The market is underestimating the impact AI will have on the entire economy. We find differentiated opportunities leveraging our proprietary credit intelligence identifying structural dislocation opportunities systematically overlooked by consensus markets. Two years prior to when capital was pouring heavily into private credit, I met a frontier researcher who wanted to create multi-agent orchestration frameworks to create autonomous companies which gave me an understanding light years ahead of Wall Street on when and how AI would change industries and sectors.

A year before the market caught up, I saw the shift that would take place in industrial tech,commodities, energy value chain from data centers, semiconductors to critical mineral exploration, SaaS & other sectors where AI would fundamentally change the unit economics of how the business is run, either rending it obsolete or helping another dominant player emerge.

SaaS companies that sell SaaS at high prices with user-based pricing are now facing pressure margin compression from AI-native competitors. If they have private credit debt from a 2021 vintage leveraged buyout, the covenant math is getting ugly.

In aerospace and defense, companies are absorbing their own supply chains — Boeing acquiring Spirit AeroSystems, Safran buying Collins & Aerospace units — because AI can now manage the coordination work that once justified outsourcing. In physical supply chains, agentic AI systems can now re-route shipments, re-allocate inventory, and engage alternative suppliers the moment a disruption signal is detected— collapsing what used to be an entire tier of logistics SaaS into a single automated layer.

In energy, AI is driving an unprecedented surge in global electricity demand set to rise more than 1 trillion kWh per year through 2030, forcing hyperscalers to vertically integrate power generation itself, with tech companies building their own microgrids and nuclear deals rather than depending on utilities.

In semiconductors, chip giants are building end-to-end AI stacks through "circular financing," locking in control from silicon to cloud. On the other side, the companies absorbing the shock of AI displacement are often not publicly traded — they are PE-backed, leveraged, and opaque.

Outstanding loans to SaaS firms grew from almost $8 billion in 2015 to over $500 billion —19% of total direct loans — by end of 2025, with a third of all private credit funds now extended into the sector. Software and technology companies now represent over 20% of BDC investments, with estimates that between 25–35% of private credit portfolios carry some degree of AI-related disruption risk. UBS analyst Matthew Mish has projected $75 billion to $120 billion in fresh defaults across leveraged loan and private credit markets by the end of 2026, warning that a faster-than-expected AI transition could trigger a broad repricing of leveraged credit and a shock to the system.

Business process outsourcers, staffing firms, legacy media companies, mid-market SaaS businesses loaded with 2021-vintage LBO debt — these are the borrowers whose revenue assumptions were written before generative AI rewrote the economics of their industries. They have no observable market prices, no liquid credit default swaps, and rating agency proxies that lag reality by quarters.

The scale of AI-driven investment is almost without precedent. Worldwide data center capital expenditures surged 57% in 2025, with America's four largest cloud providers — Amazon, Google, Meta, and Microsoft — growing their CAPEX by 76%. Oracle more than tripled its own spending to build out the Stargate project. That momentum shows no sign of slowing: full-year 2026 data center CAPEX is forecasted to surpass $1 trillion, with global data center securitization volumes already topping $30 billion in 2025 — nearly tripling from just over $10 billion in 2024. The global AI data center market, valued at $236 billion in 2025, is projected to reach nearly $934 billion by 2030.

Upstream from the data centers themselves lies another credit story that is only beginning to be priced: the resource companies — miners, explorers, energy developers, and critical minerals producers — that must supply the physical inputs for this buildout. AI is expected to spark a ten-year critical mineral supercycle, as the massive energy and hardware needs of new data centers place pressure on global supply chains already under strain from net-zero targets. The materials in question — copper for grid infrastructure, lithium and cobalt for energy storage, rare earths for semiconductors and magnets, nickel, graphite, and gallium for chips and cooling systems — are not abundant where they are needed. Exploration activity plateaued in 2024, with real investment growth of just 2% after adjusting for inflation, and today's low mineral prices are not providing the signal to invest — leaving new entrants most exposed to uncertainty. At the same time, the Pentagon became the largest shareholder of MP Materials, the only fully integrated rare earth magnets producer in the US, in a first-of-its-kind deal backed by $1 billion in committed financing — explicitly intended as a blueprint for other industries of national security interest.

Private credit is stepping into this gap directly. Copper, lithium, nickel, cobalt, and rare earths are fundamental to both data centers and clean power infrastructure, and private credit is now emerging as a critical bridge financing for shovel-ready, policy-backed projects— sitting between the long lead times of conventional mining, which can exceed a decade, and the urgency of AI infrastructure demand that cannot wait. Starting in 2025, strategic partnerships in the critical minerals space became transactional and operational, with cross-sector deals emerging between data center operators and mineral producers — a new model where end-users actively participate in upstream material science to solve their own infrastructure challenges. The traditional leverage taxonomy — low, medium, high — was built for a world where mining was cyclical and slow. In a world where AI has made copper and rare earths into instruments of geopolitical competition, and where exploration companies are raising debt against decade-long offtake agreements, the credit risk profile of a lithium developer looks less like a commodity play and more like infrastructure finance. That distinction matters enormously for anyone pricing private placements in this space.