1 00:00:00,290 --> 00:00:02,780 S1: All right, Mike, welcome to unsupervised Learning. 2 00:00:03,730 --> 00:00:04,899 S2: Hey, thanks for having me, Daniel. 3 00:00:06,040 --> 00:00:08,170 S1: Very cool. So I've been wanting to get you on 4 00:00:08,170 --> 00:00:11,080 S1: the show for quite some time, and we've been talking 5 00:00:11,080 --> 00:00:14,500 S1: about doing it. I really love what you're doing over 6 00:00:14,500 --> 00:00:18,639 S1: there on Return on Security. I kind of see you as, like, 7 00:00:18,640 --> 00:00:24,280 S1: the Nate Silver or the 538 of, uh, the security industry. Like, 8 00:00:24,280 --> 00:00:28,750 S1: you just kind of seem to know what's going on, and, uh, 9 00:00:28,750 --> 00:00:32,530 S1: just really impressed with what you're doing. Can you tell us, um, 10 00:00:32,530 --> 00:00:35,230 S1: about what? All you have, uh, sort of in the 11 00:00:35,229 --> 00:00:37,780 S1: hopper and going on in different projects you're doing. 12 00:00:38,350 --> 00:00:41,350 S2: Yeah. Awesome. Uh, well, thanks for that intro. And, uh, 13 00:00:41,350 --> 00:00:43,690 S2: and the kind words, too. I love that, uh, the 14 00:00:43,690 --> 00:00:47,680 S2: concept of, uh, the 538. Um, you know, I really 15 00:00:47,680 --> 00:00:50,440 S2: am just trying to at return on security, capture the 16 00:00:50,440 --> 00:00:54,610 S2: entire industry, uh, of cybersecurity. Um, and I've, you know, 17 00:00:54,610 --> 00:00:56,470 S2: through the years of working and I've seen a bunch 18 00:00:56,470 --> 00:00:59,200 S2: of different variations of people looking at the industry from 19 00:00:59,290 --> 00:01:02,050 S2: either a product centric kind of view or from an 20 00:01:02,050 --> 00:01:05,290 S2: investment banking kind of view. Uh, or for like a 21 00:01:05,290 --> 00:01:08,380 S2: public equities, like, you know, the big public companies that 22 00:01:08,380 --> 00:01:10,840 S2: get traded and talked about a lot. Uh, but I've 23 00:01:10,840 --> 00:01:13,480 S2: never seen anybody kind of pull it all together, uh, 24 00:01:13,480 --> 00:01:16,000 S2: in a way that you can understand if you're not 25 00:01:16,000 --> 00:01:18,940 S2: an investment banker. Uh, and so that was kind of 26 00:01:18,940 --> 00:01:21,280 S2: one of the things I was trying to do return 27 00:01:21,280 --> 00:01:23,980 S2: on security was to kind of catalog all these things, 28 00:01:23,980 --> 00:01:26,919 S2: but speak for the voice of the practitioner, uh, and 29 00:01:26,920 --> 00:01:28,510 S2: the CSO. And that's, that's kind of what led me 30 00:01:28,510 --> 00:01:33,070 S2: to this. And just, um, most of my experience, uh, 31 00:01:33,069 --> 00:01:35,290 S2: through security has been on the engineering side. So I 32 00:01:35,290 --> 00:01:37,420 S2: was always kind of front and center with buying tools, 33 00:01:37,420 --> 00:01:42,940 S2: deploying tools, operationalizing tools. Um, and I had a lot 34 00:01:42,940 --> 00:01:46,330 S2: of opinions because of that. Yeah. So it just started, uh, 35 00:01:46,330 --> 00:01:48,610 S2: kind of writing them down and, uh, capturing this stuff. 36 00:01:48,610 --> 00:01:50,920 S2: And I thought, uh, maybe I can help other people 37 00:01:50,920 --> 00:01:52,750 S2: understand this space better, too. 38 00:01:53,350 --> 00:01:57,370 S1: Yeah, that's really cool. So who is the main audience 39 00:01:57,370 --> 00:02:01,210 S1: like the ICP here? You mentioned CSOs. That definitely makes sense. 40 00:02:01,210 --> 00:02:04,420 S1: So security leaders, but would you say also like senior 41 00:02:04,420 --> 00:02:08,110 S1: practitioners as well like technical people or what do you find. Yeah. 42 00:02:08,410 --> 00:02:11,350 S2: I would definitely say that. Definitely security leaders. Uh, and 43 00:02:11,350 --> 00:02:13,570 S2: then a lot of the senior practitioners who are often 44 00:02:13,570 --> 00:02:17,140 S2: the ones informing or advising the leaders on what to buy, 45 00:02:17,139 --> 00:02:19,750 S2: what to what kind of direction to go in, what 46 00:02:19,750 --> 00:02:23,440 S2: kind of strategy to put together. Um, and that I 47 00:02:23,440 --> 00:02:25,330 S2: kind of made this also for me as I was 48 00:02:25,330 --> 00:02:27,910 S2: missing kind of these aspects of what, uh, you know, 49 00:02:27,910 --> 00:02:31,120 S2: the weekly newsletter could kind of give like, what's the 50 00:02:31,120 --> 00:02:33,010 S2: market even look like right now? And what should I 51 00:02:33,010 --> 00:02:36,250 S2: be thinking about ahead of time? Um, so I'd say 52 00:02:36,250 --> 00:02:39,820 S2: that that audience of security people makes up about 80% 53 00:02:39,820 --> 00:02:43,690 S2: of the audience. Um, the other 20% is actually a 54 00:02:43,690 --> 00:02:46,870 S2: healthy dose of cybersecurity founders. Uh, they use a lot 55 00:02:46,870 --> 00:02:49,480 S2: of it. What I produce is like market intelligence for 56 00:02:49,480 --> 00:02:53,320 S2: themselves against competitors or whatnot. Um, and then a lot 57 00:02:53,320 --> 00:02:56,110 S2: of the rest of that is also, uh, VCs or 58 00:02:56,110 --> 00:02:59,470 S2: private equity funds, uh, who are also doing similar kind 59 00:02:59,470 --> 00:03:02,109 S2: of market intelligence and trying to keep track of startups 60 00:03:02,110 --> 00:03:03,790 S2: where they are in their journey and, you know, when 61 00:03:03,790 --> 00:03:05,169 S2: they might want to raise more money. 62 00:03:06,010 --> 00:03:09,430 S1: Yeah. That's perfect. I mean, I don't know how much 63 00:03:09,430 --> 00:03:11,650 S1: that was an accident, but you really did create the 64 00:03:11,650 --> 00:03:14,110 S1: best resource for all those different groups. 65 00:03:14,650 --> 00:03:17,050 S2: Well, thank you. It was kind of an accident, but, uh, 66 00:03:17,050 --> 00:03:19,750 S2: just kind of was born out of, uh, just like, 67 00:03:19,750 --> 00:03:21,790 S2: just trying a bunch of different things and getting some 68 00:03:21,790 --> 00:03:25,660 S2: kind of outside help, uh, from friends who were in finance. Um, 69 00:03:25,660 --> 00:03:27,460 S2: so I kind of wanted to make it sure that 70 00:03:27,460 --> 00:03:29,620 S2: they could digest it, too. So I asked some friends 71 00:03:29,620 --> 00:03:34,839 S2: who do, uh, investment banking or, uh, M&A activities and say, like, 72 00:03:34,840 --> 00:03:38,080 S2: how do you normally want to see financial data or 73 00:03:38,080 --> 00:03:40,630 S2: money data, things like that, like what makes sense? And 74 00:03:40,630 --> 00:03:42,550 S2: then I kind of try to apply that in security. 75 00:03:42,550 --> 00:03:45,160 S2: And uh, it was honestly a lot of luck to, uh, 76 00:03:45,160 --> 00:03:46,330 S2: putting it all together. 77 00:03:47,460 --> 00:03:50,460 S1: Yeah, I feel like it's really congealed over, like the 78 00:03:50,460 --> 00:03:53,520 S1: last year or so, like where you kind of found 79 00:03:53,520 --> 00:03:56,940 S1: that voice and you found the audience and you know 80 00:03:56,940 --> 00:04:01,740 S1: what they are looking for. Yeah, really, really strong. So 81 00:04:01,740 --> 00:04:04,350 S1: I guess I've got a number of questions for you. Um, 82 00:04:04,350 --> 00:04:06,750 S1: but they all kind of have the same theme, which 83 00:04:06,750 --> 00:04:10,740 S1: is what the hell has been going on? Um, like, 84 00:04:10,740 --> 00:04:14,190 S1: so for like the last, I don't know, I can't 85 00:04:14,190 --> 00:04:16,710 S1: even remember how long it's been. Is it like three years? 