The Rise of AI-Native Companies and Product Leadership with SC Moatti of Mighty Capital

When you think about identity, I would say maybe it has a little bit more to do with ownership. And why I say that, first of all, when you look at patents and intellectual property, there's some regulation that's going on worldwide right now. But just looking at the USPTO that's saying like, how are we going to evolve patent protection and copyright protection to better address AI? And essentially the, the risk they're mitigating against is man versus machine. Like, we do not ever under any circumstance want machines to own anything. And so any discovery, any content, anything that was created or invented or allegedly invented by a machine is not going to be owned by the machine. Which is interesting because when you think of all the startups today who are saying like, oh, my music startup is creating music with an AI, or my drug discovery startup is creating drugs invented by an AI, what does that mean in terms of ownership? It could completely disrupt and change industries that are built on copyrights. But I also think that it relates to one of the key risks of AI today, this idea of ownership.

Welcome to another episode of our Traction podcast. This is the Next Wave. The Next Wave is a different take on our podcast where we're bringing back some of our most favorite guests that have either graced our stage or joined our podcast 10 years ago, five years ago, and we're going to revisit their topics to see do they still hold up in today's world of AI. Traction is brought to you by LaunchAcademy CA, one of Western Canada's main tech hubs and an international accelerator program that provides you access to the Canadian startup Visa as well as boast AI your go to partner for R and D Tax Credit. We are super excited to have Essie Moady from Mighty Capital joining us today. A little bit about Essie is the founding Managing Partner at Mighty Capital, a Silicon Valley venture firm that capitalizes on the growing importance of product managers and enterprise success. She earned her reputation as a genius at making products that people love during the cloud mobile era when she built products that billions of people used. At Siebel Systems and Meta, won industry awards and nominations from the Wall Street Journal and the Emmys, and wrote an award winning bestseller on what makes great product. As a venture capitalist honored on the Kaufman Top 30 index, she invested in pioneering companies like Amplitude, Grok and Canela. Essie earned a Master's in electrical engineering and a Stanford mba, is a Kaufman Fellow and a member of ypo. Essie, welcome back to Traction.

It's so great to be back. Thanks for Having me, Ray.

No, you were a awesome guest. You've had a couple appearances on our podcast as well and definitely a wealth of knowledge and experience and we want to tap back into that. Please tell our audience a little bit about Mighty Capital.

So Mighty Capital venture capital firm that capitalizes on the growing importance of product managers in business success. We're deploying our search fund. We, we have invested in companies like Amplitude and Rock and Canela, many of which you already know. The value we bring to our entrepreneurs is access to a massive community of half a million product managers, which in many cases are buyers of your technology or could even acquire your company. Anyways, you can learn more at Mighty Capital.

We're going to cover a topic that you brought up in 2018 live on stage at Traction in Vancouver. It's about building great products. And as our format are going to revisit some clips from your talk and dive into is that still relevant today, how things have changed or what the evolution is going to be moving forward from that topic. So let's kick it off with your first clip from 2018 that covers the basics of a great product.

What are the basics of a great product? And one thing I've learned through hosting literally like hundreds of dinners with dozens and dozens of product leaders and building products myself is we think of our technology now as an extension of ourselves. And so when we think what's a great product, we have to think what's a great person? Who are we when we are our best selves? And so I use the Mind, Body, Spirit framework to describe that. Starting with the body role, we all want to look good and we expect that our technology is also going to look good. What does beauty mean for technology? And I'll go into detail. It's of course a lot more than pretty pictures. Then the next rule, the spirit role. We all want to have meaningful lives. And so we all also expect that our technology is going to give us meaning. So it's going to be personalized. But then what does that mean for how we use data? And then the last rule, the mind rule. We're all here because we want to grow, we want to keep learning, and we expect that our technology is going to do exactly the same. And what does that mean? How do we build technology that learns.

Body, spirit, mine, that worked well in the mobile era, even into the SaaS era. How does that translate into AI and even Web3 for that matter, does the body, spirit mind process still hold true today?

