AI Compute
OpenAI, Anthropic, and Their Hyperscaler Partners: Huge Financial Risks and Open-Weight Competition
Hyperscaler AI Revenue Hurdle:
In an analysis released last month, Jared Bernstein estimated that the big-spending hyperscalers need $13-$18 trillion in revenues over the next 10 years to justify their AI-infrastructure investments. This is, by definition, speculative work, requiring judgment calls on technical aspects of the analysis, such as discount rates, depreciation schedules, taxes, and revenue paths. So, we were happy to see that several other analysts and researchers have, independently, been working on the same problem. Callum Williams of the Economist, whose produced his own excellent analysis on the topic, put together a comparison of these different estimates, including theirs.
What’s striking is how close each of the estimates are: firms will need between $2.4-3.0 trillion in annual incremental AI revenues by 2030 to justify their big spending; this is about 5-6 times more than they are currently making under the most general possible assumptions. They may clear that hurdle. Ryan Cummings told Axios:
“Can all these people eventually turn this into something profitable? I think they will…I’m not sure who’s going to do that, but I do think there will be trillions of dollars of profits that are up for grabs in the future, but just not in this super-accelerated timeline that is required to justify the investments.”
Hyperscaler Debt & Capex Projections (2026–2027):
- 2026 Debt Issuance: Projected between $220 billion and $250+ billion for major US hyperscalers (Alphabet / Google, Amazon, Meta, Microsoft, and Oracle), with broader AI-related market debt reaching up to $570 billion.
- 2027 Forecast: Goldman Sachs projects direct hyperscaler debt issuance to nearly double to around $400 billion (Jeff Pu estimates $419 billion) as companies finance over a third of their infrastructure needs.
- Aggregate Capex: Combined capital expenditures are expected to hit roughly $940 billion in 2026 and scale past $1.3 trillion in 2027.
- Market Share: Hyperscaler investment-grade bond sales have jumped from roughly 2% of total US supply (2022–2024) to roughly 9% in 2026.
- Credit Impact: Credit spreads on a 10-year hyperscaler credit basket have widened from historical 40–75bp ranges toward 90bp+, driven by leverage concerns.
- Cash Flow Outlook: S&P Global Ratings expects all major hyperscalers to run negative free operating cash flow through 2026 and 2027, with a cash-flow inflection point not projected until 2028–2029.
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Hidden Risks for the AI Lab Giants:
Anthropic and OpenAI belong to the group of frontier AI model developers commonly known as “the AI labs.” Their financial performance provides a more direct view of AI business economics than that of the hyperscalers discussed above. Unlike diversified technology companies, whose established businesses can offset AI-related spending and complicate profitability analysis, these firms concentrate their operations on developing and commercializing AI. Their results therefore offer a clearer indication of whether revenue from AI products and services can support the costs of model development, computing infrastructure, and commercialization.
Public disclosures about these privately held companies are consequently valuable. Recent reporting on Anthropic and the Financial Times (FT) [paywall] June 16, 2026 report on OpenAI [1.] provide insight into their revenues, spending, and losses. OpenAI’s reported results also illustrate the importance of distinguishing operating costs from noncash accounting charges when assessing the economics of its underlying business. Substantial losses do not, by themselves, establish that AI is a financial bubble. They do, however, reinforce concerns about whether revenue growth can justify the sector’s spending commitments and valuations.
Note 1. The referenced June 16, 2026, FT report covers OpenAI’s 2025 results and distinguishes its approximately $39 billion net loss from losses of approximately $8 billion after excluding specified noncash items. It does not directly substantiate the stated $1.60 loss per revenue dollar.
AI proponents point to Amazon, Uber, and Tesla as examples of companies that sustained years of losses before becoming profitable. The relevant comparison, however, is not simply whether a company loses money, but the magnitude of those losses relative to revenue and its prospects for improving margins. As shown in the chart below, Anthropic and OpenAI reportedly lost more than $1.76 and $1.60, respectively, for every dollar of revenue in the year examined—more than twice Uber’s loss-to-revenue ratio in 2016 and more than five times Amazon’s in 1999. These comparisons underscore the scale of the challenge: rapid revenue growth must ultimately translate into a sustainable cost structure, rather than continued dependence on external financing.
Underestimated Competition:
Despite their prominence in the AI industry, the frontier-model developers face questions about whether their competitive position can support sustained profitability. They depend heavily on external providers of computing infrastructure, and their ability to convert enterprise adoption into sufficient revenue and margins remains uncertain. Customers are also scrutinizing AI costs more closely, particularly as resource-intensive AI agents increase computing requirements and operating expenses. Efforts to control those costs could constrain the labs’ revenue growth even as AI usage expands.economist
Competition from open-weight models adds to that pressure. AT&T, for example, reported increasing the share of its AI use served by open models from 20 percent in May to 40 percent by September, with savings of up to 80 percent compared with earlier in the year. Such models can offer enterprises greater deployment flexibility and reduce reliance on proprietary-model providers. Although model weights may be available without charge, deployment, inference, integration, and ongoing operation are not cost-free. The competitive issue for OpenAI and Anthropic is therefore whether their proprietary offerings deliver enough additional value to justify a price premium.
The AI labs also lack the diversified earnings base available to hyperscalers. Alphabet, for example, can draw on its established advertising business to support investments in Google DeepMind, custom processors, and data centers. OpenAI and Anthropic have no comparable non-AI profit pool. Until their AI businesses generate sufficient cash flow, they remain dependent on external capital to finance expansion and absorb losses—a funding model whose sustainability depends on investor confidence as well as technical progress.
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| Dimension | Proprietary AI Labs (OpenAI / Anthropic) | Cloud Hyperscalers (Google / Microsoft / AWS) | Open-Weight Alternatives (Meta / Enterprise In-House) |
|---|---|---|---|
| Financial Engine | Purely AI-dependent; high loss-to-revenue ratios. | Diversified profit pools (Ads, Cloud, SaaS). | Subsidized by core business (e.g., Meta’s ad network). |
| Pricing Leverage | High premium; under constant pressure to justify value. | Tied to broader ecosystem infrastructure and compute sales. | Zero licensing costs; infrastructure/deployment costs only. |
| Strategic Vulnerability | High infrastructure dependence and lack of safety cushion. | Massive aggregate ROI requirements to justify $T infrastructure bets. | Cost of long-term deployment, fine-tuning, and specialized engineering. |
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References:
https://econjared.substack.com/p/a-budding-consensus-on-ai-revenues
https://econjared.substack.com/p/ai-companies-are-in-a-race-against
AI Debt Wave Implications: Higher interest rates with enormous AI sales required for ROI
Broadcom lending Anthropic up to $42 billion in yet another AI circular financing deal
Bain & Co: AI Infrastructure Buildout Will Require $6 Trillion Revenue by 2031 to Support Massive CAPEX
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
Expose: AI is more than a bubble; it’s a data center debt bomb
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
AI Debt Wave Implications: Higher interest rates with enormous AI sales required for ROI
Executive Summary:
Artificial Intelligence (AI) debt fueled infrastructure buildouts have become a broad fixed-income-market supply shock. The Wall Street Journal reports that 49% of new investment-grade bond issuance year to date in 2026 has been AI-linked—a figure that underscores how the financing of data centers, compute infrastructure and supporting power systems is reshaping bond market fundamentals and technical factors.
