The Federal Trade Commission has a two-word name for how the AI industry funds itself: circular spending. It sits in a staff report the agency published in January 2025, describing the deals between the largest cloud companies and the largest AI labs. Not a critic’s phrase. Not a short seller’s. The government’s own words, in a federal document, for the way the money moves.

Here is the arrangement that phrase describes. A cloud company invests billions of dollars in an AI lab. The lab then agrees to spend a large share of that same money buying computing power from the company that just funded it. The money leaves and comes home. The FTC found that these partnerships “include cloud commitments that require AI developers to spend a large portion of their CSP partner’s investment on cloud services from their partner,” and it named that feature, in plain type, circular spending.

That finding has been public for more than a year. So has a second fact, from a different stack of documents: the same small group of companies now floods Washington with more lobbyists than almost any other industry. What nobody has done is lay the two records side by side. Do that, and a single machine comes into focus. A money loop that funds its own demand, and a legal wall going up to keep anyone from regulating it. Same companies. Both halves. It is the first thing to understand about the AI economy, and it is hiding in plain public records.

The loop

The loop runs through three partnerships, and the FTC studied all three. The agency’s 6(b) study examined Microsoft and OpenAI, Amazon and Anthropic, and Google and Anthropic, the largest such deals in the industry. As the report tabulated the publicly reported figures through September 2024, Microsoft had put 13.75 billion dollars into OpenAI, Amazon 8 billion into Anthropic, and Google 2.55 billion into Anthropic. Microsoft’s own quarterly filing that fall put its total funding commitments to OpenAI at 13 billion, accounted for under the equity method.

These are not ordinary stock purchases. The FTC found the deals hand the cloud partners “significant equity and certain revenue-sharing rights,” and leave the door open for one company to fully acquire its partner down the line. They carry “consultation, control, and exclusivity rights,” including board seats and preferential treatment. The company writing the check also gets a hand on the wheel.

Then the money comes back as cloud spending. That is the circular part, and the FTC was direct about why it matters. The structure, in the agency’s reading, is one avenue through which a cloud provider may aim to reduce the size of the loss it might otherwise take on the billions it pours into a partner. The lab gets discounted computing it could not afford on the open market. The cloud gets its money returned as revenue, plus equity, plus a view inside a rival’s operation. The report found the arrangement reaches all the way down to the silicon, with “co-development plans for CSP-designed semiconductor chips” tuned to the labs’ models. Money, equity, control, and custom hardware, all moving in a ring.

The entanglement runs deeper than money. The FTC found the partnerships give the labs discounted access to the scarce computing they cannot get elsewhere, let the two sides embed their own engineers inside each other’s companies, and share training data along with detailed performance and financial figures on the models themselves. The people, the data, and the hardware are braided together as tightly as the cash. Unwinding one company from the arrangement would mean pulling all of it apart at once.

The agency also listed risks it thought were worth watching. In Section 5 of the report, staff flagged that these partnerships could limit other AI developers’ access to computing power and engineering talent, the two scarcest inputs in the field. They flagged that the deals could raise the cost of switching providers, through exclusivity terms and technical lock-in that make leaving expensive and slow. And they flagged that the arrangements hand the cloud partners access to sensitive financial and technical information, including confidential chip designs and a partner’s own customer and revenue numbers, which those same cloud companies could use to build products that compete with the labs they fund.

That section is contested, and the objection came from inside the agency. Commissioner Andrew Ferguson, joined by Commissioner Melissa Holyoak, filed a concurring and dissenting statement on January 17, 2025. Three days later Ferguson became Chairman of the FTC, the job he still holds. He voted to approve the report and said why: it “sheds light on three Big Tech-AI partnerships,” and “Congress, state officials, and the public deserve to understand how these partnerships work.” What he objected to was Section 5. The study was fast and narrow, he wrote, covering three partnerships between five companies, and “the limited, brief nature of the study should foreclose the drawing of broad conclusions about the AI industry and its future, or even about the partnerships themselves.” His instruction to readers was blunt: “Readers should skip Section 5 of the Report, or read it with tremendous skepticism.”

Fine. Skip it.

Nothing else in this piece needs it. The money loop, the equity and revenue-sharing rights, the board seats, the chip co-development, the embedded engineers, the shared training data and financial figures, all of that sits in the parts of the report Ferguson voted to publish and called valuable, drawn from what he described as “company documents produced in response to the Commission’s Section 6(b) orders.” The speculation about what it might mean is the part he wanted struck. What these companies actually signed is not in dispute.

It is worth noting what Ferguson did not say. He did not say the arrangement is harmless. His own statement holds that the Commission “must remain a vigilant competition watchman, ensuring that Big Tech incumbents do not control AI innovators in order to blunt any potential competitive threats.” His argument is that a study run in under a year should not be the last word. That is a reasonable thing for a regulator to say, and it cuts both ways. If a year was not enough to draw conclusions, it was not enough to rule anything out either.

The report’s own limits are real and worth stating plainly. It covers three partnerships. It reflects what the companies disclosed as of September 2024. It was aggregated to protect trade secrets. It says outright that it is “not a formal legal or economic analysis” and accuses no one of breaking the law. Circular spending is not a crime. The concern here is not illegality. It is that this structure concentrates enormous power in very few hands, and that the ordinary means of checking that power are being closed off one at a time.

What happened next

The FTC published those numbers in January 2025. Every one of the three partnerships has been rewritten since, and the public record of what replaced them is the strongest evidence in this piece.

Start with Amazon and Anthropic, because the arithmetic is right there in the announcement. On April 20, 2026, the two companies said Amazon “is investing $5 billion in Anthropic today, with up to an additional $20 billion in the future,” securing “up to 5 gigawatts (GW) of capacity for training and deploying Claude.” In the same announcement, Anthropic said: “We are committing more than $100 billion over the next ten years to AWS technologies.” That covers Amazon’s own Trainium chips, Trainium2 through Trainium4, plus Graviton processors and the option to buy future generations of Amazon silicon.

Read those two sentences together. Amazon puts in up to 25 billion dollars. Anthropic commits to spend more than 100 billion dollars back with Amazon. The purchase commitment running the other way is roughly four times the size of the investment. That is the arrangement the FTC described in January 2025, at ten times the scale, announced in public by the companies themselves. It is also a large share of the 2026 capex wave now reshaping the American power grid.

Google and Anthropic expanded too. On April 6, 2026, Anthropic announced a deal with Google and Broadcom for “multiple gigawatts of next-generation TPU capacity that we expect to come online starting in 2027,” Google-built chips supplied through Broadcom, with the vast majority of the capacity sited in the United States. Anthropic did not disclose the dollar terms. It did say its run-rate revenue had “surpassed $30 billion,” up from roughly $9 billion at the end of 2025, and pointed back to an earlier pledge to “invest $50 billion in strengthening American computing infrastructure.”

Microsoft and OpenAI went the other direction on some terms. On April 27, 2026, Microsoft announced an amended agreement. Its license to OpenAI’s models and products runs through 2032 but is “now non-exclusive.” OpenAI “can now serve all its products to customers across any cloud provider.” Microsoft “will no longer pay a revenue share to OpenAI.” Those are real changes, and they loosen exactly the kind of exclusivity the FTC described.

The same announcement says what did not change. Microsoft “remains OpenAI’s primary cloud partner,” and OpenAI’s products still ship first on Azure “unless Microsoft cannot and chooses not to support the necessary capabilities.” Microsoft “continues to participate directly in OpenAI’s growth as a major shareholder.” And revenue share payments from OpenAI to Microsoft “continue through 2030, independent of OpenAI’s technology progress, at the same percentage but subject to a total cap.”

So here is the honest accounting. One leg of the arrangement got looser. The other two got dramatically larger. Ferguson was right that a one-year study could not tell you where this was going. Sixteen months of company announcements can. The money still leaves and comes home, and the sums involved have gone from billions to hundreds of billions.

