Jake Aaron Villarreal: I'm Jake Aaron Villarreal, born and raised in Silicon Valley, and here to take you behind the scenes to share what it's like to be a startup founder, the journey they're on, the problems they've solved, the products they build in an effort to make our lives better. I'm excited to have with us today Mike Conover, co-founder and CEO of BrightWave. Mike, welcome to the show.
Mike Conover: Hey, thanks for having me. Appreciate it.
Jake Aaron Villarreal: Well, excited to have you. I know we've spent a few iterations of trying to get on the schedule and I know you're real busy and we're busy over here. So, full dance card for sure. Yeah, we're here. So, let's have some fun. Maybe if I can, I'll just go through a little bit of your background before we jump in, and yeah, we'll have some time to to talk about your origin story and then kind of talk about your company, BrightWave here.
So, a little bit more about Mike. He leads BrightWave, which is an AI powered diligence and research platform purpose-built for investment professionals. A pioneer in the field of artificial intelligence, Mike established and led open-source LLM engineering at Databricks where he created Dolly, the language model that followed or that showed the world how easy it is to build LLMs that can talk and reason. So Mike, you're right in the middle of this whole AI boom that's happening and excited to to learn about you in particular. How did you initially get into technology and engineering and become an entrepreneur? Maybe just take us back a little bit into who you were as a kid.
When I started this podcast, I had this vision of going behind the scenes to share what it's like to be a startup founder in Silicon Valley, where I'm born and raised. And weekly, we dive deep in the trenches to find the wins, the losses, and the lessons learned with the hopes that it helps you run a better startup. But here's the thing. If you've gotten any value out of these conversations or been inspired at all, hit the subscribe button. And I get it. Your podcast feed's probably overflowing. But to stay up to date what's going on in Silicon Valley, stick with us because at the end of the day, we're all just trying to build something that matters. And we're better when we do it together. So, hit that subscribe button and follow us wherever you find your podcasts. Now, let's get back to the show.
Mike Conover: Oh, goodness gracious. Let's see. I think my whole life I have had a really clear sense that the world is governed by systems that are more complex than we can apprehend in our day-to-day lives. And you a concrete example of this you notice that lungs and trees have similar structures. Why is that? Or that the when you hold a leaf I mean you you asked man you take us way back when you hold a leaf up in the sunshine you see that vascular structure and it kind of looks like city blocks that's weird why is that so I think for much of my life I was confronted with these kinds of observations that intuitively seemed mystifying but as an adult and particularly in graduate school, I discovered that the tools of computation and mathematics can give us really tractable answers to those questions.
So it turns out that both of those things that I described are space filling fractals that are solving very similar problems. So lungs and trees are both interested in gas diffusion. And that branching structure where you have a central base and then many offshoots that fill a three-dimensional space. That's a very efficient way to respire. And cities and leaves or like even slime molds that are growing on a surface are cons concerned with the distribution of nutrients. And you can think of cars and people and information as sort of flowing through the vascular structure of a city. And
[Meet Mike Conover: Background and Journey]
with this science that I studied as part of my PhD, complexity science, you can model these systems and understand processes like how diseases spread on the airline network. So pandemics or you know my work was focused on how information spreads through very large groups of people. So we had access to the Twitter Twitter data set very early on and we're looking at large scale structures of communication networks you know on the order of hundreds of thousands of people and it was just a lifelong fascination with what I thought was something that was just innately beautiful about how the world functions that grew into a sense that we can actually start to change how we behave on the basis of what we learn using these analytical techniques and we can make more informed decisions about policy with respect to should we shut down the airline network in the case of a pandemic. It turns out the answer is no and you can prove that statistically or how how are technologies like recommender algorithms going to shape the structure of society. It's like those are tractable problems in the face of data science and machine learning.
Jake Aaron Villarreal: Yeah. Wow. You've got a lot of deep knowledge there. transitioning from that sort of mindset into getting into technology and then working at Databricks like what was the transition from that to a tech company and what was it that you learned there that you felt like you know I think I could make a jump and go start my own company.
