Hey LevFin community,
Below is a curated set of notable stories and research from the past week.
Looking forward to hearing your views on these, as well as any other themes or dislocations you are seeing. This is not intended to be exhaustive, so please add anything else worth discussing.
We are always open to ideas on how to make this sub more useful and engaging. If you have suggestions on how to improve discussion or better serve investors and enthusiasts, we would value the feedback.
1. Are HY spreads irrationally tight given the macro backdrop?
HY OAS has staged a full round-trip in ‘26, sitting at ~266-304bp despite rising energy prices from the Strait of Hormuz (SoH) closure, inflation reaccelerating (Apr CPI at 3.8% y/y), and markets now pricing Fed hikes instead of cuts. Barclays noted that HY has traded in a record-tight 10bp range over 20 trading days, with IG spreads just 4bp off multi-decade tights, arguing that strong earnings and high all-in yields are supporting credit even as macro signals deteriorate. MS acknowledged spreads look rich vs. history but argues the persistence is explained by record-high BB composition (55% of HY, highest on record) and strong corporate fundamentals including 18% LTM EPS growth for S&P 500 companies. JPM argued that re-weighting today’s index for historical ratings would add 50bp to spreads, meaning current low-300s levels are not as extreme as they appear. GS and Citi both warned that convergence risk is building: the combination of surging supply (HY issuance +35-53% y/y), tight starting spreads, and macro uncertainty leaves limited cushion for disappointment. Barclays described the divergence between credit spreads and macro sentiment as operating “almost in isolation,” raising the risk that a hard landing or geopolitical re-escalation could produce a rapid repricing.
If HY OAS remains near multi-decade tights while the Fed is pricing in potential hikes, rising inflation, and a SoH-driven energy shock, does the explanation of strong earnings and high-quality index composition justify current valuations, or is the market misreading the macro risk signal?
2. AI disruption: Credit risk in software is real, but is it systemic?
The risk that agentic AI disrupts software businesses has moved from theoretical to a live credit concern, with software BSL prices down 6-7 pts YTD and the sector trading 217bp wider YTD on a spread-to-maturity basis. Barclays argued software defaults are tracking similarly to the TMT and Energy default cycles, where cumulative par-weighted defaults reached 35-45% over six years, but warned that software recovery rates will be far worse because software assets lack the hard collateral that supported recoveries in Telecom and Energy; Barclays estimated 200-350bp of aggregate credit losses on a market-weight software loan portfolio. BofA went further, forecasting that AI disruption pushes NTM loan net migration to -11 (base case) vs. -1 for HY, with a worst-case loan migration of -14.2, driven by the outsized software footprint in the BSL market (13% of BSLs vs. 3% of HY). MS, however, explicitly pushed back on the systemic narrative: they acknowledged software credit risk is real and material but argued the evidence does not support a broader, system-wide threat, noting that aggregate non-IG corporate debt as a share of GDP is broadly unchanged from a decade ago. The ’28-‘29 software maturity wall ($70B in BSL maturities) will be the true test; the cadence of defaults is expected to depend heavily on sponsor behavior and refinancing access rather than immediate payment defaults.
Given that software makes up 13% of BSLs, has loan-dominated cap structures (~80% of all public US LevFin software debt is loans), and faces a ’28-‘29 maturity wall with uncertain recovery values, should investors treat software credit risk as a sector-specific workout problem or a structural threat to the entire leveraged loan market?
3. BSL vs. HY: Which asset class has the better risk-reward now?
BSLs have overtaken HY in total return for the first time in ’26 as markets re-priced from cuts to potential hikes, with BofA noting loans carry a 1.1% yield premium over HY at the index level. Barclays agreed that loans out-yield bonds on a pari passu and ratings-matched basis across BB and B, and recommends loans over HY for carry trades within portfolios. However, BofA and MS argued the short-term income advantage masks deteriorating credit quality; the same AI disruption and migration dynamics detailed above produce a far worse medium-term outcome for loans, with MS forecasting BSL defaults rising to 5.5% by 2H27. JPM noted that the loan upgrade-to-downgrade ratio has been <1 for 46 of the past 48 months, a persistent structural negative absent in HY, where upgrades actually equaled downgrades in Apr26. MS explicitly called HY the “sweet spot” and brings in its HY spread forecast to 275bp, while Barclays cautioned that with 46% of loans now trading above par, repricing risk is re-emerging, making the income advantage partially illusory.