The shield

The companies inside the money loop are also among the largest lobbying forces in Washington. Public Citizen, a nonprofit watchdog, found that more than 3,500 lobbyists worked on AI issues in 2025, more than one in four of every registered federal lobbyist in the country. The overwhelming majority of that work, 82 percent of it, was done on behalf of corporate interests. The AI lobbying force grew 168 percent between 2022 and 2025. Sludge, working the same disclosure filings, put the precise count at 3,570 lobbyists, or 26 percent of everyone registered.

The growth is steeper than even that suggests. Public Citizen found the number of distinct lobbyist-and-client relationships working AI jumped 265 percent over those three years, from 1,672 to 6,110. Software and services became the single largest lobbying sector in the country by that measure, with about 1,448 lobbyists, close to 30 percent of the entire AI lobbying push. Lobbyists working specifically on data centers grew from 68 in 2022 to more than 400 in 2025, close to six times as many. An industry that barely registered on K Street four years ago now sits near the center of it. I have written before about the money behind AI policy and about the policy revolving door that moves people between the agencies and the firms they regulate.

Look at who is doing the spending. Public Citizen’s count of the top AI-lobbying operations in 2025 lists the US Chamber of Commerce with 91 lobbyists, Microsoft with 63, Meta with 55, Intuit with 51, and Amazon with 48. The names at the top of that list are the same names inside the money loop.

And the policy showing up is built to remove the biggest threat to the loop. On December 11, 2025, the White House issued an executive order titled “Ensuring a National Policy Framework for Artificial Intelligence.” It orders the Attorney General to stand up an AI Litigation Task Force whose only assignment is to challenge state AI laws in court. It directs the Commerce Department to identify state AI laws it considers onerous and hand them to that task force. It moves to cut states that keep the targeted regulations off the non-deployment portion of federal BEAD broadband funding, the money for planning, administration, and outreach, unless they fall in line. And it reaches further still, directing the FCC to weigh a federal reporting standard that would override conflicting state rules, and the FTC to spell out when a state law that forces changes to an AI model’s output is preempted by federal law. States have been the one level of government actually writing rules for this industry, and the fight over state AI laws had been running in 45 of them. The order is designed, layer by layer, to preempt them. It is the enforcement arm of the White House framework released earlier that year.

No one can prove the lobbyists wrote that order, and I am not going to claim they did. But the shape is hard to miss, and it is not the first time the question has come up about Big Tech’s hand in an executive order. The firms that dominate the money loop are among the heaviest spenders shaping AI policy, and the policy that arrived, federal preemption of state law, happens to sweep away the one venue that had started to regulate them. Public Citizen’s J.B. Branch put the stakes plainly: “Congress now has a once-in-a-generation opportunity to decide whether AI becomes another chapter in the story of unchecked corporate power.”

I asked Public Citizen how the two halves fit together. Eileen O’Grady, a researcher there and co-author of Generative Influence, told Laterstack:

“Last year’s AI lobbying surge and the preemption push are two parts of the same play. Big Tech spent heavily to shape federal policy and is effectively cashing in through the government’s attempt to wipe out state laws that would have created common sense guardrails for the industry. We can expect to see federal AI lobbying continue to intensify as preemption plays out in Congress and the courts, especially if bills like the draft Great American AI Act advance. We might also see more pressure directed at the executive branch, which has become the industry’s most effective route now that the direct legislative attempts have stalled.”

The bill she names is real and not yet law. Representatives Jay Obernolte, a Republican from California, and Lori Trahan, a Democrat from Massachusetts, released the Great American AI Act as a discussion draft on June 4, 2026. It has not been formally introduced. It carries a three-year preemption of state laws governing how AI models are built, while leaving states their authority over how those systems get used.

Her last point is the one to sit with. She reads the executive branch as the industry’s most effective route now that the direct legislative push has stalled. The December order came from the executive branch.

The same hands

Set the two records next to each other and the machine is whole. Microsoft and Amazon are principals in the arrangement the FTC called circular spending. Microsoft and Amazon also sit second and fifth on the list of the country’s biggest AI lobbying operations. The hands that built the loop are the same hands building the wall. This is not two stories about the AI industry. It is one story about a small number of companies that fund themselves in a circle and are working, out in the open, to keep anyone from stepping in.

None of the individual facts here are secret. The FTC report is on the agency’s website. Public Citizen published its lobbying count. The executive order is posted on the White House site. The partnership terms are on the companies’ own newsrooms. The pieces have been sitting in the open, in separate places, waiting for someone to set them on the same table. Standard Oil looked permanent too, right up until someone wrote the whole thing down in one place.

A quick word on why I am writing this. I am not trying to tell you what to think. I want to lay out what the public documents actually say and let you weigh it for yourself. How these companies are funded, and who gets to set the rules for them, touches things people feel directly, like prices, competition, and how much real choice they have. You can follow all of that without taking a political side, and it is already on the record.

No brakes

Two forces usually correct a concentration of corporate power, the market and the government, and in the AI economy both are being closed at once. The market is the first. Competitors move in, customers leave, the advantage erodes on its own. But a loop that funds its own demand does not wait on the market’s permission to keep running. Government is the second. Regulators and legislators draw the lines. But you cannot regulate a machine whose owners are writing the rules, and the December order is aimed squarely at the level of government that was trying.

The bill for all of it lands somewhere. Five gigawatts here, multiple gigawatts there, and the power and water to run them come from somewhere real. In Arizona that has already turned into the data center bill you never voted on, paid through electricity rates by people who were never asked.

That is the machine, at least the part you can already prove from public documents. It is also only two layers of it. The full stack runs from the chips and the packaging bottleneck that decides how many of them get built, up through the clouds and the labs, to the money loop and the law wrapped around it. The rest of this series follows it the whole way down.

Laterstack contacted Microsoft, Amazon, Google, OpenAI, and Anthropic for this piece. None provided an on-the-record comment.

Where every number came from

Every figure and quote in this piece comes from a public document. Here is where each one lives, in the order the claims appear.

The loop

1. The phrase “circular spending,” the cloud commitment finding, the equity and revenue-sharing rights, the board seats and exclusivity terms, the chip co-development, the discounted compute, the embedded engineers, the shared training and performance data, the Table 1 investment figures, and the report’s own scope limits: FTC Staff Report on AI Partnerships and Investments 6(b) Study, Federal Trade Commission, January 2025. The circular spending language appears in Finding 3 on page 19.

2. Microsoft’s own accounting of its OpenAI position, stated as “total funding commitments of $13 billion” under the equity method: Microsoft Form 10-Q for the quarter ended September 30, 2024, U.S. Securities and Exchange Commission. Note that this figure and the 13.75 billion in FTC Table 1 are not identical. The FTC tabulated publicly reported investment; Microsoft reported its own booked funding commitments. Both are cited here as each states them.

3. Amazon’s investment in Anthropic and the Trainium chip arrangement: Amazon to invest additional $4 billion in Anthropic, Amazon.

4. The dissent from Section 5, the instruction to read it with skepticism, the vote to approve the rest of the report, and the “vigilant competition watchman” line: Concurring and Dissenting Statement of Commissioner Andrew N. Ferguson, joined by Commissioner Melissa Holyoak, Matter No. P246201, January 17, 2025.

What happened next

5. Amazon’s $5 billion investment, the up to $20 billion that follows, the 5 gigawatts of capacity, and Anthropic’s commitment of more than $100 billion to AWS over ten years: Anthropic and Amazon expand collaboration for up to 5 gigawatts of new compute, Anthropic, April 20, 2026.

6. The multiple gigawatts of next-generation TPU capacity from 2027, the Broadcom supply arrangement, the U.S. siting, the $30 billion run-rate revenue figure, and the $50 billion American infrastructure pledge: Anthropic expands partnership with Google and Broadcom for multiple gigawatts of next-generation compute, Anthropic, April 6, 2026. Confirmed by Google Cloud, which discloses no financial terms.

7. The non-exclusive license through 2032, the any-cloud provision, the end of Microsoft’s revenue share payments to OpenAI, the primary cloud partner and major shareholder language, and the revenue share running to Microsoft through 2030 subject to a cap: The next phase of the Microsoft-OpenAI partnership, Microsoft, April 27, 2026.

