CONTRIBUTORS
Stephen Dover, CFA
Chief Market Strategist
Head of Franklin Templeton Institute
Jonathan Curtis
Chief Investment Officer,
Portfolio Manager
Franklin Equity Group
United States
Franklin Templeton Institute’s Stephen Dover interviews Jonathan Curtis, Co-Chief Investment Officer of Franklin Equity, on artificial intelligence (AI) and its impact on the economy and markets.
1. Markets are pricing real AI disruption, not a bubble
Recent volatility across equity markets, particularly within software and digital knowledge work, signals a growing recognition that AI-driven disruption is no longer theoretical. Attention is shifting from whether AI will reshape economic activity to how quickly it will alter business models, earnings durability and capital allocation.
Curtis emphasized that he believes this market behavior is not driven by speculative excess. Rather, investors are attempting to price tangible disruption to software economics, adjacent forms of digital knowledge work and companies whose earnings assumptions rely on potentially fragile AI infrastructure spending plans.
At its core, this environment reflects accelerating awareness that rapid advances in AI capabilities are beginning to translate into tangible economic consequences.
2. AI capability gains are hitting economics faster than fundamentals show
The pace of improvement in AI models continues to exceed expectations. Scaling laws remain intact, token costs are declining rapidly, and performance continues to improve across a widening range of economically meaningful tasks.
On benchmarks such as GDPval1 (which emphasize real digital work rather than abstract academic performance), results continue to move decisively higher. Across a growing set of digital job categories, AI-generated output is increasingly preferred to human output.
3. Software is ground zero for disruption
Software development represents the first major category of digital knowledge work where advances in AI are translating into tangible and dramatic economic consequences. Over the past six months, software stocks have faced sustained pressure, a trend that has intensified alongside rapid progress in agentic coding capabilities from leading model providers.
AI is materially increasing developer productivity, altering the supply dynamics of software itself. As application development costs fall, barriers to entry decline and enterprises gain greater ability to develop internal tools rather than rely solely on third-party vendors. Investors are increasingly focused on how these dynamics affect software business models and long term cash-flow durability.
4. Competition among AI model providers is reshaping infrastructure spend expectations
Competition among AI model builders is intensifying with direct implications for capital allocation, particularly among the most capital-intensive and loss-making providers. Investors are increasingly questioning whether the full scale of announced infrastructure deployment plans will ultimately be realized.
This skepticism does not reflect the weakening of AI demand. Instead, it reflects uncertainty around which platforms are likely to capture enterprise and consumer share and how infrastructure spending may be distributed across providers. That uncertainty has placed pressure on companies positioned as the most direct beneficiaries of individual model developers.
5. Capital rotation is rational
Viewed through this lens, recent market volatility appears rational. Capital is rotating toward areas that clearly benefit from AI adoption and segments where disruption risk is perceived to be structurally lower, particularly within the physical economy.
Investment implications
Curtis expects volatility to remain elevated as AI-driven disruption unfolds along powerful exponential curves. This volatility should be viewed as a feature of the transition rather than evidence that underlying trends are faltering.
One of Curtis’ convictions remains firm: Demand for AI compute is likely to remain exceptionally strong. This supports a durable investment case for leading semiconductor manufacturers, suppliers of semiconductor capital equipment and companies aligned with the broader AI infrastructure buildout, including utilities, industrials and materials.
Curtis also sees underappreciated productivity gains ahead for early AI adopters with control of large, compounding datasets and a willingness to rebuild workflows around agentic systems, particularly across health care, financial services, customer service, marketing and technology.
Real-world constraints matter. Curtis acknowledges power availability, engineering and construction capacity, and natural resource inputs represent both bottlenecks and sources of underappreciated pricing power, growth and profit margin expansion for well-positioned providers.
The bottom line
Markets are not panicking about AI; we believe they are grappling with the economic impact of rapidly accelerating AI capabilities. Software is the first area to feel that pressure, but it will not be the last. For investors, the task is to distinguish overstated disruption risk from areas where AI-driven demand, particularly for compute and infrastructure, remains durable.
Endnotes
- Source: “Measuring the performance of our models on real-world tasks.” OpenAI. September 25, 2025. “GDPval measures model performance on tasks drawn directly form the real-world knowledge work of experienced professionals across a wide range of occupations and sectors, providing a clearer picture on how models perform on economically valuable tasks.”
Glossary
Scaling laws: The idea that AI performance improves predictably as you add more data, computing power, or training time.
Seat-based pricing models: Pricing where you pay per user or license, regardless of how much value or output the software delivers.
Outcome-based pricing: Pricing tied to results achieved; customers pay based on measurable outcomes or value created, not usage.
WHAT ARE THE RISKS?
All investments involve risks, including possible loss of principal.
Equity securities are subject to price fluctuation and possible loss of principal.
Small- and mid-cap stocks involve greater risks and volatility than large-cap stocks.
Investment strategies which incorporate the identification of thematic investment opportunities, and their performance, may be negatively impacted if the investment manager does not correctly identify such opportunities or if the theme develops in an unexpected manner. Focusing investments in the health care and biotechnology sectors carries much greater risks of adverse developments and price movements in such industries than a strategy that invests in a wider variety of industries.
Any companies and/or case studies referenced herein are used solely for illustrative purposes; any investment may or may not be currently held by any portfolio advised by Franklin Templeton. The information provided is not a recommendation or individual investment advice for any particular security, strategy, or investment product and is not an indication of the trading intent of any Franklin Templeton managed portfolio.
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