Chris Selland works at Squark.AI, a startup company that uses AI and machine learning with customer data from things such as games and delivery services to help with predictions of delivery time.
Highlights:
Sal Daher Introduces Chris Selland
Squark AI and What It's Solving
"... All of those types of predictions is essentially what we do with the model. Now, we do have some other customers using us for other purposes. For instance, a very large package delivery firm is using us, actually, for prediction of packages that might be late..."
"... This, I think, highlights really the importance of networking and staying in touch with people that you've worked with successfully. Make sure that you stay in touch with them because you will have that twice annual conversation that you might have with someone that you worked two companies back with..."
Squark in the Gaming Industry
The Future of Squark
Advice to the Audience
ANGEL INVEST BOSTON IS SPONSORED BY:
Transcript of “Squark.AI”
Guest: Chris Selland
Sal Daher: I'm really proud to say that the Angel Invest Boston Podcast is sponsored by Purdue University entrepreneurship and Peter Fasse, Patent Attorney at Fish & Richardson. Purdue is exceptional in its support of its faculty of its top five engineering school in helping them get their technology from the lab, out to the market, out to industry, out to the clinic. Peter Fasse is also a great support to entrepreneurs. He is a patent attorney specializing in microfluidics and has been tremendously helpful to some of the startups, which I'm involved, including a startup, came out of Purdue Savran Technologies. I'm proud to have these two sponsors for my podcast.
Sal Daher Introduces Chris Selland
Welcome to Angel Invest Boston, conversations with Boston's most interesting founders and angels. I am Sal Daher, an Angel Investor who is just very curious about learning more on how to build technology companies, life science companies, and today we're going to be talking to Chris Selland. Say hi, Chris.
Chris Selland: Hi there. Thank you for inviting me back Sal.
Sal Daher: Awesome. Chris is an alum. You can look up Chris at DipJar: Frictionless Giving and as a really interesting company in which I invested and we had an exit and Chris is somebody who is very familiar with this whole software world, building software startups as a CEO. He is now onto another venture called Squark AI and I want you to tell us what problem you're solving and explain how the company came about.
Squark AI and What It's Solving
Chris Selland: Sure. Why don't I slightly reverse the order of those? First of all, I'm not sure I'm still on the DipJar site since I did leave DipJar at the end of last year. Although I am still an advisor to the new CEO, but I'm no longer on dipjar.com, I don't think.
Sal Daher: Not dipjar.com. DipJar: Frictionless Giving on our website. You are on the podcast.
Chris Selland: Absolutely.
Sal Daher: Eternal.
Chris Selland: DipJar was a great experience. I was there a little over four years. Thank you for your support as an investor. It was great working with you and the Walnut team and some of the other investors. There was a lot that happened there, but it was a great four years but I did wind up leaving at the end of last year, but a little earlier last year, I actually met, to your other question, the two co-founders of Squark. A guy named Dan Hess and another guy named Judah Phillips and they had started squark a few years ago.
Squark was fully incorporated in early 2021. There was also a predecessor company prior to that called VisiData. These guys had been basically building solutions for customers and to your question about the problem, the problem was really two things. It was, first of all, just how do I use some of this new AI/ML and for those who don't know, I think most people on the tech podcast would know that stands for artificial intelligence and machine learning.
Sal Daher: When we say machine learning, basically we mean machines looking at unstructured data and drawing inferences from that, just learning. You program the machines with data, you don't program it with code. Please continue.
Chris Selland: It's less programming and more training. In other words, you build models and you train the models with data and then the models produce output and predictions, and forecasts. That's essentially how AI/ML works and that's what machine learning really is. It's model development, model training. Model development is the programming part. Model training is when you feed it data and then you basically produce output and so, exactly. Dan and Judah had started this company early '21, again, there was a predecessor business as well and had actually built out a first-generation platform.
We have a V1 platform live and it's a pretty good V1. It's basically a SaaS application. We call it AI as a service, which has essentially four different model types we support and they're pretty common model types in the tabular data space. Squark is more around tabular data. What I mean by tabular data, there's different kinds of AI and ML models. Tabular data is basically like a customer database or some other structure.
Actually, it's not necessarily unstructured, although there's some code in there for things like large language models and doing things like sentiment analysis on text but a lot of it's just also around Sal's a customer, he has this much in his account. He's been with us this long, he spends as much every year, and multiply that by all of our customers, that kind of data.
Sal Daher: Cool. What's the term for somewhat structured data, tabular? You said tabular data?
Chris Selland: Usually, it gets referred to as tabular data. It's not necessarily unstructured data.
