Welcome to Stewart Squared podcast with the two Stewart Alsops! In this episode, we tackle the evolution and current state of artificial intelligence, with Stewart Alsop II critiquing the hype surrounding generative AI and questioning its tangible benefits to individuals, while Stewart Alsop III discusses how AI is becoming an effective tool for tasks like coding and research. Together, they reflect on how previous tech booms, like expert systems in the 80s and neural networks in the 90s, fizzled out, drawing comparisons to today’s AI landscape. For more insights, check out Stewart Alsop II’s article Still Haven't Seen Any AI Around Here in his Substack which was based on this episode, What Matters (To Me) is the series.

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Timestamps

00:00 Introduction to the Stewart Squared Podcast

00:32 Diving into Artificial Intelligence

01:44 The Evolution of AI: A Historical Perspective

03:06 The Internet and AI: A Symbiotic Relationship

03:55 Generative AI: Hype vs. Reality

06:29 Practical Uses of AI Today

07:25 AI in Enterprises: Challenges and Opportunities

08:46 The Future of AI: On-Device and Personal Data

11:52 AI and Market Dynamics

20:36 The Business of AI: Deals and Investments

22:48 The Financial Struggles of OpenAI

24:38 Microsoft's Stake in OpenAI

26:55 The History of Technological Innovation

28:35 The Hype and Reality of AI

30:56 The Management Styles of Tech Giants

37:43 The Influence of Wealth and Power in Tech

42:17 The Future of AI and Society

42:44 Newsletter Insights and Final Thoughts

Key Insights

1. Generative AI skepticism – Stewart Alsop II expressed frustration with the current generative AI hype, particularly questioning its tangible benefits for individuals. He sees the focus on large language models as an overhyped trend that, while impressive, doesn’t provide enough real-world value for most people, especially for those who don't rely on AI for basic tasks like writing.
2. AI’s historical context – Drawing from his extensive experience in the tech industry, Stewart Alsop II compared today’s AI boom to past tech cycles, such as expert systems in the 1980s and neural networks in the 1990s, both of which failed to deliver on their promises due to technological limitations like the lack of data and computing power. He warns that today’s AI might follow a similar path, with more talk than substance.
3. AI for personal productivity vs. enterprise use – Stewart Alsop III highlighted the practical uses of AI in his personal work, like coding and research, contrasting his father's skepticism. While acknowledging AI’s challenges, he underscored how AI tools can streamline processes, such as prototyping applications and gathering information quickly, though many people haven’t learned to use these tools effectively yet.
4. The potential of on-device AI – The episode explored the future of on-device AI, especially in products like Apple’s iPhones, which could shift the AI focus from massive server-based models to smaller, more efficient systems running directly on personal devices. This approach could provide more personalized and privacy-friendly AI experiences by leveraging individual data locally, unlike cloud-based models.
5. Business models and AI economics – The conversation took a deep dive into OpenAI's financial situation and partnership with Microsoft. Stewart Alsop II highlighted the aggressive profit-sharing agreement between OpenAI and Microsoft, where Microsoft receives the majority of OpenAI’s profits until a massive sum is repaid. This raised questions about the sustainability of AI companies and how financial pressures might impact the future of the industry.
6. Enterprise struggle with AI adoption – Despite the buzz, both Stewarts agreed that enterprise adoption of AI remains challenging. While Stewart Alsop III sees some value in using AI for tasks like making meetings actionable and coding prototypes, he pointed out that many organizations struggle to integrate these technologies meaningfully, often due to a lack of education and understanding.
7. AI and privacy concerns – The discussion emphasized the critical role privacy plays in future AI developments. Apple’s stance on user data, keeping personal information on the device and out of large corporate data pools, was highlighted as a potential game-changer. This contrasts with companies like Google and Facebook, which have historically monetized personal data. The Stewarts questioned whether individuals could eventually profit from their own data, but remained skeptical about the feasibility of such a model.