Generating a paragraph of legal analysis costs just $0.01 in inference fees. But verifying that this analysis is accurate in your jurisdiction, hasn't missed key precedents, and can actually be adopted in court—that still requires a human lawyer spending two hours. An AI-generated code snippet might pass the tests and look fine, but will it crash under edge conditions? Does it introduce race conditions under concurrency? If it causes a production outage, who is liable? None of these problems can be solved simply by burning a few more kilowatt-hours.
Generation costs are dropping exponentially; verification costs are not.
This is the destiny of probabilistic systems. Bitcoin's probabilistic nature is constrained by the structural design of the blockchain—wait six blocks, the probability is negligible, and the process is automated. But AI's probabilistic nature is diffused across the semantic layer. There is no automated "six blocks" mechanism that can serve as a safety net. Every output may require human intervention to judge, and human judgment is expensive, slow, and unscalable.
What does this mean?
It means that the truly valuable AI systems will not be the ones that can generate the most tokens, but the ones that can close the loop from "generation → verification → execution → feedback → accountability." On this chain, generation is the cheapest link. Verification and accountability are what cost real money.
It also means that measuring efficiency by "cost per token" is wholly insufficient. The real metric should be "verified, adopted output per kilowatt-hour." A system that uses ten times the electricity but yields verified diagnostic conclusions is vastly more "efficient" than a low-power system whose conclusions nobody dares to trust.
I believe this will be the most profound shift in the AI industry over the next five years: moving from a competition of who generates the most and the fastest, to a competition of whose output is credible and who can bear the liability for that output. It took Bitcoin fifteen years to convince mainstream society that its probabilistic consensus was reliable. AI will have to walk the same path, and along the way, there is no ready-made mathematical proof from Nakamoto's whitepaper to borrow.
An Unexpected Lesson from Bitcoin Miners
Back to Bitcoin. AI can take notes on the pitfalls Bitcoin encountered during its energy controversies.
When China banned mining in 2021, the farms migrated to Kazakhstan, Texas, and Northern Europe. By 2025, the share of renewable energy in the global Bitcoin network reached 52.4%—hydro 23.4%, wind 15.4%, and nuclear 9.8%. Miners didn't flock to these areas out of ecological enlightenment; they went because the electricity was the cheapest. And the electricity was cheap because renewable energy was oversupplied and stranded.
Miners turned into the grid's "sponges": absorbing capacity when power was abundant, and shutting down to yield it when the grid was stressed. Some mining farms in Texas signed demand-response agreements with the grid—when a heatwave hits, they power down to let residents run their ACs.
AI data centers running inference services can't be turned on and off on a whim. But training runs can. Scheduling massive training jobs during off-peak hours at night, and anchoring site selection to renewable energy layouts—these are the paths carved out by miners voting with their feet.
Bitcoin's fifteen-year energy debate proved one thing: any digital system that burns electricity at scale will eventually be dragged to the real world's negotiating table. How much power was burned, how much carbon was emitted, whose resources were crowded out—these questions don't disappear just because the system runs "in the cloud."
A Kilowatt-Hour
I keep thinking about Microsoft resurrecting Three Mile Island. Beyond the sheer drama of it, it exposes a profound contradiction: we are industrially manufacturing the two most intangible concepts in human civilization—trust and intelligence—using the most tangible, physical means imaginable. Electricity, silicon, cooling water, concrete.
At their lowest level, Bitcoin and AI belong to the same category: they are probability machines. Both use energy to buy the guarantee of being "probably right," and then employ additional engineering and institutional scaffolding to approach being "definitely right." It took Bitcoin fifteen years, burning the electricity equivalent of a mid-sized country, to finally compel the mainstream financial system to accept its probabilistic consensus. Trillion-dollar assets now flow across this mechanism, running to this day without a central bank's guarantee.
AI is only at the starting line of this journey. It can generate increasingly more things, but the gap between "probably right" and "definitely right" hasn't been reliably sealed. What does it take to seal it? Better verification mechanisms, clearer chains of liability, more mature industry standards. None of these can be solved by simply burning more electricity, nor can they be built in a year or two.
Meanwhile, the underlying physical bill is inflating at breakneck speed. The capex intensity of tech companies has eclipsed that of oil giants, an entire nation's economic growth is tethered to data center construction, and the baseload power draw of millions of agents hasn't even begun to hit the budget.
"What is this kilowatt-hour calculating?"—Three years ago, this was an internal debate among tech communities. Today, it dictates grid planning, nuclear policy, semiconductor export controls, and the trajectory of regional economies.
Years ago, Bitcoin was mocked as "burning electricity to mine thin air." Fifteen years later, we look back and see that electricity forged a probabilistic trust network with a global market cap over a trillion dollars. What will the electricity currently being burned by AI forge? That depends on whether we can find a reasonable engineering compromise between probability and certainty. And it depends on who gets to define what "reasonable" means.
The latter question is far more difficult than the former.
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