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.
Two things are happening in Washington at the same time. AI policy is being written. And the money spent to influence it is setting records. Whether those two facts are related is left, as always, to the reader and the disclosures, both of which are public.
Last year, AI lobbying pulled in about $130 million, and one in four federal lobbyists now works the issue, up from one in nine in 2023. Those are filings, not accusations. What they describe is a policy field being shaped at the same moment the spending around it climbed to its highest point on record.
The money, by the numbers
In the first quarter of 2026 alone, eleven top tech firms spent $20 million, about $226,000 a day. Meta led at $7.1 million, with Amazon and Google behind it. The AI labs posted their biggest lobbying quarters ever, Anthropic at $1.6 million and OpenAI at $1 million, with Anthropic outspending the company it is racing. Across 2025, lobbyists filed more than 3,500 reports mentioning AI for 774 different organizations, up 400 to 500 percent since 2020. Industries tend to spend at that pace when something is being decided.
What the spending is near
The largest single item on the table is one provision in a 269-page draft. The Great American AI Act, released June 4 by Representatives Jay Obernolte and Lori Trahan, pairs new federal safety and transparency rules for AI developers with a three-year freeze on state laws that regulate how AI models are built. The freeze is not a new idea. The industry has sought a version of it since the Senate voted 99 to 1 to strip a ten-year moratorium out of last year’s budget bill. The Business Software Alliance supports the draft; Public Citizen and the AFL-CIO oppose it. The line between those two camps is worth reading slowly.
Whose problem it solves
The structure rewards a second look. Fifty state legislatures writing fifty different AI rules is the outcome the industry has spent years arguing against. One federal standard, shaped with its input and with state rules paused, is the outcome it has spent years arguing for. The safety obligations and the preemption arrive in the same bill. Which half of that bundle the spending was tracking is a question the disclosures let a reader work out without much help.
It is also the third entry in a sequence. The industry helped shape the text of the federal AI executive order, and the people who write AI policy keep moving into the labs. Money, text, and personnel have a way of pointing the same direction.
What sits closest to home
A freeze this narrow leaves most of what people deal with day to day untouched. State rules on AI in hiring, deepfakes, child chatbot safety, and algorithmic pricing govern how AI is deployed and used, and the draft leaves all of that with the states, alongside civil rights and consumer protection. What it pauses is the layer underneath, the state rules on how the models themselves are built. That layer is invisible to most readers and central to the companies, which is part of why the fight over it draws the spending it does.
The room where it happens
The people writing the rules face a quieter version of the same pressure. State legislators and attorneys general lose their lane the moment preemption passes. Federal staffers drafting the standards sit across the table from the best-funded lobby in the city, and often from former colleagues now on its payroll. Who remains in that room to argue the other side is a fair thing to ask, and an easy thing to overlook.
Where it stands as of late June
The Great American AI Act is a draft, not a law. It has not been introduced or voted on. The next AI lobbying disclosures land in the fall and will almost certainly run higher. Three things are worth watching: whether the Act is formally introduced, which members sign on after the money moves, and how the spending shifts around both.
None of this is illegal. Every dollar is reported, every meeting is logged, every bill is public. That is exactly why it rewards a close read. The record is sitting in the open, waiting for anyone willing to follow it from the check to the clause.
Correction, June 26, 2026: An earlier version of this piece said state protections on AI in hiring, deepfakes, child chatbot safety, and algorithmic pricing could be frozen by the three-year preemption. The Great American AI Act discussion draft preempts only state laws that regulate how AI models are developed, and it expressly preserves state authority over how AI is deployed and used, including those areas. The passage has been corrected.
FAQ
How much is spent on AI lobbying?
In 2025, lobbyists reported roughly $130 million for AI-related work, and one in four federal lobbyists now works on AI, up from one in nine in 2023. In Q1 2026, eleven top tech firms spent $20 million, about $226,000 a day.
What is the Great American AI Act?
