Diameter Capital gained access to a unique opportunity to buy Twitter's debt from its underwriting syndicate. They achieved this not just through relationships, but by actively sharing their proprietary research and data analysis on the company. This made their banking partners "smarter" and built the trust needed to execute the exclusive transaction.
Roughly one-third of the private credit and syndicated loan markets consist of software LBOs financed before the AI boom. Goodwin argues this concentration is "horrendous portfolio construction." As AI disrupts business models, these highly levered portfolios face clustered defaults with poor recoveries, a risk many are ignoring.
The widespread adoption of weight-loss drugs is reducing demand for products like wine bottles and beer cans. This introduces a significant, unforeseen secular risk to packaging companies, a sector traditionally considered a safe bet by credit investors who often focus only on coupon payments and credit ratings.
Scott Goodwin highlights that while major banks report stable consumer credit, they overlook the explosive growth of online lenders like Upstart and SoFi. This hidden leverage, often ending up on insurance company balance sheets, means the US consumer is far more indebted than traditional metrics suggest.
While most investors chased Nvidia, Diameter Capital focused on the infrastructure needed for AI inference. They identified that AI models must transmit data out of data centers via commercial fiber. They bought distressed debt in a telecom company at 30 cents on the dollar, which recovered to par after signing billions in contracts with hyperscalers.
Goodwin argues against the passive "index-hugging" approach to credit focused on coupon payments and agency ratings. Diameter's edge comes from approaching credit like an equity long-short fund, constantly analyzing what macro and sector trends will change security prices over the next 3 to 24 months to generate total return.
While financing AI chips is a growing market, Goodwin warns against taking junior residual risk. His team consulted top Silicon Valley and big tech experts about the value of AI chips in 3-7 years and found that "none of them have a clue." This fundamental uncertainty makes junior positions a dangerous gamble, not a sound investment.