AI Is Leveling Up The Underdogs—Now What?

[00:00:00] Tessa Burg: Hello, and welcome to another episode of Leader Generation, brought to you by Mod Op. I’m your host, Tessa Burg, and today I am joined by Mel Morris. He’s the CEO of Corpora.AI, and they have launched a brand new AI research engine. But before we get into learning more about Corpora, Mel, who is an experienced entrepreneur in the tech sector has seen many waves of new technology come through the market is gonna share his insights on AI automation and the future of AI and business.

[00:00:32] Tessa Burg: Mel, thanks so much for joining us today.

[00:00:35] Mel Morris: Well, thank you Tessa. I really appreciate the chance to chat through. I’ve been looking forward to this all week, sir. Let’s go for it.

[00:00:41] Tessa Burg: Awesome. And before we jumped on this call, you told me a little bit about your background and it is quite impressive. You are, you’re a true entrepreneur.

[00:00:50] Tessa Burg: You see what’s possible, you know how to build businesses, create value for your audience. So tell us a little bit about yourself and your journey.

[00:00:59] Mel Morris: Sure. Well, I mean, my, my journey started out, uh, literally, uh, in the 1970s, uh, having literally, uh, just left high school. I wanted to get into technology. Jobs were very hard to come by.

[00:01:11] Mel Morris: I ended up getting an interview and and started a job with a company making pork pies and sausages, and I was doing sort of data collection type activities. I could see the computer through the window, but that’s about as close as I got to It got quite bored. One day, one of the ladies there sAId, oh, why are you looking so grumpy?

[00:01:30] Mel Morris: I sAId, well, I really want to get into writing software. And she sAId, oh, my husband’s actually the computer manager for EW Bliss. It was a company owned by Gulp and Western of all people. And, uh, she sAId, when are you go see him? So I went to see him. He sAId, well, look, take two weeks off work. Come and see if you can write software with us and the team.

[00:01:47] Mel Morris: Let’s see how you get on. We’ll do some basic trAIning. And I sAId, well, I’ve run out of all my vacation time, so I’ll just have my resignation in and I’ll come and try the two weeks. If it doesn’t work out, we know what’s gonna happen. If it does, happy days for, for all of us. I was about three, four days into it, and, uh, I was offered the job.

[00:02:06] Mel Morris: And there was this young guy, Jed Don, who was about 26. He was probably one of the first people to ever get a computer science degree in the UK. It was that early in terms of tech. And, uh, he decided he was gonna teach me everything he could so he could put his feet up and just watch me do things. And I, I found that was fa fascinating, but extremely rewarding.

[00:02:26] Mel Morris: And I learned very quickly how to write software in low level languages to understand how machines operate and what happened and set me off on a path. Basically really set me up in really good stead going forward. But as, as things progressed, I, I found I had a knack for writing software. Um, I was given lots of opportunity to do that, but at 18, I got a team of 15 people working for me, uh, in, in, uh, what was then called National Bus Company.

[00:02:53] Mel Morris: And the head of their software development team. And two, two years later, I started my first consulting business. And then since then there’s been seven or eight tech companies that I’ve started, uh, developed and then built up and sold on. And, uh, I, I enjoy that. I enjoy the creation of technology. I enjoy building it.

[00:03:12] Mel Morris: I enjoy taking it to market. I’m not so keen on the bit that follows that on the basis that once it becomes very repetitive in things, I tend to get bored and wanna move on to other things. But I love challenges. One of my, my key phrases with the tech team is, until you come back and say it’s impossible, it probably isn’t worth as much as you think it is.

[00:03:31] Tessa Burg: Hmm. That’s a great piece of guidance and some of the, um, companies that you’ve led, chAIrman of King Digital EntertAInment. The Global Hit Candy Crush Saga. I’ve definitely heard of that. Have you, over your entire journey, are there universal truths or patterns that you’ve seen that are happening agAIn today and that you’ve learned from?

[00:03:56] Tessa Burg: And so when you look at the changing marketing landscape, uh, what’s going on in the world as far as how we buy and sell, evolving and AI. What sort of truths have you unearthed that you can apply to navigate? Uh, what’s going on?

[00:04:15] Mel Morris: Well, I think the first thing is that with all new technologies, we always, certAInly from the point of view of entrepreneurs and developers of technology, we nearly always start out with this view that it’s gonna rule the world.