Mike Conover: Yeah. And so a little in terms of timelines, you know, I've been building AI machine learning systems for almost 20 years at this point and fresh out of school went to LinkedIn, worked on homepage newsfeed relevance there. So you go to linkedin.com, you'd see our updates, but still had in research interests, was a research liaison as part of their economic graph challenge. And you know, BrightWave, just to to make clear, is a system that is able to perceive more than a human is able to and reason about the contents of very large data rooms or collections of documents. Whether that's private market investors who are looking at due diligence in a deal process or a long-only investor who is confronted with a coverage universe of 80 to 120 plus names and during earning season is just drowning in a sea of data chaos. And the
[The Fascination with Complexity and Systems]
through line is bodies of information that exceed the limits of a human's ability to connect the dots. And that's like what all of this has in common. So at LinkedIn, we published a p a paper in nature communications. So one of the top scientific journals in the world using 500 million job transitions. So you can think of a resume as describing a network where you move from company A to company B. and we draw a line between those two companies based on how many people move from company A to company B. You can predict next quarter changes in S&P 500 market cap based on where high talent individuals move. That sort of realization, that manifestation of the fact that you can model these systems and then make meaningful predictions about the world.
Obviously, the 2010s were characterized by the sort of growing commercialization of that fundamental paradigm, which is that you create systems that instrument the structure of the world, whether it's mobile devices and geocoordinate data or impression and action tracking for consumer web uh and predict what is going to happen in the future on the basis of very large data sets. And so after I left grad school, the through line was really how can we use machine learning to make better and better decisions about how to act. Now the challenge up until you know 2020s roughly you know the transformer paper came out in 2017 but you know these models really became useful in the 2020s was that you historically had to field a team of PhDs and machine learning engineers to do anything about these data sets.
And with language models, you have a system that is able to, if you think about, and this is kind of a technical digression, but like the attention matrix in a language model is able to see language models don't read left to right. They see the totality of a passage simultaneously. And that is so different from how humans interact with the world. And so with the advent of chat GPT, you saw the potential of a system that was able to really deeply understand bodies of content that exceed what a human is able to do. And you even just at the order of, you know, 128,000, you know, token context window, that's 100 pages of text roughly. And a model is able to see all of that at once. That's just such a transformative technology even if it doesn't you know absent agentic behavior that that moment was so galvanizing for me in terms of conviction that this class of technology would change how we interact with the world and that it would change how we build the kinds of systems that we use to make more informed decisions about how we should be in the future.
Jake Aaron Villarreal: So if you look at the type of systems that have been in the space that you're in, which is really pulling together a lot of data for investors doing diligence and trying to assess, you know, should we bet on putting our money in this company or in that company or, you know, what type of reports can we pull together that are going to be more accurate as we predict where we think we, you know, our growth can be? what was the problem that you saw that wasn't already there in the market that you felt you could apply with BrightWave to help you know solve or provide value in that space.
Mike Conover: So I think it starts with the human element you know and any you know sort of in the context of the audience and focus this podcast like you have to start with pain. What is it that people are doing currently that is either not a great use of their time is ex tedious and
[Transitioning to Technology and AI]
time-consuming is just the thing that you as a technologist can say there there's a better way we don't have to do it like this anymore in finance the problem of getting to conviction the number of factors that are relevant to an investment decision are very are numerous and you know you step in pre-term sheet let's say a private credit investor pre-term sheet. You step into a data room of 6 to 800 pages of material. That's a board consent. That's going to be shareholder agreements. It's a confidential investment memo. It's, you know, vendor contracts. It's just all of this raw signal but without any structure on top of it. And you are in a competitive deal process. you need, you know, it's frequently these are leveraged investments and so getting it right and understanding quickly is there a deal killer in here or can we put on this decision with confidence quickly.
We've historically just thrown warm bodies at the problem and it's you've got these young associates and analysts who work extremely hard to make sure that there are guarantees around comprehensiveness and accuracy that are really at the limits of what a person is able to do in the course of a week and you read a thousand pages of content or even just browse through a thousand pages of content, it's very difficult to connect the dots across all of that material. And that's where language models in a system like BrightWave is so powerful in terms of augmenting human capabilities. And our view is very just like concretely that AI systems do not replace judge investor judgment or experience.
