AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics
00:12:07 · Dan Turchin, Jeremy Burton
[00:12:07] S1: It was prescient because you foreshadowed an AI era when there's so many more applications, so much more software, and most importantly for your business, so much more telemetry data, and it's so... Observability is infinitely harder. [00:12:24] S0: Yeah. I mean, you know, back when we started, a terabyte a day, five terabytes, so that was a lot. I would tell you we have midsized companies, let's say a thousand to 2,000 employees, that have got 30 to 50 terabytes a day at this point, and and large enterprises that are 500 to a petabyte of of data a day. So the the data volumes are quite astronomical. And just to put that in perspective, when I started at at at EMC fifteen years ago, we had one customer in the whole world that had a petabyte of data. Since the beginning of time to to to 2010, they they collected a petabyte of data. We have some customers that will will collect that every two days. [00:13:09] S1: Unbelievable. You have had a catbird seat through your board role and now obviously through Observe on really exciting epic journey at Snowflake. I had the pleasure of working under Frank Slootman when he was at ServiceNow. You presumably did when he was EMC. He famously left ServiceNow to lead Snowflake. Now Sreedhar is the CEO there. And by the way, on this podcast, we recently had Arnon Kishuri, who's the chief people officer at Snowflake. Could you give us a little bit of an an insider's tour of the culture at Snowflake? And I know it's contribute... It's it's it's a big factor in why Snowflake has been so wildly successful.
00:13:49 · Jeremy Burton
[00:13:49] S0: Yeah. I mean, look, I go back to sort of the two founders, Benoit and and Thierry. I I knew I knew both from Oracle. I knew Thierry better. I I actually played soccer with with Thierry. He he was a far better engineer than he was soccer player, which is which is which is great. Right? Because... So so they they... There's a... Like, the engineering culture, I'd say, in the company is incredibly strong. It's a very sort of warm accepting, almost collegiate type place. You know, the the product has been wildly successful. I think, you know, when Frank came in, you know, his power alley is scaling from a few 100,000,000 to sort of low billions. And I think probably then the very early engineering centric culture sort of broaden to include the sales team, you know, when you... You've gotta start to land, like, some of the biggest and and and largest accounts in the world, then, you know, you've you've gotta build up more muscle on the go to market side of the house. And then, look, I think Sreedhar now coming in. It's actually been... I I... Obviously, I knew Sreedhar from from from from being on the board. And I think the big challenge of not just Snowflake, but any company the size of Snowflake is there's a new world out there. There's a new reality around AI. You know, Snowflake and and any other company, you know, created before the last couple of years is not an AI native company. And so how do you get a company that's 10,000 people to to to think that way? And I think Sreedhar just deserves massive, massive credit. Every bit as much credit as maybe Frank would... Deserves for scaling the the go to market side of the business for for really getting the company on the same page around AI and what it means to be an AI native company, and it's painful. You know? There's no easy way to do it. I mean, and, you know, observe. I mean, I ran a company of a couple 100 people. It's it's quite easy to get a relatively new company onto a new trend because you don't have many people, and, you know, by nature, have been in a start up. I think you are much more attuned to the to the latest trends as they come along. So, yeah, I think, you know, Sreedhar is is different to Frank, is different to Bob Muglia, who was the original CEO. But I I do believe in sort of a a leader for a time, and Frank was certainly the right guy to bring in after...
00:16:26 · Jeremy Burton, Dan Turchin
I mean, Bob was the right guy to bring in initially. He's sort of your engineer's engineer and deserves a lot of credit for innovating, like, pricing model and and all kinds of wonderful things. But, Frank, to take the company public and scale it, and I think for the AI era, you you... You've got Shreedhar in. And as I said, I've been amazed, actually, by just the the depth to which AI is perpetrating every pocket of the organization. It it it is something to be old. [00:16:58] S1: I had a chance to walk the halls of campus at Snowflake with Arnon recently Mhmm. In Menlo Park, and it... There's an energy about it that I hadn't seen. Yeah. Maybe I think a fair analogy since the early days of ServiceNow. There there was really a really good morale. It felt like, you know, everyone was there to do great work. Must be different, though, to be acquired in after being at Observe, and it's certainly, you know, on the sidelines of Snowflake, but what... What's the transition been like?
00:17:25 · Jeremy Burton
[00:17:25] S0: Yeah. We we are probably quite fortunate, I think, in that Snowflake said, well, look. You would do... You guys are doing well as a separate company. Observability is a different business to sort of traditional, you know, AI data cloud pipelines, data warehousing, data engineering, all that kind of stuff. So why don't you keep running the thing over here? You keep, you know, going to the software engineering leaders, CTOs, and so on, selling observability, and then let's figure out where the leverage points are. I mean, Snowflake does have great relationships in so many big accounts. It's not that their relationships maybe are responsible for observability, but they've got good access to the people who are. So the nice thing is I am still able to run Observe. I was a big believer early on, and I've always felt this way that when you join a bigger organization, yes, you're gonna have your own traditions within your team. And if you like that, that creates your culture. But you can't deny that you're part of something bigger and better. And so I was quite insistent that day one, we're on the close. We moved to the Snowflake offices in Menlo Park and be be part of the team. I I didn't sort of want this us us and them mindset. It's like, okay. We're we're in this to be... To make a difference for Snowflake. Let's be part of team Snowflake. But, of course, we can retain our traditions and things that we have done as a small company to to sort of, like, mold the culture.
00:18:57 · Dan Turchin
[00:18:57] S1: Now the AI tooling companies would say, including the Foundation Labs, say, we own the cognition, and the data layer is a commodity. And presumably Snowflake and maybe your competitors would say, your cognition is dumb without the context provided by our data. Where do you where do you come out on on this? Who who owns the value from the perspective of the user and and the enterprise when nobody wants to cede any ground to to to anyone else?
00:19:31 · Jeremy Burton
[00:19:31] S0: It's a... It's an an entertaining debate. I think I think in the business that we're in observability, when you think about hundreds hundreds of terabytes a day, okay, if you if you if you just wanna point an LLM at your hundreds of terabytes of telemetry and have it go figure it out, like, good luck. It's just not happening. And so what what we do is not not just manage the volume of telemetry and, you know, sort of put it in a in a big dumpster for you to to dive into. We actually do a lot of work on the data to structure it, to organize it, and to relate it. And that context, particularly when you've got hundreds of terabytes of data a day, that context is critical. Whereas I think that the frontier models, clearly they can pick up knowledge around Kubernetes or infrastructure, it's all on the Internet, and those models are trained on the Internet, and so, yeah, it can probably do a decent fist of providing good context around Kubernetes troubleshooting. But if you're a bank, and you have a mobile banking application, okay, the knowledge about how that works is not on the public Internet. The models are not trained on it. Right? We have the data from that... Of those kinds of applications. We understand the environment, and and it is that environment that is the key context for the agent in the troubleshooting flow. And I and I think what's happened sort of thus far, not surprisingly, is, hey. We've, you know, we've got LLMs. We've got agents. Let's point them at this, this, and this, and this, and let... Let's see what they can do. And I think what we're finding is that we can burn a lot of tokens very easily, and we can blow our budget very, very easily. And I do think the thinking has gotta sort of come back or that the pendulum's gotta come back towards the center, which is... By the way, if you if you provide more context to the LLM, you will burn less tokens because you... You're gonna give it more of a clue of what to do next or where to go look next. And so my my view of this is we've got to sort of balance the the CPU burn with the GPU burn. A few queries run on your enterprise data will give you vital context that you can supply to the LLM so that you can burn less tokens over here.