The five largest U.S. hyperscalers plan to spend roughly $660 billion to $690 billion on AI infrastructure in 2026 alone, according to Futurum Group research published in February.
That figure nearly doubles what the same companies spent just 12 months earlier, per CreditSights projections.
Nvidia stock is up, Broadcom stock is up, and every brokerage in America has an AI picks list ready for your screen. If you are a long-term investor, you might assume you have already heard everything worth hearing about this trade.
You have not.
A senior technology specialist at Wellington Management, one of the oldest active managers in the country, just laid out a thesis that reframes the entire AI opportunity. His argument does not center on which stock to buy next quarter; it centers on a shift that has barely begun.
The AI ecosystem has layers that most investors overlook
Brian Barbetta is a technology and AI specialist at Wellington Management and co-portfolio manager of the Vanguard Wellington U.S. Growth Active ETF (VUSG) and the Vanguard Global Equity Fund (VHGEX).
In a Q&A published on Vanguard’s advisor insights page, Barbetta broke the AI sector into four distinct layers that matter for your portfolio decisions going forward.
Four layers of the AI sector you should understand
- Infrastructure: Companies building the physical backbone, including data centers, power systems, and semiconductor makers like Nvidia and Broadcom.
- Enablers: Foundational large language model creators such as OpenAI and Anthropic, plus cloud providers such as Google, Microsoft, and Amazon
- Applications: Software companies like Adobe that embed AI into products you already use, including coding copilots and image generation tools
- Beneficiaries: Health care providers, banks, and financial institutions using AI to improve efficiency across their existing operations and services
Most investors focus only on the first layer, buying chipmakers and hoping for the best without understanding the full value chain. Barbetta pointed out that the hyperscalers now span multiple layers at once, which makes their competitive position uniquely durable.
Reasoning models changed the AI game in a single year
If you think AI peaked with the launch of ChatGPT in late 2022, Barbetta’s timeline suggests you are behind at least one major leap. He identified the introduction of reasoning models in 2025 as the single biggest evolution since ChatGPT’s debut shook global markets.
The original large language models simulated the most statistically likely answer to your question, which made them impressive but unreliable. Reasoning models changed the process entirely by allowing the AI to critique and refine its own responses before delivering a final answer.
These newer models require significantly more computing power, which explains the record-breaking infrastructure spending you keep reading about. Goldman Sachs projects that hyperscaler capital expenditure from 2025 through 2027 will reach $1.15 trillion, more than doubling the $477 billion spent over the prior three years.
For your portfolio, this means the demand driving AI infrastructure stocks is not speculation about a distant future or wishful thinking. Real usage of these systems has grown exponentially, and the computing requirements behind that usage keep expanding in measurable ways.
Agentic AI could be the real portfolio game-changer
Here is where Barbetta’s thesis takes a turn that should get your attention if you invest in technology or use it daily. He described agentic AI as the next major unlock, a system that does not just answer your questions but completes tasks on your behalf.
Imagine giving your AI assistant access to Excel, Outlook, Bloomberg, and the internet, then asking it to complete a full research project. That is not science fiction or marketing hype, according to Barbetta, though he cautioned it remains early to say whether 2026 delivers it.
What agentic AI could mean for everyday investors
The practical implications extend well beyond Wall Street trading desks and institutional research departments into your household decisions.
Barbetta offered a simple consumer example that captures the scale of what could happen if agentic AI reaches mass adoption soon.
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Instead of asking AI to plan your vacation, you could let it take control of your browser and actually book it for you completely. The same logic applies to managing your finances, scheduling appointments, comparing insurance quotes, and handling routine administrative tasks.
For investors, the key insight is that preparing all data for AI today is labor-intensive and limits how useful these systems truly become. Letting AI interact directly with software programs could dramatically expand the total number of use cases and revenue streams available.
Why this AI cycle looks different from the dot-com crash
Every time AI stocks rally, someone compares the moment to 1999, and you have probably heard the dot-com bubble analogy more than once.
Barbetta pushed back on that comparison directly by pointing to one critical difference separating today’s environment from that earlier era.
More AI Stocks:
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Most AI spending today shows positive, measurable return on invested capital across both infrastructure and the cloud, he told Vanguard.
The infrastructure being built is producing attractive returns using reasonable financial assumptions, unlike the speculative dot-com buildout of the 1990s.
Where the real AI bubble risk exists right now
Barbetta did not dismiss the AI bubble risk entirely, and you shouldn’t either if you are building a long-term portfolio around AI exposure. He specifically flagged pockets of overexcitement in private markets where some investors may not fully understand the risks they are taking.
He also warned about neocloud companies that rent GPU computers to hyperscalers at inflated prices driven by the current supply shortage. When that supply eventually catches up with demand, these companies could face severe economic challenges that their current valuations do not reflect.