86 00:04:16,710 --> 00:04:22,170 S1: Maybe three years, maybe a year and a half where. So, like, 87 00:04:22,170 --> 00:04:28,729 S1: the economy is like. Or in cybersecurity specifically. We had 88 00:04:28,730 --> 00:04:31,040 S1: a boom. I can't remember when the boom kind of, 89 00:04:31,190 --> 00:04:34,310 S1: I guess the boom was post-Covid. It was it was 90 00:04:34,310 --> 00:04:38,440 S1: like a post-Covid, like hiring boom. And then there was 91 00:04:38,440 --> 00:04:42,670 S1: like all this excitement and sort of froth. And then 92 00:04:42,670 --> 00:04:46,930 S1: at some point, um, there was like, just like the 93 00:04:46,930 --> 00:04:50,500 S1: crash that came after that. Right. So how would you 94 00:04:50,500 --> 00:04:52,840 S1: describe this? Like, it seems like you've got to be 95 00:04:52,839 --> 00:04:56,500 S1: like the, the, uh, the expert here and one of 96 00:04:56,500 --> 00:04:59,169 S1: the top experts in the world on this, and I'm 97 00:04:59,170 --> 00:05:02,200 S1: trying to find out, of course, it's multivariate, but, like, 98 00:05:02,200 --> 00:05:08,800 S1: what was this? Was this all the result of anti-Covid 99 00:05:08,800 --> 00:05:14,450 S1: or over hiring from Covid? Or was it maybe pushed 100 00:05:14,450 --> 00:05:19,720 S1: back on? Um. Uh, policies inside of companies being a 101 00:05:19,720 --> 00:05:23,140 S1: little bit too pro employee. And companies are like, look, 102 00:05:23,140 --> 00:05:26,229 S1: we only I've got like this theory of like, uh, 103 00:05:26,230 --> 00:05:30,280 S1: the Alaskan fishing boat thing where it's like, I need 104 00:05:30,279 --> 00:05:34,719 S1: absolute killers on this boat. Only the best. And like, 105 00:05:34,720 --> 00:05:36,820 S1: if you say your ankle hurts, you can't come on 106 00:05:36,820 --> 00:05:41,170 S1: the boat, right? Right. If if your priority is your family, 107 00:05:41,170 --> 00:05:44,380 S1: you can't come on the boat. You want work life balance. 108 00:05:44,380 --> 00:05:47,740 S1: You're not coming on my crew. And so I felt 109 00:05:47,740 --> 00:05:50,650 S1: that happening at the same time. And I just I'm 110 00:05:50,650 --> 00:05:52,359 S1: not as close to the data as you are, so 111 00:05:52,360 --> 00:05:57,170 S1: I'm wondering what you think. Cause the boom caused the crash. 112 00:05:57,170 --> 00:05:59,240 S1: And where are we now? Yeah. 113 00:05:59,570 --> 00:06:02,060 S2: That's a great question too. And I think honestly, the 114 00:06:02,060 --> 00:06:06,110 S2: the crash was building long before Covid happened. Um, if 115 00:06:06,110 --> 00:06:10,429 S2: basically if you look after the Great Recession in 2008, uh, 116 00:06:10,430 --> 00:06:15,020 S2: in the US, when the housing market at the first crash, uh, 117 00:06:15,020 --> 00:06:21,109 S2: and it effectively recovered around 2012, 2013, that was a 118 00:06:21,110 --> 00:06:24,380 S2: time at which interest rates had become very, very low 119 00:06:24,380 --> 00:06:28,909 S2: to stimulate kind of growth in the economy that stimulated investments, um, 120 00:06:28,910 --> 00:06:33,050 S2: outside of traditional banks. And so money was a effectively 121 00:06:33,050 --> 00:06:37,460 S2: very cheap to borrow, which, yeah, venture capital loves startup 122 00:06:37,460 --> 00:06:41,330 S2: founders love and that uh, that the old habits from 123 00:06:41,330 --> 00:06:44,120 S2: early days of Silicon Valley and like the pre com 124 00:06:44,120 --> 00:06:47,810 S2: bust era continued on. So it was uh, it was 125 00:06:47,810 --> 00:06:50,030 S2: very easy to grow a company quickly. It was very 126 00:06:50,029 --> 00:06:52,820 S2: easy to raise a lot of money quickly. It was 127 00:06:52,820 --> 00:06:56,870 S2: very easy to scale. Um, it wasn't impossible to, to 128 00:06:56,870 --> 00:06:59,540 S2: raise a couple hundred million dollars and then go IPO 129 00:06:59,540 --> 00:07:01,250 S2: in this time frame, which is, you know, kind of 130 00:07:01,250 --> 00:07:04,280 S2: the ultimate goal of, uh, you know, companies to raise 131 00:07:04,400 --> 00:07:08,539 S2: venture capital money. Um, so all of those things played 132 00:07:08,540 --> 00:07:11,480 S2: out across the entire industry, like, uh, not just cyber, 133 00:07:11,480 --> 00:07:15,739 S2: but the entire set of industries. Uh, it just so happened, though, 134 00:07:15,740 --> 00:07:18,320 S2: at the same time, tech had a, you know, a 135 00:07:18,320 --> 00:07:21,290 S2: total run. Uh, they did really, really well as a 136 00:07:21,290 --> 00:07:25,580 S2: general sector. And then cybersecurity did even better within the side, 137 00:07:25,580 --> 00:07:29,360 S2: that small sector of tech. And it almost seemed recession, uh, 138 00:07:29,360 --> 00:07:33,380 S2: recession proof in some ways. Um, and any time there 139 00:07:33,380 --> 00:07:36,470 S2: was news funding went up or investments went up, any 140 00:07:36,470 --> 00:07:39,470 S2: time there was a breach, investments went up. Anytime there's 141 00:07:39,470 --> 00:07:42,350 S2: global conflict, investments went up. And so like, it kind 142 00:07:42,350 --> 00:07:45,860 S2: of just kept happening in cybersecurity. Uh, and so there 143 00:07:45,860 --> 00:07:48,620 S2: was really a good ten year or so period in 144 00:07:48,620 --> 00:07:51,590 S2: cybersecurity where things were just as good as they could be. 145 00:07:51,590 --> 00:07:54,200 S2: It could only go up into the right. Uh, and 146 00:07:54,200 --> 00:07:57,290 S2: so a lot of people flocked to that. A lot 147 00:07:57,290 --> 00:08:00,530 S2: of investments flocked to that, uh, a lot of people 148 00:08:00,530 --> 00:08:03,380 S2: that would have put their money elsewhere, dumped it all 149 00:08:03,380 --> 00:08:07,160 S2: in cybersecurity. Uh, some became specialists, some became just tourist 150 00:08:07,160 --> 00:08:12,530 S2: investors who'd, you know, invest occasionally. Um, and then as 151 00:08:12,530 --> 00:08:16,430 S2: you go up to Covid area, Covid era policies had 152 00:08:16,430 --> 00:08:19,610 S2: to kind of reverse all of this low interest rate 153 00:08:19,610 --> 00:08:23,690 S2: phenomenon that happened across the world. Uh, and each country 154 00:08:23,690 --> 00:08:26,210 S2: took a different approach, but the US took a pretty 155 00:08:26,210 --> 00:08:29,150 S2: hard approach. Uh, the US Federal Reserve, that is, took 156 00:08:29,150 --> 00:08:32,780 S2: a hard approach on ratcheting interest rates way up to 157 00:08:32,780 --> 00:08:36,470 S2: kind of slow down the possibility of having another recession 158 00:08:36,470 --> 00:08:40,640 S2: or even a depression in some ways. Um, and since 159 00:08:40,640 --> 00:08:42,770 S2: most of the money in the world invested in tech 160 00:08:42,770 --> 00:08:45,470 S2: and cyber comes out of the US or originates in 161 00:08:45,470 --> 00:08:49,430 S2: the US, uh, that hit the tech sector super hard 162 00:08:49,429 --> 00:08:54,410 S2: and hit cybersecurity sectors very hard. Um, so imagine where 163 00:08:54,410 --> 00:08:57,110 S2: you've raised, you know, a couple hundred million dollars over 164 00:08:57,110 --> 00:09:00,470 S2: the past 3 or 4 years prior to about 2022 165 00:09:00,500 --> 00:09:04,189 S2: is when, uh, early 2022, when this happened. And then 166 00:09:04,190 --> 00:09:06,800 S2: you might be worth $1 billion. You might be worth, 167 00:09:06,800 --> 00:09:09,800 S2: you know, a couple billion dollars. But then now, because 168 00:09:09,800 --> 00:09:12,350 S2: you've spent all this money and time and effort into 169 00:09:12,350 --> 00:09:15,530 S2: growing kind of at all cost, as opposed to like 170 00:09:15,530 --> 00:09:19,310 S2: growing a product market fit and growing like products in 171 00:09:19,309 --> 00:09:21,440 S2: a way that people actually want to consume, not just 172 00:09:21,440 --> 00:09:24,990 S2: to compete or winner take all. People are now left 173 00:09:24,990 --> 00:09:28,170 S2: with like, wait, sales don't match what my valuation says. 174 00:09:28,170 --> 00:09:30,990 S2: Sales don't keep up with the money I raise. And 175 00:09:30,990 --> 00:09:34,980 S2: so there was a super hard reckoning, um, from a 176 00:09:34,980 --> 00:09:38,280 S2: lot of things like operational efficiency became the name of 177 00:09:38,280 --> 00:09:40,620 S2: the game. Um, so it was no longer enough to 178 00:09:40,620 --> 00:09:43,200 S2: raise money. You actually had to, like, make money as 179 00:09:43,200 --> 00:09:45,959 S2: a company, which, uh, kind of cracked me up thinking 180 00:09:45,960 --> 00:09:46,890 S2: about it, but. 181 00:09:46,890 --> 00:09:50,040 S1: Yeah, let me let me jump in there real quick. So, um. 182 00:09:51,870 --> 00:09:54,300 S1: I've heard. Uh, do you listen to Scott Galloway? 183 00:09:54,750 --> 00:09:55,350 S2: I have. 184 00:09:55,590 --> 00:09:59,970 S1: Yeah. Yeah, yeah. So he talks about this a lot. How? Um, 185 00:09:59,970 --> 00:10:02,310 S1: I'm not sure he says it like this, but there's essentially, like, 186 00:10:02,309 --> 00:10:05,459 S1: two phases or two different vibes that a company can 187 00:10:05,460 --> 00:10:09,530 S1: be in, which is like this. Kind of like a 188 00:10:09,530 --> 00:10:15,170 S1: fantasy phase where you disconnect fully from the underlying financial 189 00:10:15,170 --> 00:10:18,620 S1: fundamentals and you're just kind of operating on, like you said, 190 00:10:18,620 --> 00:10:23,600 S1: growth at any cost or just like this state of 191 00:10:23,600 --> 00:10:28,160 S1: belief and hype, kind of like a crypto surge or something. 