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Clarke’s argument in brief
In a March 9, 2026 interview, Clarke—Blue Owl’s global head of institutional capital—described private credit as entering its most difficult environment since 2008. The market is roughly $2 trillion in size, according to Top1000funds.com, and investors are debating whether artificial intelligence has changed the risk profile of both software borrowers and digital infrastructure.
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Clarke’s answer is not that every loan is safe. His point is that outcomes depend on how portfolios are built and managed. “You’re not going to get them all right,” he said, but added that Blue Owl’s experience across cycles makes him confident that a repeatable process, selectivity and attention to loss rates can protect institutions.
That position is an investment thesis, not an independent forecast. It also does not mean Blue Owl is maintaining software exposure unchanged. In its technology-finance commentary, management said software remains the largest part of the book but that it intends to reduce exposure at the margin and concentrate on companies where it has the highest confidence. Management also said software risk has been repriced and that it continues to mark the portfolio carefully.
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Why software risk has become a credit question
Public-market repricing has made the concern visible. Reuters reported in 2026 that more than $800 billion had been erased from the market capitalization of the S&P 500 software and services index since January 28, as investors considered whether AI had shifted from a software tailwind to a source of substitution and obsolescence.
That index move is not the same as a realized loss on a private loan. A listed share-price decline can change valuations and influence marks, refinancing terms and exit assumptions without causing a borrower to miss a payment. A credit loss generally requires a deterioration in the borrower’s ability to repay, a restructuring, a default or collateral recovery below the amount owed. Private-credit managers therefore have to separate mark-to-market pressure from actual impairment while still recognizing that lower valuations can weaken sponsors’ options and reduce recovery values.
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What can still go wrong in software loans
- An AI feature can make a product less differentiated, slowing growth or increasing customer churn.
- Customers may delay renewals while they test cheaper or internally developed alternatives.
- Lower software valuations can make refinancing or an equity cure more difficult even if interest is being paid.
- Concentration in one software category, sponsor, geography or customer segment can turn a single technology shift into a portfolio problem.
Blue Owl’s stated response is selective reduction rather than a blanket exit. That distinction matters: Clarke is arguing that process and diversification can manage risk, not that all software borrowers are insulated from AI.
Why Blue Owl is positive on data centers
Clarke’s August 25, 2025 commentary describes generative-AI demand as driving a technological arms race and increasing demand for high-density data centers. He calls data-center investment a generational opportunity, but explicitly warns that an opportunity does not guarantee results, profits or success.
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The central requirement is partner quality. Clarke says investors should favor operators with scale, global reach, local presence and vertically integrated platforms, as well as a record of working with hyperscalers. Those capabilities can help an owner secure power, navigate permitting, deliver complex facilities and keep equipment productive as technology changes.
His warning is deliberately concrete: he says he could fly over Northern Virginia or Silicon Valley and see hectares of decommissioned data centers. In other words, demand forecasts alone do not prevent obsolete facilities, stranded power capacity or projects that cost more to build and operate than their contracts support.
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How the financing structures are meant to reduce downside
Blue Owl’s May 7, 2026 technology-finance earnings transcript describes digital-infrastructure opportunities that include GPU-financing structures with investment-grade counterparties. Management cited amortization, premium returns, tight documentation and corporate guarantees as features that can create contractual cash flow and improve lender protection.
The same transcript says data-center opportunities are being evaluated through Blue Owl’s IPI business, with natural limits because some projects fall into non-qualifying categories. That means a project can be connected to the AI theme and still be rejected if it does not fit the platform’s underwriting rules.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Software credit versus data-center infrastructure
The two exposures carry different failure modes. A lender should test the following six dimensions rather than assume that either sector is automatically defensive.
| Risk dimension | Software-credit exposure | Data-center infrastructure exposure |
|---|---|---|
| Revenue durability and visibility | Recurring software revenue can be durable, but renewals and growth may weaken if AI changes customer needs. | Contracted or structured cash flows can provide visibility, subject to tenant, project and contract quality. |
| Sensitivity to AI substitution or obsolescence | Directly exposed when an AI tool replicates a product’s core function or reduces willingness to pay. | Benefits from AI compute demand, but facilities and equipment can become obsolete if designs, power or cooling systems cannot adapt. |
| Counterparty or tenant credit quality | Depends on the software borrower, its sponsor support and the diversity of its customers. | Depends heavily on the operator, hyperscaler or other tenant, plus the enforceability of guarantees and contracts. |
| Construction, power and delivery execution | Usually less exposed to physical construction, though implementation and product development still affect revenue. | Material exposure to land, permits, grid interconnection, construction schedules, equipment delivery and operating expertise. |
| Diversification | Can be diversified by borrower, software category, geography and industry, but concentration can build unnoticed. | Can be diversified by operator, tenant, geography and facility type; local power or permitting constraints can create regional concentration. |
| Downside protection | Relies on covenants, collateral, sponsor support and conservative leverage, with recovery affected by changing technology valuations. | Can include amortization, guarantees, tight documentation and contractual cash flows, but recovery depends on the usefulness and location of the facility. |
Are hyperscaler commitments enough?
No. A hyperscaler relationship can improve tenant quality and revenue visibility, but it does not remove construction risk, power constraints, technology turnover or counterparty concentration. A project may have strong demand in its market and still fail to earn an acceptable return if it is late, overbuilt, poorly located or dependent on one tenant.
Clarke’s framework therefore treats hyperscaler experience as a selection criterion, not a guarantee. The preferred partners are those that have repeatedly delivered infrastructure, understand local markets and structure investments around cash generation and downside protection.
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What investors should monitor next
For software portfolios
- Changes in renewal rates, customer concentration and net retention after customers adopt AI alternatives.
- Leverage and liquidity under slower growth, including the ability to refinance without relying on a higher valuation.
- Whether portfolio marks reflect operating performance and realistic recovery values rather than only public-market sentiment.
- Exposure limits by software subsector and the manager’s willingness to reduce positions where confidence has fallen.
For data-center financings
- Evidence that power, permits, construction milestones and equipment deliveries are secured, not merely projected.
- The operator’s scale, local presence, vertical integration and history with hyperscalers.
- Tenant credit quality, contract duration, termination rights, guarantees and payment mechanics.
- Amortization, covenants and documentation that preserve lender control if a project underperforms.
- Whether the facility can remain useful if demand, chip design or cooling requirements change.
The practical takeaway
Clarke is not saying AI has made software credit risk disappear, nor that every data center is a winner. He is drawing a line between thematic exposure and underwriting discipline. Software lenders must identify borrowers whose products can withstand substitution and keep portfolios diversified. Data-center lenders must demand experienced operators, credible power and construction plans, strong counterparties and structures that convert demand into protected cash flow.
For readers asking whether AI will wipe out software private-credit investments, the evidence supports a narrower answer: some borrowers and strategies will be vulnerable, while selective managers may still meet return objectives. And for readers asking why data centers look attractive as software risk rises, the answer is that AI increases infrastructure demand—but only projects with sound execution and downside protection are positioned to turn that demand into durable returns.
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