Fivetran CEO George Fraser

Auren Hoffman (00:00.76)

Hello, fellow data nerds. guest today is George Fraser. George is the co-founder and CEO of 5Tran, a category leading data integration platform that does over 300 million revenues. George, welcome to World of Daz.

George Fraser (00:12.836)

Nice to be with you, Aaron. It's been a little while.

Auren Hoffman (00:14.06)

Super, super excited. Now, how does the rise of AI workloads change what customers need from data pipelines?

George Fraser (00:23.342)

We're trying to figure that out ourselves. I mean, we're monitoring the situation closely. So far, the answer seems to be not much. Like, 5Tran has been delivering text data to data warehouses for 10 years. And that has historically been sort of opaque. There's not a lot you can do with it. And now there's something you can do with it, these language models. From one perspective, what they fundamentally do is they give you the ability to machine process unstructured text data.

Auren Hoffman (00:25.486)

You

Auren Hoffman (00:53.92)

And, and are, are we going to see, do you think that's going to like change how people are actually like processing the data in some sort of way?

George Fraser (01:03.746)

Yeah, I think the most obvious use case is RAG. We do that at 5Tran. It's been very successful here. We have an internal RAG chat integrated into Slack. It is used by salespeople. Sales engineers support people to answer the arcane questions about the zillions of data sources we support that we get every day. If you're talking to someone at 5Tran, there's a decent chance that in the background, they're talking to this thing.

And we found that once we got all the data together, building the actual interface on top of it was the easy part. The hard part, as always, is getting all your data in one place.

Auren Hoffman (01:40.076)

Yeah. It's just getting all that knowledge and stuff like that. And then how, how do you, how does one like help tune it? Like if it's saying one thing, cause maybe it's like accessing like an old knowledge base or it's, it's having, you know, or, it's having some sort of, like, how do you actually tune it over time? How do you say, in the future you should respond more like this or something. Okay.

George Fraser (02:04.366)

We don't, we just do retrieval and we update the underlying data that's getting put into the context window and that ends up being how it gets solved.

Auren Hoffman (02:11.822)

Okay, so just putting newer newer data over time should make the answers like better and better.

George Fraser (02:15.668)

It's well, it's always searching against the latest version of our docs, our support tickets, and that's working pretty well for us.

Auren Hoffman (02:18.071)

Yeah.

Auren Hoffman (02:23.062)

Is that going to just like alleviate needs for things like sales engineers and things like that?

George Fraser (02:29.304)

You know, the value that people provide is often much more complex than you realize and less obvious, and they do a lot of different things, and the one everyone focuses on

Auren Hoffman (02:35.532)

Yeah.

Auren Hoffman (02:39.36)

Especially since you're kind of a lot of it's just like understanding that what the customer has it's almost like being consultant to the customer, right? Which is hard to train. Okay.

George Fraser (02:45.942)

Yeah, so I don't think so. I think that we will discover that mastery of the details of every single data source is not the only thing you need to succeed in this role. Similarly to like, I think AI is already playing a big part in medicine. It's a very analogous situation. Doctors have to have a mastery of this huge range of subjects.

but that's not going to make doctors disappear. Being a guidelines bot is not the only thing that doctors do.

Auren Hoffman (03:22.434)

But it could be, if you had, if you're bringing three people to the sales call, maybe you bring two in the future. There could be some scenarios like that, right? Or no?

George Fraser (03:31.086)

Totally, yeah, I think sales engineers will be less overworked in the future.

Auren Hoffman (03:38.294)

Yeah, yeah, they're able to concentrate more on the higher value stuff. The data warehouse itself is kind of evolving. Like, how do see that playing out?

George Fraser (03:47.748)

Yeah, it's kind of a jump ball right now for this next layer of applications. The thing that sits on top of the data, everyone is trying to be the center of that universe. And the traditional...

Auren Hoffman (04:01.774)

And do you think they'll be like a clear winner over time or do you think it'll just be very, very, very fragmented?

George Fraser (04:09.528)

I think it's gonna be very fragmented, if I have to guess. I think our experience has been that the sort of application layer is like pretty well served by sticking together a bunch of open source things. And it's a scenario where it actually kind of works pretty well to put together your own little set of building blocks because

A lot of it is about, like I said, curating the data correctly and then curating that retrieval step correctly, at least as far as RAG goes. The application layer is kind of thin and benefits from customization. so I don't, that tends not to lead to a single giant winner, but you know, I'm not a VC. I'm not in the prognostication business nor are they really. Who is?

Auren Hoffman (04:46.051)

Yeah.

Auren Hoffman (05:01.602)

Now, tech vendors that, basically like a company has all these different tech vendors as part of it. Sometimes they'll have like thousands of tech vendors and it almost like constitutes like a very unique DNA and where no two companies are the same. If you look at somebody's like tech stack, like what does that tell you about the company itself? Like if I looked at your DNA, I might know that

You know, you're a man and you're maybe even of a certain height and some other types of things. What could I learn about the, from a company just by seeing the tech stack.

George Fraser (05:39.34)

Yeah, you could see how old it is based on the types of vendors it uses. You could see if it's done a lot of &A. So companies that have done a lot of &A will have many sales forces, many of the same thing. One of our largest customers is OpenAI, but they have exactly one of each thing because they are so young. Whereas if you look at

Auren Hoffman (05:50.604)

Good point. Yep. Yep.

Many of the same things. Yeah. Yeah.

Auren Hoffman (06:05.506)

Yeah. Yeah.

George Fraser (06:09.292)

LVMH, another big 5Tran customer, they have many copies of each thing. And then I think, you know, on the data side, you can tell how much power the practitioners have in the organization. When the practitioners are sort of disempowered and the decisions are being made by people five levels up, they tend to buy things that speak to exciting subject matter to IT leaders like master data management and

Auren Hoffman (06:37.933)

Yeah.

George Fraser (06:38.67)

tend not to work very well, whereas when the practitioners have a lot of power in the decisions, you tend to see tools that solve real problems and work really well.

Auren Hoffman (06:50.53)

And does that mean, but also when the practitioners are doing it, you may have more vendors, right? Cause you may have like little bespoke things that solve a specific problem.

George Fraser (06:59.918)

Probably, probably. That makes sense.

Auren Hoffman (07:03.502)

There are a lot of like, there's this whole kind of open source revolution in the area. you know, whether it's like duck DB, post grass, all this other stuff that's going on, like what's your take on this open source versus closed source stuff in the data infrastructure layer.

George Fraser (07:19.534)

Well, Postgres is like, we're sort of watching the end of the movie. That's been going on for a long time, the takeover.

