Hedge Fund giant Bridgewater Associates recently warned that the artificial intelligence (AI) spending boom is entering a “dangerous” phase as major technology companies increasingly rely on external financing to fund and sustain mounting AI infrastructure costs.
According to Greg Jensen, the firm’s Co-Chief Investment Officer, this “dangerous” phase creates conditions that could allow for a significant market bubble that could rival past technology bubbles, as the escalation signals growing market anxiety about whether the massive capital investments will ultimately generate sufficient profits to justify the valuations.
The fundamental problem Bridgewater identifies and speaks on is that AI infrastructure costs have grown far beyond what companies can fund internally. For instance, according to a UBS report, AI data center and project financing deals surged to $125 billion through November 2025, up from just $15 billion in 2024, and representing an eight-fold increase in a single year.
It is why Jensen stated clearly that “going forward, there is a reasonable probability that we will soon find ourselves in a bubble,” based on the fact that companies’ internal cash flows prove insufficient to cover the explosive growth in data center and computing infrastructure demands.
Additionally, this warning comes during a sensitive time for the AI market, especially following Oracle’s recent announcement of capital expenditures (capex) reaching $50 billion, exceeding earlier estimates, and triggering a sharp 13% stock price decline. This sheds light on how even industry leaders are pushing financial limits to continually fund and sustain their AI ambitions.
The Unsustainable Scale of AI Infrastructure Investment
The magnitude of projected capex these tech giants involved in the AI arms race have reveals the scope of the challenge of adequately funding AI infrastructure. For instance, Bank of America credit strategists have warned that AI capital spending by major tech firms will consume 94% of their operating cash flows in 2025 and 2026, rising from 76% in 2024, and threatening the structural limits of what companies can self-fund without borrowing on further relying on external capital.
There is also the unprecedented demand for computing power, as it requires a data center buildout of humongous proportions. However, this infrastructure expansion faces many physical and logistical constraints. And at the same time, valuations across the entire AI ecosystem have skyrocketed, especially with the U.S. becoming increasingly concentrated around this technology, which in turn creates a fragile foundation if these AI infrastructure investments fail to materialize.
Conversely, the shift toward external capital has also become unavoidable as the scale of AI infrastructure requirements goes way beyond what individual companies can fund from their own pocket based on the fact that it involves funding everything from advanced chip production to massive power and cooling systems needed for next-generation data centers.
Circular Financing Structures: A Red Flag
Adding to Bridgewater’s concerns are increasingly complex interconnected financing arrangements that echo problematic patterns from past technology bubbles like the dotcom bubble burst in the early 2000s.
The financial firm made note of how chip-makers are investing in AI companies that purchase their products, which is creating circular dependencies. For instance, Nvidia recently invested up to $100 billion in OpenAI, with plans for the ChatGPT-maker to use those funds to purchase Nvidia chips and rent capacity from Oracle, which itself also buys Nvidia hardware.
Industry analysts call this “circular financing” and warn about the structure creating a market concentration among a select few, which could lead to a bubble.
This structure of funding and buying also resembles the vendor financing schemes that characterized the telecom bubble of the early 2000s, where companies artificially inflated demand through circular financial relationships rather than genuine market need.
However, Bridgewater also notes that while this current structure closely resembles and shares traits with past bubbles like the dot-com era, it differs in the urgency expressed by many tech executives who frame AI infrastructure investment as a competitive principle that is necessary for long-term survival as opposed to the general consensus of it merely being a business opportunity.
And this is exemplified in the way Alphabet, Meta, and Amazon have continuously committed to aggressive spending regardless of immediate profitability pressure. This move has so far contributed to a market concentration where just five companies now account for 30% of the S&P 500’s weight.
Can Tech Giants Avoid the AI Bubble?
While the concentration of AI-driven growth bolsters U.S. equities, it also introduces systemic vulnerabilities, especially as Bridgewater has flagged underappreciated risks including policy shift under President Trump and global supply disruptions that could derail timelines.
As such, the core question remains whether Big Tech can translate this monumental investment into sustainable economic advantages before the weight of external capital and financing becomes too heavy and demands for its returns.
