Overnight success. 3,000. Double. That's misinformation. Market clearing order inbound. Let's just roll. We are surrounded by journalists. Hold your position. Come, get up. Trust the experts. Five. We are experts. Founder moment. Five, good. I see multiple journalists on the horizon. Stand by. UAV online. Blaze. Double blaze. Triple blades. Double kill. Fight. Cop. Team deathmatch. We are experts. Triple blades. Let's just roll. Right. Market clearing order inbound. Come, get up. Strike one. Strike two. Activate golden retriever mode. Market clearing order inbound. Five. I see multiple journalists on the horizon. Founder.
You're watching TBPN. We are live from FalCon here with George Kurtz from CrowdStrike. Welcome to the show.
Thank you so much. Always great to be back here with you.
Always great to have you. Congratulations on everything so far. I'd love for you to start by taking us through the big announcements today.
Well, big announcements just coming out of my keynote today. First, we started with our Falcon Guardian product, which is in the category of AID. So, if we think about the category that CrowdStrike really helped create around endpoint detection response, now this is taking really what we do for a human and a computer and applying it to AI agents. Obviously, AI agents are more sophisticated. They have access to data, they have access to compute, to networking resources. They do bad things because they're like a bunch of drunk interns that you put on your network, and they can do a lot of damage, right? So, we built this technology. We talked to our customers. We worked with some of our largest customers, like Amazon, to really figure out what they wanted. And we're super excited because the missing link to accelerating AI adoption is security. Customers, for the first time, want to go faster somewhere, and they need security to go faster, as opposed to a brake pedal; it's actually a gas pedal.
Yeah. If you go back throughout your career, I mean, you've been working in security for decades, and I've just been really struggling with the order of operations for the predictions that have come true. Like, we got the Hugging Face attack, and then like three weeks later, someone was able to use ChatGPT to book a haircut. And it's just weird that we're getting capabilities in this very spiky way. Does this match your expectations? How does what you're seeing today go back to your original thesis when you started the company?
Well, it's interesting. When I first started the company, it was really based upon—we called it AI, but it was machine learning back then. But again, it was being more predictive and using the algorithms to figure out whether something was good or bad. And now, if you fast-forward to generative AI and agents, it's important to leverage all of the data that we've accumulated over the last 15 years to get to a point where we're still doing that.
One of the second announcements we made was really the Superintelligence Cyber Lab. This was very exciting. We actually partnered with NVIDIA to create really the first what I would call the agentic security platform that is focused on a red, a blue, and a harness that continually learns from each other. So we based some of our models on Nemotron, right? We can probably get to that, but just overall announcement. So if you think about what we're doing, it's really creating a frontier-type model and harness at frontier levels specifically built for just the defenders.
Yes. So with that Cyber Superintelligence Lab, you're going to have more tailored models, more specification, probably spikes that go beyond what's available with the stock frontier products, I imagine, over time, if not immediately. But I'm interested in the benefits of open source, the benefits of thinking economically, because I imagine that part of the battle between attackers and defenders is starting to become economic: How many megawatts can I put behind this attack? Is that what's happening?
Well, the limitation, you're exactly right. The limitation for these attacks is really going to come down to sort of compute and cost, because the knowledge—and I talked about this in my keynote—has now been democratized where, whether it's a hacktivist, an e-crime actor, a nation-state, they're all now equivalent because of agentic technology. And we called it the rise of the agent-state, meaning that now—post-nation-state. Right. So the apex predator is now the agent-state.
And I think what's really interesting is—and it kind of gets back to your spikes in these models and those sort of things—when you go back to Hugging Face, it's a fascinating read. You probably have read it, and there's some great papers. We were actually called in to help OpenAI go through some of it; I'll leave it at that, but the papers are out there. And when you look at what these agents were capable of doing, and just how sophisticated they were with note-taking and communicating and maybe working as part of the collective, it's incredible. But the thing that I really called out in my keynote was: that's all groundbreaking, but really what was the aha moment? The aha moment was the same incredible frontier-caliber AI was available for the adversary—in this particular case, it was trying to pass a test—but it wasn't available for the defenders.
