AI Note Taking App: How to Choose One You Keep

Choosing an AI note taking app is mostly a decision about the next five years, not the next five minutes. Notes accumulate, and the cost of moving them rises every month. The feature that sells the app is rarely the one you will care about once you have two thousand notes.

So the question worth answering first is not which app has the best summarisation. It is whether you own the files.

The four jobs a note app has#

Most tools do one of these well and advertise all four.

Capture. Getting something in with almost no friction. If capture is slow, you stop capturing, and everything downstream is moot.

Structure. Linking, tagging, folders, properties. This is where AI is least useful and most oversold, because structure is a decision about what matters to you.

Retrieval. Finding the note six months later. This is where AI genuinely changes things: semantic search finds the note whose words you have forgotten.

Drafting. Turning notes into something else. An outline, a proposal, a summary of everything you wrote about a client.

Retrieval and drafting are the real wins. Capture is a design problem. Structure is your job.

Why file ownership decides the long game#

Two philosophies, and it is worth choosing deliberately.

Database apps store notes in their system. You get structure, collaboration and polish immediately. You also get a dependency: their export format, their pricing, their terms, their continued existence.

File-based apps store notes as plain text files in a folder you control. Less polish, no collaboration out of the box, and complete portability. Any tool, any assistant, any future app can read them.

I use Obsidian for exactly this reason. My notes are Markdown files on my disk, which means every assistant I use can read them and no vendor can change the terms on my own thinking. Obsidian vs Notion covers the trade-off without pretending one is universally right, and Obsidian second brain covers the setup.

The uncomfortable truth: migrating a second brain is painful enough that most people do not, which means whichever you pick, you will probably still be in it in three years. Choose accordingly.

AI does not fix messy notes#

This is the expectation that causes the most disappointment.

If your notes are fragments written for a version of you that remembered the context, a model reading them later has the same problem you do: not enough to work with. It will produce a plausible summary of something you cannot verify, which is worse than nothing.

Four habits make notes usable by both future you and any assistant:

Descriptive titles. "Client call notes" is useless at scale. "Acme wants weekly reporting, budget confirmed" retrieves.

Conclusion first. Put the decision or the point at the top. Everything else is supporting detail.

Complete sentences for anything that matters. Fragments are fine for a shopping list and terrible for a decision.

Date and source. Especially for numbers. A statistic with no source is a rumour you will eventually cite.

None of this needs a special app. All of it makes every AI feature work better.

The setup I actually run#

Files on disk, synced, plus an assistant that can read the folder.

• Plain Markdown notes in a folder structure that is shallow rather than clever.
• A daily note as the capture surface, so nothing needs a filing decision at the moment of capture.
• Weekly processing: the day's captures get titles, conclusions and a home. Twenty minutes.
• An assistant with access to the folder for retrieval and drafting. Obsidian MCP covers how to connect one properly, and Obsidian AI covers the wider workflow.

The meeting recorder feeds this: transcripts land as notes, summaries get titles, and the raw transcript stays in case the summary missed something.

What the AI features are actually worth#

Judge the built-in features by whether they survive contact with a real backlog.

Semantic search is the one that earns its place. Finding a note by what it was about rather than by the words you happened to type is a genuine change, especially past a thousand notes.

Automatic summarisation is useful for long captures, such as a transcript, and close to pointless for a note you wrote deliberately. If you wrote three paragraphs on purpose, a two-line summary of them adds nothing.

Automatic tagging is the most oversold. It produces a tidy-looking taxonomy that nobody uses, because tags only help when they mean something to you. Let it propose tags for a backlog you are cleaning; do not let it own your structure.

Ask your notes interfaces are only as good as the notes. This is the feature that makes the case for writing clearly better than any argument about discipline.

The pattern: features that help you find things are worth paying for, features that claim to organise your thinking are worth ignoring.

Choosing between the main options#

You want structure and a team: a database app. Accept the dependency, use the collaboration, and export regularly so you know you can leave.

You want ownership and flexibility: a file-based app. Accept that you are building the system rather than buying it, and that the payoff arrives in year two.

You mostly want meeting notes: a dedicated meeting tool that writes into whichever of the above you chose. Do not make your note app do recording badly.

You want all of it in one place: you will compromise on something. Decide which compromise before you buy, not after two thousand notes.

The short version#

Choose on file ownership first, retrieval second, features last. Write notes a stranger could read, because in six months that is who you are. Process weekly, keep sources with numbers, and connect an assistant to the folder rather than depending on a built-in feature.

If you want a second brain that is already structured for this, my AI brains are built exactly that way, and AI literacy training covers teaching a team to use one.