Auren Hoffman (07:26.638)

You think that you think we're actually it's in decline now?

George Fraser (07:31.252)

No, it's not that it's in decline. It's like in a zenith, but it's like all those wheels were sent in motion like 10 plus years ago. The funny thing about Postgres, it's so popular. Listen, Fivetran runs our application on top of a single vertically scaled Postgres database. It's continuous with the one that I set up over 10 years ago. There is in theory a continuous write ahead log through all that time. However,

Auren Hoffman (07:38.871)

Yeah, yep.

Auren Hoffman (07:48.318)

Yeah, it's amazing. Yeah.

George Fraser (08:00.47)

There are much better databases out there that have come out of the research groups. It's just so hard for a application database to get traction, any new thing. Postgres is not actually the greatest technology in the world. I'm not saying it's bad, but it's sort of the incumbent at this point. Yeah.

Auren Hoffman (08:10.316)

Why is that? Yeah.

Like you guys are still on it for a reason. Is it just cause like there's inertia and everyone knows, I mean, Postgres is great. Everyone knows how to use it. And there's all these other tools that are built on top of it. you could, you know, AWS has or Azure or whatever they have other internal tools to help scale it.

George Fraser (08:28.639)

the

George Fraser (08:33.654)

Yeah, it's impossible to change. The way to change your production database is to start a new company. It's like that. That's why it's so hard.

Auren Hoffman (08:39.982)

But you think if someone's starting a new company, like I still know people who started new companies who started on Postgres. you're, but you're saying that is, that would be less likely today.

George Fraser (08:49.214)

Well, at that moment when you start a new company, you're under a very different set of considerations. The very best thing is not necessarily you want something cheap, you want something that's available in the AWS console or whatever else you're using, you want something that's very safe, like this is not something where you want to take risks. I mean, a counter example would be Snowflake, which was built on FoundationDB, which they chose because they believed that

Auren Hoffman (08:56.792)

Yeah.

Yeah, it's cheap, it's easy, you know how to do it, it's fast.

Auren Hoffman (09:09.08)

Yep.

George Fraser (09:19.064)

they had like a unique set of challenges that they faced that were solved by this sort high-risk choice of FoundationDB. But that's very unusual.

Auren Hoffman (09:29.07)

Where do you, and there's this kind of like, how do you see companies like Snowflake, Databricks, how do you see them evolving over time?

George Fraser (09:39.48)

You know, I think data lakes are really gonna change things for those companies. The rise of truly vendor neutral storage formats is a really significant development and they're leaning into it to their credit, but it's really gonna change their role. think traditionally their role has been as sort of the keeper of the database. And when I say database, I mean it in the academic sense, like the data itself, the logical model, not.

not the query engine, not the particular instantiation of it. And now, especially at the largest companies that generate the lion's share of their revenue, you're seeing people move towards vendor neutral data lakes. And then it becomes a question of the value that they can offer higher up in the stack.

Auren Hoffman (10:31.16)

You're still seeing that like a Databricks is last I checked was about like a 2.6 billion ARR and still growing like crazy fast. So somehow it's, they're still like gaining all this share and stuff. Like how do you see these things kind of happening over time?

George Fraser (10:48.206)

Well, even though everyone likes to talk about Databricks and Snowflake as like being in some kind of battle, I like to joke that the thing about the war between Databricks and Snowflake is the customers don't know about it. Yeah, yeah, they mostly are doing different things, even though they try to do each other's thing. And I think Databricks has benefited enormously from all of the hype around AI.

Auren Hoffman (10:59.67)

Yeah, and the customers have both often. Yeah, most customers.

Auren Hoffman (11:16.108)

Yeah.

George Fraser (11:16.622)

They have been flogging that tagline data plus AI for 10 years. It's like skating to where the puck is going 10 years in advance. And it is there now. So they are really benefiting from that. And I think that's why they've grown really fast recently.

Auren Hoffman (11:20.43)

Yeah

Auren Hoffman (11:34.061)

Now wrote this article on evidence-based businesses. Where do you see most companies go wrong where it's like using data for business decisions?

George Fraser (11:43.332)

They use crappy data where they would be better off using no data at all. The best thing is evidence-based decisions that are based on high quality evidence. The second best thing is just introspection, stories. Go out. It's not your gut. It's like, just talk to people one at a time, but be systematic. Really listen if at the end...

Auren Hoffman (12:00.05)

Using your guide or whatever. Yeah. Yeah. Stories. Yeah. Anecdotes.

George Fraser (12:12.312)

Like you think that all 10 people told you the same thing, like you weren't listening. Like the world is not like that. There should be contradictory information, but just sort of be like an anthropologist, just like go out there and collect stories and be systematic. That's second best. The worst is what most people do, which is they use crap low quality data that's highly confounded that they analyze it to death. know, if you put a complicated enough model on a data, it will tell you anything.

Auren Hoffman (12:17.624)

Yep.

Auren Hoffman (12:41.09)

Yep. You could p hack anything, right?

George Fraser (12:41.122)

And it will end up telling you what you want to hear. And you will do it without realizing it. This is the thing people don't understand about p-hacking is it's like subconscious. You do it, not realizing you're doing it. And then at the end, you're like, look, this data said this conclusion that I was predisposed to believe. it's like, you made it tell you that.

Auren Hoffman (12:45.955)

Yeah.

Auren Hoffman (12:59.854)

Ha

Auren Hoffman (13:05.144)

What can companies do better there? What's kind of a simple thing companies should start thinking about doing better?

George Fraser (13:11.502)

Well, I think you, what's a simple thing? A really simple thing is when you do something, when you make a big decision or you make a change to your business, decide in advance how you will say whether it worked or not and write it down, right? Say before you change the sales territories or the marketing strategy or whatever. Here is how I will evaluate this after the fact.

Auren Hoffman (13:40.184)

Okay.

George Fraser (13:41.4)

And then try to stick to that. Sometimes you won't be able to stick to it. You'll realize that that was your plan was that it's impossible to evaluate it the way you do. But really try to do that because that will prevent you. That is the only way to prevent yourself from torturing the data until it tells you what you want to hear. is to decide in advance how you will evaluate it.