A June 2026 bipartisan draft from Representatives Jay Obernolte and Lori Trahan that pairs federal AI safety rules with a three-year preemption of state laws governing how AI models are built. As of late June 2026 it is a draft, not law.
Who benefits from AI preemption?
The largest AI developers and platforms, who would face one federal standard instead of dozens of state laws. Civil-society and labor groups, from Public Citizen to the AFL-CIO, oppose it.
What does it mean for consumers?
The draft is narrower than it first sounds. State protections on how AI is used, including hiring, deepfakes, and child safety, stay with the states. The three-year freeze applies only to state laws that regulate how AI models are built, with a federal safety and transparency framework put in their place.
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Dean Ball helped write the federal government’s AI rulebook. In July, he starts work at OpenAI. That one sentence is the AI policy revolving door in miniature, and the door is spinning in a clear direction.
Ball served as senior policy adviser for AI and emerging technology at the White House Office of Science and Technology Policy, where he was a primary author of the administration’s AI Action Plan, the document that set federal expectations on chip exports, AI safety, and how Washington deals with the companies building frontier models. On July 6 he becomes head of a new OpenAI team called Strategic Futures, reporting to chief strategy officer Jason Kwon and working on catastrophic risk, recursive self-improvement, labor-market effects, and the relationship between frontier labs and governments. He keeps a non-resident fellowship at the Foundation for American Innovation. The same week, Noam Shazeer, who co-wrote the 2017 paper that made modern AI possible, left Google for OpenAI. One of those moves is talent. The other is governance.
OpenAI did not respond to a request for comment.
Here is the part worth sitting with: the AI policy revolving door is not new. The door between Washington and industry has been turning for decades, and it turns in both parties. During the Obama years, the Tech Transparency Project counted 258 revolving-door moves between Google and the federal government. The Pentagon version is older and larger, with hundreds of senior defense officials cycling into contractor boardrooms, a pattern the Project On Government Oversight has tracked for years. When Biden staffed his administration, advisers openly described technology firms as the new Goldman Sachs, the way that bank once seeded every Treasury. We covered the policy-text version of this in how the AI executive order got written, and the thinning line between state and company in Britain’s sovereign AI push. Ball is the AI era’s turn of a very old wheel.
That history is also Ball’s defense, and it is a fair one. Government needs people who actually understand the technology, and you do not get that understanding without moving talent in and out of the field. The door has always swung both ways. Someone who helped write a framework is not a regulator signing off on OpenAI’s compliance, and keeping a public fellowship is more transparency than most bother with. The distinctions are real.
The mistake is hunting for the villain. There isn’t one, and that is the whole problem. Power has stopped needing corruption now that it can simply hire the referee. The defense industry took generations to perfect this move, Wall Street took decades, and AI ran the same play in about three years, in the open, announced over press releases. What we are watching is a narrow class of people learning to write the rules and own the upside inside the same career, and calling the combination expertise. The public was never at that table. It only gets the bill.
For everyone outside this world, the takeaway is plain. The AI policy revolving door is why the rules about the AI in your bank, your hospital, and your benefits keep getting written by a circle of people who end up at the companies those rules cover. No one has to break anything for the public to lose its seat at the table, and that is a harder problem to fix than corruption, because nothing illegal ever happens.
Watch who moves next. The names leaving government for the labs are a better map of where AI policy is actually heading than anything published in the Federal Register.
FAQ
Who is Dean Ball?
Dean Ball was the senior policy adviser for AI and emerging technology at the White House Office of Science and Technology Policy and a primary author of the administration’s AI Action Plan. In July 2026 he joins OpenAI to lead a new team called Strategic Futures.
What is the AI policy revolving door?
The AI policy revolving door is the movement of people between the government roles that write AI rules and the companies those rules govern. The pattern is not unique to AI. It has long run between the Pentagon and defense contractors, and between the Treasury and Wall Street.
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Update, July 2026: This piece has been updated to reflect the AI Hardware Plan that Liz Kendall formally launched at London Tech Week on 8 June, and the first Sovereign AI Fund allocations announced on 16 April.