The shield

8. The AI lobbyist count, the corporate share, the growth figures from 2022 to 2025, the data center lobbyist growth, the lobbyist-and-client relationship growth, the software and services sector share, the top lobbying operations by headcount, and the J.B. Branch quote: Generative Influence, by Mike Tanglis and Eileen O’Grady, Public Citizen, February 24, 2026.

9. The 3,570 lobbyist count, the 26 percent share of all registered federal lobbyists, and corroboration of the top lobbying employers: AI Boom on K Street: One in Four Lobbyists Now Work on AI, by David Moore, Sludge, February 24, 2026.

10. Federal lobbying spending by the AI developers themselves, $3.13 million by Anthropic and $2.99 million by OpenAI in 2025: AI’s Biggest Builders Are Now Its Biggest Lobbyists, by Phoebe Liu, Forbes, February 20, 2026.

11. The AI Litigation Task Force, the Commerce Department review of state AI laws, the BEAD funding condition, the FCC reporting standard, and the FTC preemption guidance: Executive Order 14365, Ensuring a National Policy Framework for Artificial Intelligence, The White House, December 11, 2025. Legal analysis of the same order, including the point that the funding at risk is the non-deployment portion of BEAD: AI Executive Order Targets State Laws and Seeks Uniform Federal Standards, Latham and Watkins, December 17, 2025.

12. The three-year preemption of state laws governing model development, and the bill’s status as a discussion draft: Obernolte, Trahan release a discussion draft of the Great American AI Act, June 4, 2026.

13. Eileen O’Grady’s comment was provided to Laterstack by email on July 22, 2026, and her attribution was confirmed by her on July 23, 2026.

Frequently Asked Questions

What is circular spending in AI?

Circular spending is the FTC’s term, from its January 2025 staff report, for cloud commitments that require AI developers to spend a large portion of their cloud partner’s investment back on that partner’s cloud services. The money is invested, then returns as revenue.

Which companies did the FTC 6(b) study cover?

Three partnerships between five companies: Microsoft and OpenAI, Amazon and Anthropic, and Google and Anthropic. The report reflects what those companies disclosed as of September 2024.

Did anyone at the FTC disagree with the report?

Yes. Commissioner Andrew Ferguson, joined by Commissioner Melissa Holyoak, voted to approve publication but dissented from Section 5, the Areas to Watch section. Ferguson wrote that readers should skip Section 5 or read it with tremendous skepticism. He became FTC Chairman three days later.

How much are these partnerships worth now?

In April 2026 Amazon announced a 5 billion dollar investment in Anthropic with up to 20 billion more, and Anthropic committed more than 100 billion dollars over ten years to AWS technologies. Anthropic separately signed with Google and Broadcom for multiple gigawatts of TPU capacity starting in 2027. Microsoft and OpenAI amended their agreement to make Microsoft’s license non-exclusive.

What does the December 2025 executive order do?

Executive Order 14365 directs the Attorney General to create an AI Litigation Task Force to challenge state AI laws, has Commerce identify state laws it considers onerous, conditions the non-deployment portion of BEAD broadband funding, and directs the FCC and FTC toward federal standards that would preempt conflicting state rules.

How many lobbyists work on AI?

Public Citizen found more than 3,500 lobbyists worked AI issues in 2025, more than one in four of every registered federal lobbyist. Sludge put the count at 3,570, or 26 percent. Public Citizen found 82 percent of that work was on behalf of corporate interests.

Phil Spencer retired from Microsoft on Thursday after 38 years. His last day is Monday. Sarah Bond, Xbox president and the person most people assumed would succeed him, also resigned. Both of them, gone within the same announcement. And neither of them is being replaced by anyone from gaming.

The press release calls it a retirement. Spencer’s own statement says he told Nadella last fall he was “thinking about stepping back and starting the next chapter.” That’s the kind of language you use when you want the exit to look calm. And maybe it was calm. But the timeline tells a different story if you’re paying attention.

The Sequence

Spencer orchestrated the $69 billion Activision Blizzard acquisition, the largest in gaming history and one of the largest in tech. That deal closed in October 2023 after a year-long regulatory fight with the FTC. Within three months of closing, Microsoft laid off 1,900 gaming employees. By January 2024, another 2,500 across the company. Studios got shuttered. Teams got folded. The kind of restructuring that happens after a mega-merger, where you cut the overlap and consolidate the headcount.

Spencer oversaw all of it. The deal. The layoffs. The integration. The reorganization of every studio under one umbrella. And then last fall, once the hard part was done, he told Nadella he was thinking about leaving.

That’s not a retirement. That’s an architect walking off a job site after the building is finished. He built the thing. He did the ugly work of merging it. And then he left before anyone could ask him to run it differently than he built it.

Bond’s Exit Is the Stranger One

Spencer at least gets the “38 years, I’m tired” narrative. Bond doesn’t. She was Xbox president. She was running the day-to-day. She was the obvious next CEO of Microsoft Gaming. And instead of taking the job, she resigned.

That’s the part nobody is spending enough time on. Bond didn’t get passed over and stay. She got passed over and left. When the person everyone expects to get promoted decides to walk instead, it usually means they saw something they didn’t want to be part of. Or they were told the direction was going somewhere they disagreed with. Either way, it’s not the behavior of someone who lost a title fight. It’s the behavior of someone who chose to leave.

Microsoft replaced both of them with Asha Sharma from CoreAI, who joined the company in 2024 from Instacart. Her first public statement included a promise not to “flood our ecosystem with soulless AI slop.” She felt the need to say that on day one. Draw your own conclusions about what the internal conversations looked like.

What the Timing Actually Says

Spencer built Xbox into a $25 billion annual revenue business. Game Pass has over 34 million subscribers. The studio portfolio, after Activision and Bethesda, is the largest in the industry. All of that was Spencer’s project.

But building the portfolio was one job. What Nadella wants to do with that portfolio is a different job. Microsoft has spent the last two years layering AI into every division. Azure, Office, GitHub, Windows. All got Copilot. All got AI integration as a strategic priority. Gaming was the last major division without an AI executive running it.

Spencer built a content empire. Nadella wants a platform. Those are different ambitions with different definitions of success. Content success means great games that sell and retain subscribers. Platform success means AI tools, procedural generation, dynamic monetization, ecosystem lock-in. Content people and platform people rarely see the world the same way.

Spencer telling Nadella last fall he wanted to leave makes more sense if you consider that last fall is also when Microsoft accelerated its AI integration roadmap across divisions. If the conversation shifted from “build the best game library in the world” to “now turn it into an AI platform,” Spencer may have decided he’d rather leave on his own terms than execute someone else’s vision for the thing he built.

Bond apparently reached the same conclusion.

What’s Left

Matt Booty got promoted to Chief Content Officer, which is Microsoft’s way of saying the studios will still make games. Sharma’s job is everything else. The platform layer. The AI strategy. The part that Nadella actually cares about.

The studios that make Halo, Elder Scrolls, Call of Duty, and Minecraft are still staffed with the same developers. Nobody is firing the game makers. But the person those game makers report to, through Booty through Sharma, has never shipped a game. And the person who spent 12 years protecting them from the platform side of Microsoft just walked out the door.

Spencer’s retirement statement was gracious. Bond’s departure was quiet. Together they read less like a transition and more like two people who decided the next chapter of Xbox wasn’t one they wanted to write.

Microsoft is testing high-temperature superconductor (HTS) cables to power its AI data centers, a technology that eliminates electrical resistance entirely. Zero voltage drops. Zero heat generation from the cables themselves. VEIR, a Massachusetts-based startup backed by Microsoft, completed a successful test of its 3-megawatt superconducting cable powering a server rack in a simulated data center environment. The pitch is elegant: replace the copper arteries of a data center with superconducting ones, and the power delivery problem shrinks by an order of magnitude. The cables themselves can be more than 10x smaller and lighter than their copper equivalents.