Yes, definitely. The answer to that is yes. And why don't I do a bit of a compare and contrast of the mobile era and the AI era. I think that criminality is generally, and I see that with almost every wave of innovation it will start with a mind type problem like solve something that has never been solved before. So in mobile it was all about can I communicate wirelessly? Right? And we had these massive brick phones in the late 90s and that was like the first era, the mind era of mobile. And in AI it was like IBM WhatsApp, the, you know, can you solve like a chess problem, go on Jeopardy. And then a few years later the next problem that gets solved is more of a body problem like user experience related to beauty, ease of use factor, efficiency. In the mobile era, it was a Nokia motorola moment of 3G. That was about six years later by the way, than from the first smartphone, which was actually also an IBM product. IBM Simon in the AI era, that's where we are today with OpenAI and Anthropic and Grok, where it's all about ease of use. These products, they kind of look more humans, right? Watson definitely looked like a robot, this one looks a lot more human. But there's something that's still missing, by the way, between the mind and the body. In mobile was about six years, in AI was about 13 years. And then the third evolution of a technology wave will be in that spirit rule, which is when these products start to be really personalized. And that is what we saw with the iPhone, which is also six years later, six years after that Nokia Motorola moment. With 3G and with AI, I don't think we're there yet. And if I apply my very peasant mass of saying it's 6 and 6 and now it's 13 and 13, it means we have about like 10 years or so to get there. And so with AI, that's why a lot of articles and consensus behind the fact that it's really just the beginning. I think there's so much more to go because even when you use these AI tools like they're super easy to use, but they're something missing. And that's this personalization.

Yeah, that's basically still in its infancy, even though AI has been around for quite a while as you've iterated in terms of actual practical applications and consumer adoption, it's so brand new. That's actually a great segue into our next clip is the essence of mobile identity and context. These are two really critical elements of AI. And like I said, we're just at the infancy. So let's get to the clip and then dive into identity and context and how that holds into the AI spectrum.

And the era we're in is the mobile era. And what's essential about the mobile era is two things. One is your identity and your context. Identity being it's me, SC using this phone or using this smartwatch, not anybody else. And second this, this tech that I'm using, whether it's the hardware or the software, it knows where I am, what I'm about to do, what I'm doing, my calendar and so on and so forth, it knows my identity and my context. So for your own products, like, what is it that using those two criteria, your identity or your user's identity and your user's context, what is it that you can do that service of the previous generation couldn't do? And this is the question you need to ask yourself.

There's a lot to unpack here. One thing that just popped in my head as I was watching that is you're talking about identity in the mobile area of that. This is my iPhone, this is my app, this is what I'm doing. In this world of AI where there's this fear of people losing jobs, white collar positions becoming obsolete, the ones that are going to survive are the ones that are going to adopt AI and know how to use AI. Is that still an issue with people that are using AI in their jobs wanting to make sure that this is Ray using this AI. This is Ray's interpretation or Ray's prompting for this AI as opposed to, okay, this is just the marketing department's use or the product manager's use of AI, how does that fall into the identity here with moving forward with these AI agents?

