Due Diligence

“AI really allows smart, talented, driven, sophisticated people to be more productive – to touch more people, have better information at their disposal, better analysis.” – David Solomon – Goldman Sachs CEO

David Solomon, CEO of Goldman Sachs, made the statement “AI really allows smart, talented, driven, sophisticated people to be more productive – to touch more people, have better information at their disposal, better analysis” during an interview at Italian Tech Week 2025, reflecting his conviction that artificial intelligence is redefining productivity and impact across professional services and finance.

David Solomon is one of the most influential figures in global finance, serving as Chairman and CEO of Goldman Sachs since 2018. Born in 1962 in Hartsdale, New York, Solomon’s early years were shaped by strong family values, a pursuit of education at Hamilton College, and a keen interest in sport and leadership. Solomon’s ascent in the industry began after stints at Irving Trust and Drexel Burnham, specialising early in commercial paper and junk bonds, then later at Bear Stearns where he played a central role in project financing. In 1999, he joined Goldman Sachs as a partner and quickly rose through the ranks—serving as Global Head of the Financing Group and later Co-Head of the Investment Banking Division for a decade.

His leadership is marked by an emphasis on modernisation, talent development, and integrating technology into the financial sector. Notably, Solomon has overseen increased investments in digital platforms and has reimagined work culture, including reducing working hours and implementing real-time performance review systems. Outside his professional life, Solomon is distinctively known for his passion for music, performing as “DJ D-Sol” at major electronic dance music venues, symbolising a leadership style that blends discipline with creative openness.

Solomon’s remarks on AI at Italian Tech Week are rooted in Goldman Sachs’ major investments in technology: with some 12,000 engineers and cutting-edge AI platforms, Solomon champions the view that technology not only streamlines operational efficiency but fundamentally redefines the reach and ability of talented professionals, providing richer data, deeper insights, and more effective analysis. He frames AI as part of a long continuum—from the days of microfiche and manual records to today’s instant, voice-powered analytics—positioning technology as both a productivity enabler and an engine for growth.

Leading Theorists and Context in AI Productivity

Solomon’s thinking sits at the crossroads of key theoretical advances in artificial intelligence and productivity economics. The transformation he describes draws extensively from foundational theorists and practitioners who have shaped our understanding of AI’s organisational impact:

• Herbert Simon: A founder of artificial intelligence as a discipline, Simon’s concept of “bounded rationality” highlighted that real-world decision making could be fundamentally reshaped by computational power. Simon envisioned computers extending the limits of human cognition, a concept directly echoed in Solomon’s belief that AI produces leverage for talented professionals.

• Erik Brynjolfsson: At MIT, Brynjolfsson has argued that AI is a “general purpose technology” like steam power or electricity, capable of diffusing productivity gains across every sector through automation, improved information processing, and new business models. His work clarifies that the impact of AI is not in replacing human value, but augmenting it, making people exponentially more productive.

• Andrew Ng: As a pioneer in deep learning, Ng has emphasised the role of AI as a productivity tool: automating routine tasks, supporting complex analysis, and dramatically increasing the scale and speed at which decisions can be made. Ng’s teaching at Stanford and public writings focus on making AI accessible as a resource to boost human capability rather than a substitute.

• Daron Acemoglu: The MIT economist challenges overly optimistic readings, arguing that the net benefits of AI depend on balanced deployment, policy, and organisational adaptation. Acemoglu frames the debate on whether AI will create or eliminate jobs, highlighting the strategic choices organisations must make—a theme Solomon directly addresses in his comments on headcount in banking.

• Geoffrey Hinton: Widely known as “the godfather of deep learning,” Hinton’s research underpins the practical capabilities of AI systems—particularly in areas such as data analysis and decision support—that Solomon highlights as crucial to productive professional services.

Contemporary Application and Analysis

The productivity gains Solomon identifies are playing out across multiple sectors:

• In financial services, AI-driven analytics enable deeper risk management, improved deal generation, and scalable client engagement.
• In asset management and trading, platforms like Goldman Sachs’ own “Assistant” and generative coding tools (e.g., Cognition Labs’ Devin) allow faster, more nuanced analysis and automation.
• The “power to touch more people” is realised through personalised client service, scalable advisory, and rapid market insight, bridging human expertise and computational capacity.

Solomon’s perspective resonates strongly with current debates on the future of work. While risks—such as AI investment bubbles, regulatory uncertainty, and workforce displacement—are acknowledged, Solomon positions AI as a strategic asset: not a threat to jobs, but a catalyst for organisational expansion and client impact, consistent with the lessons learned through previous technology cycles.

Theoretical Context Table

Essential Insights

• AI’s impact is cumulative and catalytic, empowering professionals to operate at far greater scale and depth than before, as illustrated by Solomon’s personal technological journey—from manual information gathering to instantaneous AI-driven analytics.
• The quote’s context reflects the practical reality of AI at the world’s leading financial institutions, where technology spend rivals infrastructure, and human-machine synergy is central to strategy.
• Leading theorists agree: real productivity gains depend on augmenting human capability, strategic deployment, and continual adaptation—principles explicitly recognised in Solomon’s operational philosophy and in global best practice.