The a16z Show · Friday, October 2, 2026
Vlad Kyle and Seema Amble discuss why companies often fail when trying to build AI solutions internally. While simple use cases like data retrieval into an ERP might be built quickly, achieving high performance (over 70-80%) and integrating complex workflows, exception handling, and diverse systems requires specialized expertise and infrastructure that dedicated companies like Leo provide.
“So what I want to say about that is like a product that we like one of our first use cases can now be somehow built by engineers within eight hours. So because it's very easy to build stuff nowadays. So obviously there's a question, okay, well, so someone can build this within eight hours, Okay, cool. But then couldn't like just procurement departments also just build everything in two months.”
“And the answer is, yes, you can build this in eight hours and you can build this, but you will only reach... 70%, let's say, like, of the performance. And the problem is, 70% of performance or accuracy or however you measure it, it depends really on the task, doesn't mean 70% automation, right? So this can mean that you're like, have 70% of the performance, but you still need to do 100% of the work.”
“And I think they quickly realized that. the internal build didn't make sense. And so we keep hearing stories of this where people are like, okay, I'm going to do the internal build. And they're like, wait a second. It's not different from what DIY has ever been in the past, which corporates have always tried, but, um, I think enterprise companies generally realize that, like, there, there's their core competency and then there's, um, building internal tools and, um, they should focus on the first camp.”