BYOM (Bring Your Own Memory)
Issue 260: Can a work/life memory interpreter facilitate personal and work context?
There is no single solution to separating work and life. What is effective for one may be a challenge for another. I don't mind having Slack or other work apps on my phone. I'd rather make the trade-off to quickly check something at my leisure to put it out of mind, instead of feeling the Sunday Scaries and drowning in messages. This doesn't work for everyone, but it works for me.
As MCV continues to decouple and technologies from AI-native workflows develop, the work-life balance becomes a work-life context. Can you ensure the right information gets passed from your personal life into the important aspects needed for work without compromising personal privacy and security? What's needed is a way to provide the right context and bring relevance to work and life.
Research emphasizes that knowledge workers, particularly in tech and creative fields, operate within what is now a "boundaryless" interface between their work and personal lives. Mobile devices and flexible work arrangements empower them to work anywhere, including on personal devices at home or in transit. This persistent access also “enslaves” some workers, making it difficult to maintain a clear separation between professional and private spaces.
An engineer may use a physical notebook for sketches and quick notes, scanning or transcribing the content later.
Creative professionals often start ideation with pen and paper or tablet-based sketch apps before formalizing work digitally.
Many use apps like Google Keep, Notion, Obsidian or voice memos on their phones to quickly capture work ideas outside regular hours or away from their work machine
Fortunately, there are technologies such as Mobile Device Management (MDM) that allow bringing those workflows more securely. Though you should be fully aware of the implications of having MDM installed and its relationship with your IT admin, this technology has evolved quite a bit since its invention decades ago. For me, the iPad Pro is my work/personal machine instead of my phone. However, I've been using MacOS' iPhone Mirroring feature for reasons like this. It’s been working quite well.
Mobile Device Management was the infrastructure layer that made Bring Your Own Device (BYOD) possible. It gave organizations the ability to separate personal and work contexts on the same hardware, while still granting individuals flexibility in how they wanted to work. AI memory is entering a similar moment. Just as BYOD forced us to negotiate boundaries around hardware, BYOM will force us to negotiate boundaries around knowledge itself. Devices were the container problem of the last decade—memory will be the container problem of this one.
From BYOD to BYOM (Bring Your Own Memory)
Memory is the capability of an AI to recall past interactions, facts, or data and use them in future reasoning or decisions. For example, this may be remembering what a user said earlier in a conversation, recalling task history when making recommendations, or persisting long-term knowledge beyond a single session (often chat). My first prompt for any LLM I use is to ask it to remember I despise emojis and never to use them in outputs.
Apple introduced Focus modes in iOS 15 as an evolution of Do Not Disturb, letting users filter notifications and even customize Home Screens by context (Work, Personal, Sleep). In iOS 16, Focus became smarter with Lock Screen pairings and filters across apps like Mail, Calendar, and Safari. iOS 17 refined this with more granular notification controls. Taken together, Focus has evolved from muting distractions to a full context-aware filtering system, a model that shows how AI memory could also be partitioned and personalized by mode rather than being “on” or “off.”
That same framing will be essential for AI memory. Not “on” or “off,” but a filter: what memory is relevant in this context? That same framing will be essential for AI memory. Not “on” or “off,” but a filter: what memory is relevant in this context? One way to achieve this is through a memory interpreter—a layer that sits between your raw personal history and the work context you’re stepping into. Imagine you’ve been doing deep personal research on a topic—reading, journaling, exploring ideas in your own voice. When you shift into a professional setting, the interpreter could filter that knowledge, stripping away casual notes, personal anecdotes, or tone, while surfacing only the relevant facts and references in a format appropriate for work. In practice, it would act like a translator, allowing the richness of your personal exploration to inform your professional contributions without oversharing or leaking unintended details. It’s not about fusing personal and work memory, but about controlled permeability—deciding what crosses the boundary, and in what form.
Personal Memory: Where you collect raw notes, research, and ideas in your own style.
Memory Interpreter: A filtering layer that decides what crosses the boundary, transforming tone and relevance.
Work Context: The secure, professional version that only includes what’s needed.
The promise of BYOM isn’t just bringing personal knowledge into work safely—it’s also about carrying professional lessons back into your personal growth without leaking sensitive details. In both directions, a memory interpreter acts as the filter.
Personal-to-work
Imagine you’re putting together a design brief:
Inputs: Kindle highlights, personal notes in Obsidian, and photos snapped on your phone.
Interpreter: Strips away references to personal experiences in the notes, filters out irrelevant Kindle passages, and cleans photo metadata so nothing sensitive carries over.
Output: A polished, professional design brief that leverages your personal research but presents it securely for your team.
Work-to-personal
Now flip it the other way. You’re in a strategy session at work and want to capture what you learned for your personal growth:
Inputs: Confluence docs with sensitive business data, meeting notes with confidential numbers, and Slack discussions with teammates.
Interpreter: Redacts metrics, names, and roadmap details, while extracting the broader lessons (e.g., “cross-functional collaboration improves when you align stakeholders early”).
Output: A distilled personal note on “collaboration frameworks” that feeds your long-term memory without exposing company IP.
In both cases, the interpreter doesn’t just decide what to remember—it decides how memory should travel across boundaries. BYOM works best not as a giant container of everything, but as a context-aware filter that makes personal and professional lives mutually reinforcing while still secure. Work-to-personal protects company confidentiality while letting you grow and carry knowledge forward.
This way, you can bring the depth of your personal exploration into work while ensuring it surfaces in the right format and keeps sensitive or irrelevant details private.
Recap
Device consolidation will occur in a post-smartphone world. People will have a device used for personal and work purposes
The role you have at a company has a major factor in what work/life methods work for you (interface vs. boundaries)
Personal and Work memory preferences/settings live on the cloud
The goal is not to have two completely separate memory graphs but one that is contextual and secure to work and life
BYOD taught us that the challenge wasn’t just about hardware—it was about boundaries. Mobile Device Management became the interpreter between personal and work devices, giving organizations control while still giving individuals choice.
With AI, the container has shifted. Devices were the container problem of the last decade; memory is the container problem of this one. The work ahead isn’t simply about how much an AI can remember, but how it remembers in context—what belongs in work, what belongs in life, and what’s allowed to cross the boundary.
The future of productivity will hinge less on what we input and more on what we allow to persist. BYOM won’t be solved by rigid separation or full fusion, but by interpreters that let knowledge flow selectively, securely, and meaningfully between our personal and professional lives. The question isn’t whether AI should remember, but how we design its memory to serve us—without erasing the boundaries that make work and life distinct.
Hyperlinks + notes
A collection of references for this post, updates, and weekly reads.
The Profile: The highest-paid woman in podcasting & the NFL star navigating stardom → and Happy Birthday,
The Sequence Radar: Two Drops, One Direction: The Week Agentic AI Got Practical
‘Blade Runner 2099’ To Premiere In 2026 On Prime Video → Please don’t be terrible!
Architecture Converges with the Human Form in Antony Gormley’s ‘Body Buildings’
Lettering artist to software founder: Jessica Hische on founding Studioworks by
I’m with you on this concept. My first real Claude Code agentic engineering project is building a simple web and mobile app to get my own thoughts, research on the web, and the outputs from GPT chats in my own curated knowledge base, unaltered by AI. The concept is to start building my own persistent and injectable “context” for when and where I need it as opposed to it living in any proprietary third-party system. I hadn’t thought much about the value of that context and the divide between work and personal knowledge, so thanks for planting the seed!
I read the thing but am not sure what this is about really. What are you trying to say?