Auren Hoffman (14:02.518)

Interesting. You know, a lot of investors when they invest, you know, let's say before they put money into a stock or something like that, they'll write down, okay, here are the reasons I'm putting money into the stock. Here are the things that I think are going to happen. Let's say there's a macro thing that's going to happen. And therefore, because of that macro thing, I think the stock is going to go up. So let's say I believe that there's this, you know, there's a war going on in the Middle East, therefore, there's going to be higher oil prices, therefore the stock of

Exxon is going to go up or something like that. It's kind of like that kind of hierarchy because sometimes the stock went up, but not because you have your belief, right? Like sometimes like the oral price, the oral price went down, but the stock still went up, right? Sometimes like your belief was right. so then you can at least like look down the chain and kind of understand over time, like why, what, what did you do that was right? What did you do that you think needs updating in the future?

George Fraser (14:41.388)

Yeah, yeah, if it doesn't, if you didn't call your shot, yeah.

George Fraser (14:56.748)

And writing it down is a key part of that story you just told. It's not only that it needs to work for the reason you thought it was going to work. You need to write it down beforehand to prevent yourself from making up a story after the fact about why you are right. And we all do this. Yeah. Yeah.

Auren Hoffman (14:58.542)

Correct. Yeah.

Auren Hoffman (15:10.06)

Which 100 % of time we will all do unless you write it down. Yeah. Even when you write it down, sometimes if it's not clear, there's like a story to be woven there. Yeah.

George Fraser (15:19.328)

always, you can always make something fit. I mean, we just had a presidential election and there is this ridiculous figure, Alan Lichtman, who claimed that he could predict every presidential election based on these 13 keys. And they were, they were a perfect example of this because they had been mostly determined retrospectively, like you could weave together an explanation of anything. And

Auren Hoffman (15:41.292)

Yeah. It's often like, yeah, they have that sometimes like who wins the NFC or the AFC and that determines the presidential election or something. Yeah, well, maybe that was true in the past, but it seems unlikely to be true always, right?

George Fraser (15:51.384)

Yeah.

George Fraser (15:56.94)

Yeah, the primary problem in making evaluations of data is actually fooling yourself. That's the main thing that goes wrong.

Auren Hoffman (16:02.318)

Yeah. It was interesting in the, in March, 2022 in the, you know, kind of like, or right when the coronavirus was hitting, I had thought stupidly that the coronavirus, sorry, 2020, 2020, March, 2020, I had thought stupidly that it was overblown and that it wasn't a big deal. So when the S &P 500 dipped, I'm like, it's completely overblown. I'm going to invest in the S &P 500.

George Fraser (16:13.986)

You mean 2020, not 22.

Auren Hoffman (16:30.062)

And so it turned out to be a good investment, but for completely the wrong reason. turned out the S and P 500 did go up massively after that, but I was wrong about my, like, it was a bad investment because it was, it was the wrong reason to invest. If that makes sense.

George Fraser (16:45.38)

That's funny. I did the exact same thing and made a lot of money. I had a bunch of money in a money market fund because I had my first opportunity to sell some 5-tran stock on secondary and it was sitting in a money market fund. And I looked at it and there was a point where the S &P 500 had dropped. The correction that had been priced in was greater than the 2008 financial crisis. And I looked at it and

Auren Hoffman (16:57.623)

Yeah.

Auren Hoffman (17:09.548)

Yeah.

George Fraser (17:15.4)

I said, this is too big. Like COVID is bad, but it's not that bad. I was following very closely at the time, the infection fatality rate. There was very little data, but there was some data from Iceland. There was some data from this cruise ship. There was some data from Ohio prisons and a couple other cases. And I said, it's worse than a flu, but it's like, you know.

three to 10 times worse or something. It's in that band. this, is not as bad as the 08 financial crisis. And I don't know, I guess it depends what your definition of overblown is, like compared to what.

Auren Hoffman (17:46.496)

Yeah.

Auren Hoffman (17:58.178)

Well, I thought the shutdowns would not, would not, I thought the shutdowns were going to be a matter of like weeks, you know, whereas it was, you know, really for like a year and, she had all these things that were impacted, obviously at fast food, had the fast food, wasn't as impacted. didn't realize that just cause like people use door dash, but I thought fast food, you know, hotels, you know, all these things that started going down, air travel, all these things that really started going down massively. and just like the economy in general, people just like.

didn't really be out of work, they would have less money. So that was all getting priced in, in like early March or kind of mid-March 2020. And so I just thought, that's overblown because we're going to come back to work very soon. Even when we shut down our office, I was like, yeah, it's going to be super temporary. We're going to be back in a few weeks. Yeah, it just took way longer to get everything moving, right? It took until the end of the year, until they started rolling out the vaccines and all these other things that took the economy moving.

George Fraser (18:53.664)

Yeah, I guess that was overoptimistic. And I also shared that view until I think early March or something at that time. And then I saw that that's not how it was going to play out. But I would say that you were right in that the correction was too big. The market came back quite quickly, by the way. There was the...

Auren Hoffman (19:09.879)

Yep.

Very quickly, yeah, by April or May it was back.

George Fraser (19:16.95)

Yeah, so my plan was was dollar cost averaging, I was buying in each week, and I stopped because it crossed the threshold. And I was like, OK, now this is a correction that I can believe in. My threshold was like, this is about 2008 at worst. And so once it hit that threshold, I stopped buying. And then it kept going up, and I didn't predict that. I wasn't right about that.

Auren Hoffman (19:21.154)

Yeah. right. So you just did too slow. Yeah.

Auren Hoffman (19:28.46)

Yep.

Auren Hoffman (19:33.078)

Yeah, yep.

Yeah. Kept going. Still going. It still hasn't stopped. Yeah. It's kind of crazy. now you, you, are there, how do you like, I'm sure you've reversed many decisions, right? Maybe in that case, that's a good example of like an investment case, but at five chain, probably reversing evidence-based decisions that you've made. How does one.

Do that. Is it the same kind of thing where you're writing it down and then when things aren't moving that way, you can reverse it somehow? Or how do you walk through that? Sometimes I feel like the reversing decision is, is, is, is harder than making it.

George Fraser (20:16.836)

many, many times in small things and big things. I think most decisions first of all are not really evidence-based. They can't be. It's not possible to make most decisions in an evidence-based way. Yeah. and I think,

Auren Hoffman (20:24.994)

Yep. Yeah, even hiring decisions is just so hard to do, right?

George Fraser (20:35.64)

you know, I try to that this is more of like a personal psychology thing. You have to try to practice being wrong and admitting that you're wrong, at least to yourself, but ideally to others as well. And if you're never doing that, then it's not that you're always right. It's just that you're refusing to admit it. So I think that's a common

That's a tactic I try to use to maintain epistemic hygiene for myself. Another tactic I use a lot is I imagine myself as the new CEO of 5Tran and I say, what do I come in here and immediately overturn? What did this previous guy do that was so crazy?

