Investing In Tomorrow: AI, Innovation, And Growth With Mike Ryan, Co-Founder, Bullet Point Network, Part 2

 

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Mike Ryan, CEO of Bullet Point Network, shares his wealth of experience in finance and reveals key investment strategies for the current market environment. Dive into trends, risk management, and timeless investment principles that can make a difference in your portfolio.

In this episode Zack Ellison and Mike Ryan discuss:

1. From Hype to Hustle: Startups Adapt to Pragmatic Capital Raising
2. Post-Bubble Capital Raising: Crafting a Credible Business Plan
3. The AI Gold Rush: Separating Hype from Prudent Investment
4. Unlocking the Power of AI: Strategies to Avoid Deception
5. Capitalizing on Scarcity for Downside Protection and Upside Returns
6. The Venture Debt Landscape: Opportunities and Risks for Investors
7. Betting on Growth: Unveiling the Formula for Investment Success

Investing In Tomorrow: AI, Innovation, And Growth With Mike Ryan, Co-Founder, Bullet Point Network, Part 2

Introduction To The 7in7 Show

There isn’t a way to truly validate it because what it is is making predictive guesses based on everything that’s out there. This is to cut to the Bullet Point Network story again for a second, we think that the best part of the answer is being very intentional about where and how you focus the AI and what knowledge pile or source set you focus the AI on. We also think it’s pretty powerful to run orthogonal versions of the same question through different AIs with different source sets because you are clearly going to get essentially from the internet versions of AI, from like the common open AI things that are out there that are running on the internet. You’re going to get the most popular things that are on the internet.

There’s compounding going on because they’re getting picked up by other AIs and becoming even more popular, so you’re getting this risk. You really want to ask yourself, “I had these five people, is 1 of the 5 smarter on this topic than the other 4? Maybe I want to listen to her a lot more than the other four. I don’t just want to take the volume of information I’m getting. I want to weight it by the quality of the provider.” That, to us, is one of the key things that we’re doing and that we think people need to do to use AI to their benefit instead of getting fooled by hallucinations.

I think that’s brilliant. I don’t know a lot about that yet, but the way I’m thinking about it is if you have AI generate a bunch of scenarios, and then you have a process, to your point, to weight those scenarios. You’re basically seeing this giant range. It’s almost like a Monte Carlo analysis. You’ve got 10,000 scenarios that AI has produced, but now you’re going to run a process that basically says, “Of those, which are the good ones and which are the bad ones? Which are most likely to be relevant?”

For us, the way we think about this is there are certain processes, and you were in the capital markets, and so some of these examples will make sense to you, but certain processes are deeply benefited by just pure automation because it really is about processing speed. It’s about pulling in as much data as you can, quickly processing it, and making an immediate prediction about something that’s very short-term. Those kinds of things are going to be machine-driven fully. I think human beings who try to intercept that or put themselves in that loop are likely going to be in harm’s way and are going to lose.

On the other hand, there are other processes that are deeply human. They need human judgment and intuition. Thinking about the long-term future of a disruptive, innovative company is not going to be something that an AI is going to do a good job of. There may be elements of filtering information, and sorting it. Our point is that you want to use AI to make this human at the end of the chain most productive. That’s how Bullet Point Network is built. It’s really making humans better at doing this fundamental analysis by essentially enabling them to do the research management and the scenario building so that a human can ultimately control the dials off and make the final decisions around.

 

 

I think back to when I was underwriting loans before the credit crisis and through the credit crisis. We spent so much time just doing what I call repetitive formulaic tasks that didn’t really add that much value. If we could get all those off our plate and automate anything that’s repetitive or formulaic and then spend our time doing the higher value, deeper thinking, I think that that’s something that’s already coming into effect with AI. I’m already using AI to automate some of the basic stuff that we do. It helps.

