AI Eats the World: Benedict Evans on the Next Platform Shift
a16z PodcastFull Title
AI Eats the World: Benedict Evans on the Next Platform Shift
Summary
This episode discusses the transformative potential of AI as a platform shift, drawing parallels with past technological revolutions like the internet and smartphones.
The conversation explores the current state of AI adoption, its potential impact across various industries, and the uncertainty surrounding its ultimate scale and capabilities, comparing it to electricity or computing in terms of its foundational impact.
Key Points
- The term "AI" often refers to new, exciting developments, similar to how "technology" or "automation" are used; once a technology is established, it's no longer considered "AI."
- Artificial General Intelligence (AGI) is viewed as either already present and integrated into software or perpetually five years away, with uncertainty about its true potential.
- Past platform shifts like the internet and mobile phones led to significant value capture by both new entrants and incumbents, with mobile enabling new behaviors like online dating and apps like TikTok.
- The current AI landscape faces a "schizophrenia" between the promise of human-level AI and the practical reality of developing new software tools, with the true impact of AI's capabilities remaining uncertain due to a lack of fundamental theoretical understanding.
- The immense investment in AI infrastructure (compute and data centers) is driven by the perceived risk of falling behind, but the ultimate demand and economic viability are still being determined, potentially leading to bubble-like conditions.
- Current AI adoption shows a bifurcation: obvious and immediate use cases in software development and specific enterprise solutions, contrasted with broader consumer adoption that is still nascent, with many users struggling to find daily utility.
- The long-term competitive landscape for AI is complex, with hyperscalers like Google and Meta facing questions about how they will leverage AI to maintain or gain market share, while companies like Apple are exploring how AI fits into their ecosystem.
- The true impact of AI will likely be in enabling entirely new workflows and business models, rather than just automating existing ones, mirroring how the internet and mobile phones created unforeseen opportunities.
- The evolving nature of AI use cases and the difficulty in predicting future demand make current forecasting challenging, akin to predicting bandwidth consumption in the late 90s or early 2000s.
- The "AI is whatever doesn't work yet" adage highlights the cyclical nature of technological adoption, where breakthroughs eventually become integrated, mundane software.
Conclusion
The current state of AI adoption is still early, with significant uncertainty about its ultimate impact and the emergence of truly transformative applications beyond immediate utility.
The focus is shifting from purely technical capabilities to product strategy and how AI will be integrated into existing workflows and create entirely new ones.
The history of platform shifts suggests that the most profound changes will come from unforeseen use cases and the redefinition of what is possible with technology.
Discussion Topics
- How can we differentiate between genuine AI platform shifts and incremental technological improvements?
- What are the ethical considerations and potential societal impacts that need to be addressed as AI becomes more integrated into daily life?
- Beyond automation, what are the most promising areas where AI is likely to create entirely new industries and redefine existing ones?
Key Terms
- AI
- Artificial Intelligence, systems that can perform tasks that typically require human intelligence.
- AGI
- Artificial General Intelligence, hypothetical AI with the intellectual capability of humans.
- Platform Shift
- A fundamental change in the underlying technology or infrastructure that enables new products, services, and business models.
- S-curve
- A graphical representation of cumulative adoption of a new technology or innovation over time, typically showing slow initial growth, rapid acceleration, and eventual saturation.
- Hyperscalers
- Large cloud computing providers such as Amazon Web Services, Microsoft Azure, and Google Cloud.
- SaaS
- Software as a Service, a software licensing and delivery model.
- Win32 API
- A set of functions and data structures used by applications to interact with Microsoft Windows.
- LLM
- Large Language Model, a type of AI model trained on vast amounts of text data to understand and generate human-like text.
Timeline
The term AI is a label for new developments, losing its distinctiveness as it becomes integrated.
The concept of AGI is debated, with some believing it's near and others seeing it as perpetually distant.
Mobile's impact was significant, enabling new companies and user behaviors, though incumbents also benefited.
There's a perceived disconnect in the tech industry's discourse on AI, oscillating between revolutionary potential and practical software development.
The physical limits of AI technology are unknown due to a lack of a strong theoretical understanding of its workings and human intelligence itself.
The significant investment in AI infrastructure is driven by the fear of missing out, with hyperscalers prioritizing investment over potential overcapacity.
The true value of AI will emerge from enabling entirely new applications and business models, not just automating existing tasks.
Current AI deployment is split between immediate, obvious uses and broader consumer adoption that still requires more intuitive integration.
The development of AI has revealed that existing business models, particularly those reliant on "boring" or complex tasks, are vulnerable to disruption.
The evolution of platform shifts often involves unbundling and re-bundling of functionalities, creating new opportunities and challenges for existing companies.
Existing questions about AI's future, such as its scaling, the role of open source, and China's involvement, remain relevant, but new product strategy questions are emerging.
The impact of AI on various industries, like retail and content creation, is still being explored, leading to new business model questions.
The ultimate role of AI in devices and platforms is still uncertain, with implications for companies like Apple, Google, and Meta.
The core question is what truly defines a business's defensibility and profitability, and how AI might disrupt those foundations.
The historical precedent of technological shifts, like the internet and mobile, shows that unforeseen "killer apps" or fundamental shifts in user behavior emerge over time.
The definition of AI itself is fluid, often referring to technology that is not yet fully understood or functional.
Episode Details
- Podcast
- a16z Podcast
- Episode
- AI Eats the World: Benedict Evans on the Next Platform Shift
- Official Link
- https://a16z.com/podcasts/a16z-podcast/
- Published
- December 12, 2025