I kept falling for the same trap. Every couple of months a new AI dropped, and I’d tell myself, “this one’s going to replace everything else.” A week later, another model would ship, and the cycle repeated. The dream of one subscription, one tab, one invoice kept slipping further away.
Then I came across a sharp take from this LinkedIn creator that put words to exactly what I’d been feeling. The author argues that picking one AI is no longer possible, and after reading their breakdown, I’m convinced they’re right.
Here’s the comparison the original poster laid out, plus my reaction to what it actually means for how you work day to day.
The dream: one AI to rule them all
Most folks I talk to want exactly this setup. One subscription, one login, one tab, one invoice on the company card. Simple. Clean. Easy to defend in a budget meeting.
The pros are obvious:
- Lower cognitive load with no switching between tools.
- One bill, one vendor relationship.
- Easier to train your team on a single interface.
- No “wait, which AI was best for this again?” moments.
But the original poster points out the cracks fast. Models are shipping every two months. They don’t care about your craving for simplicity. The tool that “wins everything” today is mid by the next quarter.
The cons of the single tool approach:
- You’re stuck with one model’s weaknesses across every single task.
- You miss huge wins on specialized work where another tool dominates.
- You’re locked into one company’s roadmap and pricing decisions.
- Quality drops in the categories your AI is mediocre at.
The reality: a specialized AI stack
Here’s the comparison this AI professional drew, and it tracks with what I see in my own workflow.
- Claude wins writing. Long form, nuanced, voice driven prose. Still the best at sounding human.
- ChatGPT wins images, search, and spreadsheets. Native image generation, web search, and code interpreter for data work.
- Gemini wins non English work. Multilingual reasoning, especially for languages outside the usual top five.
- Gamma wins decks. Slides done in minutes, not hours. Nothing else comes close yet.
Each one is the king of its own corner. Try to use ChatGPT for a 2,000 word article and the voice goes flat. Try to use Claude for a slide deck and you’ll spend an hour fighting it. The mismatch costs you time, quality, or both.
Pros and cons of running a stack
What you gain:
- Best output for every job, no compromises.
- Faster total work time once you know what goes where.
- You stay flexible as new models ship.
- Less risk if one provider raises prices or breaks something.
What you give up:
- More subscriptions. Maybe four invoices instead of one.
- More tabs, more logins, more passwords.
- A small learning curve to remember which tool fits which task.
- More context switching during a single project.
Why “just pick one” stops working
The post’s author makes a point I keep coming back to. Models ship every two months. The leaderboard reshuffles constantly. What was best in January is third place by April.
That means the “pick one” strategy isn’t really a strategy. It’s a hope. A hope that whatever you chose stays best. It won’t.
A stack approach, on the other hand, treats AI tools like any other professional toolkit. Carpenters don’t pick one tool. Chefs don’t use one knife. Designers don’t open one app. Specialization wins because the work itself is varied.
How to actually set up your stack
Based on the breakdown this contributor shared, here’s a simple way to start without burning through cash.
- Map your work into four buckets: writing, visual and data, multilingual, and slides.
- Assign the winning tool to each bucket. Claude for writing. ChatGPT for images, search, spreadsheets. Gemini for non English. Gamma for decks.
- Test each tool on a real task you do weekly. Not a demo task. A real one.
- Compare outputs side by side. Keep the one that wins.
- Cancel the tools that lose. A stack is only worth it if every tool earns its slot.
The point isn’t to subscribe to everything. It’s to subscribe to the best tool per category, and stay willing to swap when something better ships.
My recommendation
If you’re still trying to make one AI do everything, you’re leaving quality and speed on the table. Build the stack. Keep it lean. Four tools max, one per category. Reassess every quarter when the new models drop.
The simplicity of one tool feels good emotionally. The output of a specialized stack feels good professionally. Pick the one that matches the work you’re actually doing.
Check out the full LinkedIn post for the side by side comparison and the prompts the author recommends to test each tool against your own workflow.