That's a great question. I would say, if I'm going to give this one like a quick yay or nay, is this still accurate? I think it's still very accurate, but it has evolved a little bit. And so I'll address your questions, but first, you know, when you think about identity, I would say maybe it has a little bit more to do with ownership and why I say that, first of all, when you look at patents and intellectual property, there's some regulation that's going on worldwide right now. But just looking at the uspto, that's saying like, how are we going to evolve patent protection and copyright protection to address AI? And essentially the risk they're mitigating against is man versus machine. We do not ever under any circumstance want machines to own anything. And so any discovery, any content, anything that was created or invented or, or allegedly invented by a machine is not going to be owned by the machine. Which is interesting because when you think of all the startups today who are saying like, oh, my music startup is creating music with an AI, or my drug discovery startup is creating drugs invented by an AI, what does that mean in terms of ownership? It could completely disrupt and change industries that are built on copyrights. But I also think that it relates to one of the key risks of AI today, this idea of ownership. If you think of companies like the New York Times or Reese Witherspoon who tried to sue OpenAI saying, I own my content and I don't want AI to quote, unquote, steal it from me or OpenAI, Sam Altman to use it for free. So one of the questions today I think is are we at a Napster moment with AI where, if you recall Napster, you probably have audiences who didn't go through this. The company grew astronomically fast because it was disrupting the music industry and making music consumption free, as opposed to having the labels get royalty fees every time you buy a CD or a dvd. And it went down in flames. But it gave birth to Spotify and itunes and all of that, right? Music as a subscription. AI today because it's changing sort of identity and ownership, it could have also one of those Napster moments that's going to completely reinvent a lot of business model around ownership, content consumption, property rights, copyright and so on and so forth. That's the first point. The second point is you're asking about job loss and people feeling nervous. I definitely see that in our large global network of product managers. 500,000. So it's critical Mass who are all innovators, they're all early adopters, they all love technology. There's a lot of anxiety, literally AI angst, I would call it. Is AI going to take my job? I think the opposite is true, particularly in product. I think right now the typical ratio we've been going by is one product manager for 10 engineers is a going ratio. When you're a product leader, you're hiring for your team, I think that ratio is going to go to one or one to two, one product manager to one engineer or two engineers. Because product managers are going to be able to do so much more with AI agents and bots and whatever than what they were able to do with just engineers. And so I think that in the near future, when a product manager and especially a product executive joins a team, it's not going to be one person joining. It's going to be one product manager with their many AI agents and AI bot augmenting the team that they're joining.

And I think that team itself might be managing an AI agent that they're marketing for or building and selling on their own 100%.

So engineers, I think have maybe a bigger threat than product managers, but even engineers and content marketers and content creators and honestly, anyone, I think they need to just shift the way they're looking at the problem. Here's how. If you go back to like when Google was invented, people before Google, there were a lot of people who were graduating and they became librarians and their skill set was, I know that this book, this piece of knowledge is on aisle X in column, whatever, abc. And so that's when they graduated, they became librarians. But post Google, people were worried, oh, librarians are going to disappear. Yes. But the people who graduated, they became digital marketers using the Google tools, using the search tool of Google became free and became a commodity. But using the AdWords and all the different Google products, that became the skillset that people were trained to do. And so with AI, it's exactly the same thing. People are concerned that OpenAI is going to take away jobs from junior lawyers because they won't be doing research. They're going to become masters of the AI tools that they're going to be using. So when you're early in your career today, like the advice I give to folks who are concerned about their job being taken away by AI, really the advice is learn to master the best AI tools, like the best tools of your generation, because that is what's going to make you mission critical at your job.

Now you are an expert on product managers, so can we maybe dive a little bit deeper into product managers and how their roles are going to evolve with AI? You said a product manager is going to come to the table with a suite of AI agents that they've already got trained on how they do work or how they approach logical process to managing products. What's this evolution looking like from your perspective?

Yes. Yeah, so that's a great question. There's, I think two things. One is what was the impact of COVID And then two, the impact of AI more recently, the impact of COVID on product was essentially product exploded and product management as a function dominates the enterprise. I'll give you a couple stats. Before COVID about 4% of the Fortune 1000 had a chief product officer. That's 2021 today, like 2024. So three years later, 40% of Fortune 1000 have achieved product officers. You can see clinics in three years. That's like massive. Faster than any function in history. These people, by the way, are also getting promoted to CEO faster than CFOs, CMOs, CROs, et cetera. And the reason being, with digital transformations being mainstream, the value of a company and a market leader comes a lot from technology and product managers. And product executives are the ones who understand how to turn technology into business value. So it's only natural that they get promoted to chief Product officer and then to CEO number two. The number of tools that support product managers has also exploded just about the same. Five years ago we had our at our annual product awards, which we've been running for eight years, we had about what, a thousand or so nominations. This year we got 10,000, 9,000 to be accurate. So 10x in five years. Which means that product managers now use a lot of tools to create products themselves. They don't need engineers to do prototyping anymore, not even to do products anymore in many situations. So they have the option of using tools. So build versus buy, right? Like buy. They have the options of building tools or products with an AI, without engineers building products with engineers, or like simply partnering, right, Acquiring and buying companies. And so all of a sudden the role of product is so much more strategic, so much less of a project manager. In fact, the discussions five years ago in that product network were a lot around project managers, product managers, what's the difference? And now it's not even a question. Product managers are completely driving business.

