According to Fortune, Goldman Sachs CEO David Solomon has joined other financial leaders including JPMorgan’s Jamie Dimon and Fed chairman Jerome Powell in expressing concern about America’s $38 trillion national debt, particularly the debt-to-GDP ratio that currently stands at 125% and is projected to reach 156% by 2055 according to Congressional Budget Office data. Speaking at the Economic Club of Washington D.C., Solomon argued that the “path out is a growth path,” emphasizing that the difference between 3% and 2% compounding growth is “monstrous” in addressing the debt issue. He specifically pointed to AI technology embedded into enterprise operations as creating a “better opportunity to have a higher growth trajectory,” while noting that current GDP growth of 3.8% in Q2 2025 provides some optimism. Solomon’s comments come amid unconventional proposals from the Trump administration, including a “gold card” visa scheme that would charge wealthy immigrants $5 million for green card privileges as detailed in his February remarks.
The Productivity Math Behind Solomon’s Argument
The core economic argument Solomon is making rests on a fundamental principle of compound growth mathematics. When a country’s economic growth rate exceeds its borrowing costs, debt becomes sustainable regardless of absolute levels. The challenge is that the U.S. has struggled to maintain productivity growth above 1.5% annually for decades, while government spending has consistently grown faster than GDP. AI represents the first potentially transformative productivity technology since the internet, but historical precedent suggests that technology adoption follows an S-curve rather than an immediate spike. The current 3.8% GDP growth Solomon cites reflects multiple factors beyond AI, including post-pandemic normalization and demographic shifts that may not be sustainable.
The Implementation Gap Between AI Promise and Productivity Reality
While Solomon correctly identifies AI’s potential, the translation from technological capability to measurable economic growth faces significant hurdles. Enterprise AI implementation requires massive capital investment in computing infrastructure, workforce retraining, and process redesign—investments that may not yield returns for 3-5 years. More critically, productivity gains from automation historically create distributional challenges: while corporate profits may rise, displaced workers and suppressed wages can actually reduce aggregate demand in the short to medium term. The banking sector itself exemplifies this paradox—Goldman Sachs has aggressively deployed AI in trading and operations, leading to significant headcount reduction rather than expanded economic capacity.
The Fiscal Reality Beyond Technological Optimism
Solomon’s growth-focused solution overlooks structural fiscal challenges that technology alone cannot solve. Even with optimistic AI-driven productivity gains, mandatory spending on entitlements like Social Security and Medicare—which are projected to consume nearly 100% of federal revenues by 2035—creates an inexorable fiscal drag. The demographic reality of an aging population means more retirees drawing benefits and fewer workers paying into the system, creating mathematical constraints that no amount of productivity improvement can fully overcome. Furthermore, the current political environment shows little appetite for the fiscal discipline needed to complement growth strategies, with both major parties embracing deficit spending as economic stimulus.
What History Teaches Us About Technology-Led Debt Solutions
The belief that technological breakthroughs will solve fiscal challenges has precedent, but the outcomes have been mixed. The 1990s internet boom did contribute to budget surpluses, but that period also benefited from the “peace dividend” of reduced military spending and bipartisan fiscal discipline. Conversely, the 2000s saw tremendous technological advancement alongside exploding deficits, demonstrating that technology alone cannot overcome structural fiscal imbalances. The current situation is further complicated by global economic interdependence—while AI might boost U.S. productivity, it could simultaneously disrupt trading partners’ economies, potentially reducing global demand for American goods and services.
A More Realistic Path Forward
The most plausible scenario involves AI providing moderate productivity gains that partially offset—but don’t eliminate—fiscal pressures. A comprehensive solution would require combining technology-driven growth with gradual entitlement reform, tax policy adjustments, and strategic infrastructure investment. The danger in Solomon’s optimistic framing is that it might encourage policymakers to delay difficult fiscal decisions in anticipation of a technological deus ex machina. While AI represents America’s best near-term hope for accelerating growth, treating it as a silver bullet for the debt crisis risks creating even greater fiscal vulnerability if the promised productivity gains materialize more slowly or unevenly than expected.
