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In the late summer of 2025, Oracle’s stock surged 25%, propelled by a single announcement: a $60 billion annual deal to provide cloud computing for OpenAI, the creator of ChatGPT.
The catch?
Michael Cembalest, the Chairman of Market and Investment Strategy for J.P. Morgan Asset & Wealth Management encapsulated it in a nutshell.
“Oracle’s stock jumped by 25% after being promised $60 billion a year from OpenAI, an amount of money OpenAI doesn’t earn yet, to provide cloud computing facilities that Oracle hasn’t built yet, and which will require 4.5 GW of power (the equivalent of 2.25 Hoover Dams or four nuclear plants), as well as increased borrowing by Oracle whose debt to equity ratio is already 500% compared to 50% for Amazon, 30% for Microsoft and even less at Meta and Google.
In other words, the tech capital cycle may be about to change.”
Cembalest’s comment that this deal signals a shift in the tech capital cycle is one that eerily recalls the speculative fervor of the dot-com era, a warning of sorts.
Together with Next Thing Technologies [ [link removed] ]
NVIDIA’s $46.74B Earnings Prove AI Is Here to Stay…And This Can Power It All [ [link removed] ]
NVIDIA shocked Wall Street again with $46.74 billion in revenue, up 56% year over year. Their CFO now expects $3–$4 trillion in AI infrastructure spending by 2030.¹ Oracle’s results tell the same story.
The company reported $14.9 billion in revenue, up 12% year over year. Cloud infrastructure sales jumped 55%, while long-term cloud contracts surged to $455 billion — a 359% increase in just one year.
The message couldn’t be clearer: AI isn’t a bubble. It’s the new economy.¹
The Problem No One Talks About:
Data centers and AI supercomputers run 24/7, devouring electricity.
But lithium batteries can’t keep up.1 They’re too expensive, supplies are tight, and China controls more than 80% of global lithium processing.
If AI is going to scale to trillions of dollars, the real constraint isn’t chips…
It’s power storage.¹
The Breakthrough That Could Change Everything
That’s why sodium-ion batteries are emerging as the solution.¹
Sodium is 1,400x more abundant than lithium, with significant reserves located in the U.S9
Batteries can be made at a fraction of the cost of lithium — up to 90% less expensive in the first year of use.6
They last longer8, are safer7, and can be built at scale using domestic materials.1
And Next Thing Technologies, an American-owned company, is bringing this breakthrough to market. Their sodium-ion batteries are designed to power everything from massive AI data centers to affordable home backup systems.¹
Think about it: NVIDIA builds the chips.
Oracle runs the cloud. But without safer, cheaper, U.S.-made batteries, the AI boom hits a wall.
Next Thing Technologies is building the infrastructure that could make it possible.¹
Already, 7,800+ investors have committed more than $7 million.3 And right now, shares are available at just [ [link removed] ]$6 with up to 20% in bonus shares.⁴ [ [link removed] ]
👉 [ [link removed] ]Click here to invest in the power behind the AI revolution. [ [link removed] ]
Are we witnessing the birth of a transformative industry or the inflation of a dangerous bubble?
The AI industry’s meteoric rise feels both exhilarating and unnervingly familiar. AI Backbone companies like OpenAI, Nvidia, and Oracle are betting billions on a future where artificial intelligence reshapes entire economies and societies. Yet, beneath the glossy projections, a circular economy could be emerging—one where tech giants cycle investment deals among themselves, and where valuations are based on promises of growth, that for now, remain speculative.
In certain ways this dynamic mirrors the dot-com boom, when companies like Nortel Networks soared on the hype of future internet infrastructure only to collapse when the math didn’t add up. Nortel’s market cap peaked at $398 billion in 2000, accounting for a third of Canada’s stock market, before plummeting [ [link removed] ] to near-zero by 2002.
The brutal lesson was clear: unchecked optimism, fueled by tantalizing theoretical profits, can lead to catastrophic reckonings.
Analyst's have projected that generative AI could add $2.6 trillion to $4.4 trillion to the global economy by 2030, but only if companies can translate adoption into scalable revenue models.
OpenAI’s valuation has soared. From $29 Billion in 2023 to its valuation this week of $500 Billion [ [link removed] ] making it the most valuable privately held company ever. [ [link removed] ]
In light of the above table, consider OpenAI’s deal with Oracle. The $60 billion figure is eye-popping, but OpenAI’s revenue streams—primarily from subscriptions and enterprise licensing—fall far short of justifying such a commitment. Despite ChatGPT’s global ubiquity, with millions using AI tools daily, the raw monetization of generative AI, for the moment, remains embryonic. This is not to say that it won’t grow to eventually fund that $60 Billion a year annual deal with Oracle but right now its embryonic.
This disconnect raises a question: how sustainable (or stable) is a business model built on promises rather than profits?
