For credit, Goldman’s warning points to more pressure on spreads for AI-exposed investment grade names while the issuance pipeline stays this heavy. New deals may need to come with a concession to attract buyers. The equity raises from Oracle and Meta suggest some of the funding burden is moving onto shareholders, a potential source of dilution worries for stocks already hit by last week’s selloff. For equity investors, the shift in focus from demand to returns argues for more dispersion within the AI complex, rewarding companies that can show margin and free cash flow gains. Together with the wealth managers’ calls to diversify, it adds to the case for broader market leadership.-Earlier:WSJ: Anthropic targets Nasdaq debut next week under ticker ANT despite tech selloff-The AI build-out is turning into a financing story, and Goldman’s message is that balance sheets and returns, not demand, will decide who comes out ahead.Summary:Goldman Sachs says more than $110 billion of AI-related bond supply has been announced since October 29, including deals of about $25 billion from Microsoft and $10 billion from OracleThe bank expects AI-related investment grade financing to total around $1.5 trillion over the next three years across bonds, data centre shell financings and chip-backed dealsSecondary spreads on the Microsoft deal widened five to ten basis points; Goldman cites supply imbalances and concern over the pace of monetizationHyperscalers have combined operating cash flow of nearly $740 billion and are protecting ratings with equity, including Oracle’s $10 billion issue and Meta’s planned $95 billion raiseUpside earnings surprises are fading; investors now focus on converting demand into margins, free cash flow and returnsGoldman remains selective on AI credit and expects capital discipline and return on invested capital to determine the next winnersGoldman Sachs expects AI-related investment grade financing to reach around $1.5 trillion over the next three years. The bank warns that the sheer volume of debt coming to market is becoming a strong technical headwind for credit, even as hyperscaler fundamentals remain solid.In a note on AI from a public investing perspective, Goldman said more than $110 billion of AI-related bond supply has been announced across multiple currencies since October 29. That includes deals of about $25 billion from Microsoft and $10 billion from Oracle. The bank expects future financing to span a range of instruments, from unsecured hyperscaler bonds to data centre shell financings and chip-backed transactions.Markets are absorbing the supply, but at a price. Goldman pointed to secondary spreads on the Microsoft deal widening by five to ten basis points after the announcement. It attributed moves like that to supply and demand imbalances, as well as some concern about how quickly AI spending will translate into revenue.The bank was more reassuring on balance sheets. It said the hyperscalers retain a large cash buffer, with combined operating cash flow of nearly $740 billion, and appear committed to protecting their credit ratings by diversifying how they fund themselves. Goldman cited Oracle’s $10 billion equity issue alongside its bond deal, and Meta’s planned $95 billion equity raise, as signs that companies are managing the ratings impact of their capital plans. Even so, it said it remains selective on credit in the sector.On equities, Goldman said this earnings season has brought diminishing upside surprises as AI spending settles into a steady, multi-year ramp. Strong demand alone is becoming less of a differentiator. Investors increasingly want to know whether that demand can be met and how efficiently it turns into margins, free cash flow and shareholder returns.Monetization is uneven, according to the bank. Newer consumer AI formats currently earn less than traditional search. Even so, Goldman noted that Google’s AI Overviews has reached 1 billion users and monetizes at core search rates, and that a quarter of users of Meta’s Muse agent use it to make purchases. Enterprise adoption is steady rather than accelerating sharply. Demand for custom AI infrastructure is strong, while uptake of off-the-shelf tools is more gradual.Goldman’s conclusion is that AI is becoming a test of capital and operational discipline as much as of technology. It expects the next generation of winners to be decided by balance-sheet capacity, pricing discipline and return on invested capital. That favours companies that can turn large order books into clean margin expansion. This article was written by Eamonn Sheridan at investinglive.com.