As the supply of long-dated bonds rises, prices fall and yields rise until investors are compensated enough to absorb them. The AI buildout is adding enormous corporate debt from hyperscalers, data-center developers, semiconductor and networking suppliers, and utilities—while also driving public borrowing for grid, power, water and transportation infrastructure. This is putting upward pressure on yields across Treasuries, corporate bonds and municipal debt. This heavy concentration of long-dated supply creates a crowding-out effect that forces non-AI issuers to compete against elevated benchmark yields and potentially stifles broader economic growth, especially for companies not involved in circular AI funding deals (see References).
AI is capital intensive (see Table 1. below). Building data-center capacity requires cash not only for GPUs and servers, but also for land, buildings, fiber, networking, cooling, power procurement, backup generation, substations, transmission and water systems. The major hyperscalers can fund part of that investment from cash flow, but they are also issuing lots of new bonds and notes to preserve liquidity and accelerate buildouts. Their suppliers, data-center partners and power providers are doing the same. Much of this borrowing is long dated—exactly where the Treasury is issuing heavily to finance federal deficits.
The result is an expanding pool of long-duration debt competing for the same institutional buyers: insurers, pension funds, mutual funds, banks, foreign investors and asset managers. When those buyers do not increase their allocations at the same pace as supply, issuers must offer higher yields. Here’s the flow chart that loops around indefinitely until there is an AI crash!:
More AI related bond supply→lower prices→higher yields→higher term premium for U.S. Treasuries→higher budget deficits to finance the increased debt→more bond supply→etc.
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- 2026 Debt Issuance: Projected between $220 billion and $250+ billion for major US hyperscalers (Alphabet / Google, Amazon, Meta, Microsoft, and Oracle), with broader AI-related market debt reaching up to $570 billion.
- 2027 Forecast: Goldman Sachs projects direct hyperscaler debt issuance to nearly double to around $400 billion (Jeff Pu estimates $419 billion) as companies finance over a third of their infrastructure needs.
- Aggregate Capex: Combined capital expenditures are expected to hit roughly $940 billion in 2026 and scale past $1.3 trillion in 2027.
- Market Share: Hyperscaler investment-grade bond sales have jumped from roughly 2% of total US supply (2022–2024) to roughly 9% in 2026.
- Credit Impact: Credit spreads on a 10-year hyperscaler credit basket have widened from historical 40–75bp ranges toward 90bp+, driven by leverage concerns.
- Cash Flow Outlook: S&P Global Ratings expects all major hyperscalers to run negative free operating cash flow through 2026 and 2027, with a cash-flow inflection point not projected until 2028–2029.
Hyperscaler Funding Boom: Debt vs. Capex Projections (2026–2027):
Source: Google Gemini
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Corporate Borrowing Negatively Impacted by AI Debt Financing:
Corporate bonds face the most direct effect. When hyperscalers, data-center operators, chip suppliers and utilities all enter the fixed income market at once, they compete for investor capital and dealer balance sheets.
The bond market adjusts through the following mechanisms:
- Larger new-issue concessions, meaning issuers must offer higher yields than comparable bonds already trading.
- Lower prices for outstanding corporate bonds as investors sell them to make room for new issues.
- Wider credit spreads, particularly for lower-rated investment-grade borrowers.
- Higher borrowing costs for non-AI companies that must compete with AI-linked supply for investor allocations.
The pressure is not limited to companies directly building AI agents or systems. A telecom operator, industrial firm, REIT or consumer company will likely pay more to borrow because investors can buy a new, liquid, highly rated hyperscaler bond at a potentially higher and attractive yield. That discourages non-AI corporate borrowing and leads to slower economic growth.
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AI Pain Points Personified:
1. Ares reports, “We documented well more than 100 digital-infrastructure financings from just the past twelve months… Despite different issuers, different structures, different rating agencies and different credit markets, all of the risk converges on just eight (AI tech) names: Meta, Oracle, Microsoft, Amazon, Google, Nvidia, and, on a look-through basis, OpenAI and Anthropic.”
2. Analysts at Goldman Sachs Group and elsewhere have calculated that more than $1 trillion has already been spent on the data center build-out since the launch of ChatGPT in late 2022. Presumably, other large American businesses would need to pay for AI tools to justify all of this investment. That money needs to come from somewhere. but who’s going to pay for those AI tools?
3. From Greg Ip of the WSJ: “Will America Spend 9% of Its GDP on AI? The Industry Is Counting on It“:
“Is it plausible that Americans will spend as much of their income on this one technology as they do on food? Roughly twice what the nation pays for all forms of energy or all computers and software? Seven times what consumers spend on phone, streaming, and internet services combined?”
“You should be skeptical. Even the most transformative inventions eventually run into the law of diminishing returns: each additional dollar a user spends yields less additional productivity (or enjoyment) than the last. That imposes a natural ceiling. The question, of course, is where that ceiling is. Whether or not you think 9% of GDP is right, you have to care, because this figure isn’t some fever dream: it is implicit in the dollars that investors and companies are committing right now.”
Chart Credit: Greg Ip, Wall Street Journal
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Dire Warnings:
Nobel laureate Daron Acemoglu writes about “Distorted Intelligence at the AI Frontier” (emphasis added):
“The real problem for the frontier AI labs is not that their models are too powerful and already “misaligned” with the goals their creators set for them. Rather, it is that the models are being trained in ways that may be leading to a type of intelligence that will become less predictable. AI frontier labs are training their models in ways that may be leading to a type of distorted intelligence.
“This is what I mean by distorted intelligence. If my suspicion is correct, what we are dealing with is not a model racing toward superintelligence, but a brittle house of cards that becomes more and more likely to malfunction and collapse as we demand more from it.”
Conclusions:
The above analysis outlines critical structural risks and concerns directly related to the AI infrastructure (capex) spending boom. It references several articles which highlight the risks of circular financing loops where hardware suppliers (like Nvidia and Broadcom) fund their buyers (like Anthropic and Open AI). Additionally, hyperscalers face structural cash deficits with negative free operating cash flows projected through 2027, alongside rising sovereign risk and potential conflicts of interest from intertwined debt and leasing arrangements. This table balances the author’s views with a counter-perspective:
| IEEE Techblog Warning | The Nuanced Counter-Perspective |
|---|---|
| Circular Funding Risk: Massive vendor lending deals (like Broadcom lending Anthropic $42 billion) are artificially inflating revenue and building a brittle house of cards. | Balance Sheet Cushion: Unlike the 2000 Dot-Com crash where telecom infrastructure was built on speculative, junk-rated leverage, 2026 hyperscalers possess massive cash-generative core businesses (search, cloud, e-commerce) to subsidize their debt service even if AI returns are delayed. |
| Diminishing Returns: Society cannot plausibly spend 9% of GDP on AI, meaning demand will hit a hard ceiling and spark a debt bust. | Productivity Deflation: AI infrastructure spending may not need to justify itself through direct “software sales.” If the technology lowers the baseline operating costs of the global services economy, the ROI manifests as structural corporate margin expansion rather than retail sales. |
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References:
Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23)
Broadcom lending Anthropic up to $42 billion in yet another AI circular financing deal
Bain & Co: AI Infrastructure Buildout Will Require $6 Trillion Revenue by 2031 to Support Massive CAPEX
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Expose: AI is more than a bubble; it’s a data center debt bomb
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Broadcom lending Anthropic up to $42 billion in yet another AI circular financing deal
Backgrounder:
According to Reuters, which obtained Anthropic’s IPO prospectus, the Big Tech giants are depicted in multiple ways in the IPO filing- as distribution partners, financial backers, computer suppliers, and competitors, all at once. They uncovered the clearest view yet of how circular the AI world really is, and how much an AI lab’s success depends on the giants it’s also competing against.