It is a genuinely impressive piece of engineering. It also will not matter for years, and the communities whose power grids are being consumed by AI expansion right now cannot wait that long.

The Physics Works. The Calendar Does Not.

The science behind HTS cables is well established. Cool certain ceramic materials below a critical temperature using liquid nitrogen, and they conduct electricity with zero resistance. No energy lost as heat in transmission. No need for massive copper bus bars and the ventilation systems required to cool them. Microsoft’s research team envisions replacing overhead power line corridors with compact underground HTS trenches, collapsing the physical footprint of data center power infrastructure dramatically. American Superconductor (AMSC), traded on NASDAQ, is a primary supplier of the HTS wire and systems that make this possible.

VEIR closed a $75 million Series B round with Microsoft among the investors. The test validated that the technology performs as promised under data center load conditions. But validated performance in a controlled simulation and deployed performance at production scale are separated by years of engineering, regulatory approvals, and reliability testing. The current pilots are still evaluating long-term maintenance costs of the liquid nitrogen cooling systems that keep the cables in their superconducting state. This is pre-deployment work. Microsoft is not installing HTS cables in Azure data centers next quarter. It is studying whether HTS cables might be viable for Azure data centers in the future.

The Gap Between Innovation and Relief

Power is the single largest bottleneck constraining AI data center expansion. Not GPUs. Not talent. Not capital. Power. The electricity cost pressures that AI data centers are already imposing on local communities are not theoretical. They are showing up in utility rate filings, in city council debates, in the monthly bills of families who live near facilities they never asked for. When a hyperscaler breaks ground on a new campus, the local grid absorbs the impact immediately. Utility companies file for rate increases. Residential customers subsidize industrial consumption through higher bills and degraded grid reliability.

The scale of capital flowing into AI infrastructure makes this a structural problem, not a temporary one. Big Tech is pouring hundreds of billions into data center construction right now, using copper cables, drawing from existing grids, and socializing the costs onto local ratepayers. HTS cables might eventually reduce the power lost in delivery. They do nothing to reduce the total power consumed by the facilities themselves. A data center running on superconducting cables still demands the same megawatts from the grid. It just wastes fewer of them in transit.

This distinction matters. The coverage of Microsoft’s HTS testing has treated it as a solution to the widening gap between AI energy consumption and available supply. It is not. It is an efficiency improvement to the plumbing. An important one, potentially, but not a fix for the fact that the reservoir is running dry.

The counterargument deserves serious consideration. Efficiency improvements compound. If HTS cables eliminate even 5 to 8 percent of power losses in distribution within a data center campus, that represents meaningful megawatts recovered at scale. A facility drawing 500 megawatts could reclaim 25 to 40 megawatts through zero-resistance transmission alone. That is power equivalent to tens of thousands of homes, freed without building a single new generation source. Multiply this across every hyperscaler campus worldwide, and the aggregate impact is substantial. Dismissing efficiency gains because they do not solve the entire problem is a fallacy. Every grid technology we rely on today was once a pilot that skeptics called insufficient.

That critique is fair, and it still sidesteps the timing question. The communities absorbing the grid strain of AI expansion today are not helped by a technology that might deploy at scale in five to seven years. Efficiency gains that arrive after the damage is done are retrospective improvements, not solutions. Microsoft’s superconductor research is a long bet on future infrastructure. It does not address the present reality that data centers are pulling power from grids built for cities, not server farms, and that the people living in those cities are footing the bill through higher rates and reduced reliability.

The production timeline is the part nobody wants to talk about honestly. This is years away from deployment. Maybe a decade before it shows up in enough facilities to matter at scale. Meanwhile the data centers are going up right now, pulling power from grids that were sized for residential neighborhoods and small businesses, not for buildings that consume more electricity than some towns. Superconducting cables are a solution to a future version of this problem. The current version, where families in Virginia and Texas and Arizona are watching their utility bills climb because a hyperscaler moved in next door, does not get fixed by a lab test in Massachusetts.

What This Means for Everyday People

If you live near a data center or in a region where one is planned, superconducting cables are not coming to help you anytime soon. The technology is real, the timeline is long, and the power draw on your local grid is happening now. Utility rate increases driven by data center demand are already being approved in multiple states. The benefits of AI accrue to shareholders and users of cloud services. The costs accrue to the communities that host the physical infrastructure. Until that asymmetry is addressed through regulation, rate structures, or technology that actually reduces total consumption rather than just improving delivery efficiency, the people closest to the machines will continue paying the highest price for progress they did not choose., Tom’s Hardware reported

For inquiries and analysis contact laterstack@proton.me

Four companies are about to spend more on artificial intelligence infrastructure in a single year than most nations spend on their entire economies. Amazon, Alphabet, Meta, and Microsoft have collectively committed between $635 billion and $665 billion in capital expenditure for 2026, a figure that represents a 67 to 74 percent increase over the $381 billion they spent in 2025. The Big Tech AI spending spree has no precedent in corporate history, and the question it raises is deceptively simple: is this the greatest infrastructure investment since the railroads, or the greatest misallocation of capital since the dot com era?

Bloomberg called the combined figure “essentially unprecedented in modern economic history, even accounting for the telecom boom of the 1990s and the construction of railroads.” CNBC reported that total spending approaches $700 billion when factoring in additional operational expenses. Fortune noted that the sum rivals the entire gross domestic product of Sweden. These are not comparisons made for dramatic effect. They are the most accurate analogies available, and even they may understate the scale.

The Numbers Behind the Conviction

Amazon leads the pack with a staggering $200 billion commitment, a figure that sent its stock plunging more than 11 percent in extended trading before closing down 5.55 percent on the day of the announcement. CEO Andy Jassy justified the spending by citing “very high demand” for AI compute capacity. The market’s response was telling: AWS revenue grew 24 percent to $35.6 billion, the fastest growth rate in 13 quarters, and investors still punished the stock. Strong demand was not the concern. The concern was whether any level of demand could justify spending at this velocity.

Alphabet committed between $175 billion and $185 billion, a figure that stunned Wall Street analysts who had expected approximately $119.5 billion, nearly double its 2025 capital expenditure. The stock whipsawed after earnings before ultimately recovering, a pattern that suggests the market is uncertain rather than hostile. Meta announced a range of $115 billion to $135 billion, again nearly doubling its prior year. Microsoft, based on its quarterly figures, is running at approximately $145 billion annualized.

Analysts at Barclays acknowledged that infrastructure costs of this magnitude will weigh on near term profitability but argued that cloud growth combined with progress from divisions like DeepMind “start to justify” the expenditure. The operative word is “start.” That is not a ringing endorsement. It is a hedge dressed as optimism.

The Railroad Analogy and Its Limits

The comparison to railroads is instructive, though not in the way its proponents intend. The railroad boom of the 19th century did transform the American economy. It also produced spectacular failures, fraudulent financing, and decades of overcapacity. Many of the companies that built the railroads went bankrupt. The infrastructure they created was valuable. The equity they issued was often worthless. The people who profited most were those who used the railroads, not those who financed them.

The dot com parallel cuts deeper. In the late 1990s, telecommunications companies spent hundreds of billions laying fiber optic cable based on demand projections that proved roughly correct but arrived on a timeline that bankrupted the builders. WorldCom and Global Crossing constructed the future and then collapsed into it.

The question is not whether AI will be valuable. It almost certainly will be. The question is whether the companies spending $650 billion will capture sufficient returns, or whether they are building infrastructure whose value accrues primarily to the customers who use it rather than the shareholders who financed it.

This Is an Arms Race

This looks like an arms race because it is one. None of these companies can afford to stop spending. Amazon cannot let Azure capture enterprise AI workloads while AWS builds capacity. Microsoft cannot let Google’s DeepMind models run on competitors’ hardware. Meta cannot afford to rent someone else’s AI infrastructure while building its own models. The logic is not “this will definitely pay off.” The logic is “the cost of being wrong about spending is lower than the cost of being wrong about not spending.” When every competitor reaches the same conclusion, the result is a $650 billion pile of chips on the table.