Liz Kendall used London Tech Week in June to formally launch a national AI hardware strategy with one feature the rest of the European pack has not put on the table. The British government plans to buy sovereign AI chips directly from British companies. Not just fund them, not just provide tax credits, be the first customer for what they build.
The centerpiece is a £1.1 billion AI Hardware Plan, built around a £750 million national supercomputer at the University of Edinburgh, due in 2030. Of that £750 million, £400 million is set aside for chips, with £150 million earmarked to buy next-generation inference chips from British firms this summer and £250 million for more specialised silicon as the technology matures. Around it sits a £120 million hardware innovation programme, a £45 million skills package, and a £150 million British Business Bank fund run with Playground Global. That plan stacks on top of the separate £500 million Sovereign AI Fund, which made its first allocations in April, seven companies led by an equity stake in Callosum plus compute access for six others.
The market thesis
Kendall’s office cites a global AI chip market growing at 30 percent annually, expected to clear one trillion dollars by the early 2030s. Capture five percent of that and Britain gets fifty billion dollars in revenue and tens of thousands of high-wage tech jobs that currently leak to the United States. Five percent of the AI chip market is a wildly optimistic number for a country that does not own a leading fab and has been losing senior design talent to California for a decade. The bet is on doing what the UK has historically done well, chip design, while keeping that talent home long enough to build something out of it. Beating TSMC was never the plan.
The market-share thesis sits inside a sharper concentration argument that Kendall has been making in public for months. At the Royal United Services Institute in late April, she put a number on the dependency, five companies now control roughly 70 percent of global AI compute, up from about 60 percent a year ago. The sovereign play is meant to push back against that concentration before it locks in. Whether a billion pounds is enough to dent a market dominated by NVIDIA, AMD, and the three US hyperscaler custom-silicon programs is the question the policy has not yet answered.
The advanced market commitment is the strongest piece of the strategy because it answers the founder’s most basic question. If a British AI hardware startup ships a working chip, the British government will buy it. That removes the early-stage commercial risk that has historically forced UK startups to either sell to a US acquirer or relocate to access US government and enterprise demand. It is the same playbook Operation Warp Speed used for vaccines, scaled down and pointed at semiconductors. The procurement instrument is also live in actual gov.uk tender records, the AIRR Expansion AI Cloud Compute procurement (notice ocds-h6vhtk-05a227) is the £250 million piece, running June 2026 through March 2029 with a one-year extension option, structured as a two-stage competitive under the Crown Commercial Service Technology Services 4 framework.
The contradiction at the center
The hardest part of the UK strategy is the one nobody at DSIT will say out loud. The same government that wants sovereign AI chips has also welcomed Stargate UK, the OpenAI partnership announced earlier this year, which depends on US-controlled compute infrastructure. The AI Growth Zone has £28.2 billion in private commitments, with NVIDIA’s £11 billion AI factory pledge as the headline number. The British government is simultaneously trying to build sovereign capability and host the US-led hyperscale expansion. Those two strategies can co-exist for a while. They start fighting when the sovereign startups need GPU allocation that NVIDIA is shipping into the Stargate facility instead.
The Stargate UK counterparty is Nscale, a London-incorporated AI infrastructure startup that has assembled a particularly fast funding stack. Companies House records show six Nscale UK subsidiaries spun up between May 2024 and September 2025. The parent raised $1.1 billion in Series B in September 2025 and a $2 billion Series C described as the largest European technology investment on record. Nscale’s investor list includes NVIDIA, Dell, Fidelity, Point72, and T.Capital. A separate $14 billion Microsoft deal commits Nscale to deploying NVIDIA GPUs across the US and Europe. The company is planning a 2026 IPO. So one company at the center of the Stargate UK build is itself a UK-incorporated entity, which complicates the simple sovereign-versus-foreign frame, but its investor base, customer base, and supply chain all flow through the same US hyperscaler ecosystem the sovereign strategy is supposed to balance against.
