You’ve heard there’s an AI bubble. You’ve heard the warnings. What nobody has bothered to explain is what the bubble actually is, why it’s genuinely terrifying, and where the specific crack runs that could bring the whole thing down. This is that explanation.
This SacredLoop piece is part of the Eric Mitchell’s AI Infrastructure series. Read Where They Stop Counting, Debunking the Fiction of Fear, The Bill Comes Due and Debunking the Fiction of Progress.
This isn’t the bubble you think it is
When most people hear “AI bubble,” they picture Silicon Valley doing what Silicon Valley does — startups with no revenue, chatbots burning cash, hype outrunning reality. That story is real. It’s just not the one that matters.
The bubble everyone is talking about is a rounding error compared to the one nobody is talking about.
The real bet isn’t on the apps. It’s on the physical world those apps run on — the steel, the concrete, the copper wire, the cooling systems, the power lines. Somewhere in the last few years, the largest corporations on earth quietly decided that AI was going to need an almost incomprehensible amount of physical infrastructure to run, and that whoever locked down that infrastructure first would own the future. So they started building.
The four companies leading this — Microsoft, Google, Amazon, and Meta — are spending roughly 725 billion dollars on AI servers and data centers this year alone [1]. That’s on top of the 410 billion they spent last year, which was already the largest technology spending binge in recorded history [1][2]. To put this year’s number in terms a human being can actually feel: the entire Apollo program — every rocket, every mission, thirteen years of putting humans on the moon — cost about 280 billion dollars in today’s money [3]. These four companies are spending more than twice that on AI hardware in a single calendar year.
By the time this buildout is done, Wall Street expects the total price tag to hit roughly 5.3 trillion dollars [4]. If you want a comparison that puts that in context, think about the two financial catastrophes Americans actually lived through: the dot-com crash and the 2008 housing crisis. This buildout is running at roughly six to seven times the total capital invested in internet infrastructure during the entire dot-com era [4][A], and roughly four times the total capital that was deployed into the bad bets at the core of the 2008 crisis [B]. Those aren’t typos. That’s the size of the thing sitting quietly underneath all the chatbot coverage.
So when people say “AI bubble,” they’re picturing a correction in tech stocks. What they should be picturing is what happens when the largest private infrastructure bet in human history turns out to be sized for a world that doesn’t exist.
Notes
[A] The apples-to-apples comparison is infrastructure capital deployed versus infrastructure capital deployed — not equity market losses versus capital deployed. Goldman Sachs’ Powering the AI Era report states that during the dot-com era, $800 billion or more was invested in critical internet infrastructure (fiber-optic cables, broadband, and servers). That is the correct baseline: $5.3T ÷ $800B = approximately 6.6×. For context, the dot-com bubble also erased roughly $6.7 trillion in equity market capitalization — a separate figure that reflects investor losses, not infrastructure capital committed. Source: Goldman Sachs, Powering the AI Era.
[B] The comparison is total subprime mortgage originations 2004–2007 (approximately $1.3 trillion, the underlying capital deployed into the flawed bet) versus total AI infrastructure capital committed ($5.3 trillion). $5.3T ÷ $1.3T = approximately 4×. Notional MBS and derivatives exposure in 2008 was far larger (approximately $13–23 trillion depending on the measure), making this the conservative framing. Source: NBER Working Paper No. 24509, Mortgage-Backed Securities and the Financial Crisis of 2008.
Who’s actually holding the bag
There’s a difference between a company losing its own money on a bad bet and a company losing borrowed money on a bad bet, and that difference is the thing that turns an industry problem into everyone’s problem.
When a company burns through its own cash on something that doesn’t work out, the people who get hurt are the people who owned shares in that company. Painful, contained, recoverable. That’s how most of Silicon Valley’s failed bets have worked historically. A startup burns through its venture funding, the VCs take the loss, life goes on.
Debt doesn’t work like that. When you borrow money to build something and the thing doesn’t generate the revenue you promised, the losses don’t stay inside your company. They travel backward through every institution that lent you the money or bought your bonds — pension funds, insurance companies, money market funds, the retirement accounts of people who have never heard of a data center and never will. The borrower made the bet. The lender absorbs the loss. And the lender in this case is effectively everybody.
