The Exchanges

Q What do you believe that most around you disbelieve?

A Okay, I mean, I'll talk about education. Many people believe education is an investment. I actually don't believe that. I think many people believe that education is an investment that makes you more productive. In a sense. And I think beyond some of the very basic stuff that you learn in primary school, I don't actually think that's, uh, that's the case. I actually view education as a consumption, as a luxury good. And what I mean by that is causality that people typically ascribe to societies getting wealthier and that, you know, education makes societies more wealthier is in fact reversed. In fact, what happens is as societies become wealthier, they start to be able to afford to give people four years, five years, eight years to develop and explore and just, you know, enjoy life. And learn about different ideas and fields. That's a luxury that people become able to afford after they become sufficiently wealthy. And so I think in a lot of ways, the government investment and, you know, pouring trillions of dollars into education is misplaced as a result because the effect they're trying to get is not the effect that they ultimately end up getting.

AI assessment note: “I actually view education as a consumption, as a luxury good.”

Q What's the biggest pro and then the biggest con? So 30 seconds each of being in Canada.

A The pros. So there's a lot of pros. One, it's cheaper. It's just, you know, you basically, you're 30% smarter because of the exchange rate being in Canada, and that actually has a significant impact on your ability to build a sustainable business, and that's helped us. Uh, and there's a lot of good individual contributor talent, you know, good engineering talent, good sales talent. The cons is there's Probably the number one thing is challenges with financing. There's not as much as robust a venture capital community. And biggest challenge I would say is lack of executive talent. There are very, very few people in Canada that have scaled businesses past a hundred million dollars. And as a result of that, it's challenging to build a business that scales to that degree because nobody else around you has, has done it before. And so we've had to, as a result, the top and import quite a bit of talent.

AI assessment note: “The pros. So there's a lot of pros. One, it's cheaper. [...] The cons is”

Q Can I ask Mike, was there an inflection point or a realization moment in your kind of learning curve of the scaling of the sales team for you?

A I don't think that there was any particular moment. I think it was just progressive kind of series of mistakes and learnings that happened over a period of years, but I can certainly talk about, you know, particular lessons, which to probably anybody that knows anything about sales, these will seem, you know, borderline cliche, but you know, when you're hiring sales reps, you need to match the sales cycle and cadence of That your business has to the experience of the rep. So if a rep is used to running, you know, twelve-month sales cycles and closing two deals a year, you shouldn't hire that person to try and run an inside sales transactional type process and vice versa. They're radically different things. You know, when you've got somebody who's a top rep and, you know, is clamoring for a promotion to management, more often than not, that's a bad idea. You need to figure out how to level them up and give them the opportunity for growth within the, you know, as an individual contributor. Because usually top reps don't make for good managers and vice versa. Good, great Sales managers are not necessarily the best sales reps. You know, we learned a lot about metrics and having to, you know, just obsessively measure everything. Whereas in engineering, that's difficult, if not impossible. I mean, you basically, you almost don't have any metrics when it comes to measuring engineering…

AI assessment note: “I don't think that there was any particular moment. I think it was just progressive”

Q of stretched There in terms of the employees and candidates, it's often something we see in SaaS businesses we evaluate. I'm intrigued. We've spoken before about the scaling of your sales team, and you've admitted to some mistakes. I'd love to start on a very tough one, and I don't mean to be a therapist on this one, but why do you think you made those mistakes when you reflect?

A I would say the reason I made many of the mistakes in sales is lack of experience. You know, my background was engineering, and so I had a pretty good intuition about how engineering should work. I worked on engineering teams, uh, You know, through undergrad. And so I was able to build that up reasonably well without too many mishaps. I should say, although certainly some, uh, in sales, I think all of my intuition about how to run, you know, a sales team based on how engineering is run was wrong. I mean, it's just, you have to operate those types of organizations radically differently. And so I just made basically every mistake in the book. You know, we hired enterprise sales reps to do transactional inside sales. We promoted great sales reps into management. We, uh, didn't know what our sales cycle and what our methodology was, and we did document things. Well, I'm pretty much every mistake you can imagine we ended up making. And, uh, you know, we certainly paid for it. Probably we'd be further ahead of if I had the experience early on and weren't, uh, weren't making those mistakes.

