Evan Reiser, CEO of Abnormal AI, and Rob Mazzoni, technology sector lead for late-stage growth private investments, join host Thomas Mucha to discuss how innovations in cybersecurity and AI are poised to disrupt everything from the software industry to geopolitics.
Evan Reiser, CEO of Abnormal AI, and Rob Mazzoni, technology sector lead for late-stage growth private investments, join host Thomas Mucha to discuss how innovations in cybersecurity and AI are poised to disrupt everything from the software industry to geopolitics.
In this episode:
3:00 – AI reshaping software
5:30 – Investing amid AI innovation
8:30 – Disruptor versus disrupted
11:30 – Intersection of AI and cybersecurity
16:45 – What’s top of mind for CIOs?
18:40 – Impact on geopolitics
23:00 – Parting words for founders
24:05 – Investment implications
Evan Reiser: First, we all have to acknowledge the nature of AI disruptions unlike anything we've ever seen, if you take, you know, one of the last big technology shifts, the cloud. The core technology, the cloud architecture, we figured it out kind of all at once. And it took us ten, 15 years to fully get the benefit and maybe there's a 20 or 30% productivity improvement to those companies to move the cloud. With AI, right? It's moving 10x faster and it's core technology, even if you just froze the progress, the foundational models today it would still take us ten years to figure out to fully apply those. But as Rob alludes like every day they're getting smarter and smarter and smarter. And so there's compounding opportunity to disrupt. Right. Because the technology's improving and the applicator technologies are improving every single day. No matter who you are, things are moving faster than you think. And the way I see businesses today is we're all, like small, slow golf carts going down the highway. And we have a bunch of F1 cars sneaking up behind us, right? If you don't kind of realize that's the case, you're probably going to get left behind.
Thomas Mucha: AI and cybersecurity are poised to be at the epicenter of innovation investment and, of course, geopolitical risk, really for years to come. Now, as their share of investment dollars has grown, so has the importance of AI and cybersecurity grown in terms of both national and economic security. So today, I'm very excited to welcome two guests who are both on the leading edge of this technological disruption, Rob Mazzoni and Evan Reiser. Now Rob is Wellington's technology sector lead for late stage private investments. So he's got a front row seat to where much of this innovation is happening. And Evan is the CEO of Wellington's private portfolio company, Abnormal AI, the world's largest AI native cybersecurity firm. He's also our first private company CEO guest to the podcast. So, Rob, Evan, thank you both for being on WellSaid.
Evan Reiser: Thank you so much for having us.
Rob Mazzoni: Thanks a lot, Thomas. Excited to be here.
Thomas Mucha: All right I'm going to start and direct my first question to you, Evan. So let's begin with the broad topic that's probably top of mind for our audience and that you're particularly well suited to address. And that's how you perceive AI potentially reshaping the software industry. So specifically, Evan, if we were to, let's say, you know, boldly speculate that software development costs dropped by ten or even 100 times, what are some of the consequences that you envision?
Evan Reiser: It's a great question, Thomas. I think, certainly the changes are going to be big. I think no one would be surprised to say that. I think there could be a lot bigger than we even expect today. I think you will see exponential acceleration of AI technology. I think some of second order consequences can get a little crazy. So I'll share some specific examples and just forgive me if it's sounding too science fiction here. I think the first one is like there's gonna be explosion of software supply, right? Every idea's gonna get built right, whether it's internal IT teams or outside in the kind of greater market. There’s gonna be way shorter product cycles. The second one is going to be a redefinition of software moats. All the execution moats are going to vanish, right? The windows of competition are gonna get very short. I think distribution and brand become way more important as like the defensive moats. Unique proprietary code bases won't matter that much, right? In the age of kind of AI generated software, obviously, I think data becomes the deepest moats, right? As these AI powered products get more powerful. There's gonna be a bit of a shift in the talent value chain, right? What used to be 10x engineer, maybe they’re a 100x engineer, right? Or 100x product manager that can run entire product line themselves. Obviously we're seeing way more internal tools and more automation. We see this our company where our kind of build versus buy equations totally shift. We're now building all these things ourselves. Right. Because the AI tools are very data hungry. We don't want to expose our internal data or customer data. And I think the cost is very cheap. So I think like there's going to be just lots of changes. And I think the final thing I can add on is there's also be a lot of like regulatory and security chaos. It's gonna be very easy to vibe code, you know, the next cool AI agent to help us inside the company. But how do you run that. How do you secure it, how do you protect it. What are kind of like a second order consequences at the software industry or societal or government level? It's going to be a little bit confusing, right? How do you do IP enforcement in the era where you have a billion people vibe coding things based on everything on the internet, right. It's very hard to do. So. Definitely a crazy time but exciting time. And certainly AI’s going to enable unprecedented amounts of productivity, and efficacy and efficiency in every business and probably all of our personal lives as well.