Compare the UK approach to what the US and EU are doing. The US went the direct-subsidy route with the CHIPS Act, fifty-two billion dollars in tax credits and grants tied to domestic fab construction. The European Commission’s Tech Sovereignty Package, which dropped earlier this month, leans on regulatory frameworks and pan-EU coordination through the Cloud and AI Development Act plus Chips Act II. The UK, no longer constrained by EU industrial policy mechanisms, can use procurement and direct equity stakes more aggressively. Whether that flexibility translates into faster results depends on whether the £500 million Sovereign AI Fund actually moves capital into UK startups before they sell to US acquirers. The fund made its first allocations in April, seven companies led by an equity stake in Callosum plus compute access for six others, though most of the £500 million remains uncommitted and the £150 million inference-chip purchase promised for this summer has not yet named a recipient.
The harder structural question is whether chip design without chip manufacturing is a sovereign capability or a marketing claim. Arm sells its designs to companies that fab in Taiwan. If the UK builds the next generation of AI design firms and they all fab at TSMC or Samsung, the British strategy has produced excellent IP and zero control of the supply chain that matters during a Taiwan crisis. The US CHIPS Act made the opposite bet, fifty-two billion dollars to bring fab capacity onshore even at the cost of slower returns. The UK is funding designers and hoping the fabs follow. The DSIT plan acknowledges this gap implicitly by funding the semiconductor research infrastructure. Whether that grows into actual fab capacity over a decade is the open question.
What skeptics will say
Skeptics will note that the British semiconductor strategy has been “about to launch” for the last three Conservative and Labour governments. The 2022 National Semiconductor Strategy promised £1 billion over ten years. Most of that money has not arrived. The current Labour government’s commitment is on a faster timeline but still depends on the next spending review surviving the next budget fight. If the chancellor cuts DSIT funding in October, the advanced market commitment shrinks and the sovereign fund slows. None of these announcements come with statutory guarantees. They are political commitments that can be reversed by political shifts.
The harder counter is that the UK is making this play after the window has narrowed. NVIDIA dominates AI training silicon. AMD is the credible second source. Custom silicon from Google, Amazon, and Meta has eaten the inference market. The remaining design real estate is in edge AI, specialized inference, and novel architectures. Those are exactly the categories where UK firms have real strength, Arm’s neural processing unit work for edge inference, Graphcore’s IPU pivot, and a cluster of smaller startups building reconfigurable AI accelerators. They are also niche markets. Capturing five percent of a one trillion dollar market is not the same as capturing five percent across all chip categories. The math gets harder when the categories where the UK has real talent are not where most of the market value sits.
What this means for everyday people
For everyone outside the UK, the development matters because every other mid-sized power is watching. Canada, Germany, France, Japan, and South Korea are all running variants of this same play. Sovereign AI, sovereign chips, sovereign compute. China has been doing this for a decade through Made in China 2025 and SMIC, with the difference being scale, China’s chip industry got hundreds of billions and a state-directed supply chain. The UK move tells the middle powers what the procurement instrument looks like when actually written into a strategy document by a country that does not have China’s resources. Expect to see the same advanced market commitment language show up in Berlin and Ottawa within twelve months.
The commitment to advancing human technology and quality of life is real here, and sovereign chip capability is a legitimate part of it. The Stargate contradiction is what to watch though, the same government funding sovereign British startups is also hosting an OpenAI build that depends on NVIDIA hardware those startups will eventually need to compete with. What makes the move matter beyond Britain is the procurement instrument itself, Canada, Germany, France, Japan, and South Korea are all running variants of this play, and London is the first to put the government’s checkbook at the center of it.
What happens next
London Tech Week has come and gone, and the plan is now formal. Three things still decide whether it becomes real industrial policy rather than a thesis, whether the £500 million Sovereign AI Fund moves beyond its first handful of allocations, whether the Treasury commits to multi-year funding stability beyond the next spending review, and whether US-UK trade negotiations on chip export controls force the strategy to bend toward Washington’s interests.
