💬 Stephen’s Update
I’ve been thinking a lot about a recent Modern People Leader conversation with Alex Shapiro, Chief People Officer at Jasper.
Every now and then, we have a guest who helps me reframe how I look at the future.
Alex did exactly that — introducing me to the Induced Demand principle and how it’s showing up in the way we build, use, and expand AI inside organizations.
A quick ‘thank you’ as well to my friend Hernan Chioso for jumping into my comments and connecting it to Jevons Paradox — the 19th-century idea that efficiency can actually drive more consumption.
Then earlier this week, the Financial Times dropped new data on ChatGPT adoption.
The curve looked far steeper than anything I would have expected.
So this week I’m pulling these threads together — Alex’s analogy, Hernan’s comment, and the FT data — to unpack what’s really happening beneath the surface of “AI adoption” and what it means to businesses.
1️⃣ The Curve Everyone’s Talking About
Source: Financial Times, 2025 – ChatGPT adoption vs. the Internet’s first decade.
The FT visualization comparing ChatGPT’s growth to the early Internet is stunning.
It’s the fastest-adopted technology in history. 🤯
But here’s the question we’re not asking enough:
What kind of adoption are we actually seeing?
Is this just faster tech diffusion — or a deeper rewiring of how work gets done?
Yes, this has opened up a lot of follow up questions.
But I think it’s clear, in this data, the AI era is here!
2️⃣ Induced Demand — The More You Build, the More You Create
As Alex Buder Shapiro put it on our 🔥🔥🔥 MPL episode:
“Urban planners once thought: build more roads → reduce traffic.
The opposite happened. More roads created more traffic.”
That’s Induced Demand.
And it’s exactly what’s happening with AI.
Each friction removed creates new capacity — and new kinds of work.
The more you build, the more possibilities appear.
In my own experiments, every workflow that got easier spawned three new ones worth exploring.
It’s not more busy work — it’s better work.
💡 Real-World Signals
A perfect example of this principle at play is Andrew Golden’s (RetailNext) recent How I Built It demonstrated this perfectly.
He used AI to vibe code a company specific engagement model and survey platform tailored to his organization.
After seeing its impact, RetailNext’s leadership wanted more AI projects like it.
That’s Induced Demand in motion: once teams see what’s possible, they want to scale it.
3️⃣ Jevons Paradox — The Efficiency Trap
In the 1800s, William Jevons noticed that when coal engines became more efficient, coal consumption went up, not down.
That paradox is repeating with AI.
As automation lowers the cost of creative and cognitive tasks, total output explodes.
AI isn’t just eliminating admin work…
It’s expanding our collective capacity for complex work.
4️⃣ What This Means for B2B Adoption
“AI makes work easier — so we do more of it.”
In enterprise settings, AI doesn’t just streamline processes — it expands ambition.
Teams launch more pilots, analyze more data, and chase ideas once deemed impossible.
For People leaders, the challenge isn’t tool rollout — it’s behavior rollout.
Success depends on how quickly organizations learn, adapt, and evolve their operating models.
That’s why the next true advantage will come from building Org Brains that learn as fast as their people do.
(Next week I’ll be jamming with Darren Murph on Org Brains and knowledge management)
Mark my words - there will be some unintended consequences. For example, with increased adoption, we’ll need better solutions for how we address tech debt in HR.
5️⃣ Designing for the New Traffic Patterns
The future of work won’t be defined by how many people use AI,
but by how fast companies can redesign around it.
If Induced Demand is inevitable, it is time for HR leaders to start asking themselves:
HR leaders - what new traffic patterns are you designing for your org?
Hit me up or reply back and let me know! Would love to hear what you’re doing.
🧭 Field Notes (Practical Tips for HR Leader)
If you’re building AI adoption programs, model for behavioral loops — not linear change.
Consider tracking:
Interaction frequency (i.e., how often is AI tech stack used)
Time to insight (i.e., how quickly are we learning from usage)
Workflow elasticity (i.e., how quickly are we able to iterate feedback)
Our view is that in the early days of changes - these are the new adoption metrics that matter.
🧩 Wrapping Up + Coming Next
The good news is that more will be revealed on the topic of AI adoption as the genie is out of the bottle. Pace of change seems to keep accelerating….
Coming Soon on Field Notes:
🔮 Org Brain deep dive with Darren Murph
🛠 New How I Built It with Vanessa Monsequeira, VP People @ Gorilla
If this issue helped you see the curve differently, share it with one person designing the future of work. (I’m working on some Huertanomics swag and will gladly send some)
And for those that haven’t yet, subscribe to Huertanomics Fields Notes for more AI for HR nuggets.
Let’s build it together.
— Stephen