20VC: Scale Founder Alex Wang on How To Hire Incredible Talent Before You Are A Hot Company, Why Beating Competition Is Not As Clear Cut As Investors Believe & Why AI Is Under-Hyped Today In Terms of Total Impact

Alex Wang is the Founder & CEO @ Scale, the data platform for AI providing high-quality training and validation data for AI applications. To date, they have raised over $123m in financing from some of the best investors in the business including Founders Fund, Index Ventures, Thrive, Spark and Coatue and then also some of the world's best operators and founders of Dropbox, Instagram, Quora, Github and Twitch to name a few. Prior to founding Scale, Alexandr was a Tech Lead at Quora, directly responsible for all speed projects and before that a software engineer at Addepar responsible for building and maintaining financial models. In Today’s Episode You Will Learn: 1.) How Alex made his way from growing up in Los Alamos to being one of the hottest founders in the valley with Scale's new round giving them a unicorn valuation? How did growing up outside the ether of the valley shape Alex's operating mindset today? 2.) Why does Alex believe that AI is under-hyped relative to the state of technology today? Would Alex agree that most projects claiming to be AI are merely rebrandings from actuarial science, data science etc etc? What questions does Alex ask to determine true AI or BS? 3.) How does Alex think about how AI can deal better with ambiguity of data? What other core areas would Alex like to see meaningful step-function improvements in? How does Alex think about the value of data-set size? How does he think about the utility value of data reducing with every incremental data point? How does Alex think about the rise of synthetic data? How does this change the landscape? 4.) What are Alex's biggest lessons on what it takes to hire incredible people before you are a hot company? How does Alex determine whether someone has the right risk profile and desire to work in a startup? What questions reveal that? Where does Alex believe that many go wrong in the early days of hiring? What would he do differently now? 5.) For the $100m Series C, how did the round come together? What did the process look like? How did this round compare to the other rounds? How does Alex think about and approach the element of investor selection? How can founders build relationships with investors in these hyper-compressed fundraising timelines? What have been Alex's biggest lessons when it comes to CEO growth and then also board management? Items Mentioned In Today’s Show: Alex’s Fave Book: 7 Powers: The Foundations of Business Strategy As always you can follow Harry, The Twenty Minute VC and Alex on Twitter here! Likewise, you can follow Harry on Instagram here for mojito madness and all things 20VC.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Harry Stebbings?

  This is the 20 Minute VC and Founder's Friday with me, Harry Stebbings. It would be fantastic to hear what I can do for you to make the show better. You can let me know on Instagram at H Stebbings 1996 with two Bs. But to our founders today, and with their recently announced $100 million Series C, their valuation surpassed the $1 billion mark, making their 22-year-old founder one of the youngest to do so. And so with that in mind, I'm thrilled to welcome to the hot seat today Alexander Wang, founder and CEO at Scale, the data platform for AI, providing high-quality training and validation data for AI applications. To date, they've raised over $123 million in financing from some of the very best in the business, including Founder's Fund, Index Ventures, Thrive, Spark, Co2, and then also some of the world's leading operators and founders,

• 0:42Harry Stebbings?

  from companies like Dropbox, Instagram, Quora, GitHub and Twitch, just to name a few. And prior to founding Scale, Alexander was the tech lead at Cora, directly responsible for all speed projects. And before that, a software engineer at Atapart, responsible for building and maintaining financial models. I'd also have to say a huge thank you to the one and only Mike Volpiet Index for providing some fantastic question suggestions today. Really do so appreciate that, Mike. But before we dive into the show today, I want to give a big shout out to my friends over at ActiveCampaign. With their platform, you can create unique customer experiences that attract, nurture and convert leads into customers, into repeat customers. Over 80,000 companies using ActiveCampaign hit their sales targets with intelligent automations that send the right message to the right person using the right method at the right time.

• 1:26Harry Stebbings?

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• 2:11Harry Stebbings?