AI assessment note: “the reason I made many of the mistakes in sales is lack of experience”

Q What's the most difficult element? If you were to go, there's one fundamental thing, be it Connecting distribution, be it production of differing content, what's the one fundamental thing that you think is the most challenging?

A So ultimately, it's go-to-market. That encompasses a lot of different things, but it's very, very difficult to take products to market in education technology. So there are tons of companies that have been started that have had amazing products that would have been very impactful and would have had great outcomes, but they just couldn't get them to market. They couldn't figure out, you know, who would pay for it, how they would pay for it, If you want to sell something in higher education, generally speaking, you know, you have to go through a committee process that can take years, sometimes three years for relatively small transaction sizes. And in K-II, in, you know, high schools, primary schools, it's actually worse. It's even more difficult. You have to go through districts. I mean, it's really a political game there. And so, you know, the kinds of companies that exist there don't focus on products. I mean, products in education technology are generally terrible because The decision makers aren't the users. And so, like much enterprise software, you end up with a pretty terrible product. And unfortunately, with edtech entrepreneurs, many of them are idealistic. So, that's what they focus on. But what they don't focus on is the go-to-market. How do you actually take your great product vision and translate it into a viable economic business? And I would argue that's where Top…

AI assessment note: “So ultimately, it's go-to-market. That encompasses a lot of different things”

Q We had Mark Mader from Smartsheet on the show the other day, and he said that people often over-index culture and under-index raw IQ. Would you agree with that statement?

A Absolutely, because most companies don't measure IQ at all, and that's wrong. I mean, there's a zero-point-six correlation between IQ and job performance, and that is, in fact, if you could base your hiring decision on one factor, you could only base it on one factor. IQ is actually that factor. Like, it's the most predictive element. Now, of course, .6 isn't great. I mean, you need a lot more data to get to a higher level of certainty, but it's incredibly predictive. Good things happen. There's a certain kind of magic that happens when you put a lot of smart people in the same building together, and so there's a sort of a hard-to-quantify element to that as well, so I would strongly agree with that.

AI assessment note: “Absolutely, because most companies don't measure IQ at all, and that's wrong.”

Q of stretched There in terms of the employees and candidates, it's often something we see in SaaS businesses we evaluate. I'm intrigued. We've spoken before about the scaling of your sales team, and you've admitted to some mistakes. I'd love to start on a very tough one, and I don't mean to be a therapist on this one, but why do you think you made those mistakes when you reflect?

A I would say the reason I made many of the mistakes in sales is lack of experience. You know, my background was engineering, and so I had a pretty good intuition about how engineering should work. I worked on engineering teams, uh, You know, through undergrad. And so I was able to build that up reasonably well without too many mishaps. I should say, although certainly some, uh, in sales, I think all of my intuition about how to run, you know, a sales team based on how engineering is run was wrong. I mean, it's just, you have to operate those types of organizations radically differently. And so I just made basically every mistake in the book. You know, we hired enterprise sales reps to do transactional inside sales. We promoted great sales reps into management. We, uh, didn't know what our sales cycle and what our methodology was, and we did document things. Well, I'm pretty much every mistake you can imagine we ended up making. And, uh, you know, we certainly paid for it. Probably we'd be further ahead of if I had the experience early on and weren't, uh, weren't making those mistakes.

AI assessment note: “the reason I made many of the mistakes in sales is lack of experience.”

Q do want to start today naturally hanging out with a lot of VCs doing the 20 Minute VC. We've interviewed over 1400, and the commonality when one says EdTech Is this kind of wincing or shriveling up at the word? So I want to start on this. And why do you think they have this automatic reaction to the space? And is there any historical or otherwise justification to it?