The dot com comparison is useful but lands somewhere in the middle. The telecom companies that laid fiber in the 1990s were right about demand and wrong about timing. The infrastructure they built was valuable. The companies that built it went bankrupt. The same pattern could repeat. The critical difference is that Amazon, Alphabet, Meta, and Microsoft have balance sheets that can absorb years of spending before profitability breaks. WorldCom did not. These companies will likely lose money on AI infrastructure in the short term and earn it back over the long term, because the use cases are not hypothetical. Enterprise AI adoption is accelerating. The compute has to live somewhere.

The deeper strategic play that most coverage misses is how this AI infrastructure connects to the next wave. Quantum computing requires classical compute for error correction, simulation, and hybrid algorithms. The companies building the largest AI compute footprints today are also positioning themselves as the infrastructure layer for quantum computing tomorrow. Amazon’s Braket, Google’s quantum lab, Microsoft’s Azure Quantum. The $650 billion is not just an AI bet. It is a multi decade infrastructure play that extends into quantum, robotics, and whatever comes after. That does not make it wise. It makes it unavoidable.

The Labor Market Disconnect

The spending announcements arrived against a labor market backdrop that makes the scale of investment more unsettling. ADP reported that the U.S. economy added only 22,000 jobs in January, a figure far below expectations. Corporate layoffs sit at levels not seen since 2009. The pattern emerging across American industry is one where companies are simultaneously increasing capital investment in AI systems and reducing investment in human workers, a dynamic that previous analysis on this site has examined in detail.

This is not a coincidence. It is a strategy. The capital flowing into AI infrastructure is explicitly intended to automate tasks currently performed by humans. The companies spending $650 billion are not adding this capacity alongside their existing workforce. They are adding it, in significant part, as a replacement for portions of that workforce. The physical constraints gating AI progress make the buildout even more capital intensive, as data centers, power generation, and chip fabrication all require massive upfront investment before returns materialize.

The macroeconomic implications are significant. When corporations redirect hundreds of billions from labor to capital, wages deflate while asset prices inflate. Workers lose bargaining power. Shareholders capture the gains. The distribution of economic benefit shifts further toward capital owners, a trend accelerating for decades and now receiving a $650 billion accelerant.

The Sovereignty Dimension

There is a geopolitical layer to this spending that receives insufficient attention. When four American companies control the majority of the world’s AI compute infrastructure, they become instruments of American strategic power whether they intend to or not. France’s decision to ban American technology platforms from government use reflected precisely this concern: that dependence on American digital infrastructure creates vulnerabilities that sovereign nations cannot accept.

A world in which AI compute is concentrated in data centers owned by Amazon, Alphabet, Meta, and Microsoft is a world in which access to artificial intelligence is mediated by American corporate decisions and American legal frameworks. The CLOUD Act, sanctions enforcement, and export controls all apply. The $650 billion is not just a corporate investment. It is the construction of a strategic asset with implications that extend far beyond quarterly earnings.

What This Means for Everyday People

For workers, the message embedded in these capital expenditure figures is blunt. The largest and most powerful companies on earth have decided that their future involves substantially more machines and substantially fewer people. That decision has already been made. The $650 billion is the execution budget.

For consumers, the near term effect will be improved AI products and services. More compute capacity means faster models, better applications, and lower prices for AI tools.

For citizens, the longer term questions are harder. Who governs an economy in which four companies control the infrastructure that powers artificial intelligence? What happens when these systems displace workers faster than the economy can absorb them? What oversight exists for investment decisions that will reshape labor markets, energy consumption, and the distribution of economic power for decades?

These are not questions that earnings calls answer. They are questions that societies must answer, and the $650 billion clock is already ticking.

For inquiries and analysis contact laterstack@proton.me

For the first time, major corporations are saying the quiet part loud.

Amazon announced plans to reduce its corporate workforce by 16,000 employees. Pinterest cut up to 15% of its workforce. Meta’s Mark Zuckerberg declared that 2026 will be when “AI starts to dramatically change the way that we work.” Across the technology industry and beyond, companies explicitly cited artificial intelligence as a driver of layoffs that affected more than 50,000 workers in 2025 alone.

This is new. Previous waves of technology-driven displacement were discussed euphemistically. Automation. Efficiency gains. Workforce optimization. The machines were taking jobs, but companies rarely said so directly. The social contract held that corporations would not publicly attribute job losses to technology, even when technology was clearly responsible.

That contract has broken.

But before accepting the narrative that AI is now actively replacing human workers at scale, we must ask a harder question: Is it true? Are these layoffs genuinely driven by AI capabilities that now exceed human performance? Or is something else happening, something that tells us as much about corporate communications and investor relations as it does about artificial intelligence?

The Case That AI Is Real

Let us begin with the bull case, because it is not without merit.

Generative AI has advanced remarkably over the past three years. Large language models can now write code, draft marketing copy, summarize documents, conduct research, and perform dozens of tasks that previously required human labor. Tools like GitHub Copilot demonstrably increase developer productivity. Customer service chatbots handle inquiries that once required human agents. Content generation systems produce text at scales impossible for human writers.

Companies deploying these tools are reporting productivity gains. Klarna, the payments company, has stated that its AI assistant performs the work equivalent of 700 full-time customer service agents. If AI can do the work of hundreds of employees, why would companies continue paying those employees?

The logic is straightforward. Technology substitutes for labor. This has been true since the spinning jenny. AI is the latest instance of a pattern that has repeated throughout the history of capitalism.

From this perspective, the 50,000 layoffs explicitly attributed to AI represent an honest acknowledgment of technological progress. Companies are admitting what has always been true but was previously impolite to say: technology replaces workers.

The Case for Skepticism

The bear case is equally compelling.

Forrester, the technology research firm, published a report in January arguing that many companies announcing AI-related layoffs “do not have mature, vetted AI applications ready to fill those roles.” The report identifies a trend of “AI-washing,” companies attributing financially motivated cuts to future AI implementation that may or may not materialize.

Molly Kinder, a senior research fellow at the Brookings Institution, noted that saying layoffs were caused by AI is a “very investor-friendly message.” The alternative, admitting that a business is struggling or that management made poor decisions, is far less palatable to shareholders.

Consider the incentives. Wall Street loves AI narratives. Companies that credibly claim to be AI-forward receive valuation premiums. Stock prices respond positively to announcements about AI adoption. In this environment, attributing layoffs to AI efficiency gains rather than business weakness is not merely acceptable. It is strategically optimal.

The skeptical view holds that AI is being used as cover. Some portion of these layoffs would have happened regardless of AI capabilities. Economic conditions, competitive pressures, overcapacity from pandemic hiring, and strategic missteps all contribute to workforce reductions. AI provides a socially acceptable explanation that simultaneously signals technological sophistication and deflects blame from management.

The Truth Is Probably Both

Reality rarely conforms to clean narratives. The honest assessment is that both factors are at work simultaneously.

Some jobs are genuinely being replaced by AI systems that now perform tasks better, faster, or cheaper than humans. The customer service representative whose role consisted primarily of answering routine questions from a knowledge base is vulnerable. The junior copywriter producing formulaic content at volume is vulnerable. The data entry clerk transcribing information from one system to another is vulnerable.

Simultaneously, some layoffs attributed to AI are opportunistic. Companies facing pressure to reduce headcount can now cite AI as the reason rather than acknowledging overcapacity or strategic failure. The narrative is convenient. Investors reward it. The workers lose their jobs either way, but the framing matters for corporate reputation and stock price.

Distinguishing genuine AI displacement from AI-washing in any specific case is difficult. Companies do not provide the detailed productivity data that would allow external observers to verify their claims. We see the announcements. We do not see the internal analysis, if any, that preceded them.

The Policy Question

For policymakers, the uncertainty creates a dilemma. If AI is genuinely displacing workers at scale, policy responses are urgent. Workforce retraining programs, social safety net expansions, and potentially more radical interventions may be necessary to manage a transition whose speed exceeds historical precedents.