192 00:10:28,160 --> 00:10:31,939 S1: And it has its own gravity and it takes on 193 00:10:31,940 --> 00:10:35,179 S1: its own form. And he said the same thing you 194 00:10:35,179 --> 00:10:38,720 S1: just did, which is at some point there is a reckoning. 195 00:10:39,580 --> 00:10:42,910 S1: Eventually you're going to get back to somebody. Maybe it's 196 00:10:42,910 --> 00:10:46,989 S1: like the replacement management team. They come in and they're like, hey, let's, um, 197 00:10:46,990 --> 00:10:49,360 S1: let's do something crazy. Let's figure out how much money 198 00:10:49,390 --> 00:10:53,950 S1: we're making and how much money we're spending. Right. Um, 199 00:10:54,160 --> 00:10:56,679 S1: so so this is really interesting. So you have an 200 00:10:56,679 --> 00:11:01,209 S1: underlying economic thing, which is kind of the biggest. Yep. 201 00:11:01,480 --> 00:11:05,800 S1: Then you have maybe related to that, you have this, um, 202 00:11:05,800 --> 00:11:09,490 S1: transition of a lot of these companies going for that 203 00:11:09,490 --> 00:11:13,090 S1: other style of company where it's not super tied to 204 00:11:13,090 --> 00:11:16,720 S1: the product itself. And the I think Scott calls it 205 00:11:16,720 --> 00:11:22,089 S1: like the fundamentals, market fundamentals. Mhm. And then um, then 206 00:11:22,090 --> 00:11:27,240 S1: you have Covid in addition, which is another disruptor. And 207 00:11:27,240 --> 00:11:31,390 S1: it sounds like all those sort of combine and. To 208 00:11:31,390 --> 00:11:35,410 S1: form the build up, but also to form the crash. Yep. 209 00:11:36,580 --> 00:11:38,410 S2: I think that's a great way to think about it. 210 00:11:38,410 --> 00:11:41,439 S2: And you know it. Also, you look at broader tech 211 00:11:41,440 --> 00:11:45,940 S2: kind of, uh, plays that happened so largely, uh, tech 212 00:11:45,940 --> 00:11:48,849 S2: sectors copy like people or companies in the tech sector 213 00:11:48,850 --> 00:11:53,530 S2: copy each other. If, if a Facebook or a Google or, um, 214 00:11:53,530 --> 00:11:57,370 S2: a Microsoft makes a move to reduce headcount, many of 215 00:11:57,370 --> 00:11:59,980 S2: them follow suit. The other big companies and then companies 216 00:11:59,980 --> 00:12:04,750 S2: start reevaluating. Maybe we should do that too. Uh, maybe 217 00:12:04,750 --> 00:12:07,479 S2: the investors certainly come knocking and they say, hey, wait 218 00:12:07,480 --> 00:12:10,600 S2: a second, shouldn't you be more profitable or more operationally efficient? 219 00:12:10,600 --> 00:12:13,420 S2: Let's let's drive that cost burn down. Uh, and so 220 00:12:13,420 --> 00:12:15,700 S2: that also added into the effect as well. 221 00:12:15,910 --> 00:12:21,550 S1: Yeah. It, it feels like both of those are super contagious. The, 222 00:12:21,550 --> 00:12:26,870 S1: the getting away from fundamentals is super contagious. In the hype, 223 00:12:26,870 --> 00:12:30,500 S1: and then the returning of fundamentals is also equally contagious. 224 00:12:30,500 --> 00:12:34,790 S1: Everyone's like, hey, what are we doing? Like, I'm talking 225 00:12:34,790 --> 00:12:37,969 S1: to Chuck and Sally over here, and, uh, they're both 226 00:12:37,970 --> 00:12:39,860 S1: saying we should get back to the books and we 227 00:12:39,860 --> 00:12:43,939 S1: should absolutely do that. It's like fashion trends almost. 228 00:12:44,750 --> 00:12:46,550 S2: Yeah, that's a good way to think about it, too. 229 00:12:46,550 --> 00:12:49,670 S2: Like what's in vogue versus what's not. Um, and a 230 00:12:49,670 --> 00:12:51,709 S2: lot of it, as much as people want to you 231 00:12:51,710 --> 00:12:54,080 S2: believe that, like, you know, the market is is based 232 00:12:54,080 --> 00:12:57,290 S2: on data and science and, um, hard proof. A lot 233 00:12:57,290 --> 00:12:59,390 S2: of times, a lot of us just feeling like even 234 00:12:59,390 --> 00:13:01,850 S2: investments are a lot of feeling, um, a lot of times, 235 00:13:01,850 --> 00:13:05,390 S2: which is why, um, not all investments, most investments don't 236 00:13:05,390 --> 00:13:08,360 S2: return any money. Uh, so, like, that's, you know, there's 237 00:13:08,360 --> 00:13:10,160 S2: you can't predict some of these things. So you have 238 00:13:10,160 --> 00:13:11,930 S2: to go with a bit of underlying feeling and gut. 239 00:13:11,929 --> 00:13:13,820 S2: And so that is contagious as well. 240 00:13:14,510 --> 00:13:19,440 S1: Um, yeah. Interesting. So what are some of the other 241 00:13:19,440 --> 00:13:21,750 S1: trends that you're seeing that you're sort of tracking that 242 00:13:21,750 --> 00:13:22,890 S1: you find interesting? 243 00:13:23,830 --> 00:13:25,270 S2: So right now, I think we've had a little bit 244 00:13:25,270 --> 00:13:28,450 S2: of a rebound effect because there is, uh, it's a 245 00:13:28,450 --> 00:13:32,170 S2: kind of a macroeconomic phenomenon where people expect things to 246 00:13:32,170 --> 00:13:34,450 S2: get better. And so if they expect it, it does 247 00:13:34,450 --> 00:13:38,800 S2: get better. It's a strange, like, chicken and egg concept. But, uh, 248 00:13:38,920 --> 00:13:41,740 S2: there was a bit of a rebound. Um, I've noticed 249 00:13:41,740 --> 00:13:46,390 S2: that 2024, even in these first 12 weeks, is already, uh, 250 00:13:46,390 --> 00:13:50,080 S2: almost as good as 2023. Um, and even though there's 251 00:13:50,080 --> 00:13:52,870 S2: been less transactions, there's almost the same exact amount of money, 252 00:13:52,870 --> 00:13:56,710 S2: so there's fewer transactions happening, but there are more, uh, 253 00:13:56,710 --> 00:14:00,280 S2: profitable each time or more, more revenue is, uh, or more, uh, 254 00:14:00,280 --> 00:14:05,020 S2: cash is exchanged hands each time. Um, and like the 255 00:14:05,020 --> 00:14:08,080 S2: M&A has also kind of been one of those, uh, 256 00:14:08,080 --> 00:14:11,440 S2: kind of second or third order effect knock ons that 257 00:14:11,440 --> 00:14:16,630 S2: have come from the fallout of 2022. So companies who, uh, 258 00:14:16,630 --> 00:14:19,810 S2: started in that time frame probably had a really tough time. 259 00:14:19,810 --> 00:14:23,500 S2: They either sink or swim very quickly. Um, and I'm 260 00:14:23,500 --> 00:14:26,950 S2: seeing companies who started early. Now they're getting acquired quicker, 261 00:14:26,950 --> 00:14:29,260 S2: which is, you know, it might be, you know, a three, 262 00:14:29,260 --> 00:14:32,140 S2: four year turnaround, uh, for an acquisition now as opposed 263 00:14:32,140 --> 00:14:34,989 S2: to like 7 to 10 in some cases. So I 264 00:14:34,990 --> 00:14:38,680 S2: think that is interesting. Uh, because, uh, the, the underlying 265 00:14:38,680 --> 00:14:42,190 S2: premise there is like companies either need a lifeline because 266 00:14:42,190 --> 00:14:44,680 S2: they can't survive and actually make sales themselves, and they 267 00:14:44,680 --> 00:14:48,850 S2: need like to be absorbed or acquired into a larger company. Um, 268 00:14:49,480 --> 00:14:51,670 S2: or they just have something that's so good that larger 269 00:14:51,670 --> 00:14:55,239 S2: companies like can't iterate or can't innovate without it. So 270 00:14:55,240 --> 00:14:56,890 S2: they buy it up real quick to try to get 271 00:14:56,890 --> 00:14:58,960 S2: it off the market and try to stay relevant with 272 00:14:58,960 --> 00:15:03,910 S2: the other large companies. Um, so I think that's that's 273 00:15:03,910 --> 00:15:08,680 S2: pretty interesting. Um, another concept I would say is, uh, 274 00:15:08,680 --> 00:15:12,160 S2: you know, obviously application security has exploded with AI. I 275 00:15:12,160 --> 00:15:15,190 S2: think that's probably like one of the quickest and, uh, 276 00:15:15,190 --> 00:15:17,080 S2: probably one of the better use cases to kind of 277 00:15:17,080 --> 00:15:20,590 S2: help support that function. Um, and it feels a lot 278 00:15:20,590 --> 00:15:24,250 S2: more tangible than, you know, an autonomous stock agent or 279 00:15:24,250 --> 00:15:27,640 S2: like a, a replace your SoC. Analysts like levels one 280 00:15:27,640 --> 00:15:30,280 S2: and two with a, you know, an all knowing agent. 