A Yes, absolutely. They are 100% correct. It is arguably one of the most difficult markets to succeed in. Over the last 10 years or so, there was a brief period of a resurgence in education technology where a lot of investment went into the space People naively thinking, wow, you know, it's time for the space to be disrupted, not realizing that the reason that it's been the same for a hundred years is the reason that it's difficult to disrupt. And out of the, you know, thousands and thousands of companies that have been funded over the last decade or so, only a handful, I would say probably 10 or 15, have really broken out past the, let's say, tens of millions in revenue milestone and, you know, are on track to potentially become public companies. Really, only a handful have actually been able to achieve that. Top Hat being one of those. So the track record is just terrible. It's really, it's a brutal, brutal market. The only thing harder is maybe healthcare or a handful of other very regulated industries. So they're absolutely correct. Most people that have invested in EdTech have lost money in EdTech, and so, yeah, there's good reason to be skeptical. You know, if I was evaluating Top Hat in the early days, I don't know if I would have invested knowing everything that I know now, just knowing how difficult the market is.

AI assessment note: “They are 100% correct. It is arguably one of the most difficult markets”

Q We had Mark Mader from Smartsheet on the show the other day, and he said that people often over-index culture and under-index raw IQ. Would you agree with that statement?

A Absolutely, because most companies don't measure IQ at all, and that's wrong. I mean, there's a zero-point-six correlation between IQ and job performance, and that is, in fact, if you could base your hiring decision on one factor, you could only base it on one factor. IQ is actually that factor. Like, it's the most predictive element. Now, of course, .6 isn't great. I mean, you need a lot more data to get to a higher level of certainty, but it's incredibly predictive. Good things happen. There's a certain kind of magic that happens when you put a lot of smart people in the same building together, and so there's a sort of a hard-to-quantify element to that as well, so I would strongly agree with that.

AI assessment note: “Absolutely, because most companies don't measure IQ at all, and that's wrong.”

Q What's the most difficult element? If you were to go, there's one fundamental thing, be it Connecting distribution, be it production of differing content, what's the one fundamental thing that you think is the most challenging?

A So ultimately, it's go-to-market. That encompasses a lot of different things, but it's very, very difficult to take products to market in education technology. So there are tons of companies that have been started that have had amazing products that would have been very impactful and would have had great outcomes, but they just couldn't get them to market. They couldn't figure out, you know, who would pay for it, how they would pay for it, If you want to sell something in higher education, generally speaking, you know, you have to go through a committee process that can take years, sometimes three years for relatively small transaction sizes. And in K-II, in, you know, high schools, primary schools, it's actually worse. It's even more difficult. You have to go through districts. I mean, it's really a political game there. And so, you know, the kinds of companies that exist there don't focus on products. I mean, products in education technology are generally terrible because The decision makers aren't the users. And so, like much enterprise software, you end up with a pretty terrible product. And unfortunately, with edtech entrepreneurs, many of them are idealistic. So, that's what they focus on. But what they don't focus on is the go-to-market. How do you actually take your great product vision and translate it into a viable economic business? And I would argue that's where Top…

AI assessment note: “So ultimately, it's go-to-market.”

Q sales model. I do want to kind of drill one layer deeper into the operations though for you and for any business really being hiring. You've said before to me, virtually all companies do it wrong, but there is a science to it. So if we take that in turn, why do you think that virtually all companies do it wrong and what are the big mistakes that they make?