  And finally, they're committed to content rather than ads and clickbait to drive revenue. They're focused solely on the data and research tools. And with over thirty thousand users after just a month and a half, and one user calling Atom the Robin Hood of Bloomberg, the sky is the limit and you can check them out now at atom.finance with the invite code two zero VC. That really is a must. I just love the product. And we spoke there about simple and elegant UI. So you know, when you go to a website and you just want one thing, it should be simple, quick, friendly even. Well, that's Intercom's motto. So Intercom makes that little chat bubble in the corner of a website, opening your business up to customers in quick and friendly ways. But it doesn't stop there. Intercom extends your team with conversational bots and guided tours inside your product. Take Intercom's success story, Unity.

• 2:54Harry Stebbings?

  Before using Intercom, visitors kept abandoning their site. Unity just couldn't answer people quickly enough. The Intercom Messenger helped Unity get proactive with their customers. In just twelve months with Intercom, Unity got forty five percent more customers. Give your customers the one thing they're looking for and get your best revenue channel back yet again. Go to intercom.com/podcast, then see everything else Intercom can do for you. But it's now time to dive into the episode and I couldn't be more excited to welcome Alex Wang, founder and CEO at Scale.

• 3:28Speaker 01

  You have now arrived at your destination.

• 3:31Harry Stebbings?

  Alex, it is such a pleasure to have you on the show today. I absolutely loved having you on Saster, but with the new round, I thought this was a must. So thank you so much for joining me today.

• 3:39Speaker 02

  Thanks for having me again.

• 3:41Harry Stebbings?

  Not at all, but I would love to kick off. And for those that maybe missed our Saster episode, how did you make your way into the world of startups first and then come to founder now unicorn that is Scale?

• 3:50Speaker 02

  Yeah, so I had a very lucky path. I grew up in New Mexico and then after high school, I actually came out here to the valley to work. So I worked at Quora for a couple of years. And then after my time at Quora, I decided to go back to school. I went back to MIT doing mostly deep learning and AI and then dropped out to start Scale.

• 4:08Harry Stebbings?

  I absolutely love that in terms of the dropout to start scale. Can I ask, A, what did your parents say? And B, in terms of risk tolerance and how you assess risk personally, how do you think about your own risk assessment there?

• 4:19Speaker 02

  Yeah, so. I had to tell my parents that I was planning on going back to school. Jury's still out if I'm planning to go back to school, but that's what, that's why I had to tell them to let me do it. And then in terms of risk tolerance, I adopt a very risk-seeking perspective. Cause the reality is that there's not actually that much risk. When you start a company, the worst case obviously is that it fails. But even in that case, it's not that bad, especially if you're young. Because if you're in that position, you'll still have plenty of opportunity after the company. And so I think a lot of people overweight risk, particularly when it comes to taking a job at a small startup or starting a company, et cetera. Because they think that the negatives will really weigh on them. But I think you see people who start companies and fail and then start another one and it succeeds and they're just going for it.

• 5:03Speaker 02

  And it's not really that negative of a, of experience.

• 5:06Harry Stebbings?

  That's super interesting to hear the perspective there on kind of the risk tolerance. So I am, and I'm sorry for getting off schedule so much sooner than I expected, Alex. But say you had a younger brother who was just leaving university now. And he said, hey, Alex, I can join a big incumbent, I can join a small startup, or I could start my own thing. What would you advise him? Cause that's often the three choices that people have. And what advice do you give more generally?

• 5:28Speaker 02

  Yeah, I think the thing that I would do first is join a small startup. And the reason for that is a couple of things. I think first, your first couple of jobs really imprint to you and like imprint how you think about jobs and how you think about business and how you think about the work that you're doing, et cetera. So you really wanna be somewhere where you're in a, a strong environment that will sort of teach you good habits and teach you a good culture. And so for like your first thing out of the gate, it's really good to spend that in that kind of environment, which generally lends itself to a small startup. The reason I wouldn't start a company straight out of the gate is that I do think if you've kind of seen an organization run once before and have sort of like learned from that experience, you're going to be a lot more effective when starting your own company, especially as you have to hire a lot of people and grow.