So maybe for our audience, if you give some examples of tools that product managers have been using for the last few years, some of the ones that have been nominated and won awards, and then we can talk to about AI agents that product managers might develop or utilize.

Yes. So I encourage you because there's 100 tools every year that win awards. So I encourage you to go to the website productsaccount.org check out the winners, because by now there's actually not really a one size fits all. It's going to depend on the need, the size of the team, the maturity of the product, the industry and so on. But some of the most popular tools, Figma, of course, Conva Amplitude, which is one of the mighty capital investments, and a number of others, big and small, like I remember, Google has been a winner, Adobe has been a winner, Google Sheets for a long time. I wish there were better solutions, but it is still a very popular one.

It works. It does the job.

Exactly. It does the job. I want to Add one more thing to the evolution of the role of the product manager, which is really, I think we were talking about all that explosion that was describing with digital transformation. That was really the impact of COVID One of the impacts of AI is really that product managers are now owning the data agenda. And I think that's really the hidden benefit of OpenAI. I was referring earlier to the Napster moment. Right now we don't think that OpenAI and Anthropic are going to end up like Napster. That's because really the risk of a New York Times lawsuit or something is very minimal. But imagine at some point there's enough sort of old school companies and content and media companies that kind of get together and say, I'm going to take my content away from these AI scale ups. Then we have a different problem. Then all of a sudden the value of these tools become nil. And what that means is that if you think about the kind of fear and opportunity this creates for anybody, that's not an AI scale up. It's if I have better data, both from a defensive standpoint, I don't let Sam Altman go and take it away from me or from an offense standpoint, like I'm going to invent new business model just like these guys in AI, data is becoming absolutely critical to your market leadership in any industry. And so mastering the data agenda, data governance, data management, data packaging, is really an important aspect of how technology can turn into business value. And so that's on the agenda of a lot of product executives.

I actually have firsthand knowledge of this as well. One of my companies that we're building is focused on the sports industry and sports teams, leagues, athletes, amateur athletes. They're sitting on a treasure trove of data, historical data, but also real time data. And historically they've always been closed with their data. They don't want it to be shared, it's proprietary, it's confidential. But now we're helping them understand how they can build specialized language models around their data for their team, for their leagues, and leverage that with AI agents and just not just automate workflows, but then create new revenue streams, use their data in different ways, open it up to third parties and see some of the amazing things that can be generated through the AI agents. So yes, people are now waking up to the value of data and all the different possibilities with that data. Now security and privacy is still very important, which we'll get into. But I think the technology is now at that point where you're able to Ease some concerns and show them that potential and opportunity that's just sitting on their doorstep. Now, we talked about, people are just waking up to this. This is a great segue. I love hitting proper segues into our next clip where we're going to talk about smooth onboarding. And obviously in mobile onboarding, UI UX has been very critical and we'll see how that is now moving forward into this new generation of technology. So cue the clip.

We have to create very smooth experiences for our products. We have to onboard somebody very smoothly by providing value before we ask for value. Then we have to get them into our product with one very single task. That's not going to get them to lose focus on why they came here to what they came here to fix. Then we have to give them very easy ways to navigate from one place to the next, like different swim lanes. Then we have to make sure that our service is well performing, that we have the basics covered, nothing's going to.

Crash and so on.