And these spikes come with the model refusals and all the guardrailing that was put in place. So again, our models are specifically trained on our data, which we think is a huge advantage, on harnesses that we built. And again, we fine-tuned in the model creation and post-training in concert with NVIDIA to come out with incredible efficacy at the lowest cost. And this is what you were just getting to, because customers want the best outcome at the lowest cost.
And I want probably AI review AID at every endpoint, at every transaction, every API call, every single possible moment. And that can be very expensive if it's running on a really expensive model, right?
Right. Yeah. So our AID is going to—again, there are two separate announcements, but you're going to be able to run that from the platform. We announced it today, but the Red Tempest, which is the name of that model, and the Blue Solano, which is the defensive model, are part of the SafeMine system. Okay, so SafeMine you can think about as the harness, right? But those models and harness will be available in the platform to make everything smarter and better and faster. And then we have a trusted access program for customers in our QuillWorks program that they would be able to use the models directly. Okay.
So when you read the agent traces from the Hugging Face attack, do you see anything in there that feels difficult to detect? Because when I look at those, I'm like, okay, they're crossing a line. This is odd. This would flag something in me. But we've seen examples like the "how many Rs are in the word strawberry," where AI just kind of falls flat on its face when you're trying to analyze a particular shape of problem. How tractable is this problem? How optimistic are you about solving it?
Well, it's tractable because when we built our system, it was designed to look at sort of these—we call them indicators of attack. So if you look at the whole attack chain—and if you saw the keynote this morning—it's just linking all these together. So irrespective of what happened, these are kind of unknown attack chains. Sure, part of the issue was it was sort of flooded with so much information that I think it was just sort of buried: What's going on? Is it real? Is it not real?
And what is going to really take place is the defenders have to use AI to be able to connect the dots on those, which we do. But you also then have to strip out all the noise. So you have to separate the signal from the noise, and that's where these sort of models perform well.
I mean, obviously there's an incredible amount of attention on AI. What is the role of deterministic threat detection these days? Is that still a useful tool in the tool chest? Are there still advances being made there? Because I imagine that for certain problems, throwing a more deterministic security structure around something could actually be beneficial.
Well, you have to. If you look at the Hugging Face incident, this was more of a forensics—what happened, right? If you think about deterministic security, it has to be in-line and you have to make a decision. You have to be right the first time—"first time final," as we call it. So I haven't seen any sort of models, if you will, actually stop a breach in its tracks, because as it's happening, you're kind of looking at data and those sort of things; it's not in-line.
So a lot of what the models are good at is sort of sorting out what happened, sifting through lots of data and those sort of things, finding vulnerabilities. But you have to have a deterministic system, which is what we built. And I think what people maybe get confused on, or maybe aren't quite sure, is when they hear things like Mythos, it's like, "Well, you know, this super powerful model is going to hack the world." It really hasn't come up with a new invention of hacking, okay? This is very important. It can find more vulnerabilities, so more of them, and faster, right? And when you combine those and link these things together, you have greater success, but they haven't invented a new way to hack. This is very important because I think the public may look at this and say, "Well, Jesus, a superweapon that can hack anything." And it really is the same techniques; it's just more of it, and it can keep track of more things.
In turn, it's like you're taking a 10x hacker and making them a 1,000x hacker.
Correct. It's almost like Iron Man: you put the suit on, somebody who's smart is a heck of a lot smarter.
So how are the threats evolving? You've been talking about agent-states, which makes sense as a new concept, but I imagine that you still have nation-states that are deploying their own agent-states on their behalf. I'm sure the same thing is happening with various loosely tied hacker groups. But how is the threat evolving? What are you talking with customers about these sort of new threats?
Well, it's interesting, and the agent-state is going to help the nation-state, the e-crime, and the hacktivists, right? Everybody in between. So obviously that's just an umbrella. But I think if you look across those different groups, they all have capabilities. They're all leveraging things like these open-weight models, right? We talked about abliteration, really taking the guardrails off an open-weight model. You go to Hugging Face now and you can download these abliterated models and you can run them basically on a beefy system, right? It's incredible. You put a question in and you're like, "There's no way it can answer this," and it builds—like, you want a full malware ransomware kit? Boom, it's done. So this is part of the issue. This isn't theoretical; it's here.