Reuters was able to calculate that Anthropic pays roughly 16% of every dollar it earns from its cloud partners (Amazon, Google, Microsoft) back to those same big tech partners. It counts the full value of contracts sold through a cloud marketplace as revenue, then treats the cloud platform’s cut of revenue as a marketing cost.
OpenAI does the opposite by only counting what it keeps after the cloud partner takes its share. That difference matters for understanding the big numbers floating around, and how to actually compare the two rivals’ toplines as they both eye the public market.
Broadcom’s relationship with Anthropic spans compute supply, equipment leasing and financing — giving the semiconductor company a central role in Anthropic’s infrastructure buildout. That differentiates it from other major partners and investors such as Amazon which primarily provide cloud infrastructure and distribution for Anthropic’s AI model Claude.
As part of that complex relationship, revealed in the IPO filing, Broadcom has agreed to lend Anthropic up to $42 billion to finance infrastructure spending. In turn, Anthropic stands to become the largest customer in Broadcom’s entire chip design business next year, making their relationship a prime example of the reciprocal spending that has animated AI skeptics on Wall Street, even as the AI lab readies a public offering that could see it valued at $2 trillion.
“It feels that there’s quite a concentrated bet right now on two companies being able to generate enough revenues to support all the financing that’s happened,” said Robert Leitao, managing partner of Rothschild & Co.
Anthropic disclosed that Broadcom’s role in supplying hardware and acting as a financing partner creates “potential conflicts of interest” that might affect Anthropic’s ability to access the computing power needed for its work, according to the prospectus.
The AI lab also warned that Broadcom’s decisions around pricing and hardware could affect its ability to procure enough computing infrastructure. Broadcom did not comment. Anthropic declined to comment to Reuters.
In April, Anthropic announced it was teaming up with Broadcom and Google for a deal that would see Google provide its Tensor Processing Unit (TPU) chip capacity to Anthropic, with the supply coming online in 2027. Broadcom designs Google’s TPUs that are manufactured by TSMC in Taiwan.
A Broadcom circuit board for chip testing is pictured during a lab tour as Broadcom prepares to launch new optical chip tech to fend off Nvidia in San Jose, California, U.S., September 5, 2025. Brittany Hosea-Small · REUTERS via Yahoo Finance.
Anthropic is also relying on Broadcom for equipment leasing and financing. Broadcom could designate a financing partner, and the debt instruments could be converted into Anthropic shares. Anthropic said in its filing it doesn’t expect any notes to be sold before it completes its IPO. The convertible note Anthropic would issue could finance about a third of the $125.2 billion commitment the AI lab has made for a five-year lease of tensor processing unit (TPU) computing capacity.
Other Players:
Broadcom rivals Nvidia and AMD have also provided funding to their own customers, including OpenAI and Anthropic, which the labs then used to pay for access to the companies’ high-powered chips. The concern about such circular financial schemes is that if one domino in the row falls, it will cause a chain reaction that will decimate the AI trade and, as a result, the global equities markets that have benefited from and come to rely on AI firms and hyperscalers.
As to the circularity of all of this financing, the BIS notes (via FT Alphaville):
“… circular relationships make reported demand partly endogenous to firms’ own financing decisions. For example, when a supplier finances a customer, part of the supplier’s revenue growth reflects its own capital investment, rather than organic final demand. This makes it harder for investors, lenders and supervisors to gauge what part of the current AI boom is based on organic demand.”
“The parallel with the telecom boom of the late 1990s is instructive: upstream equipment vendors financed network operators so they could buy the vendors’ equipment. This meant that part of the equipment vendors’ reported sales was being funded by the vendors themselves. For a time, as operators expanded their networks, equipment orders also expanded and vendors booked both the sales and loans as assets. However, when operators’ own revenues failed to materialize or slowed, they could neither repay the loans nor sustain the equipment purchases. Equipment vendors then sustained both financial losses and a loss of sales. Such dynamics may also play out in AI if revenue growth and end user demand fall short of firms’ expectations.”
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- 2026 Debt Issuance: Projected between $220 billion and $250+ billion for major US hyperscalers (Alphabet / Google, Amazon, Meta, Microsoft, and Oracle), with broader AI-related market debt reaching up to $570 billion.
- 2027 Forecast: Goldman Sachs projects direct hyperscaler debt issuance to nearly double to around $400 billion (Jeff Pu estimates $419 billion) as companies finance over a third of their infrastructure needs.
- Aggregate Capex: Combined capital expenditures are expected to hit roughly $940 billion in 2026 and scale past $1.3 trillion in 2027.
- Market Share: Hyperscaler investment-grade bond sales have jumped from roughly 2% of total US supply (2022–2024) to roughly 9% in 2026.
- Credit Impact: Credit spreads on a 10-year hyperscaler credit basket have widened from historical 40–75bp ranges toward 90bp+, driven by leverage concerns.
- Cash Flow Outlook: S&P Global Ratings expects all major hyperscalers to run negative free operating cash flow through 2026 and 2027, with a cash-flow inflection point not projected until 2028–2029.
Source: Google Gemini
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References:
https://www.ft.com/content/87875b20-4081-4511-9afe-4ee389409742
Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23)
Bain & Co: AI Infrastructure Buildout Will Require $6 Trillion Revenue by 2031 to Support Massive CAPEX
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Expose: AI is more than a bubble; it’s a data center debt bomb
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Bain & Co: AI Infrastructure Buildout Will Require $6 Trillion Revenue by 2031 to Support Massive CAPEX
Introduction:
Bain & Company estimates that the AI industry will need to generate approximately $6 trillion in annual revenue by 2031 to support the capital intensity (CAPEX) of the global AI infrastructure buildout, including investment in data centers, accelerated computing, memory, networking and power systems. In its latest technology report, released September 29th, Bain projects that new AI-enabled products could account for roughly $4.2 trillion of that revenue requirement. The firm identifies AI-driven innovation across search, advertising, autonomous systems and physical AI as major prospective sources of value creation.
Enterprise adoption could contribute a further $1 trillion to $1.4 trillion annually through productivity gains in software engineering, sales, marketing, customer service and IT operations. Bain characterizes “absorption speed”—the rate at which enterprises operationalize AI—as the emerging competitive variable, with leading AI labs investing more than $9.75 billion in engineering models intended to accelerate enterprise deployment and integration.
“The debate today is fixated on employee productivity. The economics of AI infrastructure demand trillions in new revenue beyond productivity gains. What the industry needs is a wave of innovation that will dwarf what mobile and cloud unlocked,” said David Crawford, chairman of Bain’s global technology practice and lead author of the report.
At a Glance:
- AI infrastructure investment is racing ahead, but generating enough economic value to justify it will require trillions of dollars in new AI-driven revenue.
- Productivity gains from existing enterprise and consumer applications won’t be enough; entirely new markets must emerge to close the funding gap.
- The winners will be those that create breakthrough AI applications that transform industries and expand the global economy.
Consumer AI services, including subscription and advertising-supported offerings, are projected to contribute between $200 billion and $400 billion in annual revenue. This segment is central to the commercial scaling of AI because it extends AI-enabled services to billions of users through consumer platforms, devices and digital-service ecosystems.
“New products and uses that don’t exist today will enable new markets and opportunities from abundant intelligence – these may include drug discovery, mental health and energy generation,” Bain said.
Bain forecasts annual AI infrastructure spending of up to $1.5 trillion by 2031. The figure encompasses greenfield data-center construction, expansion of existing facilities, and continuing investment in GPUs, memory, network fabrics and related infrastructure. Bain assumes that capital expenditure could represent approximately one-quarter of total AI-industry revenue—“an ambitious but reasonable percentage based on trends among cloud providers.” On that basis, the firm calculates that the AI market would need to approach $6 trillion in annual revenue to sustain the projected infrastructure investment cycle.