Thomas Mucha: Yeah, I can vouch for the policy confusion. I spent a lot of time in Washington, and pretty much every meeting I have with senators or representatives these days ends with, “I gotta wrap this up. I've got a symposium on AI that we're trying to figure out that I got to run to.” And so, I'm feeling that in my world, too. So, Rob, Evan just laid out a very fast changing landscape. So how are you thinking about moats for software companies? And what I mean here is that in a world where great software can be built with little friction. And as Evan said, even without developers, does software become commoditized and less defensible from your perspective?
Rob Mazzoni: Yeah. Thomas, that's a great question. I would say that Evan's right that things are changing at an incredibly fast pace today. If you look at the foundation models which are powering much of this wave of innovation, their revenue growth is unlike anything that we've seen before. One of the leading LLMs has exceeded a $4 billion revenue run rate, just two and a half years after it began its initial monetization. And another leading LLM has gotten to a scale that's even greater than that. With primarily businesses as customers, to my knowledge, that's the fastest growth ever from first monetization. This hypergrowth shows that enterprises are quickly adopting AI for jobs like software development. Think customer support and copilots for, you know, all of us knowledge workers. The global economy is something like $100 trillion of GDP. Knowledge workers make up something like 40% of that. So this is $40 trillion that's potentially getting reshaped by AI. That's what's so exciting about being a growth investor in technology today. But for the digital economy, the 15% of global GDP that's already powered by technology, we think the deck chairs could be shifting quite significantly. Data to train and refine AI models, the distribution to millions of customers, trust among those customers and high ROI, we think are all keys to winning in the AI era. Incumbents, if you think about those vectors, generally excel along the first three of those. They have lots of data. They have millions, if not hundreds of millions of customers. And they have trust that's developed over years, if not decades. What startups can do to compete is move really fast to build high ROI products that customers feel so compelled to buy, even though it's sold by an upstart company. High ROI from AI startups is, I think, what's driving some of the parabolic startup growth that we've seen. I think the last comment I'll make on this idea of moats is on execution. We focus, at the private growth stage, a lot on company leadership, even at this stage when you could say, oh, it's all comes down to the numbers. Where we've gotten things wrong, I would say in private investing has often been not leaning hard enough into exceptional management teams, which I would say tend to build and lead teams that execute competition. They're often far more adaptive to change, and they compound their businesses at higher rates and for longer periods of time. In this way, the moat today is relentless execution, as it always was. But maybe it even matters more today in a period of, you know, massive transformation like we're seeing.
Thomas Mucha: Yeah. So the disruptive nature of the industry means you better have the right person at the helm. So, Evan, we've talked a lot about change already and disruption already, but as an AI native cybersecurity company, you're trying to navigate this pace of change. So from your perspective, how do you think about staying the disruptor versus becoming the disrupted?
Evan Reiser: First we have to acknowledge, just like the nature of AI disruptions unlike anything we've ever seen, if you take, you know, one of the last big technology shifts, the cloud. The core technology, the cloud architecture, we figured it out kind of all at once. And it took us ten, 15 years to fully get the benefit and maybe there's a 20 or 30% productivity improvement to those companies to move the cloud. With AI, right? It's moving 10x faster and it's core technology, even if you just froze the progress, the foundational models today it would still take us ten years to figure out to fully apply those. But as Rob alludes like every day they're getting smarter and smarter and smarter. And so there's compounding opportunity to disrupt. Right. Because the technology's improving and the applicator technologies are improving every single day. No matter who you are, things are moving faster than you think. And the way I see businesses today is we're all, like small, slow golf carts going down the highway. And we have a bunch of F1 cars sneaking up behind us, right? If you don't kind of realize that's the case, you're probably going to get left behind. I have probably at least one meeting a day on AI transformation with our R&D and IT teams, like, well, what are we doing this week to make progress? And I feel like I'm being left behind. So back to your question how do we think about disruption. There's kind of three different categories of disruption. I think AI’s disrupt the world of ways. One is market disruption. There's some problems that exist in the world today that are gonna become more important in the age of AI, right. There's some emerging problems. Right. And so I think every business to think about how is AI effecting the markets and like what gets bigger or smaller or more or less important? The second one is, you know, AI is also a product disruptor. The way you solve those old problems and the new problems, the way to do that is different in AI. AI enables new things that weren't possible in the past or may have required or impractical may required a million humans do work 24 hours a day now can be done, you know, very practically. So I think it's kind of the second dimension. So we're looking at how can we apply to all of our, our products, our solutions, and our internal processes. And the third one is AI’s an enterprise disruptor. Right. The way businesses should work in the future in the age of AI is way different. We can have AI today that listens to half of all of our customer feedback calls and aggregates all the information, generates product improvements, submits a pull request, reviews them within five minutes of a customer giving some feedback. Those things weren't possible in the past. No matter how smart your company was. I think all of us need to think through like, how should a company work in the age of AI, rather than kind of like just making incremental improvements? Even though I think Abnormal is advanced in our own AI transformation, I think we still have a long ways to go. And the opportunity ahead of us is so, so incredibly high. We're still in the very early innings. Right. I know that we're going on a very extreme transformation journey very quickly. And I think that other companies that don't are gonna get left behind very quickly.