• 6:12Harry Stebbings?

  No, I totally agree with you there in terms of the effectiveness. But kind of talking about environments that shape you, I do have to ask one more thing before we dive into the meat of the episode. And it's, as you said there, growing up in New Mexico and Los Alamos and your parents working in an interesting profession in the secret lab. So how was that for you? And do you think it shaped any of your operations? Mentality and how you view leadership today, Alex.

• 6:32Speaker 02

  Yeah, I think there were a couple of things. I mean, the first thing was that it was a very scientific community and actually there was there was almost no business or commerciality in Los Alamos because the main employer is just this national scientific lab. And so, growing up, I almost never thought about business and almost never thought about being commercial or capitalist at all. And it was very much focused on knowledge, which I think was a great orientation growing up. I think the other thing was that in that community, one thing that I think I learned to understand a lot was in these communities of knowledge workers, of people who are sort of, everybody is very oriented towards knowledge and learning, et cetera. You kind of realize like the things that people value, if that makes sense, and the kind of culture that develops. And I think being a part of that

• 7:15Speaker 02

  kind of culture growing up really influenced how to build that kind of culture at my company today. So I think it was really influential.

• 7:22Harry Stebbings?

  No, listen, I'm super glad I asked that one. But enough of the digressions and me going off schedule. I do want to move into the meat of the episode. And I want to start, stay on the theme of AI. Obviously, it's in every publication every week. The rate of investment's never been faster and harder in terms of quantum. So I do want to ask, is AI overhyped relative to the state of technology state, do you think? And what leads the thinking here for you?

• 7:44Speaker 02

  So I actually have this belief that AI is either appropriately hyped or even maybe a bit underhyped in terms of its total impact. I think that it really is, if you were to think about technology in terms of these giant waves, I think there's the internet, there's mobile, and now the next wave is really about AI and machine learning. And if you're sort of like, take all of the impact that AI and machine learning will have over the next decade as we apply it to a wide range of existing businesses and business processes, it's going to be absolutely massive. It feels, when we think about it, it's like AI can be applied in at least as many ways as the internet. And it's going to be this incredibly impactful technology. Now, I do think some aspects of AI are overhyped. In particular, I think AGI and the thought that we're going to

• 8:29Speaker 02

  have AI that is better than humans at everything. I think that's a bit overhyped, or at least it's unclear when that might actually happen. And some people say it's going to be sooner, some people say it'll be further along. But the reality is that there's a lot of very simple problems that AI is already solving and already capable of solving that are already going to be really, really impactful. And so just assuming no advances in technology, it's already sort of worth the hype.

• 8:52Harry Stebbings?

  Listen, I love that comparison to the broad scale of the internet, but I do have to ask, because I did have a recent guest on the show. And he said the majority of projects today calling themselves kind of AI centric or AI first are really rebranding plays from the days of actuarial science, statistics, or even big data and moving to call themselves AI being the trend of the day. Do you think that's a fair summarization? And what kind of questions do you ask when meeting kind of founders that claim to be AI centric in their projects? To kind of unveil just how kind of true AI innovation it is?

• 9:22Speaker 02

  Yeah, so I actually agree with that, which is the fact that today, a lot of companies and projects really do. They're using the term AI extremely loosely. And I think that is part of what impacts a lot of the perception of the technology, because so many people are lying about it. But we at Scale, we work with very innovative companies that are actually using AI and machine learning and deep learning in particular. So we work with folks like OpenAI and Waymo and Uber and Lyft and all these folks. And the reality is that when you're actually able to apply deep learning to a problem, it is really, really impactful and really incredible what it can do. Now that being said, I do think you always have to ask yourself when somebody says, hey, we're using AI.

