When Will the AI Trade Stop Falling: The Financing Prisoner's Dilemma Behind Meta's Massive Bond Issuance

Over the past week, AI trades have come under a fresh round of selling pressure, with U.S. equity and bond markets simultaneously under strain. The Nasdaq posted its worst single-day performance in months, the risk premium on U.S. high-yield bonds rose to a 17-month high, and yields on CCC-rated junk bonds climbed to their highest level since 2020. While headline index volatility has been limited, the repricing of risk in underlying assets is accelerating.

One of the focal points fueling market concern is the massive financing plan Meta unveiled after reporting third-quarter results. According to a Barclays research report published on October 23, Meta chose to raise funds early partly because other companies in the AI ecosystem have already begun or plan to begin financing, and the capital market's capacity to absorb new supply is becoming increasingly limited.

This reveals the "prisoner's dilemma" facing AI giants: every company has an incentive to raise funds first, but if the giants collectively accelerate bond issuance, the new supply could push up financing costs and in turn squeeze the financing room available to all participants. Meanwhile, hedge funds continue to cut leverage, and spread dispersion among AI-related credit assets is widening.

Over the past week, index performance has masked sharp divergence among individual stocks. Goldman Sachs data show that its long-short portfolio of AI beneficiaries versus AI risk stocks has fallen about 55% from its June high, erasing all of its year-to-date gains; momentum strategies have also seen a sharp drawdown, falling to extreme levels not seen since the 2008 financial crisis.

On Thursday, the S&P 500 fell 1.2% even as 335 of its constituents rose; the equal-weighted S&P 500 gained 0.7%, while the market-cap-weighted SPY closed lower. Goldman Sachs data show that hedge funds deleveraged on seven of the previous eight trading days, and Thursday's selling was driven entirely by long liquidation.

Credit risk concentrates in lower-rated borrowers

According to Bloomberg, the average risk premium on U.S. high-yield bonds has risen to a 17-month high, and the primary market has seen its largest wave of pulled deals this year; yields on CCC-rated junk bonds have risen to 15.85%, a new high since 2020, and the spread between CCC and BB rated bonds is approaching the peaks seen in 2016 and 2020.

The leveraged loan market is also under pressure. The volume of deeply distressed loans priced below 60 cents on the dollar has risen to its highest level since March 2020. JPMorgan data show that over the past 12 months, the volume of loans priced at 80 cents on the dollar or below has surged nearly 90%.

Notably, even as CCC-rated bond spreads widen sharply, the broader credit market has not deteriorated in tandem, indicating that risk is concentrating in issuers with weaker debt-servicing capacity. Robert Cohen, head of corporate credit at DoubleLine Capital, noted that some CCC-rated borrowers may move toward debt restructuring, but this does not mean the entire credit cycle has reversed.

Goldman Sachs data show that spreads across multiple rating buckets—from investment-grade and high-yield credit derivatives to lower-rated cash bonds—have hit cycle highs. R. Lakshmanan, head of Asia-Pacific credit at CreditSights Singapore, believes that after a succession of multiple global shocks to the market, the repricing of credit risk is gradually becoming a sustained trend.

Meta's bond issuance exposes the AI financing "prisoner's dilemma"

Meta's financing plan has pushed the tension between AI capital expenditure and credit market supply into the foreground. The company narrowed its 2026 capital expenditure forecast to $135 billion to $145 billion and expects fourth-quarter spending of $49 billion to $59 billion; free cash flow turned negative after dividends, and management also expects to raise hundreds of billions of dollars starting this autumn.

Barclays noted in its report that revenue growth at hyperscale tech companies is materializing, but capital expenditure continues to climb, and the financing question has shifted from "whether we can raise funds" to "when to raise, how much to raise, and how to raise it."

Meta said in its filing that, given that other companies in the AI ecosystem have begun or plan to begin financing, the capital market's capacity to absorb new supply is becoming increasingly limited, making early financing practically necessary.

But when all companies choose to raise funds first, individually rational decisions can translate into pressure on the market as a whole: a concentrated influx of new bond supply pushes up issuance costs and limits the narrowing of credit spreads. To avoid excessive borrowing that could damage their credit ratings, companies are also beginning to consider equity financing, convertible bonds, asset-backed securities (ABS) and off-balance-sheet financing channels.

Barclays estimates that AI-related credit assets total about $680 billion, spanning hyperscale tech companies, semiconductors, data centers and hardware companies; they account for about 7.5% of the investment-grade bond index's market value but contribute about 13% of the index's duration times spread (DTS) risk exposure.

Overall, AI credit assets carry a spread premium of about 35 basis points over the investment-grade index, with the premium reaching about 55 basis points for bonds with maturities exceeding 10 years. Meanwhile, spread dispersion among AI issuers has risen from 24 basis points to 65 basis points over the past year; even excluding Oracle and the data center sector, the measure is still up about 60% to 37 basis points.

Since mid-September, credit spreads on Oracle, Meta, SpaceX and several data center operators have widened significantly more than the index; shorter-dated bonds from Microsoft, Google and Amazon have been relatively resilient. The market has begun to differentiate based on issuers' financing needs, balance sheets and long-term risks, rather than treating AI as a single credit theme.

The yield curve also reflects pressure at the long end. Barclays data show that over the past year, the average 2-year to 5-year, 5-year to 10-year and 10-year to 30-year yield curves of hyperscale tech companies steepened by 137%, 140% and 68%, respectively. Among them, Meta's 10-year to 30-year curve spread reached 68 basis points, the steepest among comparable issuers.

The steepening of the long-end curve reflects rising risk compensation investors demand for long-term debt. As AI companies continue to expand capital expenditure, the market must not only judge whether investment can be converted into revenue, but also assess the scale of financing, the timeline for cash flow improvement and the impact of new bond supply on financing costs.

Therefore, whether the AI trade can stop falling hinges on whether the massive financing can be absorbed by the market, whether credit spreads can stabilize, and whether risk differentiation among AI companies continues to widen. Until these pressures ease, periodic rebounds may still be constrained by rising financing costs. Risk disclosure and disclaimer

Markets carry risk, and investment requires caution. This article does not constitute personal investment advice and does not take into account the specific investment objectives, financial situation or needs of individual users. Users should consider whether any opinions, views or conclusions in this article are appropriate for their particular circumstances. Investing based on this article is at the user's own risk.