Every few weeks a new model launches and the headlines say it's the best one yet. Sometimes it's true. Either way, you feel the pull to move, to redo your setup around whatever just topped the charts. That instinct, chasing every release, will cost you more time than any model will ever save you.
The opposite instinct is just as expensive. Practices that picked a tool two years ago and never looked up are running on something two generations behind, paying more for worse output and not knowing it. So the answer isn't loyalty and it isn't chasing. It's having a reason.
Here's how I decide. I don't switch because a model is newer. I switch when a model is clearly better at a job I actually do. I work in Claude and ChatGPT every day, and which one I reach for depends on the task, writing, analysis, research, and on which one is currently stronger at that specific thing. The frontier moves, so the answer changes, and that's fine, because I'm choosing per task, not marrying a brand.
For a practice, the version of this that matters is simpler. Don't build your operation around a single model you can never change. Pick tools that let you swap the model underneath when a better one shows up, so a new release is an upgrade you opt into, not a migration you dread. That one design choice is what separates people who benefit from the pace of AI from people exhausted by it.
And when you do test something new, give it a real job and a real window. Not a five-minute demo that dazzles. A month on actual work, measured against the tool you're already using. Most of the time you'll learn the new one is marginally better and not worth the switch. Occasionally it's a clear step up and you move. Either way you decided on evidence, not on a launch-day headline.
You don't need the newest model. You need to not be locked out of it when it matters.
Frequently asked questions
Should I switch to a new AI model every time one launches?
No. Switch when a model is clearly better at a task you actually do, not because it's newer. Chasing every release costs more time than it saves.
How do I evaluate a new AI model?
Give it a real job for about a month and measure it against your current tool. Decide on evidence from your own work, not on launch-day benchmarks.
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© Audrey Campbell 2026