That’s why the debt layer of this buildout is the thing that keeps people who understand financial systems up at night. Not because the numbers are big, but because when borrowed money finances a bet that goes wrong at this scale, the crater doesn’t stay in tech. It goes looking for whoever is holding the paper. By late 2025, debt tied to AI infrastructure had grown to 1.2 trillion dollars — making it the largest single segment of the entire investment-grade bond market, surpassing even US banks [5]. That’s not a rounding error in the bond market. That is the bond market.
Now here’s the part that should genuinely frighten people, because it’s the same mistake that made 2008 as bad as it was.
Markets are supposed to protect against this kind of contagion through credit ratings. When the system works correctly, investors can confidently hold debt while understanding and pricing the risk they’re assuming. The catastrophe happens when those ratings diverge dramatically from actual risk — when paper that should be rated as speculative gets stamped as safe, and ends up in portfolios that were never designed to absorb that kind of loss.
In 2008, the catastrophic variable wasn’t simply that there was a lot of mortgage debt. It was that the debt had been rated, packaged, and sold as if it were safe. AAA-rated instruments backed by subprime mortgages. The gap between the perceived quality of the paper and its actual quality is what made the contagion global and instantaneous. Every institution that thought it was holding a safe asset discovered simultaneously that it wasn’t. That’s what froze the system.
The parallel here is almost exact. AI infrastructure debt — bonds issued by Microsoft, Google, Amazon, data center REITs, utility companies locking in decades of AI-driven power demand — carries the credit rating of its issuers, which happen to be some of the most creditworthy entities on earth. They’re the largest, most profitable corporations in human history. The debt gets rated accordingly and ends up in the safest, most conservative corners of institutional portfolios: pension fund reserves, insurance company holdings, money market instruments, sovereign wealth funds. The places designed to hold only the most boring, reliable paper.
But the credit rating reflects the borrower’s balance sheet, not the validity of the assumption the debt was sized on. Microsoft’s bonds are AAA because Microsoft has a fortress balance sheet — not because AI infrastructure demand projections are guaranteed to be right. The quality of the paper and the quality of the underlying bet are two entirely different things.
The entire bet rests on one assumption: that demand for AI power will continue to grow at something close to its current trajectory for decades. That assumption has two specific ways it can fail. Growth in user demand could fall short of projections for any number of reasons — cost, competition, a fundamental capability ceiling. Or someone could discover a way to make these systems dramatically more efficient, collapsing how much energy they need per interaction. Neither of these risks is exotic. Both have precedent. And there is nothing mutually exclusive about them — the most dangerous scenario is the one where both bite simultaneously.
You don’t have to be a financial analyst to see the problem with debt carrying the world’s safest rating when it’s actually a multi-decade bet on the energy appetite of a technology that has never been properly stress-tested for efficiency.
To understand just how fragile those assumptions already are, look at OpenAI — the company whose growth is the primary justification for the entire buildout. In 2024, OpenAI spent roughly 3.8 billion dollars in cash just to keep its models answering questions in real time — roughly 38 times what it cost to train GPT-4 in the first place [6][C]. In 2025, they brought in about 13 billion dollars in revenue and still lost around 14 billion [7]. More money going out than coming in, at scale, years into the AI boom.
OpenAI is the demand signal. It’s the reason the hyperscalers are building, the reason the utilities are signing 20-year contracts, and the reason the bond market keeps lending. And right now the demand signal is hemorrhaging cash on the assumption that costs will eventually come down and revenue will eventually catch up. The people buying those bonds are betting that math works out. Over decades. Against assumptions that have never been independently verified.
When those assumptions crack — and the rest of this piece examines exactly how — the repricing won’t just hit speculative paper or junk bonds. It will travel straight into the safest, most widely held corner of the global financial system. The institutions that thought they were holding the most conservative possible assets will discover they were holding the risk the whole time, just dressed up in a better suit. That’s not a market correction. That’s a confidence crisis. And that’s precisely the mechanism that made 2008 nearly unsurvivable.
Notes
[C] The 2024 inference figure (~$3.8B) is sourced from leaked Microsoft internal documents reported by TechCrunch, November 2025 [Reference 6]. GPT-4 training cost is based on Sam Altman’s public statement at MIT EmTech Digital, April 2023: “It’s more than $100 million,” as reported by Wired. No audited figure has been published by OpenAI. Critically, OpenAI’s training costs are largely non-cash, paid via Microsoft Azure credits under their investment agreement. Inference costs are paid in cash. The $3.8B inference figure therefore represents cash burn against a non-cash training baseline, making the operational leverage even more extreme than the ratio alone suggests. $3.8B ÷ $100M+ = approximately 38×.