Sal Daher: Chris, before we get too far along in this, let me just get the names for the transcriber. Chris Selland, that's S-E-L-L-A-N-D, and the two founders are Judah, Judah with an H at the end.
Chris Selland: Phillips, P-H-I-L-L-I-P-S.
Sal Daher: Dan Hess, H-E-S-S.
Chris Selland: Correct.
Sal Daher: Please continue. You've got tabular data. You're doing AI as a service, meaning you're trying to make it so that people who have databases can bring in a bit of machine learning to pick up trends and to learn more about the databases that they have, that they already own.
"... All of those types of predictions is essentially what we do with the model. Now, we do have some other customers using us for other purposes. For instance, a very large package delivery firm is using us, actually, for prediction of packages that might be late..."
Chris Selland: Let me give you an example of the type of thing that we do. We refer to it as customer data modeling. Almost all, and I say almost all of our customers today are doing some form of customer data modeling. It essentially means providing access to their customer data, which is usually in some database or databases or other available place. We connect to all sorts of different data sources; Amazon S3 buckets, and Snowflake. Actually, more than half of our customers are on Snowflake right now, Google Sheets, Excel. We can really import and ingest data from just about any source.
I've got all of this data about what my customers have done and I want to build models for what I think and our models think the customers will do. Will they be more loyal? Will they purchase more? Will they be more satisfied? If they are more satisfied, how will that affect their future purchases? Will they churn? All of those types of predictions is essentially what we do with the model. Now, we do have some other customers using us for other purposes. For instance, a very large package delivery firm is using us, actually, for prediction of packages that might be late.
In this case, it would be packages that are what they call high-value packages, high-risk packages, for instance, frozen food. If you're shipping a shipment of frozen food, you want to make sure that it's not going to be late because if it's late, it arrives thawed, it's ruined. That's a problem. There's a lot of other use cases for this, but the majority of our customers today are using us for customer data modeling.
At our stage of the company, that's really what we're focused on. It's a very common use case. Marketing wants to know who's going to respond to a campaign, what campaign should I run that are going to get the biggest response? Customer success wants to know who is or isn't satisfied and how that's going to affect their ability to renew, to upsell, to do more business with us, to recommend us. There's a lot of use cases around customer data, so that's really where we're focused.
Sal Daher: Excellent. You met the founders before the sale of DipJar. What was the thing that drove them to hire a full-time professional CEO?
Chris Selland: It was a combination of things. First of all, one of the things that happened with DipJar in 2022 was the ownership of the company. We sold the company, as you alluded to earlier, in 2020. The ownership was not in the Boston area. They were in a number of cities, but the lead investors were in Pittsburgh. Early in 2022, they asked me to move the company to Pittsburgh, and I did. They asked me to move as well and I said I would consider it. My wife and I talked it over, and I actually had an apartment in Pittsburgh. Lovely city.
Spent a lot of time there, but decided I wanted to stay in the Boston ecosystem. I had this in the back of my mind anyway, and again, had a great four years with the company, but I just decided really wanted to-- not just me, but my wife and I wanted to stay in Boston. Also, having spent so much time, and this gets into what attracted me in the data and analytics space, as I think we talked about last time we talked, I was five years at Vertica Systems. Earlier in my career, I spent a lot of time in the customer interaction CRM space with a company called SoundBite that was a company that IPO'd back in I think late '07 that was very much around customer interaction.
I was also with a company for two years called Unify Software in the data space, so I had a lot of background in data, analytics, customer analytics. Initially, I met Dan and then I met Judah, and I saw and heard what they were doing. I was also very impressed that they on a relatively modest amount of capital had actually launched a V1 and had paying customers, had a few hundred thousand dollars in ARR, and seemed to have happy customers using the product every day. I said, "Wow, this is not just a raw startup, it's certainly not pre-revenue."
We've got revenue, we've got customers, we've got a product which by the way is really where I excel, because my background is really on the scaling, the go-to-market side of the business, so I get really excited about this. Initially I agreed to be an advisor, because I hadn't really formally flipped the switch and decided I was going to move on, but as the year went on and as we got to talking I decided to come on board full-time. The other thing of course, maybe it's implicit from the podcast that we're on, but they were looking to raise money. They did have some angel money already raised from a number of angel investors--
Sal Daher: Including TBD Angels.
Chris Selland: Including TBD Angels.