So you would assume the fundamentals of onboarding somebody into this new era of AI agents is going to be the same. But what happens in five years from now when you have that product manager that's coming to a new position, but already has five to 20 agents that they're customized to use and leveraging to build a new product?

Yes. So it's so funny to watch that clip again because I think that at the time it was a key differentiator like that onboarding. We were all used to these horrible desktop experiences and the Napster experience compared.

To the Spotify experience.

Exactly. And so having a smooth onboarding was actually a way to stand out, get adoption and engagement much faster. I think now it's table stakes. Loading time at some point was a differentiator and now obviously it isn't. I think onboarding is pretty much the same. If you don't have a great, really smooth, easy onboarding, don't even bother. But if I project your question, like, what's going to make a difference? It's no longer having a really smooth onboarding, it's actually having no onboarding at all. Like at some point there was this trend called invisible products. I think that trend is going to come back with a vengeance, saying like, hey, if I'm an AI and I actually start to think about personalization, right. That missing piece that we were talking about, the spirit rule, the ultimate personalization is I don't have to do anything, right? Everything is done for me. It's personalized. And so if you think about Projecting out what's going to be the top of the tip of the spear. Best in class version of onboarding is no onboarding at all. Not even no download, no open, no pull up the window, just talk to me the way that in the movie her he has some earpiece that is essentially his soulmate kind of thing. So I do think that's really the future of onboarding, which with AI, one.

Of the other scenarios is going to be you have an autonomous AI agent engaging with another autonomous AI agent. So obviously is there an onboarding process required there or do they just sync automatically or do they need to get certain permissions to move forward? Like we're just at the embassy. Like this is. I'm just hypothetically thinking, but it's inevitable, like that's going to happen. You're going to have raise AI agent wanted to talk to SC's AI agent to coordinate a podcast appearance. What is that going to look like?

Yes, it's such an exciting future. Like right now we're talking human to agent communication. What about agent to agent and then multiple agents to multiple agents? Couple of things there. One is as an anecdote, we have been using AI at Mighty Capital ever since we started the firm in 2018. So when that talk took place and we been using an AI for scheduling, in about 5% of cases I get the situation and we schedule hundreds of meetings a year with entrepreneurs in 5% of cases I get this flaming email saying, you absolutely should fire this Mark Curtis. He does such a terrible job. It's an AI. And then in 5% of situations I get this email saying, your assistant Mark Curtis is incredible. He's available at any time. So responsive, so polite. And of course I'm very upfront. This is an AI. But one of the things that Mark Curtis does not do very well is communicate with other, like with calendly, for example, with other kind of AI or automated systems. And so I think we are very far from that agent to agent communication. I will also say we're very far from it in terms of our ability to be in front of have two AI talk to each other. Another great example being I'm in San Francisco where we have a lot of waymos. One of the ways you get into a traffic jam is if you put two waymos in front of one another, they just cannot compute the interactions with another waymo. Anyway, so one of the challenges there, in addition to that kind of rule based or complex type of interactions, is the energy consumption that comes with that kind of scaling and Right now we're in a phase where, you know, AI agents and bots are completely subsidized by the big cloud platforms. Right. So a lot of companies are getting started because Microsoft, Google, AWS will say, here's $500,000 in credit, please spend it on my platform. And then they report, oh, we have all this revenue, which we've comped, but you look at my revenue growth, that music at some point is going to stop once they've built enough capacity to justify charging, and so on and so forth. And the last time something like that happened was during the, the Enron scandal about 20 years ago, where essentially we had that sort of revenue trading where it's like, I'll buy your computers if you buy my plugs, but we will just keep all of that on the shelf. And it generated a massive accounting scandal that led to Sarbane's Oxley regulation. That is a kind of massive compliance industry. So there is definitely the risk of an Enron moment in AI when that music of getting all those free credit stops. And the advice I have for entrepreneurs or product managers who are building is make sure that your unit economics, like the profitability of your products in there, because that music will stop for sure.