And instead it's much more like that accountant in 1978 who would manually write out spreadsheets longhand and you go to that. This is before the advent of VisiCalc which came out in 1979. You say first computational spreadsheet what is your job? They said what I run the numbers. It's cognitively demanding it's time-consuming and it's important to the business. It feels like real work. But with Excel, we have a tool that is just the dominant solution to that class of activities. And it it is not the case that there are fewer finance professionals because Excel exists. And I see a very similar sea change with respect to the sophistication of the analysis and the perceptiveness of the kinds of questions you can ask a management team because you're stepping into the discussion armed with all of the material facts.
Jake Aaron Villarreal: Yeah, I could see a really good value proposition of, you know, where is the pain in the process and what tools or technology can you use to accelerate how you get to the answers that can give you better, you know, a better feeling of making those decisions. But how did you jump from LinkedIn or Databricks or 20 years of, you know, technology into the financial tool to tools that really are helping financial analysts or financial people in that sector like what how did you see the opportunity there? Yeah. And what why is it important to you?
Mike Conover: Well, the markets are the ultimate manifestation of how humans negotiate value. And it's not the only way that we account for value, but it is a very important mechanism for doing so. And it's just fundamentally an interesting problem. And just as an example, prior to prior to BrightWave, I used to do algorithmic sports betting and not a lot, but was using machine learning models to price MMA fights. I like to box. I like to wrestle. You know, there's a lot of good data on what's happening in the course of UFC fights. And that problem of pricing something and knowing something about the world that nobody else has noticed is just intrinsically interesting to me. I just find it to be an interesting problem. And you connect that to all of the, you know, what I shared about this sense and the statistical reality that our lives are governed by very complex
[Identifying Market Gaps in Investment Tools]
systems that are really only with computing technology. It it seemed very natural to apply this to the markets and these investment decisions. It it just seemed like a very and in addition, you know, the pain is really real. I think in contrast to I don't know what the Uber for dogs is for the AI era, but there are a lot of things that look like that where it's like that's kind of interesting, but it's not that the problem is not that acute. And you think about the hourly rate for a lot of these investment professionals and the opportunity cost of not doing higher order analytical thinking or not having time to source and identify new deals or not having time to really invest in the relationship with the management team to understand on a human level are these people we want to do business with. It's a pretty high opportunity cost and so the you know the fit between technology what I find intrinsically interesting and then the need for the market made it a pretty clear decision.
Jake Aaron Villarreal: Yeah. Well, there's a lot of money in that space. So any way to get an edge to help you make a better decision when you're placing big bets is certainly worth exploring and experimenting with. What's the product actually look and feel like? So, if you're just, you know, on a laptop or going through it, like, walk us through the visual if you can.
Mike Conover: Well, so let me step back and talk a little bit about what AI products feel like generally and how different BrightWave is from standard issue AI product. chat as a form factor such exceptional product market fit so quickly that the industry and a lot of businesses have become really fixated on that as the main way that you interact with AI technologies. It's a very powerful paradigm. It's great at iterative fast feedback loops that allow you to refine and very quickly direct the sequence of questions and interactions. But the design problem that we solve at BrightWave is how do you reveal the thought process of a system that is operating autonomously and has considered tens of thousands of pages of content to a human in a way that's useful and interpretable. And that looks much more like a knowledge surface and what we might have historically considered a data product than a chat interface.
And so you step into BrightWave and you are able to define blueprints that allow you to apply common investing templates to large collections of documents. So, if you have an investment memo worksheet that anybody is, you know, you're going to spend 20 to 40 hours filling out what's the fundraising timeline, what are the management biographies, what's the growth model, what are international expansion plans, what are the key risks of this business, what's the ask. A lot of that material is spread over so many different documents. And you're doing this for every look that your company, your fund is taking at an opportunity. the ability to perform a deep research-like autonomous parse of this body of content that in the form of a data room and get highly configurable templatized research outputs that is not a chat interface that's a configuration system for defining what is the deliverable how should it look and feel what should it focus on that is fairly different from I'm typing all these questions into chat GPT and having to you know get 500 words at a time, a thousand words at a time and copy and paste this into PowerPoint.
