George Fraser, Fivetran + dbt Labs | theCUBE + NYSE Wired: Mixture of Experts

Palo Alto Studio Connection Silicon Valley and Wall Street. I'm John Fost here with Dave Vol, my co-host. Welcome back to the Cube Studio here at the New York Stock Exchange. I'm Jim Allen, co-host of NYC Wired Mixture of Experts. And today we're going to have a conversation about how every company wants AI agents. But here's the problem. Most companies aren't actually ready for them. Fiverr research found that 15% of organizations say they're fully prepared to run agenda in production even as nearly 60% are investing millions in it. Joining me now to unpack that gap is George Fraser, CEO and co-founder of Fiverron. Welcome George. >> Great to be with you. >> So I guess we'll just get straight into it. George, those are some interesting findings that you guys released earlier this year. Help us understand the gap. What does it mean to be agentic AI ready in production? >> Yeah, so I think the thing that we're seeing companies uh deploy at scale successfully so far is using coding agents for software engineering uh as assistants to their software engineers. Uh and that's I don't want to diminish that at all. That's a that's a huge deal. A lot of people including Fiverr are getting a lot out of that. Um but if you want to do uh general purpose knowledge work with coding agents with agents uh in general um you have to connect those agents to your company's data. Uh and that is something that we are seeing most companies are at a very early stage with. They're still figuring out what that should look like, what the underlying infrastructure uh needs to be to do that. And the good news is there are some there are some great uh patterns uh that can help companies do that. But that's the thing where today I think most companies are at a very early stage. >> So Fiverr, you guys have had an interesting year. You've just fully closed the DBT acquisition. >> Talk me through >> the merger. I'm sorry. Merger. Apologies. They need to be very specific about that. Talk me through, I guess, this kind of moment that you guys are at from an industry perspective. I mean, it's clearly very opportunistic. That's a great synergy. making sure the data is clean and ready and useful, I guess, which has been a long-term challenge across enterprise and many industries. But bring me up to speed on where you guys are in September 2026 as the world goes back to school. >> Well, the uh from our perspective, uh AI agents are a new audience for the thing that we've been doing all these years, which is helping companies get all their data in one place and organize it. You could say uh without oversimplifying too much that Fiverr gets all your data in one place and DBT organizes it. Uh DBT is the tool that customers use to organize their data and that has always been important. In the past that's mostly been important for doing things like business intelligence and reporting. But now it turns out that that is the key to uh connecting AI to your company's data. the same data infrastructure um that uh you've been using for years uh to prepare your data for uh decision support as it is sometimes called is uh the ideal infrastructure for uh connecting AI to your company's data and the uh the companies that are furthest along on this journey uh are really the frontier labs uh open AAI and Anthropic and if you read what they write on their own blogs this is exactly what they have and they have taken uh the data infrastructure that they built to support reporting and they have put their AI on top of it and now the AI knows about the internal data as well as the external world. So data has always been somewhat of an opportunity and a challenge, right? Especially if you think about the world of enterprise and the verticals that sit within that. Now we're hearing about this whole world of agentic. And I mean, you know, we hear all the time data is gold, right? Like garbage in, garbage out. Why is an AI agent fundamentally more demanding when it comes to data than let's say a chatbot was 3 years ago? Like what's truly changing underneath the hood? Well, the comparison is really not between agents and chat bots between but between agents and people. So, historically the main carriers of your company's uh centralized data about everything happening in your company was human beings uh through reporting interfaces like BI tools and analysts writing SQL queries and AIs are great at writing SQL queries and they're great at writing ad hoc reports. Uh and so one of the things that we're seeing for the companies who do succeed in getting the infrastructure all wired in, getting their AI agents connected to their company's data is it immediately puts a lot of new pressure on their data infrastructure. It's uh it's a lot uh there's a lot of value that it brings, but it also creates a lot of cost because they run a ton of queries um because they are so very good at it. And but the good news is that what we've seen is that this is a problem that can be solved with optimization. You don't need to build out a whole new data infrastructure to support AI agents. You can solve this problem by optimizing the infrastructure you have. And we've been doing a lot of work uh on that at DBT and Fiverr to make it uh cost feasible to connect your AIS to your data. We hear a lot here on the cube and NYC wired