Thomas Mucha: Hey, you gotta keep tinkering with that golf cart, huh?
Evan Reiser: Yeah gotta start swapping some jet engines pretty fast. I think.
Thomas Mucha: I think AI is obviously a geopolitical disruptor as well, one of those key areas that's disrupting my research, of course, is cybersecurity. So, Rob, let's delve deeper into that. It's one of my favorite topics on this podcast. Wellington has invested, of course, in several private cyber security firms over the past 18 months. So, Rob, from your perspective, what's really driving this interest?
Rob Mazzoni: Evan's the expert here, but I'll share a few key observations on what we're seeing. I would say first, AI agents are exploding in usage. OpenAI, for example, has browser using agents that will research trips, then book flights and hotels for us. They'll be sending emails and money on our behalf quite quickly here. We'll certainly want that buttoned up from a cybersecurity perspective. Agents are also very hungry for data to carry out these tasks, and finding a way to get those agents provisioned access to data at the right time to do their jobs. Then deprovision that access and also make sure the wrong agents don't get access to sensitive data, that's going to become increasingly important from a cyber security perspective. That's number one. Second, we've seen that AI is lowering the barriers to building software. Evan has touched on this multiple times already in this discussion, if software, including entire applications, are getting built much more quickly and often by non developers, we need strong cybersecurity practices to make sure those applications are not vulnerable, don't contain harmful code, etc. I've heard a founder of a high growth cyber company say there's no way today to vet software code as quickly as AI can now create it. And then the third thing is that an unhealthy byproduct of all this investment in AI infrastructure, model training applications built atop frontier models is that it's putting more tools in the hands of attackers to spin up attacks. To use Abnormal’s language, you need good AI working on the behalf of companies against this proliferation of bad AI that's in the hands of attackers. So I think the combination of these three factors is driving higher growth rates for cybersecurity companies today.
Thomas Mucha: Yeah. So there's a lot of live action in this space. You know, I think our Value Creation Team, recently surveyed portfolio companies about their AI usage, about 18% are using AI in their cybersecurity operations today, according to that research. But more than half say they're planning to ramp up their AI usage in cyber. So, Evan, what do you foresee then in terms of AI agent adoption across enterprises? And you know what will this mean for security firms?