[00:04:27] Mel Morris: We always think now we’ve thought there were many generations of things. And, and so we, we start with that, that view. Uh, and I think Gar has a great thing called a hype cycle, which covers a lot of this sort of, from a, from a perspective of what does it mean? And it says we start out with these inflated expectations.

[00:04:44] Mel Morris: We then get a little bit sort of disgruntled when it’s not quite performing as well as we hoped. And then we start to go into what they call this, this plateau. There’s sort of, and then we get this realization of what the real value of something is. And so, you know, that’s happened many times over, but I’ve never seen it happen with the intensity and the speed with which AI has actually gripped things.

[00:05:05] Mel Morris: I mean, I’ve lived through the, the dot.com boom, the, the bubble of 2000, and how that happened. And, and this is very different. So to a lot of people, AI only actually existed after ChatGPT was launched.

[00:05:19] Tessa Burg: Mm-hmm.

[00:05:21] Mel Morris: It had been going on for decades, but actually that was the first time people got introduced to it and, and, and even then, I was so excited to play around with it.

[00:05:28] Mel Morris: It’s fantastic. Look at all these things it can do, and then eventually you start to realize that yes, it can do lots of things, but sometimes it gets things badly wrong and sometimes it doesn’t quite do what you expect it to do. So agAIn, we, we all had this expectation that it was gonna do things and now a reality is setting in that it’s impressive, but we have to be really sort of careful about how we plan about using things to get the best out of it.

[00:05:54] Tessa Burg: Yeah, I agree. It’s interesting. We see different reactions depending on individual’s roles within a marketing organization, but also depending on their level of comfort with technology at all. You know, so some people came in at the beginning of digital marketing and so they’re very comfortable with that set of technologies, sort of not understanding or realizing that they have learned new things before.

[00:06:19] Tessa Burg: Now you know, they, yeah, they’re, they feel like they’re in this. You can’t teach an old dog new tricks, but. What, and, and I was telling you before the call, we did a client survey and there is like this distribution of where AI falls in terms of priority and what I found most interesting, when you ask marketers what do they need to get done this year?

[00:06:43] Tessa Burg: What’s keeping them up at night? It’s how do I best reach my customers? How do I better understand my customers? How do I show an ROI in my marketing? How do I maximize my budget? Those challenges have always existed, and they’re not saying AI is something that has to get done. They’re saying, I have the same challenges.

[00:07:05] Tessa Burg: I gotta figure out how to solve them. But is it, how will AI impact business as they try to always strive to be bigger, better, faster, more efficient?

[00:07:21] Mel Morris: It’s very interesting. Uh, you know, certAInly, uh, I think the way that I approach this is to say, firstly, be very careful in understanding what you think is the competition, because AI has this sort of happy knack of being able to benefit those with the least skills or talent the most.

[00:07:39] Mel Morris: It upskills the bottom of the tree, therefore the, the average moves up with that. And so now all of a sudden people have to fight harder. Now, today I think it’s not just a question of marketing message. Pretty much any company now can produce really credible presentation materials. They can produce really great business strategy documents with the help of AI. Those companies might never have been able to do that previously. They wouldn’t have had the, the skillset couldn’t have afforded to hire people in to do that.

[00:08:10] Mel Morris: So for marketeers, I think it’s really quite challenging now for them to understand that it is going to get harder to make your message become unique and stand out. That’s gonna be much, much more difficult. And people that actually don’t have the, the basic skill sets are still gonna produce really credible materials.

[00:08:29] Mel Morris: So, so you’ve gotta be really mindful of that. And so understanding that, that the game moves up from the bottom, it moves up, and therefore we’ve gotta fight harder to stand out. We’ve gotta become more empower, and we’ve gotta really understand where our really true, unique qualities are in what it is that we’re trying to market.

[00:08:48] Mel Morris: Everyone else would be putting out things that says they do all the same things. But actually when you dig down, there always will be those unique strengths that companies have. And you have to focus on those and make sure that they’re not lost in the noise of what everyone else is doing.

[00:09:04] Tessa Burg: And when we focus on what unique strengths are, I mean that’s really positioning and it’s almost like people have to set aside being threatened by the average moving up.