Auren Hoffman (21:29.282)

And how often do do it? Like you can't do that every day or you just go crazy. So is it like a once a year thing, a once a quarter thing that you kind of like think about that? Yeah.

George Fraser (21:36.408)

This mental exercise? No, I do that like every week. I don't know, maybe I am driving myself crazy, but.

Auren Hoffman (21:40.748)

Okay, got it. every week, what would, what would somebody. So every, every week you're like, okay, well, what would somebody who just came in here would be doing differently?

George Fraser (21:53.858)

Yeah, mostly I come up with lot of the same answers and I'm trying to put those things into action, but it's not like a new revolution every week.

Auren Hoffman (21:56.557)

Yeah.

Okay.

Auren Hoffman (22:03.33)

But presumably somebody who replaced you, who had that same idea of what to do differently would do those things very, very, very quickly, right? Or no.

George Fraser (22:13.228)

Yeah, yeah. And I mean, it's just sometimes big changes are hard to make. So like, for example, the big one right now is there are some aspects of our pricing model that are really a little too clever. Like, I definitely like over thought it a little bit and we're trying to make some simplifications to that.

Auren Hoffman (22:36.244)

But then you have to wind through all your contracts with other yeah, okay. Yeah.

George Fraser (22:38.656)

It's a huge, it's a huge project, right? So that's, that's like the prime example right now.

Auren Hoffman (22:43.663)

And too clever meaning like just too hard to understand for everybody both for the company and for the customer.

George Fraser (22:46.85)

Yeah, yeah, it's a little, and 5Trend is very hard to price, but it's a little too hard to understand right now. And so we're going to make some adjustments that are not going to be material in terms of like the company's revenue, but hopefully we'll make it a little easier to explain to each new person who comes to the door.

Auren Hoffman (22:53.165)

Yep.

Auren Hoffman (23:01.494)

Yeah, yeah. Yep. Yeah, it is hard when someone's like, okay, I have this bill and, you know, I've seen it in certain certain different tech stacks we use. You have this bill in October and you're just trying to like understand like, why did I get this? But I just don't understand. There's like this number of workers doing this number of tokens between this number like it becomes very, very hard to kind of go through and predict.

George Fraser (23:27.768)

Mm-hmm. Yep.

Auren Hoffman (23:30.092)

What companies do you think do pricing really well?

George Fraser (23:33.86)

I think Snowflake has done pricing really well. They have basically changed nothing for their entire existence. Bob just hit the bullseye on the first shot. And he really helped me figure out 5Trans pricing model. So we joke about this. It's like, man, I didn't hit the bullseye as well as you did, Bob. He's like, nope. And who else has done pricing really well?

Auren Hoffman (23:36.13)

Yeah.

Auren Hoffman (23:44.336)

Ha!

Auren Hoffman (23:55.122)

I'm

George Fraser (24:07.936)

mean, good pricing models, they're familiar. People call them simple. Sometimes they're not actually simple, though. It's just that they're extremely familiar. They gear off something people already know. And they align to willingness to pay. They align to material cogs, if you have material cogs, which not everyone does. Those are the basic characteristics of a good pricing model. And they reduce friction.

Auren Hoffman (24:14.359)

Yep.

George Fraser (24:36.888)

They make it easy to become a customer. That's key. And that is a criteria that is failed by get in touch pricing, like where there is no public pricing model and you just say you have to talk to a salesperson, that is a huge source of friction. And a lot of people, a lot of salespeople like those models because they see the customers at the end of the pipeline and it gives them like a high degree of control. And the ones who go past that gate,

Auren Hoffman (24:47.052)

Yep. I hate that.

Auren Hoffman (25:01.144)

Yep.

George Fraser (25:05.55)

who see that and proceed, there's a heavy selection that takes place there. So then it looks really good at the other end. The problem is that you just eradicated 80 % of your pipeline by having that.

Auren Hoffman (25:12.898)

Yeah.

Auren Hoffman (25:17.548)

Yeah, I never, I know, I don't even like to engage with companies, which by the way, with most of them, went about and I see it. I'm like, I can't, I can't just sign up and use it. First of all, like if I hate when I can't sign up and use it. so that just annoys me. Like, why can't I just sign up and start paying for it right now in some sort of small way and start using it. And then, and then, and then I want to have a sense of, okay, if I do scale my organization scales greatly, like what is the pricing? Cause so many times you get bitten the butt.

George Fraser (25:20.898)

A lot of people are like that. This is the problem.

Auren Hoffman (25:45.87)

where you're like doing something and then, then all of sudden it's like 10X more than you thought it was going to be. And then you have to do this whole unwinding of like moving to a new vendor, didn't realize it. And we've probably, probably every everybody like me has experienced that multiple times.

George Fraser (26:00.706)

Yeah, and all of those negatives you just described, they prevent people from becoming customers in the first place. so if you look at your existing pipeline or your existing customers, you'll never see these issues. This is an example of where the only way to find this stuff out is like introspection. You're not going to discover this by looking at data. If you look at data on pricing, it'll tell you, well, you can look at surveys. Surveys is actually how you address this.

Auren Hoffman (26:07.02)

Yeah, you don't even want to engage. I don't want to talk to a salesperson for that reason. Yeah.

Auren Hoffman (26:16.109)

Yep.

Auren Hoffman (26:28.846)

On the flip side, a lot of salespeople will say, well, well, we, you know, it gives us less power or look, we have these two customers and they're doing the exact same thing and one's paying twice as much. Like so many enterprise software companies have that. Like, so are we going to have to go back to the one paying more and give them a price break or, like how does one start to, once you have enough customers, how you start thinking about it? Yeah.

George Fraser (26:49.804)

It's, yeah, it's a trade off. Like if you're trying to extract the maximum willingness to pay out of each individual customer and your salespeople are really good negotiators, which not all are by the way, then, you know, custom pricing is the way to do that. The problem is it sacrifices every other characteristic, velocity, conversion rate through the funnel.

Auren Hoffman (27:08.643)

Yep.

Auren Hoffman (27:13.687)

Yeah.

George Fraser (27:20.064)

That's my feeling about that.

Auren Hoffman (27:23.052)

Now you've been like kind of working on building like an environment where internally we're changing one's mind is celebrated. How do you like Institute that from like a culture standpoint?