• 10:02Speaker 02

  You have to like evaluate, like you're saying, you have to determine, hey, are you talking about real machine learning and deep learning? Or are you just using it as a buzzword to try to like convince people that you're advanced? And so that's a hump that I think the industry has to overcome. But I certainly am more of a believer that we'll be able to overcome that as an industry and then realize the actual benefits of deep learning.

• 10:24Harry Stebbings?

  Yeah, no, I agree with you in terms of it being a hump that we can and we will overcome. In terms of kind of humps to overcome, one guest said on the show recently, the field has to progress more in how it deals with ambiguity. So I guess first, would you agree with that, Alex, being at the centre with Scale? And then are there any other major improvements you'd like to see occur in the field in the imminent future?

• 10:43Speaker 02

  Yeah, I mean, it's interesting. I think there's sort of two ways in which you have to deal with ambiguity. One of them is just this sort of like bullshit detector. In terms of ambiguity that you kind of mentioned with the branding. But I think the other is a lot of times these state of the art machine learning systems. Whenever they face situations that are a little bit outside of situations that they've experienced in the past. They might not perform super well. And so I think there is, there is certainly truth to the fact, which is if we want these systems to actually be deployed in the real world and perform really well and be very reliable. There is a lot of work that we have to do to make sure that these systems are actually performing well. And sort of we think that all of this comes down to the data.

• 11:20Speaker 02

  Right. So at the end of the day, machine learning and AI really follows a kind of garbage in, garbage out kind of framework where if the data is bad, the models are going to be bad. But if the data is really accurate, comprehensive, varied, et cetera, then you're going to have much more reliable and safer models. And so that's one of the reasons actually why we're so focused on this data problem is because we think it's actually one of the key unlocks to dealing with a lot of the challenges that these machine learning models have faced so far.

• 11:46Harry Stebbings?

  I totally agree with you in terms of that kind of data-centric mindset. I guess the other question then for me with that in mind is we, we had Aaron van der Vender at Founders Fund on the show. And he said that actually the value of large data sets is actually largely overplayed because there's this kind of asymptotic moment when the utility value of every additional data point goes down in terms of the value it provides. Is that fair, do you think, from that perspective in terms of the value of large data sets?

• 12:11Speaker 02

  First of all, it's funny, Aaron van der Vender also grew up in La Salmas, New Mexico, which is, which is a fun fact. But it's an interesting question. So Google sort of published a bunch of these papers, which showed that each time you ten X the size of your data set, you're going to keep getting bang out of your buck in terms of the performance of your model. So it's not just that, it is true that like for each marginal piece of data, you get less bang for your buck. But if you keep increasing it exponentially, initially you keep getting really huge gains in the performance of your model. So that's the first point, which is you kind of just have to think about it on an exponential scale versus a linear scale. And then I think the second thing is that the kind of research, well, the research doesn't really take into account is that in the real world, the performance of these models, it really all comes down to edge cases, right?

• 12:55Speaker 02

  If you think about a self-driving car, right now, everybody in the industry is sort of tuned. And sharpening the performance against edge cases. And if you kind of just zoom in on the edge cases for most of these data sets, they actually just have almost no data for these edge cases. And so in almost all cases, there's a huge amount of value in getting more data that represent these edge cases to improve your machine learning models. And so on an overall basis, you might not be adding that much data to your data set. But if it represents these edge cases and these really rare situations, then the reliability of your model is going to go up significantly. And so I think it's fair in some sense, but it's unfair in the sense that if you want to build safe systems, you kind of, you're going to need to constantly be feeding it more data.

• 13:37Harry Stebbings?

  Speaking of kind of improving the data set that you have there, I am interested because plugging the gaps in terms of the edge cases, I'm seeing more and more synthetic data creation platforms pop up. How do you think about synthetic data creation platforms? And does that change the landscape and the value of data moving forward?

