This week’s stories play out at a distance—in financial markets, academia and big tech—but they point clearly toward future directions for publishers. At the other end of the scale, I’ve written about my hands-on experience with Claude Code and agentic AI, which you could start using today.

I had a couple of conversations this week that inspired a quick coda to my previous post on using Claude Code for prototyping ideas. The day after I posted it, I spoke with Tom, one of my most thoughtful and creative friends—someone who is about as far away from AI boosterism as it would be possible to get. A decade ago, he had spent several thousand pounds with a web developer building a location-aware mobile website. He asked how much information Claude Code needed to build something similar. I copied his sixty-word WhatsApp, clarified half a dozen questions that Claude asked in response, and it took under two minutes to replicate the core functionality.

Last week, Ethan Mollick posted about creating a functioning game with Claude Code and a one-shot prompt. As a first project with Claude Code, it seemed ambitious but appealing. I started with a single paragraph prompt to develop a short, simple adventure game in the style of the Sierra On-Line games I remember from the late eighties, with an AD&D 2nd Edition vibe. Getting a first, testable version was almost indecently fast. It then took several rounds of iteration to improve it, including a separate workstream to create retro style screens (Claude doesn’t have native image generation). The total active investment of time was about twenty minutes, with Claude Code running in the background while I did other things.

There are two big themes in this week’s newsletter: utility and accountability, and they don’t sit easily together. The productivity gains many of us are seeing from AI are increasingly in tension with unanswered questions about training and transparency.

There’s an uncomfortable paradox at the heart of Generative AI adoption: the promise from AI vendors and consultants like me is that the technology will boost productivity. And for the most part, this holds true with my clients. But the gains are unevenly distributed. Dan Hon wrote in his newsletter recently that his friends who use LLMs brilliantly in their coding work do so because they bring very particular skills and experience that don’t translate to the average user—a challenge amplified by what Ethan Mollick calls AI’s ‘jagged frontier’ of uneven capabilities. Scale this up to enterprise level and you hit a bigger problem: AI can create efficiencies for individual, power users, but it doesn’t necessarily deliver the company-wide transformation the C-suite was sold on. You’ve probably seen headlines claiming that most AI pilots fail to deliver measurable P&L impact. Are the promised gains genuinely elusive, or do we need to reframe what we’re looking for?

This week’s headline stories sit at the uncomfortable boundary between assistance and dependence. From lost intellectual scaffolding to new AI “coworkers”, the question isn’t just what AI tools can do—but how we should use them without dulling the human thinking they’re meant to support.

This week’s AI stories are less about capability and more about control: how models are trained, when licensing works, and where consent is quietly assumed rather than granted. For publishers, these mechanics are starting to matter more than the rhetoric.

Happy New Year. The first AI stories of 2026 aren’t about speed or scale, but about authenticity—who is speaking, what can be trusted, and where responsibility sits when things go wrong. That’s uncomfortable territory for publishers, but increasingly unavoidable.