George Fraser (27:33.828)

I'm not sure I've really succeeded at that. I'm still working on it. I try to cite examples of myself. I try to live by example. I try to encourage people to do experiments when possible. If an experiment is not practical or not worthwhile, I encourage people when you make change, make it all at once so that you can just see the discontinuity. If whatever you did worked, try to like write down

in advance, how, what is your gonna be, what is your success criteria for this thing? you know, every quarter, I think we do a little better at it. It's tough though. It's not the prevailing culture of the world. The prevailing culture of the world is to have arguments and to win the argument, which is like not getting to the truth.

Auren Hoffman (28:26.082)

Yes. George, quick time out. I think you're moving your thing a little bit too much. No, no problem. No problem. OK, next question. You did this acquisition of HRV, is one of the, sorry, HVR is one of the biggest in the data integration space. How do you approach integrating these two larger companies?

George Fraser (28:34.611)

sorry.

George Fraser (28:42.52)

HVR.

George Fraser (28:55.916)

Well, we.

There's sort of two sides of it. There's the technology integration and there's the people integration. And within people integration, there was sort of engineering and there was sales. The people integration we did really fast. It was a considerably smaller company by head count, by revenue. was like 30 % of the combined company, but it was a private equity backed company. hadn't grown as fast. And so it was very lean and that helped.

There was a very similar culture in engineering. And so that was really easy.

Auren Hoffman (29:35.886)

Were you like very quickly like replacing people's emails to an at 5trin.com email and stuff? So then they're on this like, they kind of fell right away on this other team rather than like kind of like, cause sometimes you might want to preserve the brand or preserve like preserve that kind of like internal culture or something.

George Fraser (29:42.582)

Yeah, that was like that. I mean, that was within like a month.

George Fraser (29:55.598)

So our goal was to fuse everything together when we did the acquisition. So the motivation of the acquisition was the companies had really highly complimentary strengths. So Fibetran was stronger in small and mid-sized companies. We were stronger in...

Auren Hoffman (29:58.474)

Okay. And how did you decide that? Because there's many different strategies.

George Fraser (30:20.79)

SAS data sources and open source databases like Postgres and MySQL, we were sort of the fast growing new kid on the block, right? And we were bigger by headcount and by revenue. But we were weak on the older databases, the closed source databases like Oracle and SQL Server and SAP, DB2. There's a bunch of them. There's a list.

Auren Hoffman (30:48.067)

Yep.

George Fraser (30:49.64)

And we were weaker on performance. Like if you have a single really large database, that was something we really struggled with. And we were weaker with big companies. That sort of makes sense. Like from a product perspective and from a sales perspective, those were our weaknesses. And then HVR was just like the perfect mirror image, strong with these older database, strong with big companies. Not the easiest thing to set up, not the easiest thing to manage. Had been around since 1998. At the time of the acquisition,

It was still a Windows application. So like amazing in many ways, but also like from another time in some ways. So the idea was we're going to combine the strengths of both companies. So we wanted to do like a total, you know, merge of the people and the technology and everything. And so we very quickly merged all the systems. That was easy. Just moved everyone over everyone's email moved. They were on Bitbucket, just moved it over to a GitHub repository. That happened like within weeks.

Auren Hoffman (31:21.87)

Okay, yeah, old school,

Auren Hoffman (31:35.415)

Yeah.

George Fraser (31:47.746)

And then the engineering teams really melded pretty fast. had a very similar culture. It turned out.

Auren Hoffman (31:52.002)

By the way, I'm sure they're and the HVR engineers are like so excited to finally be on Git like, there's probably a bunch of things that they're like, really? okay. okay. I thought they would have been like, woohoo, finally. We like, okay.

George Fraser (32:00.356)

No, absolutely not engineers are so cantankerous about this stuff ask Ask two engineers about their opinion about ID ease or version control or whatever and you'll get ten opinions Okay, and they will know no one will be happy about that's for sure, but it was fine. It was fine And then so that that part was pretty easy the sales team was a little tougher

Auren Hoffman (32:12.012)

Alright, skip right, yeah. Okay, that's fair. Yeah, that's fair.

George Fraser (32:27.268)

because there was different sales cultures, as makes sense, because they were selling more to older, larger companies. We were selling to smaller, younger companies. And there was overlap.

Auren Hoffman (32:29.334)

Yeah. Yeah.

Auren Hoffman (32:36.034)

Probably they're also using very different sales tools, even, right?

George Fraser (32:40.268)

Both companies were using Salesforce. I think their Salesforce is probably in better condition. In retrospect, we should have done the opposite. We should have adopted their instance and moved all of our stuff. That actually would probably be in a better spot today. so that took more time. There were kind of a couple iterations of that. And then from a technology perspective, there was a mixture of strategies. In some cases, we just rewrote things.

Auren Hoffman (32:41.837)

Yeah, okay.

Auren Hoffman (32:50.05)

Okay, got it. Yeah, yeah.

George Fraser (33:09.884)

We took the same people and the same tactics about things like how you queue data internally and reimplemented them in 5Trans codebase. In some places, we tried to actually have one codebase call the other. The first tactic has been a lot more successful, and we're sort of moving everything over time to that strategy of just rewriting rather than trying to actually integrate the code. It's a multi-year process, though.

Auren Hoffman (33:36.93)

Really? Okay, because that in some ways that sounds like harder or it sounds like it would take a lot longer.

George Fraser (33:43.286)

It's it's harder upfront, but then it works better. The problem is troubleshooting. When things go wrong, it's so hard to troubleshoot like two code bases communicating through RPC that were written with very different philosophies is really a nightmare. And meanwhile, a lot of the big ideas from HVR were totally possible to reimplement in another code base and we had the same people. And so it was kind of like,

Auren Hoffman (33:46.637)

Yeah.

George Fraser (34:12.696)

just asking them to speed run the same idea. Like for example, they had a really good serialization format to queue the data internally. And so we were like, same guys, like come on in here and just like do it again, but this time in Java rather than in C. And, know, they basically implemented the same format. They changed a couple of small things that they regretted about the first implementation. It didn't take that long and it was highly successful.

Auren Hoffman (34:15.404)

Yeah. Yeah.

George Fraser (34:42.338)

Vibe Trane's a lot faster now and this is one of the reasons.

Auren Hoffman (34:45.922)

And would you, if you did something kind of similar, would you run a lot of the same playbook today or what are some of the big things you do differently?

George Fraser (34:52.536)

You know, I think acquisitions are like super unique and like it's highly, it's highly circumstance dependent. And no matter what, like you'll, some, you'll regret some aspects of your strategy and most acquisitions fail, right? So acquisitions are tough.

Auren Hoffman (34:56.78)

Yeah.

Auren Hoffman (35:09.134)

Yeah, of course. Yeah. Yeah. Most acquisitions have a negative ROI. It's kind of like venture investing every once in a while. Some of them have like a 10 X, you know.

