Legendary Hacker Matt Suiche on Cyberwar in the Age of AI

11:05Matt Suiche:Is that projection, Joe? I don't delude myself.

11:08Tracy Alloway:No, I like actually like I sort of look at my screen and I know that I'm being inundated with contextless garbage and slop and propaganda and so forth. I'm curious how you monitor the situation actually as someone who takes these topics seriously and doesn't just sort of become an overnight expert, you know, the day after bombings begin. And like, how do you pay attention to what matters? How do you actually know what's real and so forth and avoid just sort of the delusion of staring at the screen and engaging with slop?

11:42Joe Weisenthal:It's a good question because there's so much of it. So I think the default reaction is to ignore most of it unless it becomes really significant. In this case, I think it comes down to looking at the actual damage. many people from the military world but also the intelligence community has been underestimating our own capabilities exactly like people used to do with north korea and now north korea are some of the best hackers in the world when we see them like targeting financial institutions whereas before they would not do like much so there is definitely like internal capabilities that are available but there is so much noise now like you say a lot of people are monitoring the situation, giving that quote-unquote overnight expert opinions, and that's becoming a lot of noise.

12:31Joe Weisenthal:But I would say that in this particular case, we have heard of the imminent threat of Iran for around 40 years. And that's also not really a new situation. So most of people would have context around it. And even for like the attack that happened last weekend, many people were expecting them for weeks, especially as they are a continuation of what happened last summer.

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14:28Tracy Alloway:I sort of would assume that cloud service providers, that it's fairly liquid. Okay, one goes down, but it can just be the same software can be run from numerous other clouds. But I saw that there were disruptions. I saw Fortnite tweeting about the fact that some of their gameplay was impaired due to the attack on data centers. How disruptive have those attacks been? Because this is, of course, a very, you know... This is where kinetic meets cyber. Yeah, and there's a lot, you know, in the future, like thinking about hardening these data centers and as you say, like making them, they're going to be increasingly targets for war.

15:05Tracy Alloway:Like how disruptive was that?

15:07Joe Weisenthal:Very good question. So I think one of the main takeaways is that it has been extremely successful. So like we said before, like a Shahid drone is around$20 ,000 and they managed to shut down two of the zones of Amazon. Actually, even if you look at the official report from Amazon, for like 36 hours, they were just saying, some objects struck the data centers before they actually explicitly said there were drone strikes. So a lot of services that have been using them have been targeted. So from like local applications, from two banks, because in a data center, you are taking care of multiple different services.

15:47Joe Weisenthal:Right. And even Versal had to reroute their data to Bombay and to exclude Middle East as deployment. So even if you take the cost of most of zero-day exploits that can go up to multi-million dollar attacks, if you are really aiming at destructing things, the cost reward of using such an attack is really efficient. So you really enter into some sort of asymmetric conflict where you can just spend like some really old material and have way more impact than someone who's going to be like cutting edge and just trying to impress with like capabilities, because at the end of the day, it does not really matter.

16:34Matt Suiche:How do governments actually build up their cyber capabilities nowadays? is because I have this image in my head of maybe 10 or 20 years ago, you know, they would recruit like a 20 year old such as yourself at the time and they would be working in a dark room, that sort of thing.

16:51Tracy Alloway:But then Red Bull, drinking Red Bull.

16:53Matt Suiche:That's right. But then, you know, we had the boom in Silicon Valley. And so you had competition from private companies. Now we have the boom in AI and again, even more competition from private companies. And at the same time, governments seem to be, I guess, seeding some of their own skill set to potentially private companies like Anthropic and ChatGPT and some others. Walk us through how, I guess, the development of governmental cyber capacity has actually shifted.

17:25Joe Weisenthal:I mean, something that didn't really change over the last years in terms of capabilities, I guess we all remember the Snowden leaks in 2013 when we started to see more about the inside of capabilities from a government, including domestic mass surveillance, global surveillance, exploitation capabilities. And since then, every other year, we have seen an history of data being leaked that belongs to the government. So in a way, things have been changing a lot, but not really much. Like most recently, there was a contractor from L3 Harris that was sentenced to 87 months sentence because he happened to sell zero day exploits to a Russian broker.

