AI didn't make orgs faster. It just exposed that orgs never had memory
AI didn't make your organization faster.It just exposed that your organization never had a memory to begin with.I've been chewing on this for a year. Here's the part nobody wants to say out loud 🧵
Honestly， I've been chewing on this question for the better part of a year. I started paying attention to AI back in 2023， which makes it three years now. And I'm a decent sample of one： I run my account solo， I write solo， I do my own ops. AI tools genuinely turned me into a one-person quasi-team. My output is more than 10x what it used to be.
But over the last six months， I've been watching friends who actually have teams - and I keep noticing the same off-kilter pattern.
One sentence： individuals are flying， organizations are crumbling.
Everyone is on ChatGPT， Claude， Gemini， Cursor. Everyone says they're 10x faster.
And yet， when you add up the whole team， output is slower than it was two years ago.
Something is clearly wrong here.
I've been trying to figure out where it actually breaks. The MIT Sloan 2026 AI Adoption report that dropped a couple of days ago gave me the most direct answer I've seen.
The 95% Number Hits Harder Than You'd Think
There's one stat in that report： 95% of enterprise AI investments produce no measurable business return.
Honestly， that one stopped me cold.
Not 50%. Not 70%. Ninety-five percent.
Meaning： out of 100 companies - that spent the money， bought the tools， trained the staff - 95 of them can't show you a single number you could put in an earnings report.
Your first instinct might be： maybe they're using it wrong？ Maybe the models still aren't good enough？
I turned it over in my head for a long time. Neither answer holds up.
The real bottleneck is something else - and it's buried in another stat from the report that most people skipped right past： more than 30% of team time is spent rebuilding context that someone else on the team already had.
What does that look like？ Let me sketch a scene and see if any of it feels familiar：
A decision got made three months ago. Today's retrospective rolls around， and nobody can find the original discussion thread.
A product question gets asked in the user chat 20 times a day， and every ops person has to copy-paste the same answer from scratch.
A new hire spends their first month scraping together fragments from Feishu， WeChat Work， email， Yuque， and half a dozen other apps， just trying to piece together "how does this company actually work？"
AI didn't make orgs faster. It just exposed that orgs never had memory
AI didn't make your organization faster.It just exposed that your organization never had a memory to begin with.I've been chewing on this for a year. Here's the part nobody wants to say out loud 🧵
Honestly， I've been chewing on this question for the better part of a year. I started paying attention to AI back in 2023， which makes it three years now. And I'm a decent sample of one： I run my account solo， I write solo， I do my own ops. AI tools genuinely turned me into a one-person quasi-team. My output is more than 10x what it used to be.
But over the last six months， I've been watching friends who actually have teams - and I keep noticing the same off-kilter pattern.
There it is. That's the truth.
AI didn't make organizations faster， because organizations never had memory in the first place. AI just turned up the volume on that fact.
Why Individual Upside Doesn't Roll Up to the Organization
I've started calling this the "AI Productivity Paradox."
The mechanism behind it is roughly this：
AI tools are personal exoskeletons strapped onto individuals. I write code in Cursor， draft articles in Claude， do research in NotebookLM - and all the memory those tools accumulate lives on my laptop， under my account.
The day I leave the company， that memory walks out with me.
The day I get promoted to a different role， that memory resets to zero.
The day I try to collaborate with a colleague， that memory just doesn't transfer.
Which is exactly why individual productivity gains don't compound at the organizational level.
Every employee is an island. Every island has a little factory on it. But there are no bridges between the islands.
This is also why， at the closed-door Sequoia AI Ascent summit a few days ago - 150 top founders， six hours of conversation - the room landed on a new definition for 2026： "the commercial year zero of long-horizon agents."
Sequoia partner Pat Grady said something that's been stuck in my head for days：
The next round of AI doesn't sell tools - it sells outcomes.