You’ve got this multiplier effect essentially for good and bad. The multiplier effect can be positive if you’re able to do more work or spend your time on higher-value work. To your point earlier, it’s negative when stories that aren’t true or facts that aren’t true, or things that aren’t necessarily optimal are perpetuated because the AI globally sucks them up and then distributes them to everybody. People are like, “This must make sense because the machine cannot be wrong.” It’s like when you read things on Google and even now, a lot of people are like, “I read this on the internet,” as if it’s true.

It’s interesting. We map our evidence in the VPN system and we always give a source and a date and a date of the source. It’s like I’m putting it in September 14th but the source is from June and the source is Deutsche Bank. We have ChatGPT 3.5 as one of those things because we want anyone to know that this piece of evidence comes from this specific model and it is an AI-generated piece of evidence. That’s not commonly done. If 10% of the articles on Bloomberg News are being written by AI, they’re not telling us which 10%. That’s fine. They’re not obligated to do it, I guess, yet.

Maybe regulators will change that, but we are not trying to just get by. We’re trying to make good decisions about investments. We want to know from this pile of information that’s mapped against this assumption, what parts of it came from AI, what parts of it came from Zack, who is a trusted source, what parts of it came from the CEO of the company who is the most informed source, but maybe a bit biased to the favorable. What parts of it come from a credentialed expert named John Smith? We basically give who’s the source, including ChatGPT 3.5 or ChatGPT 4.0 is the source.

It’s a shame that with ChatGPT, it doesn’t give you the ability to get the source. Eventually, they’ll have to develop an AI tool that lists specifically. Here’s where we got it. It’s like what you’re doing. Basically, it makes tons of sense. It’s basically having that trace dependence, being able to look back and say, “Where did every piece of this come from?” We have to go back and look. We know where to find it. Also, for publicly traded companies or any company, you want the ability to audit. It’s not going to pass the master point where you tell your investor you lost the money because ChatGPT told you this is a secret trade.

It gives you no protection, no CIA benefit, and that’s why people won’t use it. I will tell you one sobering fact that, again, I think is something that as people start using AI instead of just talking about it and dreaming about it, will come more to the fore. It is never going to be possible to cite a specific source for a specific fact. However, you can, and we do cite the sources that it’s drawn from, as well as the chunks, vectors, and pieces that are being used.

The way these large language models work is they take in all the information and they make a prediction or a guess about what the next sentence would be. Just like you were having it suck in data and then write sentences for you on the news. It’s basically using that methodology, which is different from a direct sourced methodology. You will always know the underlying sources.

You can know the chunks and the subvectors that you’re taking it from if you’re careful in building it the way we’re building it, but you will not actually know where each particular item came from unless you, the human being, open up those materials and read them. That’s one of the things we encourage people to do. We give you the sources, we tell you where it came from. By the way, you can go open up some of these things and take a quick browse through them yourself and you’ll see where it came from. You’ll see the context around it. You might not agree with ChatGPT 4.0 after you read it yourself.

The key to AI-driven investment success? Using AI to make the human at the end of the chain more productive. Share on X

Mike, I think one of the differentiators for investors going forward is this can be people who read more. It’s always been one, but more so than ever because nobody reads anything anymore.

I think you’re right.

It’s a goldfish. Imagine if you’re the person who just reads more, you don’t even need to be smarter. You’re stuck in so much more information that other people aren’t gaining.

The irony of that, in particular in this day and age, Zack, to your point, is it’s so easy now to get summaries, and now AI-generated summaries compounding that. Reading the ultimate underlying source material and then forcing yourself to critically look at things that agree with your view and things that oppose your view, so you’re getting the best argument for the other side. If you do both of those things, if you read a lot, and if you read diverse and opposing views, you’re going to have a huge advantage in this world, particularly if you’re a good reader, a fast and highly comprehension reader.

How Should Investors Approach AI?

The last question on AI, for now, is how should investors play it? We know there’s a lot of noise. How do you find the signal through the noise and not lose your shirt in the short-term but not miss the trend in the longer-term?

