The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
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Ben Scharfstein predicts AI agents will drive software writing costs to zero
“One of the things that we really believe is the cost of writing software is going to zero in that world.”
Ben Scharfstein estimates enterprise AI solutions are 70 percent unglamorous integration work
“What does the end-to-end solution look like? Which only 30% of it might involve AI, and the rest is that schlep that you just have to do to have an impact.”
Ben Scharfstein predicts AI agents will replace systems integrators within a decade
“It's hard for me to think that in 10 years, there's going to be a super robust systems integrator ecosystem. Probably agents will just do it.”
Ben Scharfstein says AI foundation model labs operate like movie studios
“I think that the, I would say my hottest take is, I think the right way to think about foundation labs is that they're like movie studios. And what I mean by that is they invest a ton of money in blockbusters that have a relatively short time spend to pay them…”
Ben Scharfstein says top enterprises reject off-the-shelf vertical AI products
“And for those companies, they actually don't want the average of their peers. They don't want, you know, the thing that everyone else is getting, what's in distribution for the models. They don't want what's been productized for everyone else because they have…”
Ben Scharfstein argues that software code itself is not a defensible moat
“Interestingly, software was never a moat. It was never something that at least, you know, he thought was leading to differentiated returns. I think that's still true, which is that You know, the software itself is not emote.”
Scharfstein: Solving end-to-end enterprise problems unlocks 10x larger contracts
“Like your job is not to build a product. Your job is to solve a problem. And that is what enterprises are going to expect. That is what's going to unlock 10 X bigger contracts with them.”
Scharfstein: Enterprise AI startups should not compete with capable internal teams
“You don't want to be competing against these internal teams, especially really capable internal teams.”
Scharfstein: Forward-deployed engineering models are not scalable outside enterprise sales
“If you're not selling to enterprises, it's difficult to think about this as a scalable business model.”
Scharfstein: Difficult 10x engineers belong on platform teams, not client teams
“That's like very hard to work with, but they're a 10 X engineer. Those people should be on your platform team and not on your forward deploy team.”
Scharfstein: Enterprise AI shift demands forward-deployed engineering for custom software
“And what we're seeing now with this platform shift is enterprises still need all of that customization. They need all of the feature set, but it doesn't exist in the software and doesn't exist anywhere. And so that's the value of this forward deployed motion i…”
Scharfstein: Rapid AI shifts make custom enterprise builds superior to static products
“That's because the industry is changing a lot. Every day, ah, it changes, you know, GPT-V comes out, and it's different than, you know, it was three months ago, and so what we need to actually build in product changes, and so we've just said, you know what, th…”
Ben Scharfstein says enterprise AI lags public state-of-the-art by 18 months
“And I would say it was probably like an 18 month lag between what you see on Twitter and what's the state of the art and what is actually working in enterprises.”
Scharfstein: Enterprise AI delay is caused by integration and UX, not models
“And I think the interesting thing is it's not because of model capabilities. It's because of change management and it's because of integrations, oftentimes with data and also just kind of understanding like what is the UX? What's the paradigm? The security, al…”
Scharfstein: AI aims to augment human work, not replace software
“What we're moving in AI is that we're not trying to replace software. We're trying to augment or automate human work.”
Scharfstein: Forward-deployed engineering must build durable software, not consulting
“And the key is that it should be the customization and a wedge into installing your software that is durable over time. It's not worth just doing this as, you know, there may be great consulting businesses to build, but that's not, you know, the pot of gold.”
Scharfstein: Enterprise AI companies must become systems of intelligence
“I think that's the key thing is you want to become a system of record system of work and eventually become a system of intelligence where you actually do the work.”
Scale AI employs over 100,000 global contributors for AI data tasks
“We have contributors, you know, a 100,000 plus contributors around the world That do these tasks.”
Scharfstein: Forward-deployed engineering must feed custom client fixes back into platform core
“Really the mandate of the forward deployed engineer is to do that. You do need to say, okay, we have 60% out of the box, but that last 40%, maybe it's a data integration, it's a visualization, it's an agent that we haven't built yet. You do need to do that wor…”
Scharfstein: Scale AI allows large enterprise clients to retain their IP
“Oftentimes the big customers want to retain the IP. We're very happy to allow them to retain that IP because, you know, it's core to their business.”
Scharfstein: Startups must retain IP and standardize features when serving small clients
“I think the smaller the company and the smaller the customer, the more that you need to retain all of the IP and build it back into the platform.”
Scharfstein: In-house forward-deployed engineering teams have a 5-to-10-year lifespan
“I think that this idea of having this forward deployed in house, it has a five to 10 year life because we just have so much software left to build.”
Scharfstein: Forward-deployed engineering roles are a factory for founders
“I think it's like a factory for founders, whether or not it's in the product role or it's in the Ford deployed engineering role or AI role, because these are people that are just doing customer discovery while they're building, which is like what being a found…”
Scharfstein: Spending 3 days on-site accelerates enterprise AI delivery by 3 weeks
“Sitting with the customer, you know, flying to six hours away and saying, I'm going to spend three days with you is invaluable. Speeds you up three weeks.”
Scharfstein: Forward-deployed deployment goals must remain separate from sales incentives
“In terms of compensation structure, I'm not an expert, but I will say that I think in the forward deployed motion, the goal is to build a repeatable process through your customers and having that orthogonal to your go-to-market incentives is important because …”
Scale AI's Ben Scharfstein: AI startups must avoid vanity revenue metrics when delivering services
“One is I think you just have to be very honest and very sober about your revenue when you do that. And you say, Hey, like we were able to chart, get to ten million dollars in revenue, but it's, Consulting-ish revenue. Our repeatable aspect is two million, and …”
Scale AI's enterprise applications make up half of its total business
“The other half of our business is the application business”
Scharfstein: Scale AI's enterprise business found PMF in past 12-18 months
“There's been an enterprise team at scale for a long time, but really just in the past 12 to 18 months has really taken off as something that's found kind of product market fit.”
Scharfstein: Scale AI splits forward-deployed teams into software, ML, and product
“At scale, we break down the forward deployed role, not just a forward deployed engineers, which, you know, Palantir made famous, but we also have forward deployed product. That's the team that I lead and forward deployed machine learning engineers or an applie…”