Sal Daher: I was just coming into TBD Angels when they had finished their round at TBD. Missed them by days. I was investing directly with Ryan Hess in Connective Health, Dan Hess's brother. Digital health is something that I focus on. AI which is just software, I have less of a competitive advantage. There are a lot of smart people here in Boston who can do software. That's why I would focus more on something like Connective Health which by the way is doing really well. Basically, they raise funds with Angel groups, and now what proof do they have that would support getting VC money?
Chris Selland: The term product market fit gets thrown around a lot, but I think it's fair to say that with significant ARR, significant base of customers using the product I think there is some degree of product market fit. What we've been in the midst of doing and are still very much in the midst of doing is putting together a seed round, and that's in process as we speak, but that was essentially my entry point. We have had some initial support from StageOne Ventures, if you know StageOne. We're pushing forward with them. Also TBD has been very supportive.
There's a few other angel investors involved as well. From TWO39, Huntington Avenue Ventures. I'm probably forgetting some, but we've got the existing investor supporting, and then StageOne has come to the table and helped us catalyze the seed round. Again, it's in process as we speak, but things are moving along nicely there. I had brought the relationship with StageOne, and that was part of the catalyst for me coming on board as CEO which happened formally actually just last week, so I formally became CEO in January as a part of the initiation of the seed round.
Sal Daher: Excellent. Congratulations.
Chris Selland: Thank you.
Sal Daher: We're recording this on the first day of February, so you've been CEO for a month.
Chris Selland: I've been super involved with the company now for a few months, but I stepped aside at DipJar fully in early December, and since early December I've effectively been working fulltime on this. There's just a lot of little details involved. By the way, I should also mention had a few other people decide to join me in this adventure-
Sal Daher: Outstanding.
Chris Selland: -really excited. First of all, we're getting Mark Lyons who worked with me at Vertica for five years. We've worked together before, was absolutely integral to our success over there as a business, was part of the product team and eventually ran the product team, and he's coming to lead product. Not unlike me with Unify, he had gone to a California company called Klaviyo that actually is or was a unicorn big data, a bit of a Snowflake competitor, and so we're really excited to get this guy Mark, who again I've worked with. He's not just this guy to me, he's a good friend and trusted colleague. He's leading product.
Then a gentleman named Tim Steele, who I actually have worked with twice as well. I mentioned Soundbite earlier, they went public in '07. Tim and I worked together there, and then we actually worked together at a company called Lumigent in the data space a few years back as well. That we got an exit, we sold to BeyondTrust. Tim and I had worked together a few times before. He's a super dynamic sales leader.
He was ironically at a company called Ironside which was actually a company that Dan Hess helped co-found in the early days doing data analytics services, but he was really excited to come on board with the software company. Anyway, we've brought on a really strong head of products and a really strong head of sales as well. They're in the company with me. We've got the two founders and the three of us who are new to the company and we're building a team around that.
Sal Daher: You're reuniting the band?
Chris Selland: [laughs] It's two different bands. We're actually three different bands in some respects, but yes, I will say, and I think I said this on the last call, the Vertica alumni in particular, that is a extremely strong group. By the way, maybe a little more to come on that front, so working on it as we speak. It's funny, because Vertica was acquired by HP in 2012 and then became part of Micro Focus, and actually, just became part of OpenText. Really, really strong, well-engineered, high-performance, big database, data platform. Just a great source of other companies.
People like Andy Palmer, Chris Lynch, some of the real notable investors and drivers of the Boston Tech Economy, Colin Mahoney, of course, who's now at AWS, who brought me into Vertica, and brought Mark in as well. Just a fantastic team of people there and a terrific alumni group that I think is a underappreciated asset for Boston Tech companies. Like I said, Tim didn't work at Vertica, he worked with me at two other companies.
"... This, I think, highlights really the importance of networking and staying in touch with people that you've worked with successfully. Make sure that you stay in touch with them because you will have that twice annual conversation that you might have with someone that you worked two companies back with..."
Sal Daher: Given the dynamic nature of technology companies and the company cycle, the development cycle of the companies, and from when they're founded to when they merge or are acquired and so forth, software, everything happens at double speed, at triple speed. You end up working with a lot of different people. When you've been in the industry for a few years, a dozen years or so, you have a group of people that you know and that you know where they stand and what they're good at, what they're not good at. I see this in other areas as well, surprisingly, even in the life sciences.
I see this, one of my sons-in-law, a lot of the moves that he makes have to do with people that he connected with in a previous company and another company and so forth. This, I think, highlights really the importance of networking and staying in touch with people that you've worked with successfully. Make sure that you stay in touch with them because you will have that twice annual conversation that you might have with someone that you worked two companies back with. You're going to learn a lot.