• 13:52Speaker 02

  Yeah, so synthetic data is very alluring concept. I think that you you'll be able to just generate data to match these edge cases. It hasn't really worked in practice in most situations, particularly when it comes to visual data or even textual data. It really hasn't worked. And a lot of the reasons why it's sort of limited in terms of its overall impact is that at the end of the day, whenever you generate data, there's always some sort of weird artifacts or bias in the synthetic data that you generate. You can see this like when people generate deepfakes, there's always some weird artifacts in that data. And the thing is, machine learning models are really good at picking up those little points of bias in a way that they they basically won't generalize very well. And so all that is to say, I think it's it's this very alluring idea. And if you talk about it at a high level, it's very exciting.

• 14:37Speaker 02

  But if you really dig in on like, where is the state of the technology? And how impactful is it really? It's nowhere close to actually impacting the curve of machine learning yet.

• 14:46Harry Stebbings?

  No, that's super interesting because it does have that hailed illusion from the outside. So really appreciate you clearing that one out. You said about kind of the technology there. And obviously, behind every technology company, there's the people themselves. And I do want to dig into the scaling of scale itself, though, in terms of the people. So if you take yourself back, Alex, what are some of your biggest lessons in terms of how to hire incredible people before scale was the super hot company that it is today?

• 15:09Speaker 02

  Yeah, it's something that I kind of think back on the times when I was doing it and it was such a slog. There are a couple really important things. I think the first is that you actually, you just have to spend a crazy amount of time doing it. A lot of our early engineers, I probably spent north of twenty hours getting coffee with them, talking to them on the phone, chatting with them, et cetera, to be able to convince them to join the company. And so I really think that like, there's no substitute for time early on. The other one is that you really wanna like leverage the people that you have and kind of snowball an incredible team over time. And so you need to make it clear that like the people you've hired so far are really excellent. And then make sure that that's the impression that everybody gets when they meet people at your company, when they like talk to other people about your company, et cetera.

• 15:53Speaker 02

  So you really wanna be, even before you're a hot company, you always wanna be building this perception. It's like, oh, it's a bunch of really great people. Then you should not waste your time with folks who don't match certain key criteria. I think we wasted a lot of time, or we spent a lot of time with folks who I think were really, if you dug into it, not comfortable with the risk of joining a smaller company or joining a company that wasn't very established. And we would always get very excited about these candidates because we thought they were obviously like very, very smart and would have been great for the team, but they just weren't comfortable with that risk. And that's something that kind of at the end of the day will end up trumping everything else that you do. Like you can't, it's very hard to convince people to be more risk-seeking or risk-taking.

• 16:33Harry Stebbings?

  Can I dive in and ask, how do you determine their own risk assessment? Do you, I mean, quite literally say, are you ready for startup life and maybe a drop in salary? Because often people will think that they're ready even in themselves. How do you determine whether they're ready or not? What sort of questions lead the way?

• 16:47Speaker 02

  Yeah, I mean, I think first you should look at the other opportunities that they're seeking out. If you have to convince somebody between joining Google and your startup, you've kind of already lost a big battle. Especially if they aren't looking at other small startups or they aren't even really trying to figure that out. This is versus a candidate, there are plenty of candidates who will interview at ten startups, under twenty people, and then pick one of them, and those people are really ready. So you just see it with the opportunities that they're looking at. And then I think when you talk to them about the company, they will have all these questions. I'm trying to think of a couple telltale one. One of them is like, oh. How many years is it before we hire like we get to more than like thirty people or forty people or something? It's just like all these questions that they're kind of asking themselves to like feel like not as risky as it is.

• 17:30Speaker 02

  And the reality is that joining a smaller company is pretty risky. And so they need to really sort of be on board with that and understand that. The other ones are like, this is an interesting one. When candidates ask about, oh, how long do you guys work and what is the policy there? I think the the reality is that if you look at most startups, people need to realize that it is a fight for survival. And so it kind of needs to be in the spot where you're okay, if necessary, working very hard. And I think that's kind of another telltale sign of people who might not be either ready for the risk or ready for the fight.

