Goldman Sachs analysts note that AI could either intensify or sharply reduce competition, depending on whether dominant incumbents compound advantages or successful adapters pull ahead.
AI impact depends on industry competitiveness and the status quo it disrupts. The analysts cite globalization, regulatory environments, and economies of scale as key modulating factors.
The economics of AI may resemble electricity or the internet: huge aggregate gains with uneven capture. Transformative technologies like railroads, electricity, and internet have historically boosted productivity while rewarding infrastructure owners with concentrated profits.
According to Goldman Sachs, data and distribution may matter more than model quality over time. If frontier models become commoditized, open-source competition will compress margins. Firms with proprietary data, workflow integration, distribution, and customer lock-in can secure durable profitability.
Corporate concentration has risen in the U.S. and advanced economies since the 1930s, driven by expansion in finance, manufacturing, information, retail, and wholesale trade.
Goldman Sachs outlines three competing theories: globalization, weak antitrust enforcement, and economies of scale. They find economies of scale the most compelling explanation.
Although profit margins have increased, only one-third of the rise is explained by higher concentration. U.S. firms raised markups as higher incomes reduced price sensitivity and price comparison.
AI disruption has two-sided implications for concentration and profitability. Industries most exposed to AI are more concentrated and enjoy higher margins. AI disruption may increase competition among them, but history shows technology and intangible capital tend to raise network concentration.