A Moody’s Ratings report published in March 2026 noted the combined backlog of contracted revenue across key hyperscalers has reached approximately $1.7 trillion. That contracted demand offers real support, but it does not guarantee that every company riding the AI wave will survive the eventual shakeout.
How Vanguard’s specialist identifies real AI winners
Not every AI stock that looks like a leader today will remain one, and Barbetta was blunt about what separates true winners from pretenders. His framework focuses on a single principle that should guide every investment decision you make inside this rapidly evolving sector right now.
The moat test that separates winners from also-rans
Companies that compete primarily on price will always have limited returns, no matter how fast their revenue grows in the short term. True long-term winners build natural monopolies or near monopolies with clear competitive advantages and defensible moats around their businesses.
Barbetta broke down what specific advantages he sees at the major players that could determine the next decade of AI market leadership.
- Google: Lowest cost to serve because of custom hardware, vast proprietary data, and enormous distribution through its applications and devices
- Meta: Applies AI to advertising technology where financial returns on investment are among the highest of any business model worldwide
- OpenAI: Built enormous consumer usage with ChatGPT, though whether that lead holds as competitors like Google Gemini advance remains uncertain
- Anthropic: Has demonstrated exceptional engineering strength in code generation on Claude, which may give it a natural edge among enterprise developers
Meanwhile, the hyperscalers share one durable advantage that smaller competitors cannot match, regardless of how clever their technology may be. They can redeploy massive amounts of capital continuously and keep growing at a scale that creates compounding benefits over long time horizons.
Market concentration creates an opportunity for active investors
If you own an S&P 500 index fund, you already have heavy exposure to the same mega-cap AI companies dominating headlines every single week. Barbetta argued that this concentration actually creates a unique opportunity for active managers willing to take calculated, benchmark-relative risks.
His approach uses a benchmark-relative active risk framework that asks one clear question about every position inside the portfolio he manages.
That question is straightforward but powerful: How much risk does each position carry relative to the benchmark, and what does it look like overall?
When a big bet against a company can generate alpha
Significant overweights and underweights are both useful tools in concentrated markets where a few stocks drive the majority of returns.
If a company is on the wrong side of a major technological change, a large underweight position can generate meaningful alpha for the portfolio.
Related: Cathie Wood buys $2 million of tumbling AI stock
When the market overreacts to a negative headline, as Barbetta says it occasionally does with AI leaders like Google, active managers can lean in.
Wellington-managed Vanguard funds have outperformed peer-group averages in 11 of 12 solely managed funds over the 10-year period ending September 2025.
For your own portfolio, the lesson is that passive exposure alone may not capture the full opportunity in an AI-driven market. Consider whether a blend of index funds and actively managed strategies might help you navigate the winners and losers more effectively.
How you can use AI to sharpen your own financial decisions
Barbetta’s advice was not limited to professional money managers or institutional advisors with Bloomberg terminals on their desks every morning. He made a direct recommendation that applies to any investor, financial advisor, or household trying to make smarter decisions with available tools.
His core message was simple: People who embrace technology advancements early can quickly become super performers in their respective fields. Rather than fearing AI disruption, Barbetta suggested leaning in and learning exactly how these tools work to strengthen your financial life.
Practical steps you can take right now:
- Load information about your investments, goals, and risk tolerance into your preferred AI model to generate personalized preparation memos.
- Use AI to retrieve and analyze financial data before making portfolio decisions, then go back and forth to refine your research approach.
- Let AI handle routine data tasks so you can focus your energy on the high-value, personal decisions that actually require human judgment.
- Start small by experimenting with free AI tools for budgeting, tax estimation, or retirement planning before committing to paid services.
The biggest risk is not that AI replaces your judgment as an investor or a professional managing your own household financial decisions.
The biggest risk, according to Barbetta, is that someone else masters these tools first and gains an advantage you cannot easily close later.
What this means for your portfolio in 2026 and beyond
The AI trade is entering a new phase, and the usual strategy of buying chipmakers and hoping for the best will not be enough going forward. Vanguard’s global chief economist Joe Davis has said the firm expects AI to stand out among megatrends for its capacity to transform labor markets.
Davis compared the current AI investment cycle to the railroad buildout of the mid-19th century and the 1990s telecommunications infrastructure surge. Vanguard projects up to a 60% chance that the U.S. economy achieves 3% real GDP growth in coming years, driven partly by AI investment.
Your next step should not be chasing the latest AI stock tip or loading up on a single semiconductor name that dominated last quarter’s news. Instead, focus on understanding which companies in your portfolio have genuine moats, real returns on invested capital, and durable competitive edges.
Agentic AI is coming, and it will reshape how you interact with your money, your advisor, and your daily life in ways that feel inevitable later. The investors who understand this shift before it becomes consensus will be the ones best positioned for what the next five years actually deliver.
