Bloomberg Intelligence Analysis: AI Demand Elasticity Justifies Startup Valuations

This analysis is based on the Bloomberg Technology interview [1] published on November 13, 2025, featuring Bloomberg Intelligence’s Breanne Dougherty discussing AI capacity demand elasticity and startup valuations.

Dougherty’s central argument challenges bubble concerns by emphasizing AI demand elasticity - every efficiency gain creates additional demand that fuels new technological revolutions [1]. This creates a self-reinforcing cycle where improvements in AI efficiency unlock new applications and generate sustained demand growth. The thesis is supported by substantial market data showing generative AI revenue projected to grow from $14 billion in 2020 to $1.304 trillion by 2032, representing 12% of total technology spend [2].

The interview occurred during mixed market conditions, with major indices showing resilience (S&P 500 +0.23%, Dow Jones +1.87%) while the Russell 2000 declined 3.39% over the past 30 days [0], suggesting some rotation away from smaller, potentially more speculative stocks. Against this backdrop, Bloomberg Intelligence tracks over 12 “deca-corn” companies (valued over $100 billion) in their datasets, with three major AI companies that would rank in the top 30 of the Bloomberg Benchmark Index if public [1]. OpenAI’s implied valuation has more than doubled over the past year, demonstrating continued premium valuations in the AI sector [1].

The demand elasticity argument is reinforced by massive infrastructure investments. Big tech companies’ capital expenditures may hit $200 billion in 2025 as generative AI demand booms [2]. Microsoft’s Azure AI business alone has reached a $5 billion run-rate, contributing a high single-digit percentage to Azure’s approximately $80 billion in segment sales [2]. This infrastructure backbone supports the thesis that current high valuations may be justified by expanding demand.

The analysis reveals strong correlations between infrastructure spending, valuation growth, and market positioning. Former OpenAI executive Mira Murati’s Thinking Machines Lab reportedly seeking funding at a $50 billion valuation [3] demonstrates how the demand elasticity thesis translates into premium valuations across the AI ecosystem.

Dougherty advocates for a benchmark approach rather than picking individual winners, positioning AI as a “continuum” investment theme extending toward quantum computing [1]. This framework suggests that current valuations reflect broader technological transformation rather than isolated speculation.

The integration between private and public AI markets highlighted by Dougherty could lead to significant market impacts when major AI companies eventually go public, potentially reshaping market indices and investment flows. The scale of these companies suggests they could become major index constituents upon IPO.

Despite her optimistic stance, Dougherty acknowledges significant risks, specifically mentioning “potential tension that can come with that in 2026” and noting that investors are experiencing periods of optimism followed by risk-off sentiment [1]. The analysis reveals several risk factors that warrant attention:

• Volatility Risk
  : Bloomberg Intelligence expects continued volatility through 2026 as investors balance FOMO with risk-off sentiment [1]
• Valuation Sustainability
  : While demand elasticity supports current valuations, the market has not yet tested these levels during significant downturns
• Regulatory Uncertainty
  : Minimal discussion of how potential AI regulations might affect demand elasticity and valuations

• Infrastructure Investment
  : The projected $200 billion in big tech capex for 2025 [2] creates opportunities for companies across the AI value chain
• Market Integration
  : The eventual public listing of major AI companies could provide new investment opportunities and potentially reshape market indices
• Continuum Investing
  : The positioning of AI as part of a longer technological continuum toward quantum computing suggests sustained investment opportunities beyond current AI applications

• Bloomberg Intelligence tracks over 12 AI companies valued above $100 billion [1]
• Three major AI companies would rank in the top 30 of the Bloomberg Benchmark Index if public [1]
• OpenAI’s valuation has more than doubled over the past year [1]

• Dougherty recommends a benchmark approach rather than individual stock picking [1]
• AI is positioned as a “continuum” investment theme extending toward quantum computing [1]
• Expected volatility through 2026 as investors balance optimism with risk management [1]

• Generative AI revenue projected to grow to $1.304 trillion by 2032 (12% of tech spend) [2]
• Big tech capex may reach $200 billion in 2025 driven by generative AI demand [2]
• Microsoft’s Azure AI business at $5 billion run-rate [2]

Other industry experts, such as Accel Partner Philippe Botteri, also see room for AI market growth, supporting Dougherty’s optimistic outlook [3]. This convergence of expert opinion lends additional credibility to the demand elasticity thesis.