Additionally, we have developed a lot of new product experiences, user interaction experiences that I think are fairly distinctive. Now in BrightWave in
[The Intrigue of Financial Markets and AI Solutions]
addition to our sense level attribution to the underlying primary sources you know these really high-quality citations we have we have an interaction where you can highlight any passage of text and say tell me more about that where's this coming from where's the evidence for this what are the implications of this and it's like in Photoshop you have brushes this is like the ability to take any piece of a living document and expand on it. It's like a magnifying glass for text and you're able to pull a thread that catches your attention. And that's one of the ways that, you know, we put our money where our mouth is when we say, I don't think these systems replace investor judgment. Instead, I think you get to brass tacks 50, 100 times faster. And then when you see something that connects with that conversation you had that was not digitized, you know, it was just that hallway conversation with management, you say, "This is material. We need to talk about this." You're able to instantaneously get the information that's going to drive your decision on an opportunity.
Jake Aaron Villarreal: Yeah. Wow. It's fascinating. You know, what's been or maybe you can share some feedback from customers that have tried BrightWave and you know, walk us through that a little bit of how it differentiates from what they've already used maybe other tools out there.
Mike Conover: Yeah, I mean, I think let me first start with some of the feedback we've heard from customers. We were talking to a managing director at a fairly large private credit fund who took a look at a research report produced by BrightWave on a deal that he had assessed and he said, "This is identifying risk factors that I wish I had seen when we originally looked at this opportunity." And so it's not only the ability to speed up, you know, we've heard this does 5 hours of work in 5 minutes. You know, it's just such a material force multiplier from a velocity and team bandwidth standpoint. That's one real way that we create value. But there's also this making sure that no stone is left unturned and that those factors that you wish you knew about when you did a deal that those are the first things you see.
So our ability to BrightWave is fairly different from other products in this category in that we've developed a technique that allows us to in a computationally efficient way read every page of every document every time. So contrast this with consumer grade language models that use lossy search techniques. You know, you would call this like retrieval augmented generation where it's kind of creating this montage of like paragraphs or snippets from documents and trying to piece something together. We use language models to read the entirety of every document for every analysis we perform. And that allows us to provide guarantees around comprehensiveness and accuracy that are just really hard to touch. And so it's a combination of things that that differentiate us from we're not the only team that has recognized that this is a real problem in a huge market.
But when you sit down with the product and where our customers sit down with the product, what they're telling us is that this looks and feels different from anything else I've used. This is so much more articulate and so much more dialed to finance specific workflows and how I want to interact with large collections of content and that the the accuracy and the quality of
[The Evolution of AI Interaction]
the analysis exceeds anything else that's available from the market. And that I mean this is you know obviously I think that's true. We work very hard to make sure that that's true. this is what people tell me. You know, we a lot of buyers are assessing four, six of these this options in this class and we consistently hear that the analysis that BrightWave puts on is just more insightful, more accurate in terms of tone and voice and just more information dense and so those you know those things make it an easy yes for pretty choosy buyers.
Jake Aaron Villarreal: Yeah. Who are the buyers?
Mike Conover: Yeah. So we f you know we are really focused on private market investors who are you know stepping into data rooms that have large bodies of material that uh goodness it's just it's you got to get up to speed real quickly and this is this is our core market.
Jake Aaron Villarreal: Yeah. Got it. Really cool. You've raised some is it seed funding or have you actually
Mike Conover: No, we've raised $21 million in the past 20 and change months. Our we had an inside investor preempt our series A. We've backed by angels from OpenAI and Databricks and Uber Solana Foundation as as well as some of the largest asset managers in the world.
Jake Aaron Villarreal: Really cool. You got great backing. You've got a team. What's the biggest challenge for the company currently?
