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AI has damaged confidence in software investments, but James Clarke of Blue Owl says that does not automatically mean software private-credit portfolios will suffer widespread losses. His position is selective rather than complacent: managers should reduce exposure to the least defensible software borrowers, diversify, mark positions carefully and rely on covenants and cash-flow analysis. Clarke is more optimistic about data centers, yet he warns that rising demand alone cannot prevent poorly chosen projects from becoming stranded assets.
What Clarke is arguing
James Clarke is identified in Blue Owl’s 2025 Investor Day materials as its Global Head of IBD. A March 9, 2026 interview in the Fiduciary Investors Series describes him as the firm’s global head of institutional capital and examines private credit, software risk and data-center finance.
His central argument is that private-credit outcomes depend less on avoiding every troubled industry than on selecting borrowers and structuring loans. “You’re not going to get them all right,” Clarke said. He added that Blue Owl’s experience across different environments gives him confidence that a repeatable process, diversification and attention to loss rates can still provide downside protection.
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Why software-credit concerns have intensified
Reuters reported that more than $800 billion had been erased from the market capitalization of the S&P 500 software and services index since January 28, 2026, as investors reassessed the effect of AI on software companies. That number describes a change in public-equity index value, not realized losses in Blue Owl’s private loan book.
A falling software-stock valuation can still matter to private credit. It may weaken a borrower’s refinancing options, reduce acquisition activity, pressure equity sponsors and make lenders reassess recovery values. But a mark-to-market decline is different from a payment default or a realized credit loss. A loan can be repriced or marked lower while the borrower continues to pay interest and principal; an actual loss generally requires a restructuring, default, sale below cost or another event that crystallizes impairment.
Blue Owl’s technology-finance commentary takes a cautious portfolio stance. Software remains the largest part of the book, but management said it would reduce software exposure at the margin, concentrate on names where confidence is highest and continue marking the portfolio carefully after the market repriced software risk.
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What makes a software loan more defensible
Clarke’s comments do not mean that every software borrower is protected from AI disruption. They imply a screening process that separates durable cash flows from products vulnerable to rapid substitution.
- Revenue visibility: recurring contracts, demonstrated renewals and customers that depend on the product for mission-critical operations are more useful than a high-growth forecast alone.
- Business diversification: exposure spread across borrowers, end markets and geographies limits the damage from one failed product category.
- Leverage and liquidity: lenders need enough cash-flow coverage and liquidity for a borrower to absorb slower growth, pricing pressure or higher investment requirements.
- Process consistency: repeatable underwriting, monitoring and covenant enforcement can matter more than a manager’s ability to predict every technology winner.
- Downside terms: covenants, collateral, guarantees and contractual repayment schedules can improve recovery prospects, although none eliminates risk.
The practical conclusion is narrower software exposure and higher selectivity, not a claim that AI will leave software credit untouched.
Why data centers look attractive to Clarke
In an August 25, 2025 article, Clarke described generative-AI demand as driving a technological arms race and increasing demand for high-density data centers. He called AI a megatrend and data-center investment a generational opportunity, but paired that optimism with a direct warning: “the opportunity does not always guarantee results, profits, or success.”
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His preferred partners have “scale, global reach, local presence, and vertically integrated operating platforms,” along with a record of working with hyperscalers. Those capabilities can help developers secure power, manage construction, operate specialized facilities and negotiate credible tenant contracts.
Clarke’s warning illustrates why demand forecasts are not enough. “I could be flying over Northern Virgina or Silicon Valley and look down to see hectares and hectares of decommissioned data centres,” he said. The spelling “Northern Virgina” appears in the quoted statement. The point is that facilities can become obsolete or uneconomic when technology, power requirements, location economics or customer needs change.
How hyperscaler demand can still fail to protect a project
A hyperscaler relationship can improve tenant quality and revenue visibility, but it is not a blanket guarantee. A project may still fail if it cannot obtain power on schedule, overruns construction costs, lacks the right cooling or networking design, misses delivery dates or depends on a customer commitment that is not sufficiently binding.
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Location also matters. Local permitting, grid access, water constraints, taxes and labor availability can change project economics even when long-term AI demand remains strong. A facility built for one generation of high-density equipment may require expensive retrofits when chips, rack designs or cooling standards change.
For that reason, Clarke emphasizes subject-matter expertise and investment structures focused on cash flow and downside protection rather than simply maximizing capacity.
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How Blue Owl describes its digital-infrastructure structures
In a May 7, 2026 technology-finance earnings transcript, management described opportunities across the broader platform, including GPU-financing structures with investment-grade counterparties. The features cited included amortization, premium returns, tight documentation and corporate guarantees.
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The same transcript says data-center opportunities are being evaluated through Blue Owl’s IPI business. Management also noted natural limits because some proposed projects fall into non-qualifying categories. That qualification matters: the firm is not presenting every data-center proposal as financeable or equally attractive.
- Investment-grade counterparties can reduce payment risk, subject to the exact contractual obligations.
- Amortization returns principal over time instead of leaving all repayment to a single maturity date.
- Corporate guarantees may add support beyond the project company, depending on scope and enforceability.
- Tight documentation can define reporting, covenants, remedies and permitted uses of funds.
- Premium returns compensate investors for specialized technology, construction and execution risks; they do not remove those risks.
Software credit versus data-center infrastructure
| Risk axis | Software-credit exposure | Data-center infrastructure exposure |
|---|---|---|
| Revenue durability and visibility | Depends on renewals, customer concentration, pricing power and whether the product remains essential. | Depends on contracted capacity, tenant commitments, utilization, power availability and operating performance. |
| AI substitution or obsolescence | AI can replace features, compress prices or make products less necessary. | AI increases demand, but equipment, cooling and facility designs can become outdated. |
| Counterparty or tenant quality | Borrower cash flow and sponsor support are central. | Hyperscaler or other tenant credit helps, but contract terms and guarantees determine protection. |
| Construction, power and delivery execution | Usually less construction-intensive, but implementation and product investment still matter. | Power interconnection, permitting, construction, grid constraints and delivery schedules are major risks. |
| Diversification | Spread by borrower, industry, geography and software category. | Spread by site, operator, geography, tenant and technology design. |
| Downside protection | Covenants, collateral, guarantees, liquidity and contractual cash flows. | Amortization, guarantees, tight documentation, contracted cash flows and asset or project controls. |
What investors should take from the thesis
AI is not a single credit outcome
AI can hurt one software borrower while increasing demand for another company’s tools or for computing infrastructure. Industry labels therefore conceal material differences in product relevance, balance-sheet strength and contract quality.
Watch realized performance, not only valuation headlines
Public-market capitalization changes are an early sentiment signal. To judge private-credit risk, investors also need payment performance, covenant compliance, amendments, restructurings, recovery values and realized losses. The Reuters figure should not be treated as a direct estimate of Blue Owl’s credit losses.
Demand must be converted into cash flow
Data-center demand becomes investable only when a capable operator can deliver a facility, secure power, maintain it and collect contractual payments under enforceable terms. Clarke’s thesis is therefore conditional on partner quality, scale, local expertise, hyperscaler experience and structures designed to protect cash flow.
Bottom line on Clarke’s position
Clarke is not saying AI has made software risk disappear or that every data center will prosper. He is saying disciplined private-credit managers can limit damage by reducing confidence-sensitive exposure, diversifying and enforcing a consistent process. He sees data centers as a stronger long-term opportunity, but only when specialized operators and carefully documented, cash-flow-oriented financing turn AI demand into durable repayment capacity.
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