The early phase of the generative artificial intelligence revolution resembled the “picks and shovels” stage of a gold rush. The focus was on building the core tools, such as semiconductors, cloud platforms, and foundational models that made AI possible on large scale. Chipmakers such as Nvidia and Taiwan Semiconductor Manufacturing Company provided the computing power with specialized semiconductors, while hyperscalers and start-ups laid the groundwork with infrastructure and large-language-model development.
Today, that foundation is rapidly transforming into an industrial build-out. From our perspective, capital spending is surging as hyperscalers and enterprises race to construct AI-optimized data centers, deploy advanced networking, and secure sufficient power. The Magnificent Seven have helped establish this trend, dedicating billions of dollars to various AI initiatives.
In our opinion, the market narrative has shifted from simply supplying the tools to full-scale commercialization, where the ability to train, deploy, and scale generative AI systems at massive scale is becoming the new competitive frontier.
Where AI Is Going
The road ahead points to both extraordinary opportunity and real separation between winners and losers. In our view, the early stage of the AI revolution was fueled heavily by investor enthusiasm, with capital chasing a wide set of AI ventures. As the industry matures, scrutiny is shifting toward companies that can demonstrate tangible progress, scalable business models, and clear proof of concept. Those that can convert investment into real-world applications and sustainable growth are likely to emerge as leaders, while less viable players risk being left behind. In our view, the baton of leadership will potentially pass on from riskier ventures to higher quality operators.
Demand for power, already a critical constraint, will continue to escalate as AI applications expand into consumer and enterprise use cases. Those with access to cheap, reliable energy and efficient infrastructure will gain an edge, while laggards risk falling behind.
Beyond infrastructure, the next frontier could be applications: productivity software, humanoid robotics, autonomous systems, and more. The AI leaders will capture value as adoption accelerates, while weaker players may struggle to monetize despite technological promise.
Across the AI value chain, we’re seeing this transition already. Palantir (PLTR) has emerged as a front-runner in enterprise AI adoption. Its Artificial Intelligence Platform (AIP) is being deployed by governments and corporations to integrate large-language models into real-time decision making. Nebius (NBIS) anchors the infrastructure layer with AI-optimized data centers, backed by a multi-year Microsoft agreement and new capital for expansion. CoreWeave (CRWV) adds cloud GPU capacity at scale, highlighted by a major deal with Meta. Together, these firms illustrate how leadership is consolidating around companies delivering real adoption, capacity, and customer traction.
Generative AI appears to be justifying the value proposition at the company level. In a recent Dallas Fed survey, ~60% of companies reported an increase in productivity (or expect one) with the implementation of generative AI. It will be crucial for companies to find ways to properly leverage generative AI and streamline processes.
For investors, the future of AI lies not just in allocating to innovation, but in discerning which companies can scale sustainably in a resource-intensive, hyper-competitive landscape. AI next-twelve-month P/E multiples are elevated. In our view, high valuations can be justified as long as they are supported by growth, cash flow, and progress. Companies that struggle could be punished with multiple contraction.
The global generative AI industry is in an exciting state of transition and transformation. Investors are shifting their focus from the “picks and shovels” of the AI revolution towards companies that are showing meaningful integration and implementation of AI applications at the software level. High quality operators are likely to be rewarded, while the laggards will be left behind. In our view, generative AI has approached a level of maturity that businesses can start to capture real-time benefits and could streamline operational processes. Headwinds, such as power constraints, continue to loom, but the aggressive industrial buildout and capital expenditure highlight profound optimism for the future.
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