OWN WORKFLOWS

“I think, you know, there there's so much narrative around the supply crunch. And no matter like, as much as we hear about it, I don't think people realize how bad it really is. Like, there is, you know, there is very, very little Slack compute available. Like, you know, we we run pretty large clusters ourselves, and we run them in, like, uncomfortably high utilization. You know, we when I'm saying we're, like, mid nineties utilization Mhmm. Most of the time.”

“I think the two or three things that I'll say is, like, you want people where you can give them whole problems. Yeah. And so, like, you know, if if you are if you feel like you are micromanaging, if you feel like you need if you feel like, you know, you you have to be involved in everything, I think that's a bit of a cop out as a founder because you're just like, I just need to be involved in everything. It's like, no. You probably don't have the right people. I think the second thing is, be very, very clear what you're optimizing for.”

“I think, like, GPUs as a service is not sticky. I think that's been seen. Like, customers generally just see that as as commodity. Imprint with the software they included is incredibly sticky. You know, like, just just like, you know, none of our top 30 customers have ever churned. You know, we're talking, like, 400% annual NDR Mhmm. Around our business. And so it's like very it's, it's very, very sticky. So I think that software layer is very important.”

“I think the LLM is a commodity. People are not saying that, but it is a commodity. Like, you can get gas from this gas station or you can get gas from that gas station. It doesn't matter. So it's not about that. It's really comes down to your company. What data does your company have that's special that your competitors don't have? Can you leverage that, and can you build AI that really understands that data?”

“you you hear these 95% of projects fail, but, like, you know, like, that's that's that's actually what you want. Like, you you like, when you are actually experimenting with new technology, if if if all of the all of your projects are failing, that means you didn't just not trying enough, you know, at the moment. So so I think when when I read the study, like, it was not a surprise for me.”

“I think, like, one, I think if you've worked at an infrastructure company like, we were once in a meeting with a bunch of AWS execs, and this was, you know, like, very senior AWS folks. All their pages went off multiple times, during our forty five minute meeting. You know? Like, it's a I I I think, like, it's it's it's very much like it's a cultural thing. But, yeah, like, I I don't you know, our, like, inference can't go down and, like, you know, we you know, you you you learn to like, you know, what's this? Like, I think Amir, my cofounder, when his pager goes off, his seven year old said, is that a p zero? Oh, is that is that is that a p zero? And so, you know, I I think that is you just have to get used to it, and that's the culture you live in.”

“I think there's, like, three paradigms or three kind of camps. The first camp is this quest for superintelligence camp. There's a second camp, which are the people that created the original technology. The scientists who created the technology got them the computer science Nobel Prize for it. They have for many years been saying that, that first camp is not gonna that's, like, not even the right approach. Third camp, which is I think what we are in, is I don't think we need superintelligence. So that's why I think we have the AGI we need. Let us just focus on solving the actual problems inside the organizations.”

“I think the big thing is data entry. How does the data appear in that database? And that's today, not completely automated. So, you know, just just to, like, I I think a company that would be well positioned to do that would actually kinda be Zoom. And a lot of people don't think about it that way. But Zoom is really should be the the the perfect data entry, application. Because that's where you're having all the conversations, and that's all the information's coming out. If you had that, that would be the full disruption of the SaaS.”

“I would I would say there is a bubble. I I would say those three camps. There is a superintelligence quest camp. I would be very worried there. We're not in a bubble in a sense that we're not spending huge amounts of capital on what we are doing. We're just trying to get actual economic value inside of this organization. So I I don't think it's binary, but there is a bubble. I mean, there are startups with zero revenue worth, you know, $10.20, 30,000,000,000. That's a bubble.”

“I do think coding is a little bit overhyped. I don't know if I would short it. It's I mean, I think it's still the future. So I think that's that's one of them. I think automating, customer service and support is a little bit overhyped. So, you know, I basically, I think the things that the industry thinks are, like, amazing and we've made great progress, we probably haven't done as much progress.”

“I do think that to, to some extent is I I think there is importance to The US that we develop our own models. I think that that would be a massive loss if that there are five companies, you know, five different labs in China that are creating open source models, and we're struggling to get one set up. So it's necessary. I also think it's inevitable. And, you know, like, the deep sea the deep sea moment a year ago, I remember someone saying to me and I thought it was, like, very well said, which is, like, and the world's changing a lot, but they said, hey. You know, we should kinda just forget Mhmm. That this is a Chinese model. We should just act like this came from Mhmm. From meta and and build and build with that in mind.”

