Welcome to Episode 52 of The HockeyStick Show. I’m Miko Pawlikowski, and this week I sat down with Maggie Engler and Numa Dhamani, co-authors of “Introduction to Generative AI (Second Edition)”, to talk about navigating the AI landscape without getting swept up in hype, fear, or misinformation.
Maggie and Numa shared what it’s like to write a technical book in a field moving so fast that a second edition became necessary just a year after the first. The conversation moved fluidly between AI agents, copyright battles, bubble economics, and the challenge of staying grounded when headlines scream about both utopia and apocalypse.
When Your Book Needs an Update Before the Ink Dries
We started by exploring why a second edition was needed so quickly. The answer wasn’t just new models or better benchmarks—it was a fundamental shift in how people think about and use generative AI.
When the first edition came out, people were still asking “What is generative AI?” By the time they started the second edition, the question had become “How do I actually use this in my daily work?” The technology moved from experiment to infrastructure in less than two years.
Maggie and Numa described the challenge of writing about a field where specific results and capabilities change weekly. Their solution: focus on teaching people how to interpret new developments rather than chasing the latest numbers.
Agents: Promise, Limitations, and Reality
We spent significant time on AI agents—one of the biggest additions to the second edition. The conversation was refreshingly balanced. No wild predictions about fully automated workflows next quarter. No dismissive skepticism either.
They explained how agents show real promise in constrained domains like coding, where you can verify results against tests. Tool use capabilities have improved. Infrastructure like Anthropic’s Model Context Protocol is maturing. But we’re still far from the autonomous systems some headlines suggest.
The key insight: agents work best when you can clearly define success and verify outcomes. The further you get from that, the more human oversight you need.
The Legal Wild West and Copyright Chaos
The copyright discussion was particularly interesting. Maggie and Numa didn’t dance around the obvious: large-scale model developers are training on copyrighted material. The question isn’t whether it’s happening—it’s what happens next.
We talked about the recent SORA controversy, where OpenAI initially told anime studios they could opt out character by character, then reversed course within days. The lawsuits, the settlements, the attempts at licensing frameworks—it’s all still being negotiated in real time.
Their take: we’re converging on some baseline principles around transparency and accountability, but the intellectual property questions will take much longer to resolve.
Bubble or Revolution? Yes.
I asked the question everyone wants answered: are we in an AI bubble?
Their response was nuanced. Yes, there are bubble characteristics—high valuations, massive investment, limited returns, lots of speculation. But no, the underlying technology isn’t a passing fad. The comparison to the dot-com era felt apt: real value underneath, correction likely, but the fundamental shift is genuine.
Maggie predicted we’ll see market consolidation and some valuations adjusting. Numa emphasized we’re moving from wild optimism toward more measured metrics and tempered hype. But the core technology will keep evolving, and returns will materialize.
Starting Points and Practical Advice
We closed by discussing how people should actually get started with generative AI today. Their advice was simple: just play with the tools. Try Gemini, Claude, ChatGPT. Most have free tiers. Experiment with prompting. See what works for you.
The hesitation people feel—not knowing the “right” use cases or perfect prompts—is the main barrier. The best way through it is hands-on exploration, not more reading.
At its core, this episode was about maintaining perspective in a field that rewards extremes. How to stay informed without getting overwhelmed. How to evaluate capabilities honestly without falling into either hype or cynicism. And what it takes to write a book that stays relevant when the field updates faster than publishing cycles allow.
