Is Now The Time?
Artificial Intelligence Could Be Moving From Experiment To Infrastructure
Artificial intelligence is not new. Machine learning, neural networks, and automation technologies have existed for decades. What has changed is the potential scale of deployment. Businesses are moving beyond experimentation and toward implementation, integration, and infrastructure.
When technologies transition from research projects to enterprise priorities, the flow of capital tends to follow. Today, investment in semiconductors, data centers, networking, power infrastructure, and enterprise software is rapidly changing as organizations build the capacity for broader AI adoption. We believe this transition from experimentation to deployment is one of the factors that make the current environment structurally different from earlier periods of AI development.
Infrastructure Precedes Productivity
Historically, transformative technologies have required substantial infrastructure investment before their economic benefits became widely visible. Electricity required decades of grid development. The internet required fiber networks, servers, and data centers before global platforms emerged. Artificial intelligence appears to be following a similar path. Compute capacity, data infrastructure, networking, and energy systems must expand before productivity gains can be realized at scale. We believe much of today’s opportunity lies within the businesses enabling that expansion.
Economic Value Accrues Unevenly
Not every company participating in AI will benefit equally. Some of the most important opportunities may exist within the infrastructure, equipment, and services required to support adoption rather than within the end applications themselves. Broad market indices are designed to reflect company size, not structural change. As a result, many of the companies building AI infrastructure, enabling deployment, or supplying critical components may represent only a small portion of traditional index exposure despite their role in the broader ecosystem.
Could parts of AI be overhyped? Is this Just a Bubble?
Possibly. Periods of technological transformation are often accompanied by speculation, over-investment, and valuation excesses. History offers many examples, including railroads, electricity, the internet, and cloud computing. The possibility of a bubble does not necessarily invalidate the underlying technology. While many individual companies may ultimately disappoint, the infrastructure and productivity gains created during these periods often become the foundation for future economic growth.
This strategy is not built on the assumption that every AI-related company will succeed. Instead, it focuses on understanding where economic value is being created across the AI ecosystem and allocating capital accordingly. While no strategy can eliminate market risk, our objective is to participate in long-term technological change without relying solely on speculative outcomes.
Why this Moment Matters
While no one can predict short-term market outcomes, we believe the current opportunity stems from the fact that much of the capital required to support AI adoption is still being deployed. Historically, some of the most significant investment opportunities have emerged during periods when infrastructure is being built and economic value is still in the process of being recognized.
As with previous technological transitions, the most visible beneficiaries may not always be the first to create value. Understanding where capital is flowing and which businesses are enabling adoption is central to our investment approach.