Mike Conover: Oh goodness, it's a long list of things that keep me up at night. It's no one thing. It is what is this class of technologies? What is this market? What is this opportunity? I think there are no reference implementations. If you are if you're building a Salesforce killer, you basically know what are the knobs that a CRM must have and you basically know how databases are going to function next year. How what is the most effective user interface to control such powerful systems? How will truly agentic processes shape the contours of these products when we have end-to-end reinforcement learning over tool use for workflow specific applications? How will reasoning models complement what seems to be a saturation in pre-training? Those are very urgent, very complex questions that that we grapple with every day.
Jake Aaron Villarreal: Yeah, there's this transformation that we are seeing very quickly about Agentic AI and marketplaces for agents and you know hiring agents versus hiring people where you have customer support and you got sales agents, you have marketing agents and you've got, you know, financial analyst agent like it's really coming fairly quickly. Yeah. As your tool or your platform continues to evolve, do you see it as an agent or do you see it more as a tool that's helping knowledge workers do their job better, faster, more efficiently?
Mike Conover: It is a tool that helps knowledge workers do their jobs better, faster, more efficiently using autonomous agentic reasoning. And I think that the thing that you buy at the end of the day is a deliverable with respect to agents, right? You Cursor is a great example. you know the code generation platform you're buying the pull request you know that there's like a lot of like how do I make the sausage but what I want at the end of the day is correct tested code that is merged to master and we we take a similar view with respect to our customers they autonomy and agentic behavior is there are user-facing considerations so we talk these are typically longer running processes whereas chat is very fast it
[Customer Feedback and Differentiation]
begins to stream As soon as you finish asking the question, you see what has this done for me? Is it on the right path? Agentic systems and we see this with some of the really long running tests that OpenAI has disclosed. You know, we might be looking at hours or days. So there are ways that a user experiences an agentic system differently. But at the end of the day, as a finance professional, as an investor, I don't care how the sausage is made. like you you can pay crowd workers so long as it's accurate and insightful and useful to me.
And so we actually don't and this is in part also just my co-founder he was the former CTO of a federally regulated derivatives exchange and clearing house. His first deep learning patent was filed in 2018. We've got a NeurIPS published research engineer on the team. We've just got like 9 ft of methodological options behind you know the first three things we try don't work. We've got six more teed up. And so I don't feel any particular need to get too hung up on like what do we call this cl this algorithm? What how do we do it? It's just does it work or does it not? It's super simple for us.
Jake Aaron Villarreal: Yeah, that's great. Sounds like you've got a really deep bench of talented people.
Mike Conover: That's true.
Jake Aaron Villarreal: And in today's world, you don't need a ton of people that do a really good job on technology, in particular in AI. It seems to be that we're seeing small teams really bring some value to the markets quickly and raise hundreds of millions of dollars very easily and you know as long as they're solving a problem and I'll
Mike Conover: philosophy on capital is that you let you know capitalization follow needs and value creation and I you know I don't need to say much more on that but on the team front our philosophy is very much that you hire a smaller number of more experienced professionals and that you're able to move faster as a result largely because of two things. One is that they are able to make the right decision the first time and so you're able to prune the search space very quickly based on the experience of these individuals and then the second is that you enjoy lower coordination costs. So if you think of the number of relationships in a business, it grows with the square of the number square of the number of people.
And with AI technology, you're seeing smaller groups of people accomplish, you know, weeks worth of work in days. And I think you just, you know, we are in a privileged position. So we the individual who oversees our front-end development, he led Instagram's integration with the Ray-Ban sunglasses and Oculus VR headsets. We've got folks from McKinsey and Goldman Sachs and FT Partners and Scale AI on our commercial team. You know, we hired the cloud architect from Anaplan to lead our infrastructure efforts. It's we've just put together a very stacked bench and people want to be a part of that and because of the experience and also the technology that we leverage from an AI efficiency standpoint, you're just able to get more points on the board faster with a smaller team.
Jake Aaron Villarreal: Yeah, that's great.
Mike Conover: And it's a fun place to work. We show up to these beautiful offices and you know,
[Navigating Challenges in AI Development]
five days a week there are people in here and it's just a fun place to be. It's interesting.