Requests for comment
Laterstack has reached out to the UK Department for Science, Innovation and Technology for comment and will update this story if they respond.
The phone call came before the signing ceremony.
An earlier draft of Trump’s June 2 AI cybersecurity executive order would have given the federal government 90 days to review new frontier models before public release. David Sacks, Elon Musk, and Mark Zuckerberg called the White House to kill it. Sacks blessed a 30-day window, and the order moved forward on those terms. The signing went ahead Tuesday. Treasury Secretary Scott Bessent now has 30 days to stand up the AI cybersecurity clearinghouse the order created.
That was day one.
The 90-day version that died
The 90-day review was the headline restraint in the original draft. Three phone calls killed it. The replacement is voluntary and runs 30 days, and the class of “covered frontier models” subject to even that review will be defined through a classified benchmarking process at NSA and CISA. Companies will not know in advance whose models trigger it. The voluntary structure matters because there is no enforcement mechanism if a company decides its next release falls outside the covered class.
The order also instructs Treasury, Homeland Security, Defense, and the NSC to consult on the clearinghouse design. The Q1 federal lobbying record shows the companies were already inside that conversation. Six firms (Alphabet, Meta, Microsoft, Nvidia, Anthropic, and OpenAI) collectively employed 307 federal lobbyists in the first three months of the year. They reported $20 million in combined federal lobbying spend.
Altman went to the Hill the next day
Wednesday, Sam Altman flew to Washington. He met with White House staff, House Speaker Mike Johnson, Minority Leader Hakeem Jeffries, Senate Minority Leader Chuck Schumer, and Senator Bernie Sanders, whose draft plan would take half the equity of every frontier AI lab and channel it into a sovereign wealth fund. OpenAI used the trip to release what the company calls a blueprint for a durable federal AI framework. The single line in the blueprint that mattered to Altman was the call for a larger OpenAI role in the Commerce Department’s Center for AI Standards and Innovation. CAISI is the office that will write the safety standards every frontier developer has to publish.
Twenty-four hours after Altman asked for a seat in CAISI, Congress moved.
The bipartisan draft
Thursday, Representatives Jay Obernolte and Lori Trahan introduced “The Great American Artificial Intelligence Act.” Their co-sponsors include two more Republicans and two more Democrats. The draft preempts state laws regulating AI model development for three years. It requires any frontier developer with more than $500 million in annual revenue to publish a safety framework and submit to semi-annual third-party audits. And it codifies CAISI into statute, with $100 million in annual funding from 2027 through 2029. Exactly the office Altman lobbied to expand the day before.
The state-preemption clause is the part that travels furthest. As of this spring, 45 states had AI bills in motion. Arizona’s 2025 AI consumer-protection laws were already on a federal preemption track under Trump’s spring framework, as Laterstack reported in April. The 1,500 state-level AI bills the country has watched accumulate since 2024 now sit on a three-year clock, assuming the draft becomes law.
Anthropic, the other major frontier lab, spent $1.6 million on federal lobbying in Q1, up from $360,000 in the same quarter last year. OpenAI spent $1 million. Both record their largest-ever quarterly outlays in the disclosures.
Inside the room
Look past the 30-day window. The room where it got drafted is where AI policy actually gets made. Access to that room is measured in millions of dollars per quarter and in the willingness of senior executives to phone a White House on a Sunday. The companies in the room change administration to administration. The Biden White House’s October 2023 AI executive order routed industry consultations through NIST and OSTP. This one routes them through Treasury, NSA, and CISA. Different agencies. Same companies. Same room. The faces and the company logos rotate. The mechanism does not.
This is what makes the EO and the Obernolte-Trahan draft worth reading together. The order created a clearinghouse. The bill, twenty-four hours later, codifies the office that will write the rules the clearinghouse depends on. Twenty-four hours after the executive who would benefit most asked for a seat at the same office, in person, with the Speaker and the Minority Leader in his calendar.