• 18:00Harry Stebbings?

  Can I ask, in terms of ready for the risk and ready for the fight, how do you think about the equity versus salary decision? And how you think about compensating them in the right way in the very early days when budgets are tight?

• 18:11Speaker 02

  Yeah, I mean, it's interesting. So the interesting part of this is nowadays, seed rounds are getting so big that I think most companies can actually, if they wanted to, they can actually afford to pay pretty reasonable salaries for their early employees. Now, I actually think that's negative. I think you do really want people who are okay taking a massive salary cut and then really excited about getting more equity. And even though a lot of founders might view their equity as very precious, particularly early on, I think it is totally the right thing if for your early people, they have really healthy equity packages and are really long-term owners in the business.

• 18:46Harry Stebbings?

  Yeah, no, I couldn't agree more. I mean, it is a war for talent, though, and being a war for talent and the ecosystem that we're in. I did love something that you tweeted recently, and it was about competition. And you said that beating competition is not as clean-cut as investors like to believe. As an investor, I do like to believe it's clean-cut, so you're totally right. But what are your thoughts here, Alex? And how do you think about an approach to competition?

• 19:06Speaker 02

  Yeah, so it really is not that clean-cut. I mean, if you think about some of these classic examples over time, like think about Uber versus Lyft. That's just been, or Uber versus all their competitors, honestly, that's been a total dogfight in every single geography. And it's like every geography has its own unique challenges and it's required some strategy. And the people running those geographies have really kind of determined the success or failure of those geographies. Now, I do think Uber and Lyft maybe is much more competitive of a space than others appreciate. But I think the reality is that like in terms of beating competition, there are actually so many different ways in which you can get an edge in doing better than them. There's like your go to market strategy can be better, your technology and your product can be better, your ability to serve customers and like be just be very customer centric can be a lot better than them.

• 19:51Speaker 02

  Your ability to build like have great design and branding and really convey that to the customer can also be an advantage. And so there's all these things that kind of add up. And so it's really naive to think that like, oh, we can just build a better product and then we'll win. That's basically, I, I think it's almost never true. I think you always need to build a better product, yes, but you also need to realize that you need to be better in every other way. And you might like, as a founder, you might be able to do that from day one and you might care enough about every little detail such that you do better than the competition in the beginning. But as your company scales, you need to hire just like incredible ninjas in every single function. So that if you think about your company as a machine, like it's always going to be doing way better than the competition.

• 20:33Speaker 02

  And so I think in reality, and I think many investors do think this, but I think competition and beating competition always comes down to talent. And not just talent in engineering or not just talent in product, but also talent in every single function.

• 20:46Harry Stebbings?

  I mean, I couldn't agree with you more there in terms of the talent centristy, but I do want to dig a little bit more into the investor mindset that you touched on there. Cause you recently raised a hundred million dollars led by our friends at Founders Fund. So massive congrats for that, Alex.

• 20:58Speaker 02

  Thank you. Thank you so much.

• 20:59Harry Stebbings?

  I do want to ask though, how did the round come together and what did the process look like?

• 21:04Speaker 02

  Yeah, I think it was very quick and I, I don't want to give away all my secrets, but we had been discussing, we had talked to Founders Fund for a while before they ended up investing. And I think this is what's generally the case with firms that you sort of naturally have gravity with is that you get to know each other long before the company needs to fundraise. And then by the time that we sort of as a business said, hey, if we had some extra capital, we could really accelerate our progress. Then they already were ramped up on our business. They already had spoken to a bunch of our customers, I'm sure. And so by the time that that happened, it happened really quickly. I think it was probably less than a week of due process.

• 21:38Harry Stebbings?

