The Best Ideas I Found in Q1 2025 Investor Letters (Part III)
From the quaterly commentary of Horizon Kinetics. Data centers, AI, and a few companies.
I’m back with a third part of the letters that I like to read and write-down the most interesting parts.
If you missed the Part I & Part II of the Q1 2025 Letters, go and read it. Quite a few interesting ideas!
TL;DR
Companies mentioned in the letter
Aris Water Solutions (ARIS)
San Juan Basin Royalty Trust
Hawaiian Electric Industries, Inc. (HE)
AI and Data Center Demand
The current AI-driven data center boom may rival the scale and cost of World War II. Meta alone plans to spend over US$60 billion on data centers in 2025, and estimates suggest that U.S. data center investment could reach US$1.8 trillion by 2030—comparable, in inflation-adjusted terms, to the U.S. war effort from 1941 to 1945. This investment cycle is largely driven by the resource-hungry nature of AI and the explosion of digital data.
The build-out of extremely large data centers has the potential to be the greatest deployment of private investment capital in history.
At the heart of the energy demand is the Large Language Model (LLM) AI typified by ChatGPT. These systems require massive amounts of computing power to perform their statistical operations—calculating probabilities for each next word in a response, not based on understanding, but on brute-force pattern matching across the entire internet.
A single ChatGPT request can require hundreds of billions of calculations, and as usage skyrockets, so too does electricity consumption. According to a Berkeley Lab study, data centers may account for as much as 12% of U.S. electricity usage by 2028, up from 4.4% in 2023. That would be a staggering shift, especially in a country with flat power generation and an aging grid.
The only AI the public has interacted with is the Large Language Model type, like ChatGPT. It’s easy to find lists of very large markets for it.
Among them: voice search and fake review detection (for e-commerce); proofreading and writing (for legal contracts, or for pretend investment research on financial news websites); data, intelligence, and cyber threat analysis and response (for commercial and military users); facial recognition, and gait and body language analysis (for surveillance and law enforcement); and back-office automation and portfolio analysis (for the banking sector). Other applications for AI include image and video generation, GPS navigation and autonomous driving, and robotics and industrial automation.
But AI isn’t just about LLMs. Horizon highlights Large Math Models (LMMs) as an even more transformative but lesser-known AI application. Unlike LLMs, which depend on pre-existing data, LMMs generate their own data through high-performance simulations. This makes them especially suited to areas like drug discovery, where novel proteins or molecules must be modeled in dynamic, complex 4D environments (three-dimensional space plus time). The result: simulations that can accelerate drug development from 10 years down to 18 months, while dramatically reducing the costs and risks of failure in late-stage clinical trials.
To illustrate the scale, the commentary cites a study that required 2 petabytes of data—equal to 13 years of high-definition video—to digitally reconstruct just one cubic millimeter of mouse brain tissue. This shows not only the computational heft of AI but also the permanent storage demands that will drive long-term growth in data center infrastructure.
Ultimately, Horizon argues that this surge in AI and data center investment is economically rational, not speculative. LMMs offer real cost savings in billion-dollar drug development processes, and their use is expanding into adjacent industries like materials science and energy. Even if some AI applications—like surveillance or job replacement—are socially controversial, the promise of curing diseases or creating better materials ensures widespread adoption. This inevitability, they suggest, will place massive long-term demand on electricity, water, and raw materials, offering critical insight for portfolio positioning.
Portfolio Positioning in Data Center companies
With trillions in capital pouring into the construction and operation of these facilities, the key question isn’t whether to invest in this transformation—it’s how.
The first approach is direct: invest in companies like GE Vernova, a manufacturer of high-capacity gas turbines for power generation. The company has a multi-year backlog and increasing capacity to meet new orders, including for small modular nuclear reactors. While such companies stand to benefit from the data center boom, they are also **massive consumers** of the very commodities that are becoming scarce—such as steel and electricity—and thus may not offer the best risk-adjusted returns.