It's really surprising. There's a tendency for people to just have their nose in the grindstone and just be thinking about what's in front of them at the moment, but maintaining those connections is really important. What is the competitive advantage for Squark? How is it gaining customers? This is a space where there are a lot of people trying to do some variation of this. What is it that helps Squark be recognized by HubSpot, and DHL and companies like that?
Chris Selland: To me, the secret sauce is really around accessibility. What I mean by that is, making this available to and usable by sometimes they say mere mortals, but by business people, business analysts, data analysts.
Sal Daher: Your hook is user experience, UX.
Chris Selland: It is, but it's also because, to your point earlier, there are a lot of AI startups. Even in this interesting funding environment, there are a lot of AI companies. If you look at what's going on in the media with all the excitement around ChatGPT and DALL·E, OpenAI and a whole of the other things going on, there's a lot of interest, there's a lot of companies getting funded. Data science has traditionally been, and I would say even up until now, has been really the domain of the data scientists who tend to be very technically oriented people that are hard to hire, relatively expensive because good data scientists are very rare, they're worth a lot of money, and they should be.
They're hard to get, and what happens in a lot of organizations is that the data science resources are just stretched so thin, or in some cases, they can't hire data scientists at all. We were, for instance, just the other day, talking to a large entity in the convenience store space. A good data scientist working for basically a convenience store company, it's very, very hard to recruit.
It's funny, when you're in a vibrant tech community like Boston, you tend to think about selling to other software companies and financial services companies and mutual funds and these industries where they have the ability to recruit good, strong data scientists, but there's a lot of other industries out there that are really struggling. Even the ones that can recruit them, they're hard to get, they're hard to leverage.
Sal Daher: Oh, you should see it in the life sciences. There's so much to be done with biostatisticians and data scientists in the life sciences. That is, one of my sons-in-laws in that area, the guy is in tremendously high demand and he is working all out. The value of being able to interpret data is so high that the person who can do that effectively is going to be incredibly in demand.
Chris Selland: Exactly.
Sal Daher: He's going to be working brutal hours, [laughs] sad to say.
Chris Selland: There's a lot of demand and limited supply. Exactly. Different industries have different capabilities of recruit people, but to that exact point you made in a life science company for instance if it was say a pharmaceutical company, you probably want your very well trained, highly knowledgeable, expensive data science people working on, like drug discovery models as opposed to supporting the marketing team who's trying to figure out what offer to send out next week.
Sal Daher: [laughs] Value from being able to analyze the data.
Chris Selland: Exactly.
Sal Daher: By the way, a data scientist would of course, they're not building new models and so forth, but with machine learning, you can get a lot of insights without tremendous commitment in terms of human input.
Chris Selland: We actually get the ability they can, and again, we need to be at least a data literate marketer but most are these days, certainly as we know, but a data literate marketer could build a model, and train a model, and test a model, and refine a model in our product. Again, that's the idea.
Sal Daher: You're giving someone who has a working knowledge much deeper knowledge, like an exoskeleton. You're making them like 10x more powerful than their experience would warrant.
Chris Selland: [laughs] We'll have to do a marketing campaign with that and test it out.
Sal Daher: Exoskeleton. Exactly. Your ML's going to say, "Yes, it works." It works at about 2% of the algorithm. Who know what an exoskeleton is. [laughs]
Chris Selland: Exactly. The interesting thing about it is, if I step back to my Vertica days again, because those are really pretty formative years.
Sal Daher: Can you just give a brief description of Vertica and what it is that they do that sets them apart?
Chris Selland: Very high performance analytic database. Basically crunch numbers very fast, produce output very quickly, high quality, so Vertica is like a Ferrari or Lamborghini database.
Sal Daher: What was the killer use cases for Vertica?
Squark in the Gaming Industry
Chris Selland: I have a lot of data and I need to analyze it very quickly, was the killer use case. That would vary by industry, but for instance, one of the industries we did a lot within Vertica and we're doing a lot with now at Squark is gaming. For instance players in a game, whether it might be, and by the way, we're working both with online gaming, like Epic games is a customer of ours, for instance, but we also work in traditional gaming.
We're actually in negotiation right now with a big casino provider, and it's interesting too because this casino provider that we're working with, we're negotiating the deal as we speak. Their casinos are not in Vegas and Atlantic City. They're in places like Missouri and Mississippi, where again, the ability to recruit data scientists is somewhat more limited than it would be in other areas. Right?