Evan Reiser: Yeah, appreciate the question, Thomas. I’ll quickly just add on to what Rob said. I think if you look at how AIs affecting the overall threat landscape, there's a couple of things which feel a little bit disturbing but I think obviously true. One is like AI it's just going to make more criminals, right? There's some criminals out there that didn't have the technical skills to do like scans and stuff. Now they can. I think the second thing is that every criminal will be able to send out more attacks. Whether it's different forms of automation or from AI generated scripts, or maybe just using the large language models themselves, we’re actually gonna see more stuff. The third piece is like every attack is going to get more sophisticated. In the past you, if you wanted to go, I don't know, hack a bank, you had to be like kind of smart, right? Break a firewall. You know how to hack a satellite. Now you can just ask the AI, like, well, tell me, what are different ways that banks send money out to people and, who's involved in that and like, where their phone numbers. How do I get ahold of them. So I think that those are three things. There are three realities that anyone who's played around with AI can, like, easily imagine. Right. So I think we're going to a more difficult place from a global security perspective. If you look at the enterprise, I think there's going to be huge, massive adoption of AI different ways, the rise AI agents. There's probably like three big cybersecurity challenges I would call out. One is there’s just the rise like the number of identities. You got more stuff inside your organization that's doing things, more things to monitor, more things to provision access to. I think that gets more complicated and more digital identities. You also have these AI agents that are very data hungry, they want access to lots of customer data, lots of internal data, finance data, HR data. So the kind of the complex graph of what things can access what things for what reasons, dealing with all the privileges and entitlements and permissions. That gets way more complicated. The third piece, which I think is like the most scary for me, is these AI agents move super quickly, right? They're making like a thousand requests per second. So part of the current globally accepted paradigm for cybersecurity is that for a lot of security use cases, you can pump a bunch of data into some system, attract things, pop up alerts, right. Show humans them. Humans can make decisions, and then you can go stop the attack. Well if the AI agent is making moves in ten milliseconds, right? You can have the world's best forensic investigators in your security operations team and you won’t be able to stop anything. So I think that requires kind of a new paradigm for how we approach global cyber security. While we're best known for doing email security. Really, we're an expert on understanding identity, behavior and context. We integrate into a bunch of different IT systems, right? From Microsoft 365 to Google to work data Salesforce. We map out all the identities, whether human identities or non-human identities. And we create these baseline models for behavior. And then we see anomalies, right? Whether it's a weird Teams message or a weird email or a weird activity from someone using a Microsoft account, we might see that as identity compromise and go automatically detect that, automatically respond to that in milliseconds. As Rob said, we're trying to be the good AI to automatically identify bad usage, whether a human or for an AI agents. That's what we do today. And I expect we can do more to protect our customers from rogue AI agents in the future.
Rob Mazzoni: Evan I just wanted to ask, you spent a lot of time talking to customers, including, chief information security officers at Fortune 500 companies. What's the top 2 or 3 things on their minds today? What are they most asking you about as a cybersecurity leader?
Evan Reiser: I would say that the biggest thing is I think everyone started to wake up and realize, like how powerful these agents can be. Very few people have gotten even a small percentage of the full opportunity. But I think we're all realizing that these things can improve the customer experience, improve all parts of our P&L, and have a big impact on how we do business. It’s becoming easier to build these applications, right? I think every CIO is frustrated cause they've seen really cool demos right? From their IT team, but it's not super clear: How do you deploy these in production? How do you give it access to sensitive data? Who's monitoring these things to make sure they go crazy and rogue. That's a big challenge. So I don't think it's quite top of mind yet right now, but I think in the next year the big theme is going to be how do I safely roll out these agents? Who's monitoring these kind of new digital identities? How do we make sure they have access to the right things and they aren't doing like abnormal crazy things? That's kind of the big concern right now. And it's less of like a tactical, like how do we fix our problem and they usually have some bigger questions that are more industry wide, like are the standards for how computers communicate with each other – Is that even acceptable? In this new age of AI-to-AI communication. So I think as a technology industry and IT industry in the cyber industry, right. We got some major questions to answer. Can humans even be in the loop and monitoring these things. Like what role can humans play in cybersecurity? How much more do we have to lean on AI as our chief perceiver, or an actor, right. Decision maker, right. Those are the bigger questions I’m being asked.
Rob Mazzoni: Thomas, I want to spin around and ask you a question here. I've heard you highlight both on this podcast and directly to our portfolio companies that these trends around AI have major geopolitical implications as well. I have also learned from you that there's never been a time in history with more active conflicts globally than today. Will the nature of geopolitical conflicts and even warfare change due to AI and cyber attacks. What are the risks of falling behind in this technological arms race?