Yeah. Especially with the announcement of Stargate and the $500 billion that's going to come into infrastructure. Yes, that seems exciting. But that, that, that infrastructure is going to be there, that that's going to start to cover a lot of opportunities that. Or cover the opportunity costs that a lot of the big players have been absorbing themselves, and they're gonna have to recoup that somehow. And so you have to start paying for it.

Yes.

So we've been pretty bullish on AI and the evolution of technology for most of this discussion. But I want to talk about a sector that is a concern. It's been a concern in the past. It's going to continue to be a concern, and that is privacy. So we're going to visit a wipe about what you had to say about privacy back in 2018. And it's very poignant and very astute on what you had to say there. We want to dive into that.

The challenge of personalization, of course, is privacy, which is a problem that everybody likes to talk about these days, but really, like, you have to break it down into, like, smaller pieces, like, what's, what's the issue? Like, what's the risk that you take as a business owner, as a product owner, when you think about somebody's privacy? And there's really, like three types of risks. One is someone uses the data of one of your users. 2 is an organization like a corporation or an organization uses this data. And three, the government uses this data. And we have different responses. Right, someone using your data. We're very clear we don't want that. We don't want identity theft, so you need to protect that from happening. Two is corporations using our data. We don't like to admit it, but we do like that. We do like to have a free Facebook, we do like to have better advertising, and so on and so forth. So a corporation using your data, you have to find ways as a business or product owner to make that possible within some boundaries. And then three, government using our data, we have very mixed feelings about that. We know historically and it's not something that we want to do. We don't want Big Brother. But on the other hand, when we look at, for example, the way countries defend from terrorist threats, they use very, very rudimentary criteria like what's your age, what's your race, what's your gender, what's your nationality? Which are not very effective. They're actually gross approximation compared to, hey, are you calling terrorists or are you meeting with terrorists? Which would be a lot more accurate.

Right.

So government using our data. We don't really have a good answer because we're not prepared to have a real debate. And so you have to sort of thread that line carefully.

I think that the three categories, corporations, individuals and government still hold very true today. But I think it's pretty obvious we're giving up more and more of our privacy for ease of life and making our lives easier by given up control over our data and privacy at the expense of security. And with individuals, I think identity theft is still very a big concern. And I can see how people would not want other individuals using AI to impersonate or intrude into their personal lives. But when it comes to the corporation side, the writing's on the wall. People are giving up so much control over their data and information just to make their lives easier. Do you agree or am I missing the mark here?

No, I think you're right on point. I think on the individual side, we are seeing, and I'm sure all of us have experienced that. I certainly have an explosion of the risk of fraud and identity theft now. It's becoming extremely difficult to wire anything from one bump to the other because so much frauds going on.

One thing, it's also deep fakes and impersonations.

Yes, 100%. And so I think the zero risk trend that we've seen in security companies that's going to continue, that's going to get even more important. The anti money laundering, we're talking a little bit web3 that is going to be here to stay. We're in a world that has a lot less of the security in the sense of the security nest that we had growing up. And so there's going to be more and more rules and restrictions and compliance. On the corporate side, I think people continue to not value corporation having access to their data in exchange for free service. I think they like the free better than they dislike corporations using their data. It's a little bit different in Europe, but it is more because there's no clear tech winner there than anything else. And they're aware of that. They're trying to course correct and create some winners. We'll see how successful that is on the government side. Something that I didn't anticipate. I still think we have a lot of progress to do on the way we manage security at our borders and government having access to data or not and how we do that. I do think that the incoming administration in the US is going to try to make a lot of changes to that with organizations like DOGE and the technology leaders and billionaires having a lot more influence in Washington and in government. So we'll see how that shapes up. The one thing I can tell you is with AI, when we talk to these chief product officers in the private account network, the number one concern is how do I build these products based on my proprietary data like we were talking about earlier and at the same time build that in a way that is trustworthy with my customers because if I do it and I lose trust because they feel used or they feel like manipulated or you know what something of course I lose my customer and that's not acceptable. So a key concern in on the AI roadmap and agenda for Chief Product Officer is really like maintaining and building trust as we're building differentiated AI products.

