The landscape of generative AI is evolving rapidly with companies like OpenAI and Meta striving for dominance. DeepSeek’s rise as a cost-effective alternative has shaken the market, prompting reevaluation of traditional business models. The podcast dives into the complexities of achieving artificial general intelligence and the associated economic implications. It also tackles the paradox of profitability in AI, comparing it to the airline industry, while emphasizing the importance of user interface in maintaining user loyalty.
The growing demand for AI skills is highlighted by diverse students enrolling in prompt engineering courses to enhance their productivity.
DeepSeek's cost-effective AI models challenge Silicon Valley valuations, potentially igniting a price war among generative AI companies.
Deep dives
Empowering Through Education
A generative AI prompt engineering class at Capital City College highlights the growing demand for skills related to AI technology. The course caters to a diverse group of students, including marketing professionals and retirees, showcasing the widespread interest in learning how to effectively leverage AI tools like ChatGPT. The curriculum aims to transform learners from merely using AI for tasks like essay writing to understanding how to engage with the technology for enhanced productivity in their daily work. This shift emphasizes the importance of teaching critical prompting techniques to help users achieve better results from generative AI.
Market Dynamics and Competitive Landscape
The entry of China’s DeepSeek, which provides effective AI models at a fraction of the cost of its American counterparts, raises questions about the valuation of Silicon Valley AI companies. DeepSeek’s ability to develop a reasoning model with cognitive capabilities similar to leading models while maintaining lower costs challenges the economic viability of traditional closed-source business models. As DeepSeek's open-source model spreads, the competitive landscape shifts, forcing established firms to reconsider pricing strategies and value propositions. This situation suggests a potential price war, where offering the lowest cost model may become paramount to attract business users.
Potential Outcomes and Profitability Challenges
Generative AI firms face high operational costs and fierce competition, exacerbating their struggle for profitability. The discussion of Jevons Paradox suggests that as AI technology becomes cheaper, consumption may increase, potentially leading to long-term profits despite current losses. However, the unpredictability remains high; companies might cut back on quality in a bid to streamline costs or, conversely, find innovative solutions that enhance user experiences. Ultimately, while the quest for advanced human-level intelligence continues, the race among AI companies may come down to which can offer superior service rather than groundbreaking technology.
The biggest companies in tech are fighting to be the leader in generative AI - even as the path to profitability for the technology remains unclear. So what’s the long game for companies such as OpenAI, Google, and Meta? And what does the rise of Chinese start-up DeepSeek mean for AI companies with massive valuations?
In the second episode in our series on the business of AI, the FT’s AI editor Madhumita Murgia speaks with FT technology reporter Cristina Criddle as well as Vahap Can, an instructor on a prompt engineering course at Capital City College, Anton Korinek, a professor in the department of economics at the University of Virginia, and Alex Chalmers, a writer, researcher formerly of Air Street Capital.
This season of Tech Tonic is presented by Madhumita Murgia, and produced by Josh Gabert-Doyon. Edwin Lane is the senior producer and Manuela Saragosa is the executive producer. Sound design by Breen Turner, Samantha Giovinco and Joe Salcedo, with original music from Metaphor Music. The FT’s head of audio is Cheryl Brumley.