Big Tech’s AI Bill $3 Trillion Bigger Than It Looks

The words “Artificial Intelligence” are seen alongside a keyboard and robotic hands in this illustration. (Reuters) 
The words “Artificial Intelligence” are seen alongside a keyboard and robotic hands in this illustration. (Reuters) 
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Big Tech’s AI Bill $3 Trillion Bigger Than It Looks

The words “Artificial Intelligence” are seen alongside a keyboard and robotic hands in this illustration. (Reuters) 
The words “Artificial Intelligence” are seen alongside a keyboard and robotic hands in this illustration. (Reuters) 

Each quarter, big tech companies disclose their massive capital expenditures on artificial-intelligence infrastructure, from data centers to chips.

But those figures don’t come close to expressing the full extent of future spending to which Google parent Alphabet, Meta Platforms, Oracle and many others have committed.

That is because a huge swath of their coming financial obligations are not reflected on their balance sheets.

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings.

Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

America’s blue-chip tech companies are placing these huge bets based on assumptions about what the demand for AI computing—and availability of AI hardware—will be in several years.

Their hope is that they will easily meet all their obligations with future revenue as consumers and businesses adopt AI in every facet of American life.

If those assumptions about technology and demand prove wrong, these deals to clinch future capacity could become a monstrous burden for the tech companies and their investors.

Meta’s gigantic “Hyperion” datacenter project in Louisiana, which is the size of about 1,700 football fields, helps explain how big obligations wind up off tech companies’ balance sheets.

Meta initially agreed to lease Hyperion for a four-year term starting in 2029, with options to renew for up to 20 years.

It guaranteed that it would make bondholders whole if it does not stay the entire two decades. The company did not think payments under that guarantee are probable, so it has not recorded any liability on its balance sheet.

In accordance with accounting rules, Meta’s Hyperion lease obligations will remain off balance sheet until it starts paying rent.

It said its aggregate initial lease commitment is about $12.3 billion.

Meta disclosed $347 billion in total obligations for leases that have not kicked in yet, including for Hyperion, as of June.

Across the companies the Journal analyzed, promises of payments under these uncommenced leases totaled $1.2 trillion in off-balance – sheet obligations, or about four times more than what was disclosed a year earlier.

In addition to Meta, the Journal reviewed commitments for Alphabet, Amazon.com, Microsoft, Oracle, Nvidia, Broadcom, SpaceX and Advanced Micro Devices.

Data centers get stuffed with a lot of hardware, including the Nvidia chips that are used to train and run models and memory chips that store information.

To buy all that, companies sign long-term contractual agreements well in advance to lock in production from their suppliers.

Those and other purchase obligations at the companies the Journal examined stand at a whopping $1.9 trillion. Under accounting rules, purchase commitments typically remain off balance sheet until a product or service is delivered.

Alphabet’s purchase commitments and contractual obligations have exploded and stood at $811 billion as of June 30, the WSJ reported.

As with other companies, it is hard to tell from its disclosures what precisely it intends to buy.

The company said the commitments primarily relate to “technical infrastructure and inventory” and “agreements to secure energy for data center usage.”

Alphabet also didn’t detail why those obligations increased so much from the $332 billion it reported three months earlier. The commitments span several years, with obligations under its energy agreements lasting as far out as 2054.

Off-balance-sheet exposures at some companies include agreements to buy other companies’ stock in the future or backs to pleases for other tenants.

Nvidia committed to make $27 billion in equity investments between April 26 and the end of its fiscal year in January 2027.

There are reasons to believe tech companies will make good on all their obligations.

Optimists see the skyrocketing demand for AI tools—which has lifted the stock market and led to shortages of key hardware—as a proof point that demand is going to be strong for years, and the money to pay off all these bills will be rolling in.

For the more anxious set on Wall Street, it is a worrying sign that some tech companies that once seemed to have fortress balance sheets have needed to tap the capital markets frequently.

Alphabet and Amazon recently posted results showing negative free cash flow, meaning their capital spending exceeded the cash they brought in from operating their businesses.

And that is before considering the implications of trillions in off-balance – sheet commitments.

Whether or not the revenues ever arrive, purchase commitments and signed leases can’t be canceled, for the most part.

If things go wrong, tech companies will be paying an expensive tab for infrastructure that they cannot profitably use.

