Beyond the magnificent 7: Managing concentration risk in the age of AI

How the AI super cycle took hold

AI arrived at a moment of extraordinary liquidity and low capital costs. Years of accommodative policy had built an environment where scale was rewarded. The companies best positioned to deploy AI—those with global reach, data, and computing capacity—expanded faster than the market itself.

At the same time, technology became the channel through which much of the world’s productivity gains were realised. The result: a handful of firms accounting for a disproportionate share of global earnings growth. In effect, AI accelerated a pre‑existing concentration trend, turning big tech into infrastructure for the digital economy.

Investors benefited enormously. But as the gap between the largest and the rest widened, diversification within indices quietly diminished. Even balanced portfolios now depend on a few companies whose influence rivals entire sectors.

Why every innovation cycle starts narrow

Technological revolutions rarely begin broadly. The early gains accrue to the firms that combine capital, talent, and distribution. In the industrial age, that meant railways and steel. In the information age, it meant internet platforms. In the AI era, it means semiconductor leaders and cloud providers that control compute and data.

This concentration is part of innovation’s normal rhythm. Capital flows first to proof of concept, the companies proving a new technology can generate profit. Once adoption spreads and competition builds, returns start to normalise. That transition from concentration to diffusion is where the next wave of opportunity forms.

The macro forces behind concentration

Concentration doesn’t just emerge from technology, it is reinforced by macro conditions.

• Liquidity. Ample liquidity supports high valuations and encourages passive inflows, which in turn channel more capital toward the largest benchmark constituents.
• Policy. Low borrowing costs and supportive fiscal spending favour growth sectors with strong balance sheets. These firms use cheap capital to consolidate leadership.
• Globalisation and scale. The network effect of global markets rewards reach. AI models trained on vast data sets benefit from size in a way small competitors cannot easily replicate.

When any of these conditions reverse, when policy tightens or competition rises, the market’s centre of gravity begins to shift. Recognising that pivot early is a macro investor’s edge.

AI and the next productivity wave

AI’s long-term economic impact will come from productivity. The first phase of the boom rewarded enablers, the chipmakers and platform providers. The next will depend on how deeply AI raises efficiency across industries.

History suggests that once a general‑purpose technology diffuses, productivity gains broaden while profit concentration narrows. Electricity and the internet followed that path. AI is likely to do the same.

Sustained productivity growth changes everything: wages, inflation, policy responses, and equity leadership. As output per worker rises, margins can expand across sectors, from logistics to healthcare to finance. The market moves from a handful of dominant innovators to a larger set of adopters and integrators. This is the macro foundation of the next diversification cycle.

The future of market leadership

Leadership will not disappear; it will evolve. The current giants may remain essential, but new winners will emerge in areas where AI meets real‑world constraints: energy, infrastructure, industrial automation, and cybersecurity.

Energy is critical because AI consumes power at unprecedented rates. Infrastructure will matter because data centres, grids, and supply chains must scale to match digital growth. Automation will matter because every productivity cycle ultimately feeds into manufacturing and logistics. And cybersecurity will matter because connected systems multiply vulnerability.

As capital flows into these areas, the market’s leadership will broaden. Investors will no longer rely solely on software platforms for exposure to the AI economy. They’ll find it across sectors that combine digital and physical productivity.

What investors should watch

• Earnings breadth. When more sectors contribute to index earnings growth, diversification is taking hold.
• Capital expenditure trends. Rising investment outside of big tech indicates diffusion of AI benefits.
• Policy incentives. Government funding for energy transition, grid expansion, and automation supports new leaders.
• Valuation dispersion. Narrowing valuation gaps between large and mid‑caps often mark the start of a broader cycle.
• Liquidity direction. Tightening conditions tend to compress multiples for over‑owned leaders, accelerating rotation.

Monitoring these signals helps investors anticipate when concentration may start to unwind.

Positioning for the broadening phase

Long‑term investors can prepare for this evolution without abandoning innovation exposure.

• Combine enablers with adopters. Keep exposure to AI leaders while adding sectors that will apply AI at scale—energy, industrials, healthcare, and logistics.
• Diversify geographically. The U.S. dominates software and platforms, Asia leads in semiconductors and manufacturing, and Europe in clean energy and automation. Owning all three captures different stages of the AI value chain.
• Use balanced vehicles. Equal‑weight or factor‑balanced ETFs reduce single‑stock influence. Active global equity funds can identify emerging leaders before they reach index scale.
• Add real‑asset exposure. Infrastructure and commodity investments benefit from the same macro drivers (energy use, digital construction, and automation) without overlapping tech valuations.
• Reassess benchmarks. Standard indices may not reflect where growth will occur next. Thematic or blended benchmarks can better capture structural change.

This approach keeps portfolios exposed to innovation while restoring diversification that pure market‑cap weighting has eroded.

The broader macro implications

AI’s influence reaches beyond markets. As productivity and automation reshape labour demand, fiscal and monetary policy may adapt. Lower inflationary pressure could extend economic cycles. At the same time, energy demand and infrastructure bottlenecks could generate new sources of investment and volatility.

For investors, that means equity leadership will rotate alongside sectoral shifts in growth, pricing power, and capital intensity. Technology will remain central, but its definition will expand—from software to systems, from code to capacity.

This diffusion is healthy. It signals that AI’s economic value is being realised across the global economy, not confined to a handful of platforms.

An enduring lesson from past cycles

Every major technological revolution follows a similar arc: concentration, diffusion, maturity. Investors who recognised that arc in earlier eras—moving from railways to industrials, from mainframes to personal computing, from internet pioneers to cloud providers—captured long compounding trends.

The next chapter will look familiar. AI is the enabler, but leadership will shift toward those who integrate it into tangible productivity. The future of diversification lies in owning both the code and the capacity.

Conclusion

The Magnificent 7 remain anchors of global equity markets, but their dominance will not define the entire AI era. Over time, growth will broaden as the technology they champion becomes part of every industry’s toolkit. The same macro forces that built concentration—liquidity, policy, and scale—will eventually fuel dispersion as investment spreads outward.

For investors, the goal is not to predict when leadership changes but to stay positioned for it. A balanced global portfolio rooted in innovation, but diversified by region, sector, and factor, offers the best path to capture AI’s long‑term promise without taking concentrated risk.