[00:09:14] Tessa Burg: I’ve heard some people say they think. People are feeling more vulnerable and exposed, or maybe questioning like, oh, maybe I’m not as good as I thought I was. As opposed to leaning into, here’s the baseline, the average has moved up. Where can I take this? Where can it go, and how can I take those unique points of views or positioning and start to amplify it?

[00:09:37] Tessa Burg: What advice would you give for marketers as they think about that positioning? I mean, it sounds easy to be like, just don’t be threatened by it. It’s like, you know, this is, these are exercises that, you know, marketers have been really good at in the past. And, um, I almost feel like maybe it’s just the flood of AI information and hype that’s overwhelming them.

[00:10:01] Mel Morris: Possibly I, I think it’s important that basically people must not dismiss the benefits of AI. It’s easy to do, so I don’t need to go and do that with AI. Maybe you don’t, but if it might save you 50, 60, 70% of the time to do that, use it to do that. Use the free time. You’ve now got to put the human element into this thing, the creativity that you as an individual can put in place and, and if you think about things, whether we talk about search or AI or whatever, the faster things are done.

[00:10:35] Mel Morris: You get into a groove with things, you get into a certAIn mindset that that feeds off that speed. If something takes 20 minutes to do, you’ve already started daydreaming about something else. If something takes two seconds, you are on it. You’re focused on it, you get that flow going with things. So even the people at the top of the tree must make sure that they’re not doing things that are taking longer to do than those at the bottom of the tree can do.

[00:11:01] Mel Morris: Because they don’t have the skills. They have to rely on AI. You have to use AI. You cannot afford not to. It’s, it’s, they’re very worst. It the very least it can be. Is, is a time saver for you. Even if it doesn’t necessarily do better quality output than you might do yourself, if it saves you time, you’re ahead of the curve.

[00:11:19] Mel Morris: Use that time wisely to use your own creativity. Then to be able to pull out the, the, the really important things, and it’s not just about saying what we unique with, it’s getting it into what does that mean? So what. What does that mean to the customers, to the, the audience you’re trying to appeal to? So what, what does it mean?

[00:11:40] Mel Morris: And if you focus on that side of things, what my uniques are, what the company’s uniques are, what the, what that means to my target audience, what are the benefits of that? You always get back to a, a, you know, an old, you know, IBM phrase of feature benefits, feature benefits, feature benefits, and I think with AI. People at the top of the tree I think can actually say, well, you know something, I’m saving 70% of my time doing all these lower level pieces using AI.

[00:12:08] Mel Morris: I’ve now got a lot more time available to be creative.

[00:12:13] Tessa Burg: You brought up two really important points that I think are I, I wanna highlight one, know who the competition is. I think a lot of people when they look at AI tech and they’re evaluating it, are trying to see, does it do everything I do? Does it do every step within this process?

[00:12:29] Tessa Burg: And what level of quality does it complete these steps? And it. We wouldn’t want it to do everything. And if your measurement for whether or not I will start is how good do I personally think that this is, then you’ll never get started. And that brings it to 0.2 where you sAId just try to use it to save time to start with.

[00:12:51] Tessa Burg: Yeah. You as a human with creativity. Yeah. You should be better than Jackie. You should absolutely.

[00:12:59] Mel Morris: I mean, I, I think there, there are three vectors to this and, and I think they apply to pretty much most of technology. And that’s speed, quality, and cost. Okay. And, and if you think about those three things, then they’re really important to keep in place.

[00:13:14] Mel Morris: What you don’t wanna be doing is using AI in the wrong ways. Now, how can that happen? Well, I’ll give you examples of it. People can start daydream. They don’t get focused. They, oh, I can ask this. I’ll try that. I’ll do this. So you’ve gotta be focused on what you’re doing. You’ve gotta understand the quality of what you’re producing.

[00:13:32] Mel Morris: You wanna do it quickly, and you wanna do it the least cost. Now, I say to people, would you use ChatGPT to up your shopping bill? Of course you wouldn’t. So find the right tools at the right time to do the job and be efficient about what you’re doing with your time. Your time is the one qua, one quantity and resource that you’re totally in control of.

[00:13:53] Mel Morris: So maximize that. If you maximize your time, you actually make sure that you become more valuable to your business, right? You start to produce more things, everything gets better. But if you sit back and think, well, I don’t need to use AI, you, you’re eventually gonna get caught up by all those people with much less skill or using AI to compete.