If AI displacement is overstated, different interventions are appropriate. The focus should be on counter-cyclical employment policy, supporting workers affected by economic downturns rather than technological transformation, and resisting the temptation to create programs addressing a problem that exists more in corporate communications than in labor market reality.

The policy response depends on accurate diagnosis. That diagnosis is currently impossible because companies have incentives to exaggerate AI’s role and no obligation to provide verifiable data.

A pragmatic approach acknowledges uncertainty while preparing for multiple scenarios. Invest in workforce adaptation programs that help workers transition regardless of whether the cause is AI or traditional economic displacement. Monitor labor market data for patterns that would distinguish AI-driven structural change from cyclical fluctuations. Require more transparency from companies making AI-related layoff claims, perhaps through enhanced disclosure requirements in securities filings.

The worst outcome would be either complacency (assuming AI displacement is overstated and being caught unprepared) or panic (overreacting to a narrative that serves corporate interests more than it reflects reality).

The Moral Question

Beyond policy, there is a moral question that both left and right should consider.

From the left, the concern is worker welfare. Fifty thousand people lost their jobs. Whether AI or management failure or economic conditions caused those job losses, the human impact is real. Families face disruption. Communities lose economic activity. The psychological toll of unemployment falls on individuals regardless of the cause.

From the right, the concern is honesty. If companies are citing AI as cover for ordinary business failures, they are misleading investors and the public. Market efficiency depends on accurate information. AI-washing, if widespread, distorts capital allocation and undermines the integrity of corporate communications.

Both perspectives converge on a demand for truth. Workers deserve to know why they lost their jobs. Investors deserve accurate explanations for corporate decisions. Policymakers deserve data that reflects reality rather than narratives constructed for strategic advantage.

The current situation satisfies none of these demands. Companies make claims. Workers lose jobs. The truth remains obscured by incentives that favor narrative over accuracy.

What the Data Actually Shows

The available evidence is mixed.

Challenger, Gray & Christmas, the employment consulting firm, documented that AI was cited in connection with approximately 50,000 layoffs in 2025. This represents a significant increase from previous years when AI was rarely mentioned explicitly.

However, total layoffs in the technology sector have remained elevated since the post-pandemic correction began in 2022. The pattern predates the current AI narrative. Companies hired aggressively during the pandemic, discovered they had overcapacity as growth normalized, and have been reducing headcount through multiple rounds of layoffs.

AI may be accelerating this correction. It may be providing cover for a correction that would have happened anyway. The aggregate data cannot distinguish between these possibilities.

What is notable is the change in rhetoric. Companies previously avoided explicitly blaming technology for job losses. Now they embrace it. This shift reflects changed incentives in capital markets, where AI capability is rewarded, rather than necessarily changed conditions in labor markets.

What This Means for Everyday People

For workers, the practical implications are the same regardless of whether AI displacement is real or exaggerated. The jobs are gone. The skills that secured previous employment may not secure future employment. Adaptation is required.

The strategic response for individuals is to develop capabilities that complement AI rather than compete with it. Tasks requiring judgment, creativity, interpersonal connection, and physical presence are less vulnerable than tasks that are routine, digital, and scalable. This advice would be valid even if AI were not a factor. It becomes more urgent if AI displacement proves real.

For citizens evaluating public policy, the appropriate stance is skepticism toward corporate narratives combined with preparation for genuine disruption. Companies have incentives to exaggerate AI’s impact. But the technology is real, and its capabilities are expanding. Prudent policy prepares for scenarios that may not materialize rather than assuming benign outcomes.

The 50,000 layoffs attributed to AI may be the beginning of a transformation that reshapes labor markets over the next decade. They may also be a convenient narrative that serves corporate interests while obscuring ordinary business dynamics.

The honest answer is that we do not know. What we know is that companies are now willing to say what they previously would not: that technology is taking jobs. Whether that statement is accurate remains to be determined.

For inquiries and analysis contact laterstack@proton.me

Frequently Asked Questions

Did AI really cause 50,000 layoffs?

Companies explicitly cited AI as a factor in over 50,000 layoffs in 2025, including major cuts at Amazon, Pinterest, and others. However, analysts debate whether AI actually replaced these workers or whether companies are “AI-washing,” using AI as a convenient explanation for cuts driven by overcapacity, economic conditions, or strategic failures. The truth likely involves both genuine displacement and opportunistic framing.

What is AI-washing in the context of layoffs?

AI-washing refers to companies attributing layoffs to AI capabilities and efficiency gains when the actual drivers may be unrelated to AI. According to Forrester research, many companies announcing AI-related layoffs “do not have mature, vetted AI applications ready to fill those roles.” Citing AI for layoffs is an investor-friendly message that can make ordinary business problems appear like technological progress.

What jobs are most at risk from AI?

Tasks that are routine, digital, and scalable are most vulnerable to AI displacement: customer service handling standard inquiries, formulaic content generation, data entry and transcription, and basic research and summarization. Jobs requiring judgment, creativity, interpersonal skills, and physical presence are less vulnerable. However, AI capabilities are expanding, and the boundary between vulnerable and protected work continues to shift.

There is a moment in the life of any dependency when the dependent party recognizes, suddenly and with clarity, the nature of the relationship. For European governments, that moment arrived when Microsoft canceled Karim Khan’s email.

Khan is the chief prosecutor of the International Criminal Court. In late 2025, the Trump administration imposed sanctions on him personally in response to ICC investigations. Microsoft, complying with U.S. sanctions law, terminated Khan’s access to his Microsoft email account. The prosecutor of an international tribunal established by treaty, investigating alleged war crimes, lost his email because an American company decided he should.

The implications rippled through foreign ministries, intelligence agencies, and government IT departments across Europe. If Microsoft could cancel the ICC prosecutor’s email at the command of the American executive branch, what else could American technology companies do? What communications could they intercept? What services could they terminate? What “kill switches” existed in the infrastructure that governments had come to depend upon?

Last week, France announced that it would ban Zoom and Microsoft Teams for public officials, replacing them with a domestically developed platform called Visio. The announcement is the most visible manifestation of a deeper shift: democratic nations are reconsidering their dependence on American technology platforms in ways that would have seemed paranoid five years ago and now seem prudent.

The Timeline of a Reckoning

The French announcement follows years of escalating concern.

In 2020, the European Court of Justice struck down Privacy Shield, the framework governing transatlantic data transfers, ruling that American surveillance law provided insufficient protection for European citizens’ data. The decision, known as Schrems II, created legal uncertainty for every European organization using American cloud services.

In 2022, the European Commission proposed the Data Act, establishing rules for data sharing and limiting international data transfers. The regulation reflected growing concern that European data processed by American companies was subject to American jurisdiction in ways that European governments could not control.

In 2025, the sanctions on the ICC prosecutor demonstrated that these concerns were not theoretical. American companies would comply with American government demands, regardless of the impact on foreign customers. The “kill switch” was real.

French Minister Delegate David Amiel stated the rationale plainly: maintaining classified exchanges and strategic information on external infrastructure poses an unacceptable national security risk. The question was not whether American platforms were convenient or cost-effective. The question was whether French government communications should be subject to the potential control of a foreign power.

The answer was no.

The Visio Alternative

Visio is not new. The platform has been in testing for approximately a year and already supports around 40,000 users within French government networks. It is hosted on Outscale’s sovereign cloud, a subsidiary of Dassault Systèmes, ensuring that all user data remains within French jurisdiction. The encryption keys are French. The servers are French. The legal framework governing access is French.

France announced that it will roll out Visio across all government departments by 2027. Leading adopters include the CNRS (French National Centre for Scientific Research), which will replace 34,000 Zoom licenses by March 2026, along with the French National Health Insurance Fund, the Directorate General of Public Finances, and the Ministry of Armed Forces.

The economic case is straightforward. France projects €1 million in annual savings for every 100,000 users migrating from licensed American solutions. For a government employing millions, the savings are substantial. But the primary motivation is not financial. It is strategic.

