Podcast Summary: "Will GenAI Turn a Profit in 2025?"
Podcast Information:
- Title: Thoughts on the Market
- Host/Author: Morgan Stanley
- Description: Short, thoughtful, and regular takes on recent events in the markets from a variety of perspectives and voices within Morgan Stanley.
- Episode: Will GenAI Turn a Profit in 2025?
- Release Date: March 3, 2025
Introduction
In the episode titled "Will GenAI Turn a Profit in 2025?" from Thoughts on the Market, Morgan Stanley's experts Joe Moore, Head of U.S. Semiconductors, and Keith Weiss, Head of U.S. Software, delve deep into the burgeoning debate surrounding the return on investment (ROI) in Generative Artificial Intelligence (GenAI). Recorded live from Morgan Stanley's annual Technology, Media, and Telecom (TMT) conference in San Francisco on March 3, 2025, the discussion sets the stage for understanding the current landscape and future prospects of GenAI in the tech market.
Market Overview: The Boom of AI and Investor Sentiment
Joe Moore opens the conversation by highlighting the explosive growth in the AI sector since the release of ChatGPT in November 2022. "Since then, the biggest tech players have gained more than $9 trillion in combined market capitalization," he states (00:06). This surge has outpaced the S&P 500 index by over double, driven by high investor expectations for an AI-centered technology cycle. However, Moore also notes significant investor concerns regarding the ROI of GenAI, given the massive investments and the still limited data on returns. This dichotomy sets the primary question of the episode: Is 2025 the year GenAI finally turns a profit?
The Innovation Cycle of GenAI
Keith Weiss provides a comprehensive breakdown of the innovation stages in GenAI. He outlines that the period leading up to ChatGPT's release was dominated by fundamental research on transformer models and machine learning (01:01). Following the release, the focus shifted to product development and scaling infrastructure. Weiss anticipates that by 2025, the market will enter the next phase—market uptake—where revenue generation from automating business processes will accelerate, validating the ROI of ongoing investments.
Revenue Projections and Market Potential
Morgan Stanley's research projects a staggering revenue opportunity driven by GenAI, estimating it to reach $1.1 trillion by 2028, up from $45 billion in 2024 (02:55). Weiss elaborates, explaining that this growth is bifurcated into enterprise software and consumer platforms. Enterprise software could account for approximately $400 billion by 2028, representing about 22% of the overall software market, which is expected to hit $1.8 trillion. Consumer platforms mirror this growth, highlighting the broad economic impact of GenAI across both B2B and B2C sectors.
Software Perspective: Catalysts and Transformation
Delving deeper into the software landscape, Keith Weiss identifies the key catalysts for the current and future growth of GenAI:
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Efficacy of Solutions: The primary catalyst for the present year is demonstrating that GenAI solutions deliver tangible productivity gains and ROI for end-users. "Proving out that they're going to drive productivity gains and yield real hard dollar ROI for the end customer," Weiss emphasizes (04:35).
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Mainstream Adoption: Within the next 12 to 18 months, Weiss expects a surge in mainstream adoption as businesses leverage GenAI to stay competitive.
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Business Process Transformation: Over the next three years, the focus will shift toward breakthrough technologies that not only create efficiencies but also fundamentally rewrite business processes, potentially leading to the emergence of new, innovative companies in the software sector.
Weiss also discusses the transformation of business models due to GenAI. Traditional seat-based pricing models for software may evolve into transaction-based or consumption-based pricing as automation reduces the need for human intervention. This shift will expand the market opportunity beyond information workers, as automation permeates various business processes (10:54).
Hardware Perspective: Bottlenecks and Investment Dynamics
Joe Moore addresses the hardware challenges that underpin AI innovation. He explains that training large language models like ChatGPT requires immense computational power, necessitating the use of tens of thousands of GPUs or XPUs (05:40). The scaling laws for AI models are complex and resource-intensive, leading to an "arms race" among hardware providers to support increasingly sophisticated models.
Moore highlights that while hardware companies strive to make advanced processors more cost-effective, the demand from AI workloads remains inflationary. This creates a "tug of war" in the market, where hardware designed to be deflationary contrasts with the rising demand for AI-specific processing power (07:20). Despite these challenges, Moore remains optimistic, noting that hardware investments continue to grow as AI becomes more integral to various applications.
Scaling Laws and Market Investments
Furthering the discussion, Weiss points out that although there has been some skepticism regarding the scalability of GPU clusters, major players remain committed to scaling their models. "There is a bit of an arms race at the high end of the market," Moore observes (07:47). He asserts that while a significant portion of hardware revenue currently supports large models, the future growth lies in inference and model customization, which will drive demand for advanced processors.
Key Catalysts in the Semiconductor Space
Looking ahead, Joe Moore identifies supply chain management as the primary catalyst for 2025. As companies ramp up the production of new hardware and custom silicon, the ability to efficiently scale and deliver these components becomes crucial. He predicts that 2026 will witness a slowdown and consolidation in hardware investments, signaling a shift of value from hardware to software (09:06).
Moore emphasizes the cyclical nature of the hardware market, cautioning that while the current growth is robust, future investments may experience periods of decline. Nonetheless, he maintains optimism about the long-term prospects of the semiconductor sector in supporting AI advancements.
Business Model Transformation and Future Outlook
Keith Weiss concludes by reiterating Morgan Stanley's optimism about the software industry's future in the era of GenAI. As AI automates more business processes, software companies will need to adapt their pricing models and expand their value propositions beyond traditional information workers. "The underlying value proposition remains the same. It's about automating, creating productivity in those business processes," Weiss explains (10:54).
Conclusion
Joe Moore wraps up the episode by reflecting on the collaborative insights shared between the semiconductor and software divisions. He underscores the importance of understanding ROI and tracking key milestones to navigate the evolving AI landscape effectively. As the Morgan Stanley TMT conference continues, listeners can anticipate further in-depth discussions on the dynamic interplay between hardware investments and software innovations in shaping the future of GenAI (12:23).
Notable Quotes:
- Joe Moore: "Since then, the biggest tech players have gained more than $9 trillion in combined market capitalization." (00:06)
- Keith Weiss: "Proving out that they're going to drive productivity gains and yield real hard dollar ROI for the end customer." (04:35)
- Joe Moore: "The hardware is designed to be deflationary because the workloads themselves are inflationary." (05:40)
- Keith Weiss: "The underlying value proposition remains the same. It's about automating, creating productivity in those business processes." (10:54)
Final Thoughts
This episode provides a thorough examination of the current state and future prospects of GenAI from both software and hardware perspectives. With substantial revenue projections and ongoing innovations, GenAI appears poised to achieve profitability by 2025. However, challenges in scaling, hardware investments, and evolving business models must be navigated carefully to realize its full potential.
For those interested in the intersection of AI, software innovation, and semiconductor advancements, this episode offers valuable insights and expert perspectives from Morgan Stanley's leading analysts.