Make An Extra Revision
For the past year, I’ve been noodling on something that James Clear once noted in his newsletter:
"The difference between good and great is often an extra round of revision.
The person who looks things over a second time will appear smarter or more talented, but actually is just polishing things a bit more.
Take the time to get it right. Revise it one extra time."
James Clear has certainly earned the right to illuminate the difference between good content and great. Though I often dash my daily blog posts off without much consternation or revision — deliberately, by design, as I consider the forcing function an feature, not a bug — I was nonetheless struck by the prospect of how much better my content might be if I gave an extra round of revision.
This idea of innovation via revision found me once more while listening to a spectacularly insightful interview with YouTube sensation Mr. Beast. He describes the difference between good and great in his world thus:
“The difference between one million views and 30 million views isn’t that the 30 million-view-creator put in 30x the effort. They just had a way better idea, and then put in 2-3x the effort. Once you understand that, you realize that the idea is so freaking important. Most YouTubers could pull triple the views with half the work if they had better ideas. It’s that extreme.”
The part that got me is the notion of putting additional effort (ie another cycle of iteration) into the particularly good idea, rather than simply coming up with another idea. All too often, when we think of innovation — and even of the quantity required to truly break through — we think in terms of discrete, “different” ideas. But iterations are every bit as much viable alternatives!
And, if you believe James Clear and Mr Beast — two smashing successes in their respective domains — perhaps the iterated ideas may actually be even more valuable than the next original idea.
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The fine folks over at Section recently interviewed me about some common mistakes I’m seeing folks make when they use AI. One of the biggest ones? The tendency to expect too little, and get just that.