The new country on the grid
Grid planners are now treating AI like another whole country showed up and plugged itself into the American power system. They’ve penciled in an extra 224 gigawatts of peak demand — that’s roughly enough electricity to power more than 160 million homes [8][E]. You’re not shaving a corner off the grid; you’re rearranging where the country’s electricity goes.
Once you see AI as a brand-new country bolted onto the grid, everything that follows is just the system doing what it always does when it thinks a permanent customer has moved in. Regulators start forecasting around that load, and utilities start pouring concrete and signing long-term deals. The 2028 estimates for how much power US data centers will consume are so aggressive — anywhere from about 325 to 580 terawatt-hours a year — that the gap between the low and high guess is bigger than the total electricity consumption of most countries on earth [9][D]. The uncertainty range alone is a nation.
AI represents the vast majority of the projected growth in data center power consumption — AI servers are expected to grow four to eight times by 2028, surpassing conventional servers entirely [9]. The rest of the sector is following AI’s gravitational pull. Which means the scale of this grid buildout, the contracts, the transmission investments, the generation commitments — all of it is essentially a multi-decade bet on AI’s continued hunger for power. Which means the scale of this grid buildout, the contracts, the transmission investments, the generation commitments — all of it is essentially a multi-decade bet on AI’s continued hunger for power.
On the back of those projections, utilities are already locking in 10- and 20-year power contracts to feed data centers that don’t even exist yet, betting that this new “AI country” will still be drawing that power, at those prices, decades from now. The concrete is being poured. The turbines are being ordered. The contracts are signed.
All of it priced on a single assumption: that AI will always need this much electricity to do its work.
Notes
[D] The 255 TWh gap between the low and high estimates (580 − 325 = 255 TWh) exceeds the total annual electricity consumption of countries including Poland (~175 TWh/year) and Argentina (~135 TWh/year). Source for country comparisons: IEA World Energy Balances.
[E] The 224 GW figure is from the NERC Long-Term Reliability Assessment, January 2026 [Reference 8]. Household equivalent derived from US EIA average residential electricity consumption of approximately 10,500 kWh per year (EIA 2023 Residential Energy Consumption Survey). 224 GW sustained output ÷ average household peak load ≈ 160–187 million homes depending on methodology. Conservative figure used in text.
Why everyone jumped off this cliff together
The first thing to understand — and it’s something almost nobody explains clearly — is that AI is not like regular software.
With normal software, most of the cost is up front. You hire engineers, build the product once, and then millions of people can use it without the bill exploding every time someone clicks a button. The work is pre-programmed, so running it is cheap. That’s why software companies historically minted money at scale: once you built the thing, the marginal cost of each new user approached zero.
AI flips that on its head. These systems are reasoning machines. Every time someone asks a real question, they have to think their way to an answer in real time. And just like you, the harder they have to think — the more steps, the more context, the more complex the problem — the more energy and computing power it takes. The meter doesn’t run once when you build the model. It runs on every single interaction.
What makes this compound is that the thing getting better with every new generation of models is precisely their capacity for that hard, complex reasoning. That means each capability improvement makes each interaction more expensive. Better models think harder, and harder thinking costs more. This isn’t a bug — it’s the design. And it scales superlinearly: each step up in reasoning capability costs more than the last [F].
As each new generation gets more capable, three growth curves stack on top of each other simultaneously. More capability means more people want to use it at all. It means each person finds more things in their life and work worth handing off to it. And it means every one of those interactions is more expensive on the back end. Demand goes up in three dimensions at once, and the cost per unit of demand goes up with it.
This makes AI companies look a lot more like utilities than software companies. Their core product isn’t an app you download. It’s metered intelligence, sold by the query and paid for in electricity and hardware time. And that reframe is what makes the power story so critical. For these companies, access to electricity isn’t a background line item — it’s the hard ceiling on how big they can get. The company that runs out of power first hits a wall. The company that locked down the most capacity has the most room to grow.
