enterprise AI

“So last year was very much the year of the proof of concept. People were getting familiar with the technology. It was their first time working with it. And so there were a lot of small tests and experiments, but this year is very much one of going to productio…”

“And although, yeah, 80% failure rate is not uncommon or call it, you know, getting thrown on the shelf, right? First of a kind is last of a kind type of scenario.”

“And we often find many of the POCs turn into, they don't require complex AI, new model development and things in that space. They require simple automation. So they require more or more data, right? Or more programmatic changes in how the application is operat…”

“Well, no, so I'm a firm believer that no, no one is going to win it all. The consumer and enterprise are different, that different, you know, like the act of software engineering is different than the act of marketing. And I'm definitely not a God model. One m…”

“In many ways, I always kind of say we haven't yet seen the ChatGPT moment, I think, in business for AI.”

“I think that this next generation wave of change will come. It will take a while, and it will be a slow, a slower than expected adoption on this complex side while the easy cases will be solved quite fast, very fast.”

“It's only valuable to train on our own internal context, A, and B, that model does not have to have read War and Peace to deliver to us that it doesn't have to be generally trained on everything that's available. It has to be trained on our content. And so the…”

“It's expected, according to Gartner, to bring in 37.5 billion dollars in revenue next year. Enterprise AI. That's up from zero in 2022 or effectively zero in 2022.”

“We're never going to be able to build systems that work out of the box in a single shot. And the one thing that we try to convey to our customers is that they need to build a prototype that's going to work 80% of the time. But then how do they get from 80% to …”

“In reality, paying customers want like a good trade-off in terms of performance for efficiency. So, you know, we'll train bigger models, but we'll deploy smaller models because it gives us that trade-off. It's like good enough intelligence to get the job done.”

“Today, I don't think customers are getting, like, in general, as people have been embracing AI, they aren't getting that level of exponential output or maybe the right way to say it is they aren't getting the level of exponential outcomes from their investment…”

“I think we're going to see a pullback and we are going to recognize, but structurally, I mean, this is the thing structurally, when you go to your entire engineering team and say, Go use as much as you can of this when there's no clear direction.”

“Again, working in enterprise AI for a company that is only focused on enterprise AI since its founding, you see how that energy distorts the proper conversation very quickly when open AI comes in. Like suddenly the way people approach it, the scale with which …”

“If you are using AI for something really important, ask yourself, who is checking the outputs? And the information that you're getting. And if it's the company that sold you the AI, you have a problem. So you, whether you're building your own evals internally …”