Sal Daher: Right. Yet they could benefit tremendous, talk about an area where you really need to look at data in a large context. I imagine casinos, they want to know for sure what particular type of game. If a dealer is a little bit off, if a roulette wheel is off--
Chris Selland: Who's loyal, who's profitable? It's funny you say that. I was actually discussing the other day, not with actually this pending customer, but with an investor who specializes in gaming. It was a relatively young associate at the venture firm, and I said something about Gary Loveman. and I don't know if the name Gary Loveman, talk about having a few years under your belt. Again, back in my Vertica days, he was the founder of so much of what we call data science as used for business, but he started at Caesars, I believe.
It was one of the casino companies. He actually was an academic, he came from academia. He moved from, I forget what university he was teaching at, but then he went to work for Caesars because gaming is an extremely data intensive business, but back to I guess where we've gone off on a bit of a thread here, but gaming is a great example. Let's say I want to actually produce an offer in the game, for instance. You need to crunch a lot of that data very quickly.
Sal Daher: You can turn around and say, "Make this offer to the player. This could be interesting."
Chris Selland: It'll keep them playing. Gamers don't really churn. They just stop playing or they go play another game, or they walk across the street to the other casino or delete the one game from their phone they don't like anymore and download another, so--
Sal Daher: Predicting that moment when the player might be at the point of switching away, making an offer, making the game a little bit more interesting, taking it in a different direction, predicting that can be very useful to keep the player engaged.
Chris Selland: 100%.
Sal Daher: That is so cool. The movie about the MIT's Blackjack Team. Semyon Dukach has been on the podcast. He was on the Blackjack Team. What casinos used to do with videos and looking for card counters and people like that, and the MIT Blackjack Team. They were doing that professionally to make money.
Chris Selland: Absolutely.
Sal Daher: The casinos were not happy about having them. There was a lot of gamesmanship at different levels involved with that.
Chris Selland: Do you remember UNICA? I don't know if you remember a Boston company called the UNICA. They were eventually acquired by IBM, but they were very successful Boston startup marketing knowledge base, they actually used Vertica inside of their platform, but it was a marketing analytics app or set of apps but one of the founders of UNICA, Yuchun Lee was part of that team. That was-- we're going back a few years, but UNICA was a big, big local success story as well.
Sal Daher: Good. We touched on the unique value proposition that Squark offers its customers and how it's gaining traction with aggressive clients, such as the DHL. I'm at the website right now in HubSpot. When you said a customer that has a lot of packages to track I imagine DHL does. Are you at Liberty to discuss what Squark is going to look like in five years or two years into the future?
The Future of Squark
Chris Selland: Oh, sure. the short answer is big and successful but, or maybe successful and big because I think success drives size. I should probably reprioritize those things but we want to be the dominant provider in particularly of serving the business community, in helping them leverage, predictive AI machine learning, for making decisions about the future, because that is one difference, by the way, we just touched on. Again, I don't want to go too far back but just to rewind to what we were talking about a few minutes ago, the difference between the--
Vertica was about crunching the data about what happened, including up to what just happened. Squark is more about looking forward. That's really the difference between what used to be called BI or business intelligence, and AI and artificial intelligence because BI is about understanding what happened and AI is about modeling what's going to happen. To me, the market opportunity for AI for business people is wide open Greenfield because it has been the domain of the data scientists, the highly technical person. Again, all of the attention being paid to OpenAI, ChatGPT and DALL.E, what's called generative AI is really, really helpful.
Not because it's what we do per se, because what we do is a little bit different from that, but that it's really getting people excited about the possibilities. I'm in a bunch of local groups, but I'm in a local group of current former heads of marketing, because I used to be a CMO, and bunch of them did a Zoom the other day, just a couple days ago, on content marketing. Obviously, ChatGPT, OpenAI, because as you may know, you probably do if you've been paying attention, you can just say, "Write me a blog post about funding an AI company or write a blog post around why AI is important." Just type that one sentence into ChatGPT and you've got a blog post.
What does that mean for content marketing? For a head of marketing, and certainly, for the organization, it means potentially, a lot less spent on human writers, if I can do that now, as has been already said, and you're starting to see more and more of this, AI is not perfect, it often makes mistakes. I was just reading something the other day about, there was-- I think it was CNET had started a site and they started giving financial information that was written by AI and they started talking about how interest works and things like that and it was just completely wrong, but you're talking about model training.
Sal Daher: Context is really difficult to get if you're talking about something really concrete, like, "Find me the best restaurants that have escargot on the menu." Easy, but comparing different things that have to do with context and so forth, like interest rates. Finance is very complicated. And the terminology is not perfect. It's going to be a while still. There's going to be a lot of hybrid models if people are going to be using these generative AI to enhance their abilities. I see this for example, in a company in which I'm an investor called FineTune Learning.