If AI is metered thinking instead of pre-built code, and if the limiting factor is how much power you can lock down, then not building enough capacity isn’t prudence — it’s losing the race. That’s the logic the big four are acting on. The 725-billion-dollar buildout is what it looks like when everybody concludes at the same moment that the real bottleneck isn’t having good ideas. It’s having enough electricity to run them.
Notes
[F] The relationship between model capability and per-query compute cost is documented in peer-reviewed literature on scaling laws. See: Accounting for Inference in Language Model Scaling Laws, arXiv:2401.00448 (2024), and Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scaling, Joule (April 2026), which quantifies how test-time compute scaling increases energy consumption per query nonlinearly.
The load-bearing assumption has a hole in it
Now it’s time to examine the specific scenario that could bring the whole thing down. To be explicit: what follows is a hypothetical, but one built directly on published research — research conducted using the industry’s own studies, benchmarks, and results.
That research makes a pointed argument: the entire industry has been wrong about where the “thinking” in these systems actually lives. And it’s impossible to properly optimize something when you’ve misidentified what you’re actually optimizing for. By definition, you end up optimizing for the wrong thing, with enormous and measurable inefficiency as the guaranteed result [G].
The full technical case — argued at length, using only the industry’s own published data — lives in the two companion pieces linked above. The short version, which is all we need for this discussion, is this: if you actually design and operate these systems around where the thinking really happens, you don’t get a marginal improvement. You get a step-change. The power and hardware required to deliver a given amount of useful AI work collapses by something like a factor of two or three.
Now hold that alongside everything we’ve just walked through:
The hyperscalers are pouring 725 billion dollars into data centers this year alone, on their way to a 5.3-trillion-dollar buildout, on the assumption that today’s power draw is simply how this technology has to work. Grid planners are carving out an extra country’s worth of electricity because they believe these machines will always be this hungry. The bond market is writing checks against utilization numbers that treat the current inefficiency as permanent. And that debt has been rated as the safest in the world — even though it’s essentially a multi-decade bet on an energy appetite that has never been verified against a properly optimized system.
The efficiency correction my research points to could arrive in one of two very different ways, and the difference between them is not academic.
If the solution requires new hardware — a redesigned chip that has to be manufactured, shipped, and deployed across the industry — then the timeline stretches years. Painful, disruptive, but manageable. The system has time to adjust. Projections get revised. Bonds get repriced gradually. Nobody likes it, but the world doesn’t end on a Tuesday.
The second scenario is the one that changes the calculus entirely.
Notes
[G] Independent measurements corroborate the general efficiency problem from multiple directions. The KAIST study (July 2026) found GPUs idle up to 54.5% of the time during agentic workloads (reported by Forbes). The GPU Efficiency Funnel framework (AI Journ, January 2026) documents real-world compute yield falling below 20% of theoretical capacity in large AI clusters. The specific architectural mechanism described in the companion pieces is distinct from both of these findings and is documented separately using the chip manufacturers’ own published benchmarks.
What happens when the patch drops
Here’s the hypothetical. Someone releases a software patch — free, public, easy to apply — that makes every AI GPU already deployed in the world run the way it was supposed to run. Not new chips. Not a multi-year hardware program. A software fix, the kind that propagates the way software fixes propagate: immediately, universally, and for free. Anyone can apply it. Anyone can verify the results. You can replicate the numbers on a consumer laptop in under 30 minutes.
Now watch what happens.
Within days, every sophisticated operator in the AI infrastructure chain runs the numbers. Not because they’re panicking — because it’s their job. A hyperscaler CFO looks at the efficiency gain and asks the question that should have been asked two years ago: “If the same hardware now does two to three times as much useful work, how much of what we’re building do we actually need?” A utility board looks at the 20-year power contract they just signed and asks whether the demand curve it was priced on still makes sense. A bond desk looks at the data center REIT prospectus on their screen and notices that the utilization assumptions in section four were calculated against hardware running at half capacity.
None of these people are panicking yet. They’re just asking the right questions. But they’re asking them at the same time, about the same assets, across the entire system simultaneously.
This isn’t entirely hypothetical. On January 27, 2025, DeepSeek released a model that appeared to achieve comparable AI performance at a fraction of the compute cost. The claim was disputed. The methodology was questioned. None of that mattered: Nvidia lost roughly 600 billion dollars in market capitalization in a single session — the largest single-day equity loss in American stock market history — because enough investors asked the question at the same time [14]. That was a disputed efficiency claim from an unverified source. The scenario described here involves something anyone can replicate and verify independently in 30 minutes. If a rumor did that to one company’s stock, consider what a proof does to an entire asset class.
