AI investing is moving beyond the market leaders that defined its first phase. Raul Moreno, co-founder of Andova AI, explains why a more selective and adaptive approach is needed as leadership rotates across the AI supply chain, and why AI itself is becoming an important tool for identifying where value is emerging.
The first phase of the AI boom was extraordinary, but relatively easy to understand. Investors backed the most visible companies linked to the sector, with Nvidia and the other Magnificent Seven companies the clearest examples.
These leaders helped define the first stage of AI investing, but they no longer tell the full story.
As the industry matures, the AI buildout is reaching deeper into the supply chain. The opportunity is no longer limited to the companies producing the most recognisable chips, models or applications. It is spreading across the infrastructure that makes AI possible, from semiconductors and memory to data centres, cloud infrastructure, networking, power and the companies applying AI inside major industries.
For the market, this creates a fragmented landscape. Leadership is rotating across different parts of the AI supply chain, and understanding where value is emerging requires a more adaptive approach than simply backing the most visible names.
That is why AI is becoming an increasingly valuable tool for investors. When used properly, it can help process larger volumes of information, identify patterns across sectors and assess where the next areas of growth may emerge.
The future of AI investing will not only be about investing in AI, but using AI to invest more intelligently in the companies powering its development.
The first phase of the AI boom
The early days of AI investing shared some characteristics with previous technology booms,including the dot-com boom of the late 1990s and early 2000s.
The companies that were easiest to understand and most clearly connected to the theme attracted the greatest attention. Capital flowed towards the most obvious beneficiaries, particularly those supplying the hardware and infrastructure needed to train and deploy new AI models.
Nvidia became the symbol of that phase. Its chips were central to the AI buildout, and its growth reflected the urgency with which major technology companies, cloud providers and enterprises were investing in AI capacity, smashing records on its path to becoming the first company to reach a $4 trillion market cap.
The other Magnificent Seven companies also became shorthand for the market’s concentration around the largest technology platforms and the companies seen as best positioned to benefit from AI adoption.
This early concentration was understandable. In the first stage of a new market cycle, investors will naturally start with the clearest beneficiaries. The companies with the most visible demand, strongest pricing power and most direct link to the theme tend to attract the most capital.
The new era of AI investing
The first phase of AI investing is giving way to a broader and more complex opportunity set beyond Nvidia and its Magnificent Seven peers.
Investors are already looking beneath the headline names and towards the infrastructure underpinning AI growth, with AI hardware and component suppliers across South Korea, Japan, and Taiwan benefiting from demand for the technologies needed to support the next phase of the buildout.
As investment moves deeper into the physical infrastructure needed to support AI at scale, understanding where value and bottlenecks exist across the supply chain is becoming far more important than simply backing the headline leaders. Leadership will not sit in one part of the stack permanently.
In one period, the strongest opportunities may sit in chips or semiconductor equipment. In another, they may sit in memory, cloud infrastructure, networking, power or companies applying AI inside major industries.
That rotation is creating a more fragmented opportunity set, where selectivity and adaptability matter more than ever.
Using AI to invest in AI
With that complexity comes a greater need for better tools. The AI supply chain is global, fast-moving and increasingly data-rich. Understanding it requires analysis of financial performance, valuation, market signals, competitive positioning and the relevance of AI to each company’s future growth.
Doing that manually across thousands of listed companies is difficult. The volume of information is too large, and the pace of change is too fast for traditional research processes to capture every relevant shift in real time.
AI can help with this challenge through real-time analysis and data processing, sifting through vast datasets and helping investors differentiate between sectors across the AI supply chain.
However, expert human judgement remains integral. Far from supplanting the role of professional investment management, AI can enhance the process by giving investors more time to interpret and evaluate information rather than collating it.
With so much attention focused on the investment opportunity within the AI boom, the competitive advantage may actually lie in utilising AI to invest.
The future of AI investing
AI investing is maturing, and it will be shaped by the ability to understand where AI is creating durable value across a wider financial ecosystem.
As AI evolves beyond software and becomes embedded across more sectors and industries, the data required to understand and differentiate its value is increasingly complex.
AI will also help to confront this challenge by processing information alongside the expert understanding of where long-term value lies, so investors can distinguish between short-term excitement and sustainable growth.
The next phase of AI investing will belong to those who can strike this balance most effectively.