It’s going to sound maybe like a broken record, but I think the answer lies in the same fundamentals that we’re always talking about, which is to invest in companies that have the right people, that have the right market that they’re going after, and have a differentiated product that is easily understandable to create a real value proposition for a real customer who will pay real money for it and has a mode around it that can’t be easily breached.

That’s what we’re looking for. When we look at these so-called AI companies, you almost want to cover the name AI and just look at them as companies and say, “Who are the people running it? What’s the market they’re going after? How differentiated is the product or solution they’re delivering? How scalable is the unit economics that they’re getting on it? How much am I having to pay for the privilege of owning a piece of that company?”

“If I lend money to the company, how likely is it that they’re going to be able to repay me?” You have to do for us the proper cashflow analysis. Ultimately, it will be a wide cone, and that’s the reality of disruptive, exciting things. No one can be precise about exactly what’s going to unfold. There’ll be a wider cone of uncertainty around the scenarios, but if you’re not thinking about how this translates into those basics, who, what, when, where, and why is the cashflow that is going to be produced?

You’re going to end up losing at it. I do think people can do it. We have that capability but we’re subjects as human beings to be infatuated and excited by exciting shiny toys like AI. In that world, you’re going to have a lot of people that are investing money behind concepts and some are investing behind just people. The concept’s key, the person is key, but how that’s going to translate into a business model with a sustainable moat is what I’m looking to do.

 

I think we agree that innovation is what predominantly drives value creation. How should investors think about where to invest, not in terms of sector but in terms of stage or structure, equity versus debt? Where are some of the opportunities that you see for this next part of the cycle?

One of the things that I think is very interesting right now is there is a big opportunity to get, for many companies, some reasonably strong downside protection, let’s say in the form of debt or in the form of yield or cashflow that is going to give you an asymmetric return profile. Why is that more available now than it was a few years ago? It’s because there’s a scarcity of other forms of capital being provided to these companies.

You could actually look at companies that are growing or have the characteristics that I’ve talked about many times in this interview, and they desire or need more capital than they can get from their traditional common equity or preferred equity source providers. That’s one great opportunity. I think that’s a pretty material one here.

On the other hand, I know we don’t want to talk specifically only about AI or about any one sector, but I do think you have to accept what is the environment that you’re investing in. If you’re investing in an early-stage company that has disruptive potential, you’re going to be taking a lot of risk. You’re hoping to be afforded a significant return on that risk. If you have enough shots on goal in a diversified portfolio of those kinds of risks, you can come out well ahead even if some of those individual investments don’t work out.

If you’re going into something that is disruptive, has a large R&D budget or a large CapEx budget or a large sales and marketing budget that’s going to play out over many years before you’re going to get to positive profitability, you’d be crazy not to think, “I’m taking a lot of risk here.” I have to understand and embrace that risk. I am not from the camp of you just don’t invest in anything like that. I think there are lots of reasons to invest in things like that, but you need to be very discerning.

You need to do that hard work to figure out will this story translate into real growth and profitability that’s sustainable with a moat. If it will, then the investments can be fabulous. If you’re just investing because the story sounds good, you’re probably at greater risk. I really do like the downside-protected structures now. I think they’re more available from some excellent companies than they were years ago.

I also like investing in some of these businesses at much lower multiples or entry prices for the equity because people aren’t assuming that they’re going to grow forever and become massively profitable and aren’t putting 20 and 25-time revenue multiples on companies as readily as they used to. That’s really attractive. There are some businesses that were good businesses but wildly overpriced, and some that are still good businesses that are now acceptably priced. That’s really fun and interactive.

I think we’re going through the process of resetting and washing out a lot of the companies that were never really going to be successful, but they were priced as if they were going to be very successful. I don’t know when the exact timing will be. It’s hard to know, but I’m thinking that 2024 will probably be one of the best vintage years for venture debt, certainly, but also for venture equity in many respects, too.