That is how AI policy gets made in 2026. Three artifacts, three days, one channel.
What this means for the people not in the room
Policy staff already know the channel exists. The Q1 lobbying numbers are public and the meeting schedules get leaked. The people who do not know are the readers and constituents who experience AI policy as a finished product. Federal frameworks descend, state protections get preempted, and the design of the safety regime gets treated as a technical question rather than a political one.
The next visible artifact will arrive in 30 days, when Bessent’s clearinghouse stands up. Watch who staffs it. The names will tell you whether the channel grew or held steady.
Requests for comment
Representative Obernolte’s office did not respond to a request for comment about whether OpenAI or Anthropic provided input on the draft bill’s CAISI language. OpenAI did not respond to a request for comment on whether the company advocated for the 30-day window over the 90-day version. Laterstack will update this story if either responds.
Josh Kushner’s venture capital firm Thrive Capital closed a $10 billion fund on February 17, 2026, its tenth and largest to date. The round was heavily oversubscribed, meaning the firm turned away billions from limited partners who wanted in and could not get a seat. The fund is double the size of Thrive’s previous raise.
The portfolio explains the demand. Thrive holds early positions in OpenAI, SpaceX, and Stripe, three of the most valuable private companies in the world. The new capital will be deployed across artificial intelligence, space technology, robotics, and life sciences.
Ten billion dollars in one firm’s hands. One family’s orbit.
The Access Question
Josh Kushner founded Thrive Capital in 2009 at age 24. His brother, Jared Kushner, served as Senior Adviser to President Donald Trump from 2017 to 2021. The family operates on two parallel tracks: political power and capital allocation. Josh has deliberately distanced himself from Jared’s political work, donating to Democratic candidates and publicly opposing several Trump administration policies.
The structural reality is harder to separate from the family name. When your last name opens doors in both Washington and Sand Hill Road, deal flow reflects that access. Thrive’s early position in OpenAI, a company whose regulatory future depends heavily on federal policy, is worth examining. The same applies to SpaceX, which holds billions in government contracts with NASA and the Department of Defense.
This does not mean political connections caused any specific investment. It means the system that produces $10 billion funds is not blind to who a founder’s family is. Access compounds the same way capital does.
Why $10 Billion Matters
The venture capital industry is concentrating. Fewer firms control more capital, and the gap between the top and everyone else is widening.
Big Tech’s $650 billion AI infrastructure commitment created a funding environment where only the largest firms can participate in the most valuable rounds. OpenAI’s latest raise was $6.6 billion. SpaceX’s last was $10 billion. These are not Series A checks. They are institutional capital plays that require institutional-scale funds.
A $10 billion fund can write $500 million checks into late-stage rounds that most venture firms cannot touch. Thrive competes not with other VCs but with sovereign wealth funds, pension systems, and the world’s largest asset managers. The firms that can participate in these rounds capture the returns. Everyone else watches from the sideline.
For founders, concentration changes the math. When a handful of firms control the largest pools of available capital, getting into those portfolios becomes the game. The power dynamic inverts. Founders do not choose investors. Investors choose founders.
The Counter-Argument
Thrive’s returns stand on their own. Early bets on Instagram, Spotify, and Slack were made before the Kushner name carried significant political weight. The firm’s track record in identifying companies that defined their categories is legitimate and documented. Limited partners invest in performance, not last names. The oversubscription reflects financial results, not political proximity.
That is a fair reading. It is also true that in a system where access, information, and relationships determine which deals you see before anyone else, separating merit from network is functionally impossible. Both things are true simultaneously.
Politics and wealth are not parallel systems. They are the same system viewed from different angles. The Kushner family is a case study in how both forms of power compound. Josh builds the portfolio. Jared builds the political capital. The access flows in both directions whether either brother intends it to or not. That is not a conspiracy. It is how the system has always worked for families that operate at this level.