What they're doing is they're really helping people who are writing tests and creating tests and so forth to do it more efficiently with machine learning, but you can't just say, "Create a test about this subject of that subject." Because it's not going to work. You need an expert to be using that, and what the AI does is it does a lot of the mundane things around that, but then you have to have a little bit of quality control, and you have to understand the context of things. In certain areas, it's acting like the exoskeleton we're talking about, 10x thing people's powers, the power of humans. I don't think copywriters are going to be out of business anytime soon--
Chris Selland: To that point, it's not extremely difficult to train a model to create a structured sentence, but training the model to model how compound interest works, or how GDP fluctuates with interest rate in the Fed, you can get in real trouble there.
Sal Daher: If you follow any company that's publicly traded. There are lots of bot-generated content out there, and you start reading it. Then you say, it picks up things like it's moved 12% in the last this and that. That's easy stuff. Then it says things about, but that's not typical for a company. It doesn't know that the company doesn't actually have revenue. It's kind of like, there are different types of companies.
There are companies that have revenue, and you're investing based on a prediction of future revenues, and so forth. Then there are companies that have products that try to get through the FDA, and they have no revenue, or very little revenue or their profitability is completely weird. The existing models can't pick it up. You read a little bit and you start chuckling with the article side, this is obviously not written by human being.
Chris Selland: We're just getting started, but it doesn't invalidate the point that people are interested, who aren't necessarily data scientists, but how can they use this to help me? Not to replace me, but to help me.
Sal Daher: There's so much to be done on that, sort of predicting customer behavior and things like that. The opportunities are just massive, and we're just beginning to scratch the surface. Great. We've covered what's going on with Squark. At this point, I think perhaps what I could do is I could do a little brief promo for the podcast, then after this, let's talk about stuff that might be of interest to you that we haven't gotten across so far that you want to get across to our listeners who are founders and Angel investors, and people who work at Startups.
Chris Selland: Sounds great.
Sal Daher: If you're finding this conversation with Chris Selland of Squark AI, CEO of Squark AI, you can get more people to listen to this by the simple expedient of going to whatever platform you listen to, for example, if you're listening on Apple podcasts, go leave a review, leave a rating, we'd like five-star ratings. Funny thing, we're talking about how the algorithms are not very subtle, you can say some disparaging things in writing. As long as you leave a five-star review, the algorithm is clueless, doesn't really pick it up. Please, no obscenities, but you can be quite frank in your written review.
The fact that it's a written review, the algorithm kind of likes that. If it's five stars, it's hunky-dory. Such as the state of these algorithms, it may change, but for now, they're still not that clever. Give us some help getting more people to find out about the conversations that we have here because the goal of this podcast is to bring out lessons for founders and investors to help them build technology companies better.
These things come out of conversations, and I learned tons with it. Let me tell you, I have learned so much in the first six seasons, so beginning our seventh season, I've learned so much during this time because of conversations, because of course I get to ask questions and elucidate things, but just listening. I learned tons of stuff from podcasts. I listen to podcasts all the time. Are you a podcast listener, Chris?
Chris Selland: I am.
Sal Daher: What podcast do you listen to besides Angel Invest Boston?
Chris Selland: I listened to some venture tech podcasts, mainly 20-minute VC and Jason Calacanis' podcast, I think it's the online podcast. I listen to those.
Sal Daher: Jason is my muse.
Chris Selland: Is he?
Sal Daher: He's been on the podcast, his example of his talking to people, and how much I learned from him in this podcast. As an Angel investor, listening to the stuff that he says, and I will be like, "Yes, he's saying stuff that makes perfect sense, and I've seen this as an Angel investor." So I said, "Jeez. What if I could do what he's doing, and direct it to the stuff that's of interest to me," because he's in Silicon Valley.
He's in an adjacent world. I'm here in Boston and my focus is companies that exist in Boston. My particular interest is life science companies, but there's so much software stuff that's going on here such as SCORE, KI and so forth. We would talk about that. I said this is a great learning vehicle. He inspired me to start the podcast and I'm very happy that he did, and he was kind enough to be on and really helped us in terms of recognition.