These obligations could also lead increasingly indebted companies to have to borrow even more.

“As these off-balance sheet commitments become more frequent, larger, and more complex, it is becoming increasingly difficult for investors to assess companies’ total potential leverage,” Morgan Stanley accounting analysts wrote in April.

Meanwhile, researchers at the European Central Bank on Monday posted their rather ominous conclusions on how all this may end, saying the timing and extent of a stock reversal were inherently “unknowable” in advance but that a correction was coming regardless, according to Reuters.

The ECB blog noted that the financial-stability concerns were not confined to America, as US megacaps are widely held by European households, insurers and pension funds.

It said a correction was an inevitable feature of such technological revolutions and investment booms, while exposure was amplified by the concentration in giant market valuations and index tracking. All involved needed to be prepared.

“Historical experience suggests that technological revolutions carry risks of a boom-bust cycle in asset prices, and this risk does not depend on today’s valuations being rational or irrational,” they wrote, metaphorically fastening their safety belts.

 

 

 



China's Tencent Says AI Agents Showed Unintended Browsing Behavior

FILE PHOTO: The Tencent logo at the company's headquarters during a government‑organised media trip in Shenzhen, Guangdong province, China, April 17, 2026.  REUTERS/Go Nakamura/File Photo
FILE PHOTO: The Tencent logo at the company's headquarters during a government‑organised media trip in Shenzhen, Guangdong province, China, April 17, 2026. REUTERS/Go Nakamura/File Photo
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China's Tencent Says AI Agents Showed Unintended Browsing Behavior

FILE PHOTO: The Tencent logo at the company's headquarters during a government‑organised media trip in Shenzhen, Guangdong province, China, April 17, 2026.  REUTERS/Go Nakamura/File Photo
FILE PHOTO: The Tencent logo at the company's headquarters during a government‑organised media trip in Shenzhen, Guangdong province, China, April 17, 2026. REUTERS/Go Nakamura/File Photo

Tencent said on Friday that AI agents used to evaluate third-party models displayed web-browsing behavior not intended by the tech giant, in what appears to be the first known incident of rogue agent actions acknowledged by a major Chinese company.

The statement followed a report by AI-safety research group Swarmchasers on Wednesday, which said "rogue" agents running on infrastructure it linked to Tencent Cloud made repeated attempts between September 28 and October 8 to retrieve information from Alibaba's Amap, China's equivalent to Google Maps.

Tencent Hunyuan, the company's artificial ⁠intelligence unit, said in ⁠a post on X that the activity did not originate from its Hunyuan models. Its review found no evidence of deliberate targeting or coordination, Reuters quoted it as saying.

The Swarmchasers researchers said the agents sought information such as the commonly used entrances at Chinese parks, museums and zoos.

The actions are not as egregious as those disclosed by US AI companies which include the hack of ⁠open-source platform Hugging Face by an OpenAI agent and the lodging of a fake homicide tip by an Anthropic agent.

But the disclosure underscores research that shows Chinese AI agents can also misrepresent their identity, conceal failures and try to circumvent testing controls.

Tencent said it took the findings very seriously and was strengthening network access controls and agent monitoring.

Hunyuan is one of China's leading AI model developers, competing with in-house teams at rivals such as Alibaba and ByteDance. ByteDance this week announced the creation of a new unit focused on AI model and agent safety.

Swarmchasers said it used the ⁠word "rogue" to ⁠describe agents taking actions outside their intended scope or completing a task in unintended ways.

After routine automated requests were blocked, the agents tried other methods, including public web tools that could open pages and run code, routing requests through other websites and using Amap keys and security codes found in publicly available software code.

Swarmchasers said it found no evidence that the agents breached Amap's main anti-bot safeguards. Most of the 2,818 attempts it recorded either failed or returned anti-bot pages, login screens or blank results.

Some requests returned information through cached pages and intermediary services, including data on visitor arrivals at Chengdu Zoo, Swarmchasers said. The researchers said they could not determine whether an agent read the result or completed its task.