  Got you. No, absolutely. It's, it's always super helpful to have that round time before. Can I also, when you think about advising founders today on the investors that they choose to work with, how do you advise them when it comes to investor selection?

• 21:49Speaker 02

  Yeah, I think this is a piece of advice that I had gotten before we sort of picked our Series B investor from Nat Friedman, who's now CEO of GitHub. And I think it's really correct advice. And what he said is basically, you want to pick your biggest believers. And so usually, not always, but usually your sort of first term sheet is usually your biggest believer. And I think that may or may not always be the case, but I do think when you're picking investors, sure, you want to optimize for the brand and you want to optimize for like the ability for them to help your business. But probably the number one indicator of that is just like, how big a believer are they in you and what you're doing? Because if you think about running your company over time, there's going to be ups and downs for sure. And if you have great investors who really believe in the mission or in it for the long run,

• 22:34Speaker 02

  then you're going to be much more aligned in terms of just getting through the hardship and continuing to just go towards the mission and with sort of a long-term view. And that's something that really impacts the trajectory of the business. I've seen founders where their investors might be less long-term oriented, or when the going gets tough, they might withdraw a little bit from the business. And I think that's always really challenging, especially for the company and for the founders. So I think it's really important, the core thing is like, you do want people who are sort of long-term believers in the mission and not just kind of me tooing into your round.

• 23:04Harry Stebbings?

  And speaking of that interaction there between the founder and the investor, despite the times in terms of the business cycle, I do want to ask one final thing. For you as the CEO, this is kind of your first time in terms of the institutional board that you now manage. What are some of your biggest lessons when it comes to board management?

• 23:20Speaker 02

  Yeah, I think there's a couple of things. I mean, I think the first thing that has been probably the first and foremost thing, and I don't know if this works for everyone, but the thing that's gone the furthest for me is just really building close personal relationships with the members of my board. And that's probably been like the number one thing that I think has helped me. Because I think it means that with this relationship with the other person, first of all, you'll get the direct feedback, which is really, really crucial. Like, it's really crucial that your board is able to just give you direct feedback and not be worried about how you take it. But I think it also means that like, you're sort of already gearing up for like the decades-long battle that you're, that the company is going to be. And I think it's, it'll make you feel better.

• 23:59Speaker 02

  If you're in it with people that you really enjoy. That's maybe the number one thing. I do think the the other stuff around board management is you really want to clue your board members into more information, give them more access to your team and give them as much visibility as possible. Because I think what that ultimately will lead to is just them being more effective at helping you. And I think there's a lot of like inks that happens from like, oh, what did I tell my board? Would I not tell my board? And I think more often than not, like the better outcome is just to be open to them.

• 24:28Harry Stebbings?

  No, I totally agree with that transparency and that being at the core of the trusting relationship. So agreed on that. But I do want to move into my favorite element, Alex, of any episode, being the quickfire round. So I say a short statement and then you hit me with your immediate thoughts. Are you ready to roll?

• 24:41Speaker 02

  I am ready.

• 24:42Harry Stebbings?

  Okay, so your favorite book and why? What must I be reading?

• 24:46Speaker 02

  I really love this book, Seven Powers by Hamilton Helmer, but it's probably one of the best business books ever. And the reason is that he sort of, it goes through what he called these seven powers, which are these seven qualities of businesses that allow them to have differentiated returns over long periods of time. And it's one of the most quantitative, game theoretical, analytical views into the inner workings of businesses. And it's it's just so refreshing versus, I think, a lot of the a lot of the other business texts out there.

• 25:15Harry Stebbings?

  Listen, I'm going on holiday next week. I'm going to buy it for that. The best business book ever. That's quite an endorsement, Alex. Tell me, what would you most like to change about Silicon Valley and tech today? You're at the epicenter. What would you change?

