Investing In AI: Beyond 'Buy The Chip'

Forbes

07-04-2025

Jacob Miller, Co-founder, Chief Solutions Officer, Opto Investments.
Generative AI may be the newest new thing, but the future of investing in AI goes well beyond the current 'buy-the-chip' frenzy—and far beyond the public markets. After all, with Nvidia worth $2.9 trillion as of March 2025, there's probably not a lot of upside left.
Headlines trumpet generative AI's ability to produce text, images and even music. But beyond the glitz, a quieter and less glamorous transformation is happening. This transformation, what we call 'boring AI,' is where machine learning meets industries long afflicted with cumbersome processes and inefficiency.
In short, AI can make old industries—such as healthcare, logistics, legal, construction and energy—new again.
In the United States alone, navigating the labyrinth of healthcare administrative tasks costs up to $265 billion annually, according to a 2023 McKinsey analysis. The promise of AI in healthcare is not just in futuristic robotics but in optimizing billing processes, coordinating labor more efficiently and ensuring compliance with regulations.
Equally tantalizing are the prospects in logistics and supply chains, sectors where global inefficiencies for large companies are estimated to reach a staggering $184 million each year, according to a 2021 Interos report. By enhancing demand forecasting, optimizing routing and automating yard management, AI can not only reduce waste but also create efficiencies.
What characteristics make a sector ripe for AI efficiency gains?
High complexity/low tech: Many industries still rely on manual processes. Automating these through AI can produce instant improvements by lowering costs and increasing speed.
Fragmented data systems: Patient records, shipment logs and legal documents are often siloed, making comprehensive data analysis difficult. AI excels at integrating disparate sources to extract valuable insights.
Entrenched inefficiencies: Long tolerated, these inefficiencies present significant opportunities for solutions that can generate cost savings and performance enhancements.
Regulatory overload: Many sectors face complex compliance landscapes where AI can efficiently monitor and manage regulations to reduce risks and streamline operations.
Crucially, each of these industries also commands enormous budgets. Even minor improvements can unlock gains on a massive scale.
Consider healthcare management again. By automating claims and optimizing staffing, AI-driven tools can reduce reimbursement times and workforce costs dramatically.
In logistics, predictive analytics have the power to revolutionize supply chains by reducing inventory costs and improving delivery timelines.
Legal operations have also seen groundbreaking changes as AI reduces time on document review and automates compliance checks. This slashes costs and accelerates processes.
In construction, AI-driven scheduling can shorten project timelines significantly, translating to substantial cost savings.
Energy sectors can use AI to manage grids more efficiently and predict maintenance needs, thereby reducing waste and enhancing service reliability.
Newcomers in the generative AI space are going to find it very difficult to access better data and build faster than xAI, OpenAI and Anthropic. There will be only a few winners.
The 'boring AI' trade, primarily accessed through private markets, offers far more entry points. For example, other highly effective avenues for building meaningful exposure to AI could be:
• Venture funds backing domain-focused startups: These include managers who fund specialized AI platforms for targeted use cases.
• Engineering infrastructure and services: Look at companies or funds building the infrastructure (data pipelines, cloud services, edge computing solutions) necessary to deploy AI at scale.
• Private equity firms leveraging AI post-acquisition: Look for PE sponsors that use AI to streamline operations in the businesses they acquire—reducing costs, improving margins and accelerating value creation. A 10% to 25% efficiency gain might be enough to drive strong returns in many spaces.
This is not to suggest that investors should avoid exposure to generative AI and those who are benefiting from it. Instead, this is a suggestion to be selective and, where possible, look beyond public markets to find deeper value from AI in the private space.
Informational purposes only. This is the personal opinion of the author and not that of Opto Investment Management, LLC/affiliates. Not investment advice. Seewww.optoinvest.com/disclaimers
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