In a world full of noise, focus on fundamentals: right people, right market, and a differentiated product. Share on X

That’s because you’re going to see a lot of the companies that are typically on three-year funding cycles, and they got funded in 2020 or 2021 and they’re going to be running low on cash. They’re going to need to raise some capital, and you’re going to get them at much lower valuations because they don’t have much negotiating power at that point.

I think premise generally holds and makes good sense. I think, of course, you have to be very rigorous about the underwriting, scenario analysis, and thorough vetting of each of those investments because there’s a big risk of adverse selection here. I do agree with you that across the batch, if you can avoid the adverse selection and if you can get in touch with the right opportunities from a sourcing standpoint, and then if you do the proper rigorous diligence, I think it’s going to be a great opportunity. I think ‘24 is going to be a great time to do this stuff.

I think defense wins championships, not in every market, but in this market, I think we’re back to that. For the last couple of years, it’s been a high-flying, fast-break type offense that’s been “the winner.” The reality is now we’re going to see those that are defensive and risk-intelligent outperform quite a bit. I don’t know when that market change will come. To me, it’s largely based around psychology. Right now, the fundamentals certainly don’t support where valuations are in the public market. I think there are still ways to fall, even in the private markets.

It hasn’t happened because people just still haven’t reached that point where they realized, “We’re actually in pretty bad shape. We’ve been asleep at the wheel.” I think that’s coming. Once that happens, I think those that are well positioned will rise to the top. I talked about this on another episode with Kelly Perdew, the managing general partner of Moonshots Capital, a seed-stage VC focused on veterans and dual-use technologies. My thinking was you don’t have to have the best product. You just need to have the most liquidity because a lot of your competitors, if you’re a startup, are going to simply run out of cash.

They’re not going to be able to fund themselves at the price or size that they need to survive. If you’re smart when it comes to your capital markets and finance function, you go out and raise probably more liquidity than you think you need, and you’re going to have a much higher probability of success. Kelly said it best. I always go back to the story because it’s so spot on. He said that it’s like going out into the woods with your friend and you see a bear. You don’t need to outrun your friend or that group of people or you don’t need to outrun the bear.

You just need to outrun your friends because the bear will eat whoever’s the slowest runner. That’s how I feel about the market right now. I don’t even think you need to have the best product. I just think you need to be the best capitalized and the most able to be conservative when you need to be. In other words, you need to have levers you can pull to reduce your cash burn and get to profitability if you need to, get to some safety, conserve cash, and link in your runway. I still think that message hasn’t gotten fully through to a lot of founders.

We shall see. It’ll be an interesting next fifteen months, but I think there’s a lot of opportunity here, Zack.

Opportunities In The Venture Debt Market

We’re almost out of time. We’re going to split this into two episodes because there’s so much to talk about. I wanted to just ask you, you talked a little bit about debt structures. Just to dig in a little bit to venture into debt, I know that you like the space. Clearly, I like it because that’s what ARI does but what are your thoughts on the venture debt market specifically over the next 5 to 10 years?

I think it’s a great time to be looking at venture debt. I know most people connote early-stage companies with venture debt. I think there will be some great opportunities with relatively early-stage companies. There are also some very good opportunities with later-stage companies, particularly private companies that are really not generating EBITDA or profits yet but have enough scale in their revenue base and levers that they are pulling and can continue to pull to improve their profitability.

The best product doesn’t always win. Sometimes, the company with the most liquidity survives. Share on X

The way I think about it, there are two very good points on a long continuum. One is companies that have $10 million or less in revenue. Many people in the lending community just won’t touch them. They’re not going to go near them. They’re just too small. By the way, that rule of thumb exists for a reason. Most of them should not be touched, but if you filter through the many and find the few that have the potential, there can be a great opportunity for an appropriately structured and appropriately sized amount of death in that capital structure.