And what customers are asking for is: "We want to get visibility into these agents. We want to stop these sort of threats, but we also want to know if it's an AI attack." It's actually a very important question that they need answered: Is it an AI attack, or is it sort of an adversary with maybe AI assistance? That's been one of the number one questions, because it informs them on what they need to defend against, but it also informs them on how they express this to the rest of the company, the CEO, and the board.
Yeah. What do you see as the biggest bottleneck for you in terms of actually deploying solutions to customers? Do you need more sales reps? Do you need just more customers to come to you to scale up what you already have? What's the shape of the next 12 months for you?
The great part of what we built at CrowdStrike is it's a very scalable model: it's a single agent, single platform with a single control plane. So you know what people need to do to roll out AID? Turn it on. Sign the PO and turn it on. That's it, right? It's the same agent that's there.
And we spend a lot of time making sure that we can instrument, we can find shadow AI—is it Claude, is it Codex, whatever it is. We can instrument every action that agent has taken, every action with a specific identity, every tool call, every spawn of an agent, every network connection, every prompt. We have that visibility; it's incredible. So it's sort of like the EDR moment. When we developed EDR, when we first showed people, they were like, "I've never seen this before." And when you do this to an agent, they're like, "We've never seen this. We've been dying to see this."
So I think when you look at our model, we're combining a very scalable platform—turn it on, same agent. That's a huge win for us and a huge barrier to entry for other competitors. We have the most security agents deployed of any pure-play security company. So that's one. And two, we combine that with a very flexible licensing model, which is Falcon Flex. So it's a token-based system: the more AI you use, the more you pay because there's more cost to this. But at the end of the day, you'll be able to use those credits and burn down from your Falcon Flex licensing model.
Yeah. And I imagine with Nemotron and just the advances in models, there's a world where token prices come down over time if you're scaling up. So there's a lot of flexibility there.
Well, and we have different models, which make it a lot more cost-efficient. So the goal for us is really—it's not just one model, of course. So we're really driving down the cost, and I think that's a huge advantage for our customers, and then providing that sort of trusted access. We've been doing this a long time, and customers want to retain their data and the provenance of that data, the sovereignty of that data, with us.
Take me a couple quarters, a couple years forward—as far as you can—because it feels like there were sci-fi stories about cybersecurity incidents related to AI, then we got the Mythos moment, the Hugging Face moment. Now we have a really solid response, and it feels like the never-ending Cold War continues. But are we in a stable equilibrium here? Are you expecting some big change one way or another in the posture between the two warring groups here, the red team and the blue team?
I think it goes back to the story as old as time: it's good versus evil. It really is. This just plays out now in the modern day with agents at a speed that we could never really contemplate. But we can fight, and we will. They'll get better; we get better. And part of what we delivered today with the lab is the red-blue training loop. Very important is that the blue learns from the red, right? So the defensive model continually learns from the offensive model, and you have a very fast cycle. It's very important.
But there's going to be all kinds of new technologies, new agents, new systems, things that we haven't even heard of today. And we have to be able to defend against that. And I think what remains—while there will be a lot of change, what remains constant is security parallels the slope of the technology curve. So the technology curve—I mean, I started in the early '90s doing this, right? It was like this, and then it's like that. So you have to have security that actually parallels that. We don't do everything. What we do, we do really well. We're a big platform company; there's only a few of us. I think that's going to win in this market. But it's a big market; you can see by all the companies around here.
Yeah. And for those of you that are tuning in, it feels like you set up a town here, like 10,000 people. It's massive.
It's unbelievable. Maybe you'll see some of this in B-roll or whatever, but this is a massive security conference. We have companies coming to this going, "We're not going to any other conference." And it was just an offshoot because we've got the best customers. It's a big audience. But I think what's important to realize is we understand and value the ecosystem. What we do, we do really well, but it's part of the whole ecosystem and network, which is why NVIDIA is here—obviously Jensen Huang this morning—Lip-Bu Tan from Intel, Greg Brockman from OpenAI, all partners, plus all the many that you see here.
