What a 90s sitcom can teach us about today’s AI arms race. A meditation on power, restraint, and why the most meaningful uses of AI may come not from scale, but from intention.

A new kind of headline is appearing: What if the AI boom is a bubble? Given the scale of investment and the mismatch in results, the real surprise would be if it weren’t. But bubbles don’t invalidate the underlying technology — they expose where strategy gave way to hype. The path forward is the same as it's always been: ask better questions, center people, and focus on meaningful, durable value.

We’re surrounded by quick takes and hot opinions about AI, but real understanding takes time. What AI Books to Read, Vol. 1 kicks off a continuing series of thoughtful reading recommendations curated by Erin, founder of orient(ai)tion. Each book offers a different lens on where we are, how we got here, and what kind of future we can still design…if we take the time to learn.

AI promised to boost productivity, but in many workplaces, it’s creating a new kind of mess: workslop—low-effort, AI-generated output that looks fine at first glance but lacks real value. This isn’t a failure of the tools, but of how we’re using them. In this piece, I dig into how we got here, what the research says, and why common-sense strategy and human judgment still matter.

Telling employees to “just use AI” may seem efficient, but it creates fear, shadow use, and short-term thinking. In reality, automation often creates more human work, not less. The real value comes when AI is used in good faith to push roles further, improve products, and build long-term growth.

Human-centered design may feel like an outdated term in an era of AI. But its spirit of care, curiosity, and clarity has never been more relevant. We’re missing an opportunity to use decades of expertise to guide this moment with wisdom.

AI adoption is happening fast, and much of it in the shadows. Employees are experimenting with tools on their own, while leaders chase the next big thing. The problem isn’t enthusiasm — it’s tool-first thinking. When organizations slow down and cut through the hype, they find the real value.

The question I keep circling back to is: How do we handle this shift better than the last one? What did we learn, and how do we apply it now?

To start answering that, I’ve developed a framework — less “how quickly can we deploy AI?” and more “how can we be thoughtful and ethical in how we move forward?”

Is questioning the AI claims and buzzy applications the same as misunderstanding what the internet was going to be twenty-odd years ago? To make sense of it, I found myself returning to 8 comparisons of what feels different (and doesn’t) between then and now.

AI ROI is too often framed around efficiency — time and money saved. But efficiency alone is not a strategy. This post explores why the better question is “How do we use AI to make our products and services better?” and how reframing ROI helps organizations move from extraction to innovation.

Talking about AI as if it's human reinforces a misunderstanding of what AI is and isn’t, and more importantly what we can reliably trust it to do. This is not to slam AI. As I’ve said many times, the technology itself is neither good nor bad. Our understanding, application, and business models based around it…. that’s another story.