“I think the application layer will exist for a number of reasons. One is because, you know, I think this idea, that what it what is valuable to a company, is, you know, the the user signal that they can gather, that only they can gather. And to the extent that that is encoded, in a model, I think a lot of their business will, be at risk. But to the to the extent that it is encoded in workflows, that is where they will be able to develop notes. So a good I think a good example of that is, say, a company like a bridge where the clinicians edits of the notes and what they do with those notes after the fact and the, the thing that happens in, inside the earmark three steps down, and that becomes a workflow that only”

“I'm very long on speech as an interaction. Like, I think keyboards are kinda basically gonna disappear completely. We haven't actually nailed speech. I know I know it feels like we have, but we haven't because you're still using your keyboard. So as long as using a keyboard, we haven't nailed speech. But I think we're this close to completely eliminating, keyboard.”

“I think the application layer will exist for a number of reasons. One is because, you know, I think this idea, that what it what is valuable to a company, is, you know, the the user signal that they can gather, that only they can gather. And to the extent that that is encoded, in a model, I think a lot of their business will, be at risk. But to the to the extent that it is encoded in workflows, that is where they will be able to develop notes. So a good I think a good example of that is, say, a company like a bridge where the clinicians edits of the notes and what they do with those notes after the fact and the, the thing that happens in, inside the earmark three steps down, and that becomes a workflow that only”

“I think the two or three things that I'll say is, like, you want people where you can give them whole problems. Yeah. And so, like, you know, if if you are if you feel like you are micromanaging, if you feel like you need if you feel like, you know, you you have to be involved in everything, I think that's a bit of a cop out as a founder because you're just like, I just need to be involved in everything. It's like, no. You probably don't have the right people. I think the second thing is, be very, very clear what you're optimizing for.”

“I think, like, one, I think if you've worked at an infrastructure company like, we were once in a meeting with a bunch of AWS execs, and this was, you know, like, very senior AWS folks. All their pages went off multiple times, during our forty five minute meeting. You know? Like, it's a I I I think, like, it's it's it's very much like it's a cultural thing. But, yeah, like, I I don't you know, our, like, inference can't go down and, like, you know, we you know, you you you learn to like, you know, what's this? Like, I think Amir, my cofounder, when his pager goes off, his seven year old said, is that a p zero? Oh, is that is that is that a p zero? And so, you know, I I think that is you just have to get used to it, and that's the culture you live in.”

“I think we have AGI. I think we have artificial general intelligence. We really have it. We absolutely have it. It's like anyone who says we need to get to AGI, that's like it's it's, it's false premise to start with. We already have AGI. I came to United States in 2009 at UC Berkeley, and back then, the definition of AGI we had, we already have satisfied that. So for thirty, forty years, we had a definition of AGI. We've already hit that. Now we're changing it and moving the goalpost.”

“I do think that to, to some extent is I I think there is importance to The US that we develop our own models. I think that that would be a massive loss if that there are five companies, you know, five different labs in China that are creating open source models, and we're struggling to get one set up. So it's necessary. I also think it's inevitable. And, you know, like, the deep sea the deep sea moment a year ago, I remember someone saying to me and I thought it was, like, very well said, which is, like, and the world's changing a lot, but they said, hey. You know, we should kinda just forget Mhmm. That this is a Chinese model. We should just act like this came from Mhmm. From meta and and build and build with that in mind.”

“I think, you know, there there's so much narrative around the supply crunch. And no matter like, as much as we hear about it, I don't think people realize how bad it really is. Like, there is, you know, there is very, very little Slack compute available. Like, you know, we we run pretty large clusters ourselves, and we run them in, like, uncomfortably high utilization. You know, we when I'm saying we're, like, mid nineties utilization Mhmm. Most of the time.”

“I think, like, GPUs as a service is not sticky. I think that's been seen. Like, customers generally just see that as as commodity. Imprint with the software they included is incredibly sticky. You know, like, just just like, you know, none of our top 30 customers have ever churned. You know, we're talking, like, 400% annual NDR Mhmm. Around our business. And so it's like very it's, it's very, very sticky. So I think that software layer is very important.”

“Royal Bank of Canada, built agents with us that basically take as soon as an earnings report comes out, so equity research analyst, their job is to put together these, you know, reports that say, like, you know, this is a buy. The agent goes, gets the earnings report, gets all the previous earnings reports, gets all the competitors, earnings reports, gets everything that's going on in the market, does the full analysis, the news, everything, puts it all together, and it can get the equity, report out in fifteen minutes from the earnings call. Industry standard is two hours.”

“Finance is all on Databricks, and it's all all the forecasting, all the sort of it's all moved to machine learning based. But it took them a long time because they had their Excel models, and they're very proud of them. We actually had external data science team build the AI models, and then eventually, they became good enough. And now, finance has taken those over and, like, you know, finance have kind of moved from Excel to Python, largely at Databricks. But it was a journey because, you know, most of the speakers speak Excel.”