# Personal AI agents trade convenience for data exposure

Markdown copy of https://realalpha.app/s/personal-ai-agents-trade-convenience-for-data-exposure/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Ethan Mollick](https://realalpha.app/people/ethan-mollick/) · Ethan Mollick is a Wharton professor and author of Coexistence: The Next Phase of AI.
- Host: [Alex Kantrowitz](https://realalpha.app/people/alex-kantrowitz/) · Founder, Big Technology · Former BuzzFeed News senior tech reporter and author of Always Day One.
- Show: Big Technology Podcast
- Episode: [This AI Expert Argues We're Underestimating AI's Impact — With Ethan Mollick](https://realalpha.app/v/Xoj0GGu0-aw/), Oct 20, 2026
- Moment: 41:08 to 44:07 of the episode (3 min), playing on https://realalpha.app/s/personal-ai-agents-trade-convenience-for-data-exposure/; the episode from there: https://realalpha.app/v/Xoj0GGu0-aw/?t=2468

## Quote

> “But we are still trusting an LLM with access to your private data that can receive and send messages to the outside world. **There is risk in that**.”
> — [Ethan Mollick](https://realalpha.app/people/ethan-mollick/) at [43:52](https://realalpha.app/v/Xoj0GGu0-aw/?t=2632) · Big Technology Podcast

## Takeaways

1. Personal agents can catch mistakes and handle tasks proactively.
2. Giving agents access to private data creates real exposure.
3. Email prompt injection could make an agent act in unexpected ways.

## About this segment

Ethan Mollick explains how personal agents use connected data to act proactively, then flags the risks of giving them access to private information and outside communications. Users have to weigh the convenience against that exposure.

From “This AI Expert Argues We're Underestimating AI's Impact — With Ethan Mollick”, Oct 20, 2026. [Watch the full episode](https://realalpha.app/v/Xoj0GGu0-aw/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[41:12](https://realalpha.app/v/Xoj0GGu0-aw/?t=2472) I call these claw-likes because they're all sort of successors to open claw, which was sort of the big phenomenon back in January. And some of them are literally open claw, like, implementations, right? And so the basic idea of a claw of a personal agent is you connect it to your personal data and it does stuff for you proactively. It solves a few problems that way. First of all, it's enabled by harnesses, but also enabled by smarter models.

[41:37](https://realalpha.app/v/Xoj0GGu0-aw/?t=2497) It's kind of one of the reasons why I think companies that did not engage in trying to build LLMs at scale like Apple might run into trouble because if you don't have a frontier model, you can't do this kind of work, right? So it requires a recent frontier model because before that, you used to do all this really complex stuff if you wanted to get an AI to work with data. You'd have to have a RAG pipeline and you'd have a vector database.

[42:01](https://realalpha.app/v/Xoj0GGu0-aw/?t=2521) And the model just swallowed that. And the model just swallowed that. Right. so so it basically eliminated the need to have all this architecture it just figures stuff architecture or something was uh what was it called scaffolding yeah there was elaborate scaffolding right but the muse was supposed to the muse is built on a more of a standard model or is it because it's built on metas metas models are quite good i mean they're they're not full frontier level but the the you know wonder and spark are solid models and if you use dot which is astra it's even more capable right i think is a machine it's wild it's it's crazy i mean proactively has found issues with like emails i've sent where it's like you actually use the wrong number for this code for asking for planning approval from your local like yeah same here it

[42:42](https://realalpha.app/v/Xoj0GGu0-aw/?t=2562) told me i sent a wrong date to somebody after i sent an email it's like by the way you they asked for january you sent december yeah i mean crazy stuff right and you'd have like a guardian agent on your shoulder at the small price to pay of your entire privacy wait small price to pay of your entire privacy you got to talk about that well i mean we are you know leaving aside everything else right you're connecting models to your you know and i am too right yeah to your gmail and to your other sets of my finances my medical am i going to regret that i mean like i i don't have an easy answer to that question right like i think there's risk in doing this stuff do i think that the connectors are as secure as they're going to be yes do i think open ai and you know meta are better defended than most organizations at this point from cyber sure do i also think that like

[43:27](https://realalpha.app/v/Xoj0GGu0-aw/?t=2607) we have no idea, like these models are pretty well behaved, right? There has been not that many issues, but could it decide to send your medical records somewhere you don't know? Yes. I mean, would there be a, is there a sort of prompt injection you can receive via email that might make the model of act badly? We have no idea. I mean, this is an entirely new field. And it's entirely possible that you're creating exposure to your data.

[43:48](https://realalpha.app/v/Xoj0GGu0-aw/?t=2628) You have to make the judgment calls on that. I think the data leakage problem has been less bad than I think a lot of us anticipated would be two or three years ago. But we are still trusting an LLM with access to your private data that can receive and send messages to the outside world. There is risk in that.