18:17Joe Weisenthal:And that's like actual exploit that belonged to the government because there was some sort of integrator. So we see like nation states or like governments like the U.S. investing like enormous amount of money into offensive capabilities, but they also keep being burned by insiders. A lot of those capabilities are also as strong as the internal coercion.

18:42Matt Suiche:But I guess what I'm asking is, you know, if you're the Department of Defense, or I guess now the Department of War, and you're thinking about developing in-house capabilities versus partnering with a company like Anthropic, and we should talk about all the drama that's going on there. How are you balancing those decisions nowadays versus, say, 10 or 20 years ago?

19:02Joe Weisenthal:My understanding is that now a lot of it is also outsourced because they cannot really develop as many capabilities internally. So now we have seen with Entropic that it had been used in the Maduro operation. And then after that, there was a pull out from Entropic because they said it was violating their ethical policy. So I would say now something that's really changing very fast is the incorporation of AI into those decisions. But as we all know, AI can also hallucinate. So even Dario, the CEO of Anthropix, said it's definitely not in a state where it can be used for fully autonomous decisions like that.

19:55Joe Weisenthal:So I would say the AI element would be the main difference. Even we start to see it now for exploit development or vulnerability discovery, but it's still too early to give a definitive opinion about it. But overall, I would say it's very similar.

20:13Tracy Alloway:Well, talk to us about exploit development, because I know that you can't just go to Claude Code and say, like, I'm working on a zero day malware attack. Help me figure this out. But, you know, I also know that there are some very talented people who pride themselves in being able to jailbreak AI and elicit outputs that the labs do not want their AIs to produce. So do you have a sense how just within the pure hacker community, AI is being employed today for these purposes or what they're able to get out of these tools?

20:46Joe Weisenthal:It's a good question. So we started to see people leveraging AI for bug discovery, which actually is becoming pretty good. I think even Entropic published an article explaining how cloud can be used for discovery into smart contracts and how it found some bugs automatically. And I think even recently they released something called Cloud for Security that was aiming at doing a code assessment. But now we're entering into this interesting paradigm shift where the cost of software is going towards zero. So if you're a company and if your cost of building software is becoming less and less, it's also hard to convince people that auditing software for security reasons is going to be more expensive than developing the actual software.

21:37Joe Weisenthal:So I think that's one of the shifts we're going to see. But when it comes down to... Wait, sorry, can you explain? Sorry, pause that last part. What did you mean by that? It's going to be hard to convince. If you're going to have like to allocate budget for like building a product. So you have most of the budget that's usually allocated for like your software engineers to build the software. and then you do some code review afterwards to make sure there is no vulnerabilities before it gets released to the public. But security's risk is usually pretty high. You cannot just rely on AI tools, at least not at the moment.

22:15Joe Weisenthal:Maybe in a year from now, it's going to be possible. So it's going to be really interesting to see how it's going to do a market shift because now with Cloud Code, And as the famous vibe coder, Joe, I'm sure you know that the cost of building software is approaching like zero if you just look on the timeline.

22:37Matt Suiche:Are you actually on this note, are you a believer in the SaaSpocalypse? Because obviously there's the argument that, well, now everyone can just create their own software fairly easily using natural language. But on the other hand, if you are a big corporation or presumably a government, you're going to want to have you're going to still want to buy software from an external provider, given some of the security concerns, given that it might not necessarily make sense for various reasons, management reasons, perhaps to recreate an entire software business in-house.

23:10Joe Weisenthal:So I'm definitely biased on that. But as someone who thinks like the cost of software is going towards zero and as someone who is like watching the software costs like collapsing, one of the things that we realized is that data is the only durable asset in the AI economy. That's why we decided to work on DB, the current startup I'm working on. Because like that's the only thing that's really going to have value long term if agents need something to transact or to take decision on. Because even if you look in terms of like in any context, if agents are designed to think autonomously, you need to have enough information to take those decisions whenever you're going to have your reasoning loops.

