The Future of AI: A $7 Trillion Bet
Updated: Sep 28
"This is the industrial revolution.”
-- Jensen Huang, Nvidia CEO
Every article and talking head is now debating, "Is AI a bubble?" We believe "yes" and "no." This perspective reminds us of our experience investing during the internet bubble 25 years ago. (Yes, we're that old.) In the following video, Steve shares our nuanced view. He explains how we're positioning client portfolios along a path less traveled. This path allows us to profit regardless of which AI model or technology ultimately wins the race.
The Big Bet on AI
A staggering $7 trillion has been committed toward artificial intelligence and data center capital expenditures over the next five years. For perspective, that’s roughly seven years’ worth of U.S. defense spending. This is the largest capital commitment we've ever seen in absolute dollars. Why would some of the smartest people on the planet, like Zuckerberg, Sergey, and Musk, bet the ranch on this?
Understanding the Investment
According to Anthropic researcher Julian Schrittwieser, AI models are roughly doubling in capability every seven months. Today’s systems are akin to a hyper-diligent, brilliant high-schooler. Yet, within just seven months, they’ll be able to reason with the experience of a seasoned 32-year-old professional.
White-collar wages total approximately $50 trillion globally. It’s easy to imagine AI making a significant portion of that $50 trillion “replaceable” or “augmentable.” If even a fraction of that productivity can be captured, it would mean a massive return on the $7 trillion investment.
The Skepticism Surrounding AI
Despite all the enthusiasm, there’s a disconnect between the vast capital outlays and the meager near-term cash flows from AI models. Today’s global AI revenue is only about $40 billion, with nearly all profits accruing to one company: Nvidia. Everyone else is losing money.
Worse, the large language models (LLMs) already feel like a commodity. Many users routinely use multiple models simultaneously. They do this because they lack conviction that any one model is the best. It’s often better to ask the same question to several models and compare their answers. People also understand that the “best” model today may be leapfrogged tomorrow by a new and improved version of a competing model.
Two years ago, OpenAI was miles ahead. Now, credible challengers like Google DeepMind, xAI, Anthropic, Perplexity, and a swarm of Chinese open-source models operate at 90% of U.S. models’ capability at just 10% of their cost. This situation does not suggest monopoly economics; rather, it signals a price war in the making.
Hence, the murmurs of “bubble” among skeptics. Yes, the AI productivity revolution may arrive — but the profits might not accrue where investors expect.
Echoes from the Year 2000
If all this feels familiar, it should. History teaches us that transformative technologies often overpromise in the short run and underdeliver in profits — until they don’t. The trick is to survive the hype cycle without missing the structural gains that follow.
In January 2000, many professional investors worried that the internet boom was in a bubble. They were right — at least in price terms. The NASDAQ soon thereafter fell approximately 75% from peak to trough. Yet the internet’s transformative impact was not a mirage. From the eve of that crash to today, the S&P Tech Index has risen roughly 10–12 times — almost twice the total return of the S&P 500.
Today’s AI boom may rhyme with that era. Prices may overshoot, yet the technology may still prove transformative. The challenge for capital allocators is to separate the price bubble from the underlying fundamental revolution.
The Path Less Traveled in AI Investment
While the AI “model layer” looks crowded, the physical build-out beneath it — the data centers, power infrastructure, semiconductors, and cooling systems — is anything but abstract. These are shovel-ready projects backed by signed contracts, zoning approvals, and megawatts of electricity demand.
Today, U.S. data centers consume about 5% of national power. By 2030, that figure could double to 10%. Every “free electron” — whether from West Texas natural gas, Scandinavian hydro, or Middle-Eastern solar — is being spoken for. This is driving a quiet boom in energy infrastructure and industrial metals. Nuclear, gas, oil, solar, wind — even coal, where permitted — are all part of the “all-of-the-above” strategy required to feed AI’s appetite for electrons.
The $7 trillion bet on AI is already underway. The electrons will flow, the models will evolve, and productivity will increase — somewhere. But as investors, our goal isn’t to predict which model wins. It’s to own the assets that get paid, no matter who prevails.
At Fielder, we believe there is a more compelling opportunity among the “picks and shovels.” This includes the enabling infrastructure and the downstream beneficiaries of AI’s higher productivity. These entities may profit regardless of which model wins the algorithmic race.
If you’d like to learn more about how we're positioning and navigating this theme, please reach out.
Yours in the Field,
Frank Byrd, CFA, CFP
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