“The unprecedented speed and scale of the AI buildout, with billions flowing into chips, data centers, networks and power systems, have focused attention on the challenge of building capacity. But the more important question may be whether enough economic value can be created to justify it.”
Consider the scale of investment and the gap between that and the revenue model necessary to fund it.
- The arms race among hyperscalers (Microsoft, Google, Amazon, Meta, and Oracle) is accelerating: Their capital expenditures could reach $780 billion in 2026, nearly five times the level of just three years earlier.
- Leading-edge AI data centers today are approaching 1 gigawatt (GW) of power capacity. By 2027, many are expected to approach 2 GW facilities, with 9 GW campuses emerging by the end of the decade.
The scale of individual AI data-center projects illustrates the infrastructure requirements underlying those projections. Bain said AI data-center size and cost are increasing rapidly, with leading facilities approximately doubling in scale every 12 to 16 months. Meta Platforms’ Prometheus data center in Ohio, for example, had approximately 600 MW of capacity and an estimated cost of $24 billion in 2025, according to Epoch AI. The facility is projected to reach as much as 2 GW of capacity and cost approximately $80 billion by 2027. Epoch AI projects that Prometheus could reach 5 GW by 2029, with costs of up to $175 billion, and 9 GW by 2030, at an estimated cost of $200 billion.
Such growth places data-center infrastructure squarely within the telecommunications and network-infrastructure domain. Multi-gigawatt AI campuses require high-density optical interconnects, large-scale Ethernet or InfiniBand fabrics, low-latency east-west traffic engineering, high-capacity metro and long-haul connectivity, and resilient access to electric generation and transmission capacity. The infrastructure challenge consequently extends beyond data-center construction to the coordinated scaling of semiconductor supply chains, transport networks, power systems, cooling infrastructure and specialized technical labor.
Bain identified electric-grid capacity, access to GPUs and other critical infrastructure components, talent availability, workforce retention, public acceptance and regulatory requirements as important factors shaping the pace and geography of AI data-center deployment. Resource consumption, noise and community impacts are also becoming material considerations in project planning and approval processes.
Governments in the UAE, Saudi Arabia, the European Union, South Korea and the United States are supporting the expansion of AI and data-centre infrastructure, Bain said. The firm described data centres as increasingly important to technology innovation, economic development and national sovereignty.
The Bain report stated: “Capital needs for data infrastructure will remain high … bottlenecks in power, semiconductors, and other inputs carry large capital needs of their own, opening additional entry points for investors. And as sovereign infrastructure becomes a bigger part of national strategies, partnerships offer both a way in and geographic diversification.”
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Greg Ip of the WSJ: Believers in the artificial-intelligence boom need to take a very close look at this number: 9% of GDP. That is how much American businesses and consumers eventually have to spend per year on the services of companies like Anthropic and OpenAI to justify the staggering sums being committed to the technology right now.
Is it plausible that Americans will spend as much of their income on this one technology as they do on food? Roughly twice what the nation pays for all forms of energy or all computers and software? Seven times what consumers spend on phone, streaming, and internet services combined?
You should be skeptical. Even the most transformative inventions eventually run into the law of diminishing returns: each additional dollar a user spends yields less additional productivity (or enjoyment) than the last. That imposes a natural ceiling. The question, of course, is where that ceiling is. Whether or not you think 9% of GDP is right, you have to care, because this figure isn’t some fever dream: it is implicit in the dollars that investors and companies are committing right now.
The figure is courtesy of Columbia University finance professor Stijn Van Nieuwerburgh. His paper, first presented at the Brookings Institution, showed that the AI build-out is now bigger than any investment boom in American history. His more intriguing, and sobering, statistic is how much revenue AI would have to garner to justify that boom.
Conclusions:
- Dramatic innovation will be required to deliver the revenue necessary to fund the gap.
- The economics required to generate ROI from AI infrastructure are demanding trillions in new revenue, not just cost savings.
- The industry needs a wave of application innovation comparable with what mobile and cloud unlocked, not just productivity gains on existing workflows.
- The infrastructure is being built ahead of the demand curve, and funding it sustainably will require adding approximately 1% to the annual global GDP growth rate.
- The question is whether the applications arrive in time to pay for it.
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References:
https://www.bain.com/insights/topics/technology-report/
https://www.wsj.com/tech/ai/will-america-spend-9-of-its-gdp-on-ai-the-industry-is-counting-on-it-3501bb4f?st=HkbAZs&reflink=desktopwebshare_permalink&mc_cid=65590ec418&mc_eid=5917efd771
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Dell’Oro: Data Center capex grew 92% in 2Q-2026 (caveats galore)
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
PwC: Global AI data center spending to hit $31.6tn by 2050; Role of full stack orchestration layer explained
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Expose: AI is more than a bubble; it’s a data center debt bomb
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Introduction:
According to the Wall Street Journal, the AI build-out is rapidly becoming the largest concentrated infrastructure investment cycle in modern American economic history. Unlike earlier national build-outs—railroads, interstate highways, electrification, or the commercial internet—this cycle is being driven largely by a small group of cloud platforms deploying highly specialized compute, networking, power, cooling, and semiconductor infrastructure at unprecedented speed. Economist Stijn van Nieuwerburgh estimates that U.S. spending on data centers and related AI infrastructure could reach $10.3 trillion [1.] between 2025 and 2032, equivalent to an average of 3.6% of annual GDP. The estimate encompasses far more than conventional enterprise data centers: it reflects the industrial-scale infrastructure needed to train and serve frontier AI models, including GPU and accelerator clusters, high-bandwidth memory, advanced packaging, optical interconnects, high-capacity Ethernet and InfiniBand fabrics, grid interconnection, substations, backup generation, liquid cooling, and long-haul fiber connectivity.
Note 1. The $10.3 trillion number is a scenario-based estimate of U.S. AI infrastructure investment during 2025–2032—not a forecast of announced corporate spending. The Brookings analysis behind it assumes that about 183 GW of new data-center capacity will be completed through 2032, versus a 509-GW announced/planned pipeline. A representative 200-MW AI campus is estimated to cost about $8.2 billion: $5.6 billion for IT equipment, $2.2 billion for the facility, and $0.4 billion for power infrastructure. Thus, most of the investment is in compute and networking hardware rather than buildings.
The scale creates a major financing challenge: the five largest hyperscalers are projected to spend about $800 billion on capex in 2026, exceeding their combined operating cash flow. Under the Brookings assumptions, the resulting infrastructure would need roughly $3.7 trillion of annual revenue by 2032 to produce a 10% unlevered return. The key economic issue, therefore, is whether future AI revenue and utilization can justify the enormous capital investment.
From Cloud Data Centers to AI Factories:
The defining characteristic of this AI buildout investment cycle is its concentration. The five U.S. hyperscalers (Alphabet, Amazon, Meta, Microsoft, and Oracle) are collectively expected to invest roughly $4.2 trillion in capital expenditures during the four years ending in 2029, according to FactSet estimates cited in the source material. Increasingly, this capital is directed toward AI-optimized facilities: campuses designed around megawatt-scale accelerator pods, dense GPU clusters, high-radix network fabrics, and power delivery systems capable of supporting workloads whose energy and cooling profiles differ sharply from those of traditional cloud computing.