281 00:15:30,430 --> 00:15:32,860 S2: It may come their that way eventually, but uh, I 282 00:15:32,860 --> 00:15:36,280 S2: think um, the, the first the shine has worn off 283 00:15:36,280 --> 00:15:38,740 S2: of the first level of AI security or security for 284 00:15:38,740 --> 00:15:41,620 S2: AI companies. Uh, and people are starting to see that 285 00:15:41,620 --> 00:15:45,820 S2: there's like real tangible benefit and like the application security 286 00:15:45,820 --> 00:15:47,260 S2: and the data security side. 287 00:15:47,260 --> 00:15:51,250 S1: When you say the first layer, you mean pre this 288 00:15:51,250 --> 00:15:54,070 S1: new gen of AI, you mean like the old AI 289 00:15:54,100 --> 00:15:58,510 S1: security companies who were like. Yeah, automate the sock. But 290 00:15:58,510 --> 00:16:01,540 S1: that was before Gen I. You're saying that the Gen 291 00:16:01,540 --> 00:16:06,070 S1: I stuff is like injecting some real, real, um, energy? 292 00:16:06,070 --> 00:16:09,729 S1: I definitely agree with that. But how? We're using it manifest. 293 00:16:09,730 --> 00:16:12,100 S2: Yeah, I'm seeing some energy. So I think there were 294 00:16:12,100 --> 00:16:15,550 S2: some last year, uh, you know, after after OpenAI and 295 00:16:15,550 --> 00:16:17,800 S2: everyone came out or like, it's 18 months ago. So 296 00:16:17,800 --> 00:16:20,080 S2: now there's a lot of early, early companies that were 297 00:16:20,080 --> 00:16:24,910 S2: trying to do something with security as like as new startups. Um, 298 00:16:24,910 --> 00:16:28,030 S2: most of them were just like, uh, proxies or casbs 299 00:16:28,030 --> 00:16:30,370 S2: or just like basically a firewall to kind of limit 300 00:16:30,370 --> 00:16:33,520 S2: the exposure that a company would have. Um, those had 301 00:16:33,520 --> 00:16:35,650 S2: a quick pop and then they've kind of like trailed off. 302 00:16:35,650 --> 00:16:39,700 S2: And then you saw the next iteration even after like, uh, 303 00:16:39,700 --> 00:16:42,820 S2: OpenAI came out and the others, uh, of like replace 304 00:16:42,820 --> 00:16:45,190 S2: the sock, but like in a different way than how 305 00:16:45,190 --> 00:16:48,700 S2: they used to say, replace the sock. Um, now more 306 00:16:48,700 --> 00:16:51,460 S2: of like, just like a the co-pilots, if you will, 307 00:16:51,460 --> 00:16:54,070 S2: like the the everyone, every product now has an AI 308 00:16:54,100 --> 00:16:57,370 S2: copilot and an AI chatbot to go with it. Yeah. Uh, 309 00:16:57,370 --> 00:17:00,010 S2: even that's starting to lose its its shine now, and 310 00:17:00,010 --> 00:17:02,380 S2: they're trying to get a lot more around help me 311 00:17:02,380 --> 00:17:05,230 S2: automate the fix. So I think that that's a pretty neat, uh, 312 00:17:05,230 --> 00:17:09,400 S2: iteration of it. Now, it's no longer just purely kneejerk, uh, 313 00:17:09,400 --> 00:17:12,760 S2: you know, cover my legal basis. It's let me actually 314 00:17:12,760 --> 00:17:16,450 S2: make appsec or things like that, uh, faster and more, uh, 315 00:17:16,570 --> 00:17:20,830 S2: more concrete for all the parties involved. Uh, so I think, uh, 316 00:17:20,830 --> 00:17:25,659 S2: that one is really interesting. Um. And then kind of 317 00:17:25,660 --> 00:17:28,359 S2: at the same, the same time, uh, a kind of 318 00:17:28,359 --> 00:17:30,790 S2: a smaller space that people don't really think about as 319 00:17:30,790 --> 00:17:34,060 S2: much that I find really interesting now is the explosion 320 00:17:34,060 --> 00:17:36,969 S2: of trust and safety, because, like, it's so much more 321 00:17:36,970 --> 00:17:40,480 S2: important now to even understand which videos, which content, which 322 00:17:40,480 --> 00:17:43,210 S2: anything you're consuming is real or not. And it's it's 323 00:17:43,210 --> 00:17:46,480 S2: more than just a social media problem. It's becoming a 324 00:17:46,480 --> 00:17:48,669 S2: company problem as well. Like it's a brand. It's a 325 00:17:48,670 --> 00:17:52,959 S2: reputational problem. Um, that extends beyond just somebody making a 326 00:17:52,960 --> 00:17:56,020 S2: deepfake video. So I think that's there's a lot of 327 00:17:56,020 --> 00:17:59,170 S2: going on that. And then you add on geopolitical contexts 328 00:17:59,170 --> 00:18:01,030 S2: and like wars on top of that stuff. And it's 329 00:18:01,030 --> 00:18:04,420 S2: it's very interesting, um, like how you, how you sort 330 00:18:04,420 --> 00:18:06,370 S2: all that out. And I think this is a place 331 00:18:06,369 --> 00:18:07,810 S2: where I can really help. 332 00:18:08,869 --> 00:18:13,760 S1: Yeah. Interesting one section I think's going to absolutely blow up. 333 00:18:13,760 --> 00:18:16,790 S1: And I don't think um, I don't think there are 334 00:18:16,790 --> 00:18:19,910 S1: many people playing here yet, but actually there's a decent 335 00:18:19,910 --> 00:18:28,879 S1: number like, um, validation. Of ground. Truth is a space 336 00:18:28,880 --> 00:18:33,560 S1: that I think needs to, well, is just going to, uh, 337 00:18:33,560 --> 00:18:38,159 S1: blow up. So, so I, I see this as. Um, 338 00:18:38,160 --> 00:18:40,949 S1: me and you having some sort of, like device. It 339 00:18:40,950 --> 00:18:42,540 S1: might be on our phones. It probably will be on 340 00:18:42,540 --> 00:18:45,150 S1: our phones because it's expensive to carry an extra device. 341 00:18:45,150 --> 00:18:49,080 S1: So basically, you and I have a pre-existing, like, uh, 342 00:18:49,080 --> 00:18:53,190 S1: signal connection. Mhm. So we go to get on this, 343 00:18:53,190 --> 00:18:56,670 S1: this call right here. And um, like I've seen you 344 00:18:56,670 --> 00:18:59,340 S1: move your hand when you move your hand also with me, 345 00:18:59,340 --> 00:19:03,149 S1: you see, see the green glitching. Yeah. The green screen. 346 00:19:03,150 --> 00:19:10,170 S1: Same happens with you. Um, and when, um, we get 347 00:19:10,170 --> 00:19:13,419 S1: ready to do a call. I think we're going to 348 00:19:13,420 --> 00:19:18,760 S1: do something like. Okay. Initiate, uh, pre-shared key connection or something. 349 00:19:18,760 --> 00:19:23,440 S1: And you put up actually on your, on your screen 350 00:19:23,440 --> 00:19:27,160 S1: and show a code which is corresponds to the code 351 00:19:27,160 --> 00:19:29,920 S1: I have, which is part of the Pre-shared. Uh, now 352 00:19:29,920 --> 00:19:33,580 S1: it's part of the video. As well. And so now 353 00:19:33,580 --> 00:19:38,050 S1: we've validated that this video feed. With a combination of 354 00:19:38,050 --> 00:19:42,220 S1: live and a combination of pre-shared. We have an actual 355 00:19:42,220 --> 00:19:46,960 S1: connection and you're not a deepfake. Um, that SpaceX is 356 00:19:46,960 --> 00:19:49,030 S1: going to absolutely heat up. The other one is going 357 00:19:49,030 --> 00:19:55,260 S1: to get crazy is, um, AI agents acting as humans? Um, 358 00:19:55,440 --> 00:19:59,940 S1: and calling APIs and everything, and basically IAM type companies 359 00:19:59,940 --> 00:20:03,690 S1: or identity type companies basically getting a handle on who's 360 00:20:03,690 --> 00:20:06,540 S1: human versus who's emulating human. Mhm. 361 00:20:07,630 --> 00:20:10,209 S2: Interesting. Yeah, I think that all makes a lot of 362 00:20:10,210 --> 00:20:12,730 S2: sense too. And it kind of plays into one of 363 00:20:12,730 --> 00:20:15,730 S2: the earlier posts you've written about that. I don't think 364 00:20:15,730 --> 00:20:18,730 S2: a lot of people have, uh, really, uh, done too 365 00:20:18,730 --> 00:20:21,070 S2: much with or like looked too far into. But it's 366 00:20:21,070 --> 00:20:24,790 S2: really you've got AI security, you've got security of AI, 367 00:20:24,820 --> 00:20:27,490 S2: you've got security for AI, but you don't have anybody 368 00:20:27,490 --> 00:20:31,300 S2: really talking about security from AI other than deep fakes. 