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“We have very smart people, and we can put these [AI] tools in their hands to make them more productive… By using AI to reimagine processes, we can create operating efficiencies that give us a scaled opportunity to reinvest in growth.” – David Solomon – Goldman Sachs CEO

David Solomon, Chairman and CEO of Goldman Sachs, delivered the quoted remarks during an interview at the HKMA Global Financial Leaders’ Investment Summit on 4 November 2025, articulating Goldman’s strategic approach to integrating artificial intelligence across its global franchise. His comments reflect both personal experience and institutional direction: leveraging new technology to drive productivity, reimagine workflows, and reinvest operational gains in sustainable growth, rather than pursuing simplistic headcount reductions or technological novelty for its own sake.

Backstory and Context of the Quote

David Solomon’s statement arises from Goldman Sachs’ current transformation—“Goldman Sachs 3.0”—centred on AI-driven process re-engineering. Rather than employing AI simply as a cost-cutting device, Solomon underscores its strategic role as an enabler for “very smart people” to magnify their productivity and impact. This perspective draws on his forty-year career in finance, where successive waves of technological disruption (from Lotus 1-2-3 spreadsheets to cloud computing) have consistently shifted how talent is leveraged, but have not diminished its central value.

The immediate business context is one of intense change: regulatory uncertainty in cross-border transactions, rebounding capital flows into China post-geopolitical tension, and a high backlog of M&A activity, particularly for large-cap US transactions. In this environment, efficiency gains from AI allow frontline teams to refocus on advisory, origination, and growth while adjusting operational models at a rapid pace. Solomon’s leadership style—pragmatic, unsentimental, and data-driven—favours process optimisation, open collaboration, and the breakdown of legacy silos.

About David Solomon

Background:

• Born in Hartsdale, New York, in 1962; educated at Hamilton College with a BA in political science, then entered banking.
• Career progression: Held senior roles at Irving Trust, Drexel Burnham, Bear Stearns; joined Goldman Sachs in 1999 as partner, eventually leading the Financing Group and serving as co-head of the Investment Banking Division for a decade.
• Appointed President and COO in 2017, then CEO in October 2018 and Chairman in January 2019, succeeding Lloyd Blankfein.
• Brought a reputation for transformative leadership, advocating modernisation, flattening hierarchies, and integrating technology across every aspect of the firm’s operations.

Leadership and Culture:

• Solomon is credited with pushing through “One Goldman Sachs,” breaking down internal silos and incentivising cross-disciplinary collaboration.
• He has modernised core HR and management practices: implemented real-time performance reviews, loosened dress codes, and raised compensation for programmers.
• Personal interests—such as his sideline as DJ D-Sol—underscore his willingness to defy convention and challenge the insularity of Wall Street leadership.

Institutional Impact:

• Under his stewardship, Goldman has accelerated its pivot to technology—automating trading operations, consolidating platforms, and committing substantial resources to digital transformation.
• Notably, the current “GS 3.0” agenda focuses on automating six major workflows to direct freed capacity into growth, consistent with a multi-decade productivity trend.

Leading Theorists and Intellectual Lineage of AI-Driven Productivity in Business

Solomon’s vision is shaped and echoed by several foundational theorists in economics, management science, and artificial intelligence:

1. Clayton Christensen

• Theory: Disruptive Innovation—frames how technological change transforms industries not through substitution but by enabling new business models and process efficiencies.
• Relevance: Goldman Sachs’ approach to using AI to reimagine workflows and create new capabilities closely mirrors Christensen’s insights on sustaining versus disruptive innovation.

2. Erik Brynjolfsson & Andrew McAfee

• Theory: Race Against the Machine, The Second Machine Age—chronicled how digital automation augments human productivity and reconfigures the labour market, not just replacing jobs but reshaping roles and enhancing output.
• Relevance: Solomon’s argument for enabling smart people with better tools directly draws on Brynjolfsson’s proposition that the best organisational outcomes occur when firms successfully combine human and machine intelligence.

3. Michael Porter

• Theory: Competitive Advantage—emphasised how operational efficiency and information advantage underpin sustained industry leadership.
• Relevance: Porter’s ideas connect to Goldman’s agenda by showing that AI integration is not just about cost, but about improving information processing, strategic agility, and client service.

4. Herbert Simon

• Theory: Bounded Rationality and Decision Support Systems—pioneered the concept that decision-making can be dramatically improved by systems that extend the cognitive capabilities of professionals.
• Relevance: Solomon’s claim that AI puts better tools in the hands of talented staff traces its lineage to Simon’s vision of computers as skilled assistants, vital to complex modern organisations.

5. Geoffrey Hinton, Yann LeCun, Yoshua Bengio

• Theory: Deep Learning—established the contemporary AI revolution underpinning business process automation, language models, and data analysis at enterprise scale.
• Relevance: Without the breakthroughs made by these theorists, AI’s current generation—capable of augmenting financial analysis, risk modelling, and operational management—could not be applied as Solomon describes.

Synthesis and Strategic Implications

Solomon’s quote epitomises the intersection of pragmatic executive leadership and theoretical insight. His advocacy for AI-integrated productivity reinforces a management consensus: sustainable competitive advantage hinges not just on technology, but on empowering skilled individuals to unlock new modes of value creation. This approach is echoed by leading researchers who situate automation as a catalyst for role evolution, scalable efficiency, and the ability to redeploy resources into higher-value growth opportunities.

Goldman Sachs’ specific AI play is therefore neither a defensive move against headcount nor a speculative technological bet, but a calculated strategy rooted in both practical business history and contemporary academic theory—a paradigm for how large organisations can adapt, thrive, and lead in the face of continual disruption.