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Search results for: “AI”

  • Will AI Intensify or Weaken Market Competition?

    We study how AI affects market competition based on a general equilibrium framework with heterogeneous firms facing idiosyncratic productivity and variable markups. Firms choose the AI technology subject to fixed costs, where AI production requires data and energy inputs. Our model predicts a non-monotonic relation of AI diffusion with industry concentration. As AI usage rises from an initially low level, large incumbent users gain market share. When AI usage is sufficiently diffused, entry of new and smaller adopters erodes the market share of incumbents, reducing industry concentration. The non-monotonic relations are robust when firms can complement AI with their own data. Our calibrated model predicts that industry concentration is likely to fall if AI adoption increases relative to the current level. In comparison, the relation of AI with the average markup depends on whether increased AI usage is driven by demand or supply factors. Our model also predicts that a modest subsidy of about 3 percent for AI adopter revenues maximizes social welfare, reflecting a tradeoff between aggregate productivity and the average markup associated with AI usage.

  • The Rise of AI Pricing: Trends, Driving Forces, and Implications for Firm Performance

    We document key stylized facts about the time-series trends and cross-sectional distributions AI pricing and study its implications for firm performance, both on average and in response to monetary policy shocks. We use the online job postings data from Lightcast to measure the adoption of AI pricing. We infer that a firm is adopting AI pricing if it posts a job that requires AI-related skills and contains the keyword “pricing.” At the aggregate level, the share of AI pricing jobs in all pricing jobs has increased more than tenfold since 2010. The rise of AI pricing jobs has been broad-based, spreading across more industries than other types of AI jobs. At the firm level, larger and more productive firms are more likely to adopt AI pricing. Firms that adopted AI pricing experienced faster growth in sales, employment, assets, and markups, and their stock returns are also more responsive to high-frequency monetary policy surprises than non-adopters. We show that these empirical observations can be rationalized by a simple model where a monopolist firm with incomplete information about its demand function invests in AI pricing to acquire information.

  • Reflections on AI Implementation and Guardrails for Community-Based Organizations

    Insights from nonprofit and philanthropic leaders on the role AI policies and guardrails can play in implementing AI in mission-driven organizations.

  • Small Businesses at the Frontier of the Generative AI Economy

    How is the spread of generative artificial intelligence (GenAI) tools affecting small businesses? Read more about the EERN roundtable with leaders of small-scale enterprises currently integrating GenAI technologies.

  • Early Findings on Small Business Use of AI

    The Federal Reserve’s Small Business Credit Survey asked about AI use for the first time in 2024. Well over one-third of small businesses reported they were already using or planning to use AI.

  • The Economic Value of AI at Work and at Home

    Aaron “Ronnie” Chatterji, chief economist at OpenAI and Sylvain Leduc, director of economic research at the Federal Reserve Bank of San Francisco, held a live discussion on the economic value of AI in both professional and personal use.

  • How Twelfth District Businesses are Rapidly Embracing GenAI

    Emerging technologies are top of mind for Twelfth District businesses. Hear how they are using GenAI and their expectations for boosting workforce productivity.

  • Understanding How Generative AI is Changing the Health Care and Legal Industries

    Although relatively new for a wide generalized audience, generative artificial intelligence (AI) has already begun to change our economy, and it seems poised to drive even more fundamental changes in the future.

  • AI-Powered Algorithmic Pricing and Monetary Policy

    The business practice of adjusting prices using algorithms powered by artificial intelligence—known as AI pricing—has grown rapidly and spread across many sectors in the economy. Unlike traditional price setting, AI pricing uses predictive analysis of large data sets to incorporate real-time changes in supply and demand conditions into pricing decisions. This enables businesses to adjust prices more quickly in response to unexpected changes in market conditions and monetary policy. Industry-level evidence suggests that price adjustments are more sensitive to monetary policy in sectors where AI pricing is more prevalent.