So do you feel that role for that product Alluded to this before, like the product managers are now going to process purposes have ownership over the data as part of their role. And so product managers no longer just about I need to build this app or I need to build this agent. It's also I need to be able to identify how we're going to govern this data officer. Yeah, we're going to have to govern this data from our customers or from our clients.

Yes, yes, that's 100% true. It's like how is data collected, managed, governed, monitored. And then also it makes, makes data management, as in data centers and choices of cloud providers and so on, a lot more of a business decision than a technology decision. So we're seeing a shift there as well. Today we're still talking about digital transformation and product managers or product executives leading digital transformation. We don't talk too much about chief Digital Officer anymore. That's really the role of the Chief Product officer. We had a few chief Data officer, chief Privacy Officer. I think all that is going to go and be rolled under the Chief Product Officer. And then I wouldn't be surprised if in a couple of years we start to see AI transformations. Right. We're already starting to hear AI native companies. What does that mean? What we're seeing, by the way on that topic is AI native is generally so far very much something that serial entrepreneurs are tackling. They've been watching the market, it goes at an incredible speed and they're ready to identify opportunities and they're trying to play their cards in a way that's AI native, as in extremely capital efficient. They are leveraging AI to build the company and therefore instead of hundreds of thousands of dollars, they need now maybe tens of thousands of dollars. So very capital efficient. Raise not so big rounds like smaller modest rounds, reach profitability or cash flow break even and then if there's the traction to back it, then raise a massive round. So when it comes to what entrepreneurs are going to do, we think that there's going to be a massive change in the, the whole Alphabet soup of series A, B and C are no longer going to want to raise all these different rounds they're going to raise. We call that at mighty capital discovery rounds to reach that cash flow break even point. And then if they're successful, okay, let's just go for it and then raise a massive.

Yeah. And the advantage of the AI native is you don't have all that legacy infrastructure and so you can build right from the beginning around AI framework. But one of the challenges is that do you not feel that AI and all the AI platforms, whether it's Grok, OpenAI ChatGPT, Claude, it's getting commoditized and it's so easy to spin something up. But the real value is in that data management. It's how do you help an entity or individual understand the value of their data, how to leverage that data, how to extract the most value out of that data set and put it to work?

Yes, 100%. I think if you think about like what are companies buying, they buy. Less cost, more productivity, more revenue. I think the less cost is not so much an AI use case, more productivity because it requires complex orchestration of tasks. That is a use case that AI can solve and therefore can be monetized for and then more revenue. That's like what we're talking about with the new revenue coming from different data model. Data management cost optimization is something that like to your point is going to be either already commoditized and free or that is going to be owned by the major like SaaS, players like Salesforce, the Adobes of the world who even if they haven't released the feature yet, they will upsell for it. And so this is not a place where as an entrepreneur I recommend playing, I recommend finding productivity plays or new revenue, better data plays.

Yeah, it's fascinating and I think the future of product managers roles are just going to keep evolving and keep getting more interesting and we've already seen such amazing growth in that sector and I think it's just going to keep growing. Like I said one to one product managers. Essie this has been an amazing discussion. I'd like to always close out our podcast by giving our guests an opportunity to put a call to action for our audience. What can our audience do for you?

I really appreciate that. Look, Mighty Capital has just raised a new fund. It's 100 million. We invest at Series A Enterprise Tech. If you're building a company that's exciting, that's in that sweet spot. A lot of our investments of course are in AI, but that's not just what we do. Please reach out and you can find me very easily on LinkedIn. You can find me on our website myd capital or you can just email me scyd capital.

Awesome. Sc this has been an amazing conversation. As always, thank you for being part of our Traction community. And as she said, if you want to reach out, she is easy to find on LinkedIn or Scmidy Capital. Awesome.

Thanks for having me and thanks for everything you do. Love what you're doing with your community.

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