Jake Aaron Villarreal: Yeah. Yeah. you know, having your experience at LinkedIn and being able to understand talent in addition to a great story and really high level individuals that are part of the team now like what's been something that you can share with other entrepreneurs that's been helpful as you recruit and grow and keep your team small but very efficient like what's worked well for you to attract the right people?
Mike Conover: That's a great question and I've thought about it a lot. LinkedIn was an incredible place to learn about culture. I think that was a very special business under Jeff Weiner's leadership and it percolated through that entire organization. Strong people have great options and so I think the talent conversation begins with a really candid discussion about whether what we're doing is a good fit with what you're trying to accomplish in your life. And I see life all-in as a portfolio optimization problem. There's your family, there's your work, there's your personal passion, there's what are your current skills? How hard am I trying to work right now? Candidly, and all of those factors are immutable on short time scales.
And I think when you get into some of the like real like DNA stuff in terms of who are you as a person, are you intrinsically motivated to do something exceptional? Do you take an orientation to the world around you which is an ownership mindset and accepts that you can't control your circumstances but you always get to choose how you respond to your environment. There's a real source of strength that accompanies that recognition of where the locus of agency is in the world that's inside you. I can teach you PyTorch. You know we can you can learn how to stand up a Kubernetes cluster. No problem. Are you an agent? Are you an actor in the world? do you happen to the world or does the world happen to you? That's much harder to learn on the job.
And so I think where we start and where candidates who get the chance to speak with BrightWave see how differently we show up is, you know, right off the rip. What are we talking about here? Who are you and what do you want to do with your life? Is the conversation that sets us apart? We've got an incredibly interesting technical problem. We have a market, you know, that is basically boundless in size and we have a technology that works. And the thing that I think is most special in in spite of all of those factors is the way that we treat each other and how seriously we take getting the right people on the team who have a mindset that is keyed for success and for pushing real hard.
Jake Aaron Villarreal: Yeah, that's great. I think a lot of times when companies grow and hire and we've been, you know, I've personally done 20,000 interviews with candidates and gone through a lot of sort of learning around the mindset of someone is it's a lot of times they might have the skills and experience and they might be a good fit for what you want them to do, but do they have the right attitude? Do they have like the right mindset when it comes to challenges you're going to go through? and are they going to continue to fight to deliver and come out on the other end versus I've done my job. I can do the best I can but at the end of the day like you know I'm just going to try and you know and that's where it ends.
And I think that the companies we've seen we've worked with a lot of startups the ones that have done well is that they find the right team that they all are fighters in some capacity and they don't take no for an answer. They find a way. It's a founders' mindset. They almost encompass the company as their own. And I think that's really important to find. It's hard to find it, but if you ask the right questions and it's not always here's who we are and here's what we do, can you work with us? It's like, are you truly interested in what we're bringing to the market? Because if you're not, you know, you can work for 10 different companies, but yeah, they have to really believe in the leaders and they have also have to believe that they can make a difference and be interested to want to be there to make it successful.
Mike Conover: And I think there's a lot in there's a lot to what you just said. There are a lot of little pieces in there. And one of the things that we hire for is a demonstrated ability to make things work. So I there's people say, you know, I listen, I'm not interested to speak with job hoppers. And it's like, well, what is it? What does that tell you about a person if you've had multiple stints of less than a year and a half or two years? And what that resume, that fact pattern on a resume tells me is that you stepped into situations where consistently you were not able to figure out a way to push through something. Something came up and it was instead of I am going to react. I'm going to embrace the author, the autonomy and agency I have and figure out a way to make this work and solve this puzzle. I'm going to walk.
Now that h impossible situations happen, but do you have a demonstrated track record of a acting with intention and making good decisions about who do you spend your time with? What kinds of businesses do you bet on? And then even if you get a bad break, are you able to create success from bronze? You know what? When we put gold on offer, you know, goodness, I know that, you know, we're going to get magic out of this process. The other piece is I like to hire people who who will exceed the limits of what we have to offer and where their talent and their ability will lead them to decide that BrightWave is no longer the place where they can accomplish the fullness of their potential.