23:54Joe Weisenthal:So software itself, if you just build it, is pretty static. Whereas the agentic feedback loops are more dynamic. But what changes is the context they take decision on. So definitely SaaS business are going to have a hard time because if anyone, including the Shopify CEO, can just rewrite an MRI software in one afternoon just to look at his back MRIs, you can imagine how disruptive it's going to be by the end of this year. I think the only thing we haven't seen yet is enterprise AI agent. So far, I would say since Christmas, people are mostly still playing around, trying to find a proper use case.

24:41Joe Weisenthal:We see a lot of consumer AI agents, open-clothed that really made agents more mainstream. But we really haven't seen yet enterprise AI agents. So as everyone is kind of scared of being replaced for their jobs, we haven't really seen actual AI agents replacing entire departments or full-on employees. So we have seen some disruption around software engineering mostly to make software engineering more efficient, especially in terms of development with shorter timelines. But we haven't seen yet proper enterprise AI agents.

25:20Tracy Alloway:How do you define an AI agent?

25:22Joe Weisenthal:So my view of an AI agent, and I like to remind people what an AI agent actually is, at the moment, most of AI agent is just like a piece of code usually written in Python or in TypeScript that's just doing a bunch of calls to like Entropic OpenAI and running in a loop and taking decisions and calling like third party tools like MCPs or web searches. So that's mostly what an AI agent is. We tend to think of AI agent as a completely different persona. But at the end of the day, it is just like a piece of software that's running as a service on a machine or on a server. So from a security standpoint, which is pretty interesting, it's just like another service or software.

26:12But people really like to think of it in another way.

26:16Tracy Alloway:Well, just so like from the security standpoint, I mean, one of the exciting aspects of AI agents is that they can work autonomously, right? You set a task and it can go out and find what it needs to do. And it seems like, okay, this didn't work. I'm going to try. It's going to try this thing. It's going to try this thing. Oh, I need to connect to this web service to get this information, et cetera. The downside of AI agents is precisely the same. The downside of AI agents is that they could do whatever they want to do. And if it accidentally deletes a bunch of files because it thinks that's what's necessary to execute the task.

26:51Tracy Alloway:So, like, I'm curious, like, from a security standpoint, like, I mean, we've already seen examples of people getting private information exposed or, as I mentioned, the example of deleting a bunch of information. Is this like a new way to think about the security threat model, the fact that the capability and the downside are one and the same? It's the same. It's sort of like hallucinations, right? The ability to like create an output and it also, you know, is hand in hand with the ability to create a wrong output, a false output. And so, you know, the ability to do to act on its own is also the ability to destroy on its own.

27:27Tracy Alloway:Is this sort of like a novel threat model or a novel paradigm in thinking about enterprise security? From an enterprise security standpoint, it is pretty much the same thing in the sense of like, if you're building software, you cannot just really like patch software afterwards and stuff because it never ends.

27:48Joe Weisenthal:Like security must be like built in and you need to have like a safe design from the beginning. what we have seen now is like whenever people do something agentic they just give like all permissions up front yeah which is like probably the worst thing you can do and if you're an enterprise as you can imagine if you just give like all permissions to an agent it just becomes murphy's laws if something bad can happen because you gave it access to it will happen so you're And I see like more data leaks for sure, because there is no safety by design in those like architectures or like those like agents, which is in terms of vulnerabilities and like exposure would be like very similar to what we have seen over the last like 10, 20 years.

28:35Joe Weisenthal:But, you know, if people are ignoring what has been done in terms of software security for the last 20 years, that's why we're going to have a lot of problems. And I think we're probably going to start to see like people, especially in enterprise, like pushing back a lot because there's compliance that needs to be like, you know, like answered to. So you cannot just give like full access to like, you know, your agent.

29:14Matt Suiche:speaking of the long arc of history one thing i really wonder is you've obviously been in this space for a very long time at this point can you describe how you think your own career and i guess coding experience would have been different if you were, say, starting out now in 2026 versus, I guess you would have started out in like the late 90s or early 2000s, maybe even before that.