A So yeah, switching gears to hiring. Most companies use their intuition when it comes to hiring. In other words, they do what seems to make sense rather than what the research and the science shows make sense. There's actually been a huge amount of research that's been done on what, in fact, predicts success in the workplace. And virtually all companies out there completely ignore this research and instead implement the same kind of hiring processes that have been shown repeatedly not to work, that have been shown to have tons of bias and be in favor of hiring basically extroverts that are charismatic rather than people who are actually going to be good at their job. And the science out there is quite clear. There's a handful of things that actually predict success in the workplace. One of those things is a work sample. I mean, that's one of the most predictive things. So you actually have people do a sample of the work that they are going to do, whether it's programming, if it's developer or doing a presentation of their sales rep, that's critical and highly predictive of success in the workplace. Uh, intelligence, you know, cognitive assessments actually are very predictive of success in the, in the workplace. Uh, yet most companies don't bother doing it or they do it, but they do it in kind of a, in a backhanded kind of way where they'll do maybe programming assessment, probl…

AI assessment note: “Most companies use their intuition when it comes to hiring.”

Q Can I ask, is there an innovation on the assessment side of it? So if I apply applied to Top Hat today for a position, what does that process look like, and how do you measure those scientific elements that suggest success?

A Yeah, so it depends on the role. You know, the interview process is slightly different depending on the role. You know, if you're a salesman, you'll go through one process. If you're an engineer, you'll go through a different process. But as a general rule, you do a basic screen to assess for high-level competencies to just To ensure that the person is worth, you know, having a conversation with. There will be, you know, a cognitive assessment and a work sample. So for an engineer, that's an assessment, and we would ask them to code something. Usually they're in a pair programming experience or a, um, you know, just an algorithmic type assessment that they, they do. Followed by a structured interview, we use something called top grading. There's lots of structured interviews that could potentially work, but the top grading format actually works pretty well. And so we use, we use that. And finally, then we do competency focused interviews and those focused interviews. So for example, if you want somebody who is a team player, you would have an interview that assesses their track record and your past performance that indicates whether or not they are in fact a team player. I mean, that's a contrived example, but things like that. So that's the general process for sales. Instead of a programming test, you do a presentation for account management or tech support. Maybe you'd have t…

AI assessment note: “basic screen to assess for high-level competencies... cognitive assessment and a work sample”

Q Can I ask if you were then advising or angel investing, maybe in a young technical mic, uh, as you were, what would your biggest advice be in terms of really scaling that sales team and not making the mistakes that you did?

A Yeah, that's interesting. So I'll say there's one mistake that, and this is going to sound paradoxical based on what I just said, but there's one mistake that almost every young founder makes, engineering founders in particular, honestly, almost a hundred percent of the time is at some point they decide, you know what, we need to start figuring out sales. And then they correctly assess, well, I don't know anything about sales. So what I'm going to do is bring in someone, you know, with a bit of gray hair and experience under their belt in order to help them figure out sales. And almost 100% of the time that ends up not working. They end up having to fire that person six months or a year or however long. Uh, the reason it doesn't work is there's several reasons, but one is because when you're a very small company without a lot of track record and you can't afford to pay the kind of money that top sales leaders expect to make, you end up almost by definition hiring somebody subpar, somebody who has had the kind of a so-so career and is looking to get into the startup game relatively late. And they almost always aren't able to figure out a model from scratch because they've generally worked in larger companies where they've had a framework, you know, within which to, to work in. So I would say resist the temptation to hire the gray hair. You know, you need to figure out sales, but…

AI assessment note: “resist the temptation to hire the gray hair”

Q do want to start today naturally hanging out with a lot of VCs doing the 20 Minute VC. We've interviewed over 1400, and the commonality when one says EdTech Is this kind of wincing or shriveling up at the word? So I want to start on this. And why do you think they have this automatic reaction to the space? And is there any historical or otherwise justification to it?