about, you know, knowledge capture, right? About tribal knowledge, about context, about the importance of building that into your AI data layer. When you think about the many of enterprises you guys operate in and the interoperability of data from system to system, what are you seeing and hearing around I guess the role of AI in actually capturing knowledge and ensuring that you know this challenge of creating structured data which has been a long-term challenge is actually being solved for in this era of AI. I mean 15% it's an interesting percentage, right? There's obviously a lot of things not still working as they should. How do you boil down those problems? >> Well, I think um first of all recognize that most of the data that you want already exists in a structured form in a system of records somewhere. I think uh sometimes people do foolish things in this area like they point uh you know AIs at a collection of uh PDFs of you know forms filled out by insurance adjusters or something only to find out that hey the contents of those those PDFs were generated by a system and the contents are all sitting in a database somewhere that you could have replicated. So, I think you want to uh make sure to first and foremost don't turn this into a more exotic problem than it actually is. The very first thing you should do is get the data from all of the systems of record you already have uh and get it in one place. And by the way, you're probably already doing that. um it's just a matter of incrementally adding uh new sources, new collections that maybe weren't relevant to the use cases that you were doing before, but are relevant to AI. Um so I think a lot of techniques that are tried and true uh actually work really well uh in terms of getting all of your company data organized for AI. I want to ask you about something I know you've spoken about this year, George, and this is this whole concept of the SAS apocalypse, right? You have been you certainly had some opinions on what you believe to be true and not so true in that scenario, but you mentioned systems of record. You guys obviously work across many and there is certainly this view that you know are we moving towards one core orchestrator like one core source of truth 10 years from now. I want to hear from you like what are your thoughts on what's happening from the perspective of fragmentation which has been a challenge I think that somewhat fed the SAS apocalypse narrative especially as it relates to unified data and I guess elevating that proposition in the world of AI like where where do you see the reality there? >> I think there will be beneficiaries of AI and there will be victims of AI from a company perspective. companies whose performance who see a tailwind, companies that see a headwind and we don't totally know who those are going to be right now. Uh there's a lot of uncertainty and I think you know the markets do price uncertainty. I think the markets maybe overreacted in around April uh and maybe they're underreacting a little bit right now. There there will be um companies that are harmed uh by AI for sure. I think the other thing that people sometimes miss is um even the companies who you know who see for whom AI uh is competition or it makes their product less relevant uh change is slow adoption of new technology is very slow uh and so we're going to see you know uh even even after the technology um becomes available that maybe means you don't need X thing anymore or there's a radically better way to do a certain task. Um, you're going to see shockingly slow progress at adoption of this across the economy because that's just how it always is. Uh, most companies, they don't actually change their behavior unless they're actually under threat. So, it's sort of a complicated nuanced answer. I don't think it's a terribly non- consensus answer actually that you know there will be there will definitely be winners and losers but it will also play out um at most companies very slowly uh and uh and so you know for businesses uh like Fiverr we just try to figure out a way to be winners uh and then we try to navigate that over time. When we think about what has happened just historically and how software has grown up in the world of tech and enterprise, there was always a level of silo that was quite intentional, right? Especially from the perspective of security and fragmentation. You didn't necessarily have one system of record or one human capable of accessing all variations of systems of record in any one firm for potentially good reason. Now, we're hearing a lot about this whole kind of unified era, right? But that security threat, it's still very very real. And perhaps in the world of Agentic AI where it's an agent accessing multiple parts of your, you know, architecture at any one time, it's even more real. Like what are your thoughts around that argument? The argument for agents accessing systems of record and how secure that opportunity is or is not at this moment. I think when it comes to internal corporate data and internal uses of that data, security concerns are mostly a pretense for company politics. Um, at big companies, a lot of people at those big companies like silos. They like it when if you want to get an answer to a question about a particular uh about something in their domain, you have to come to them and they get to shape the answer