The artificial intelligence boom represents a “super-duper micro cycle that will outlast many investing careers,” according to Scott Goodwin, cofounder and managing partner of Diameter Capital Partners, a firm managing approximately $25 billion in assets. Speaking on the Goldman Sachs Exchanges podcast, Goodwin outlined his firm’s strategy for capitalizing on AI’s growth beyond the obvious semiconductor plays that have dominated headlines.
Diameter Capital has identified less obvious bottlenecks created by AI demand, particularly in credit markets. In 2023, the firm purchased unsecured debt of a midsize telecommunications company, betting that as companies transition from training AI models to deploying them in production, demand would shift from chips alone to the networks and infrastructure that carry data. “It had to leave the data center. How would it leave? It would leave on the commercial fiber, the pipes,” Goodwin explained. This strategic bet paid off handsomely when the telecommunications company signed more than $10 billion in contracts with hyperscale cloud providers, and the debt rebounded to face value.
The firm also made “a big bet” on a satellite company tied to wireless spectrum, which similarly paid off after the company sold spectrum assets and the debt returned to face value. These investments highlight Diameter’s thesis that AI infrastructure extends far beyond semiconductor manufacturing to include telecommunications networks, fiber optics, and wireless spectrum.
However, Goodwin issued warnings about emerging risks in AI-credit markets, particularly in chip financing. Some investors are taking on “residual risk”—the riskiest slice of chip-financing deals—betting on what hardware might be worth years into the future. This is problematic because cutting-edge firms frequently refresh their technology, making chips quickly obsolete. “We call up really smart people in Silicon Valley, we call up really smart people at Big Tech companies and ask them what the residual value is on these chips three, four, five, six, seven years forward,” Goodwin said. “None of them have a clue.”
Looking ahead, Goodwin believes the next phase of AI investment isn’t just about infrastructure spending but about competitive disruption. The key question, he argues, is identifying which companies will successfully adopt AI to gain advantages over peers—and which will fall behind. This competitive cycle, he suggests, will last longer than the current capital expenditure cycle, presenting sustained investment opportunities.
Key Quotes
This is a super-duper micro cycle that will outlast many investing careers
Scott Goodwin attributed this quote to his partner Jonathan Lewinsohn, emphasizing Diameter Capital’s view that AI represents a long-running, disruptive cycle that will create investment opportunities for decades to come.
It had to leave the data center. How would it leave? It would leave on the commercial fiber, the pipes
Goodwin explained the rationale behind Diameter’s 2023 investment in telecommunications debt, recognizing that AI deployment requires robust network infrastructure beyond just data center chips.
We call up really smart people in Silicon Valley, we call up really smart people at Big Tech companies and ask them what the residual value is on these chips three, four, five, six, seven years forward. None of them have a clue.
Goodwin warned about the risks of chip financing deals, highlighting that even industry experts cannot predict future chip values due to rapid technological obsolescence—a critical concern for investors taking on residual risk.
Who are the companies, who are the entities that are going to adopt AI and take a step forward versus their peers? And who are going to be the losers?
Goodwin outlined what he sees as the next phase of AI investment, focusing on competitive disruption rather than infrastructure spending—a cycle he believes will be longer and more interesting than the current capital expenditure boom.
Our Take
Diameter Capital’s strategy reveals a sophisticated understanding of AI’s economic ripple effects that many investors may be missing. While semiconductor stocks have captured headlines and massive valuations, the real infrastructure play may be in telecommunications and data transmission—the unglamorous “pipes” that enable AI deployment at scale. This insight is particularly valuable as the industry shifts from model training to inference and deployment.
The warning about chip financing residual risk is sobering and suggests potential overheating in certain AI credit markets. When even Silicon Valley experts cannot predict chip values years ahead, investors betting on residual values are essentially speculating on unknowable technological trajectories.
Most intriguingly, Goodwin’s focus on competitive disruption as the next investment phase suggests we’re entering AI’s true value-creation period. The companies that matter won’t necessarily be those building AI infrastructure, but those successfully deploying it to gain market advantages—a much harder investment thesis to execute but potentially more rewarding.
Why This Matters
This analysis from a major credit investor provides crucial insights into the evolving AI investment landscape beyond the semiconductor stocks that have dominated market attention. As AI transitions from the training phase to widespread deployment, the infrastructure requirements are shifting, creating opportunities in telecommunications, fiber networks, and wireless spectrum—sectors that may be undervalued by investors focused solely on chip manufacturers.
The warning about residual risk in chip financing is particularly significant as it highlights potential overexuberance in certain AI-related investments. With even industry experts unable to predict chip values years into the future, investors taking on this risk may face substantial losses as technology rapidly evolves.
Most importantly, Goodwin’s emphasis on competitive disruption over capital expenditure signals a maturation of the AI investment thesis. The next wave of value creation will come from identifying which companies successfully leverage AI for competitive advantage—a more nuanced and potentially longer-lasting investment cycle than the current infrastructure build-out. This perspective is essential for investors, businesses, and policymakers trying to understand where AI’s economic impact will be most pronounced.
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