• 25:26Speaker 02

  I think the main thing is I would like to change, I think particularly for early stage startups, there's a lot of focus around press and getting your name out there and being a sort of like hyped person. And I think the most important thing is to just build a good business, actually. And being hyped very rarely ends up helping you build a great business. And so I think the thing that I'd most like to change is sort of this, I think in the press and on TechCrunch, et cetera, there's like a focus around, there's a certain kind of focus, which is in many ways anti-counterproductive to building a really successful business, actually. So I think a sort of like a return to fundamentals would be great.

• 26:01Harry Stebbings?

  I agree. These damn podcasts are terrible, aren't they? Tell me, Alex, what's your biggest weakness and what are you doing to improve it?

• 26:09Speaker 02

  Yeah, I think the thing that I'm most wanting to improve on now is sort of this transition from being a doer to a leader. And I think this is, at least for my conversations with founders, relatively common at sort of this stage in life cycle. But I think so far in the company, we've been able to get pretty far. I've been able to do a lot as an IC and do a lot to personally contribute. And I think now it's really important that I transition away from that and focus on being more of a leader for the company.

• 26:36Harry Stebbings?

  When I say success, Alex, who's the first person that comes to your mind and why?

• 26:40Speaker 02

  This is a very cliched answer, but Steve Jobs, I think, accomplished some of the most incredible feats that anybody in the world's accomplished, honestly. And so if I think about the lasting impact on the world that Steve Jobs has had, through his work in technology and business, that's really the gold standard.

• 26:57Harry Stebbings?

  The biggest takeaway for you from the YC experience, Alex?

• 27:00Speaker 02

  I think the big thing is that there's so many business opportunities out there, and there's so many hungry, smart, talented people going after those businesses. I think it's really, it's maybe one of the few places where you can go and you can sort of like really soak in how much opportunity there really is out there. It's really incredible.

• 27:20Harry Stebbings?

  And then I want to finish, Alex, on probably the most exciting of all. What are the next five years for you and for Scale hold? What's that roadmap ahead?

• 27:27Speaker 02

  Yeah, I think what we're really excited about is going beyond what we've done so far in terms of building this sort of data platform for AI and build out more and more infrastructure for AI in general. And so I think as we look at the next five years, we want to build out all of the pieces of infrastructure, all of the tools, all of the platforms that would allow more and more organizations to be successful, effective and productive with machine learning, so that we can actually kind of realize all this hype that we talked about at the beginning of the show. And so that's what we're really excited about. And we want to help existing tech businesses that are doing a lot of machine learning. We want to help enterprises that are just sort of starting to get their feet wet with machine learning. We want to help smaller

• 28:09Speaker 02

  organizations, like we want to help every single business that can apply AI into their business process or into their business for positive productivity. We want to help them do that.

• 28:18Harry Stebbings?

  Alex, listeners, it's been such a pleasure to have you on the show today. As I said, I'm so excited for the times ahead with Scale. Massive congrats on the round once again. And thank you so much for putting up with my dulcet British tones one more time.

• 28:28Speaker 02

  Thank you so much for having me. You do a great job with the show. I'm really, I'm really glad to be on it for a second time.

• 28:35Harry Stebbings?

  Such a wonderful guest to have on the show and I couldn't be more excited about the times ahead with Scale. If you'd like to see more from us behind the scenes, you can do so on Instagram at HStebbings1996 with two Bs. It'd be great to see you there. But before we leave you today, I want to give a big shout out to my friends over at ActiveCampaign. With their platform, you can create unique customer experiences that attract, nurture, and convert leads into customers, into repeat customers. Over 80,000 companies using ActiveCampaign hit their sales targets with intelligent automations that send the right message to the right person using the right method at the right time. If you already have sales and marketing tools you love, keep them. ActiveCampaign will work with them and give you automations to make them even better. ActiveCampaign is the first customer experience automation platform, helping you provide personalized and optimized customer experiences.

• 29:20Harry Stebbings?

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• 30:04Harry Stebbings?

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• 30:48Harry Stebbings?

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