On Jevrons Paradox
Let's talk about one of those awesome counterintuitive moments in tech that keeps product leaders up at night.
You know how we're all obsessed with making AI more efficient at the moment? Well, what if I told you that making AI more efficient might lead to us using... more of it?
The Plot Twist That Started It All
Picture this: It's 1865, and an economist named William Stanley Jevons is having a "wait, what?" moment. He's looking at steam engines getting more efficient, using less coal per trip, when he notices something that probably made him slightly confused – people were actually using more coal overall, not less.
A Case of History Rhyming
Fast forward to today's tech landscape, where we're seeing this same pattern play out in AI (because of course we are). Have you heard about DeepSeek? They just dropped this AI model that runs on cheaper hardware. Watching the market reaction was like seeing a remake of your favourite movie – you know the plot, but it's still fascinating to watch.
The stock market had a "moment of uncertainty" (aka tech stocks doing their best roller coaster impression), Satya Nadella jumped in with the tech leadership equivalent of "I've seen this film before" – pointing out that making AI more efficient isn't going to slow down adoption... it's going to send it through the roof, and everyone calmed down.
When Efficiency Gets Complicated
Remember when having a website was a big deal? Now your local coffee shop's cat has an Instagram account. That's the trajectory we're on with AI – as it gets more efficient and accessible, suddenly everyone's thinking about how to use it. And I mean everyone. But just because an AI model is more efficient doesn't mean it's more truthful. It's like having a super-fast car with a questionable GPS – you might get somewhere quickly, but is it where you wanted to go?
What This Might Mean for Your Strategy
Think Exponentially, Not Linearly
When planning your AI strategy, don't just account for what you need today. The moment it becomes more efficient, you'll probably find seventeen new use cases you hadn't even considered, which suddenly becomes a lot of potential futures to think about.
The Sustainability Paradox
We need to plan for both increased efficiency and increased usage. It's like trying to diet while working at a chocolate factory – possible but requires serious strategy and discipline.
Quality Over Speed (Sometimes)
Remember: just because we can do something more efficiently doesn't always mean we should. Sometimes the scenic route leads to better enjoyment of destinations.
The Philosophical Bit
There's something deeply human about this whole paradox – the more accessible we make something, the more ways we find to use it. It's like giving a kid Lego... they never just build one thing and call it a day, right?
As someone who lives and breathes product strategy, I can't help but see this efficiency paradox as both a challenge and an opportunity. It's not just about making AI more efficient – it's about understanding how that efficiency will reshape our entire approach to building great products.
Are you seeing this pattern in your own work? How are you balancing the push for efficiency with the need for quality?
