It wouldn’t be far-fetched to describe the current AI bull run as being one of the most concentrated leadership cycles in modern market history. A handful of mega-cap technology companies have driven an outsized share of index returns, capital flows, and sentiment. Recognizing the fragility of this setup, many investors have been gradually reassessing concentration risk and exploring whether leadership may broaden.

One popular response has been to rotate toward value and cyclical sectors—energy, industrials, materials, transports, and select financials—on the premise that the next phase of the AI cycle may favor the beneficiaries of adoption rather than the architects of innovation.

According to a recent poll conducted by BlackRock, only about a fifth of investors still consider large US technology firms a compelling investment for the coming year. By contrast, 54% see energy providers as offering stronger opportunities, while 37% view energy infrastructure as a better avenue than the traditional Big Tech leaders. 

The underlying argument here does make sense. 

AI’s value is increasingly being captured not by the companies building the technology, but by those integrating it into real-world operations—what experts call the “physical reality” of AI. Utilities, industrials, and logistics firms are now positioned to benefit from surging demand, efficiency gains, and margin expansion, often with far less competitive disruption risk than software companies.

In fact, just the amount of energy demanded by AI is set to more than double over the next decade, placing grid operators, power providers, and infrastructure developers squarely at the center of this growth story.

Data centers, which operate around the clock and require constant cooling and high-performance servers, create structural demand for electricity that is unlikely to wane. As such, investors are increasingly recognizing that supporting AI at scale is not just about software platforms—it’s about ensuring reliable, sustainable energy and robust transmission networks.

Given these broader dynamics, financial advisors and portfolio managers need to appreciate that the next phase of market leadership is likely more diffuse and infrastructure-driven. The opportunities for alpha may no longer reside solely in software innovators but in the ecosystem that enables AI at scale—from energy generation and distribution to industrial operators, logistics firms, and transportation networks.

Building a 2026 AI Barbell Portfolio

Top investment strategists, such as Patrick Huber, of LGT Private Banking, are increasingly advocating for a strategic barbell approach to portfolio construction in 2026. With the AI cycle entering a phase where infrastructure and adoption matter as much as innovation, concentrating solely on mega-cap software exposes portfolios to both valuation and disruption risks. 

The recommended allocation splits roughly into 40–50% on the growth side—companies enabling AI through energy infrastructure, physical security, and other critical bottlenecks—and 50–60% on the value/cyclical side, targeting high-cash-flow firms in industrials, logistics, banks, and pharma that are integrating AI to improve efficiency and margins.

This balance allows advisors to capture alpha across the AI ecosystem while mitigating concentration risk. The growth allocation benefits from continued capital spending and structural demand, while the cyclical allocation profits from “productivity arbitrage,” as legacy companies re-rate on improved profitability. Active stock selection, rather than passive tech ETF exposure, ensures portfolios avoid overvalued software facing competitive disruption.

Needless to say, however, even with this balanced approach, risk management is essential. Portfolio managers would need to assess valuation ceilings, Big Tech CapEx slowdowns, or shifts to debt-funded AI spending. Any red flag on any of these fronts would signal the need to reassess allocations, potentially trimming growth exposure and increasing emphasis on cyclical beneficiaries. This is because the barbell framework is not static—it requires dynamic monitoring to respond to changes in AI spending patterns, economic indicators, and sector-specific developments.

Beyond 2026: The Convergence of Physical and Digital

Ultimately, the transition to a barbell strategy reflects a maturing market that recognizes AI is no longer a localized "tech sector" event. It is a fundamental rewiring of the physical economy. 

By balancing the high-octane growth of energy and infrastructure with the disciplined efficiency of industrial adopters, the ideal 2026 portfolio should seek to turn the volatility of innovation into the stability of a new industrial revolution.

This shift marks a profound change in how investors must think about risk and opportunity. The old paradigm, where alpha was concentrated in a handful of software giants, no longer captures the breadth of potential value creation. Today, the engines of growth are distributed across the economy—embedded in power grids humming with data center demand, in factories orchestrated by intelligent robotics, and in logistics networks optimized by AI agents. Each of these sectors represents a tangible, structural contribution to productivity, providing a foundation that is less susceptible to hype cycles yet still capable of delivering outsized returns.

In this environment, the role of the advisor evolves from simply picking the “next big tech winner” to orchestrating portfolios that can dynamically capture alpha across an increasingly complex AI ecosystem. Monitoring sector rotations, infrastructure trends, and adoption metrics requires continuous data analysis, scenario modeling, and strategic rebalancing—tasks that can quickly become overwhelming when done manually. 

One way to excel in this environment is to leverage automated, rules-based portfolio construction, where advisors and retail investors alike can implement barbell allocations with precision, adjusting exposure to growth and cyclical sectors in real time as market conditions shift. Surmount’s strategic tools help bridge this gap by providing both the visibility and the automation required to act decisively.

Rather than reacting to each market headline, users can rely on systematic frameworks that translate data into actionable allocations, preserving upside potential and controlling downside risk.