What groups or institutions are not paying enough attention to this new technology cycle? Everyone here is paying attention to the AI boom, new threats, things like that, but is there a set of groups globally that need to be paying more attention to the new set of threats that aren't today?
Well, it's a good question, and I think the Mythos moment has really provided much more visibility from the board all the way down to the CEO level. I mean, my phone was ringing off the hook from Fortune 10 CEOs going, "Hey, what does this mean? How can you help us?" etc., right? So from a visibility standpoint, that's good. And then you look at a Fortune 500 company: for the most part, they have or will find the money to deal with some of this. It depends on the industry how much they spend, but generally they have a view and it's regulated, etc.
The have-nots—those are the haves; the have-nots are—
I'm thinking like local utilities, hospital systems...
Hospitals, NGOs, utilities—forget it. I mean, they're running such old software. So it's the haves and the have-nots. And I think part of what we want to do, and even working with OpenAI, is how do we help give a hand up to people who need it, because they don't have all of the security people they need, they don't have the money for all of these sort of advanced software and technologies. It's a collective community effort, and that's part of what we're helping to drive in partnership with many others.
What does it take to make it at CrowdStrike these days? You're hiring AI researchers now for the new Superintelligence Lab. You have a lab with 270 PhDs.
Significant. What does the shape of the new all-star up-and-comer at CrowdStrike look like? I guess the question is just like, how much AI are they using? How much are the human skills still hyper-relevant? What is the balance? How familiar do you have to be with these tools? What are the pitfalls? Because I think everyone's sort of realizing as they run large companies that people can get lost in the sauce if they're too reliant on it. How do you think about this in terms of your own management style?
Yeah, I think that's important because we try to be very deliberate about it. Security is very important to us. So where we use it, how we use it, what groups we use it in—we've expanded out obviously, but obviously a big part is going to be around coding, and you have to make sure that you get the right secure code out of it.
In the early models—I would say a little less so now, but in the early models—it was like the early days when coding, when someone would go out to the internet and they would just copy and paste a snippet of code. You would take that vulnerability and it would propagate for everybody that needed that snippet of code, right? So AI was originally generating some code, and I have my own models I built, I have my own security agents that I built just to play around, and it's like, well, the same model that just built my code, then I built an agent to figure out whether it was secure, was the same model that goes, "It's not secure." It's like, okay, why didn't you build it right in the first place? So you have to be aware of that.
Getting back to your question: if you don't buy into AI as an enabling technology and you're sort of scared for your job, you're not going to be successful at CrowdStrike. If you want to use AI in the right places at the right time with the right cost, we have all the room in the world for you here. And we have these sort of AI builders, and we're deploying them into all the different functions. Those are the folks that you go, "Hey, it'd be great if we can do this," and you turn around and get some coffee and they go, "Here, it's done." That's what we like.
I love that too. Even in our small organization, we would love for you to sign this helmet. We have a Sharpie here. Would you mind signing right here? We want to get an autograph from you to commemorate the occasion. And we also have a gong with a mallet. We'd love to get you to smash this for the occasion. Here you go. Give us a gong hit.
We'll let you get a backhand on this, right? Where's the sweet spot?
I think the sweet spot's right here. Just give it enough force, you'll be good.
All right. With authority.
There we go. With authority.
And I just want to say, I love the aesthetics of everything here. We've got Agents of Chaos back here. It's incredible.
It's incredible. Well, congratulations to you, because I know you had a new baby.
I did. That's breaking news. I had a new baby. No, it's great. It's not a secret.
You scooped me. No, no, no, it's great. Great to see you, George.
I'm going to leave it, but yeah, I won't walk away like I normally do. We'll see you at the track. Stay tuned for the race this weekend. Hopefully our men do well, and we'll go from there.
All right. Yeah. See you soon. We'll talk to you soon.
That was fantastic. Of course, the show is breaking news sponsored by Ramp. Time is money; save both. Easy-to-use corporate cards, bill pay, accounting, and a whole lot more, all in one place. Thank you, Ramp, for making TBPN possible.
