Persistent ai
12 Aug 2025Context Engineering X Experience ERA = Persistent AI?
Here’s something that drives me crazy about ChatGPT: every conversation begins with me explaining myself all over again.
“I’m a product manager working on…”
“My team is struggling with…”
“Just to give you context, my company…”
It’s exhausting. I spend half my time re-teaching the AI who I am, what I’m working on, and what I actually need help with. And then the session ends, and tomorrow? We’re back to square one. (Yes, I know “projects” and I know I can’t always do in those realms@)
But here’s the thing - this isn’t just annoying, it’s completely backwards from how intelligence actually works.
Think about your best colleague or closest friend. They don’t need you to re-explain your entire professional situation every time you talk. They remember that you’re dealing with that difficult stakeholder, that your product launch got pushed back, that you prefer direct feedback over sugar-coating. They build on previous conversations.
That’s about to change with AI, and it’s going to be massive.
From Prompt Hacking to Context Engineering
Everyone’s obsessing over prompt engineering - finding the magic words to get better AI responses. But honestly? That feels like polishing a fundamentally broken interaction model.
The real shift would be context engineering - Clearly it’s been discussed including by Shopify dude - building AI systems that maintain persistent, evolving understanding of your actual reality.
I came across this interesting chapter from upcoming book from DeepMind researchers David Silver and Richard Sutton called “The Era of Experience.” Their core argument? We’re moving from AI that learns from static human data to AI that learns continuously from ongoing experience with the world. They have done this RL in DeepMind where the system as whole can potentially think and is not constrained by human data.
And when you combine that with persistent context about your life? That’s when things get interesting.
The Two Things That Change Everything
1. AI That Actually Remembers Your Life
Imagine an AI assistant that genuinely knows:
- What you’re working on right now and how those projects are evolving
- Your communication style with different people (formal with your boss, casual with your team)
- Your decision-making patterns and what’s worked for you in the past
- Your goals and the specific ways you like to make progress on them
- Your schedule, your preferences, your ongoing challenges
Not because you told it once, but because it’s been paying attention.
This isn’t some distant future thing. Humane was a bit ahead of its time but what’s to stop someone from using a whoop band of smartphone for repurposing if user consents.
2. AI That Learns From Working With You
But here’s where it gets really wild. The Era of Experience paper talks about AI that doesn’t just learn from training data, then stop. Instead, it continuously learns from every interaction, every outcome, every bit of feedback.
Your AI doesn’t just know that you prefer concise summaries - it learns that when you’re stressed (which it can tell from your writing patterns), you need even more directness. It notices that its morning briefings help you stay on track, but afternoon check-ins just add noise.
It’s learning from the experience of actually being your AI assistant.
Why This Hits Different
The Switching Cost Problem (That’s Actually Great)
Once you’ve spent months building this kind of relationship with an AI system, switching becomes almost impossible. You’d lose:
- All that learned context about your work and personal patterns
- The AI’s understanding of your relationships and communication preferences
- Months of experiential learning about what actually helps you vs. what doesn’t
- The accumulated knowledge of your decision-making history
It’s like switching best friends. Technically possible, but you’d be starting from zero.
From Reactive to Actually Helpful
Current AI waits for you to ask questions. Context-aware AI that learns from experience can:
- Notice patterns you miss (“Hey, you always struggle with focus on Mondays after travel - want to block your calendar differently?”)
- Prepare things before you need them (“I see you have that difficult client call at 3pm - here’s a summary of their recent concerns”)
- Connect dots across different parts of your life (“This new project opportunity aligns with those career goals you mentioned last month”)
This isn’t just better software - it’s like having a thought partner who actually pays attention.
The Strategic Landscape (Or: Who’s Going to Win This?)
Apple: The Privacy Play
They already have the hardware access (your phone knows everything about your daily patterns). Plus, their privacy-first approach might be the only way people will trust an AI with this level of life context.
The bet here is that people will choose privacy-protected context over raw capability.
Google: The Data Goldmine
Gmail, Calendar, Search history, Maps - they already have the most comprehensive digital context on most of us. They just need to connect the dots properly.
The challenge? Privacy skepticism. Do you trust Google with an AI that knows this much about your life?
Microsoft: The Work Context King
Office 365, Teams, LinkedIn - they understand your professional world better than anyone. But consumer context? That’s a bigger challenge.
OpenAI: The Dark Horse
They have the best models and the most engaged users, but no direct access to your persistent data. They’d need partnerships or hardware plays to really compete here.
The Real Opportunity
Honestly? I think there’s room for someone completely new to win this. Someone who builds the context layer that works across different AI providers. Let users maintain control over their data while still enabling these powerful personalization features.
Build the “context infrastructure” that everyone else builds on top of.
Because here’s what I think: the company that figures out how to ethically capture your real context, enable AI to learn from actually helping you, and make you genuinely trust them with your data? They don’t just win market share. They become indispensable.
What This Actually Means for Us
The Personal Impact
Your AI becomes less like a search engine and more like… well, like having a really good assistant who’s been working with you for years. Someone who knows your patterns, anticipates your needs, and gets better at helping you over time.
The Weird Implications
- Job displacement: If AI can learn from experience and maintain context, it starts approaching what we consider uniquely human capabilities
- Privacy paradox: The more context you share, the more valuable the AI becomes, but also the more you’re locked in
- AI relationships: When your AI assistant knows you better than most humans do, what does that do to human relationships?
The Bottom Line
We’re moving from AI-as-tool to AI-as-colleague. From starting every conversation from scratch to building on months or years of shared understanding. From generic responses to genuinely personalized intelligence.
And unlike previous tech transitions, the switching costs here might be unlike anything we’ve seen. Once your AI really knows you, leaving becomes almost unthinkable.
The era of experience isn’t coming - it’s here. The question is: who’s going to build the AI that you’ll never want to leave?