The other end of the spectrum is companies that have $100 million of revenue, roughly maybe more. They are still not profitable in an EBITDA sense, but they are doing things that clearly put them on the path to that profitability. I think that’s a perfect opportunity for a variety of debt and debt-like structures for those companies.

There’s a third category that I’m spending a decent amount of time on right now, which includes IP-rich companies with very strong patent, trademark and trade secret portfolios. Those companies may have unique salvage value over and above their cashflows. Those are the three areas that get my attention in terms of the debt universe right now, to be very specific on your very broad question.

I agree with you. Those are three gaps within the gap. Broadly, with SVB going down and other regional banks pulling back, there’s just a huge gap in the venture debt market in terms of the amount of capital available. Just for statistics’ sake, I’ll leave you with this. In 2022, there was about $35 billion deployed in venture debt in the US. Through the first half of 2023, $6.5 billion. On an annualized basis, the amount of venture debt that’s been deployed is about two-thirds lower. That’s by volume and also by a number of deals.

There’s been a total dearth of venture capital in general, and that also leads to less venture debt as well. I don’t think anybody’s going to step up to fill that gap because the banks aren’t going to do it because they’re going to be saddled with commercial real estate books going down the tubes. Consumers that are over levered, that have too much credit card debt and people that can’t afford to move because mortgage rates and housing prices are too high. I think there’s going to be a gap for a while. I don’t think it’s going to be this short-term dislocation. I think this is more like a 5 to 10-year.

I think you’re in a great space, and I agree that there’s an immediate term, 1 to 2-year opportunity, and there’s a 10-year opportunity because people will learn how to use these tools in their capital structure appropriately, and more investors will get comfortable with the risks that do exist. Lending to any one of the three categories of companies I mentioned is risky business. You need to be careful, thorough and rigorous about your vetting.

As I said, in passing, but I want to double emphasize it, each of those three categories has many companies that are not credit-worthy, and you shouldn’t lend any money to them on any terms. However, there are a few, a subset of those companies that are enormously credit-worthy. The fact that none of them can get money means that you can lend to the enormously credit-worthy ones at terms that are very attractive because there aren’t any real other alternative sources for them. It’s a tremendously important vetting game because the reason most people avoid lending to companies like this is because most companies like this don’t deserve your money and they’re not going to be able or willing to repay you.

This is where VPN comes in very handy, in the sense that you guys help de-risk a lot of these risky opportunities. Where others are not able to generate the insights that you are, I think it adds a ton of value. You’re basically taking opportunities that don’t have as much transparent data because of private markets and there are other stages. There’s a lack of data, but then you’re thinking, “How can we scenario test this in a bunch of different ways,” and, by extension, stress test it and figure out where there will be chinks on the armor.

Whereas there’s going to be a weakness that you might not be able to see with the naked eye, but using deep analytical processes, sometimes harnessing AI, not always, but many times. You’re going to be able to find potential weaknesses that the traditional lender or the traditional capital provider couldn’t.

 

We like to think so. We think that the very process of doing the analysis helps you make a better decision. If you add to the process of doing the analysis, doing it in a rigorous, thorough, computer-assisted way using Bullet Point Network gives you a real advantage because you do want to think about the good, the bad, and the ugly scenarios. You also want to put realistic odds on each of them. That’s what we’re doing.

We’re not only doing scenario modeling, but we’re adding odds and probabilities or likelihoods to these scenarios. That combination, we think, is very useful and very powerful. We’re also humble in knowing that all models are wrong, including ours, but some are very useful if properly executed. We’re trying to make our models rigorous, thorough, and useful for decision-making.

You know that with the idea of probability adjusting because I would say lay people will tend to think about discrete outcomes, “This is going to happen or it’s not,” but investors who are trained think about everything in terms of probabilities. I might say this is the most likely outcome, but maybe that outcome is only 25% likely to happen.