29:38Joe Weisenthal:Yeah, mid 2000. Well, I would say like what has changed is back, even like we even without going to like the 2000s, like back in the Snowden days, when the global surveillance program was being exposed like a lot of people were really like scared of it and scandalized by it and pushed back and people really cared about privacy was like now we're entering in a new arc where very few people care about privacy where you see like the CEO of Entropic being asked why he refused to work with the US government. And he says, well, they wanted to do a domestic surveillance program. So that's against or like a safety like chart.

30:31Joe Weisenthal:So this is all aspect of people relationship with data, which I guess is very different. In terms of software, obviously, like now you can write more things. Anyone can write anything. But I think we're still in this weird adequate software phase where we know what AI can do, but it cannot really do anything more, anything less yet. We still haven't seen the actual use case for it because it's obviously very exciting and it feels like a lot of it is very different from before, but we don't really have like any evidence of how it's really helping in national security i know it's been helping people who have been analyzing like epstein's emails because there's a lot of data and that makes it faster but in term of like real use case i don't think the like it feels like the current world is very different than before but there's so much noise and so much like slop all over that in a way it is pretty similar.

31:43Joe Weisenthal:I don't know if that makes sense.

31:45Tracy Alloway:Well, I mean, your company is ONDB.ai. So you must think that it's going to be used or that, you know, or that there's clearly something there. Actually, tell us about, I'm actually really, I'm on your website right now. It looks really interesting because it's something I've been thinking about. But you must have some vision for like where it's going and that there will actually be significant demand for these services?

32:10Joe Weisenthal:Sure. I mean, like I was saying, I think now we've like, anything that's agentic, building software, which is like the main use case so far for AI, is going to make software like going to zero. So the cost of building software is going to zero.

32:24Tracy Alloway:So that's real. So like, in your view, there's no question that already AI, I mean, talk about it. It's a pretty big use case right there, bringing the cost of building software to zero.

32:34Joe Weisenthal:Yeah, I mean, like, even if you look at the author of CloudCode, he said they didn't write like code since November. So like a lot of people are like this, you know, even us internally, like, we definitely like use CloudCode a lot. But if software is going to zero, like what's left in terms of like the internet layers? So our conclusion is that data is the only thing that's going to be like timeless in the AI economy. so building like a layer for that especially now there's like all those innovations around like payments and stablecoin that you can use to actually like pay like anything online yeah so we think like okay if you have like issues like entropy or like open ai just scrapping internet and using like public internet so you may as well find ways of charging like bots or agent for your data so at least you can have this new like revenue unlock that's going to emerge in this like new economy because i think a lot of the traditional like economic model like for instance like we said like with sass are going to be like disrupted a lot so there is a completely like new market around how people are going to consume data and i think people people would just be ready to pay like more to have high quality data because the more noise there is the more you want to make sure that the data you have access to is going to be like valuable and real so yeah even from a security standpoint you know like once you build like an infrastructure layer you can have this like built-in security to make sure that the data you give back or the access like for the interface that you define is actually secure because even if you'd look at open claw for instance one of the top skill was malware.

34:25Joe Weisenthal:So like, people just like living into like wild west, but they just run everything. So it's us anticipating that enterprise is going to look very different, and they won't just run like anything they find online.

34:38Tracy Alloway:Yeah, you know, this is one of the things that I've encountered in my vibe coding for is, is that one of the annoying things is, okay, you want the agent to like go out and grab some information or query some database or whatever. and then it's like, okay, like, let me know when you've gotten an API. They're like, okay, come back and get an API. And then you have to go to a website and then you have to like, get out your credit card and you have to set up an account and then you like get an API key and then you copy and paste it and so forth. And that's very annoying. I want, what I want is for the agent to just be able to go there and say, oh, you know, like, let me just pay you with some stable coins, et cetera.

35:19Tracy Alloway:Just go out and get the information on its own without having this human in the loop. But it also occurs to me, and this is something that I've asked about with others, which is that once I'm just entirely operating in the terminal and the agent is going out and scraping information for me, et cetera, why do we even have a free public internet anymore? And so I'm curious whether the direction of the internet and information in general is just entirely paying microtransaction or fees for data consumption so that the data then arrives in some usable form in the terminal that I'm operating.