The Broader European Context

France is not acting alone.

The European Union has been constructing a regulatory framework to assert control over digital infrastructure for years. The Digital Markets Act imposes obligations on large platforms designated as “gatekeepers.” The Digital Services Act establishes content moderation requirements. The GDPR created the world’s most stringent data protection regime.

These regulations represent an attempt to exercise sovereignty over digital space without building alternative infrastructure. They regulate American platforms operating in Europe rather than replacing them. The French decision represents a different approach: building domestic alternatives and migrating away from American platforms entirely, at least for government functions.

Other European nations are watching. Germany has expressed similar concerns about digital sovereignty. The Netherlands has conducted assessments of risks associated with American cloud providers. The UK, despite its closer alignment with the United States, has begun examining the security implications of platform dependence.

Beyond Europe, the backlash is global. India has endorsed “Made in India” alternatives to American platforms. Australia has examined social media platform risks. International organizations have begun diversifying their technology providers to avoid the vulnerability that the ICC prosecutor experienced.

The American Perspective

From an American perspective, this development is strategically problematic but legally predictable.

American technology companies operate under American law. When the American government imposes sanctions, American companies must comply or face criminal penalties. Microsoft did not choose to cancel Khan’s email because it wanted to. It complied with legal requirements imposed by the executive branch.

The same logic applies to surveillance. American intelligence agencies have broad authorities to compel American companies to provide data, particularly data involving foreign nationals. The CLOUD Act of 2018 clarified that American companies must provide data to American law enforcement regardless of where the data is stored. European data on American platforms is American data for legal purposes.

This is not a bug in American law. It is a feature. American policymakers deliberately constructed a legal framework that extends American jurisdiction to data processed by American companies worldwide. The framework serves American intelligence and law enforcement interests.

The consequence is that foreign governments now recognize what American law always implied: using American platforms means accepting American jurisdiction. For routine commercial purposes, this may be acceptable. For sensitive government communications, it increasingly is not.

The Technology Nationalism Question

Critics will characterize the French decision as technology nationalism, a retreat from the globalized digital economy that has generated enormous value over the past decades.

The critique has merit but misses the strategic context. Technology is not neutral. The platforms through which governments communicate, store data, and conduct operations are infrastructure as critical as roads, ports, and power plants. No serious nation would allow a foreign power to control its physical infrastructure. The question is why digital infrastructure should be different.

The honest answer is that it should not be different, and nations are belatedly recognizing this reality.

The American government understood this long before European governments did. American defense and intelligence agencies do not use foreign platforms for sensitive communications. American sanctions law reflects an understanding that technology control is a form of power. American policy has consistently sought to maintain American dominance over global digital infrastructure.

European governments are now applying the same logic in reverse. If technology control is power, then dependence on American technology is subordination. Strategic autonomy requires technological autonomy, at least for critical government functions.

The Market Implications

For American technology companies, the French decision represents the beginning of a trend that will accelerate. Government contracts in democratic nations outside the United States will increasingly require sovereign infrastructure. The addressable market for American platforms in sensitive applications is shrinking.

This creates opportunities for European technology companies that can provide sovereign alternatives. It creates opportunities for American companies willing to establish genuinely independent subsidiaries operating under local legal frameworks. It creates complexity for multinational organizations that must now navigate diverging technology ecosystems.

The fragmentation of the global digital market along national and regional lines was always a possibility. American platforms benefited from a period of relatively uncontested global expansion. That period is ending. The kill switch that canceled Khan’s email demonstrated the risks of dependence, and nations are responding rationally by reducing that dependence.

What This Means for Everyday People

For ordinary citizens in France and elsewhere, the shift to sovereign platforms will be largely invisible. Visio will replace Zoom in government offices. Most citizens never attended those meetings anyway.

The deeper implications are systemic. The fragmentation of the global digital economy into sovereign blocs will reduce efficiency and increase costs. Interoperability between national systems will be imperfect. The seamless global communication that characterized the internet’s first decades will become more constrained.

Whether this is a price worth paying depends on values that reasonable people weigh differently. Privacy and sovereignty have costs. Convenience and efficiency have costs. The Khan incident forced a reckoning with tradeoffs that had been deferred, and nations are making choices about which costs they are willing to bear.

France has chosen sovereignty over convenience. Other nations will make their own calculations. The era of unquestioned American dominance over global digital infrastructure is ending. What replaces it remains to be determined.

For inquiries and analysis contact laterstack@proton.me

Frequently Asked Questions

Why did France ban Zoom and Microsoft Teams for government use?

France announced it will phase out American video conferencing platforms for public officials due to security concerns about data sovereignty and potential foreign government access to communications. The decision follows the 2025 incident where Microsoft canceled the ICC prosecutor’s email account in compliance with U.S. sanctions, demonstrating that American companies will comply with American government demands regardless of impact on foreign customers.

What is Visio and how does it differ from American platforms?

Visio is a French-developed video conferencing platform hosted on Outscale’s sovereign cloud, a subsidiary of Dassault Systèmes. Unlike American platforms, Visio keeps all data, encryption keys, and traffic within French jurisdiction and under French legal frameworks. The platform has been in testing for a year and already serves 40,000 government users.

Will other countries follow France’s example?

Multiple countries and international organizations are reconsidering their dependence on American technology platforms. Germany, the Netherlands, India, and Australia have all examined digital sovereignty concerns. The International Criminal Court itself dropped Microsoft as a service provider following the sanctions incident. The trend toward sovereign digital infrastructure is accelerating across democratic nations.

Microsoft posted $81.3 billion in revenue and $4.14 in non-GAAP diluted EPS for Q2 FY2026 on January 28. Both numbers beat Wall Street expectations — analysts had called for $80.27 billion and $3.97 respectively. The stock plunged roughly 10% the next day, erasing $357 billion in market value. The second-largest single-day loss in U.S. stock market history.

The message from investors was blunt: beating earnings does not matter if you cannot prove the AI money machine actually works.

Azure Growth Hits a Wall of Expectations

Azure revenue growth slowed to 39%, down from 40% the prior quarter and below the institutional “whisper numbers” that expected AI tailwinds to accelerate growth. CFO Amy Hood guided Q3 Azure growth to 37%-38%, signaling further deceleration.

Capital expenditures surged 66% to $37.5 billion — well above the $34.31 billion analysts expected, and putting Microsoft on a $148 billion annual run rate for AI infrastructure spending. Hood confirmed two-thirds went to short-lived assets like CPUs and GPUs — hardware that depreciates fast and requires constant replacement.

Hood also revealed something telling: if Microsoft had allocated all GPUs that came online in Q1 and Q2 exclusively to Azure customers, “the KPI would have been over 40.” Translation — the slowdown is partly a strategic choice. Microsoft is reserving compute capacity for Copilot and its partnership with OpenAI rather than selling it to enterprise customers.

Meta Showed Receipts. Microsoft Showed a Bill.

The contrast was brutal. On the same reporting day, Meta Platforms posted massive AI spending and its stock jumped 8%. The difference: Meta demonstrated that AI spending was directly fueling record advertising revenue. Tangible receipts. Microsoft is still asking Wall Street to trust the process.

Barclays analyst Raimo Lenschow noted the company “will not really accelerate Azure further from here, due to the law of large numbers and extra capacity being used for its own, higher-margin, first-party offerings.” Wedbush’s Dan Ives called 2026 “the inflection year for AI and MSFT.” Bernstein analyst Mark Moerdler suggested management “made a cognizant decision to focus on what is best for the company long term rather than driving the stock up this quarter.”

Laterstack Editorial Take

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life — across class, industry, and influence. Microsoft lost $357 billion in a single day — not because the business is failing, but because Wall Street is starting to ask the question Silicon Valley does not want to answer: where is the return? The market is not punishing AI investment. It is punishing AI investment without proof of monetization. That distinction matters for every company in the AI infrastructure race, and for every lawmaker weighing subsidies and tax incentives for data center buildouts. The “spend now, monetize later” era has an expiration date, and the clock just got louder.