Thomas Mucha: Yeah. Thanks, Rob. Those are really important questions. They do occupy a lot of my research agenda. I'll make two big points. And yeah, you're right. We've got more than 60 active conflicts happening around the world right now. That's double the number of where it was just five years ago. And what's happening is you've got this long geopolitical cycle coming to an end amid rising great power competition and other historic stresses. So we're getting a lot of wars. And this, of course, includes the biggest conflicts in Europe in decades, the biggest wars in the Middle East in decades. I can go on and on. But in this more unstable and dangerous geopolitical backdrop, what I'm finding is, is that national leaders are focusing more, of course, on national security. And I don't mean only bullets, bombs, missiles, other traditional defense applications, which of course they are. But also this so-called gray zone conflict. And what I mean by that are actions that fall just short of actual military conflict. They're designed to be deliberately ambiguous. But they are intended to degrade the military intelligence and the economic capacities of adversaries. And gray zone conflict has a lot of dimensions. We're seeing more sabotage. We're seeing interference in domestic politics, disinformation campaigns and others. But at the top of that list is cyber for a lot of reasons. And that's because these kinds of attacks fit the mold in terms of, you know, they offer plausible deniability. They're open to both state and non-state actors. I've been talking a lot about criminals. These are important non-state actors in the geopolitical arena. They can be difficult to find and track. They're relatively inexpensive to operate. And, of course, they can do all sorts of important damage to adversaries, from intelligence gathering to, crippling of critical infrastructure, even to military weapons and other aspects of actual battlefield warfighting. So the use of cyber warfare, both offensive and defensive, is central, I think, to how conflict is playing out today. We're seeing it in Ukraine. We're seeing it across the Middle East and elsewhere. And so I think it's an increasingly important tool in the toolbox of military leaders, political leaders and these non-state actors for the future. And the second point, and it's related, of course, is the military application of AI. And almost every national security policymaker I speak with in Washington, Brussels, Paris, Berlin, all across Asia, they underscore the centrality of AI in the current great power competition and this unstable geopolitical backdrop. For the simple reason that whoever wins AI, meaning whoever applies it most effectively across their military and economic capabilities, is going to have a huge leg up as this new world shapes up. And that's why we've had so much focus, for example, here in the States on semiconductors and other AI inputs, there's a core national security issue that flows through the whole AI picture. And so I do think we should expect AI, to continue to be a key driver of geopolitical tensions, really for the foreseeable future. The demand curve on this is certainly going higher. And again from my perspective as the geopolitical strategist here, it goes beyond today's dominant market narrative, which is largely about, improving efficiency, lowering costs, boosting margins and the like. I think we're on the cusp of some very potentially dramatic developments given these rapid changes in the national security, economic, and especially the technological environments. And so, AI is central to all of this. It's critical to great power competition, right? It's China, it's the United States, it's Russia. Everyone is trying to get here first. So, I'm going to end this conversation with a question from both of you. We’ll start with Evan. And you know we've said a lot about how the next let's say, 3 to 5 years might go from an AI, cybersecurity, a nation state perspective. So, Evan, any parting words for how startup founders, should best position themselves? And then, Rob, you can jump in. I've got a question for you: How best can investors position themselves in this very dynamic environment?
Evan Reiser: First of all, we talked about some heavy topics. Like I am still an optimist, right. We are seeing one of the most interesting technologies going to change the world, I think for the better in most places. So I think my first advice like let's stay optimistic, right. Even though there are some practical realities we have to confront and solve. I think AI is the uber trend, right? There's never been a more interesting and powerful technology, it may be the last invention of mankind. So I think, like you got to start with AI. And my advice to other founders entrepreneurs is the best place to be is to be looking at what markets or what problems become way more important with AI, and where can those problems be better solved, and maybe new novel ways with AI? I think if you're in that zone, ideally both zones, right. That's like a very interesting place to be. And just because this is a very unique window of opportunity with the way that we live our lives, the way businesses work, the way we built products is going to fundamentally change. And I just can't think about a more exciting time to be in technology development.
Rob Mazzoni: Yeah. And I would just I would just tack on and say that, you know, all investors are hyper focused today with good reason on which sectors and companies are AI beneficiaries and losers. This is a tidal wave, to Evan's point, that if you can identify the winners, the ride should be long and sizable. It's also probably a good time as an investor to find some safer havens from AI crosshairs. You know nothing is fully safe as AI becomes more powerful. But for us, with our current investment horizon, that's been some areas of financial services, including credit and brokerages, health care including devices. I also made the point about company leadership mattering even more in a period of transformation. I might go a step further to say that investors should find management teams that are taking meaningful swings to reshape their organization, to get on the right side of these trends by adopting AI tools, hiring differently, thinking more critically about the product roadmap now and what's attainable now using AI to ship better products faster and capitalize on their customer’s trust, and mandating heavy AI usage throughout their organizations. Because my take is the most front foot leaning teams are both least likely to get disrupted and also the most likely to capitalize on this wave.
Thomas Mucha: All right, well, you both ended with some relatively optimistic comments there. I'm going to take that as a win. And thank you both for being with us here on WellSaid, that's Rob Mazzoni of Wellington Management and Evan Reiser, CEO of Abnormal AI. Thanks again for being here, guys.
Evan Reiser: Thank you, Thomas.
Rob Mazzoni: Thanks, Thomas, that was fun.
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