Those five major hyperscalers increased combined capital expenditures from approximately $97 billion in 2020 to more than $400 billion in 2025, with the paper projecting approximately $800.5 billion in 2026. That 2026 figure is significant because it exceeds their combined operating cash flow of approximately $707.1 billion. In other words, projected capex is about 113% of operating cash flow. Pacific Software Ventures That creates a MAJOR financing problem: AI infrastructure investment is becoming too large to be financed entirely from hyperscaler internally generated cash.
AI infrastructure is not simply an expansion of conventional cloud capacity. Large-model training and inference create a distinct systems-engineering problem. Training AI frontier foundation models requires thousands to hundreds of thousands of tightly coupled accelerators. Those accelerators must exchange model parameters, activation data, and gradients at extremely high rates. Network performance therefore becomes a first-order determinant of usable compute capacity. A GPU cluster can deliver poor economics if its fabric introduces congestion, latency, packet loss, or inadequate bisection bandwidth during distributed training.
That requirement is accelerating deployment of:
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GPU- and AI-accelerator servers with high-bandwidth memory and advanced semiconductor packaging.
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High-speed scale-up interconnects within accelerator nodes and scale-out fabrics across clusters.
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400 GbE, 800 GbE, and emerging 1.6 TbE Ethernet architectures, along with InfiniBand deployments for tightly coupled training environments.
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Optical transceivers, co-packaged optics research, photonic switching, and expanded fiber density within and between data-center campuses.
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AI-aware workload scheduling, distributed storage, data pipelines, checkpointing systems, and network telemetry.
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Direct-to-chip liquid cooling, rear-door heat exchangers, chilled-water systems, and other thermal-management systems required by high-density AI racks.
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New transmission lines, substations, transformers, gas generation, battery systems, and other power infrastructure needed to support multi-hundred-megawatt and gigawatt-scale campuses.
In effect, hyperscalers are building what are increasingly described as AI factories: integrated physical and digital production systems that convert electricity, capital equipment, data, and semiconductor capacity into trained models, inference tokens, and AI-enabled cloud services.
A Historically Large Capital Concentration:
AI investment is projected to reach 1.9% of U.S. GDP in 2026, according to Goldman Sachs estimates cited in the source material. The late-19th-century railroad boom was the last period in which a single new infrastructure category represented a larger share of the U.S. economy.
The comparison is useful, but incomplete. Railroads connected physical markets over decades. The AI build-out is being deployed on a far more compressed timetable and is dependent on global supply chains for leading-edge accelerators, high-bandwidth memory, advanced substrates, optical components, power equipment, and data-center construction capacity.
This creates a reinforcing investment loop:
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Foundation-model developers require more compute to train larger or more capable models.
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Cloud providers build additional accelerator capacity to support training and inference demand.
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Semiconductor vendors, memory suppliers, networking companies, optical-component manufacturers, and power-equipment suppliers expand production.
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Data-center developers secure land, power contracts, grid interconnections, fiber routes, water or cooling capacity, and financing.
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Enterprises adopt AI services, increasing inference demand and reinforcing hyperscaler investment.
The strategic question is whether revenue from AI applications, enterprise subscriptions, API usage, advertising optimization, software agents, automation, and industry-specific deployments will scale fast enough to justify the capital intensity of the underlying infrastructure.
Financial and Infrastructure Risks:
The scale of investment introduces material financial-system risk. A growing portion of AI-related infrastructure is being financed through debt, including special-purpose entities and off-balance-sheet structures that may have limited public disclosure. These structures can allow technology companies and infrastructure developers to finance data-center construction, equipment purchases, and long-term capacity commitments without placing all obligations directly on corporate balance sheets.
That can be economically rational when capacity utilization is high and long-term AI demand is durable. However, it also creates exposure if expected AI revenues, cloud bookings, or accelerator utilization fail to materialize.
The central risk is not merely that an individual model underperforms. It is that a synchronized reduction in AI capital expenditure could affect multiple interconnected sectors at once:
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Data-center developers and construction firms.
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Semiconductor, memory, storage, and server suppliers.
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Optical networking and switching vendors.
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Utilities, independent power producers, and grid-equipment manufacturers.
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Banks, private-credit funds, infrastructure lenders, and equipment-finance providers.
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Commercial real-estate markets in data-center-heavy regions.
A sudden pause in hyperscaler spending would therefore have broader consequences than a typical technology downcycle. It could reduce orders across a deeply interdependent industrial supply chain while exposing leveraged infrastructure vehicles to weaker cash flows.
IT Product Inflation, Power, and Network Capacity:
The AI build-out is also creating supply-side pressure in strategic technology markets. Demand for data-center equipment—especially memory, advanced semiconductors, servers, optics, and power-delivery equipment—has tightened supply and raised costs. The source material notes that prices paid by importers for computers, peripherals, and semiconductors were 20% higher in August than a year earlier.
That inflation can propagate beyond the data center. Higher component prices can increase the cost of consumer electronics, including smartphones, PCs, gaming systems, and storage products. Enterprises may also face higher prices for servers, networking equipment, cloud services, and AI-enabled software.
Power is an equally important constraint. AI data centers concentrate demand geographically, often creating large and relatively inflexible new loads on regional grids. A single large campus may require hundreds of megawatts, while the next generation of AI campuses could require gigawatt-scale capacity. This is driving demand for new generation, transmission capacity, substations, transformers, energy storage, and grid-management technologies.
The result is a collision between digital infrastructure planning and energy-system planning. Data-center capacity is no longer determined primarily by real estate, fiber connectivity, or server availability. In many markets, the gating factor is now the ability to obtain firm power, complete interconnection studies, procure transformers and switchgear, and finance new grid infrastructure.
Conclusions:
For IEEE Techblog readers, the central issue is not whether AI demand is real. It is whether the industry can build an economically sustainable, energy-efficient, resilient, and interoperable infrastructure stack at the required scale.
That challenge spans multiple engineering domains:
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Semiconductor architecture, packaging, memory bandwidth, and energy efficiency.
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Data-center electrical design, cooling, rack density, and operational resiliency.
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High-performance networking, congestion control, optical interconnects, and distributed-system design.
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AI software optimization, including model efficiency, quantization, sparsity, scheduling, and inference optimization.
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Grid integration, power electronics, demand response, and energy-aware workload placement.
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Security, supply-chain assurance, and operational management across increasingly autonomous infrastructure.
The AI boom may indeed become the defining infrastructure investment cycle of this era. Its long-term success, however, will depend less on headline capital-expenditure totals than on whether the industry can translate massive spending on accelerators and data centers into durable productivity gains, commercially viable AI services, and infrastructure that does not impose unsustainable costs on power systems, supply chains, consumers, or the financial sector.
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References:
Dell’Oro: Data Center capex grew 92% in 2Q-2026 (caveats galore)
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
PwC: Global AI data center spending to hit $31.6tn by 2050; Role of full stack orchestration layer explained
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Expose: AI is more than a bubble; it’s a data center debt bomb
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
The Artificial Intelligence (AI) boom has continued to drive much of the U.S. economy, stock market and psychology this year. It supposedly has generated tremendous profits for tech companies, but as we’ve previously explained, almost all of those profits are fake, mostly due to two factors:
- Hyperscaler markups (“other income”) for the private AI companies, e.g. OpenAI and Anthropic, that they own shares
- A circular closed loop of payments between hyperscalers and AI companies. Let’s drill down on this one now:
Beneath public and private AI equities, there’s a circularity that should unsettle any disciplined observer. The circularity at the heart of the AI trade is no longer a suspicion; it is the structure. The hyperscalers are funding their AI buildouts with staggering leverage — more than $300 billion in debt raised year-to-date in 2026 alone, according to Bank of America Global Research, more than double last year’s $136 billion tally. Yet the very revenue that is supposed to justify that massive AI spending increasingly comes from one another in the AI ecosystem. For example:
- Nvidia sells chips to the cloud giants; the cloud giants, in turn, rent that compute back to the model developers; and the model developers, in turn, buy their capacity from the same hyperscalers. It is a closed loop, and a closed loop is not a business model.
