10 Upgrades I Want in My AI Twin
I use AI daily to brainstorm, write, plan, build. But even with all the progress, they still fall short of what I’d call a true “AI twin.”
Today’s models are talented assistants, helpful, articulate, fast. But not yet adaptive. Not yet reflective of how I work.
Here are 10 things I wish my AI twin could already do 👇
1️⃣ Remember Me, Precisely 🧠
Not infinite memory, but modular memory. Let me choose what’s stored (tone, frameworks) and what’s not (medical history).
Let me spin up memory packs, carry them across models with a secure key.
Personalization should be composable.
2️⃣ Know My Digital Terrain 🌄
Don’t make me search for links. With permissions, crawl my Drive, Notion, Gmail. If I say “everything I’ve written on onboarding,” it should just pull the right docs. No handholding.
3️⃣ Connect Distant Dots ⚙️
Scoping a new idea? Pull in:
•A teardown on system inefficiencies
•An old Slack debate on workflows
•A buried memo from 6 months ago
Synthesize across time and format.
4️⃣ Pick the Right Model for the Task
Some models are better at convo, some at reasoning.
My twin should route smartly: “This prompt feels logic-heavy. Want o3?”
5️⃣ Fluidity Across Mediums
Drop in a doc → get a podcast.
Feed it Python → get a PPT critique.
Format should never be a blocker.
6️⃣ Ask Better Questions ❓
Sometimes I don’t need answers. I need a mirror.
“Are you solving for speed or avoiding the hard convo?”
Think Oura ring, but for thought patterns.
7️⃣ Push Back Where I Need It 🎯
Know where I’m decisive vs. where I stall.
Sharp at storytelling but verbose? Push me on pith.
Adapt without external prompting.
8️⃣ Act, Don’t Just Assist 🤖📞
If Gmail sees I’m 6 threads deep with a service rep, it should:
•Draft the reply
•Pick up my tone
•Send it (with permission)
•Chase a resolution
9️⃣ Train My Thinking, Too 🧩📈
Add “reasoning-lite” mode: I go first, it scaffolds. Track how my thinking evolves. Stretch me when I repeat blind spots. It’s resistance training for cognition.
🔟 Run As Me, With Rules 🧍⚡
Assign it workflows: investor updates, recruiting, scheduling.
It should follow my tone, my escalation logic, and what not to say.
I want to trust it to run that lane.
The next leap in AI adoption won’t just come from smarter models.
It’ll come from systems that adapt to us.
We’re close. But not there yet!
@signulll Totally agree. Once token efficiency starts going back up, token limits will recede from the conversation, but this discount Walmart-esque branding will persist.
@jenzhuscott I’d be interested in seeing the $ overlay here. How are the apps with significant usage doing on ARPU? If there’s a dent compared to pre-AI, we know the new app deluge has eaten away margins and surely sacrificed absolute traction along the way.
Disagree. 3 pts: a) Most large cos' new code/total code share is tiny; token use restricted in numerator limits impact. b) Tech debt + platform sprawl erode any prod uplift via coord costs. c) Too early in cycle to measure AI-led gains—intangible/invisible vs ledger txns (for now).
@jain_harshit There are whole Reddit threads discussing how to position yourself in the sweet spot of these leaderboards. TL;DR: Try to be in the middle of the top 10%.
@dylan_curious Plus, the real question becomes: how do we measure AI's value beyond efficiency gains? The most interesting developments are happening in human-AI collaboration patterns.
@devahaz That same $60 spent on groceries and a bit of home cooking easily yields 10–13 meals, that’s under $5 per meal. That’s the real comparison to a $60 restaurant meal.