In a recent piece, I made the case that we should stop trying to build "perfect" AI. That imperfection is not a failure mode — it's intrinsic to how these systems work. Here, I want to go one step further: not just to excuse AI's flaws, but to explore how we can use them. How we can design with imperfection in mind.
We seem to have stumbled into a strange contradiction: we demand perfection from our machines, while tolerating imperfection from ourselves. The same people who chuckle at human error in the workplace will denounce AI systems for the slightest misstep. "It hallucinated a fact!" Yes. And you never have?
A week ago I tried switching my web browser's default search engine from Google to Perplexity.
A useful way to understand the successes—and the stagnation—of big tech is through the lens of organisational advantage. Not just what a company did, but how it was set up to win.
The 20th century belonged to specialists. Experts carved out narrow domains and dug deep. The system rewarded it — the best knee surgeon, the best COBOL programmer, the best ad copywriter. Depth was the differentiator. But AI tilts the game board.
Have you ever tried to create a language from scratch? I began creating one for fun a while ago, and I've been trying to teach it to ChatGPT,. Langaf is a fictional language that is similar to English and uses a base twelve counting system.
Documenting how an APIAPI A set of rules and protocols that allows different software applications to communicate with each other. APIs define the methods and data formats that applications can use to request and exchange information. works is easy. Documenting what you can do with it is the hard part, and it's the part that actually matters.
As a product manager, making decisions is a big part of the job. While we all want to make the right decision every time, sometimes things don't go as planned. It can be tempting to try to stick with a decision, even when it's not working, but I've learned that it's better to be able to reverse course and make a new decision.
Life can often feel like a journey towards a specific goal, be it happiness, wealth, expertise, or fame. And just like in artificial intelligence, achieving our goals can be seen as an optimization problem. But what if we thought of life itself as an AI search problem? What lessons could we learn from the various search approaches used by computer scientists and mathematicians?