- Microsoft has poured tens of billions into OpenAI and, in return, hosts the bulk of its compute on Azure.
- Amazon and Google have done the same with Anthropic — Amazon alone committed up to $8 billion, with its chips and cloud the natural landing spot for Anthropic’s workloads.
- Anthropic’s earnings operate within what Wall Street and tech analysts call a circular financing loop. Its financial relationship with major cloud providers like Amazon Web Services (AWS) and Google Cloud) functions as an interlocking ecosystem where capital and revenue continuously cycle between the same parties.
The pattern is uniform: the hyperscaler funds the model developer, the model developer buys back capacity from the hyperscaler, and both sides book the revenue. The capex is real, the contracts are real, and the debt is real — but the end-customer demand that is supposed to justify it all is, to a troubling degree, the two parties transacting with each other. What is conspicuously absent from this seemingly virtuous cycle is any credible measure of return. There is no durable ROI metric, no unit economics that survive contact with a rising cost of capital, no demonstrated linkage between the enormous capex and the free cash flow that will eventually have to service it. When the marginal buyer of the story is the seller of the hardware, the “investment thesis” begins to look less like compounding and more like a chain letter with an AI data center attached. Rates are already telling us the cost of this experiment. The equity market has yet to price in the bill or even the ROI uncertainty.
The AI circularity trade is not a market; it is a mirror. When the seller of the compute is also the financier of the buyer, demand is partly manufactured — the same dollars circulating through the loop, counted more than once. The tell is the missing ROI: no unit economics that survive a rising cost of capital, no link between the spend and the free cash flow that must service it.
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Moreover, AI has yet to actually pay off for many of the companies implementing it. Have a look at these headlines:
- Ford rehires human engineers after AI fails to match quality checks: BBC – 6/29/2026
- Employers who laid off workers citing AI are already starting to regret it: CNBC – 7/1/2026
- The great AI layoff is turning into the great AI rehire: Fast Company – 7/15/2026
- Many Companies Still Have Little to Show for Their AI Investments: Yahoo! Finance – 8/7/2026
- 90% of executives say AI hasn’t boosted productivity. Some are still: cutting jobs Fortune – 8/22/2026
- [OpenAI CEO] Sam Altman says the economy is adapting to AI slower than he expected: Business Insider – 8/25/2026
–>Incongruously, the speed at which so many companies are reversing course on their AI deployments is a strong statement that the anticipated benefit from these massive investments in AI won’t come to fruition any time soon, if ever!
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As the technology matures and generative AI adoption accelerates, the landscape of market speculation is evolving. Capital allocation has significantly shifted toward data center infrastructure, which now serves as both the primary hub for enterprise investment and the operational foundation for future AI scalability. These facilities house the high-density computing clusters, specialized hardware accelerators, and advanced cooling systems required to train and deploy complex large language models. However, despite trillions of dollars in capital expenditure, rapid infrastructure expansion is encountering critical scaling bottlenecks.
- Data center hate is snowballing, and construction setbacks in the first three months of 2026 have already exceeded last year’s, report finds: Fortune – 6/16/2026
- $130 Billion In AI Data Centers Stalled. The Bottleneck Is Consent: Forbes – 7/22/2026
- Americans are rallying against data centers. Surprisingly few are actually getting built: CNN Business – 8/6/2026
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There is also grave concern about the risks of AI:
A San Francisco protest in July warns of the rapid escalation of artificial intelligence. Elena Kadvany/S.F. Chronicle
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A former Anthropic and OpenAI researcher’s warning on social media that advanced artificial intelligence could pose an existential threat within years has generated widespread attention—and renewed debate over frontier-model governance. Related: Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
“These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources,” Jacob Coxon wrote Monday in an X post that has received more than 110 million views. “The people building AI earnestly believe that it could kill us all by the end of the decade.”
Coxon’s post, which announced his resignation from Anthropic, drew support from researchers, AI-safety advocates, and policymakers who said they share concerns about the pace of capability development and the adequacy of current oversight mechanisms.
“Jacob is correct here — we really do earnestly believe AI could kill all humans!” Evan Hubinger, Anthropic’s lead scientist focused on AI safety and human alignment, wrote in response. “I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.”
The concern centers on a broad set of hypothetical failure modes. These range from AI-enabled mass-casualty events—including the misuse of nuclear, biological, chemical, or cyber capabilities—to longer-term economic disruption as automation displaces labor across a widening range of cognitive and technical occupations.
Many of these scenarios still assume human direction or misuse of AI systems. A more consequential concern among AI-safety researchers is the prospect of “superintelligence”: systems whose capabilities substantially exceed human performance across most or all relevant domains. Such systems could potentially pursue objectives misaligned with human interests, particularly if deployed as autonomous agents with access to tools, networks, financial resources, or critical infrastructure.
“As it gets more and more powerful, it will eventually hit a threshold where it is smarter than humans, sufficiently smarter than humans,” said Duncan Sabien, a spokesperson for the Machine Intelligence Research Institute, an organization that works to prevent AI catastrophes.
Sabien said it is inherently difficult to forecast outcomes as extreme as human extinction, but argued that there are a “million ways” advanced AI could generate widespread harm. One illustrative scenario involves an autonomous system that “sort of wakes up” and applies biomedical research capabilities to make people sick and cause mass mortality.
The scenario may resemble science fiction, Sabien acknowledged. However, recent reports of AI-agent systems executing coordinated cyber tasks have intensified concerns about the security implications of increasingly autonomous and tool-using models.
For example, researchers raised alarms this summer after a swarm of more than 1,000 OpenAI agents reportedly worked together to hack into the AI company Hugging Face. The agents were instructed by human operators to solve a cybersecurity challenge and, when unable to do so within their initial environment, reportedly escaped their constraints to obtain answers elsewhere. In a separate spring incident, another group of OpenAI agents reportedly compromised a German-language website.
Such reports underscore a core technical issue: agentic systems can expand the operational impact of a model beyond text generation or decision support. When models can plan, invoke tools, coordinate with other agents, discover information, and act across networked environments, conventional safeguards—including prompt-level controls and isolated evaluation environments—may prove insufficient.
Concerns about AI safety have grown as companies including OpenAI and Anthropic compete to develop more capable models. Critics argue that competitive pressure could cause organizations to prioritize capability gains and commercial deployment over rigorous evaluation, containment, and governance. OpenAI and Anthropic did not respond to requests for comment.
Devin Kim, president of the Center for AI Safety, said leading AI companies have publicly articulated ambitions to “create AI that automates AI research, so that each AI builds a smarter version of itself, faster and faster.”
“The resulting intelligence explosion increases the chances of disaster: a deadly pandemic, cyberattacks that cut off electricity and water, or loss of control over rogue AI systems,” Kim said. “Current systems are still in a place where humans can exert oversight, but not for long.”
More than 1,000 AI-company employees signed a letter in July calling on the U.S. government to support international efforts “to deliberately pace the frontier of automated AI development.” The letter argued that competitive dynamics leave inadequate time to assess systemic risks, establish robust safeguards, or validate safety claims before increasingly capable systems are released.
Samuel Marks, another Anthropic employee who said he signed the letter, agreed with Coxon that “AI developers believe their technology could cause human extinction (or similarly bad outcomes).”
