Meta Archives

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.

The Glasses Play

Zuckerberg is not retreating from hardware. He’s shifting where the money goes.

Ray-Ban Meta smart glasses sold 7 million units in 2025, tripling the combined sales of 2023 and 2024. Meta and EssilorLuxottica are reportedly in talks to double production to 20 million units by end of 2026. Some reports say 30 million if demand holds.

On the Q4 2025 earnings call, Zuckerberg described them as “some of the fastest growing consumer electronics in history” and drew the smartphone analogy: “Billions of people wear glasses or contacts for vision correction. And I think that we’re at a moment similar to when smartphones arrived, and it was clearly only a matter of time until all those flip phones became smartphones.”

Google is making the same bet with Android XR. Apple is reportedly shifting resources from Vision Pro toward its own glasses project. The industry consensus is forming: the face computer of the future sits on your nose, not strapped to your forehead.

Meta’s stock rose 4 percent on the news of budget cuts to the metaverse division. Wall Street is not mourning.

What’s Left Behind

CTO Andrew Bosworth told employees that Meta will “double down on bringing the best Horizon experiences and AI creator tools to mobile.” Horizon Worlds is being repositioned as a Roblox competitor on phones. Mobile engagement is reportedly up 4x. Meta acknowledged internally that “having to build everything twice, once for mobile and once for VR, is a tremendous tax on the team.”

That’s an admission. When you describe building for your own VR platform as a “tax,” you’ve made the decision. You just haven’t said it out loud yet.

Quest hardware continues. The app store stays open. But the first-party content investment that was supposed to make VR a platform, not a peripheral, is being dismantled. Twisted Pixel made Deadpool VR. Sanzaru made Asgard’s Wrath, one of the best-reviewed VR games ever. Both studios are gone. Meta spent roughly $400 million acquiring Within, the company behind Supernatural VR, fought the FTC to close the deal, and then put the app into maintenance mode with no new content or features.

Third-party developers are watching. CNBC reported that Meta’s cuts have “sparked fears of a VR winter.” Meta was the primary funder, employer, and content buyer for the VR ecosystem. When the biggest player pulls back on content investment, the indie studios and game developers who built for Quest are left without a patron.

The Honest Read

Smart glasses are working. The product has traction. The pivot makes financial sense for Meta right now. None of that changes the fact that Zuckerberg renamed the entire company around a vision that produced $83 billion in losses, a social platform with fewer users than a mid-tier mobile game, and a VR productivity app that lasted four years before being deleted.

The smartphone analogy is convenient. But Ray-Ban Metas with cameras and AI assistants are not the metaverse. They’re a consumer electronics product with good margins. Calling them the next step in the same journey is a rewrite, not a continuation.

The question nobody at Meta wants to answer: if smart glasses were always the real play, why did the company spend $83 billion on something else first?

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

Meta has hired Alan Dye, Apple’s longtime head of user interface design, in a move that signals the company’s push into AI-equipped consumer devices. Dye, who has led Apple’s interface design since 2015, will oversee design for hardware, software, and AI integration at Meta, reporting directly to Chief Technology Officer Andrew Bosworth.

The hire is a significant blow to Apple, which has faced a wave of departures from its design and executive teams over the past several years. Dye helped shape the look and feel of Apple’s latest operating systems, apps, iPhone, Apple Watch, and Vision Pro headset. His exit continues a trend of key talent leaving Apple since Jony Ive’s departure in 2019.

Why This Matters

Meta is clearly signaling a shift. By bringing in Dye, the company aims to compete on design as aggressively as Apple, especially as AI becomes central to consumer devices. Dye will lead a new design studio at Meta, focusing on integrating AI features into hardware and software across Meta’s products, including virtual and augmented reality devices.

Apple, meanwhile, is promoting Stephen Lemay, a longtime designer, to replace Dye. Lemay has been instrumental in Apple’s interface design since 1999. CEO Tim Cook emphasized that the company’s design team remains strong, but Dye’s departure highlights the ongoing challenge of retaining top creative talent in Silicon Valley.

What This Means for Apple and Meta

For Apple, Dye’s exit adds to a period of leadership change. In recent months, COO Jeff Williams retired, AI head John Giannandrea left, and hardware chief Dan Riccio retired last year. Other executives, including Johny Srouji and Lisa Jackson, are reportedly considering their futures.

For Meta, the hire signals ambition beyond software and social networking. The company is betting that world-class design, combined with AI integration, can help it compete in consumer hardware. Dye will be joined by Billy Sorrentino, a senior director from Apple, and will oversee several of Meta’s existing design leaders.

The move could reshape the tech landscape, creating a new battleground between Apple and Meta for design, AI, and next-generation devices.

Why Meta Shifted the Timeline

The delay followed a series of internal reviews. Business Insider reports that CEO Mark Zuckerberg told teams to take more time to strengthen the business case and ensure the product delivers a meaningfully improved experience. Updated memos from XR executives Gabriel Aul and Ryan Cairns said the additional months will give teams more room to refine the details and make sure the launch meets expectations.

This type of timeline reset is not new for Meta’s hardware efforts. The company has been pushing aggressively into AR and MR for several years, yet the market still lacks clear mainstream demand. When a device as important as Phoenix gets pushed back, it is often a signal that teams are adapting to realities that are not always visible from the outside.

Budget Shifts Add More Context

The delay also arrives just days after Bloomberg reported that Meta plans to reduce its metaverse budget by as much as 30 percent for 2026. A budget reduction of that scale does not necessarily indicate retreat. It does however speak to prioritization. XR hardware requires stable supply chains, careful pacing, and a clear narrative that can be communicated to users and investors. Meta has struggled with all three at different moments in recent years.

When a company adjusts both its timeline and budget in the same window, it usually means the broader strategy is evolving. For readers following the XR sector, these details are important. They help piece together a clearer picture of how Meta is navigating a market where innovation moves quickly but adoption moves slowly.

A Pattern Worth Paying Attention To

Mixed reality continues to hover between potential and practicality. Companies see the long term opportunity, but the path to a viable mass market device remains uneven. Meta’s delay is one more sign of this tension. Hardware can only advance as fast as the technology, economics, and user habits allow, and those elements do not always align.

This moment is a reminder to step back and observe the larger pattern. Product delays are rarely only about schedules. They often point to shifts in ambition, risk tolerance, or internal confidence. When patterns repeat, they tell a story long before the companies involved choose to spell it out.

Meta is still investing heavily in the future of AR and MR. The timeline adjustment for Project Phoenix simply positions the company to choose a path that is more measured and potentially more sustainable in a market that is still writing its own rules.