That’s how 2008 started too. Not with a crash. With a question. The question was: “Are the mortgages backing these securities actually worth what we think?” The moment enough people asked it at the same time, the answer didn’t matter. The asking was the event.
In 2008, the trigger was comparatively slow. Default rates crept up over 18 months. Rating agencies were slow. Banks were slow. There was time — not enough, but some — for the system to pretend it wasn’t happening. In this hypothetical there is no slow phase. The information is public, replicable, and binary. Either the efficiency gain is real or it isn’t, and anyone can check in 30 minutes. There is no 18-month ambiguity window. There is no hiding it in a model. The moment the patch is credible to one major player, it’s credible to all of them simultaneously. They all ask the same question at the same time.
The cascade from there follows a specific and brutal sequence.
One. The equity repricing.
Microsoft, Google, Amazon, and Meta are not just four large companies that happen to be in AI. These four companies, along with a handful of others whose fortunes are tied to the same AI capex cycle, collectively represent nearly a third of the entire S&P 500 [10]. They’re not a sector. In a very real and measurable sense, they are the market — at the most concentrated index weighting in the history of modern investing [10]. Which means every 401k, every pension fund, every target-date retirement fund, every passive ETF that tens of millions of Americans were told was “diversified” is loaded with exactly these names. When the market decides their capex plans were built on a broken efficiency assumption, it doesn’t reprice “the AI sector.” It reprices the index. The rotation out happens in milliseconds. The retail investor finds out when they open their app and discover their accumulated wealth is a fraction of what it was hours earlier. The people with access and speed have already moved. The rest of us are left holding what they sold.
Two. The debt starts asking questions.
The 1.2 trillion dollars in AI infrastructure bonds — already the largest single segment of the investment-grade market — were priced against utilization assumptions drawn from hardware running at the efficiency levels the patch corrects [5]. When the equity repricing hits, the bond market doesn’t wait to see how cash flows shake out. It asks whether the underlying utilization projections are still valid. They aren’t. The assets are still real — the data centers are still standing, the GPUs are still humming — but the revenue they can realistically generate to service the debt is being revised down in real time by every analyst on every desk simultaneously. That’s not a slow burn. That’s a margin call.
Three. The utilities are left holding contracts that no longer make sense.
The 224-gigawatt demand increase that regulators planned around, the 20-year power purchase agreements, the generation and transmission investments made on the assumption that AI would always be this hungry — all of it was priced for a world where the parking brake stays on forever. It doesn’t. The utilities can’t tear up the contracts. The stranded costs get pushed somewhere: ratepayers, taxpayers, or bankruptcy proceedings. Either way, it lands on someone who wasn’t in the room when the bet was made. [8]
This is where it stops looking like 2008 and starts looking worse.
In 2008 we came within days of a complete global financial freeze. Ben Bernanke wrote in his memoir The Courage to Act that within days of Lehman's collapse, the commercial paper market — the mechanism by which virtually every large company in America funds its payroll and day-to-day operations — was hours from total seizure [11]. The Fed and Treasury improvised tools with no clear legal basis, deployed them without political consensus, and stopped the bleeding by the narrowest of margins.
They could do that in 2008 because of a specific set of conditions that no longer reliably exist.
The federal debt-to-GDP ratio going into 2008 was roughly 35 percent — today it stands at over 122 percent [12][H]. That room is gone. The Fed’s balance sheet never normalized after 2008, and was expanded again dramatically after 2020. The institutional credibility that allowed the Treasury to guarantee money market funds, the Fed to backstop commercial paper, and the G20 to coordinate a unified global response — that credibility was built over decades and has been substantially spent. The bipartisan political mechanism that passed TARP within two weeks, under enormous pressure, with leaders from both parties standing together — that mechanism is functionally gone [13]. The global coordination that amplified the US response in 2008-09 depended on a level of institutional trust between major economies, particularly the US and China, that has been systematically dismantled.