369 00:20:31,300 --> 00:20:33,460 S2: But like, that's just like the tip of the iceberg 370 00:20:33,460 --> 00:20:35,590 S2: where this could go and what, you know, could be 371 00:20:35,590 --> 00:20:39,250 S2: launched against you or as a person or as a company. Um, 372 00:20:39,250 --> 00:20:43,000 S2: so I think that space, obviously it'll include misinformation and 373 00:20:43,000 --> 00:20:46,120 S2: disinformation kind of signals, but there's got to be more 374 00:20:46,119 --> 00:20:49,030 S2: than that. Like what else is being undertaken against me 375 00:20:49,030 --> 00:20:53,350 S2: from a personal like an OpSec standpoint, almost. Um, so 376 00:20:53,350 --> 00:20:55,209 S2: I think that space is going to just going to 377 00:20:55,420 --> 00:20:57,490 S2: as people get more, uh, attuned to this and they 378 00:20:57,490 --> 00:21:00,670 S2: get more paranoid, I think that's going to become, uh, um, 379 00:21:00,670 --> 00:21:04,330 S2: have a greater impact on the world of privacy than 380 00:21:04,330 --> 00:21:08,020 S2: privacy or regulation ever will, if that makes sense. 381 00:21:08,020 --> 00:21:12,129 S1: Yeah. Yeah, I think that's right. Yeah. One way I 382 00:21:12,130 --> 00:21:15,190 S1: like to think about that is like a, uh, and 383 00:21:15,190 --> 00:21:18,820 S1: this is a little bit in the future according to like, this, this, uh, 384 00:21:19,690 --> 00:21:25,210 S1: plan or idea that I have, but it's like a continuous, uh, defender. 385 00:21:25,480 --> 00:21:28,630 S1: So you essentially have like a digital assistant, uh, which 386 00:21:28,630 --> 00:21:30,820 S1: is what I call, like the permanent AI that you 387 00:21:30,820 --> 00:21:35,080 S1: have with you. And it's a continuous filter for everything 388 00:21:35,080 --> 00:21:38,770 S1: coming in to see you. Um, um, and it could 389 00:21:38,770 --> 00:21:42,879 S1: actually detect when you're being, like, overly flattered for the 390 00:21:42,880 --> 00:21:46,360 S1: purpose of manipulation because they're in sales. Yeah. Um, so 391 00:21:46,359 --> 00:21:48,550 S1: one's like, oh, yeah. You know, that outfit looks really 392 00:21:48,550 --> 00:21:50,290 S1: great on you. And by the way, I love that 393 00:21:50,290 --> 00:21:53,649 S1: talk that you gave. I especially love slide number seven 394 00:21:53,890 --> 00:21:57,970 S1: from your talk in Berlin. And, uh, and suddenly you're like, 395 00:21:57,970 --> 00:22:00,189 S1: oh wow. I just feel really cool. And then they 396 00:22:00,220 --> 00:22:02,860 S1: hit you with the sales pitch, right? Right. And so 397 00:22:02,859 --> 00:22:05,350 S1: you could have something pop up in your glasses or whatever. 398 00:22:05,350 --> 00:22:11,260 S1: It's like NLP detected manipulation, uh, expect inbound sales pitch 399 00:22:11,260 --> 00:22:14,380 S1: or something like that, right? Or if, um, you're getting like, 400 00:22:14,380 --> 00:22:18,250 S1: this really threatening language that's coming at you, it might 401 00:22:18,250 --> 00:22:22,449 S1: actually just rewrite that for you. Um, so, uh, one example, 402 00:22:22,450 --> 00:22:26,830 S1: I was talking with a friend named Joel the other night, and, um, 403 00:22:27,640 --> 00:22:31,060 S1: an example of this would be like, say, someone's been 404 00:22:31,060 --> 00:22:34,810 S1: traumatized by a partner and the partner is constantly emailing 405 00:22:34,810 --> 00:22:38,290 S1: them or texting them or something. And the the texts 406 00:22:38,290 --> 00:22:42,399 S1: are like they're life disrupting. It's like a paragraph of, like, 407 00:22:42,400 --> 00:22:47,739 S1: nastiness and narcissism and like gaslighting and whatever, under the 408 00:22:47,740 --> 00:22:50,739 S1: pretense that they're actually agreeing when to pick up the 409 00:22:50,740 --> 00:22:56,740 S1: kid with shared custody, your AI just rewrites it. Pick 410 00:22:56,740 --> 00:23:02,050 S1: up Jimmy at for. The whole paragraph is gone. It 411 00:23:02,050 --> 00:23:06,040 S1: just leaves the content so it doesn't. It strips out 412 00:23:06,040 --> 00:23:08,740 S1: all the triggers and everything. So I think that I 413 00:23:08,740 --> 00:23:11,710 S1: filtering thing is really powerful. Yeah. 414 00:23:11,830 --> 00:23:13,990 S2: Well that makes a lot of sense. And it kind 415 00:23:13,990 --> 00:23:16,900 S2: of plays into the whole like, uh, personal digital sovereignty 416 00:23:16,900 --> 00:23:19,000 S2: and like being able to control that aspect of you 417 00:23:19,000 --> 00:23:22,300 S2: online and offline. Uh, that makes a lot of sense. 418 00:23:22,660 --> 00:23:25,690 S1: Yeah. Yeah. And the other one that I think is 419 00:23:25,690 --> 00:23:30,320 S1: really interesting is like, um. We're going to have to 420 00:23:30,320 --> 00:23:35,480 S1: validate true content. This one I've not seen a space 421 00:23:35,480 --> 00:23:38,630 S1: really heat up yet with either, and maybe because it's 422 00:23:38,630 --> 00:23:44,960 S1: just so hard, but, um, essentially watermarking and validation like 423 00:23:44,960 --> 00:23:49,790 S1: trusted feeds so that um, for, for example, I love 424 00:23:49,790 --> 00:23:53,120 S1: UFC stuff. I'm all into fighting stuff. So when I 425 00:23:53,119 --> 00:23:56,780 S1: watch fight videos now, I'm having this really weird experience 426 00:23:56,780 --> 00:24:03,950 S1: where I'm having fight videos narrated by Joe Rogan. And 427 00:24:04,280 --> 00:24:07,640 S1: I'm like watching the fight video and I'm enjoying it 428 00:24:07,640 --> 00:24:11,930 S1: and I'm like three, four minutes in before I finally 429 00:24:11,930 --> 00:24:16,979 S1: catch some weird word. And then I realized for the 430 00:24:16,980 --> 00:24:23,690 S1: entire four minutes. Joe Rogan is a multi-millionaire. I am 431 00:24:23,690 --> 00:24:27,530 S1: on a very small YouTube channel. Um, he didn't come 432 00:24:27,530 --> 00:24:31,700 S1: to this channel and narrate this. Interesting. This person went 433 00:24:31,700 --> 00:24:35,810 S1: and collected all the clips and he just got the voice. 434 00:24:35,810 --> 00:24:39,950 S1: He wrote the script. Um, or maybe even I wrote 435 00:24:39,950 --> 00:24:43,909 S1: the script, but it's all being set in Joe Rogan's voice. Um, 436 00:24:43,910 --> 00:24:47,000 S1: which is fairly harmless. I mean, it makes the videos 437 00:24:47,000 --> 00:24:51,710 S1: quite nice, uh, quite enjoyable, but, like, what happens when 438 00:24:51,710 --> 00:24:55,700 S1: that's actually news instead, right? What happens when that's something 439 00:24:55,700 --> 00:24:59,720 S1: really important? Um, or if it's like a Joe probably 440 00:24:59,720 --> 00:25:02,810 S1: doesn't care. Maybe he does. I doubt he does. But 441 00:25:02,810 --> 00:25:06,709 S1: a lot of, uh, Hollywood celebrities actually would, and definitely 442 00:25:06,710 --> 00:25:11,510 S1: government officials really would. So, um, that thing should either 443 00:25:11,510 --> 00:25:17,300 S1: either have a fake symbol or lack an authentic symbol. Right? 444 00:25:17,300 --> 00:25:21,710 S1: Whereas the official symbol coming out of, uh, Rogan's feeds. 445 00:25:23,170 --> 00:25:26,619 S1: Should have the have the symbol right or lack the 446 00:25:26,619 --> 00:25:29,530 S1: fake symbol. That is an entire industry that's going to 447 00:25:29,530 --> 00:25:33,700 S1: be a multi-billion dollar industry. Um, yeah. And I'm sort 448 00:25:33,700 --> 00:25:35,260 S1: of just waiting for that to happen. 449 00:25:35,890 --> 00:25:38,410 S2: I think that's a really good concept too. And I've, 450 00:25:38,440 --> 00:25:40,600 S2: you know, I've talked to actually a few startups who 451 00:25:40,600 --> 00:25:44,110 S2: are doing that. Exactly. But they're they're trying to basically 452 00:25:44,109 --> 00:25:50,260 S2: create truth out of multiple sources for news reports. And 453 00:25:50,260 --> 00:25:52,630 S2: the goal of this, you give it to corporate communications 454 00:25:52,630 --> 00:25:56,170 S2: teams to let them decipher what's real and what's not. 455 00:25:56,170 --> 00:25:58,630 S2: Like who actually said this thing. And it really comes 456 00:25:58,630 --> 00:26:02,170 S2: down to using AI to go find, uh, the original 457 00:26:02,170 --> 00:26:04,659 S2: sources or the original, and the original shares the original 458 00:26:04,660 --> 00:26:07,000 S2: posts and then tie it back, kind of like to 459 00:26:07,000 --> 00:26:09,939 S2: information actors on the back end to say, oh, this, 460 00:26:09,940 --> 00:26:12,910 S2: this post that was widely shared by this person on 461 00:26:12,910 --> 00:26:17,859 S2: Facebook originated from a Russian disinformation mill. This person is 462 00:26:17,859 --> 00:26:19,869 S2: also associated with these 12 other things. It has a 463 00:26:19,869 --> 00:26:22,390 S2: high degree of chance that this is all fake. And 464 00:26:22,390 --> 00:26:23,979 S2: so then it gives you like kind of risk scores. 