AI Eats the World? A Reality Check with Benedict Evans

June 4, 2026

AI Summary

5 min read

In 2023, AI coding assistant GitHub Copilot had a run rate of about $9 billion. By early 2025, that number had jumped to $47 billion. That single data point, as analyst Benedict Evans explains in this conversation, captures the most important thing that has happened in AI over the last year: coding has found product-market fit. Customers are pulling the tool out of developers' hands. But that clarity, Evans argues, is also a trap. It narrows the industry's focus onto one use case while the deeper, harder questions about who captures value, what becomes a product, and how the rest of the economy will use this technology remain wide open.

Why Coding Won, and Why That Isn't the Whole Story

The fact that software development became AI's first killer app is not surprising in retrospect. As Evans puts it, the first thing people did with PCs was make computers, and the first thing people are doing with large language models is make more compute. The people who are most comfortable experimenting with the technology are the ones building it. But the shift from "kind of useful" to "really changing everything" happened only in the last six months, driven by agentic coding workflows that actually work.

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What you'll learn

1 (00:00) **Introduction & Episode Framing** - Benedict Evans returns to discuss his updated "AI Eats the World" presentation, reflecting on what has changed in the last year.
2 (02:12) **What We've Learned in the Last Year** - Evans identifies the major shift: diverging product strategy and the emergence of agentic coding as the first clear product-market fit.
3 (03:48) **Why Coding Became the First Killer Use Case** - Evans explains that software developers were the first to experiment with LLMs, making coding the natural first application.
4 (04:56) **What This Means for Engineering Jobs** - Evans argues it's too early to know the impact on junior vs. senior engineers, team structure, or careers.
5 (06:08) **OpenAI's Strategy & The Anthropic Pivot** - Evans analyzes OpenAI's "everything all at once" strategy versus Anthropic's focus on coding, which achieved product-market fit.
6 (08:07) **Consumer Adoption: The Weekly vs. Daily Gap** - Evans highlights the persistent gap: only ~10% of users are daily active, while 30-40% are weekly.
7 (11:10) **The Pricing Crunch & The Mobile Data Analogy** - Evans draws a direct parallel between the current AI token pricing crunch and the mobile data pricing crisis of 2009-2010.

+ Full timestamped outline available in the app

Guests on this episode

Benedict Evans

Show Notes

Erik Torenberg speaks with tech analyst Benedict Evans about the current state of AI, what has changed over the past year, and which questions remain unanswered.

The conversation covers coding agents, foundation models, AI infrastructure spending, software economics, and the tension between today's AI excitement and the long-term realities of technology adoption. Evans discusses why coding has emerged as AI's first breakout use case, how previous platform shifts can help frame the current moment, and why many of the most important questions about AI remain unresolved.

Along the way, they explore the future of software, enterprise adoption, consumer behavior, and whether AI models ultimately capture value themselves or become infrastructure for the next generation of applications.

Resources:

Follow Benedict Evans on X: https://x.com/benedictevans

Follow Erik Torenberg on X: https://x.com/eriktorenberg

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