Your 2026 resolution: Go on an AI diet
Stop binging on the all-you-can-eat AI buffet
Maybe it’s just me, but the last few months of 2025 felt so chaotic. Every week brought some “gamechanging” AI news. Latest models! New tools! AGI! Our LinkedIn feed was full of people launching yet another AI startup, showcasing what they built in 30 seconds, new workflows that would “completely revolutionize” our job, and reasons why we should rethink our career.
It’s hard not to feel FOMO these days. And it’s exhausting — trying to keep up with every AI development is now a full-time job. And I would argue that it’s actually making you worse at your actual job.
Your 2026 resolution shouldn’t be to try more AI tools. It should be to go on an AI diet.
“It’s like Christmas everyday!”
I remember Tobi Lütke, founder of Shopify, excitedly made that statement about the rapid AI development. Every new AI tool felt like a revelation. ChatGPT went from text to vision to voice to search. Replit and Lovable made prototyping instant. Cursor made coding accessible. Claude got Projects, artifacts, and extended context.
It’s tempting to drop everything and try the new tools immediately. After all, you don’t want to be the dinosaurs that adopt the tools a little too late. It was a sensible mindset when AI was new. Exploring these tools was super important — you needed to understand what was possible.
But guess what, Tobi? Nobody works at Christmas. Nobody gets anything done — there’s too much food to eat, too many presents to open, too many people to talk to.
I’m not thinking that the more AI tools you try, the less value you extract from any of them. When you chase every new release, you’re constantly in beginner mode, never developing mastery. You’re optimizing for novelty, not utility. You’re spending more time learning tools than solving problems. You’re reacting to what’s new instead of choosing what’s useful.
Every hour learning a tool you don’t need is an hour not getting better at something that actually matters for your work.
What you need in 2026: Careful curation, not consumption
Not “what’s new?” but “what’s essential for my actual work?” Not “what’s possible?” but “what’s worth the learning curve?”
Going on an AI diet means being deliberate about what you let into your workflow.
1. Audit what you actually need
Start with the job, not the tools. What problems am I solving repeatedly? What tasks genuinely benefit from AI acceleration? What capabilities would create leverage in my specific role?
For a PM, this might be research synthesis, data analysis, competitor research, prototyping for specific validation questions, drafting specs after thinking is done.
Unless it’s actually useful for you, there’s no point learning 17 different ways to generate images and edit videos.
2. Choose your core stack
Pick the tools you’ll master, and learn it deeply. Most PMs only need one general LLM, one prototyping tool, and maybe one specialized tool for your domain.
That’s it. Everything else is optional. Resist the urge to add more just because something new dropped.
The power isn’t in having access to 20 tools. It’s in deeply understanding 3 tools so you can use them without thinking.
(Sorry, can’t resist adding this meme)
3. Set a “time budget” for new tools
Literally ration how much time you spend evaluating new AI tools. Maybe it’s two hours per month. Maybe it’s one new tool per quarter. Whatever feels sustainable.
When something new launches, ask: Does this solve a problem I currently have? Is it meaningfully better than what I already use? Is the switching cost worth the marginal improvement?
If the answer to all three isn’t “yes,” it’s a no.
4. Stop following AI news daily
The AI news cycle is designed to create FOMO. Every release is “groundbreaking.” Every update is “game-changing.”
Most of it doesn’t matter for your actual work.
Check in monthly, not daily. Let other people be the early adopters. You’ll hear about the truly important developments anyway. By being more intentional about your learning, you actually reserve your brain space for things that are truly important.
Your job is product management, not AI journalism.
5. Measure by outcome, not output
The wrong metric: “How many AI workflows and agents have I built?”
The right metric: “How much better or faster am I at the work that matters?”
If adding a new tool doesn’t measurably improve your outcome, it’s noise. Quoting a classic from Peter Drucker: “There is nothing more useless than doing with great efficiency something that should not be done at all.”
Bonus point: Maybe don’t build that AI feature?
Just because you can add an AI feature doesn’t mean you should.
Every product is getting an AI button now. Most of them are solving problems users don’t have, adding complexity users don’t want, and competing on “AI-powered” as a feature rather than actual value.
Before building any AI feature, ask: What user problem is this solving? Be specific. Does AI actually make the solution better, or just different? Will this create real value, or just check the “LOOK, WE HAVE AI” box? Are we adding this because our users need it, or just because our competitors have it?
The best AI features are invisible. They make existing workflows faster or easier without making users roll their eyes and think, “Yay, another AI feature I don’t need.”
It’s time to tighten your belt
Going on an AI diet as a product builder means ruthlessly cutting AI workflows and features that don’t create clear, measurable value. Having the clarity to say “we don’t need AI here” when that’s true.
The PMs who thrive in 2026 won’t be the ones with the longest list of AI tools in their repertoire. They’ll be the ones who mastered a few selected tools deeply, ignored the noise, and spent their energy solving real problems instead of chasing shiny objects.