to that question. They don't like all the data to be in one place and accessible to everyone. And I think so I I think a lot of these security concerns are actually about something else. Uh there are legitimate uh security concerns for sure. But you know cryptography works uh role-based access control works. We have tools to solve these problems. If you look at companies who are far down the curve of uh agents accessing internal data, what you typically see first of all is they curate that data usually with DBT. Uh and they curate a subset of data that they are comfortable being broadly accessible to the entire company. The the what exactly that is is different at every company. Um but there's a lot of important stuff that you can that is acceptable to be uh to be accessible to everyone. And so this is a simplified view. But the the pattern is you have a small number of people the data team who have access to everything. They curate it into uh a subset that can be accessible to the company more broadly. You create a role that has access only to that broader subset. That is the role that the AI agent uses to access the database. Like these are not new ideas. Uh they do work. So I don't mean to dismiss security concerns. They are real. But we have great tools for dealing with them. We have had these tools for decades. And if you're in a leadership position and you're trying to solve uh this problem, I would urge you when people are flagging security concerns, you know, pull up those threads because sometimes hiding behind those security concerns are actually just good old-fashioned corporate politics. Well, I certainly see your point, George. So, talk me through what's actually happening in the industry at this moment. You know, what are you seeing from the perspective of adoption like that 15% of folks that are getting it right? Are there any verticals you seem to be adopting AI and kind of meeting that moment sooner, maybe in a more efficient way? you know, talk me through what you're seeing broadly, the bird's eyee view of enterprise and AI adoption and agentic adoption right now. >> Yeah, the the companies that are further down that curve are generally technology companies. So, you see the frontier labs themselves are like this. Uh, Fiver Train is like this. Um you've seen companies like RAMP and Sarah BRS publish uh details of how they uh have curated their data for access within the company and and all of these examples are like I said using very similar patterns. They they take the same data infrastructure that they built for internal reporting. They add additional data sources that didn't make sense until you had agents able to access uh the data. a lot of unstructured data sources. Text uh was not super useful in the pre-ai era, but now we can actually comprehend it. Um they they use a lot of the same tools. Curation is super important. Uh it turns out uh dbt great tool for curating data for AI just like it was for uh reporting. Um and then interestingly um the the semantic model seems to be the lynch pin of a lot of this. You have to put a semantic model over the data uh that describes things like you know this this column of this table it represents revenue you can add it uh if you want to know the relationship to go is in this table over here and the way you join to that is through this other column that that kind of information needs to get um specified very precisely but that's that's kind of the lynch pin uh at this present moment it seems of making AI successful able to navigate the central database of everything happening in your company. So that's the pattern we see. We see it most commonly in technology companies. A lot of them have written about it and you can go read about it and I think this is the pattern that you're going to see everyone else emulate as this spreads throughout the economy. >> It's certainly an interesting moment right when we think about some of the tactical challenges. Something as simple as a unified taxonomy can actually be a hurdle in this agentic world too. So George, last question to you. Just finished this merger. Seems like it's a very interesting time in the world of AI and enterprise technology. What's ahead? I mean, how are you guys thinking about this next year out as you think about moving forward together with one unified and synergistic offering to the market? Yeah, I mean we are 100% focused on how do we take what we've built and make it as useful as possible for this amazing new use case of connecting AI uh to to data. Um so we're focused on solving cost problems uh making sure that people are going to be able to get all these benefits without having insane uh data infrastructure costs. Um and we are focused on this semantics aspect. uh making sure that we make it as easy as possible for people to uh curate uh their data into a simplified form that you know as we talked about earlier is acceptable to give broad read access to to the company and um represents a simplified view of their business that an AI agent is going to be able to navigate successfully. >> Well, George, great conversation. and wish you guys all the best as you move ahead on this next chapter. Thanks so much for joining us in NYC Wired. >> Thank you very much. >> I'm Jem Allen here at the Cube Studio at the New York Stock Exchange. This is NYC Wired Mixture of Experts. Thanks for watching.