[00:14:11] Mel Morris: There’s a 10 x factor sometimes with AI, you know, make sure you are using it.

[00:14:17] Tessa Burg: Yeah. No, I, I just love that piece of advice. So when we look at your current company. Where does it sit in adding value to the audience? I mean, I’m sure you applied this a same approach. You thought about where it could deliver value. You thought about getting to know the audience better and your company’s strengths. Tell us a little bit about Corpora.AI.

[00:14:38] Mel Morris: Well, it, it’s, it’s an interesting story because we, we’ve had the technology development for several years as a lot of the AI companies have, by the way, we all think of AI as being, you know, October 22 when ChatGPT was launched.

[00:14:50] Mel Morris: But we’d all been doing this stuff for almost decades before that. So, you know, there’s a lot of history behind what, what we are doing and what the whole AI movement has been doing. But, but from the point of view of Copora, we wanted to set out, again, looking at those things, quality, speed, and cost. Our belief was at the end of the day, we are actually helping people research things.

[00:15:15] Mel Morris: That’s all about trying to look at vast quantities of information. You wanna be able to do that very effectively. You wanna be able to do it very quickly and you assure as hell, we want to do it very cheaply as well. So, so when, when you think about those things, they, they’re the things that we focused on.

[00:15:30] Mel Morris: So are we necessarily gonna do things 10 times better? Probably not. Because most of the tools are pretty much similar or you can be able to do it much faster. Absolutely. We’ll be able to do it much cheaper. Yes, we will. So those are the things that we are focusing on, but we’re approaching this whole AI space, not from the point of view of large language models, reasoning models, and that side of it.

[00:15:53] Mel Morris: We’re approaching it from the other end, which is the data. So with the data that we have, we’re able to actually be able to answer questions much faster. We’ll be able to look at information far more deeply, and the quality of the results you can get are a function of those two characteristics. So we hope to be able to deliver high quality results much faster and very much cheaper.

[00:16:18] Tessa Burg: And that really aligns to what you were talking about earlier. Make sure you know what problem you’re trying to solve. I know marketers have lots of challenges when it comes to data, and one of them is getting the right data efficiently. I still see a lot of people pulling data from multiple resources, trying to compile them themselves, putting things in Excel sheets, spending a bunch of time organizing, and then it just takes got forever to get to the part where you’re thinking about it.

[00:16:47] Tessa Burg: And I think this is a great example of where AI like helps take some of that, what you were saying, like. Are, where are you spending your time? Like maybe you shouldn’t be spending your time just compiling and organizing, you get to spend more time on the actual thinking.

[00:17:02] Mel Morris: I think it’s interesting. I think one of the things that we’ve seen with AI is that people tend to rely on it probably too much.

[00:17:10] Mel Morris: You know, they’re, they’re sort of almost wanting it to do their job for them, and there’s always a danger with that. You never know whether, has it really looked at the breadth and of information avAIlable? What? What the judgment and reasoning used, so, so you’re almost being presented with a set of answers.

[00:17:29] Mel Morris: You don’t really know necessarily the formulation of the information that went into that result. So, so it’s very important that people have the tools that allow them to rapidly look at things from many different perspectives. So for example, if we were, let’s say we were building, I mean, we saw Donald Trump announce this week that Americans are gonna build the most powerful war plane ever.

[00:17:50] Mel Morris: Okay. So, so from that perspective, you could look at that topic and say, well, how might you want to look at what that might include? Well, you can look at it from the point of view of a pilot. You could look at it from the point of view of a, a, a scientific politician looking at how to advise governments on how they should position these things, where they should sell them, what they should do.

[00:18:11] Mel Morris: You could look at it from the perspective of how effective would it be in a warfare environment. There’s so many different angles you could take. So the information that’s is vast even on that type of topic, but, but it’s not just. You can’t possibly look at all the information. Therefore, you have to be able to dis distill it down to the things that really matter to you.

[00:18:34] Mel Morris: And, and that’s quite a challenge with, with AI because people are saying, well, I, I want to look at a sixth generation war plane. I want it to do this, this, and this. What should I do? And the half expecting it to come out with the designs for Boeing to, to follow or, you know, to tell exactly what weapons, it’ll ridiculous using.

[00:18:54] Mel Morris: ChatGPT, these sorts of tools to actually get to write the entire job for them. And, and I think in so doing, they get a really fancy report out, but they don’t know the substance behind it. They’re not quite sure the accuracy of it. So, so what we’re saying is here, you’ve got to really look at the amount of data available.