What This Means for Everyday People
When $10 billion concentrates in one firm holding positions in the companies building artificial intelligence (OpenAI), controlling space access (SpaceX), and processing global payments (Stripe), the decisions that firm makes affect everyone. Which AI companies get funded determines which AI products exist. Which space ventures survive determines who controls orbital infrastructure. Which payment systems scale determines how money moves across borders.
You will never meet Josh Kushner. But the companies his firm funds will shape how you work, pay, communicate, and travel. The people who allocate capital at this scale have more direct influence over daily life than most elected officials. The difference is that no one voted for them.
For inquiries and analysis contact laterstack@proton.me
OpenAI officially retired GPT-4o from ChatGPT on February 13, 2026, along with GPT-4.1, GPT-4.1 mini, and OpenAI o4-mini. The default model for all ChatGPT users is now GPT-5.2. Existing conversations that used GPT-4o will continue displaying previous responses, but all new messages route through the successor. The model that brought generative AI into mainstream consciousness lasted less than two years.
That timeline deserves attention. GPT-4o launched in May 2024 as OpenAI’s multimodal flagship, the model that could see, hear, and speak. It powered the voice mode that made ChatGPT feel like a conversation instead of a search bar. It was the model that crossed the chasm from early adopter curiosity to something your dentist asked about. And now it is gone, deprecated alongside three other models in a single support page update that reads like a firmware changelog.
Twenty Months From Flagship to Landfill
The compression of AI model lifecycles has no precedent in consumer technology. Microsoft supported Windows XP for thirteen years. Google maintained the original Gmail interface for nearly a decade. GPT-4o got twenty months. The replacement, GPT-5.2, had already captured the majority of ChatGPT usage before OpenAI pulled the trigger, according to the company’s deprecation notice. A fifth generation model made the fourth generation irrelevant not through a dramatic announcement but through quiet user migration. Most people switched without being told to.
This is the new rhythm. OpenAI also announced GPT-5.3 Codex this week, a model specifically designed for advanced coding tasks. The gap between model generations is no longer measured in years. It is measured in months. Each new release does not supplement the previous one. It replaces it entirely. The company is treating its own models the way fast fashion treats inventory: produce, ship, liquidate, repeat.
There is a version of this story that is straightforwardly good. GPT-5.2 is measurably better. Consumers get a superior product without lifting a finger. The companies that adapted fastest to GPT-4o’s capabilities will adapt fastest again. For the casual user, this deprecation is invisible progress. That reading is not wrong. It is just incomplete.
The pattern parallels a broader acceleration we are seeing across the industry. ByteDance, Alibaba, and DeepSeek all launched new models in early February, each one designed to leapfrog whatever existed the month before. The competitive dynamics are compressing timelines further. No company can afford to maintain an older model when a rival’s newer one is cheaper and more capable. The result is an industry where the product you built your business on can become obsolete before your annual contract renews.
The Developer Problem
For the hundreds of millions of casual ChatGPT users, this deprecation changes nothing. They were already on GPT-5.2 without knowing it. The real impact lands on the developers, enterprises, and startups that built workflows, fine tuned models, and integrated API calls around GPT-4o’s specific behavior. Every model has idiosyncrasies. Prompts that worked perfectly on GPT-4o may produce different outputs on GPT-5.2. Fine tuned models need retraining. Edge cases need retesting.
OpenAI’s API deprecation timeline is separate from the ChatGPT consumer deprecation, but the signal is the same: build on our platform and accept that the foundation shifts beneath you every few months. This is the trade off that every company using third party AI accepts, and it is one that Big Tech’s $650 billion AI capex bet is designed to lock in. The more infrastructure you build on someone else’s model, the harder it becomes to leave. The more frequently that model changes, the more dependent you become on the provider to keep things working.