A Yes, absolutely. They are 100% correct. It is arguably one of the most difficult markets to succeed in. Over the last 10 years or so, there was a brief period of a resurgence in education technology where a lot of investment went into the space People naively thinking, wow, you know, it's time for the space to be disrupted, not realizing that the reason that it's been the same for a hundred years is the reason that it's difficult to disrupt. And out of the, you know, thousands and thousands of companies that have been funded over the last decade or so, only a handful, I would say probably 10 or 15, have really broken out past the, let's say, tens of millions in revenue milestone and, you know, are on track to potentially become public companies. Really, only a handful have actually been able to achieve that. Top Hat being one of those. So the track record is just terrible. It's really, it's a brutal, brutal market. The only thing harder is maybe healthcare or a handful of other very regulated industries. So they're absolutely correct. Most people that have invested in EdTech have lost money in EdTech, and so, yeah, there's good reason to be skeptical. You know, if I was evaluating Top Hat in the early days, I don't know if I would have invested knowing everything that I know now, just knowing how difficult the market is.

AI assessment note: “Yes, absolutely. They are 100% correct. It is arguably one of the most difficult markets”

Q Can I ask, is there an innovation on the assessment side of it? So if I apply applied to Top Hat today for a position, what does that process look like, and how do you measure those scientific elements that suggest success?

A Yeah, so it depends on the role. You know, the interview process is slightly different depending on the role. You know, if you're a salesman, you'll go through one process. If you're an engineer, you'll go through a different process. But as a general rule, you do a basic screen to assess for high-level competencies to just To ensure that the person is worth, you know, having a conversation with. There will be, you know, a cognitive assessment and a work sample. So for an engineer, that's an assessment, and we would ask them to code something. Usually they're in a pair programming experience or a, um, you know, just an algorithmic type assessment that they, they do. Followed by a structured interview, we use something called top grading. There's lots of structured interviews that could potentially work, but the top grading format actually works pretty well. And so we use, we use that. And finally, then we do competency focused interviews and those focused interviews. So for example, if you want somebody who is a team player, you would have an interview that assesses their track record and your past performance that indicates whether or not they are in fact a team player. I mean, that's a contrived example, but things like that. So that's the general process for sales. Instead of a programming test, you do a presentation for account management or tech support. Maybe you'd have t…

AI assessment note: “basic screen... cognitive assessment and a work sample... structured interview, we use something called top grading”

Q Can I ask if you were then advising or angel investing, maybe in a young technical mic, uh, as you were, what would your biggest advice be in terms of really scaling that sales team and not making the mistakes that you did?

A Yeah, that's interesting. So I'll say there's one mistake that, and this is going to sound paradoxical based on what I just said, but there's one mistake that almost every young founder makes, engineering founders in particular, honestly, almost a hundred percent of the time is at some point they decide, you know what, we need to start figuring out sales. And then they correctly assess, well, I don't know anything about sales. So what I'm going to do is bring in someone, you know, with a bit of gray hair and experience under their belt in order to help them figure out sales. And almost 100% of the time that ends up not working. They end up having to fire that person six months or a year or however long. Uh, the reason it doesn't work is there's several reasons, but one is because when you're a very small company without a lot of track record and you can't afford to pay the kind of money that top sales leaders expect to make, you end up almost by definition hiring somebody subpar, somebody who has had the kind of a so-so career and is looking to get into the startup game relatively late. And they almost always aren't able to figure out a model from scratch because they've generally worked in larger companies where they've had a framework, you know, within which to, to work in. So I would say resist the temptation to hire the gray hair. You know, you need to figure out sales, but…

AI assessment note: “resist the temptation to hire the gray hair. You know, you need to figure out”

Q I could not agree with you more. I'm very pleased you said that. What keeps you up at night, Mike?

A I would say there's a lot of periodic, you know, bubbles and market cycles that freak me out. You know, it seems like certainly the crypto mania is concerning. I worry that it's going to get large enough to where I'll actually dislocate the global economy, uh, just like the real estate, uh, bubble did, uh, not too long ago. You know, I worry that there's a bubble in venture financing. It seems like just like many years ago, there are deals being done that really on the face of it don't make a lot of sense. So that's, that's something I'm concerned about day to day, because when it comes time for top head to IPO or Republic, that's ultimately going to impact My outcome. So I always prefer the market to stay as rational as, uh, as possible.