[00:19:14] Mel Morris: You gotta understand the perspective you want to take in analyzing that data. What are the real things under the surface? And you don’t wanna turn for the meeting and someone says, well. Where did this number come from? I have no idea. It came from ChatGPT. It’s, that’s not really defensible. So we want to see AI being used by people to allow them to do their job better, not necessarily to do their job for them.

[00:19:38] Mel Morris: That’s a dangerous place to go.

[00:19:41] Tessa Burg: Yeah, I agree. And when you start to see people letting specifically ChatGPT do their job for them, or even coding tools, and we were talking about that earlier. Yep. There’s some like almost tell, like tells where you can see it’s too wordy. It’s over designed. It is not specific to this audience or like, did you generate this or did you think about it?

[00:20:06] Tessa Burg: And those who are early on in their journey or they, or to your point, have lower skills, are overusing the AI and the tools and they, it isn’t happening in a process where quality is. Quality testing is introduced, but I think the bigger problem is too, a lot of leaders have to, the way to embed quality is that you are very clear on expectations, goals, and the next, the points it occurs in the process.

[00:20:38] Tessa Burg: Because, for example, when we were talking about code assistance, anyone now can build quote unquote an app, but yep. You don’t create value until someone that you’re serving a client or a customer is able to use what you built to meet their own goals and objectives. And you can spend all day writing mediocre copy, mediocre content, but if people, mediocre code, mediocre apps.

[00:21:07] Tessa Burg: But if people aren’t able to get tremendous value or generate an emotion, that’s what marketers are all about. That takes creativity and that takes empathy, then you’re not gonna be that AI doesn’t matter how much crap, it turns out, it won’t be able to scale and scale at a value equation.

[00:21:27] Mel Morris: Yeah, I mean, I think the work that, that sums this up is if you, if you take the Nth degree and say, well, we’ve got, let’s say 10,000 companies trying to attack a particular challenge with a, with a software development, alright?

[00:21:39] Mel Morris: It’s not inconceivable to say, well, okay, so I produced this code. Do I know if the, the code that’s been produced might infringe patents that other people have? Do, do I actually understand exactly how it works and what it does? Could I defend it? If someone said, well, we think you’re infringing our patent, you wouldn’t have a clue.

[00:21:57] Mel Morris: You wouldn’t know. But, but more important than that, across those 10,000 companies that now all used AI to produce the code, the word that was summed up is ubiquity That likely to have. Ubiquity across all of the solutions and, and for a marketeer that is a nightmare. We’re now one of 10,000 people that can do this.

[00:22:18] Mel Morris: And if you’re not careful, it’s then a question, well, who will do it the cheapest? Who will do it the fastest? That’s all that matters then. Alright, so, so we almost take out functionality out of the picture. They all do the same thing supposedly. I mean, interestingly enough. One of my young Gen Z developers here, the, the other week who we’d started down the path of really starting to understand how you could AI to develop code.

[00:22:41] Mel Morris: And he actually said, I want you to build me a Candy Crush lookalike. And it actually produced a match three game running in Python on his desktop in less than a minute. My point is, If we are saying now that actually the value of creative software is diminished, it’s important that the software that we’re producing stands out. It’s, it’s also important to make sure that it doesn’t just stand out as being functionally different, but it has to stand out in terms of getting traction as well, and, and that’s much more challenging, particularly when there’s 10,000 apps all doing the same thing.

[00:23:19] Mel Morris: I mean, just for the user to go down, say, oh, I’ve searched for a max three game and the app store now got 10,000 games that are doing max three games, that’s a challenge. And, and we have to be really careful of these things. So I, I think ubiquity summed up a lot of what happens with these things and, and I think what we’re trying to search for is how do we get really high value content?

[00:23:42] Mel Morris: And I think that requires AI and human endeavor in combination. To be able to get the best result. And I think that’s gonna be the case for quite a long time to come. But the average of the rest will be really good and we have to be careful with that.

[00:24:00] Tessa Burg: Yeah, I love that message. ’cause it is a balance, right?