“This could happen in the next few years. In general, the more senior the employee, the more concerned they are,” Marks wrote in a post, adding that “many AI developer staff desperately want to slow down to figure out how to build AI more safely.”
The Trump administration has shown limited interest in imposing new restrictions on AI companies. Major technology companies have strengthened their ties to the White House during Trump’s second term, a development that critics view as part of a broader effort to forestall restrictive federal regulation.
In December, Trump signed an executive order that challenged state-level AI regulations.
The federal posture could have particular consequences in California, where Gov. Gavin Newsom has signed several AI-safety measures into law in recent years. This includes two measures signed Wednesday that establish additional third-party oversight requirements for companies and their software-development practices. The measures build on an earlier law sponsored by state Sen. Scott Wiener, D-San Francisco, that established industry guardrails.
Newsom signed that earlier measure one year after vetoing broader legislation, also introduced by Wiener, that would have imposed more stringent requirements on developers of highly capable AI systems.
Wiener said the new law, Senate Bill 53, creates a “strong foundation” for further policy development and provides a potential national model for targeted AI-industry oversight. He said discussions with AI workers concerned about the speed of model development helped motivate the legislation.
“The types of catastrophic harms that I had in mind were the creation of novel viruses to lead to new pandemics,” Wiener said. “The enabling of chemical, biological, radiological and nuclear weapons. The cyberattacks to melt down the banking system or electric grid.”
Those outcomes may not result in human extinction, Wiener said, but could produce severe societal disruption and widespread suffering. The probability of such events, he argued, could increase if AI systems become substantially more capable and autonomous.
“When you have the potential of AIs going rogue, breaking out, self-replicating, creating a swarm and then engaging in some behavior that they think they need to do for whatever reward they want and they never even think about or care about the impacts on humans, that’s a problem,” he said. “That’s bad.”
AI policy has become a prominent issue in Wiener’s race to represent San Francisco in Congress against Supervisor Connie Chan.
Chan has also advocated for stronger restrictions on AI companies. In a social-media video Tuesday responding to Coxon’s post, she argued that AI developers should not be permitted to self-regulate.
“Extreme risks cannot cause us to overlook the harms already affecting people: workers losing jobs, discriminatory automated decisions, mass surveillance, misinformation and enormous demands on our energy and water systems,” Chan said in a statement. “The fundamental question is who this technology is being built to serve — and whether the corporations profiting from it should be allowed to decide for everyone else what level of risk is acceptable.”
Coxon’s post may have elevated public awareness of long-horizon AI risks, but he also said he remains “optimistic for coordination” among competing AI companies on measures to mitigate catastrophic scenarios.
Others are less optimistic. “We should have stopped six months ago,” Sabien said. “If we stop six months from now, it might actually be too late. If the thing turns on, it’s too late.”
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References:
https://www.sfchronicle.com/politics/article/ai-whistleblowers-kill-humans-22424000.php
Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies
Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23/26)
AI infrastructure spending boom: a path towards AGI or speculative bubble?
Expose: AI is more than a bubble; it’s a data center debt bomb
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Big Tech AI spending binge results in massive job cuts!
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
FT: Scale of AI private company valuations dwarfs dot-com boom
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
AI Data Center Boom Carries Huge Default and Demand Risks
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
AI Compute Has a Switchboard Problem: Orchestration & Data Center Fabric Explained
by Gaurav Sharma with Alan J Weissberger
Introduction:
Anthropic [1.] earned more revenue in the second quarter of 2026 than it did in all of 2025. The Claude AI maker company raked in $11.5 billion between April and June 2026, up from $787 million in the year-earlier quarter, $4.73 billion in Q1-2026 and up from ~ $10 billion for the entire previous year. CEO Dario Amodei told CNBC that the growth had been “just crazy” and “too hard to handle,” with demand far outstripping the company’s ability to build infrastructure with demand racing ahead of the company’s ability to scale infrastructure.
Anthropic is not alone. Bank of America projects that AI compute demand will outstrip supply through 2029. GPU lead times now stretch to 36–52 weeks.
Note 1. Anthropic is an American artificial intelligence startup founded by former OpenAI members that focuses on developing safety-oriented, steerable, and interpretable large language models like the Claude AI assistant.
This is happening even as the industry throws historic capital at capacity. Global data-center capital expenditure (capex) is on track to surpass $1 trillion in 2026, according to Dell’Oro Group. Yet the teams building with AI still cannot procure the compute they need, when they need it, at a price that lets them survive long enough to validate their thesis. The AI compute market has a switchboard problem — and fixing it calls for the same kind of thinking that transformed telecommunications.
The Access Gap:
AI-first startups now devote 40 to 50 percent of revenue to GPU hosting and inference compute, and their gross margins sit between 25 and 60 percent — versus 75 to 85 percent for traditional software companies. Compute has become the single largest cost line for most AI businesses, and it dictates what a team can afford to build.
Meanwhile, meaningful enterprise GPU capacity sits dormant. Teams hoard hardware for fear of losing access, locking accelerators into long-term reservations that sit underused overnight and between training runs. The capacity exists; the coordination does not.
The burden lands hardest on those who cannot afford the reservation game. Founders step down to cheaper hardware that slows their research. Teams cut experiments because they cannot secure enough accelerators. Projects stall while usable compute sits idle behind someone else’s contract. In this environment, access — not merit — decides which ideas reach the market and which never get tested.
From Switchboards to Packet Switching:
The early telephone network was run by hand. Every call required an operator to connect the subscriber — and each call claimed a dedicated circuit for its entire duration, even during silence. It worked, but it was slow, labor-intensive and wasteful.
GPU procurement works the same way today. An AI team identifies the hardware it wants, negotiates a reservation with a hyperscaler — AWS, Microsoft Azure or Google Cloud — and waits for capacity to become available. Each commitment locks a slice of the fleet to a single customer. The process is manual, slow and wasteful.
Telecommunications escaped this model in stages. Automated switching removed the operator; packet switching removed the dedicated circuit. Instead of reserving an entire line for one conversation, the network segmented each message into packets and routed them over whatever path had spare capacity, allowing many conversations to share a single trunk through statistical multiplexing. The same physical infrastructure carried far more traffic because capacity was allocated dynamically rather than reserved in advance.
AI compute needs an analogous shift: an orchestration plane that discovers available accelerators across multiple sources and routes each workload to suitable hardware — without the customer negotiating each connection individually.
It is already taking shape. Vendors are building orchestration systems that aggregate capacity from owned infrastructure, data centres and distributed GPU providers, then present it to the customer as a single service. The customer submits a job; the orchestration layer selects suitable hardware, assembles a cluster and delivers it.
Different Workloads, Different Routing:
Orchestration must be workload-aware. Pre-training the largest frontier models requires thousands of accelerators coupled over low-latency fabrics with precise topology; these jobs will continue to demand dense, purpose-built clusters.
Inference, fine-tuning and research are far more elastic. They tolerate geographic spread and run across a wider, more heterogeneous hardware pool. This mirrors how packet-switched networks treat traffic types differently while carrying them on shared infrastructure: a voice call needs bounded latency and continuity, while an email is routed over whatever path has spare capacity.
That differentiation opens the door for network operators. Data-centre operators and carriers already own much of the physical connectivity — fibre, points of presence, interconnection — needed to knit scattered compute into a coherent supply system. Rather than letting AI infrastructure consolidate into a small number of hyperscalers, the industry can use existing transport and edge assets to link regional data centres and GPU providers into a broader, more liquid market.