AI assessment note: “there's a lot of periodic, you know, bubbles and market cycles that freak me out”

Q sales model. I do want to kind of drill one layer deeper into the operations though for you and for any business really being hiring. You've said before to me, virtually all companies do it wrong, but there is a science to it. So if we take that in turn, why do you think that virtually all companies do it wrong and what are the big mistakes that they make?

A So yeah, switching gears to hiring. Most companies use their intuition when it comes to hiring. In other words, they do what seems to make sense rather than what the research and the science shows make sense. There's actually been a huge amount of research that's been done on what, in fact, predicts success in the workplace. And virtually all companies out there completely ignore this research and instead implement the same kind of hiring processes that have been shown repeatedly not to work, that have been shown to have tons of bias and be in favor of hiring basically extroverts that are charismatic rather than people who are actually going to be good at their job. And the science out there is quite clear. There's a handful of things that actually predict success in the workplace. One of those things is a work sample. I mean, that's one of the most predictive things. So you actually have people do a sample of the work that they are going to do, whether it's programming, if it's developer or doing a presentation of their sales rep, that's critical and highly predictive of success in the workplace. Uh, intelligence, you know, cognitive assessments actually are very predictive of success in the, in the workplace. Uh, yet most companies don't bother doing it or they do it, but they do it in kind of a, in a backhanded kind of way where they'll do maybe programming assessment, probl…

AI assessment note: “Most companies use their intuition when it comes to hiring.”

Q In terms of hiring, we had Mariam Nafisi from Minted on the show last week, and she said that internal upscaling is the most important Important thing in a company. So I'm intrigued. How do you think about the debate of whether to promote that internal candidate or hire that maybe more experienced external candidate?

A I think companies need to develop both of those muscles. Internal hiring is essential to show people career progression, to show them that there's a, you know, a future with the company. And not only that, but sometimes the very best candidates are people that you hire when they're very young. Because generally speaking, when someone is Incredibly talented. As soon as they land somewhere, you know, that company is going to try to hold onto them, you know, for dear life. And so as a result, people who are experienced paradoxically are, as far as the distribution, it's more challenging to find top performers because they said that the good ones are already locked in somewhere. And so when you hire people that are, you know, fresh out of school or quite junior, and then give them the opportunity and empower them to, to level up, that's one of the most effective ways to get really strong top performers in the business. But at the same time, you know, sometimes you don't have the skill set in the organization, and you need to level up, and so the ability to hire externally is crucial as well.

AI assessment note: “I think companies need to develop both of those muscles.”

Q Can I ask Mike, was there an inflection point or a realization moment in your kind of learning curve of the scaling of the sales team for you?

A I don't think that there was any particular moment. I think it was just progressive kind of series of mistakes and learnings that happened over a period of years, but I can certainly talk about, you know, particular lessons, which to probably anybody that knows anything about sales, these will seem, you know, borderline cliche, but you know, when you're hiring sales reps, you need to match the sales cycle and cadence of That your business has to the experience of the rep. So if a rep is used to running, you know, twelve-month sales cycles and closing two deals a year, you shouldn't hire that person to try and run an inside sales transactional type process and vice versa. They're radically different things. You know, when you've got somebody who's a top rep and, you know, is clamoring for a promotion to management, more often than not, that's a bad idea. You need to figure out how to level them up and give them the opportunity for growth within the, you know, as an individual contributor. Because usually top reps don't make for good managers and vice versa. Good, great Sales managers are not necessarily the best sales reps. You know, we learned a lot about metrics and having to, you know, just obsessively measure everything. Whereas in engineering, that's difficult, if not impossible. I mean, you basically, you almost don't have any metrics when it comes to measuring engineering…

AI assessment note: “I don't think that there was any particular moment. I think it was just progressive”

Q Can I ask, when is a stretch a stretch too far in expectation of ability, maybe?

