Cofounder andCEO @ScribeHow. Here to make AI agents work and help companies own their specialized intelligence. Alum @greylockvc @mckinsey @princeton
San Francisco, CA
Joined June 2010
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Scribe tested a frontier model on 30 enterprise tasks to see what context helped it most.
With a company wiki: ~55% better than the model alone
With step-by-step guides of how people do work: ~70% better
With both: ~60% better. Worse than the guides alone.
More context =/= better performance. It has to reflect how work actually gets done.
The stat going around today, that 10% of workers capture 90% of AI productivity gains, is a predictable reflection of how companies have been thinking about AI: as an individual productivity tool. Which means the gains accrue to individual employees and can leave with them.
Treating it like a talent problem is not the corrective.
Companies need to see that how work gets done is an asset (specialized intelligence) that they can own and benefit from beyond the sum of productivity gains. Their focus should be on capturing it.
Just spoke to the Head of AI at a huge company.
The #1 thing they're struggling with is... what do I do with the AI laggards at our company?
Like most companies, they’ve found that a Super Pareto Principle is true within their org. 10% of their employees are responsible for 90% of the uplift in productivity/token usage from software engineering to non-technical knowledge work.
They’ve spent a lot of time & resources on AI enablement from LLM foundations to function-specific trainings and skill building. And yet, the power law of productivity increases hasn’t budged.
So now they’re left with a few sobering thoughts and hard questions:
1) 90% of people are overpaid and 10% are underpaid
2) We’d be better off allocating tokens proportionally to the power law, but how do we do that without killing culture?
3) Significant layoffs are likely warranted and wouldn’t impact overall output, but cultural backlash is a big concern?
4) How do we hire more power law people as a non technology company?
5) Is all of our efforts on training and enablement ACTUALLY worth it?
6) How do we not experience a brain drain where the top 10% all leave because they want to be surrounded by other AI-native 10x’ers?
The more a company trains its AI on email and wikis, the more its agents learn the job from whoever writes the most.
That’s a strange way to pick your process expert. The people who are great at their jobs rarely write down how they do it. Most vocal =/= most authoritative.
Jennifer Smith retweeted
The best, most comprehensive take on AI I’ve read. After a year of confusion and fear about the future, I finished it feeling like a weight had physically lifted.
It’s long. Make the time.
@vgr is undefeated.
contraptions.venkateshrao.co…
Jennifer Smith retweeted
😱 youtu.be/0kybbAvzBLo - a company spent $200M automating a process that cost $100M.
It is what Jennifer Smith @scribeceo, co-founder and CEO of @ScribeHow, says she heard from a healthcare company while advising enterprises on AI.
Before founding Scribe in 2019, before ChatGPT made AI mainstream, Jennifer was at @GreylockVC, where she interviewed more than 1200 executives about the problems keeping them up at night.
The pattern she kept hearing:
“I don’t know how my business actually works.”
Companies could see salaries, tools and outputs. But they could not see how work actually happened. That knowledge lived only in people’s heads.
That insight became Scribe: a $1.3B AI company with 6M+ users, used by 94% of the Fortune 500, and now past $100M in ARR.
In our new interview, Jennifer explains:
▪️Why most AI pilots fail before they scale
▪️Why more data can actually make AI agents worse
▪️How companies should turn internal know-how into an AI-ready asset
▪️ Whether AI will replace consultants, @McKinsey and knowledge workers
▪️The one human skill she believes will matter most
Her most contrarian point: enterprises do not have an AI model problem. They have a context problem.
“AI isn’t your solution until it understands your situation. That starts with your workflows.”
Full interview with Jennifer Smith from @HumanXCo in Amsterdam is now live on @aicryptominds YouTube➡️ youtu.be/0kybbAvzBLo
What is the biggest reason AI projects fail inside large companies: bad data, unclear ROI or lack of context?
"Knowing what to ask, how to find the information that matters for AI transformation, and THEN being able to build it successfully, is where all the alpha lies."
Agree with every word.
The next thing leaders need to realize is that they cannot and will not find that information manually.
Town's $108M Series B announcement and Ramp's Glass announcement are clear signs that AI agents have a problem leaders should be paying attention to.
Agents can't create automate work and deliver the value they're supposed to without figuring out how companies work... and most businesses have no record of how they work.
I've thought about this problem for the last decade, and I predict we're about to see a lot more companies try to build in this category.
I wrote about which approaches will and won't work.
Companies love to talk about processes as if there’s one right way and everyone follows it.
But the largest dataset on knowledge work in existence (Scribe’s) says the opposite: the most common version of a process only happens 7% of the time.
Run it 100 times and you’ll get around 50 different versions. Train an agent on the most common one, and it’ll match how your people actually work just 1 in 14 times.
That’s why we built Autocapture: so your people and your agents learn how work really gets done.
See it in action:
scribe.com/capture/autocaptu…
The most valuable thing at your company isn't in any system. It's the stuff your best people just know and have never had time to write down.
It’s your company’s specialized intelligence. And until today, there's never been a system built to help you own it.
We just launched Autocapture at Scribe: it turns how your teams actually work into documentation, automatically. No one has to press start or stop anymore.
What's never been encoded becomes visible. Structured, actionable guides, for people and agents.
See the announcement: scribe.com/library/autocaptu…
Every AI transformation plan has the same first move: connect the knowledge base, import the transcripts, let the model sort it out.
But more inputs isn't the same as better inputs. What matters is whether any of it reflects how your company actually works today — most of what gets written down is already stale by the time it's uploaded.
We've been building toward a different answer. October 6:
scribe.com/event-registratio…
Last week at @HumanXCo , someone asked: "If you started an AI company right now, what would you build?"
Everyone named uniquely human things: Enjoyment. Family. Learning. Rest.
One way to create that space: work differently. When AI takes the manual work off someone's plate, they get to focus on the things that energize them.
I believe human potential is the strongest force in the universe. Right now, we're squandering it, burying it under work that was never worth any person's time.
On October 6, we're announcing something from @ScribeHow built to change that.
scribe.com/event-registratio…
Every exec I talk to is saying the same thing: “AI knows everything except how my company works.”
That's because it runs on general intelligence that's rented – same labs, same models, but no unique edge. The companies that harness their specialized intelligence now will build a generational advantage that’ll only compound.
Join us on October 6 to learn how.
scribe.com/event-registratio…
Your company's most valuable asset isn't codified in any system.
It’s your specialized intelligence: the judgment calls, the workarounds, and the exceptions that people make every day without thinking twice.
When they leave the company, it leaves with them.
On Oct 6, we're changing that. Tune in live at 1pm ET / 10am PT.
getscribe.how/announcement20…
Jennifer Smith retweeted
Your company's most valuable asset isn't codified in any system.
It’s your specialized intelligence: the judgment calls, the workarounds, and the exceptions that people make every day without thinking twice.
When they leave the company, it leaves with them.
On Oct 6, we're changing that. Tune in live at 1pm ET / 10am PT.
getscribe.how/announcement20…