Sounds like a comment about supply， but the more I sat with it， the more I think he's actually describing the demand side：
Customers don't want tools anymore - because tools get installed on individuals， and individuals don't move org-level metrics.
Ten ChatGPT seats don't help me. What I actually want is for every conversation， every decision， every piece of feedback inside my company - from yesterday to today - to be captured， searchable， and reusable.
Once you start thinking this way， the problem clicks into place：
No matter how smart an agent is， if it doesn't know what your organization is thinking， it's just a smart fool.
It can write perfect copy， but not the one sentence that captures your brand voice.
It can answer every generic question， but not "did we actually ship the fix for that bug last week？"
It can hand you a polished market analysis， but it doesn't know you killed that exact direction three months ago.
OK， I'm wandering - what I'm trying to say is： the problem was never the model. The problem is that the organization never gave the model a place to learn.
A Few Products Are Trying - But None of Them Is the Savior
Let me be honest about something here：
There are already some products taking a swing at this space. But frankly， none of them have solved the whole problem.
The one I've been watching most recently is Lucius - they just closed a $3M seed round two days ago， led by the Future Capital Discovery Fund. This is the third startup from founder Zhao He， and his first two both died on the same rock： users won't even write the documentation.
His angle this time is interesting： if people refuse to write the docs， let the AI sit there and listen， learn， and capture them on its own.
How does it actually work？ Their loop looks roughly like this：
A user asks something in the community chat → the AI tries to answer with what it already knows → if it can't， it auto-creates a task for the ops team → ops answers → the AI captures the answer， structures it， and files it into the knowledge base → next time someone asks the same thing， the AI handles it.
No prompts to write. No rules to configure. It's like a new intern who quietly sits in the chat， listens， and slowly figures things out.
The early-user numbers： community self-resolution rate went from 29% to 88%， and ops time spent on repeat answers dropped from 3 hours a day to 20 minutes.
But here's my cold water： it can't handle complex consultations from high-value customers， it can't generate or execute code， and at its core it's still a "load-shedder for high-frequency， repetitive scenarios."
What it really does is carve out the most time-wasting 30% of standardized repetitive work. It's not replacing your team.
You can't expect it to take over your business. But you can use it to make sure your team never gets asked the same question 20 times again.
Is that enough？ For a lot of small teams， I think it actually is.
But for anyone holding out for the fantasy of a "fully autonomous AI company，" it's nowhere close.
So my read on Lucius is - it's an interesting sample， not the destination.
This category is just getting started. A pile of similar "organizational memory layer" products will show up over the next year， and who actually breaks out is anyone's guess.
Image
This is their official Discord community if you want to try it： https://discordhunt.com/en/servers/lucius-lab-1484054485020966956
Lucius is currently offering a launch promo with 400 free actions - if you run a community of your own， give it a spin.
The One Thing I Actually Want to Say
I've rambled a lot. Here's the part I really mean：
The winners of the next era won't be the companies with the strongest model. They'll be the companies with the deepest organizational memory.
It took me a long time to be willing to write that line down， because it implies that most of the energy we spent over the past three years "chasing the strongest model" was pointed in the wrong direction.
Models get refreshed every three months. The moat is pathetically shallow.
But a company that has accumulated two years of conversations， decisions， feedback， and brand voice - that's not something you can copy， and it's not something a competitor can catch up to overnight.
So if you let me give one line of advice to three kinds of people， here's what I'd say：
To founders： Don't go all-in on the bleeding-edge model. Find a vertical scenario and make your "organizational memory" as thick as possible. Models will keep changing， but organizational memory is the thing that compounds.
To managers： Stop buying your team more AI tools. First ask whether your team has a single place where every conversation actually gets captured.
Without that foundation， every additional tool just accelerates the chaos.
To individuals like me： Even if you're a team of one， start building your own Context Layer.
Your project notes， your customer conversations， your writing material - these are the most valuable assets you'll own over the next five years.
Honestly， I haven't fully figured this out either.
