Artificial intelligence is reshaping not only the technology sector, but also how investors define value. In a recent webinar, Matthew Tuttle, CEO and CIO of Tuttle Capital Management, and Frances Newton, CIO of Tuttle Wealth Partners, discussed how the rapid expansion of AI infrastructure is creating new investment opportunities while challenging traditional approaches to portfolio construction.
Key Takeaways
- Nearly 63% of advisors surveyed allocate less than 15% of client portfolios to energy, utilities, industrials and materials despite their growing role in the AI buildout.
- The AI expansion is shifting capital allocation beyond traditional software into physical infrastructure, driving massive investments in energy, utilities, industrials, and raw materials.
- To navigate potential software disruption, market experts recommend capturing value in durable, physical assets that supply the resources AI requires to operate.
AI’s Infrastructure Buildout Is Creating New Investment Opportunities
A central theme of the discussion was the shift from investing primarily in AI developers to investing in the infrastructure that enables AI to scale.
Tuttle highlighted the growing importance of bottlenecks such as data centers, semiconductors, photonics and energy infrastructure. He argued that the record capital expenditures of hyperscale technology companies represent a structural shift: AI growth increasingly depends on investment in hard assets, not just software innovation.
To capture this opportunity, Tuttle introduced the heavy assets, low obsolescence (HALX) framework, which underpins the Tuttle Capital Heavy Assets Low Obsolescence ETF (HALX). Rather than targeting the companies building AI models, the strategy focuses on businesses that own or operate the essential infrastructure supporting AI, including power, transportation and other capital-intensive assets.
To illustrate the gap between the AI narrative and portfolio positioning, the panel shared the results of a live audience poll. Nearly 63% of advisors reported allocating less than 15% of client portfolios to energy, utilities, industrials and materials. The experts said the results suggest that many investors continue to view AI as a software story rather than an infrastructure one.
Electricity was a key example. Both speakers noted that connecting new data centers to the electrical grid can take five to 10 years in many regions. As a result, technology companies are increasingly securing long-term power agreements and investing directly in energy infrastructure to support future AI growth.
See More: Inside HALX: Top Holdings, Weightings, and What Makes the ETF Unique
Diversifying AI Exposure Beyond Technology
The webinar also explored why investors may need to broaden how they think about AI exposure.
Tuttle divided the opportunity into two categories:
- Digital bottlenecks: Companies involved in advanced semiconductors, memory and photonics. These businesses may benefit directly from AI adoption but can also experience greater volatility because of technology cycles.
- Physical bottlenecks: Utilities, energy producers and infrastructure companies that supply the electricity, transportation and materials needed to support AI expansion.
He argued that combining exposure to both groups can create a more balanced AI allocation because they often respond differently to changing market conditions. Tuttle also discussed his H.E.A.T. framework — hedge, edge, asymmetry, themes — as a way to evaluate portfolio construction, risk management and thematic opportunities.
Newton approached diversification from a macroeconomic perspective, explaining that gold is best viewed as protection against fiat currency debasement rather than a consistently reliable hedge against equity market declines.
Rethinking Value Investing in the AI Era
The speakers also questioned whether traditional definitions of value investing remain sufficient as AI reshapes competitive dynamics.
Tuttle discussed how generative AI tools, including Claude and ChatGPT, are increasingly capable of automating tasks once performed by employees. He used this trend to illustrate what he described as the “SaaSpocalypse” — the pressure facing some software companies as AI democratizes information and analysis.
As a result, the panel argued that low valuation multiples deserve closer scrutiny. A company trading at a discount may not simply be undervalued; it may instead be facing long-term disruption as AI changes how value is created.
Newton added a macroeconomic perspective, highlighting how higher interest rates, bond yields and Federal Reserve policy can create additional headwinds for traditional value-oriented investments.
How Tuttle Builds the HALX Framework
Continuing the earlier discussion, Tuttle explained that the HALX framework screens for companies with three defining characteristics:
- Significant physical assets;
- Low risk of technological obsolescence; and
- Lower exposure to AI-driven business model disruption.
Newton framed the approach as a long-term wealth strategy designed to help investors avoid value traps in knowledge-based industries while emphasizing businesses with durable assets that may better preserve purchasing power in an inflationary environment.
Focusing on Durable AI Infrastructure & Tangible Assets
While AI can improve the efficiency of these businesses, it cannot replace the underlying assets they own and operate. The broader takeaway was that as AI adoption accelerates, investors may benefit from looking beyond the companies developing the technology to those providing the essential infrastructure that enables it.
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