35:55Joe Weisenthal:Yeah, no, I think you're raising some really good points. We need to talk after. You can be our new design partner, you know? Yeah, I can be a consultant. He's going to hack all the API keys for you.

36:07Tracy Alloway:I can be a consultant. No, I hate having to deal with all these API keys. Why am I still using my printer?

36:11Joe Weisenthal:Well, you're completely right. See, I told you, you're a famous live coder. But the US case you're describing now makes entire sense. That's why we position ourselves as a trusted provider, where instead of having to go everywhere to get data, you have this single point and unified access. A bit like open router for AI models, but for data providers. Because if you think about it, when you use Cloud now in your terminal, the level one is basically you asking the model itself for information. but as we know, the model may be a few months old and doesn't have access to all the information. So the second level is the agent, like Cloud Code or Cloud for Desktop or ChatGPT doing web searches.

37:01Joe Weisenthal:And that's just them looking on the internet, doing a Google search, et cetera. But that's still not giving you access to the actual relevant data that you would need where, for instance, you would have those API keys and stuff. So like the level after that is basically access to private information. So in private information, usually the one that's valuable, like in the case of Bloomberg, for instance, Bloomberg has a lot of extremely valuable information, but you can only have access to it through the terminal once you have the subscription. Or like any SaaS services, if you think of like SaaS platform as just like some fancy UI where you can like browse the database, but the data that's valuable is just in the database directly.

37:45Joe Weisenthal:so if you'd have access to those apis and we not have to create like 1 million subscriptions left and right because there's already like someone who's doing the integration for that kind of like as a programmatic like api marketplace but the integration now is like much easier and especially if you trust it because now what we have seen also is like whenever you use all those tools they make you install like mcps but more and more people are like moving away from mcps they're just using like skills because like you just said we start to spend more time into the terminal so like the terminal and anything that cla is becoming like a natural interface for like agents even for humans because you don't need to try to understand those like fancy ui and ux you just say like okay i want to do abc just do that so i think it just makes more sense to have like this interface for it.

38:40Joe Weisenthal:And like, like you said, you know, like, otherwise it's just like, it becomes too complicated.

38:44Matt Suiche:Just on this note, can we talk a little bit about, I guess, institutional knowledge of code? Because I remember one of the things that happened in the early days of AI development. I mean, not that early. I think it was like 2017 or something back when Facebook's AI lab still existed. What was that called again? Like the acronym was FAIR or something. Yeah, they had like a little Facebook like experimental lab. Anyway, they invented a bunch of chatbots. And the chatbots started talking to each other in pretty much incomprehensible language. But like they clearly understood what each other were saying.

39:20Matt Suiche:And so I'm just wondering if you extrapolate that to AI generated code, could we have a situation where the models are constantly iterating on themselves, they're constantly talking to each other. And so we end up with a system that becomes very, very difficult for human engineers and coders to actually understand.

39:42Joe Weisenthal:Yeah, I mean, I think what we're going to start with is like humans, you know, like us are going to move towards like creating like markdown files as like a programming language. So everything is just going to be like normal, like language. But for the machine itself, obviously, yeah, I remember that video that is like gibberish, like voice transfer thing. Well, if you think about it, it's not that much different from voice to text in that sense. And at the end of the day, it's just like bits being transferred. Because even whenever you connect on a web page, you write it in text, but behind it's just like bytes that are being exchanged.

40:22Joe Weisenthal:So agents still need to agree on the protocol that they're going to use and not necessarily an encryption format, but an encoding format. So once you know what it is, it just becomes like a reverse engineering problem or like a forensic problem where you're just like, okay, this is what's being used. When those packets are being sent, let me just decrypt it. Once you know the protocol, that's... So I don't think we're going to end up in a situation where we would have no idea of it because you're always going to have people who are pretty good reverse engineers. but at the same time you're also going to have your like ai assistant who's going to help you to like reverse engineer those things so in a way even if it happens like you are not like alone with your red bull and your laptop no ai agents we all gonna have ai agents yeah so that's also like the reality of things so we're far from just like the clippy uh plugin that we used to have in Microsoft Office.