The second, more prevalent approach is indexation, which appears to offer wide exposure to AI beneficiaries through large index funds. Sectors like health care, financials, and IT—which dominate the S&P 500—are deeply linked to AI adoption. Pharmaceutical firms can use AI for drug discovery, banks for operational efficiencies and balance sheet optimization, and tech giants for cloud services. However, Horizon cautions that this index-based exposure is deceptively concentrated. With over 66% of the S&P 500 now tightly coupled to the AI value chain, there’s an increase in correlated risk rather than true diversification.
What’s more, the companies driving the AI revolution—like cloud providers and pharmaceutical giants—are consumers, not providers, of the critical resources required to support this transformation. These include natural gas, water, steel, and land. Yet, resource producers make up a minuscule portion of the indexes: less than 4% overall, with metals mining at 0.25%, and natural gas exposure only about 1%. Water and land-based providers are practically invisible in benchmark ETFs.
Horizon emphasizes the paradox: while AI-linked companies absorb the lion’s share of investor capital, the enablers of this infrastructure—such as natural gas producers or water logistics firms—remain neglected. The imbalance suggests that resource scarcity, not just AI capabilities, may drive future returns. They even note that some water ETFs contain little actual exposure to water as a commodity, and that REITs holding data center infrastructure suffer from structural limitations like high leverage and poor per-share value compounding.
The team proposes a contrarian thesis: rather than following capital flows into the high-visibility AI names, investors might achieve superior returns by focusing on limiting-factor assets—those commodities and infrastructures without which the data center revolution cannot happen. These assets are not only under-owned and under-indexed, but also poised to benefit from inevitable supply-demand imbalances.
Portfolio Insights
Horizon Kinetics challenges the conventional framework used by index-based investing and traditional stock screeners, which rely heavily on descriptive attributes—such as sector classifications, market caps, trading volumes, and historical earnings growth. These measures, the team argues, are inherently backward-looking and offer little predictive value when it comes to future performance.
Instead, Horizon advocates for focusing on predictive attributes—less quantifiable factors that indicate a company may have outsized return potential over time. One example is the presence of dormant assets: unmonetized land or legacy holdings that may not show up meaningfully on the balance sheet but could generate substantial value under the right circumstances. Another example is a company caught in a time arbitrage opportunity, where market participants heavily discount near-term uncertainty and fail to price in a more favorable long-term outcome.
This concept of “equity yield curve” investing is central to Horizon’s approach. Just like bond investors demand a higher yield for lending over longer durations, equity investors often discount future returns too steeply when there’s uncertainty about timing. But if the payoff is large and the resolution timeframe is reasonably knowable—even if it’s two or three years out—then the annualized return can be enormous. Horizon shares an historical case from Pacific Gas & Electric preferred shares during bankruptcy proceedings, where a patient investor could realize a 35% annualized return simply by waiting for the resolution.
The takeaway is that high-conviction, time-dislocated opportunities—especially those with some contractual or legal pathway to value realization—often exist outside the visibility of index-based investors. Horizon is deliberately positioning its portfolios in such opportunities today, especially where they intersect with the AI and data center infrastructure themes. While the broader market pours capital into obvious “AI winners,” Horizon is focused on the inputs: companies with strategic exposure to electricity, water, land, and hard infrastructure that will be indispensable for supporting this multi-trillion-dollar AI buildout.
This approach favors companies with misunderstood timelines, unconventional assets, or regulatory structures that temporarily obscure their intrinsic value—but that offer asymmetric returns once revalued. These are not fast trades, Horizon acknowledges, but rather deliberate long-duration bets with a high probability of success for investors willing to wait.
Comments on holdings
Aris Water Solutions (ARIS)
Aris Water Solutions is a niche player in the energy and water infrastructure market that Horizon believes is deeply undervalued. Although classified as an oilfield services company—typically an unpopular sector among ESG-focused and institutional investors—Aris plays a critical role in managing one of the most underappreciated bottlenecks in shale oil production: water.