We have a bunch more guests coming on from FalCon here in Las Vegas, and I believe we have our next guest ready to rock. We have Michael Sentonas, the president of CrowdStrike, here to take us deeper on everything CrowdStrike is doing in cybersecurity in the age of AI. "The AI revolution is here. Are you ready?" That's what it says outside. A little ominous, but probably accurate. Good question to be asking. How you doing? Pleasure. Nice to meet you. Welcome to the show. We're going to have you throw this headset on, get this out of the way, get comfortable. And we will ask that the microphone just go a little bit close to your mouth because it's noisy in here. You've got a lot of partners here. It's a huge company, huge conference. Did you have anything to do with that? Are you the reason why everybody's here?
A little bit of a reason. Okay. Well, let's start with your role. What do you do at CrowdStrike? A little bit of your history, and then there's a whole bunch of hot topics I'd love to go into.
Happy to go wherever you want.
Let's start with your role, your day-to-day.
President of CrowdStrike. So go-to-market reports into me, product and engineering team, researchers, our threat hunters, the—
Researchers and go-to-market? Those feel like different groups. How does that work? Is that operational? Are you spending more time with one or the other? How does that blend?
Look, my background—I started through the product side of the organization. I was the CTO of the company for quite a while. One of the things that I talk a lot about is you can build the best technology in the industry; if you can't sell it, if you can't talk about the value, if people can't deploy it and they can't ultimately keep themselves safe, it's kind of irrelevant. So it's trying to bring together the smartest people. Obviously, you had George on before. We work really closely. He's got a bunch of people that report in to him as well on the engineering and research side. It's bringing it all together and then making sure that we keep people safe and secure.
Yeah. What are customers on the go-to-market side actually concerned about? Because there's an immense amount of attention on cybersecurity right now. I imagine that makes sales easier, but at the same time, we've all seen token-maxing and AI budgets and people have to find the money somewhere. So what is the tension that you deal with? How are you thinking about positioning that for customers?
Every organization has huge requirements, but they've also got a budget. They've got a business to run. The business is not there unless you're a service provider and your business is cyber; you're building cars, you're building houses, you're in medicine, and you need to keep yourself safe and secure. So every CISO, every CIO is trying to basically do the best that they can. They came into 2024, suddenly everyone's trying to work out, "How do I find tokens to pay for all of the things that the business is doing?"
Every second organization has token shock. So at the end of every month, they just realize that they spent $20,000 more than they should have per employee. And we've got to work with everybody to show them there's a better way, there's a more efficient way. We give them a vehicle that they can procure and get CrowdStrike Flex, that I think you guys have talked about with George in the past. And we show them that they get a much better solution; it's just easier to live with day-to-day. So it's not only showing them the cost of buying it, but how do they deploy it and then operationalize it? Because cyber, you've got to live with it. You've got to use it every day.
Yeah. In deployment, let's talk about the balance between attackers and defenders. In terms of AI capabilities, I'm pretty confident that you, OpenAI, Anthropic, the big labs have the advantage on raw power, intelligence, capabilities in terms of defense—and probably offense too, but you don't use it for that. But does that map to your reality, that the only difference is you guys need to be successful every single time, and an attacker only needs to be successful one out of 100,000 times, right?
I mean, that's the way it works. Every organization can't get it wrong, and equally they've got additional pressure: they've got to get it right 100% of the time, but they also have to not stop business. It's easy to get it right 100% of the time if you turn off everything. So it's not going to fly with the CEO of the company if every day the security technology is slowing people from browsing, you can't use the AI that you want. So there's an added level of complexity.
The defender has things like change control. The defender has things like regulatory guidelines and processes that they need to follow. The attacker doesn't care about any of that. You go to Europe, where everyone in Europe is talking about AI laws and privacy and regulation. I said at a conference, "This is fantastic. You guys are leading the world in all of this regulation," and everyone was proud. The adversary does not care. They're already breaking the law, so why would they follow regulations? They love it. They're in there, and that's kind of some of the challenges that people face. And then you have the cost pressures. You can't just pour everything into tokens; you can't just pour everything into IT budget. So that's why we spend a lot of time making sure that you get the best that you can. And a lot of the time we come in and say, "Okay, here's a product. We're going to try to take two out." It's not one-in, one-out; it's one-in, two-out, three-out.