I'm still juggling more than a dozen AI tools. I still re-enter the same idea into different places. I still routinely fail to find an insight I had three months ago that I was sure I'd remember.
So this isn't a "I figured it out， follow me" tutorial. It's a letter from one practitioner in the AI era to another one fumbling through the same fog.
If you've felt that same off-kilter pattern of "individuals flying， teams crumbling" - then we're in this together.
Let's take our time， and figure it out together.
（This piece is synthesized from the MIT 2026 AI Adoption report， notes from the closed-door Sequoia AI Ascent 2026 summit， and recent industry developments. Lucius is mentioned as one example， not as a recommendation.）
One sentence： individuals are flying， organizations are crumbling.
Everyone is on ChatGPT， Claude， Gemini， Cursor. Everyone says they're 10x faster.
And yet， when you add up the whole team， output is slower than it was two years ago.
Something is clearly wrong here.
I've been trying to figure out where it actually breaks. The MIT Sloan 2026 AI Adoption report that dropped a couple of days ago gave me the most direct answer I've seen.
The 95% Number Hits Harder Than You'd Think
There's one stat in that report： 95% of enterprise AI investments produce no measurable business return.
Honestly， that one stopped me cold.
Not 50%. Not 70%. Ninety-five percent.
Meaning： out of 100 companies - that spent the money， bought the tools， trained the staff - 95 of them can't show you a single number you could put in an earnings report.
Your first instinct might be： maybe they're using it wrong？ Maybe the models still aren't good enough？
I turned it over in my head for a long time. Neither answer holds up.
The real bottleneck is something else - and it's buried in another stat from the report that most people skipped right past： more than 30% of team time is spent rebuilding context that someone else on the team already had.
What does that look like？ Let me sketch a scene and see if any of it feels familiar：
A decision got made three months ago. Today's retrospective rolls around， and nobody can find the original discussion thread.
A product question gets asked in the user chat 20 times a day， and every ops person has to copy-paste the same answer from scratch.
A new hire spends their first month scraping together fragments from Feishu， WeChat Work， email， Yuque， and half a dozen other apps， just trying to piece together "how does this company actually work？"
There it is. That's the truth.
AI didn't make organizations faster， because organizations never had memory in the first place. AI just turned up the volume on that fact.
Why Individual Upside Doesn't Roll Up to the Organization
I've started calling this the "AI Productivity Paradox."
The mechanism behind it is roughly this：
AI tools are personal exoskeletons strapped onto individuals. I write code in Cursor， draft articles in Claude， do research in NotebookLM - and all the memory those tools accumulate lives on my laptop， under my account.
The day I leave the company， that memory walks out with me.
The day I get promoted to a different role， that memory resets to zero.
The day I try to collaborate with a colleague， that memory just doesn't transfer.
Which is exactly why individual productivity gains don't compound at the organizational level.
Every employee is an island. Every island has a little factory on it. But there are no bridges between the islands.
This is also why， at the closed-door Sequoia AI Ascent summit a few days ago - 150 top founders， six hours of conversation - the room landed on a new definition for 2026： "the commercial year zero of long-horizon agents."
Sequoia partner Pat Grady said something that's been stuck in my head for days：
The next round of AI doesn't sell tools - it sells outcomes.
Sounds like a comment about supply， but the more I sat with it， the more I think he's actually describing the demand side：
Customers don't want tools anymore - because tools get installed on individuals， and individuals don't move org-level metrics.
Ten ChatGPT seats don't help me. What I actually want is for every conversation， every decision， every piece of feedback inside my company - from yesterday to today - to be captured， searchable， and reusable.
Once you start thinking this way， the problem clicks into place：
No matter how smart an agent is， if it doesn't know what your organization is thinking， it's just a smart fool.
It can write perfect copy， but not the one sentence that captures your brand voice.
It can answer every generic question， but not "did we actually ship the fix for that bug last week？"
It can hand you a polished market analysis， but it doesn't know you killed that exact direction three months ago.
