The real AI assistant problem no one talks about

You have ChatGPT open in one tab. Claude in another. Gemini somewhere. Perplexity for research.

Same question, four different assistants, hoping one gives you the perfect answer. But here is what nobody is saying: the real AI assistant problem no one talks about is not which one is best. The problem is that you are using all of them.

We have created a new kind of context switching, and it is destroying the productivity gains AI was supposed to deliver.

The fragmentation multiplies

I came across this research from the American Psychological Association that stopped me cold. Task switching can cost up to 40% of productive time. Not 4%. Forty percent.

Now add AI assistants to that equation. Workers already switch between apps about 1,200 times per day, wasting roughly 4 hours each week just reorienting themselves. Every switch between ChatGPT and Claude is another context break. Another mental reset. Another productivity hit.

The math is brutal. If each context switch costs you 20-80% of your productivity for that task, and you are bouncing between four different AI assistants throughout your day, you have just turned your productivity superpower into a productivity drain.

This is not theoretical. Global productivity losses from context switching cost hundreds of billions annually. We are now adding AI assistant switching on top of that.

Why we keep adding more assistants

Each assistant has its strengths, right? ChatGPT for conversation. Claude for long documents. Gemini for Google integration. Perplexity for research with citations.

So you end up using them all. Most people today use more than one AI assistant, but none of them share context. You repeat the same prompts. You paste long context over and over. You lose track of what you discussed where.

Sound familiar? This is exactly what happened with enterprise software. Every department got their own tool. Marketing had theirs. Sales had something else. Operations used another system. Nobody talked to each other. We called it tool sprawl and spent billions trying to fix it.

Now we are doing it again, but with AI assistants.

Gartner estimates that a significant portion of enterprise AI investment is duplicative, and that is just at the organizational level. At the individual level, we are duplicating work across multiple assistants every single day.

The hidden cost nobody measures

Here is what the real AI assistant problem no one talks about looks like in practice.

You start a research task in Perplexity because you want citations. Good choice. Then you realize you need to write something based on that research, so you copy everything to ChatGPT. But ChatGPT gives you something that needs refinement, so you try Claude for better writing. Then you want to share it in a Google Doc, so you ask Gemini to format it properly.

Four assistants. Same task. Each switch costs you mental energy, time, and context.

This gets worse when you are working on something complex. You build up context in one assistant over multiple prompts. Then you need a different capability, so you switch assistants and lose all that context. You either waste time rebuilding it or you proceed without it and get worse results.

The productivity research is clear about this. Switching between tasks can substantially reduce productivity because of the cognitive load of constantly reorienting yourself. Every time you jump to a different AI assistant, you are paying that tax.

What actually works

Pick one assistant for 80% of your work. Just one.

This sounds limiting until you realize that the real AI assistant problem no one talks about is not capability gaps between assistants. The problem is context fragmentation. The problem is decision fatigue about which assistant to use. The problem is rebuilding the same context across multiple platforms.

Choose your primary assistant based on what you do most. If you write a lot, pick the one best at writing. If you research constantly, pick the one best at research. Use it for everything it can reasonably handle.

Then, and only then, use specialized assistants for specific tasks they genuinely excel at. Not because you think they might give a slightly better answer. Not because you want to compare responses. Only when there is a clear capability gap that matters for that specific task.

McKinsey found that a substantial majority of organizations have adopted AI, but many are stuck because they cannot move from pilots to production. Part of that is tool fragmentation. When everyone is using different assistants with different approaches, you cannot build consistent workflows.

The same principle applies to individual productivity. Consistent workflows beat perfect tools.

The path forward

Stop chasing the perfect AI assistant. Start optimizing for workflow consistency.

That means accepting that your primary assistant will not be perfect at everything. It means occasionally getting a response that is good enough rather than switching assistants to find the theoretically optimal answer. It means building up context and expertise with one tool rather than spreading yourself thin across many.

The companies winning with AI are not the ones using the most AI tools. They are the ones who figured out how to integrate AI into actual workflows. The same applies to you.

Pick your primary assistant. Build your workflow around it. Use other assistants deliberately and sparingly. Stop treating AI assistant selection like an optimization problem and start treating context preservation like the productivity driver it actually is.

Because the real AI assistant problem no one talks about is not that we lack good options. The problem is that we have too many options and we are using all of them at once.