The second most likely is 15%. Who knows? There’s a whole range. I think that’s where it gets pretty interesting because that’s not how most people think. If you can price according to the probabilities and risk weight according to the probabilities, and those probabilities are more refined and more accurate than how others are thinking, you’re going to have a competitive advantage in everything that you do.

We think so. We think the combination of mapping evidence to assumptions, putting center points and ranges on them, and then making combinations of assumptions to produce logical cashflow scenarios and logical valuation scenarios is going to give you an advantage, but you have to actually do all three steps.

We’re just about out of time, so I wanted to ask what themes you’d like to leave everybody with or any thoughts about the next part of the cycle. In the next 3 to 5 years, how should people be thinking about investing in innovation, and protecting themselves too?

Final Thoughts On Investing And Innovation

I will end with my optimism. I believe you should never bet against growth. I think most, if not all, fundamental growth comes from innovation of one kind or another. I’m a big fan of finding things that are truly better. They are truly a better mousetrap, to use the analogy. The difficulty is wading through the many things that sound promising and finding the few that will ultimately deliver sustainable growth, profitability, and a defensible load. I think the way that you do that is by a combination of story, qualitative judgment and numbers quantifying that.

Finding growth in innovation means waiting through the hype and identifying true value. Share on X

Whether you use Bullet Point Network strategic scenario model framework or whether you use a yellow pad and just jot down your notes and think about the story and the numbers and the odds of different outcomes occurring, I think you’ll be in very good shape in terms of going through the process of finding the winners, avoiding the losers, and protecting your capital. Defense may win championships in sports. Defending your capital is also very important because there are lots of ways to lose money, including investing in hype that doesn’t translate into results. It’s also possible to falsely believe that you’re going to be able to turn a company around.

One thing I will say that may be counterintuitive but there are just very few successful turnarounds in tech innovation stories. You do want to find things that are working and back them. It’s very rare that you turn around a technology and have it become a winner. That’s more common in an industrial, manufacturing or distribution context. In a product technology context, it’s very rare. You definitely want to find things that are working and then back them to scale as opposed to turning things around.

It’s been great having you on, Mike. It’s always a learning experience. One thing I wanted to mention, last thought here, is sometimes the risk is in not doing something. That’s what I like to remind people. You can be in treasuries because they’ve got a higher yield now, but the reality is when you’re doing that, there’s an opportunity cost and you might miss some really incredible opportunities on the innovation side.

Great being with you again. Thanks for your time as well.

Closing Remarks And Double Episode Announcement

Thank you, Mike. Thanks, everybody for reading. This will be a two-part episode with Mike because we went over time. You get a double dose of learning.

 

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• Zack Ellison on LinkedIn
• Applied Real Intelligence (A.R.I.)
• 7 in 7 Show with Zack Ellison on Apple
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• 7 in 7 Show with Zack Ellison on YouTube
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• Mike Ryan on LinkedIn
• Bullet Point Network
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• Mike Ryan on Twitter
• From Star Apprentice to Leading Venture Capitalist with Kelly Perdew
• 7 in 7 Show Disclaimer

 

About Mike Ryan

Mike Ryan is the Co-Founder and CEO of Bullet Point Network. An active private investor, entrepreneur, and board member, Mike has almost 30 years of investment, capital markets, and management experience.

In addition to founding and overseeing BPN, Mike served as Board Chair of the Alpha Partners Technology Merger Corp and has been the CIO of MDR Capital, his family office, since 2009. Before founding Bullet Point Network, Mike served as Head of Public Equity and Absolute Return at the Harvard endowment, Head of Global Securities (overseeing equities, fixed income, currencies & commodities) at Credit Suisse, and Partner and Head of Global Equity Products at Goldman Sachs, where he spent the first 18 years of his career.

Mike has served as a director, committee chair, and board chair on numerous corporate and non-profit boards and is a venture partner or advisor to several growth equity and credit funds. He graduated summa cum laude from Yale University with a B.A. in economics and was an Academic All-American in basketball.