The AI boom’s second act is being financed not just by venture dollars but by borrowing, as companies sprint to build the data centers and buy the chips needed to train and run large language models. That shift is changing who can compete and how quickly and introduces new risks for enterprises buying AI services.
Oracle’s $300 Billion Bet
The most visible example is OpenAI and Oracle’s $300 billion contract. According to the Wall Street Journal, to deliver, Oracle must invest heavily up front. KeyBanc analysts estimate the company may need to borrow roughly $25 billion annually over the next four years. Oracle already carried about $82 billion of long-term debt as of August and had a debt-to-equity ratio of nearly 450%, far higher than Alphabet’s 11.5% and Microsoft’s ~33%.
Nebius struck a $19.4 billion deal to supply Microsoft and said it would fund the build with cash flow and debt, while CoreWeave has relied on creative financing to climb the ranks of AI compute providers. Oracle’s stock jumped after the OpenAI contract was disclosed, an investor vote of confidence despite years of expected cash burn.
Demand vs. Debt
The wager is that demand will catch up. But the liabilities are large and the timelines tight. Moody’s flagged significant risks tied to equipment, land and power costs and gave Oracle a negative outlook in July. Analysts note OpenAI would need to scale to more than $300 billion in annual revenue by 2030 from about $12 billion today to justify the spending tied to the Oracle pact.
Consumer willingness to pay remains limited, and contracts could be postponed, renegotiated or reassigned if usage lags. Oracle could, in theory, lease capacity to another buyer if OpenAI falters, but the financing model is getting “bubblier by the day,” Wall Street Journal reports.
Private Credit Steps In
For now, the debt taps are open because equity has been plentiful and momentum remains strong. PYMNTS reporting shows AI captured 42% of U.S. venture capital in 2024, up from 36% in 2023 and 22% in 2022. That continued into this year: AI startups raised $104.3 billion in the first half of 2025, according to PitchBook data.
But venture money alone won’t fund the infrastructure build. Private credit is stepping in: the AI boom represents as much as a $1.8 trillion opportunity for non-bank lenders by decade’s end, according to Carlyle estimates reported by PYMNTS. UBS strategists warn that private credit to tech swelled by roughly $100 billion in the past year to $450 billion. Individual operators are tapping those markets directly; Nvidia-backed Lambda recently closed a $275 million credit facility to expand AI data centers and GPU fleets.
Implications for Model Development
If debt becomes the decisive input, the winners in foundation model training may be those with the cheapest capital and the strongest balance sheets. That could entrench a small set of compute landlords and model providers and make the cadence of model upgrades more sensitive to credit conditions. Should funding tighten, providers might ration training runs, slow parameter growth or prioritize paying customers over research, choices that could affect the pace and openness of LLM progress.
Implications for Enterprise Buyers
Borrowing-driven build-outs are ultimately repaid through pricing. Enterprises may face higher or more variable AI service costs, along with vendor concentration risk if a large provider must renegotiate capacity or refinance. Many CFOs are already scrutinizing ROI; PYMNTS reporting finds typical corporate AI deployments run from $50,000 to $500,000 for practical use cases, with large programs stretching into the millions. That spend is contributing to a projected 9% rise in global IT outlays this year, led by AI and cloud.
Bottom Line
Debt can accelerate the AI infrastructure race, but it also adds fragility. For banks, lenders and corporate adopters, the key questions now are less about model accuracy and more about balance sheet durability, who is financing the compute behind the demo, on what terms, and how those obligations might shape the availability and price of enterprise AI over the next cycle.
Source: https://www.pymnts.com/