41:26Joe Weisenthal:Now you can have this CLI interface which just give commands and then it's led that into like, okay, I understand what I need to do, you know, and that's it.

41:36Tracy Alloway:No, I love interacting with just the CLI now and every time I have to go to the web it feels like some sort of failure. I'm like, oh, I have to go to this bright website. Yeah, and I just want the information right there on the black screen communicating back and forth in English.

41:53Joe Weisenthal:You know, that's like feeling familiar for you.

41:56Tracy Alloway:You know, something I'm thinking about is I imagine that there's a lot of like crusty old Linux and Unix programmers who are like, oh, this code is this isn't high quality code that the AI that the chatbots produce. This is slop code. It needs all kinds of fixing and so forth. From your perspective, is the code itself of good quality or of improving quality? how just the lines of code itself from your standards is a good stuff yeah it's pretty good

42:25Joe Weisenthal:and even when it's bad you just tell it okay like this is bad you know like just do it better you know like if you're using like negative rewards you know if you say okay like this piece of code is garbage you know it's going to understand better because you kind of give like a strong emotion whereas like if you say oh it's okay you know you just be like okay like whatever you know If it's okay, it means it's passing the adequate test. Whereas if you would say, okay, that's garbage, you write it. I always do this.

42:53Matt Suiche:So if I ask a bot for something, I will always say, after the first version, do better. And just see what it comes up with. And then just try to iterate on that.

43:01Tracy Alloway:This is tough because when you're talking in English, the brain deludes itself into thinking that you're talking to... Or when you're talking in human language, any language. The brain deludes itself. You feel like you're talking to a person.

43:12Matt Suiche:so you have to be nice well then i feel like i don't have that problem jake oh really but then

43:18Tracy Alloway:i feel like i like i don't want to say oh this is garbage i don't but from your perspective it's actually sort of important to to be firm with the bot and you get better results by being

43:28Joe Weisenthal:more sharp with it yeah because it's the equivalent of a negative reward like just like positive reward if you if it does something you'll say okay like that's great that's exactly what I tried to explain, then it was like, okay, like, that's like a reference point was like if it starts to go on a tangent, you just say like, oh, that's completely like out of the line. Redo this. Like, why are you doing this? You know, like, the more explicit you are, the better it's going to understand how far it is from the requirement. It's tough love. I just have to get better.

44:01Tracy Alloway:You know, I'm sort of conflict avoidant.

44:03Matt Suiche:I know.

44:03Tracy Alloway:And I like being nice to people. So I just have to get...

44:05Matt Suiche:Joe still says please and thank you. So the basilisk doesn't get them.

44:09Tracy Alloway:Yeah, that's right. So I just have to be like, no, this is trash. This is garbage. You totally, we're all dumber for having seen this code. Okay, this is good to know.

44:18Joe Weisenthal:I mean, it's a good point. You know, like all those like AI companies that are recording all the prompts, you know? So who knows if they are keeping it, if there is retention around it, you know? Like we know the OpenAI is keeping them, you know? Like they can get supone, is this how you pronounce it? Yeah, so like who knows? in years from now. If there's full-on autonomous robots managed by the Department of War because they think it's lawful.

44:45Matt Suiche:We all get social scores based on our AI prompts. How well we're treated.

44:49Tracy Alloway:These thoughts creep into my head unironically where it's like at some point is there going to be some, am I going to regret having, I don't know. They were there in my head.

