The Next Repricing: Credit Markets

Recently I wrote about what I called the repricing phase across markets, labour, and capital formation.

Much of the discussion since has focused on software. Investors are increasingly debating how AI may reshape the economics of the sector. Margins may compress. Feature differentiation may erode faster than expected. Entire categories may become easier and cheaper to build.

The more interesting and potentially overlooked story may not be software itself.
It may be credit.

Every major technological disruption eventually moves up the capital stack.

First equities reprice. Then credit. We may be entering that second phase.

One of the most insightful ways I have seen the current moment described came from Chamath Palihapitiya. For years the debate around software companies focused on when their cash flows might eventually deteriorate. Ten years from now? Twenty years from now?

AI is forcing investors to ask a different question. Not “when”. “If”.

If those future revenues and backlog actually remain durable at all. Once investors begin asking “if” instead of “when,” the entire valuation framework shifts. Multiples compress, discount rates rise, and investors demand wider margins of safety.

As Chamath noted, when uncertainty rises the safest thing investors can do is re-rate companies with lower multiples and higher discount rates, embedding a larger margin of safety into the investment.

That repricing is already underway in public markets, but public equities are only one layer of the capital stack. The next layer is credit.

Over the past decade the private credit market has grown to more than $1.7 trillion globally, becoming one of the fastest-growing segments of institutional finance.

Software quickly became one of its most attractive lending sectors. The logic was simple. SaaS companies generated recurring revenue, high gross margins, and required relatively little capital to operate. To lenders, that combination looked like stable and predictable cash flow. In many cases software revenue effectively became collateral.

Private credit funds financed leveraged buyouts, growth capital, and recapitalizations across the software ecosystem. Private equity firms relied heavily on these credit markets to fund acquisitions, platform strategies, and roll-ups.

But the entire model rests on one core assumption: predictability.

Credit works when the future looks like the past.

Innovation destroys the predictability credit depends on.

We are beginning to see early signs that the market is reassessing these assumptions.

Tech-focused private credit firm Blue Owl became a visible example after its shares declined sharply during the recent AI-driven sell-off in software. The move triggered broader questions about how exposed private credit lenders may be to software companies whose long-term economics are becoming harder to forecast.

Large credit platforms moved quickly to reassure investors. Leaders at firms like Blackstone and Apollo emphasized that their exposure to software lending remains diversified and manageable. At the same time, liquidity dynamics in private credit are also drawing attention. BlackRock recently maintained redemption limits on one of its credit funds, highlighting how sensitive these vehicles can become when investor sentiment shifts.

None of this suggests a systemic issue today, but it does highlight something important.

Private credit has built large lending markets around the assumption that software revenues behave like stable infrastructure assets. If AI introduces more uncertainty into those revenues, lenders will eventually begin pricing that risk differently.

Private credit now sits at the centre of the modern leveraged buyout ecosystem.

Private equity acquisitions depend heavily on debt financing, and software has been one of the most active sectors for leveraged transactions and roll-up strategies over the past decade. If lenders begin pricing more uncertainty into software revenues, the implications could ripple across the entire deal ecosystem.

Leverage multiples could fall.
Credit spreads could widen.
Deal economics could shift.

And when credit models change, the effects rarely stay contained within private equity. They flow directly into M&A activity, startup exit markets, and venture capital liquidity.

Over the past decade many venture-backed software companies have ultimately exited through private equity acquisitions or sponsor-led roll-ups. Those exits depend heavily on a credit system willing to finance acquisitions at attractive terms. If credit markets become more cautious about software revenues, the downstream effect could be slower M&A activity and longer venture holding periods.

Credit works when the future looks like the past. When technological change accelerates, that assumption becomes harder to rely on.

This dynamic highlights an important distinction in how investors capture technological change. Private credit funds finance mature companies with leverage. Their models depend on stability, predictability, and measurable financial performance.

Early-stage investors operate under very different conditions. They capture innovation risk before predictability exists.

If innovation breaks predictability, credit becomes less reliable. In that environment, the value of early-stage ownership increases. Illiquidity in venture investing is often framed as a disadvantage compared to credit strategies that promise steady yield and shorter duration, but that illiquidity is not a flaw. In many ways that illiquidity becomes the feature as it is the cost of capturing innovation risk before the credit markets arrive. By the time a company becomes stable enough for lenders to finance it, much of the asymmetry has already been realized.

There is another layer to this story that may become increasingly important over the coming decade. Traditional credit underwriting relies heavily on static financial statements and periodic reporting cycles. Lenders analyze quarterly results, covenant ratios, and historical performance to evaluate risk.

What happens when financial infrastructure itself becomes programmable? As AI reshapes how companies operate, technologies like Ethereum may enable financial systems where credit risk can be evaluated continuously rather than periodically.

Instead of relying solely on static reports, lenders could analyze live revenue streams, transaction flows, and operational data in real time. In other words, underwriting could move from periodic reporting to continuous evaluation. This intersection between AI, programmable finance, and capital markets deserves much deeper exploration.

This is exactly why the future of finance will increasingly be shaped at the intersection of AI, capital markets, and programmable infrastructure. I will explore that idea deeper in the upcoming weeks.

For now, the key point is simple. The repricing phase may not end with software valuations. The next layer to watch is credit and when credit models begin to change, the ripple effects rarely stop at a single sector.

Marcus Daniels