Photo by Joshua Sortino on Unsplash

My calendar app now takes 40% longer to open. It's not because my phone got slower—it's because there's an AI agent sitting between me and my schedule, trying to be helpful by "learning my patterns" and "predicting my needs." I don't need predictions. I need to know what time my 2 PM meeting is.

This isn't a complaint about a single app. It's what's happening across the entire productivity software ecosystem right now. Developers, desperate to stay relevant in the AI arms race, are bolting machine learning features onto tools that were working perfectly fine before. The result? A generation of applications that are slower, buggier, and often actively counterproductive.

The Feature-Creep Spiral

Six months ago, Notion released an AI assistant. Within weeks, Microsoft added one to OneNote. Evernote followed suit. Asana, Monday.com, ClickUp—they all scrambled to announce their own AI capabilities. The competitive pressure is real, almost suffocating. Product managers see competitors launching AI features and panic. Feature requests flood in from marketing departments claiming customers "demand" this stuff. Nobody wants to be seen as falling behind.

But here's what actually happens: A task management app that used to let you quickly capture a thought, organize it into a project, and assign it to a team member now first asks: "Would you like me to analyze this task for you?" It suggests priority levels based on vague patterns. It offers to auto-assign tasks to team members. It wants to summarize your project status. You end up clicking "no thanks" fifty times a day, or worse, you get a "helpful" suggestion that's completely wrong and wastes your time having to fix it.

Take Slack, which I use daily. The new AI-powered message search is supposed to be smarter than keyword search. Except it's not. It misses obvious messages and returns irrelevant ones. I've started going back to the old search because it actually works. The new feature doesn't make my job easier—it makes me work around it.

The Performance Tax Nobody Discusses

Here's something the tech press barely covers: AI features make apps heavier and slower. Notion's AI requires persistent background processes. Grammarly's AI-powered writing assistant can add half a second to every keystroke if you're typing in a browser. That half second doesn't sound like much until you're writing an important email and you're sitting there, watching the text lag behind your fingers.

A friend of mine switched from Google Tasks to a simpler to-do app called Todoist, which also recently added AI. She noticed her laptop's fan spinning up more often. She checked the task manager and saw that Todoist was using 8-12% of her CPU even when she wasn't actively using it. She disabled the AI features and the CPU usage dropped to nearly zero. One feature she'd never asked for was silently eating 10% of her computer's resources.

The battery impact on mobile is even worse. Apps using AI assistants drain batteries 15-25% faster than their pre-AI versions, according to testing I've seen from independent reviewers. If you've noticed your phone dying faster lately, your apps might be the culprit—especially if they've recently added AI capabilities.

When Artificial Intelligence Is Actually Artificial Incompetence

The real problem isn't that AI is bad. It's that most of these implementations are fundamentally solving problems that didn't exist. When you ask an AI assistant to summarize your Slack conversation history, it often gets key details wrong. When it suggests meeting times, it ignores timezone information. When it auto-drafts an email response, it frequently misses context and sounds robotic.

A product manager I know at a large tech company told me (off the record) that their team spent three months building an AI feature for their project management tool. It was smart, well-integrated, and technically impressive. Then they gave it to beta testers. Users disabled it within days. It was too slow. It made wrong suggestions. It got in the way. The company shipped it anyway because executives had already announced it to investors. Now it sits there, unused by most people, draining resources.

There's also the trust problem. If I can't rely on the AI to get things right most of the time, I won't trust it with anything important. And if I don't trust it, why would I let it run in the background learning my patterns and habits? Yet companies keep pushing these features as if trust is something users will grant simply because the AI exists.

The Workarounds We're Building

Smart users are developing workarounds. Some people are deliberately choosing older, simpler versions of apps that don't have AI yet. Others are switching to open-source alternatives that let them disable AI features entirely. A few have gone full analog—returning to paper planners and notebooks—because at least paper doesn't try to predict their needs.

The irony is that the most productive people I know still use relatively simple tools. They use email, spreadsheets, and basic to-do lists. They haven't adopted the fancy AI-powered everything because they don't need to. They know what works and they stick with it.

So what should you do? Start questioning every new AI feature that appears in your apps. Does it actually solve a real problem you have? Or is it solving a problem the company invented? If it's the latter, disable it. Your battery, your CPU, and your sanity will thank you. The productivity apps of the future should be smarter, sure—but only in the ways that actually matter to you, not in the ways that matter to quarterly earnings reports.