In the Permian Basin, every barrel of oil generates around four barrels of contaminated wastewater, which must be safely transported and disposed of, or preferably recycled. Aris operates permanent pipeline infrastructure that transports and recycles this water, earning steady fees while avoiding the risks and capital intensity of actual oil production.
The company’s historical challenges—an inauspicious 2021 IPO, private equity sponsors, and its small-cap status—have kept it out of favor. But Horizon sees a turning point: Aris has shown margin expansion and volume growth while operating below capacity, and its pricing power should increase as regulators limit disposal permits. It also has optionality: early signs of electric power and data center development in the Permian could drive future demand for industrial water use.
At a valuation of just 8.5x next year’s pre-tax cash flow, Horizon sees a high-margin, high-utility business that is completely overlooked by conventional investment frameworks.
San Juan Basin Royalty Trust (SJT)
San Juan Basin Royalty Trust is a textbook Horizon-style “equity yield curve” investment: a security trading at depressed levels not because of fundamental impairment, but because of time-dislocated expectations.
The trust holds royalty interests in a natural gas-producing basin in New Mexico, has no employees, no debt, and a simple business model—distribute cash to shareholders. In early 2023, it was paying over $4/share in annualized dividends, which at the time was greater than the share price. But when operator Hilcorp increased capital expenditures eightfold in 2024 to drill and modernize production, distributions temporarily ceased. Under the trust’s structure, all capital outlays must be repaid before resuming payouts.
Horizon has modeled the capex payback and, under conservative gas pricing assumptions, expects distributions to restart around May or June 2025, likely yielding ~17% at current prices. Even modest increases in natural gas prices could sharply increase the NAV and cash flow outlook.
Additionally, the San Juan Basin is one of the few U.S. gas sources with direct pipeline access to Southern California—a region prone to energy shocks and premium pricing. Horizon believes the market is overlooking this because of the current zero dividend, small market cap (~$200 million), and lack of analyst coverage.
Hawaiian Electric Industries, Inc. (HE)
HE is one of Horizon’s most contrarian positions—a regulated utility that suspended its dividend after becoming entangled in the aftermath of the devastating 2023 Maui wildfires.
The stock dropped more than 70% in a matter of weeks, as lawsuits and political pressure raised concerns of bankruptcy. However, Horizon believes those fears were overblown. As of early 2025, a settlement agreement has been approved by Hawaii’s Supreme Court that outlines a US$1.92 billion liability payment over four years, with no rate hikes required. HE has already secured part of this funding through the sale of its banking subsidiary and available credit facilities. While dividends are off the table in the near term, the company’s core electric utility segment remains stable, profitable, and now operates without the complexity of banking regulation.
Horizon estimates that normalized earnings power exceeds US$150 million annually, which, once legal liabilities are settled, could support a return to dividend payments and a re-rating of the stock. The market, however, remains focused on short-term noise and fails to see the medium-term path to recovery. Horizon sees this as a classic time arbitrage: the fundamentals are still intact, and if the business resumes payouts in 3–4 years, today’s valuation implies a deeply discounted entry point for a regulated utility with a near-monopoly customer base.
The hidden diamond in Data Center:
The Recurring Business of Shared Cloud Responsibilities.
The overlooked insight here is that while AI and data centers are grabbing the headlines, the real constraint in this buildout isn’t technology—it’s resources. The market continues to price AI like it’s software: infinite scalability, low marginal cost, perpetual growth. But the physical world has limits—land, energy, water, even regulatory bandwidth. We’re about to find out how hard it is to pour trillions into capex without tripping over those limits.
The risk isn’t that AI growth stops. It’s that the capital cycle shifts—AI demand stays high, but resource scarcity drives bottlenecks and cost overruns. That’s where patient capital can play: finding those underappreciated infrastructure and resource bets, the literal inputs that make this whole thing possible.