Goldman Sachs reported Q3 2026 net revenues of $18.7 billion on October 13, 2026 — up 23% year over year and the second-highest quarterly figure in the firm's history. Diluted EPS came in at $17.01, up 39%, with return on equity at 18.7% and return on tangible equity at 20.5%.
Against that backdrop, CEO David Solomon used the call to lay out Goldman's view of the AI infrastructure investment cycle as one of the firm's most significant near-term opportunities.
Solomon cited external studies estimating that the U.S. data center buildout could require more than $10 trillion of investment through 2032. At that scale, he said, the annual capital requirement would represent approximately 3.6% of U.S. GDP.
Demand for compute currently exceeds supply, he said, and that imbalance is what creates financing opportunities for Goldman across its capital markets, lending, and advisory businesses.
In regards to where Goldman sits in the AI investment cycle, Solomon said they are not a builder of AI systems, but a financier of the infrastructure those systems run on.
Total financing revenues reached a record $12.7 billion in the first nine months of 2026, representing over 40% of combined FICC and equities revenues. Equities financing revenues in Q3 alone hit a record $3.37 billion, up 96% year over year, driven by elevated prime balances and Asia activity.
At the same time, Solomon acknowledged the limits of visibility into how the cycle evolves. "No one knows how the demands for capital and compute will evolve as the broader AI ecosystem matures," he said.
He also noted that rising rates could pressure project returns and that the cycle will at some point "recalibrate."
Solomon also addressed AI governance directly, framing it as a subject where Goldman's existing risk management practices are directly applicable.
He named rigorous testing, independent review, continuous monitoring, and clear human accountability as the framework for responsible deployment. He acknowledged "the accelerating pace of AI technology and the unchecked advancement of frontier models" as a broader concern, but argued the answer is not to halt progress.
Analysts pressed on multiple AI-related angles during the call. Evercore ISI's Glenn Schorr raised the question of how agentic trading, tokenization, and prediction markets could affect Goldman's markets business over time.
Bank of America's Ebrahim Poonawala asked whether financing has become the real constraint on AI capital expenditure. Solomon said the firm is watching both dynamics closely.