George Fraser (35:15.907)

Yeah.

Auren Hoffman (35:20.59)

All right, some personal kind of questions. Before 5Tran, you know, you were like a scientist, right? You had a PhD in neurobiology. What are some like the big open questions in neuroscience right now?

George Fraser (35:34.432)

all of them. Why are we here? What is the physical basis of human consciousness? It's the question of questions and we have no idea. We don't even know how to state it precisely. A lot of neuroscientists get annoyed when you bring it up and they're like, that's not a scientific question. And I'm like, that is the most important question in the universe. And obviously it's a neuroscience question and you're just like giving up.

Auren Hoffman (35:36.149)

You

Auren Hoffman (35:58.039)

You

Auren Hoffman (36:03.822)

mean, now that we like interacting with AI, it's like clearly passed the Turing test. You know, there's clearly past Turing test quite a while ago. What how is that like kind of changed your mind about things or or open your mind about things in neuroscience world? Correct, yeah, but like if you had asked me five or six years ago, I mean, as a layman, would have said, that's that's the test. That's that's but then it turns out that was really easy.

George Fraser (36:20.536)

Well, but turns out the Turing test is a terrible test.

George Fraser (36:32.952)

Yeah, it, well, I wouldn't say it was easy, but.

Auren Hoffman (36:35.212)

Sorry, easy, it was easy, like now, now like every system has passed that test.

George Fraser (36:39.884)

Yeah, we passed it and yet still there's so much that they can't do. So clearly that is not a great test of like general AI. I think one of the

Auren Hoffman (36:48.236)

Yeah. It's like, we keep moving the goalpost. It's like, you know, in the eighties, it was chess or something like we just keep moving the goalpost on what it means to be conscious or what it means to be AI or right.

George Fraser (36:59.656)

I mean, I think the thing that is really surprising about AI and has always been surprising about it, and I go back not only to chess, to like, know, work queue planning software was a kind of AI, is that we make these huge leaps and these AIs astonish us with their capability along some dimension. And then, but then they're bizarrely weak on others, right?

Auren Hoffman (37:13.175)

Yeah.

George Fraser (37:27.646)

and things that seem easy to us are still like elude these AIs. The most surprising thing I think about language models has been that precisely the things that commander data struggled with, sort of human interaction, emotion, they're great at, they can blather, they can write poetry, they're so good at all that stuff.

Auren Hoffman (37:48.343)

Yeah.

Auren Hoffman (37:52.14)

Yeah, their EQ is off the charts.

George Fraser (37:54.964)

And the things they struggle with until the latest iteration of the models, and I think they put this into the training set because they had been called out as a weakness of AI, so I think they sort of cheated to solve this problem, is you ask an AI to do, or a language model to do arithmetic in base nine. They couldn't do it. And it's not that hard. If you know how that kind of math works, it's not that hard.

Auren Hoffman (38:15.085)

Yeah.

Yeah, yeah.

George Fraser (38:23.38)

to do long-form arithmetic in base math, in base nine, but they couldn't do it. And there continued to be these like bizarre gaps in there. So ironically, like the things that Commander Data was great at, turns out super hard for these language models to do. And the things that he struggled with are easy.

Auren Hoffman (38:25.016)

Yeah.

Auren Hoffman (38:37.784)

So sometimes it's like, it's so interesting when whenever I meet like a human who's super talented in one vector, they're like the best guitarist or the best, you know, mathematician or whatever. They're like, they're often so terrible in another area. Like it doesn't always you would think it like translates really well from one to something else, even if it's like something somewhat analogous, like, you know, a great scientist is sometimes terrible at

policy involving science or something, you know, something that's like right tangential to, to what they're, what they're doing.

George Fraser (39:15.294)

It's hard to say. Who knows? Maybe the great scientist is actually really good at it and you're just wrong, right? It's like, I don't know. It's hard to evaluate. I think in general, in people, all intellectual skills are highly correlated. That's what IQ is. It's just the observation that people who are smart in one way are almost always smart in every other way. And AIs are not like that at all.

Auren Hoffman (39:16.972)

Yeah, okay. You disagree? Yeah.

Auren Hoffman (39:23.192)

That might be right. That might be right. Yeah. Yeah. Yeah.

Auren Hoffman (39:32.79)

Yeah, yep.

George Fraser (39:44.898)

they, going back to chess again, it's like you can make an AI that's so good at playing what we think of as a very intellectually difficult task, but then it can't do like, but we struggled for years to do things that humans would think of as easy, like driving, know, driving, we're just getting now. I think people find driving to be much easier than playing chess, to be great at it, right? But it's a much harder AI problem.

Auren Hoffman (40:05.654)

Yep. Yep.

Auren Hoffman (40:10.924)

Yeah, it's a point. It's a really good point. Now, you've also said your brain is not a computer. Why do you why do you think that? Because I know there's a big debate around that.

George Fraser (40:19.948)

Yeah, I think it's just a bad analogy. I think like...

Auren Hoffman (40:24.338)

You don't think there's a scenario where like we could upload our brain and have basically the same function on a machine or something. Okay.

George Fraser (40:32.034)

I didn't say that. I think that it's very mysterious how consciousness works. And I think you can simulate physical things in a computer. the problem with the analogy of brains and computers is that computers are primarily software driven, right? You have a general purpose CPU that can basically do anything. And then you write software that runs in that CPU. So they're very programmable. Brains are very much the opposite. We come with very much pre-baked.

A great example of this, you know, these language models to get to like competence, to get to sort of human like ability to talk. They are trained on, the numbers are not public, but people think perhaps like a trillion words to train like a state of the art language model. A human being by the age of, by becoming, by the time of becoming a toddler where they're like pretty fluent in language, they've heard like tens of millions of words.

So that is like a 100 million times difference in the size of data. Like we come kind of pre-programmed with something really important. And so, you know, it's much more of a like of a...

Auren Hoffman (41:35.596)

Yep. Yeah, it's crazy.

Yeah. Yeah.

Auren Hoffman (41:45.757)

Right, probably even in our lifetime we don't hit the same kind of level as a as a yeah. Yeah

George Fraser (41:49.696)

Not even close. No, not even close. So that's a very different system you're describing there.

Auren Hoffman (41:57.356)

Okay, so for those of you not watching on video, there's like there's a bed behind you. I know you've got an interesting theory about beds and videos. So explain to the audience your theory about beds and videos.

George Fraser (42:08.558)

Well, I have to credit my boyfriend with this theory, think. But I think there's a U-shaped relationship between your seniority in your job and the probability that there's a bed in the background on video.