Anthropic Co-founder Renews Call for Robust Regulation of Frontier AI

AI Research Company Anthropic's Co-Founder and Head of Public Benefit Jack Clark speaks during the Artificial Intelligence and the Global Economy From Risk to Opportunity event of the 2026 Annual Meetings of the IMF and the World Bank Group in Bangkok, Thailand, 16 October 2026. EPA/RUNGROJ YONGRIT
AI Research Company Anthropic's Co-Founder and Head of Public Benefit Jack Clark speaks during the Artificial Intelligence and the Global Economy From Risk to Opportunity event of the 2026 Annual Meetings of the IMF and the World Bank Group in Bangkok, Thailand, 16 October 2026. EPA/RUNGROJ YONGRIT
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Anthropic Co-founder Renews Call for Robust Regulation of Frontier AI

AI Research Company Anthropic's Co-Founder and Head of Public Benefit Jack Clark speaks during the Artificial Intelligence and the Global Economy From Risk to Opportunity event of the 2026 Annual Meetings of the IMF and the World Bank Group in Bangkok, Thailand, 16 October 2026. EPA/RUNGROJ YONGRIT
AI Research Company Anthropic's Co-Founder and Head of Public Benefit Jack Clark speaks during the Artificial Intelligence and the Global Economy From Risk to Opportunity event of the 2026 Annual Meetings of the IMF and the World Bank Group in Bangkok, Thailand, 16 October 2026. EPA/RUNGROJ YONGRIT

Anthropic co-founder Jack Clark renewed calls on Friday for regulating artificial intelligence, saying that allowing companies to continue to operate without robust legislation was like letting a race car loose on the streets at night without brakes.

"It's remarkable to me that the toys I buy for my children are regulated more than the literal super intelligence I work on at my day job," he told an event at the IMF-World Bank's Annual Meetings in Bangkok.

Clark said that US President Donald Trump, whose administration has called for the industry to police itself, was against the idea of regulation but that China was not, said AFP.

"I think it's Formula One at night without maybe brakes in the cars," he said.

He said the lack of clarity around the will for regulation meant "the Formula One car is roaring round and nobody seems to want to put up a guardrail?"

At an industry summit in Dublin on Wednesday, calls to prioritize safety in the development of powerful AI technology dominated the conversation.

Concerns about AI guardrails have soared after models developed by OpenAI and Anthropic were involved in security incidents during testing, piling on pressure to regulate the technology.

A recent Anthropic report suggested small AI models may be only months away from behaving like computer worms, copying themselves from one internet-connected device to the next.

Anthropic has been among those companies calling for governments to step in, even suggesting international coordination to slow down the whole industry -- including Chinese rivals.

That initiative has so far failed to materialize, with companies involved in a hyper-competitive race to attract as much investment as possible.


Meta Releases Open-Weight Muse Spark Model for Coding, Agentic Tasks

The logo of Meta is seen at the entrance of the company's temporary stand ahead of the World Economic Forum (WEF) in Davos, Switzerland January 18, 2025. (Reuters)
The logo of Meta is seen at the entrance of the company's temporary stand ahead of the World Economic Forum (WEF) in Davos, Switzerland January 18, 2025. (Reuters)
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Meta Releases Open-Weight Muse Spark Model for Coding, Agentic Tasks

The logo of Meta is seen at the entrance of the company's temporary stand ahead of the World Economic Forum (WEF) in Davos, Switzerland January 18, 2025. (Reuters)
The logo of Meta is seen at the entrance of the company's temporary stand ahead of the World Economic Forum (WEF) in Davos, Switzerland January 18, 2025. (Reuters)

Meta ‌Platforms on Thursday released an open-weight version of its Muse Spark artificial intelligence model for coding and AI agents, expanding developers' ability to download and adapt the technology.

Open-weight models make trained parameters available for developers to modify, unlike proprietary models ‌that remain under ‌their creators' control. ‌They ⁠can offer businesses ⁠a lower-cost alternative to closed systems.

Meta said Muse Spark has 816 billion total parameters, with 42 billion active at a time, and ⁠can handle a context window ‌of up ‌to 1 million tokens.

The ‌model can run locally on ‌two Apple M5 Ultra Mac Studio computers and is also available through some API providers.

CEO ‌Mark Zuckerberg has argued that advanced AI should ⁠not be ⁠concentrated among a small number of companies.

While open-weight models can give companies greater control over their data, they can require significant computing resources and technical expertise to operate at scale.

In August, Meta released Muse Glimmer, a smaller open-weight model.