In a 2021 O'Reilly Radar piece called "Rebranding Data," I noted the following:

When the hype fades—when changing the name fails to keep the field aloft—that hype dissipates. At that point you’ll have to sell based on what AI can really do, instead of a rosy, blurry picture of what might be possible.

Post-bubble AI (or whatever we call it then) will be judged on meaningful characteristics and harsh realities: “Does this actually work?” and “Do the practitioners of this field create products and analyses that are genuinely useful?” (For the investors in the crowd, this is akin to judging a company’s stock price on market fundamentals.) Surviving long-term in this field will require that you find and build on realistic, worthwhile applications of AI.

That was when "AI" still referred to ML/AI, more than a year before genAI started making waves. But the lesson still holds.

Today, in 2026, we're starting to see those first cracks form in the genAI sales machine. Enterprises are slowing their purchases of AI-based tools as they think through the value prop and try to understand the technology's place in the company.

I hope the enterprises mentioned in this Wall Street Journal piece continue to ask the hard questions and demand real answers from their vendors. That's how we'll collectively surface the truly useful tools and meaningful use cases for genAI.