A I mean, it happens a lot. It's super challenging. The way we tend to handle it is you almost give the person the role before you promote them. In other words, you give them the opportunity to show that they're able to do the role before you actually give them the job title. We've certainly made The mistake of promoting people too early, and unfortunately, that usually doesn't end well, because what tends to happen is you can't demote them, because that's, you know, politically or whatever, from an optics perspective, you can't really do that, and at the same time, you know, they're, they're not leveling up as fast as the business needs, and so ultimately, that almost always ends up with the person leaving the company, so promoting someone too early is, is quite disastrous, like, that's really something to, to avoid.

AI assessment note: “you almost give the person the role before you promote them”

Q Can I ask, when is a stretch a stretch too far in expectation of ability, maybe?

A I mean, it happens a lot. It's super challenging. The way we tend to handle it is you almost give the person the role before you promote them. In other words, you give them the opportunity to show that they're able to do the role before you actually give them the job title. We've certainly made The mistake of promoting people too early, and unfortunately, that usually doesn't end well, because what tends to happen is you can't demote them, because that's, you know, politically or whatever, from an optics perspective, you can't really do that, and at the same time, you know, they're, they're not leveling up as fast as the business needs, and so ultimately, that almost always ends up with the person leaving the company, so promoting someone too early is, is quite disastrous, like, that's really something to, to avoid.

AI assessment note: “give them the opportunity to show that they're able to do the role before”

Q Mike, I'm utterly flummoxed then. I wasn't expecting that. So, tell me, why are Where's the opportunity then that you see that maybe the past hasn't produced and the current investor community doesn't see quite yet?

A For sure. And I'll even say this, you know, when I speak to entrepreneurs, you know, a lot of folks come to me for advice on starting companies in the education technology space. One of the first things I ask them is, you know, are you sure you want to go into this market? I mean, this is, you're probably going to fail. This is one of the most difficult markets. In all likelihood, it's going to be brutal and frustrating for you. You should maybe consider something else. You know, just Because that's the nature of it. So, you know, yeah, the question is, you know, what, what is it the top hat has done that made it somewhat unique? The way that companies typically sell software in higher education, I'll focus on higher education because that's, you know, a rat. The way they typically focus on selling software is by going top down to the enterprise. You know, you go to the CIO, the provost of the university, you tell them this amazing software that you developed and how it's going to help them. And then very quickly you get bought down into, uh, You know, if they even give you the time of day into a multi-year committee style decision-making process where a million people can say no, but a handful of people can say yes. Where the incentives and things that are motivating people are oftentimes not what you think they are. You know, teaching, as surprising as it may be, is really ve…

AI assessment note: “what is it the top hat has done that made it somewhat unique?”

Q Mike, I'm utterly flummoxed then. I wasn't expecting that. So, tell me, why are Where's the opportunity then that you see that maybe the past hasn't produced and the current investor community doesn't see quite yet?

A For sure. And I'll even say this, you know, when I speak to entrepreneurs, you know, a lot of folks come to me for advice on starting companies in the education technology space. One of the first things I ask them is, you know, are you sure you want to go into this market? I mean, this is, you're probably going to fail. This is one of the most difficult markets. In all likelihood, it's going to be brutal and frustrating for you. You should maybe consider something else. You know, just Because that's the nature of it. So, you know, yeah, the question is, you know, what, what is it the top hat has done that made it somewhat unique? The way that companies typically sell software in higher education, I'll focus on higher education because that's, you know, a rat. The way they typically focus on selling software is by going top down to the enterprise. You know, you go to the CIO, the provost of the university, you tell them this amazing software that you developed and how it's going to help them. And then very quickly you get bought down into, uh, You know, if they even give you the time of day into a multi-year committee style decision-making process where a million people can say no, but a handful of people can say yes. Where the incentives and things that are motivating people are oftentimes not what you think they are. You know, teaching, as surprising as it may be, is really ve…

AI assessment note: “what is it the top hat has done that made it somewhat unique?”