What This Means for Everyday People

If you work at a company paying for Microsoft 365 or Azure, watch closely. Microsoft is prioritizing internal AI development over cloud capacity for paying customers — which could mean slower feature rollouts, capacity constraints, or price increases as the company recoups its investment. The startup ecosystem feels it too — when the biggest cloud provider signals that AI infrastructure costs are accelerating faster than revenue, smaller companies building on that infrastructure absorb the pressure first.

For anyone holding Microsoft stock in a 401(k) or index fund, you watched real money evaporate not because the company failed, but because it spent aggressively on a future that has not materialized yet. The $357 billion wipeout is a stress test for the entire “spend now, monetize later” thesis driving Big Tech AI investment.

The Bottom Line

Microsoft’s numbers were fine. The problem is that “fine” does not justify $37.5 billion quarters. Wall Street is not punishing the results. It is punishing the gap between what Microsoft is spending and what it can prove. Until Copilot and Azure AI show enterprise adoption at scale, every earnings call is a referendum on whether the biggest AI bet in corporate history will pay off.

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Microsoft has released another Copilot ad, this time holiday-themed, featuring users asking the AI to assist with lighting, cooking, decorations, and more. The ad is festive and cinematic, showing smart lights pulsing to music and toy production “delays” blamed on elves drinking too much cocoa. But testing the prompts from the ad reveals a different story: most of the Copilot actions do not work as advertised.

In the spot, a homeowner asks Copilot to sync holiday lights to music using a website called Relecloud. On screen, lights pulse to a song. The issue is Relecloud is not a real company, but a fictional example Microsoft has used in past case studies. When tested in real applications like Philips Hue, Copilot can identify some buttons correctly but often hallucinates elements that do not exist and misguides users.

Other ad scenarios include scaling a recipe, following IKEA assembly instructions, and checking HOA rules for decorations. Copilot often gives incomplete calculations, mislabels steps or ingredients, and defers judgment to the user rather than providing actionable guidance. In some cases it claims to highlight buttons or text on screen when nothing is actually there.

Even when shown real apps, Copilot struggles to reliably complete tasks. Recipe scaling only partially works, assembly instructions are misread, and lighting automation frequently fails to perform as intended. The ad’s holiday cheer masks the reality that these AI features are far from ready for everyday tasks.

Microsoft insists all Copilot responses in the ad are real responses generated by the AI at the time, shortened for brevity. Still, the disconnect between ad depiction and actual functionality points to a broader pattern: the promise of seamless AI assistance often outpaces what is technically achievable.

For consumers, this serves as a reminder that technology marketing can exaggerate capabilities, and even widely used AI assistants may not deliver on advertised promises. Understanding these gaps helps set realistic expectations for home automation, AI tools, and digital assistants.

For questions, tips, or inquiries, email us at hello@laterstack.com.

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id Software, the studio behind Doom, has voted in favor of forming a “wall-to-wall” union, a structure that covers every employee across the company. While not unanimous, a majority supported the unionization effort, signaling a notable shift in the video game industry’s labor landscape.

The new union will partner with the Communications Workers of America (CWA), which has previously worked with parent company ZeniMax on union initiatives. Microsoft, ZeniMax’s owner, has already recognized the effort, following a labor neutrality agreement made last year with CWA and ZeniMax employees.

“The wall-to-wall organizing effort at id Software was much needed; it’s incredibly important that developers across the industry unite to push back on all the unilateral workplace changes that are being handed down from industry executives,” said Andrew Willis, id Software producer and CWA committee member.

Key priorities for the union include protecting remote work policies. “Remote work isn’t a perk. It’s a necessity for our health, our families, and our access needs,” said Chris Hays, Lead Services Programmer at id Software. Hays also emphasized the importance of worker protections regarding the “responsible use of AI” in game development.

Workers began organizing roughly 18 months ago, a process accelerated by the mid-year closure of several Bethesda studios by Microsoft. CWA Local 6215 President Ron Swaggerty expressed optimism about negotiations, stating, “We look forward to sitting across the table from Microsoft to negotiate a contract that reflects the skill, creativity, and dedication these workers bring to every project.”

id Software’s latest title, Doom: The Dark Ages, recently won an accessibility award at The Game Awards, highlighting the studio’s commitment to inclusive gaming. The unionization marks a significant moment for the gaming industry, signaling growing momentum for labor rights in a sector historically resistant to organized labor.

This move is part of a broader trend in tech and gaming, where employees increasingly challenge top-down executive decisions and advocate for transparency, inclusion, and worker protections.

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For inquiries, tips, or submissions: hello@laterstack.com

Quantum technology has always been hard to explain. It deals with particles so small and strange that even experts struggle to describe what is happening. Yet behind the complexity lies a truth that is starting to surface. Quantum might soon become as transformative as artificial intelligence and possibly even bigger.

The field that once lived only in theory is now moving into hardware. Companies like Microsoft, Google, and Quantinuum are racing to build quantum computers, sensors, and communication systems. Microsoft’s latest Majorana chip is one example, designed to make quantum systems more stable and less error-prone.

For years, AI dominated the headlines. It was easy to understand, easy to use, and quick to deploy. Quantum, on the other hand, required labs, lasers, and near absolute-zero temperatures. But things are changing fast.

Researchers are finding ways to build qubits, the core building blocks of quantum computers with synthetic diamonds, allowing them to work at higher temperatures and with greater efficiency. Companies like Element Six, a subsidiary of De Beers, are now creating industrial-grade quantum diamonds in partnership with Amazon Web Services, paving the way for more accessible hardware.

The potential scale is enormous. Analysts at McKinsey project the quantum sector could reach 97 billion dollars by 2035. That is still smaller than AI’s trillion-dollar forecast, but the real measure is not in money, it is in impact.

Professor Sir Peter Knight of Imperial College London describes it simply: “Problems that would take the age of the universe to solve could one day be computed in seconds.”

That kind of power could change everything from drug discovery to energy systems. In healthcare, quantum computers could map endless combinations of molecules to design precise, personalized medicines. Google’s new Willow chip already demonstrated how a quantum processor could solve a problem in five minutes that would take the world’s fastest supercomputer ten septillion years.

The same capability could transform global industries. Airbus is testing quantum algorithms to load cargo more efficiently, saving thousands of kilos of fuel. The UK National Grid is investing in quantum models to optimize how power flows across thousands of generators. Even navigation could change, researchers at Imperial College London recently tested a quantum compass that works underground where GPS fails.

These breakthroughs are not limited to science. They also touch national security. Experts warn that quantum systems will eventually break today’s encryption standards, unlocking everything from government secrets to personal data. This looming moment is known as Q-day, when a fully operational quantum computer becomes powerful enough to decrypt traditional systems.

Governments and tech firms are already preparing. Apple and Signal have rolled out post-quantum encryption keys designed to withstand these future attacks. But older encrypted data remains vulnerable. Intelligence agencies are already harvesting and storing data they cannot yet read, waiting for the day quantum decryption becomes possible.

Professor Alan Woodward of the University of Surrey calls it “harvest now, decrypt later.” Once Q-day arrives, everything encrypted by older systems could become transparent overnight.

Still, many experts believe quantum’s benefits will outweigh its risks. Quantum sensors already enable more precise brain scans, helping doctors study movement disorders and childhood epilepsy without keeping patients still. In transportation, quantum navigation could keep airplanes and subways connected even when satellite signals drop.

As Rajeeb Hazra, CEO of Quantinuum, told the BBC, “We as consumers will touch the impacts of quantum computing in almost every walk of our lives. It could be as big as AI — if not bigger.”

The question is no longer whether quantum works. It is how soon we will be ready for it. The technology that once lived in cold labs is warming up fast. And when it arrives, the world may have to rethink not only how we compute, but how we secure, measure, and even understand information itself.

Read the original report by Zoe Kleinman at BBC.

For more Laterstack analysis, explore Quantinuum Helios quantum computer could bring quantum breakthroughs closer to real life and NVIDIA just built the bridge between quantum and classical computing.