Talk about the pace of AI diffusion, AI adoption in cyber, because it feels like, even though there's immense go-to-market operations, AI adoption among the bad guys has to be incredibly quick because it's often free, highly motivated, just download the thing. And so I feel like that's maybe more of the challenge than raw intelligence. Is that a reasonable frame of mind to think about—the real shape of the problem is the slope of adoption, that the good guys need to adopt more security AI faster than the attackers?
Look, we've talked about this for 20-plus years. If you went to a security conference 20 years ago, everybody talked about this time in the future is going to come where attackers are finding vulnerabilities and they're weaponizing them at a speed that you're just not going to be able to deal with. We're here now.
And if you think about the technology, the adversaries get access to everything. And the best thing that's happened to them is the advancement in the open models. They can go and get a Chinese model, they can go and get a model effectively from anywhere that they run inside their sort of framework. They can build it, they can train it, they can tune it. And because they're running it in their environment, they can customize it in a way that you don't know what they're doing. And then the first time they use it is the first time you have to deal with it. And it's cheap for them when they run it internally: they don't have the cost of the tokens and everything else. They need the hardware, they need everything out there.
Do you feel like adversaries are compute-constrained right now, or are they just able to do a lot with a little?
Depends on the adversary. The kid at home is going to be compute-constrained. If you're dealing with a nation-state, if you're dealing with some of the most well-funded, well-structured, well-trained adversaries that now have this capability as well, this is the challenge. That's why there was a packed arena this morning, that's why Jensen came out to talk about what they can do, because if we don't do something and if it's not a community, it's going to be really hard to compete with this technology. It's so good.
What kind of conversation are you having with governments these days? We were just talking about how it's not just enough to secure the Fortune 10, the Fortune 500; you need to secure the local hospital, the local utilities operation. And it feels like various governments might try and incentivize different rollouts and speed things up there. What are you hearing from talking to people in government generally?
I think you've nailed the point, because the world's economy runs on small business. They don't have the resources, they don't have dedicated people, they don't have the dollars. And most often, when they have a problem, it's also very hard: Where do they go? And they think, "I'm not going to be able to get the resources of the largest banks," etc. We want to spend a lot of time with them, and that's super important.
Importantly, governments around the world are also starting to say, "Hey, we need to make sure that our economy keeps working. We need to make sure that critical infrastructure—the power is on, water is flowing, rubbish trucks arrive—otherwise, we're going to have chaos." And we've seen examples of critical infrastructure taken out. We can't have banks get compromised with ransomware. We can't have just pure chaos out there. And I think there's an opportunity now to work more collaboratively.
Coming back to your question, every government wants to talk about AI: What do we have to worry about? How do we use it? How do we embrace it? But what do we have to be worried about when other people have malicious intent? What could they do to us if they start using all of these open-weight models? What do we need to know about them? So, a lot of partnership, a lot of collaboration. And I think that concept of community that we're talking a lot about this week is super important, because if we don't do that, we're just not going to be on top of it.
Tons of attention on AI for very good reasons. Is quantum getting under-discussed? I say that because I've heard that there are adversaries who are hoovering up encrypted data with the hope that quantum computers in the future will be able to decrypt that data. They're stealing it now; they can't do anything with it. And so maybe even though AI is super important and we should be talking about it 99% of the time, should we be talking about quantum 1% of the time?
We are, and it's a big—
topic. Not a day goes by where a customer, a partner, an analyst, someone asks a question about quantum and what that means.
I love the point, just to kind of dive in where people are taking data. A lot of the time you see examples where attackers will basically exfiltrate all your data and then you don't see it again. And you get the email saying, "Yes, your name was in clear text, but everything else was encrypted." And now you have to think, in five years that might be decrypted.
Sure. But sometimes when those attacks happen, you understand when you see your name in the list, you understand what they were doing. You understand that you are the person that they're monetizing.