465 00:26:23,980 --> 00:26:27,460 S2: Then you can say corporate cops can say, we know 466 00:26:27,460 --> 00:26:29,830 S2: this is fake. Uh, next one, you know, so they 467 00:26:29,830 --> 00:26:32,230 S2: can they can move on and they can then have their, 468 00:26:32,230 --> 00:26:35,379 S2: you know, just kind of situational awareness per, per company 469 00:26:35,380 --> 00:26:37,600 S2: around that. But then that could that could easily extend 470 00:26:37,600 --> 00:26:41,290 S2: to like video contexts as well. Um, but it's it's 471 00:26:41,290 --> 00:26:44,230 S2: fascinating a time to be in this space just because it's, 472 00:26:44,619 --> 00:26:48,820 S2: I think the, the, uh, technology hype cycle and the 473 00:26:48,820 --> 00:26:52,600 S2: curve has it occurred it occurred like every other, you know, 474 00:26:52,600 --> 00:26:55,360 S2: platform we've seen with like, mobile and cloud, which might 475 00:26:55,359 --> 00:26:57,280 S2: have taken ten years. But this a lot of this 476 00:26:57,280 --> 00:26:59,500 S2: has happened in like 18 months. And it's gone through 477 00:26:59,500 --> 00:27:02,560 S2: a couple iterations of that loop. Um, so it's just 478 00:27:02,560 --> 00:27:04,510 S2: so hard to keep up with it all, uh, and 479 00:27:04,510 --> 00:27:06,070 S2: know what you're actually looking at. 480 00:27:06,550 --> 00:27:11,109 S1: Yeah. I don't think it's even close to where it's going. 481 00:27:11,109 --> 00:27:14,649 S1: It's it's not even close. I mean, I'm very much 482 00:27:14,650 --> 00:27:19,810 S1: I'm a self-described, uh, very pro AI, but I feel 483 00:27:19,810 --> 00:27:24,459 S1: like I see this really clearly, and we're barely getting started. Um, 484 00:27:24,460 --> 00:27:28,330 S1: so when I hear about AI hype and there, you know, 485 00:27:28,330 --> 00:27:33,600 S1: there's some places where, uh. People got over their skis 486 00:27:33,600 --> 00:27:36,360 S1: and they got overly excited about a very small thing, 487 00:27:36,359 --> 00:27:41,379 S1: like a giant company that generates. I don't know. Uh, 488 00:27:41,380 --> 00:27:44,770 S1: Midjourney images and sells them or something. That would be 489 00:27:44,770 --> 00:27:49,090 S1: more crypto like, or more NFT like, where it's way 490 00:27:49,090 --> 00:27:53,920 S1: over inflated for what it is, right? Right. Whereas most 491 00:27:53,920 --> 00:27:56,980 S1: of I, especially the stuff we've just been talking about, 492 00:27:56,980 --> 00:28:00,670 S1: hasn't even started yet. And it's not even possible without AI. 493 00:28:01,240 --> 00:28:04,030 S1: The hype cycle is not even. It's not that it 494 00:28:04,030 --> 00:28:06,670 S1: hasn't got to the top. It hasn't even started yet. 495 00:28:07,000 --> 00:28:10,480 S1: And that's not counting for like the real, the real 496 00:28:10,480 --> 00:28:15,820 S1: core stuff where all of your entire corporate data is 497 00:28:15,820 --> 00:28:20,460 S1: inside of a data lake. That I can see. And 498 00:28:20,460 --> 00:28:22,890 S1: now you could basically ask any question and take any 499 00:28:22,890 --> 00:28:26,040 S1: action as a result of that, which is kind of 500 00:28:26,040 --> 00:28:29,100 S1: the long term thing. And people haven't even really started 501 00:28:29,100 --> 00:28:33,179 S1: doing that because of the limitations of context. Windows and Rag. 502 00:28:33,180 --> 00:28:36,750 S1: It's like that's all still building up. There's there's a 503 00:28:36,750 --> 00:28:41,160 S1: few hype phases that are kind of really small and inconsequential. 504 00:28:41,160 --> 00:28:45,690 S1: But the ultimate S-curve of AI, I think, is just starting. 505 00:28:46,620 --> 00:28:50,070 S2: Yeah. No, I totally agree. And I think, um, you know, 506 00:28:50,070 --> 00:28:52,080 S2: we it's one of those things you can't even imagine 507 00:28:52,080 --> 00:28:55,560 S2: it because we didn't even imagine generally available. I on 508 00:28:55,560 --> 00:28:58,230 S2: your phone like 18 months ago, at least the average 509 00:28:58,230 --> 00:29:02,280 S2: person didn't. Yeah. Um, and now the average tech person, like, 510 00:29:02,280 --> 00:29:05,489 S2: uses it multiple times a week. Um, and even the 511 00:29:05,490 --> 00:29:08,640 S2: average non tech person is starting to at least know 512 00:29:08,640 --> 00:29:11,250 S2: what it is in general. Um, which is, which is 513 00:29:11,250 --> 00:29:14,220 S2: pretty impressive. Um, but yeah, we're just kind of scratching 514 00:29:14,220 --> 00:29:17,820 S2: the surface on what's, what's possible there, which I think 515 00:29:18,240 --> 00:29:20,610 S2: it's going to take kind of like a step function to, 516 00:29:20,640 --> 00:29:24,420 S2: to get that next level of AI security kind of realm. Um, 517 00:29:24,420 --> 00:29:27,030 S2: you know, every investor I talked to has that as 518 00:29:27,030 --> 00:29:28,890 S2: part of their core thesis. Now they they have to 519 00:29:28,890 --> 00:29:33,300 S2: do AI, some variation of security because nobody knows where 520 00:29:33,300 --> 00:29:35,100 S2: the rocket ships going, but they know it's a rocket ship. 521 00:29:35,100 --> 00:29:38,220 S2: So they, they want to latch on in some way. Yeah. Um, 522 00:29:38,700 --> 00:29:41,010 S2: and uh, it's, it is going to make some really 523 00:29:41,010 --> 00:29:44,400 S2: cool stuff possible that otherwise wouldn't be. And I think, uh, 524 00:29:44,400 --> 00:29:46,770 S2: I also would not be surprised in terms of trends 525 00:29:46,770 --> 00:29:49,020 S2: to see like the, you know, the 1 to 2 526 00:29:49,020 --> 00:29:53,700 S2: person security unicorn company. Uh, yes. Like they oh, you 527 00:29:53,700 --> 00:29:56,580 S2: can just build out your own complete attack surface management 528 00:29:56,580 --> 00:30:02,100 S2: platform using AI and self-hosting it or, um, and as 529 00:30:02,100 --> 00:30:04,560 S2: long as you can, uh, then learn the sales motions 530 00:30:04,560 --> 00:30:06,570 S2: and get that going, you can get bootstrapped, you can 531 00:30:06,570 --> 00:30:09,510 S2: get going, you can get funding, um, or may not 532 00:30:09,510 --> 00:30:11,910 S2: even need funding. So I think like it's going to be, um, 533 00:30:12,540 --> 00:30:14,940 S2: a pretty open world. Uh, which would be better, I 534 00:30:14,940 --> 00:30:17,760 S2: think for like, you know, competition would be better for 535 00:30:17,760 --> 00:30:21,900 S2: practitioners eventually. Um, because all that stuff is constantly expanding 536 00:30:21,900 --> 00:30:24,900 S2: and collapsing in the market, like, because security is not 537 00:30:24,900 --> 00:30:28,080 S2: a winner take all market at all. Like from a 538 00:30:28,080 --> 00:30:32,790 S2: product standpoint, it's a many, many people can be winners 539 00:30:32,790 --> 00:30:36,030 S2: in this space as well. Um, even if some of 540 00:30:36,030 --> 00:30:37,890 S2: the players are trying to act in a zero sum way, 541 00:30:37,890 --> 00:30:42,600 S2: like there's there's not a singular like dominant player across 542 00:30:42,600 --> 00:30:47,700 S2: all fields. There's like pockets of dominance across multiple fields. Um, 543 00:30:47,700 --> 00:30:49,890 S2: so I think it's just going to make that bubble 544 00:30:49,890 --> 00:30:53,160 S2: even bigger and maybe, maybe more challenging for practitioners for 545 00:30:53,160 --> 00:30:55,440 S2: a while. I hope I can help with that with security, 546 00:30:55,440 --> 00:30:57,630 S2: like make a little more sense of that. But, um, 547 00:30:57,960 --> 00:30:59,220 S2: it's going to be interesting. 548 00:31:00,410 --> 00:31:03,140 S1: Yeah, I think you're absolutely right. 