The AI boom will flourish as long as confidence runs high, said Goldman Sachs (GS) CEO David Solomon.
“There’s plenty of liquidity in the system if the world continues to remain as optimistic,” Solomon said during a Tuesday interview hosted by the Economic Club of New York. He was asked by CNBC’s Leslie Picker how he felt about the sheer size and number of AI mega deals coming to the equity markets for capital.
“We are definitely in a moment where there’s more greed than there is fear,” Solomon added.
After raking in $17 billion in profits last year, Goldman Sachs is positioned for potentially another banner year, driven by market volatility and the AI gold rush.
Its IPO team landed the coveted lead spot for the listing of Elon Musk’s SpaceX. The offering, which involves 22 other banks, is expected to be complete next week. The rocket and satellite company is widely expected to use the capital to accelerate its AI ambitions.
Goldman Sachs was also named as the private placement agent for an $80 billion equity raise from Google parent company Alphabet (GOOGL).
“The stock’s trading very well,” Solomon said of the Alphabet raise, describing the deal as the largest follow-on equity raise ever. “This is the first actual concrete data point for bringing something of this scale, and it’s encouraging.”
Additionally, Goldman is vying for a lead spot in the public offerings of AI model makers Anthropic and OpenAI. The rivals are looking to go public sometime later this year. Anthropic filed its confidential IPO paperwork on Monday, while ChatGPT maker OpenAI is reportedly working toward the same goal.
Separately, Goldman is backing both OpenAI and Anthropic’s efforts to launch ventures that aim to accelerate AI deployment in enterprises. Analysts forecast 2026 will rank behind 2021 as Goldman’s second-highest year ever for profits, according to data compiled by Bloomberg.
But demand for AI infrastructure and compute is “not going to go in the straight line that everybody’s now currently projecting,” Solomon said. Potential speed bumps include further technological changes and the shifting costs of manufacturing and distribution.
Demand for AI computing power “ultimately has to be bought by enterprises,” Solomon added. “Enterprises broadly will go slower at [using AI], they will be slower to change, they will be slower to adapt than I think some of the current expectation.”
AI usage would likely differ based on industries, he explained. While higher-margin businesses can afford to invest and experiment more, lower-margin businesses have fewer resources and incentives to adopt AI.