And the idea here and this I got this LinkedIn Reid Hoffman and Jeff Weiner talk about this a lot. the idea of a tour of duty where your trajectory will eclipse what you can do at this business. But I want to work with you for a window in time and change the slope of that trajectory and give you a career experience that is career defining but not necessarily need to hold on to you for nine 10 years in order to for this to be a productive transaction. And I think that's a that sets the tenor of the relationship which is that you are an incredible person and I will work hard to earn your partnership and recognize that you are destined for great things and that may not be here.
Jake Aaron Villarreal: Yeah, I like that tour duty and we read that too and I think it makes a lot of sense. Yeah, I mean you progress as a person and as a company and you know at some point one outgrows the other and you have to decide like okay it's a good time to make a separation or maybe evolve and become a leader for the company in a different capacity. So really insightful for the listeners like on that topic.
Mike Conover: I think so much of our lives with if we do not act with intention can be characterized by like there are a lot of relationships professionally that unless you make a point of it can feel
[Building a High-Performance Team]
like we're having a conversation at this level but really what's going on is some subsurface dynamic that we're not talking about. And I think that's a really unhealthy way to live your life and a really unhealthy way to run a business. And to your point about leadership, I think executives at every and leaders, not just executives, but leader at every scale of an organization set the high watermark for that team and everybody pattern matches on, you know, culture is what you tolerate. What does this person find acceptable? How do they behave? How are they showing up? How are they talking about culture? What matters?
And you have this need for authenticity in the workplace that I think is frequently not met by the ways that we show up for one another. And so when I have a conversation with a candidate where I'm like, "What are you trying to do with your life?" And I really want to know the answer to that question, that sets the tenor for the whole relationship. And we're going to we're going to keep having that conversation. And if and if what you want to do with your life is start a company or you know break into a different industry, how can we set you up in a way that is mutually beneficial so that you can pursue those objectives and the business still succeeds because you spend time with us. Now we're having the real conversation and you don't have to pretend like you're never going to take another job and it's not this cynical bargain and it creates a sense of trust and trust is consistency over time is consistency over time. Now we can have the hard conversations and we can talk about what's really going on. What are the real problems that we face as a business? How are we going to solve them? And I think that's a much more interesting way to work. It's a much more interesting way to live your life.
Jake Aaron Villarreal: Yeah. Yeah. I was listening to ironically a podcast a few months back and it was about leadership but it was also about investing in the people that you hire but also expecting a return on your investment. So, you know, you spend a lot to get the right people, but you also expect them to stay for a certain amount of time so that the return on what you've provided, there's a mutual benefit, but also they're not here for 16 months or 18 months and they're gone and you felt like you didn't get out of it what you want to do, too. So I think there is a mutual side of that ROI but at the same time I want to go back to the leadership question you talked about because as a leader of a company and that if that company evolves at some level the leaders also evolved. So for you particularly, how have you evolved as a leader when you started maybe in your career or maybe your company and where you're at today? Like what's a breakthrough that you can share that you've learned that you're using that's been really good to to kind of learn?
Mike Conover: Yeah, I think the biggest evolution that I've gone through as a person and it is catalyzed by my professional life has been an embrace of humility as a source of strength and I did you ever listen to Run the Jewels? Yeah. Yeah. rap group, there's a lyric which is, "You can't crush me. I'm dirt." And I think that it captures very like succinctly how I feel about humility, which is that I'm wrong about most things already anyway. And to the point at the beginning of the conversation, the world is vast and unknowable, and I'm just a speck of dust floating through space on the surface of this planet with really limited faculties for understanding what's actually going on.