549 00:31:03,170 --> 00:31:05,840 S2: There's the there's the big trend of, uh, you know, 550 00:31:05,840 --> 00:31:10,430 S2: personal digital sovereignty, like that kind of concept of being 551 00:31:10,430 --> 00:31:13,340 S2: security secure from AI and all the kind of attacks 552 00:31:13,340 --> 00:31:16,550 S2: on you as well. Um, there's going to have to 553 00:31:16,550 --> 00:31:21,290 S2: be some more iterations around, uh, improvements in AI security 554 00:31:21,290 --> 00:31:23,300 S2: in general for it to be like the next iteration 555 00:31:23,300 --> 00:31:26,420 S2: for like for to be beyond just like I think 556 00:31:26,420 --> 00:31:29,000 S2: a lot of what we've seen right now is just been, uh, 557 00:31:29,000 --> 00:31:33,530 S2: adding AI to existing capabilities as opposed to AI creating 558 00:31:33,530 --> 00:31:37,070 S2: the new capability. Uh, so we're not quite there around 559 00:31:37,070 --> 00:31:39,890 S2: that curve yet. And then once whoever makes that next 560 00:31:39,890 --> 00:31:44,570 S2: step is going to, like, do really well. Um, but 561 00:31:44,570 --> 00:31:47,480 S2: it's obviously there's, uh, there's a context and there's a 562 00:31:47,480 --> 00:31:50,660 S2: data problem that has just got just expanded now with the, 563 00:31:50,660 --> 00:31:56,120 S2: with AI. Um, because more, more AI means more cloud workloads, 564 00:31:56,120 --> 00:31:59,180 S2: which means more tech service means more configuration mismanagement. All 565 00:31:59,180 --> 00:32:04,070 S2: these things are just have have expanded. Um, like you said, I, uh, 566 00:32:04,070 --> 00:32:09,890 S2: exaggerates the thing or highlights the thing. It isn't the thing. Yeah. 567 00:32:11,540 --> 00:32:15,620 S1: Yeah, I like what you said about a single person 568 00:32:15,620 --> 00:32:20,120 S1: company launching and getting really big because you mentioned like, oh, 569 00:32:20,240 --> 00:32:22,820 S1: as long as they can learn the sales motion well, 570 00:32:22,820 --> 00:32:24,680 S1: what if they don't have to because they just hire 571 00:32:24,680 --> 00:32:28,580 S1: an AI company? Yeah, that's automated sales. Right. And then 572 00:32:28,580 --> 00:32:32,180 S1: you hire a company that's automated support and then you've 573 00:32:32,180 --> 00:32:36,650 S1: got automated like uh, EA's. So you've got like your 574 00:32:36,650 --> 00:32:40,520 S1: personal assistants and basically all of your company infrastructure is 575 00:32:40,520 --> 00:32:46,380 S1: kind of mostly outsourced. Service companies that are AI based. 576 00:32:46,380 --> 00:32:50,190 S1: And then you basically have, like yourself, a co-founder and 577 00:32:50,190 --> 00:32:53,790 S1: maybe 1 or 2 people. Yeah. And maybe it's just you. 578 00:32:53,790 --> 00:32:56,490 S1: And like one person I would say is probably one 579 00:32:56,490 --> 00:33:00,180 S1: of the smallest versions. I'm sure there will be actual 580 00:33:00,180 --> 00:33:04,950 S1: single founders who make pretty big companies as well. Um, 581 00:33:04,950 --> 00:33:08,700 S1: Altman's been talking about that as well as the first unicorn. 582 00:33:08,790 --> 00:33:13,770 S1: One person. Yeah. So I think that's very possible. Um. 583 00:33:16,790 --> 00:33:23,870 S1: What about the balance of private equity VC versus startups like. 584 00:33:25,240 --> 00:33:29,830 S1: I feel like this last down period. Oh, by the way, 585 00:33:29,830 --> 00:33:33,850 S1: what are the years for these? These periods? 21 was 586 00:33:33,850 --> 00:33:35,710 S1: that the start of the down? 587 00:33:36,100 --> 00:33:40,810 S2: So 22 was like the start of the down basically. Okay. Yeah. 588 00:33:41,110 --> 00:33:45,310 S1: 22 we started the down 23 was down and 24 589 00:33:45,340 --> 00:33:46,930 S1: were starting to see the uptick. 590 00:33:47,020 --> 00:33:50,350 S2: But yeah a bit of a rebound. Um, and um, 591 00:33:50,530 --> 00:33:54,250 S2: you know 2021 late 21 had a few signs of distress, 592 00:33:54,250 --> 00:33:56,410 S2: but not like no one thought the fed would raise 593 00:33:56,410 --> 00:33:59,590 S2: the rates so high. Yeah. Or keep on raising it. 594 00:33:59,590 --> 00:34:02,739 S2: And like the so that it just kind of had 595 00:34:02,740 --> 00:34:05,290 S2: like a an iterative effect there and just like kind 596 00:34:05,290 --> 00:34:06,730 S2: of kept making things worse. 597 00:34:07,300 --> 00:34:12,910 S1: Right. So 22 and 23, uh, private equity did really 598 00:34:12,910 --> 00:34:15,940 S1: well because they're swooping in, grabbing things. Right? 599 00:34:16,210 --> 00:34:16,810 S3: Yep. 600 00:34:16,810 --> 00:34:20,110 S1: And VCs did not do so well. Startups didn't do 601 00:34:20,110 --> 00:34:23,799 S1: so well. And now there's a whole bunch of companies 602 00:34:23,800 --> 00:34:27,070 S1: that are kind of sitting out there. They've been growing 603 00:34:27,070 --> 00:34:31,150 S1: slowly and they're kind of large and bloated. Mhm. I'm 604 00:34:31,150 --> 00:34:33,640 S1: sure you've seen a bunch of these and they're just 605 00:34:33,640 --> 00:34:36,400 S1: kind of like stagnating. And every time they have to 606 00:34:36,400 --> 00:34:39,250 S1: go to investors, investors are like tired of hearing from them. 607 00:34:39,250 --> 00:34:42,580 S1: And they're like yep you got to do something like 608 00:34:42,580 --> 00:34:45,580 S1: I've heard the same thing. You're not growing. It's not 609 00:34:45,580 --> 00:34:50,920 S1: super exciting. Um, what does this look like as things 610 00:34:50,920 --> 00:34:55,210 S1: start to boom now? And I think it's going to accelerate, 611 00:34:55,210 --> 00:34:58,900 S1: but who knows. But as it starts to boom, who 612 00:34:58,900 --> 00:35:02,890 S1: starts to benefit? Uh, I guess the private equity benefits, 613 00:35:02,890 --> 00:35:06,160 S1: if the stuff they bought cheap starts to be worth more, 614 00:35:06,190 --> 00:35:10,239 S1: that's pretty obvious. But I guess VC and startups are 615 00:35:10,239 --> 00:35:13,510 S1: the main benefits if things start going up. Is that right? 616 00:35:14,030 --> 00:35:16,790 S2: Yeah. I mean that's that's exactly right. So like, if, 617 00:35:16,790 --> 00:35:18,680 S2: you know, if the bets the P made at the 618 00:35:18,680 --> 00:35:21,200 S2: acquisitions and like the takeovers they made and they and 619 00:35:21,200 --> 00:35:23,569 S2: they and by the way, P has been very good 620 00:35:23,570 --> 00:35:27,350 S2: at buying companies and making them super operationally efficient for 621 00:35:27,350 --> 00:35:29,090 S2: a couple of years, getting a return on their investment 622 00:35:29,090 --> 00:35:31,969 S2: and selling it. Uh, they just had a much better 623 00:35:31,969 --> 00:35:33,980 S2: time doing that, you know, the past couple of years 624 00:35:33,980 --> 00:35:37,339 S2: than they had before. Um, you know, VCs, a lot 625 00:35:37,340 --> 00:35:39,980 S2: of when it comes to, uh, venture investing, you know, 626 00:35:39,980 --> 00:35:43,310 S2: most investments don't make money at all. Uh, and typically, 627 00:35:43,310 --> 00:35:45,530 S2: only a very small portion of what you invest in 628 00:35:45,530 --> 00:35:48,860 S2: actually makes up 80 or 90% of the the value 629 00:35:48,860 --> 00:35:53,180 S2: that you get back. Um, and so, uh, some did 630 00:35:53,180 --> 00:35:55,730 S2: really well, uh, at the time and some and, but 631 00:35:55,730 --> 00:35:57,980 S2: a lot of them did not. So like they, they 632 00:35:57,980 --> 00:36:01,940 S2: struggled their companies shut down, uh, funds shut down. Uh, 633 00:36:01,940 --> 00:36:05,690 S2: they didn't return any money to their investor base. Um, 634 00:36:05,690 --> 00:36:07,489 S2: so that's all bad. And that makes it harder to 635 00:36:07,489 --> 00:36:09,470 S2: raise more money next time, or get people to commit 636 00:36:09,469 --> 00:36:12,380 S2: to give you money to go invest next time. Um, 637 00:36:12,710 --> 00:36:17,210 S2: you know, there's a couple carry overs that will be constant. Um, 638 00:36:17,210 --> 00:36:21,049 S2: we're we're too close to when operational efficiency was the 639 00:36:21,050 --> 00:36:24,050 S2: king to go away from it now. So it's like, yeah, 640 00:36:24,050 --> 00:36:25,670 S2: we might be bouncing back, but you got to be 641 00:36:25,670 --> 00:36:29,120 S2: a lean bounce back. You cannot be overinflated. I agree you, 642 00:36:29,360 --> 00:36:33,080 S2: you never hire at 200, you know, percent or like bro, 643 00:36:33,110 --> 00:36:35,090 S2: you know 1,000%. Like the memory. 644 00:36:35,090 --> 00:36:36,140 S1: Is too fresh. 