Diameter Capital's Total-Return Credit Strategy Amidst AI Disruption

In a landscape where conventional wisdom often prioritizes immediate gains and visible metrics, Scott Goodwin, co-founder and managing partner of Diameter Capital, offers a potent counter-narrative. This conversation reveals the hidden consequences of focusing solely on surface-level data, particularly in credit markets. Goodwin emphasizes a "total return" perspective, urging investors to map out sector and macro trends over multiple years, not just quarters. The hidden implication? Many seemingly stable sectors harbor secular shifts that, if unaddressed, will erode value. This analysis is crucial for active investors, portfolio managers, and anyone seeking to build durable advantage by anticipating the second and third-order effects of market dynamics, rather than just reacting to the present.

The Unseen Currents Beneath the Surface of Credit

The prevailing narrative in credit markets paints a picture of relative stability: spreads hover at historically high percentiles, private credit booms, and defaults, while present, are not yet systemic. Yet, Scott Goodwin, with his characteristic skepticism honed over years at Diameter Capital, urges a deeper look. He points to subtle but significant signs of stress, particularly within the consumer lending space and the broader housing market. The issue isn't just that the low-income consumer is struggling; this has been a persistent trend. The real concern, as Goodwin highlights, is the creeping strain into segments previously considered stable, driven by the growth of non-bank lenders and the migration of loans away from traditional bank balance sheets. This shift means crucial credit data is less visible, making it harder to gauge true consumer leverage.

The housing market, too, presents a complex picture. Despite a slowdown since 2022, Goodwin identifies an opportunity arising from the very cyclical issues plaguing the sector. The lack of new home production, while a problem, also creates a space where strategic capital deployment could yield significant returns as market dynamics potentially shift.

Beyond these macro trends, Goodwin illustrates how secular shifts can silently dismantle seemingly safe sectors. The rise of GLP-1 drugs, like Ozempic, is a prime example. While a boon for human health, it presents a distinct challenge for companies in the beverage and packaging industries, sectors previously considered low-risk lending targets. The penetration curve for these drugs is steep, suggesting a lasting impact on demand for certain consumer goods. This highlights a critical failure of conventional, ratings-focused credit analysis, which often overlooks such profound, long-term demand shifts.

"We really want to approach credit from more of a total return perspective, more of how an equity long-short fund would. So, what's happening in this industry from a sector macro over the next three months, six months, 18 months, 24 months, and what is that going to mean for security prices and individual companies?"

This perspective, akin to that of an equity long-short fund, requires a deep dive into industry dynamics, anticipating how these forces will impact security prices over extended horizons. It's about understanding the "why" behind the numbers, not just the numbers themselves.

Twitter: A Masterclass in Proactive Engagement and Data-Driven Insight

The story of Diameter Capital's involvement with Twitter (now X) serves as a powerful case study in how proactive engagement and deep data analysis can unlock significant value, even in complex, non-traditional situations. While a syndicate of banks held the debt, clipping coupons for years, Goodwin and his partner John Lewinson were meticulously tracking online advertising data. They saw an inflection point coming, a potential rebound in ad revenue that the market, focused on the banks' existing positions, seemed to be missing.

This wasn't about simply waiting for an opportunity; it was about creating one. Diameter proactively engaged with the banks, presenting their proprietary data and analysis. This transparency and willingness to share research, Goodwin explains, helps their bank partners win deals and, in turn, creates opportunities for Diameter. They leveraged their Limited Partner (LP) network, specifically family offices with expertise in the tech and media sectors, to gain further insight. This collaborative approach allowed them to structure a bid for the Twitter debt, ultimately acquiring it at a favorable valuation.

"We were pulling online data, scraping the data, trying to figure out what the ad dollars were that were going to Twitter. And we saw in Q4 of 2024 that they were really starting to re-inflect..."

The outcome was remarkable: what was once a distressed holding for banks became a significant win for Diameter, with the debt's value soaring post-acquisition, particularly given the rumored valuation of XAI. This demonstrates how understanding the underlying business drivers, even for unrated debt, and actively cultivating relationships can lead to outsized returns.