45:01Matt Suiche:Truly a brave new world. Matt, just one last question from me, But going back to cybersecurity and the current situation with Iran, what are you on just the lookout for? Like what would pique your interest the most to see in that particular space? I think now in that particular space, because I'm one of those people who think it's related to the Epstein files, you know,

45:26Joe Weisenthal:like it's just more about like getting more Epstein files related stuff to see if there is like more connections to it, you know. so I think that will be the only thing that would kind of be like digital that will like spike my interest because now we have seen you know like just those like all drones can do like so much damage and that Iran demonstrated that they can be like really precise with their attacks so now I think it's more about seeing like which direction it's gonna go to and how long it's gonna to last which is like the the big like question mark because there's so many other components like the energy sector you know like how is it we have seen the price of like memory like increasing like a lot so now now if they're starting to block like the detroit of almost you know like like what's gonna happen to like the cost of data centers and like memory and ai in general you know like we're going towards on one side you know we're going towards like the cost of tokens and inference going down but that may also like bring the cost up you know so if you're gonna use like ai for like your next generation wars but if your enemy can just like increase your cost of token and inference what does that even mean like do you even need ai in the first place is it even relevant so i think there's these all like asymmetric like warfare that's gonna happen that we really haven't seen yet and i think that's gonna be like really interesting but the same time, there's so much noise and so many things happening at once that it's becoming extremely hard to focus and extract what's really relevant.

47:02Matt Suiche:Yeah, definitely feels like that. Tough choices potentially coming for Havelock as your token costs go up. Matt, it was so wonderful to reconnect again. Thank you so much for coming back on Oddlots. And yeah, you'll have to get back to Joe about those APIs.

47:17Tracy Alloway:Yeah, I'm happy. Let's go. The most famous web provider on Twitter now. Yeah, bring me on as a consultant. Sounds good. Take care, Matt.

47:24Matt Suiche:Thanks, Matt.

47:24Tracy Alloway:Bye-bye.

47:37Matt Suiche:Joe, always good to catch up with Matt.

47:40Tracy Alloway:Super interesting. It's incredible how much is happening right now at this particular nexus, especially, obviously, the anthropic stuff. But, you know, it's interesting. You know, you think about cyber warfare and you think about, OK, we're going to hack into a system and take out critical infrastructure. But another thing you can do is just attack a data center directly.

47:58Matt Suiche:Just send a drone to a data center. I thought that was really interesting. That sort of like we got very used to thinking about cyber as like this thing that exists only in code. But now you have this like new front of kinetic warfare where the two like really intersect.

48:14Tracy Alloway:Yeah, they do really intersect. And yeah, these are like huge national security vulnerabilities. And he pointed out, I mean, certainly today, but, you know, was this in anyone's threat model? Thinking about the risks to it, you know, the cheapness of drones, the ability to take them out. Super interesting. Also, just this idea like, yes, you know, obviously, again, as your observation, the point we think of like cyber attacks, like we're going to take out this whole thing. But at least in the warfare context, his point, like most of it is like before the war, et et cetera, the sort of information gathering, spy craft and so forth prior to the actual attacks.

48:51Matt Suiche:But it is interesting to see Israel in particular use some cyber attacks as a sort of sower of chaos in Iran. I can't imagine what it's like to actually be on the ground there at the moment for many reasons. But like, you imagine just being there worried about your physical safety and then the traffic lights aren't working as well.

49:10Tracy Alloway:Well, right. And also just think like, wait, there's cameras everywhere? or how much is being recorded, create a sense of paranoia among everyone about everything.

49:21Matt Suiche:And also the interesting thing, obviously this is very topical in markets, but the SaaSpocalypse idea, Matt in particular, seemed pretty bearish on the outlook for existing software companies, I guess. And I did think his comments about what that would mean for security budgets within organizations were pretty relevant and worrying.

49:42Tracy Alloway:Absolutely. All right.

49:43Matt Suiche:Shall we leave it there?

49:44Tracy Alloway:Let's leave it there.

49:45Matt Suiche:This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

49:50Tracy Alloway:And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our guest, Matt Swish. She's at mswish. Follow our producers, Carmen Rodriguez at Carmen Armin. Dashiell Bennett at Dashbot and Kale Brooks at Kale Brooks. And for more Odd Lots content, go to Bloomberg.com slash Odd Lots for the daily newsletter and all of our episodes. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash Odd Lots.

50:12Matt Suiche:And if you enjoy Odd Lots, if you like it when we talk about the intersection of kinetic and cyber warfare, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

50:43Thank you.