Nvidia, another pillar of the AI boom, exemplifies this paradox. The company’s chips power the data centers driving AI innovation, and its revenue has soared. [ [link removed] ] [ [link removed] ]Yet for sometime now, whispers [ [link removed] ] surround its sales. Industry analysts, such as @JustDario [ [link removed] ] on X, have questioned who exactly is buying these chips [ [link removed] ] and how those deals are structured [ [link removed] ].
These details are murky [ [link removed] ] at best and in some cases downright opaque [ [link removed] ]. Are tech giants stockpiling hardware in anticipation of demand, or is this a game of pass-the-debt, where purchases are financed [ [link removed] ] by funds borrowed against inflated valuations?
This lack of transparency harks back to the dot-com era’s frenzy, when companies like Pets.com burned through cash on unproven business models, only to fold when investors demanded results.
Or not?
The scale of OpenAI and Oracle’s ambitions are most starkly illustrated by its energy demands [ [link removed] ] and it is only when those numbers are examined that the scale of the task ahead rears its head.
Oracle’s proposed data centers would require 4.5 gigawatts of power, enough to supply power to 3 million homes.
For context, Cembalest references the Hoover Dam [ [link removed] ], which powers 1.3 million homes, generates roughly 2 gigawatts annually (depending on water levels) and cost $900 million (in 2025 dollars) to build in the 1930s.
Replicating that today, adjusted for modern costs, environmental regulations, and geographic suitability would be a multibillion-dollar endeavor.
Nuclear power offers a more realistic solution, but even that is daunting.
The Vogtle Electric [ [link removed] ] Generating Plant’s recently activated Units 3 and 4 in Georgia, which cost $37 billion and together they produce 3.4 gigawatts.
To meet Oracle’s needs, an additional reactor would be needed, conservatively pushing costs toward $55 billion just to build the energy infrastructure required to feed the datacenter.
$55B would be before accounting for building the data centers themselves or the chips to fill them.
Perhaps the power stations will be funded by the federal government which will defray the cost, but Government intervention will bring inevitable oversight and delays as it is no small thing to put up three nuclear reactors. Also, there are already questions being asked about the percentage of public state electricity consumed by data centers.
With nearly 40% of Virginia’s power now being routed to data centers, this very quickly is going to become a political issue.
No seriously, show me the money?
Debt is the undercurrent that is pulling this enterprise into uncharted waters. Oracle’s debt-to-equity ratio, already orders of magnitude higher than Amazon’s, signals a company stretching its financial limits. OpenAI, too, despite its latest valuation which is staggering, is banking on a future where its they becomes the Google or Apple of AI for the next quarter-century.
Sam Altman’s vision is certainly bold: If OpenAI truly can deliver the world’s most advanced AI, today’s investments will look like pocket change and he will rightfully go down in history as one of humanity’s greats alongside Jobs, Tesla, Edison and Newton.
But the scale of borrowing—coupled with an as yet unproven revenue model—invites comparisons to Enron, which infamously booked theoretical future profits before they had been made (granted this was done with the complicity of their auditor Arthur Andersen.)
While no one accuses OpenAI or Oracle of Enron’s criminality, their plan’s total reliance on speculative gains raises red flags. Cembalest’s quote suggests that even titans of Wall Street’s are starting to feel skeptical about the viability of these grand plans.
Even when an industry is mature, titans can fall. Blackberry was once the ultimate communicator but was blown to dust by the iPhone, and the AI “market” is no where near as mature as the consumer cellular market was in 2007.
The AI boom’s parallels to the dot-com era are not just financial but cultural. In the late 1990s, the internet was hailed as a transformative force, much as AI is today. Yet, the gap between vision and eventual execution proved fatal for many early companies. Nortel’s collapse left thousands jobless and investors ruined, a cautionary tale of what happens when hype outpaces reality.
Today’s AI enthusiasts argue that the technology’s potential—its ability to revolutionize healthcare, education, and industry—justifies the risk. But potential is not profit, and the logistical hurdles of scaling AI are gargantuan. From power generation to chip supply chains, the infrastructure required demands not just capital but time, political will, and environmental trade-offs.
The AI industry could indeed reshape the world eventually, Jeff Bezos certainly thinks so, [ [link removed] ] delivering fantastical breakthroughs that entirely justify today’s gambles. But the path forward requires sobriety, not exuberance. Policymakers must grapple with the environmental and economic implications of AI’s growth, while investors should demand transparency in how deals are structured and funded. For the public, the challenge is to separate genuine innovation from speculative froth. The dot-com bust taught us that not every revolution delivers on its initial promises, even if they do in the end.
As we stand on the cusp of AI’s next phase, the question looms: are we building a new era of progress, or chasing shadows of a boom that cannot last?
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DISCLAIMER
Please refer to our full Disclosures page [[link removed] [ [link removed] ]] to see important information regarding the statements made herein, sometimes identified by superscript numbers (¹, ², etc.).
Forward-looking statements, performance and progress claims (cost, safety, longevity), and market data are speculative estimates based on current assumptions, involve risks, and are not guaranteed. Past performance does not predict future results, specific data requires verification, third-party mentions are informational only, and offer terms may change without notice.
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