Auren Hoffman (42:20.387)

So like super low seniority, there's a bad super high seniority, there's a bad. Okay.

George Fraser (42:25.034)

Exactly. Super low seniority, you're not worried about it. You're like, I'm a junior associate or whatever. Everyone expects this. Then once you get to the other end of the spectrum, you're like, I don't care people. I'm secure.

Auren Hoffman (42:30.029)

Right.

Auren Hoffman (42:35.406)

I've heard that about webcams as well. You have the crappy webcam at the low end and the super crappy webcam when you're like Jeff Bezos or something. You have a good web cam. Right. Exactly. When you're at the trillion dollar market gap, I expect you to have a very crappy webcam. What is a conspiracy theory you believe?

George Fraser (42:43.97)

That's funny. I'm not at that level yet. See, I'm very particular about webcams. Yeah, but maybe someday, you know, I can aspire to get to the point where I don't care.

George Fraser (42:58.02)

Exactly.

George Fraser (43:03.46)

Santa is the real conspiracy theory. And this, I think, is part of why people believe in conspiracy theories as adults. Because when you're a child, you are the victim of this conspiracy theory, and then you discover that it's real. The conspiracy, mean, not Santa. Yes. Right. Like, Santa is a global conspiracy of adults to deceive children. Like, think about that for a second.

Auren Hoffman (43:05.142)

Okay. Yes.

Auren Hoffman (43:17.164)

Yeah. the conspiracy is real. Right. Right. Obviously, you've the tooth fairy, the Easter bunny. Yeah.

Auren Hoffman (43:30.51)

One of things I like about the Santa conspiracy is like at some point, I don't know when that is, but let's say some point you're like nine, 10 years old, you get in on the conspiracy and then you actively participate against all the other younger. It's like, there's a point where you just like, you just get like, it's like you get read in very quickly.

George Fraser (43:40.046)

Yeah.

George Fraser (43:50.296)

And what is the lesson that you are learning by having this experience?

Auren Hoffman (43:54.274)

You're learning to lie and deceive.

George Fraser (43:56.994)

What is your favorite answer you've gotten to this question, by the way? Yeah.

Auren Hoffman (44:00.084)

On conspiracy theories. I I believe that there's conspiracy theories everywhere. So I'm into it. And usually when you read something that is, let's say negative about a company in the New York Times or something, it's pretty rare that the reporter came up with it on his or her own. Usually it got fed by a competitor or somebody who had a reason.

for that story to get out there. so, and then so whenever I read anything, I often try to figure out, who's benefiting from this? And there's like a pretty good chance that somebody in that subset of people who are benefiting is involved in that story.

George Fraser (44:50.286)

That's a fair one. So that's your favorite answer that you've gotten to this, though? I would think there would be something more wild. okay. But that's not what I asked. I said, what's your favorite answer that you've gotten to this?

Auren Hoffman (44:53.616)

that's my own answer. Maybe not my favorite answer. Yeah. Yeah. That's a good point. Yeah. I know. I, well, Santa's pretty good. I would say it's definitely up there. It's one of my favorites because I never thought about it as like a global conspiracy, but it's clearly one of the biggest conspiracies that there is. One of the things about the tooth fairy and you have kids. Okay. So one thing about the tooth fairy is like, there's been no inflation in tooth fairy things. like

George Fraser (45:06.616)

what it is.

George Fraser (45:22.668)

It's still like a quarter.

Auren Hoffman (45:24.142)

Well, sorry. like when I would, so maybe there has been inflation, but like my kids get, my kids have gotten a dollar. and, and I think people have been getting a dollar for like 25 years. like I don't think it's gone up at all. I think you just get, you just get the dollar and you would think like, and even if you're super wealthy, like, I don't think your kid is getting like $50 or $20 or something when their tooth comes out. Maybe they are, but.

George Fraser (45:50.51)

That is interesting. I'm trying to remember. What did I get? I feel like it was coins, but I'd have to check with...

Auren Hoffman (45:55.532)

Yeah, sometimes we do that like the silver dollar, the Susan B. Emly, whatever that type of dollar as well, the coin dollar, but it's still $1. So it's kind of a very, so I feel like kids are really getting the low end of the stick. they're not, we're just not keeping up with the purchasing power parity of a tooth lost has gone down pretty dramatically. All right.

George Fraser (46:16.088)

That's funny. Well, you know what? Things have to go down. Otherwise, there's no productivity gains. You got to find the savings somewhere.

Auren Hoffman (46:23.872)

It's a point. Yeah, it's a very good point. Prices have to go down somewhere. Our last question, we ask all of our guests, what conventional wisdom or advice do you think is generally bad advice?

George Fraser (46:36.514)

Well, I think the precautionary principle is terrible advice. When in doubt, err on the side of caution. First of all, it's not a useful principle at all, because there's always some further, it has no limiting principle. You can just go to infinity of cautiousness. And second of all, I think in general people take too little risk, not too much. So I think the precautionary principle is extremely popular and absolutely terrible advice.

Auren Hoffman (47:05.848)

How do you deal with like these Cassandras that are out there? Because like they're occasionally, right? Right. And, you know, there's a guy, you know, there was this, if you remember like the levies in New Orleans when Katrina came 20 years ago, you know, there's a guy who was like writing about the levies all the time. And then, you know, of course, in the levies broke, it's like, we should have put the, you know, we should have done the rejiggered the levies, put some money behind it so they wouldn't have broke.

George Fraser (47:06.297)

that has.

Auren Hoffman (47:35.99)

And clearly that would have been a good thing. But then you dive into all the other things the same guy said, like none of them have still come to pass. And it would have cost potentially hundreds of billions of dollars to do everything that he wanted to do. So how do you actually dive into these Cassandras? There's so many out there, right? I mean, and I live in DC, like DC is full of Cassandras.

George Fraser (47:53.404)

You I mean me personally you just Yeah Utter contempt is the right answer. I mean first you have to understand that It's hard to persuade other people of this. That's a different question. But but first you have to understand that If you evaluate people's predictions in retrospect There will always be someone who is right on everything. It kind of gets towards what we were talking about earlier about

Auren Hoffman (48:20.92)

right.

George Fraser (48:23.028)

Writing down your theory of success for whatever it is you're doing in advance to avoid fooling yourself But if you just go out and look in the world, there's always gonna be someone who looks like a great prognosticator in advance and worse There are a lot of people out there who sort of hack the system. So they make a lot of predictions. They're basically like mentalists that seem That are are just specific enough

Auren Hoffman (48:42.423)

Yeah.