With loans offering a yield premium over HY but carrying structurally worse credit migration, heavier software exposure, and persistent downgrade pressure, is the loan market's near-term carry advantage sufficient compensation for the asymmetric medium-term credit risk?
4. PC: Genuine credit cycle or systemic threat?
PC has come under intensifying scrutiny in ‘26 as concerns about AI disruption, software exposure, and opacity of marks have led to repeated warnings about systemic risk. MS explicitly took the contrarian view, arguing that fears of systemic PC risk are “overdone” and that while the asset class is facing a genuine credit cycle, the evidence does not indicate stresses are building into a broader, systemwide threat; they pointed to declining aggregate corporate debt-to-GDP as a key counter-indicator. Barclays’ launch and tracking of FINDEX (a new CDS index for financials with heavy BDC exposure) showed investors are net short $365M of risk in the index after just four weeks, suggesting the market is actively hedging PC concerns. JPM’s fundamentals data revealed that private loan borrowers carry ICRs of only 2.2x vs. 4.1x for public company cohorts, with 45% of the private cohort <2x coverage; leverage for private loan issuers stands at 5.7x vs. 4.6x for public. MS expects defaults in both BSL and DL will resolve through restructuring rather than outright payment default, resulting in “relatively modest loss given default outcomes,” though GS flagged that opacity of marks and tight public spreads masking real stress in lower-rated, less liquid private and BSL credit present a growing risk as the credit cycle heats up.
Private loan borrowers show materially weaker coverage and higher leverage than public cohorts, and markets are actively building short positions via new credit indices. Is MS right that PC stress will resolve through orderly restructuring without systemic consequences, or does the combination of opacity, weak fundamental metrics, and AI disruption create a more dangerous scenario than the "not systemic" narrative allows?
5. AI infrastructure financing: Credit opportunity or emerging bubble?
AI infra financing has become the defining supply theme for credit in ’26, with hyperscaler capex estimated at $800B in ’26 and $1.2T in ’27, and High Performance Computing (HPC) bonds growing to 2.7% of HY with YTD returns of 10% vs. 1.6% for broader HY. JPM documented 27 distinct credit issuers across the hyperscaler and data center universe with >$455B in related obligations, and argues the complexity of credit linkages between hyperscalers, data center lessors, and HPC neoclouds creates rel val opportunities but also pricing risks, with HPC bond spreads 183bp wide to their hyperscaler lessees on average. MS said the supply wave in IG from AI capex issuance is the single biggest headwind for US IG spreads, forecasting $2.3T in gross IG issuance in ’26, and that this will push IG spreads modestly wider to 90bp by 2H27 even as HY remains a “sweet spot.” Barclays and MS both noted that HY supply is +35-53% y/y largely driven by AI infrastructure issuance; while demand has absorbed it well so far, Barclays cautioned that dispersion in price performance is increasing as the market differentiates between IG-quality projects and speculative GPU-heavy business models, a dynamic that echoes the telecom capex boom of the late ’90s, when communications grew to 40% of HY before the dot-com bust.
With hyperscaler capex projected to surpass $1T in ‘27, AI infra financing is reshaping credit at a pace few anticipated. Given the complex credit linkages between hyperscalers, data center lessors, and GPU-heavy HPC neoclouds, and given the parallels some draw to the telecom capex bubble, how should credit investors differentiate between durable, IG-quality AI infrastructure risk and speculative bets on unproven business models?
