Those pointing fingers at Nvidia (NASDAQ:NVDA) for an “AI bubble” are missing the mark. The real overreach isn’t coming from the company that’s actually constrained by compute resources.
Instead, it’s the legion of publicly traded and private companies whose valuations are built on Nvidia’s narrative, even though they don’t possess the hardware, the energy infrastructure, or the distribution channels that bring that narrative to life.
The harsh reality is that the biggest danger in AI stocks right now is the mindless, easy money that’s treating every CEO who mentions AI as a stand-in for Nvidia’s market control.
This trend is already warping tech indices, spilling over into digital asset markets, and preparing a correction for anything that depends on the “AI story” rather than actual infrastructure, energy, or real-world distribution.
Nvidia’s $5T Headline Is a Distraction From the Real Excess
Nvidia’s market cap recently surpassed $5 trillion, pushed by a jump in demand for AI chips. It took the company mere months to leap from a $4 trillion valuation to this new peak. The stock has also surged nearly 30% this year, and an astonishing 1,200% over the last five years, prompting critics such as Apollo’s Torsten Slok and Alibaba’s Joe Tsai to sound the alarm bells about a potential bubble. But they’re missing the point when it comes to Nvidia.
Unlike the late ’90s tech boom, which was all about selling dreams and banner ads, the chipmaker occupies a real choke point. They control high-end computing power and the energy needed to operate it. Training and deploying cutting-edge models demands hardware and power that most AI companies simply don’t have, and finding alternatives isn’t straightforward.
And yes, Nvidia’s worth might be inflated, but it’s grounded in a physical bottleneck that’s already holding back major players in the tech world. The same can’t be said for those companies whose high valuations are based on the idea that Nvidia’s limitations somehow protect them.
OpenAI’s “Rough Vibes” Memo Exposed the New Fault Line
The disparity between tangible infrastructure and the buzz surrounding AI startups became painfully obvious following the leaked memo, allegedly from OpenAI’s Sam Altman.
Just as Google was gearing up to launch Gemini 3.0, the memo surfaced. It painted a picture of a “rough vibes” competitive landscape, acknowledged Google’s advancements, and cautioned about a possible revenue growth deceleration of at least 5%. That’s not the typical language of a company with significant pricing power.
The financials attached to that moment were even more revealing. OpenAI was projected to blow past $20 billion in 2025 revenue from roughly $4.3 billion in the first half of the prior year and around $4 billion across 2024.
Yet a Reuters report suggested a cash burn of more than $8 billion in the coming year and cumulative losses potentially hitting $115 billion by 2029. Revenue and cost move in lockstep because every incremental user sits on top of rented infrastructure.
In contrast, consider a company like Google (NASDAQ:GOOG) (NASDAQ:GOOGL) , which has a unified operational model. Its artificial intelligence efforts are built on a unique system. This system includes a large global user base that constantly generates data, custom Tensor Processing Units designed for its specific tasks, and distribution through widely used tools.
For companies like OpenAI, they must rent compute, source data under increasingly contentious conditions, and persuade users to adopt new platforms. Those are not temporary disadvantages that will vanish with the next product release; they are structural gaps.
So, if growth was to slow down for some of the most hyped contenders, these gaps would be brutally exposed, and stock prices that were essentially options on unlimited compute and frictionless adoption would most likely re-rate.
The Physical Bottleneck Exposing Speculative Claims
The most damning fact for the AI hype complex is not a softening in demand — it is the inability to deliver the compute and power needed to meet that demand.
The “Magnificent Seven” and the next tier of AI hopefuls are competing over a finite pool of high-end accelerators and grid capacity. Backlog numbers and power delays tell a harsher story than any bullish investor deck.
For instance, CoreWeave’s (NASDAQ:CRWV) revenue backlog sits at around $55 billion, and the company has scaled back its 2025 capital expenditures by as much as 40%, pointing to setbacks in delivering power infrastructure.
At the same time, Oracle (NASDAQ:ORCL), with a backlog nearing $455 billion linked to significant contracts with Meta, OpenAI, and xAI, is also facing capacity constraints, even as it turns away potential clients. This is not a demand problem, but rather a physics problem.
That constraint acts like a live-fire stress test. Companies that have secured long-term access to megawatts, datacenters, and hardware pipelines can keep scaling. Those whose entire business plan depends on renting someone else’s compute, while that very compute faces severe allocation constraints, will be exposed.
A Lesson for Risk Assets
The above scenario won’t be limited to the AI space alone. Bitcoin’s recent struggles, including a retest of the $80,000 support level, can be looked at as a preview of the capital flight that will occur when the overhyped AI stock bubble pops.
The liquidity drain from a tech equity correction will most likely wash over the entire risk-on spectrum, and crypto will not be spared, especially considering there are more than 1,300 AI-related tokens currently in the market. This is the inevitable consequence of a market that has rewarded narrative over substance.
While that shakeout will be brutal, it will be necessary in order to force capital to be reallocated away from copy-cat AI trades and toward teams building real, scalable infrastructure.
This reckoning will also highlight a new structural advantage: privacy. Countless AI systems process sensitive documents and databases without hardware-level security for “data in use.” This should create a massive opportunity for firms building confidential AI and decentralized compute networks, a far better use of capital than funding marketing-heavy startups that simply shout “AI” the loudest.
Benzinga Disclaimer: This article is from an unpaid external contributor. It does not represent Benzinga’s reporting and has not been edited for content or accuracy