Chris Selland: That's great. I would say there's actually a bit of a dearth of Boston Tech podcast. There's yours, of course. I do listen to VentureFizz fairly frequently as well. Oftentimes I know the different founders on the VentureFizz podcast, so I listen to those. I also do listen to some political podcasts and some sports podcasts. We don't need to really get into those things. It's usually a driving thing. I enjoy podcasts in the car, and I actually wish there was a better podcast reader/tool/queue. I get frustrated because there's always so many I want to listen to, and keeping track of the ones I want to listen to is not the easiest. I really haven't found the ideal tool for doing that yet.
Sal Daher: It's funny that you say that, by the way, I've been on the VentureFizz podcast. I've had Keith Klein on the podcast, and I've been on Keith's podcast. It's interesting, VentureFizz tends to be later-stage ventures that are already funded because Keith, he's a recruiter and he's interested in people who can actually hire people. I'm interested in companies that are trying to raise money at an earlier stage. We're not competitive in any way. Keith is a tremendous guy, or I think he's very, very capable, very smart, and I've learned a lot from him.
It was very nice that he was on the podcast, and I've been on his podcast as well. Anyway, it's funny that you mention, there is a person whose name will go on mention. He is one of the Super Angels in Boston. This is a guy who has founded since the '70s, dozens of technology companies. I interviewed them for this very podcast. It's the only place you're going to find this guy interviewed online. He's got plenty of money. He doesn't need money. He's not looking for notoriety. It was like four years after I recorded his podcast, he says to me, "Hey, Sal. I listened to the podcast."
"What do you mean you listened to the podcast?" "Yes, I listened to it. I just bought a new car that had a podcasting button." The interface on the car made it really easy for people to pull up podcasts. The rest of the technology this guy had, and this guy is a guy that is a genius. This guy's an absolute math genius, he's founded a bunch of technology companies, but the interface for the podcast wasn't available in the devices that he was involved with.
There's so much that could be improved in podcasting. I wonder if we just couldn't go back to something that looks like a radio, remember radios? The little knob. Then you can go through and then it can say, "Yes, you can listen to this, you can listen to that." Then the radio walks you. An actual device that is large enough for people a certain age to see the letters, they'll see the screen, but it's shrink-wrapped, single purpose. This is back to DipJar. Kind of like DipJar for podcasting.
Chris Selland: Simplify it.
Sal Daher: Optimized, simplified device.
Chris Selland: It's that too. It's also that there's so much content out there these days that even for podcasts I like, you just can't listen to everything. You just don't have the time. That's the hard thing is flipping around and saying, "I want to listen to episode 3 and episode 8 and episode 21."
Sal Daher: Follow the podcast so that you have that thing, that choice because otherwise, you'll never find it. It's like a needle in the haystack.
Chris Selland: 100%. Then when you're trying to find it when you're driving, organizing, it can be challenging. That's where I wish there were better tools out there. Anyway, but modern problems, as they say.
Sal Daher: I wish there were a podcast listening app service that you subscribe to, and they make money from giving you a really, really good podcast experience. You spend a month training it, here's an idea for someone out there. You say, give me a month training it. Then after that month, your subscription kicks in and we're going to show you the podcasts based on the 30 days of listening that we have that will interest you.
You don't have to search, it's going to pop up. We know you're interested in this, you're interested in that, reinforcement, the networks and we're going to pull stuff and you're going to be amazed. When it misses, it'll learn something. You don't listen all the way through and so forth. It's got to be somebody that'll do that. That's what AI is--
Chris Selland: Use case for AI, another use case.
Sal Daher: It's the user interface that we need to create, one that's better, Chris. We're thinking about wrapping-up the podcast now, is there a point that you want to get across to our audience?
Advice to the Audience
Chris Selland: I guess just going to every day right now and maybe it's fresh in my mind. Last night I went to an event that First Republic Bank put on downtown.
Sal Daher: I love First Republic Bank.
Chris Selland: First Republic Bank is great.
Sal Daher: Really capable. Good systems. They have excellent capable people.
Chris Selland: They did a great event last night. They had a few VCs talking about the tough environment we're in right now. It is a tough environment. It's a challenging environment to raise.
Sal Daher: Where was this event?
Chris Selland: It was downtown Boston. It was at a WeWork in the Back Bay.
Sal Daher: Oh, WeWork in the Back Bay.
Chris Selland: WeWork in the Back Bay. I think partly because that's fresh in my mind but also partly because I'm doing it every day. It's a challenging time to raise money, but I would say two things. One which first of all I feel very gratified that we actually have a product and a saleable product right now because it wouldn't necessarily be available to a pre-revenue company, but it's funny when I'm talking to investors these days, I tend to use this line. I just say if it really came down to it from a dollar from a customer, to dollar from investor, I strongly prefer the customer dollar.