[00:24:03] Tessa Burg: Yeah. Like you, it is not, should you use AI, should you not? Can it this, can it not? It’s not, it’s not black and white. It’s finding how to navigate the gray and keep testing, keep trying. See where it helps save you time. But the challenges that we’re facing, getting, elevating our strengths, communicating those strengths in a way that’s personalized to our audience and delivers value to them is still what we’re, we’re trying.

[00:24:31] Tessa Burg: That’s still our path forward and requires collaboration and strategic thinking.

[00:24:38] Mel Morris: Oh, it does. And, and you only have to look at some of the, the, the sort of horror stories that we’ve already seen. The, the stateside, uh, uh, lawsuit where the, the, the, uh, the guys actually came out with a set of, uh, case law, which never existed.

[00:24:51] Mel Morris: The cases never actually existed and ended up being content of court. A similar thing happened in Australia only a few weeks ago. And so, you know, we, we look at, let’s say you’re a massive consulting firm. Now, you know, you’ve got some people inside there who blacked their way into the job and they’re able to produce really high quality looking output in the way of consulting reports, but were entirely AI generated.

[00:25:17] Mel Morris: What value did that actually create? Because if the customer realized that, then they’ve just done the prompts themselves.

[00:25:24] Tessa Burg: Right?

[00:25:24] Mel Morris: Right. They wouldn’t bothered paying a big fortune to a consulting firm to do that. So we have to get into a point where we can distinguish. Well, a call high quality human AI hybrid content that actually exhibits the best of both, and it’s getting that balance of the best of both.

[00:25:44] Mel Morris: And coming back to your marketeers, that’s what they have to do. We have to be able to show our own qualities as well as making sure we absolutely use AI as much as we need to to ensure we’ve got the time to bring out that final. Twist of lam or whatever it is that we might need to put in place to be able to make this thing stand out.

[00:26:07] Tessa Burg: I, yes, I agree. So when we think about Corpora and its strengths, and I would say there’s like 13 new startups a day back to like that sea of like, geez, how do you even differentiate? Where do you feel like. Maybe it’s from the legacy of the product and that this isn’t new. This didn’t just hit market, but where are you finding in this new company that you’re leading or not new company, but new to me, um, that it delivers the most value and unique position that you’re owning?

[00:26:39] Mel Morris: Well, I’ll give, I’ll give you three, three different data points on this. Uh, we have produced really, really brilliant product for exploring data on. October 22, the world changed ChatGPT was launched and and I said to my team that day, I said, watch this. This is gonna change everything. The expectation, the way people interface with applications, the way you do things, it’s gonna change.

[00:27:11] Mel Morris: We have to address that. Alright, now we are not. AI language model. So we have to understand how our data sets can play and be enhanced by AI and can also enhance AI itself. So we started down the path and we’ve got to a stage where in the last three months we’ve seen so many announcements that deep seek revelation.

[00:27:37] Mel Morris: Right. That came out. It rocked the market. I think a lot of it was overstated, but it rocked the market. There was some really clever stuff in the middle of all that, but it had a massive impact. Now since then, when we launched Corpora as a, as a research engine back in, in November, December last year, and since then I think there’s been five companies come out with what they call an AI deep research tool.

[00:28:03] Mel Morris: Right. That’s happened. And since then we’ve seen announcement after announcement, and I believe now the time for someone to have a unique play is about two weeks in the AI space. Within two weeks, somebody comes and said, me too, I can do that. We can do that. Look, we can do that. We can do it nearly as well or better or cheaper or whatever.

[00:28:25] Mel Morris: So those things are happening all the time. It’s a two week lifetime between you. Having a unique position to now being one of many people doing that, and in many ways people underestimate that AI. Already today, large language models are capable, so capable that you can take someone else’s product offering.

[00:28:48] Mel Morris: You can teach an existing AI system or get help from it to upskill the AI com components to compete with what that other product’s doing. It’s happening sometimes in hours, but it’s certainly happening within days and a couple of weeks. So, so you have to be able to look for enduring strengths and and benefits.

[00:29:10] Mel Morris: And we, we we’re saying, look, we think we can do a better job with the data. That’s a, that’s a very easy one to be able to, to understand. People look at two sets of output. They either believe that’s got more debt or hasn’t, but that’s something that they can, certain subjectivity to. But when it comes to speed and cost, we know what we’ve got is capable of actually bringing about orders of magnitude benefits in being much faster and much cheaper.