OK， I'm wandering - what I'm trying to say is： the problem was never the model. The problem is that the organization never gave the model a place to learn.
A Few Products Are Trying - But None of Them Is the Savior
Let me be honest about something here：
There are already some products taking a swing at this space. But frankly， none of them have solved the whole problem.
The one I've been watching most recently is Lucius - they just closed a $3M seed round two days ago， led by the Future Capital Discovery Fund. This is the third startup from founder Zhao He， and his first two both died on the same rock： users won't even write the documentation.
His angle this time is interesting： if people refuse to write the docs， let the AI sit there and listen， learn， and capture them on its own.
How does it actually work？ Their loop looks roughly like this：
A user asks something in the community chat → the AI tries to answer with what it already knows → if it can't， it auto-creates a task for the ops team → ops answers → the AI captures the answer， structures it， and files it into the knowledge base → next time someone asks the same thing， the AI handles it.
No prompts to write. No rules to configure. It's like a new intern who quietly sits in the chat， listens， and slowly figures things out.
The early-user numbers： community self-resolution rate went from 29% to 88%， and ops time spent on repeat answers dropped from 3 hours a day to 20 minutes.
But here's my cold water： it can't handle complex consultations from high-value customers， it can't generate or execute code， and at its core it's still a "load-shedder for high-frequency， repetitive scenarios."
What it really does is carve out the most time-wasting 30% of standardized repetitive work. It's not replacing your team.
You can't expect it to take over your business. But you can use it to make sure your team never gets asked the same question 20 times again.
Is that enough？ For a lot of small teams， I think it actually is.
But for anyone holding out for the fantasy of a "fully autonomous AI company，" it's nowhere close.
So my read on Lucius is - it's an interesting sample， not the destination.
This category is just getting started. A pile of similar "organizational memory layer" products will show up over the next year， and who actually breaks out is anyone's guess.
Image
This is their official Discord community if you want to try it： https://discordhunt.com/en/servers/lucius-lab-1484054485020966956
Lucius is currently offering a launch promo with 400 free actions - if you run a community of your own， give it a spin.
The One Thing I Actually Want to Say
I've rambled a lot. Here's the part I really mean：
The winners of the next era won't be the companies with the strongest model. They'll be the companies with the deepest organizational memory.
It took me a long time to be willing to write that line down， because it implies that most of the energy we spent over the past three years "chasing the strongest model" was pointed in the wrong direction.
Models get refreshed every three months. The moat is pathetically shallow.
But a company that has accumulated two years of conversations， decisions， feedback， and brand voice - that's not something you can copy， and it's not something a competitor can catch up to overnight.
So if you let me give one line of advice to three kinds of people， here's what I'd say：
To founders： Don't go all-in on the bleeding-edge model. Find a vertical scenario and make your "organizational memory" as thick as possible. Models will keep changing， but organizational memory is the thing that compounds.
To managers： Stop buying your team more AI tools. First ask whether your team has a single place where every conversation actually gets captured.
Without that foundation， every additional tool just accelerates the chaos.
To individuals like me： Even if you're a team of one， start building your own Context Layer.
Your project notes， your customer conversations， your writing material - these are the most valuable assets you'll own over the next five years.
Honestly， I haven't fully figured this out either.
I'm still juggling more than a dozen AI tools. I still re-enter the same idea into different places. I still routinely fail to find an insight I had three months ago that I was sure I'd remember.
So this isn't a "I figured it out， follow me" tutorial. It's a letter from one practitioner in the AI era to another one fumbling through the same fog.
If you've felt that same off-kilter pattern of "individuals flying， teams crumbling" - then we're in this together.
Let's take our time， and figure it out together.
（This piece is synthesized from the MIT 2026 AI Adoption report， notes from the closed-door Sequoia AI Ascent 2026 summit， and recent industry developments. Lucius is mentioned as one example， not as a recommendation.）
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