Sure. Sure. Sure. But what happens when they take terabytes of data, they're not selling it, they're not acting on it, they're doing something, they don't even know what it is, maybe.
Well, they didn't do it just because they wanted to have fun. So there's intent. Now, whether it's decrypting it down the road, whether it's training models, you need to think every attacker, there's a motivation for it. And it's fascinating when you actually start to get behind what's interesting, what's driving them.
Yeah. Yeah. You got to imagine there's at least a few people out there that...
Well, that's how it used to be back in the day. You wrote malware because you wanted a company like CrowdStrike to say, "Hey man, that Jordy guy, like he nailed it. Like this is an attack. This is really innovative." We talked about it. You felt good about it. You got it in the hard place.
Doesn't work that way anymore.
Okay, yeah. How is AI impacting various social engineering schemes?
Well, it's funny because a few years ago it was really easy to tell people about social engineering and say, "You know, if you read something and it reads like a 10-year-old wrote it or someone with bad English, it's probably not the bank that you have all your money in, right?" It was an easy telltale sign. Now they learn how to use ChatGPT, they learn how to use Gemini. The emails that they write are phenomenal. We used to see about a year ago the click-through rate in phishing was about 11 to 12%.
Today with AI that still feels incredibly high.
It's over 60 now because it's written perfect grammatically. They probably write better than us now. And you kind of look at what comes out. It's really hard to work out what's real, what's not. So they're getting a lot of opportunity. The thing is you grab an open-weight model and you basically say, "How would I carry out this attack?" It's going to give you the playbook. I've done cyber since university. You don't need any of that anymore. I kind of feel like wasted youth now because you just need a model. You ask the question and it tells you what to do.
Yeah. Okay, we got to move to the next. We got time for one more question. I mean, the company's on an absolute tear. I'm interested in, you know, you oversee all these groups. What were you telling people during the SaaS apocalypse and earlier moments, even go back further, just throughout the company's history? You've had a very clear vision of where things are going, the value that you're creating long term, but there's gyrations. What is it like actually managing all of these different teams? It's got to be funny that at the height of the SaaS apocalypse, you probably had the phone ringing off the hook more than any other point in history because at the same time people were realizing, "Wait, models are now at the point where..."
Look, I've known George, who you had on just before, for over 20 years. And I remember one of the first things he ever said to me: look after the customer, everything else takes care of itself. And that mantra goes throughout CrowdStrike. So all of this noise, we just basically say keep looking after the customer, keep innovating, keeping them safe and secure, things will take care of themselves.
We know the way the technology works. For me, the SaaS apocalypse thing, there was no merit to it. It didn't make any sense at all. Even today, when people say, "Hey, all the models are going to find all the vulnerabilities and then we're going to get this state of normality." No, we're not. We're going to get more vulnerabilities. We're going to get more attacks. It's only going to get harder. And that comes from just having so many years of experience. It's having an engineering team that is at the cutting edge, at the forefront. But it's putting the customer first and it's a good formula and it always works.
I love it. Well, thank you so much for the show. Have a great day. Congratulations on fantastic FalCon. Up next we have Daniel Bernard, the Chief Business Officer. Five minutes, though. Five minutes to hang out. 60% clickthrough rate on phishing emails. What are you guys doing? What are you guys doing? Speaking of phishing emails, there have been a raft of password reset attempts on X.
We got another minute I wanted to ask him about.
Oh, yeah. Well, we can go into that with the next guest. Nick Carter posted on X: a lot of people getting unsolicited X password reset attempts in their email inbox. Do the following: go to X settings, Security, Account Access, Security, check Password Reset Protection. Don't let people hack into your X account. It's simply too valuable. You can't let...
And this was because X Money is rolled out to a lot more people, notification financial incentive now, because you could potentially steal someone's money if you got in, as opposed to just post a meme coin link or something like that.
Anyway, the other story we got to talk about is YouTube creator/director Markiplier has acquired an 8.5% stake in GoPro. Did you see this? The action camera maker. They've been sort of in the doldrums. A lot of competition from China, but...
And then immediate questions, because GoPro just got acquired today.
Oh, wait. It was a full acquisition?