The smart investors tend to chuckle at that right. It's a pretty obvious statement. I think it's easy to lose sight of that when you get so focused on raising money. It's focused on your business, focused on your customers because that's something you can control. The craziness of the venture environment is, the markets go up, markets go down. Right now they're a little bit subdued. A lot of LPs are sitting on their hands.
A lot of LPs and a lot of funds that we've been talking to are tech executives, and they see 10,000 layoffs at Google and 18,000 at Amazon. They start to say, "Wow, those are my LPs," and they're going to pull back on their commitments and so on and so forth. It's a challenging market focus business, I guess is the main thing. Focus on the customers as I focus on the business.
Sal Daher: Even in the normal times focusing on the customer dollar, focusing on building the business makes your fundraising easier. The more developed, the more mature your company is, the easier it is for you to get that investor dollar. With life science companies, with software companies, developing, moving the maturity of the product, moving the maturity of the technology forward builds the company, makes the company more fundable and that is job one. A very big danger for founders is getting caught up in a raise that's too big and that distracts them from building the product, building the technology which is ultimately what is going to pay off for the investor.
Which is also going to make getting investors easier. Michael Mark, my mentor in angel investing in so many ways, he's always emphasizing that. Don't be too ambitious on your raise. If people throw money your way, take it, don't turn your nose up at money, but on the other hand if a raise isn't going fast, try to see what you can accomplish with the resources you have so far because you can come back and then the raise will be easier if you move to the next level in terms of proving out the viability of your company.
Chris Selland: I think right now there's a little bit of a capital backlash because of so much capital flowing in '20 and early '21 and even into early last year, but people were talking about this last night too. People get valuation expectations based on the last couple years. They're not realistic anymore but just focus on the business, focus on customers, grow the business, like you said, do what you can with what you've got.
Increase your runway as much as you can and I do think we're going to look back and this is the last point I wanted to make on this time right now as frustrating and challenging as it can be from some perspective just because everything that's going on. Again I think all layoffs have been announced in the big tech companies are really exacerbating this short term but I think in the long run we're going to look at this as a great time to build a business. You got to focus on building the business.
Sal Daher: I think what's going to happen is that it's a time when a thousand flowers will bloom. I don't mean it in the ironic way that Mao Zedong meant with a thousand flowers bloom and then he threw them all in jail in front of a firing squad. I don't mean it in that ironic sense. A lot of these people who are being laid off at places like Google, I think it's because they're basically figuring out that with the AI that they have, that they can do a lot of coding with AI going forward and they don't need so many people to do the stuff. These very capable people are going to be freed up.
These are resources that'll be freed up. These people are entrepreneurial. Somebody who has been six, seven years at Google is no slouch, is no dummy. That person is going to figure out some angle, some area that it's just too small for Google to pay attention to. That could be a huge company still. I think we're going to see a lot of entrepreneurial talent coming out. I think it's going to be exciting times. The technology is very promising. These large companies are now freeing up a lot of human capital, a lot of entrepreneurial energy that is going to do amazing stuff.
Chris Selland: I 100% agree. You have to be a little scrappy and creative right now, but it's worth it.
Sal Daher: Remember the tech crisis back in 2000 when so many of these companies just evaporated? A few companies hunkered down like Amazon. Amazon was a bookseller. It was Barnes & Noble online, but they did their homework. They didn't get distracted. They minded their business and they developed into the giant that they are because of this focusing. Whatever they do, they have their process and all this stuff. It's very, very difficult to compete with them in particular areas. The amazing thing about AI is that the opportunities that the verticals are exploding. You're not going to be head-to-head with a lot of these players. Human ingenuity is going to be even more prized. I think it's very positive.
Chris Selland: Likewise.
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Sal Daher: Great. Chris Selland, CEO of Squark.Ai. A company that is bringing the power of machine learning on tabulated data to the hands of people who are on the front lines of the marketing and setting strategy for the companies. I thank you very much for making time to be on the Angel Invest Boston podcast.
Chris: Thank you for the invite, Sal. It's a pleasure. It's great to be back and it's always fun to work with you and keep doing the great work you're doing. The community really needs it and benefits from it. I'm honored to be on. Appreciate it.
Sal Daher: I love it. I learned so much and I learned from you as well. This has been a really instructive conversation for me. I thank our listeners for listening. This is Angel Invest Boston. I'm Sal Daher.
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I'm glad you were able to join us. Our engineer is Raul Rosa. Our theme was composed by John McCusick. Our graphic design is by Katharine Woodman-Maynard. Our host is coached by Grace Daher.