AI's Ripple Effect: Beyond the Hype to Infrastructure and Software

The conversation around Artificial Intelligence often fixates on the LLM infrastructure plays, like NVIDIA. Goodwin, however, directs attention to the less obvious, yet equally critical, infrastructure and software implications. He frames AI not just as a capex cycle but as a "super duper microcycle" that will reshape industries for decades. Diameter’s strategy has been to identify the beneficiaries of this shift beyond the chip manufacturers.

Their investment in a mid-sized telecom company, providing commercial fiber access for AI inference, exemplifies this approach. By acquiring unsecured debt at a deep discount, they positioned themselves to benefit from the massive build-out required to move AI processing out of data centers and into practical application. Similarly, their investment in a satellite company with significant spectrum holdings anticipated the growing demand for mobile data driven by AI-powered devices and services.

"When people actually started using the AI for inference... it had to leave the data center. How would it leave? It would leave on the commercial fiber, the pipes."

This focus on the "pipes" -- the essential infrastructure enabling AI -- illustrates a systems-thinking approach. It’s about understanding that the AI revolution requires more than just processing power; it demands robust connectivity.

The analysis then pivots to the software sector, specifically Software-as-a-Service (SaaS) leveraged buyouts (LBOs). Goodwin expresses concern about the vintage of deals originating before the widespread adoption of generative AI. Many of these LBOs, he argues, were built on models that assumed a certain trajectory of growth and recurring revenue, a trajectory now threatened by AI's disruptive potential. Companies that cannot adapt quickly enough, or whose business models are rendered obsolete by AI-driven efficiencies, face significant risks. He highlights Diameter’s own past mistake in a SaaS investment, underscoring the difficulty of recovering value when subscriber churn accelerates and loan documentation is weak. This leads to a preference for originating post-GPT SaaS deals, where the AI risk is better understood and potentially mitigated.

Actionable Takeaways for Navigating Complexity

• Embrace a Total Return Mindset: Shift focus from immediate coupon clipping to multi-year sector and macro trend analysis. This requires understanding industry dynamics and anticipating how they will impact security prices across different time horizons.
• Map Secular Shifts Beyond Obvious Indicators: Actively identify industries undergoing fundamental, long-term change driven by technology or consumer behavior (e.g., GLP-1 drugs impacting packaging). These are often overlooked by traditional analysis.
• Leverage Proprietary Data and Networks: For complex situations like distressed debt, proactively gather and analyze alternative data sources (e.g., ad spend, user engagement) and engage with LPs and industry experts to build a comprehensive view.
• Prioritize Infrastructure Beneficiaries of AI: Look beyond chip manufacturers to companies providing essential connectivity and data transmission services (e.g., fiber optics, spectrum) that will power AI adoption.
• Scrutinize SaaS LBOs with Pre-AI Vintages: Exercise extreme caution with software companies financed before the widespread impact of generative AI. Their business models and competitive moats may be significantly eroded. Focus on deals where AI is understood as a factor, not a threat.
• Build Deep Credit Knowledge for Cyclical Opportunities: In sectors experiencing cyclical downturns (e.g., housing), invest time in understanding the nuances of individual credits. This allows for rapid deployment of capital when opportunities arise from market dislocation.
• Cultivate Transparent Banking Relationships: Share research and insights with bank partners. This can foster trust, create deal flow, and position your firm as a valuable counterparty.

Key Action Items

• Immediate Action (Next Quarter):
  • Review existing credit portfolios for exposure to secularly challenged industries (e.g., traditional packaging, pre-AI SaaS LBOs).
  • Initiate research into commercial fiber and spectrum providers as potential beneficiaries of AI infrastructure build-out.
  • Engage with key banking partners to share proprietary insights on specific sectors to strengthen relationships.
• Medium-Term Investment (6-12 Months):
  • Develop a framework for analyzing consumer leverage that incorporates non-bank lending data and alternative sources.
  • Begin deep-dive research into specific housing market sub-sectors where cyclical distress may present opportunities.
  • Explore opportunities in chip financing, focusing on senior tranches with clear amortization schedules and double-digit yields.
• Longer-Term Investment (12-18 Months+):
  • Build out expertise in identifying and underwriting post-GPT SaaS deals, understanding how AI can be a benefit or a manageable risk.
  • Establish a systematic process for monitoring and analyzing AI adoption trends and their impact on software company performance.
  • Develop a strategy for identifying and capitalizing on dislocations in sectors undergoing significant technological or behavioral shifts.