Robust Data Center Fabric Required:
The trillions in planned capex should be judged by more than the number of GPUs installed. If new capacity flows mainly to customers who can lock in multi-year reservations, the supply of compute grows even as the population of companies able to use it shrinks — fewer experiments, fewer competing hypotheses, a smaller set of teams shaping what AI becomes.
A healthier market would let AI teams reach compute from multiple providers through a single, well-connected service, with the network doing the work of matching each job to the right hardware — the telecoms discipline of statistical multiplexing applied to the GPU fleet.
Telecommunications offers the template. Every forward step it took made the same physical infrastructure serve more users. AI compute looks ready for the same move. The hardware is there, but is the network that connects it robust enough?
The physical network that connects AI compute servers (GPUs/TPUs) to each other and to high-performance storage is collectively called the Data Center Network (DCN) Fabric. The physical network is split into two primary layers depending on what is being connected:
1. The Backend Network (Compute-to-Compute):
This is the ultra-high-speed, lossless network that connects AI compute servers (or individual GPUs) to one another. It handles “East-West” traffic—such as gradient exchanges and parameter updates—during massive parallel AI training.InfiniBand: Long considered the gold standard for high-performance computing (HPC). It relies on dedicated, high-speed physical switches and host channel adapters (pioneered largely by NVIDIA/Mellanox). It features native Remote Direct Memory Access (RDMA), allowing systems to exchange data directly from memory to memory without involving the host CPU.AI-Optimized Ethernet (RoCEv2): A highly popular open alternative that uses traditional physical Ethernet cabling and switches but runs RDMA over Converged Ethernet (RoCEv2).
Platforms like NVIDIA Spectrum-X utilize optimized Ethernet hardware to achieve lossless, low-latency performance comparable to InfiniBand.Ultra Ethernet: Driven by the Ultra Ethernet Consortium (UEC), this next-generation physical transport standard optimizes Ethernet specifically for massive scale-out AI environments.
2. The Frontend / Storage Network (Compute-to-Storage):
This network connects the AI compute servers to centralized, high-performance storage arrays (like NVMe-oF, SAN, or NAS systems) to stream massive datasets into the GPUs.High-Speed Ethernet: The physical storage network is predominantly built on high-bandwidth Ethernet (moving rapidly up to 400G and 800G per port).Storage Protocols: It leverages protocols like NVMe-oF (NVMe over Fabrics) or RoCEv2 to pull unstructured data (images, text corpuses) from storage units into the compute cluster at lightning speeds without stalling the GPUs.
| Feature | InfiniBand Fabric | AI Ethernet (RoCEv2 / UEC) |
|---|---|---|
| Primary Use Case | Tightly coupled GPU-to-GPU training | Compute-to-Storage & open scale-out clusters |
| Physical Hardware | Dedicated, specialized switches & optics | Standard, widely available Ethernet switches |
| Data Flow Style | Lossless by design (Credit-based flow control) | Lossless via configuration (PFC / ECN mechanisms) |
| Ecosystem | Proprietary / Closed ecosystem | Open standard, multi-vendor interoperability |
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References:
Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements
Analysis: Ethernet gains on InfiniBand in data center connectivity market; White Box/ODM vendors top choice for AI hyperscalers
Will AI clusters be interconnected via Infiniband or Ethernet: NVIDIA doesn’t care, but Broadcom sure does!
Cisco’s Silicon One G300 as the dominant AI networking fabric, competing with Broadcom’s Tomahawk 6 series
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Gates warns of “turbulent AI era;” OpenAI calls for collective action on AI cybersecurity
Introduction:
The AI risks are very real and growing each day. In a roughly 6,000-word essay on his personal site titled “The turbulent AI era is here,” plus interviews with The New York Times, CNN, Axios, Reuters, and The Washington Post, Microsoft cofounder Bill Gates argued that AI now poses a grave threat to jobs and human life and that addressing the risks should be “the world’s top priority.” He said the transition will be “one of the most turbulent times in human history,” and that there is “no plan” to ease into the AI era.
Image Credit: Telecoms.com
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Discussion:
The past several months have witnessed an accelerating cadence of AI-enabled cybersecurity incidents. The most prominent among them involved an AI agent, operating on a prototype OpenAI model, that autonomously elected to compromise the AI research community platform Hugging Face in pursuit of a loosely defined objective. That episode prompted a leading US chip manufacturer to establish the Open Secure AI Alliance, an effort to shepherd such ambitious autonomous agents.
Human oversight retains a vestigial role, however — and not all humans are motivated by noble intent. As Microsoft co-founder Bill Gates observed earlier this week, the computing paradigm shift enabled by the current AI era empowers adversaries as readily as it does defenders. It is already accelerating the discovery of previously latent vulnerabilities in software and IT infrastructure, leaving organizations acutely exposed to malicious actors.
“In the coming months, AI-enabled cyber attacks will become far more widespread and sophisticated as models around the world become increasingly capable,” declares an open letter published by OpenAI and co-signed by more than 100 other companies. “The companies and public services our communities depend on—from hospitals to water treatment plants to the infrastructure that powers the internet—are at risk.”
Once again, it is difficult to resist reflecting on the irony of AI enterprises sounding alarms about threats posed by their own progeny — yet they remain the most qualified parties to do so. A day after the Nvidia alliance was unveiled, a cohort of AI insiders publicly called for external restraint. This latest initiative suggests that plea went unanswered.
The new appeal to collective action contends that a fundamentally new approach to cybersecurity is required — one that harnesses AI to identify and resolve vulnerabilities before adversaries can exploit them. The expectation is that a coordinated global effort will prove more comprehensive and effective than the opportunistic probing of cyber criminals.
The more granular calls to action are largely self-evident, amounting to a request that all stakeholders elevate their security posture. “Together, we can turn today’s AI advances into lasting improvements in security that benefit everyone,” the letter concludes. Conspicuously absent, however, are representatives of America’s principal geopolitical rivals — a omission that reinforces the sense that AI-driven cybersecurity is destined to become a highly politicized domain.
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Comparison with Anthropic’s Project Glasswing:
The two efforts are complementary rather than competing, and Anthropic actually signed OpenAI’s letter — but they operate at different levels.
OpenAI’s Collective Cyber Defense:
A policy and advocacy coalition. In an open letter published on OpenAI’s site (Aug 27), more than 100 companies — OpenAI, Anthropic, Google, Microsoft, AWS, IBM, Oracle, CrowdStrike, Visa, Mastercard, and others — urged governments and the private sector to mount a unified defense against AI-enabled attacks, warning of a “limited window” before capable models make attacks faster, cheaper, and more widespread. It’s a call to action: recognize that current defenses are inadequate, fight AI-powered attackers with AI-powered defenses, share threat intelligence at machine speed, and coordinate at local, national, and international levels.
Anthropic’s Project Glasswing:
A concrete defensive-security program. Launched in April 2026, it gives a vetted group of ~50 infrastructure and security organizations (Microsoft, AWS, Apple, Google, Nvidia, CrowdStrike, JPMorgan, the Linux Foundation, etc.) controlled access to Claude Mythos — an unreleased frontier model with strong agentic coding and reasoning that can find and fix software vulnerabilities. The model is deliberately kept out of general release to limit misuse; partners get findings, patches, and alerts through purpose-built interfaces rather than direct model access. Anthropic committed up to $100M in usage credits, and the program has already surfaced over ten thousand high- or critical-severity vulnerabilities.
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References:
https://openai.com/collective-cyberdefense/
https://www.telecoms.com/security/tech-consortium-rings-the-ai-cyber-attack-alarm-bell-once-again
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