Goldman Sachs Group, Inc. (GS) executives detailed their artificial intelligence strategy for operational efficiency as new data showed the top 100 registered investment advisor firms now manage more than $1.6 trillion in client assets, double what they controlled just two years ago, according to Padi Raphael, global co-head of third party wealth at the firm.
Key Takeaways:
- Top 100 RIA firms doubled assets to $1.6 trillion in two years, while audience size grew just 15%.
- Goldman Sachs is deploying AI across 10 specific use cases, achieving 20–50% productivity gains in software development.
- Current M&A cycle is corporate-led as firms pursue scale to afford AI investments and capture margin advantages.
Raphael shared the data Tuesday during the Goldman Sachs Annual RIA Professional Investor Forum held at the firm’s New York City office, where president and chief operating officer John Waldron outlined how the bank is deploying AI across 10 specific use cases rather than attempting to “boil the ocean” with hundreds of projects simultaneously.
While assets at the top 100 RIA firms doubled, the audience size grew just 15% over the same period, Raphael noted. The concentration of growth among fewer but larger firms reflects new operational challenges tied to scale, technology deployment and talent management.
Addressing the technology challenge, Waldron said AI is currently inflationary and in a build phase where compute infrastructure costs are rising 10% to 30%. All the inputs for building AI capability are getting more expensive, from data centers to specialized chips, making the technology a near-term cost driver rather than an immediate efficiency gain.
The technology will eventually drive productivity gains, but capital expenditures will get more expensive over the next 12 to 24 months before efficiency curves kick in.
AI Deployment and Operational Efficiency
Goldman Sachs selected 10 use cases under what Waldron called the “one Goldman Sachs” architecture, focusing on lending processes, client onboarding, data infrastructure, trade ledgers, liquidity platforms and software development. The bank chose to concentrate resources on fewer initiatives rather than spreading efforts across dozens of projects.
The firm meets weekly with military precision to track return on investment across those projects, Waldron said.
Early results show software developers achieving 20% to 50% productivity gains using AI coding tools. Rather than reducing headcount, Goldman Sachs is maintaining staff levels while increasing project output.
The bank is lowering its zero-based budget line and completing more projects with the same number of developers, Waldron explained.
The bank measures “gross benefit” across three categories, Waldron explained: productivity gains from existing staff, hard cost savings from process automation, and foregone investment where planned hiring becomes unnecessary.
Waldron warned RIA executives against attempting too many AI initiatives at once, saying the worst outcome is trying to tackle 15 to 20 projects without accomplishing any of them.
Beyond technology deployment, Waldron advised RIA leaders to expand front office, middle office and back office functions in lockstep when scaling operations. Goldman Sachs interviews candidates for skills through the first four rounds, then switches exclusively to cultural fit assessment in subsequent interviews.
The approach aims to prevent the common scaling mistake of prioritizing speed over cultural alignment.
“When you’re scaling fast, you can lose the cultural piece fairly quickly,” Waldron said. He recommended breaking growing firms into smaller community units rather than operating as one large organization, similar to dividing a university campus into residential colleges rather than housing everyone in a single dormitory.
Market Dynamics and Portfolio Construction
The U.S. economy remains strong despite a “K-shaped” dynamic where lower-income consumers face pressure, Waldron said. Chief investment officers he speaks with remain over-allocated to U.S. equities and increasingly comfortable with that positioning, despite ongoing debates about dollar dominance and U.S. market concentration.
Even global asset allocators who question their U.S. exposure continue gravitating back to the same stocks in practice.
Current market conditions favor a “bigger is better” merger and acquisition cycle led by corporate buyers rather than private equity sponsors, Waldron said. Companies are pursuing scale to afford AI investments and capture margin advantages, with larger firms generating better performance and commanding higher valuations.
The stock market is rewarding scale with premium multiples, creating pressure on smaller competitors to consolidate or risk falling further behind.
On private credit, Waldron said the asset class belongs in client portfolios but wished for better industry-wide disclosure and discussion around suitability and liquidity. Some SpaceX and similar venture capital liquidity events in 2026 and 2027 will likely recycle back into private markets, he added.
Waldron closed by urging privately held RIA firms to remain private as long as possible. “This quarter to quarter thing is challenging when you’re trying to build enduring value,” he said.
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