Like you and I are having a conversation. I don't know what your internal monologue is like. And we're like both really trying to be present with this dialogue. Now, multiply that by everybody you've ever met, all the conversations that you've they've had that you're not party to, all of the facts and data that just it's just very much a scientific approach, which is, you know, can we falsify this hypothesis? I believe something may be true about the world until I get evidence that contradicts my hypothesis and then I discard it and find one that's more useful. The orientation
[The Importance of Mindset in Recruitment]
there is the knower versus the learner mindset. And this again comes from LinkedIn and a an individual a coach there Fred Kofman. He talks about that idea that you embracing that my source of strength as a person is does not come from being right but from rather from getting the right output from my process and that if you give me if you say I disagree or here's data that contradicts what you think we should do or your plan is garbage we need to go in another direction. It's like I believe that you are a reasonable person generally trying to do the right thing and that you have information that I need. So my job is not to convince you that I'm right, but rather to elicit that information from you. And now I can very with great facility adapt to new information and update my model and my priors that allow me to behave more expertly in new situations.
And so with respect to the startup journey, that approach, that humility, which enables me to step into a user research call or a sales discovery call with a beginner's mind and say like, listen, you if you think this is a bad idea, you're right. Like I don't need to there's this idea that if only customers would just XYZ and that nobody's going to just do anything. either they love it and they want to use it or you need to change how it functions. And that rather than clinging to ideas about, you know, how the product should behave or what market we should focus on or how we should operate as a business. Instead, we, you know, happily kill our gods and move on to, you know, it's like things that you really thought were true. It's like if the market tells you that's not true, just believe the market, you know, and that is a very efficient like way to get a lot of reps on deck as a business. And so I think that's, you know, a very long running through line and one that has definitely shaped the way that we are building BrightWave.
Jake Aaron Villarreal: Yeah, that's great. You've got a lot of good knowledge and like the space you're in. I know that I don't know specifically like with financial analysis and understanding all the data that's going to make it a good bet on a deal like how big that opportunity in the market is. What I do know is that there is a ton of companies that are aging out, but you've got a lot of people that are retiring that have built pretty good-sized businesses. There's no one to take them over. So in the private equity space, I know there's a lot of growth over the next 10 years and there's going to be a need for lots of tools and technology that's going to help them make the right choices. So I just think timing is such a important factor of whatever you bring to market. And if that aligns well with the solution you're bringing, it just helps gives you an advantage in success. Yeah. And it sounds like you have the timing right. Now it's about executing and getting the brand out and growing the company. So really excited. What are you excited about as you look to continue to build your product in 2025? We're already in March, but for the next yeah, you know, 10 months, 11 months, 5 months, whatever it is, what are you excited about as what's on the road map for the product?
Mike Conover: I mean, we we are building the definitive platform for understanding very large bodies of investment research. It it is I mean it is the same thing that we've been doing for months and months now which is executing monomaniacally on this vision for a system and a user experience that allows you to deeply understand tens of thousands of pages of material hundreds of times faster than was previously possible. There are a lot of really interesting sub-problems both from a product development standpoint as well as technically within that space. But we have really winnowed the field and we say no to a lot of really interesting stuff because if it's not painful, it's not prioritization and our plan is really well defined and it all lines up around creating this system that has never existed before and that when our work is done, I do believe that the way investment professionals approach the market will not be the same.
Jake Aaron Villarreal: Yeah. Really cool. Well, if anyone wants to find BrightWave or find you, Mike, where do they go?
Mike Conover: BrightWave.io. Reach out on LinkedIn. We are hiring high talent, experienced professionals across all job families, both commercial and and engineering. We tend to fit the job to the person rather than the other the other way around because we hire for intrinsic attributes rather than specific technical skills. And we, you know, to the extent that you're interested in changing your approach to investment research, get in touch.
Jake Aaron Villarreal: Very cool. Well, there you have it, Mike. Thanks so much for coming on and sharing your story. I think it's phenomenal what you're building and can't wait to see what happens in the future. For all the listeners that are listening, thanks for spending your time with us today. It means a lot to me that you spent it with us. My name is Jake Aaron Villarreal signing off for now, but can't wait to catch up with you all in the next episode. Until then, Mike, the world, take care. If you like what we're doing, don't forget to subscribe, leave a review on Apple Podcasts or wherever you listen. Follow us on YouTube where we go behind the scenes to learn what it takes to be a startup founder.
.jpg)
.webp)
.webp)
.webp)
.webp)