The opposite argument has weight. Developers who treated GPT-4o as a permanent foundation were making a bet they should not have made. The documentation always warned that models would be deprecated. The companies that built abstraction layers, maintained model agnostic architectures, and tested across providers are fine today. The ones that hardcoded GPT-4o into production workflows chose convenience over resilience. That is a developer problem, not an OpenAI problem. Both readings contain truth. Which one you land on depends on whether you believe platform providers owe stability or whether users owe themselves adaptability.
The counterargument is that rapid deprecation is exactly what progress looks like. GPT-5.2 is measurably better than GPT-4o across every benchmark. Clinging to older models out of nostalgia or convenience slows the entire ecosystem. OpenAI’s willingness to kill its own darlings is precisely what makes it the market leader. The companies that survive are the ones that adapt to new models quickly, not the ones that demand backward compatibility forever. Every technology platform has upgrade cycles. AI’s are just faster.
The progress argument is valid, and it misses the point. Nobody is mourning GPT-4o’s capabilities. The concern is the business model underneath. OpenAI is training an entire economy to build on infrastructure it can unilaterally retire, and calling it innovation. Imagine if every commercial landlord could demolish your office building with thirty days notice and hand you a key to a different one across town. The new office might be better. You still lost everything on your walls. The companies that survive this era will not be the ones that build the best prompts. They will be the ones that build the thickest insulation between their products and the model provider’s deprecation schedule. The rest are renting intelligence on someone else’s terms and calling it a strategy.
What This Means for Everyday People
If you use ChatGPT, your experience just got better and you probably did not notice. GPT-5.2 is faster, more accurate, and handles complex reasoning more reliably than GPT-4o did. The transition was designed to be invisible.
The deeper implication is about control. When the tools you rely on can change overnight without your input, you are not a customer. You are a passenger. The AI companies are driving, and the destination changes whenever their engineering team ships a new model. For businesses, that means building contingency into every AI integration. For individuals, it means understanding that the AI assistant you are talking to today will not be the same one you are talking to in six months. The personality, the quirks, the way it phrases things, all of it is disposable. The only constant is the subscription fee.
This analysis assumes two things worth questioning. First, that the pace of deprecation will continue or accelerate. It is possible that model improvements plateau and lifecycles stabilize, the way smartphone upgrade cycles eventually slowed from annual breakthroughs to incremental refinements. Second, that dependency on a single provider is the default path. Open source models from Meta, Mistral, and others offer an alternative for companies willing to trade convenience for control. Whether that trade off is worth it depends on how much you trust OpenAI’s deprecation schedule to align with your business needs. That question does not have a universal answer.
For inquiries and analysis contact laterstack@proton.me
OpenAI announced it will start serving ads inside the free version of ChatGPT and its $8 per month ChatGPT Go over the coming weeks. The move is long expected, but it raises questions about how a company known for AI innovation balances revenue with user trust.
The company reached $13 billion in revenue last year and expects to triple that this year, according to an anonymous source. Most of that revenue is being spent on cloud services and data centers to support AI infrastructure. OpenAI plans to spend $115 billion between 2025 and 2029, a figure that dwarfs the budgets of most tech companies.
Ads in ChatGPT will not change the answers it provides, OpenAI says, nor will advertisers influence the responses. Still, the method of ad delivery is unlike anything seen on the web. Chatbots generate text instead of web pages, which makes standard display ads impossible. Instead, OpenAI will tailor ads based on the questions users ask and prior queries, with an option to disable personalization.
This approach exposes the tension between AI monetization and the trust users place in the service. ChatGPT is used for everything from coding to personal advice. If users start to perceive any subtle influence from advertising, the credibility of the platform could erode.
It also highlights the scale of AI’s infrastructure demands. OpenAI will use Cerebras chips that consume hundreds of megawatts of electricity, equivalent to powering tens of thousands of households. OpenAI is not alone; companies like Microsoft and Google are also investing heavily in global AI compute, with significant cost and environmental considerations.
This is a moment where technology, business, and ethics intersect. Every ad served is a decision about how much users pay with attention and how much companies pay for compute. AI growth has costs that go beyond money, and users are only beginning to notice the trade-offs.
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