So the honest accounting looks like this: a crisis larger in notional exposure than 2008, faster in propagation than 2008, more concentrated in the assets most widely held by ordinary Americans than 2008, hitting a government with less fiscal capacity than 2008, a central bank with less dry powder than 2008, a political system less capable of coordinated emergency response than 2008, and a global architecture less able to coordinate than 2008.
The honest statement isn’t that this is guaranteed to produce a global depression. It’s that every condition that allowed us to narrowly avoid one in 2008 is now either gone or severely degraded — and the thing coming is bigger and faster than what we faced then. If you ran that scenario a hundred times, how many times does the narrow 2008 escape repeat? And how many times does it go the other way?
That’s the hypothetical. That’s what a free, public, easy-to-apply software patch — one that simply makes existing AI hardware run the way it was designed to run — does to a 5.3-trillion-dollar bet priced on the assumption that today’s waste is permanent.
The patch doesn’t cause the crisis. The crisis was already locked in. The patch just makes it impossible to pretend otherwise.
Notes
[H] The 2007 pre-crisis figure of approximately 35% reflects federal debt held by the public as a percentage of GDP (White House OMB historical tables). The current figure of 122.6% (Q1 2026) reflects total federal debt as a percentage of GDP per Federal Reserve FRED series GFDEGDQ188S. Using the same publicly-held measure for 2026 yields approximately 99–100% of GDP — still roughly three times the pre-crisis level. Both measures confirm the same directional argument.
The part where smart people should be embarrassed
None of this requires a conspiracy theory. It doesn’t require hidden data or whistleblowers or bad actors. The flaw in the story — where the thinking actually lives, how much efficiency is being left on the floor, what that means for the cost assumptions underlying trillions in infrastructure spending — has been documented in the open the entire time. The chip companies published the benchmarks. The cloud teams logged the utilization losses. The labs released papers showing how much better things get when you treat the runtime as the structure that actually matters. The evidence isn’t hiding. It’s in their own PDFs and blog posts and production logs [G].
What never happened was the one move that would have changed everything: someone with their hand on the money asking, “If this is how the system actually works, what does it do to the size of the bet we’re making?”
The engineers at the chip companies measured the inefficiency, logged it, and published the graphs. The infrastructure teams saw the utilization losses in their dashboards. The financial analysts priced the bonds and modeled the capex returns. Everyone optimized their own slice. Nobody got paid to put the pieces together and ask whether the efficiency number the entire financial model rested on was drawn from a system working correctly.
That’s not a conspiracy. It’s something almost more troubling: a room full of very smart, very well-compensated adults who built the largest private infrastructure bet in history on top of a technical assumption their own research quietly disproves. The career incentives didn’t reward the question. If you’re a hyperscaler CFO and you raise the issue of whether your capex commitment is sized on a broken baseline, you’re not being prudent — you’re being the person who killed the deal. If you’re a bond analyst asking whether the utilization assumption was calculated against hardware running at half capacity, you’re not being thorough — you’re being the person who spooked the market. The system selected against the question. Not through malice. Through the ordinary human instinct to not be the person who stops the party.
The result is 5.3 trillion dollars in committed capital sitting on a technical assumption that has never been stress-tested at the financial layer — even though it has been stress-tested, repeatedly, at the technical layer, by the companies doing the building [4].
Their numbers. Their measurements. Their published research.
Nobody added it up.
Until now.
Jason Hubbard is the founder and CEO of Sacred Loop AI and an independent AI architect and researcher. He builds systems at the edge of what current AI can do and documents the gap between what the industry claims it built and what it actually built.
His work examines AI infrastructure, system design, model performance, and the technical decisions hiding beneath the industry’s marketing.
Read Jason on Medium | Follow Jason on X | Connect on LinkedIn
Read More:
Glossary:
AI — Artificial Intelligence
GPU — Graphics Processing Unit
CFO — Chief Financial Officer
GDP — Gross Domestic Product
Fed — Federal Reserve
TARP — Troubled Asset Relief Program
REIT — Real Estate Investment Trust
ETF — Exchange-Traded Fund
S&P — Standard & Poor's
MBS — Mortgage-Backed Securities
NBER — National Bureau of Economic Research
IEA — International Energy Agency
EIA — Energy Information Administration
NERC — North American Electric Reliability Corporation
TWh — Terawatt-hours
GW — Gigawatts
KAIST — Korea Advanced Institute of Science and Technology
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