[00:29:41] Mel Morris: And I’m not talking about, you know, 1.5 times or like deep six, four or five times faster, faster, or, you know, four or five times cheaper. I’m talking about orders of magnitude faster and orders of magnitude cheaper. And going back to my point previously that the faster something is, and the cheaper it is, the more you will do.

[00:30:02] Tessa Burg: Mm-hmm.

[00:30:03] Mel Morris: You’ll use it more. You’ll use it more, you’ll get more benefit from it. You’ll end up getting into that, that real flow of things that you’re able to ask fast, rapid questions. Think about search when search comes back really quickly and gives you the answer you want, you. If search comes back with a load of really disparate answers, you’re not sure what they are, eventually you stop using it because you’ve run out of time, not because you’ve necessarily found the answer.

[00:30:30] Mel Morris: So there’s a real correlation for the humans in this thing, which says the quicker and faster and cheaper it is to do something, the more inclined they’re gonna be to use it, and more likely the more productivity I’ll have with that. So we we’re actually placing our bets on those three factors. Speed, quality, cost.

[00:30:49] Tessa Burg: And quality is so important because given if everyone has access to the same models, if everyone has access to the same apps, it really comes down to what are you putting through? What’s the data and data out within those models and those apps? And so that’s where your business is focused, is getting the most value in the quality and making sure it becomes a habit for people who know how to ask the right questions too.

[00:31:19] Mel Morris: Well, we, we’ve seen, we’ve seen this and, you know, the, the existing quotes, deep research offerings that are out there we don’t think are particularly deep. Right. We know they’re very expensive and we know they’re very slow. So, so we, we, we, we see the competition, we see what they’re doing. We watch ’em intensely, and we’re sitting back and, and we’re not actually in competition with them.

[00:31:40] Mel Morris: We can make all of these AI offerings faster, cheaper, and better. We are agnostic to the models involved. So, so our, our play here really is to make sure we can showcase what our capabilities are, and that those three characteristics are really evident when people use our technology. So that that’s where we sit and that’s what we’re trying to do.

[00:32:02] Mel Morris: And, and, you know, it, it’s, uh, it’s, it’s very interesting. The Deep Seek thing to me was a really interesting, you know, data point for AI because, you know, listen. The what, what those guys did. I think from the point of view of the PR that they did was phenomenon. I think the tech is good, but, but fundamentally, I think it got way outta control in terms of how the market reacted to that.

[00:32:26] Tessa Burg: Yeah, I agree. But it was fun to test. I mean, we jumped, it was, you know, like, let’s learn more.

[00:32:32] Mel Morris: I did too. Me too.

[00:32:35] Tessa Burg: I loved the conversation that came out of it, but to your point, it was like I was, I was like, man, like I never spent so long on Twitter as I did after Deep Seek. And, you know, people brought up some great points, pointed different types of research points to different security and governance and compliance concerns.

[00:32:53] Tessa Burg: I’m like, the most education I, I got in such a short amount of time. Um, but yeah, it didn’t change. I. Our approach as much as you know, ’cause we’re also not building foundational models. But to your point for our company, your company, anyone in tech, or even selling any product, staying focused on the problems you’re trying to solve and where you provide value is, is really important.

[00:33:21] Mel Morris: Uh, I think Deep Seek did a really good job at that. And, and of course, uh, you know, again, that they then, of course ended up with more traffic than they could possibly deal with. Uh, which again, and, and basically it wasn’t because it did a better job. It was because basically fundamentally it was cheaper and faster.

[00:33:36] Tessa Burg: Right.

[00:33:37] Mel Morris: It didn’t even say it was doing a better job. It just says we are cheaper and faster. That’s all they said.

[00:33:41] Tessa Burg: Yep. So, uh, our time is up. This has been such an engaging, interesting conversation, so thank you, Mel, for coming on today and sharing your insights. Uh, if people want to talk to you more, where can they reach you

[00:33:57] Mel Morris: Ever so easy. So, my email address is: [email protected].

[00:34:04] Tessa Burg: If you wanna hear more episodes of Leader Generation, you can find them on our website at modop.com. That’s M-O-D-O-P dot com. You can look under The Vanguardian or search for Leader Generation wherever you listen to your podcast. Until next time, Mel, I hope we get another chance to talk. I hope you have a great week.

[00:34:23] Mel Morris: Thanks, Tessa. Really enjoyed it. Take care.