George Fraser (48:48.814)

that they can go back and retrospect and pull them out and say, look, I was right about this thing. Yeah, and it's so toxic. I know, and I think some people do it without realizing it. And you just have to understand the sort of nature of like large sets and retrospective analysis and how dangerous that is. And yeah, have like really high, just like don't listen to them. I'll tell you a funny story.

Auren Hoffman (48:51.766)

Yeah, here's the tweet. Yeah, yeah.

Auren Hoffman (48:58.67)

It's like a game, people do it all the time.

George Fraser (49:18.37)

along these lines. So my grandfather was a OBGYN in a small town in Ohio. A fun fact, the football coach, Jim Harbaugh, was delivered by him and was actually named after him. Yeah, so my grandfather, Jim Fraser, he was an OBGYN, he delivered an entire generation. And

Auren Hoffman (49:19.223)

Yeah, sure.

Auren Hoffman (49:38.577)

my gosh, amazing.

George Fraser (49:45.924)

This was back before you could tell what sex a baby was going to be. And he was a little bit of a prankster. And so sometimes he would tell the mother that he could tell. And he would say, he would look at her, he would do an examination, it's gonna be a boy. And then he would write down in the chart, girl. And if he was right, they'd be like, doc, you predicted it, it was a boy. And if he was wrong, he'd be like, no, I said it was gonna be a girl. Look, you can see right here.

And I think he eventually got caught by some of the Catholic families who had a lot of children. And that's kind of a harmless example, but this is what people do. I always think of that story when you look at, like, prognosticators. It's like you just plant a bunch of seeds, and then after the fact, you go back and say, look at this one. This one grew into a tree.

Auren Hoffman (50:26.327)

Yeah.

Auren Hoffman (50:31.47)

And how do you, like in your own company, there are, there are going to be these kind of, cause it seems like there's so many things that can go wrong. There's so many, it's, it's, can't patch everything. It's like, there's going to be a, and and how do you, there's some things that you have to patch. Like there's some security thing that you have to go fix or there's some thing that you have to do that is like, you, you, you can't not, you can't say we're not going to fix everything, but you also can't go fix everything. So how do you even know how to deal with that?

Like there's defense and offense. can't always play offense. Like you have to play defense sometimes, right?

George Fraser (51:02.776)

Well, this is more of just a general, yeah, this is more of a general question about risk. I think that first of all, the tendency is to take no risk, right? Getting towards that precautionary principle. So that's the main thing you end up fighting against is you're actually pushing people to take more risks. And so you make the point that like, hey, zero risk is not the right answer. You look at

Auren Hoffman (51:19.468)

Yeah, yeah, yeah.

George Fraser (51:31.446)

like, well, how did you come to the decision that this was the right thing to do? And then you sort of look at someone's framework and you say, would this framework ever lead to taking any risk? And if the answer is no, it's not a good framework. You point out to people that like, if nothing ever goes wrong, you didn't take enough risk, right? Like things should go wrong. Bad things should happen. If nothing bad happens, then you played it too safe. There's a great Elon Musk quote about engineering that like,

Auren Hoffman (51:41.603)

Yep.

George Fraser (51:58.71)

If you never put any parts back, you didn't take enough out. And I think there's sort of endless cousins of that principle. It's a great principle. It's like if you never sort of reverse something, you didn't go far enough in some direction.

Auren Hoffman (52:02.569)

haha

Auren Hoffman (52:15.18)

Yeah, it's interesting it like FX Capital or seed fund, we do a lot of deals. so occasionally, one of them turns out to be a scam or whatever it might be. And there's always a question internally, what should our scam rate be? It shouldn't be zero because then we're not moving fast enough or not. We're doing like too much due diligence on everything. It's just like the shrinkage rate at Walmart can't be zero. They have to frisk everybody on the way out, which would be a bad experience.

So you always have to kind of figure out like there's some sort of optimal rate that you have to kind of understand.

George Fraser (52:48.804)

That I absolutely agree with that. The hardest thing is there are a few things where the rate you're going for is actually zero. There are certain kinds of hacks. At 5Tran, the prototypical example is we must never put the customer's data in the wrong customer's destination. That would be fatal, right? That needs to happen zero times. And although it's funny, I've heard some stories.

Auren Hoffman (52:54.478)

Correct. Yeah.

Auren Hoffman (53:04.81)

Yes, yeah, that would be disastrous.

So you at least have to know what that is, right? Yeah. What's fatal?

George Fraser (53:15.042)

Yeah, so it's like, how do you gauge risk on that dimension? You sort of look at close calls. You say like, okay, like, first of all, this is the thing that really needs to happen zero times. So this is one of the few cases where actually zero is the right answer. And then you look at, you look at like close calls and other things that are distant from it and say like, okay, those should not be zero. If those are zero, then we're being too aggressive. But

Auren Hoffman (53:29.132)

Yeah.

Auren Hoffman (53:39.533)

Yeah.

George Fraser (53:41.316)

The core thing does actually need to be zero and that's how you calibrate. those are the hardest ones and not everything is like that. Like getting sued is not like that. Like lots and lots of things that I think a lot of people would think of you want to happen zero times. Like once a company gets big enough actually zero is the wrong answer about that thing.

Auren Hoffman (53:48.424)

Yep.

Correct. Yeah.

Auren Hoffman (53:58.094)

How do you like, there's certain parts of the organization, whether it be like the general counsel's office or the HR office or something like that, that are, that are, are going to be generally like that's kind of their job is to be a little bit more risk averse. Like how do you actually work with those parts of the organization?

George Fraser (54:16.086)

you tell them that's not their job. There's a whole list. It's legal, security, SRE. Yeah, even procurement can actually be kind of like this. You tell them, no, no, no. That is not your job to advocate for one side of the equation. Your job is to balance it. you tell them, tell me about how you've made mistakes in both directions.

Auren Hoffman (54:18.38)

Yeah, okay, yeah.

Auren Hoffman (54:27.764)

Yeah, yep. Because they're trying to get like the best price or something or all the time. Yeah. Yeah.

Auren Hoffman (54:45.016)

Yeah.

George Fraser (54:45.058)

you haven't, then you're not actually doing your job.

Auren Hoffman (54:48.014)

All right, this been amazing. Thank you, George Frazier for joining us on World of Dazs. I follow you at FrazierGeorgeW on X, by the way, think you need a new name on X, but I follow you at FrazierGeorgeW on X. And I definitely encourage our listeners to engage with you there. This has been a ton of fun.

George Fraser (55:06.136)

Nice talk to you.