645 00:36:36,350 --> 00:36:37,969 S2: Yeah, it's too fresh. Like, I think we have to 646 00:36:37,969 --> 00:36:40,250 S2: have another 4 or 5 years of, like, a bull 647 00:36:40,250 --> 00:36:42,800 S2: run for them to say, yeah, forget that. Let's just 648 00:36:42,800 --> 00:36:45,560 S2: go big again and like, uh, but you also have 649 00:36:45,560 --> 00:36:48,650 S2: to remember at the same time, the goal posts have 650 00:36:48,650 --> 00:36:51,860 S2: been significantly moved on both ends of the spectrum. So 651 00:36:51,860 --> 00:36:55,940 S2: now you have to have less people be operationally efficient 652 00:36:55,940 --> 00:36:58,670 S2: and burn less cash. So that's bottom in the spectrum. 653 00:36:58,670 --> 00:37:00,920 S2: At the same end, if you want to go public, 654 00:37:00,920 --> 00:37:03,109 S2: it's a lot more of a higher barrier now. So 655 00:37:03,110 --> 00:37:05,850 S2: it's not you can't just be a $1, a 1 656 00:37:05,850 --> 00:37:10,700 S2: billion uh, valuation. You need to be three, $4 billion valuation. 657 00:37:10,700 --> 00:37:12,950 S2: You can't just be a $100 million in RR, which 658 00:37:12,950 --> 00:37:15,980 S2: used to be the magic number to make companies go public, 659 00:37:15,980 --> 00:37:18,469 S2: or at least start the roadshow. Now you need to 660 00:37:18,469 --> 00:37:21,469 S2: be 200 or 300 million. And so like the pole 661 00:37:21,469 --> 00:37:23,930 S2: or like the, the the narrowing of the pyramid is 662 00:37:23,930 --> 00:37:26,630 S2: like gotten like even tighter now. So you have a 663 00:37:26,630 --> 00:37:31,190 S2: lot more competition, which means sink or swim is like 664 00:37:31,190 --> 00:37:34,310 S2: a lot more aggressive. Um, and so that's why everyone 665 00:37:34,310 --> 00:37:37,160 S2: has kind of been decrying, oh, platform ization is finally 666 00:37:37,160 --> 00:37:40,129 S2: happening in cybersecurity. Like we're finally going to have less vendors, 667 00:37:40,130 --> 00:37:42,859 S2: which is will never, ever be true. Um, it'll just 668 00:37:42,860 --> 00:37:47,569 S2: go through pockets of of consolidation. Always like to explain 669 00:37:47,570 --> 00:37:50,300 S2: it as, uh, it's more of an accordion like parts 670 00:37:50,300 --> 00:37:52,880 S2: of it are open and like expanding and parts of 671 00:37:52,880 --> 00:37:55,370 S2: it are closed and contracting, but you're still like you're 672 00:37:55,370 --> 00:37:59,330 S2: making music, but you just like but it's not going 673 00:37:59,330 --> 00:38:02,150 S2: to look like, you know, one company, Palo Alto, is 674 00:38:02,150 --> 00:38:03,770 S2: not going to rule them all, even if you want 675 00:38:03,770 --> 00:38:07,880 S2: them to, um, tomorrow and like so this they will 676 00:38:07,880 --> 00:38:11,090 S2: never be that one. True. Uh, so it's now become 677 00:38:11,090 --> 00:38:15,859 S2: harder to, to do that. Um, and then all the while, like, 678 00:38:15,860 --> 00:38:17,239 S2: private equity is still going to have a pretty good 679 00:38:17,239 --> 00:38:18,620 S2: run for the next 3 or 4 years, because a 680 00:38:18,620 --> 00:38:21,610 S2: lot of those companies who raise money in 2020 or 681 00:38:21,610 --> 00:38:25,010 S2: 2019 or even 2021, some of them are still just 682 00:38:25,010 --> 00:38:27,350 S2: limping on, and they may have another a few more 683 00:38:27,350 --> 00:38:30,530 S2: years of runway before they need to go private. Like 684 00:38:30,530 --> 00:38:32,900 S2: they may have raised too much money, like some of 685 00:38:32,900 --> 00:38:36,020 S2: these companies raised close or more than $1 billion in 686 00:38:36,020 --> 00:38:39,620 S2: funding may not have anywhere else to go. Uh, and 687 00:38:39,620 --> 00:38:41,660 S2: less like a Cisco or like a, you know, a 688 00:38:41,660 --> 00:38:45,020 S2: Microsoft buys them, uh, so either they'll die a slow 689 00:38:45,020 --> 00:38:47,780 S2: death or they'll go take, you know, they'll private equity 690 00:38:47,780 --> 00:38:50,690 S2: or go shape them up and, you know, possibly dismantle them, 691 00:38:50,690 --> 00:38:54,860 S2: possibly resell them. Um, so there's a lot of more 692 00:38:54,860 --> 00:38:57,680 S2: complexity now, I think, than there used to be, uh, 693 00:38:57,680 --> 00:38:59,779 S2: in terms of, like, making a go of it. 694 00:39:01,120 --> 00:39:06,879 S1: Yeah. Interesting. I really like your accordion thing. I think 695 00:39:06,880 --> 00:39:11,140 S1: that makes a lot of sense. I do think platform 696 00:39:11,140 --> 00:39:16,880 S1: migration is absolutely a thing, I think. That's like the 697 00:39:16,880 --> 00:39:19,160 S1: permanent home for anything that I used to have this 698 00:39:19,160 --> 00:39:27,920 S1: thing where it was like, idea. Um, then, um, application, then, um, 699 00:39:28,550 --> 00:39:32,719 S1: operating system. So it's like things move through that phase 700 00:39:32,719 --> 00:39:35,630 S1: where it's like, uh, it's like a web app or 701 00:39:35,630 --> 00:39:38,089 S1: a little utility or something, and then it gets brought 702 00:39:38,090 --> 00:39:41,390 S1: into the OS and it's kind of absorbed the the 703 00:39:41,390 --> 00:39:45,380 S1: knowledge is now so known and understood that it just 704 00:39:45,380 --> 00:39:46,940 S1: becomes part of the furniture. 705 00:39:47,450 --> 00:39:48,290 S3: Right? 706 00:39:48,440 --> 00:39:51,890 S1: Um, but especially with AI. So I think one of 707 00:39:51,890 --> 00:39:55,250 S1: the biggest things happening with AI is the fact that 708 00:39:55,250 --> 00:39:58,370 S1: it's so much easier to start a company and actually 709 00:39:58,370 --> 00:40:02,299 S1: be effective, kind of going back to the whole, um, 710 00:40:02,300 --> 00:40:05,719 S1: unicorn thing. But forget about the unicorns. Like I'm about 711 00:40:05,719 --> 00:40:10,010 S1: to launch a SaaS myself and it's just me. And 712 00:40:10,010 --> 00:40:11,390 S1: like a dev. 713 00:40:11,860 --> 00:40:12,610 S3: Nice. 714 00:40:12,610 --> 00:40:15,340 S1: And it's a full size, right? It's a full size. 715 00:40:15,340 --> 00:40:17,870 S1: It's like end to end like it. And there's a 716 00:40:17,870 --> 00:40:21,100 S1: million people like me who are doing this because of AI. 717 00:40:21,219 --> 00:40:24,910 S1: So that is the accordion, but the bottom of the 718 00:40:24,910 --> 00:40:29,920 S1: accordion or whatever. The new ideas, the new companies, the 719 00:40:29,920 --> 00:40:33,430 S1: new sauces that are out there for the platforms to 720 00:40:33,430 --> 00:40:37,180 S1: pick and choose which features they want via acqui hire. 721 00:40:38,050 --> 00:40:38,500 S3: Is. 722 00:40:38,500 --> 00:40:42,130 S1: Going to be much larger because I think the barrier 723 00:40:42,130 --> 00:40:45,219 S1: to get in to be a startup person five years 724 00:40:45,219 --> 00:40:47,200 S1: ago is way higher than it's going to be in 725 00:40:47,200 --> 00:40:48,100 S1: two years from now. 726 00:40:49,190 --> 00:40:52,339 S2: Yeah, I think that's true. And I think, um, I 727 00:40:52,340 --> 00:40:54,590 S2: agree with everything you said too. And I think, you know, 728 00:40:54,590 --> 00:40:57,200 S2: the concept of bootstrapping or just doing it on your own, 729 00:40:57,200 --> 00:41:00,890 S2: with your with your friends and family support, uh, is 730 00:41:00,890 --> 00:41:03,980 S2: going to become a lot more realistic, um, than it 731 00:41:03,980 --> 00:41:05,690 S2: used to be. Like, you see this in other industries, 732 00:41:05,690 --> 00:41:08,000 S2: but you don't really see it much in cybersecurity. Like 733 00:41:08,000 --> 00:41:09,920 S2: maybe like think of, like canaries, like kind of the 734 00:41:09,920 --> 00:41:12,620 S2: only one I could think of that's, uh, truly like, 735 00:41:12,620 --> 00:41:16,109 S2: kind of bootstrapped, but like most. Uh, you know, it's 736 00:41:16,110 --> 00:41:19,950 S2: hard to get behind that model. Um, and I think, 737 00:41:19,950 --> 00:41:21,719 S2: you know, a lot of things that I've been trying 738 00:41:21,719 --> 00:41:24,569 S2: to also help people kind of realize the return on 739 00:41:24,570 --> 00:41:27,600 S2: security is that, like, you know, understanding, like who the 740 00:41:27,600 --> 00:41:31,890 S2: investments or investors are behind a given, you know, company 741 00:41:31,890 --> 00:41:34,410 S2: you can kind of see, like repeatability and like track 742 00:41:34,410 --> 00:41:37,710 S2: records of success or like playbooks they use often. And 743 00:41:37,710 --> 00:41:39,900 S2: you kind of say like, okay, I know that it's 744 00:41:39,900 --> 00:41:42,570 S2: going to, you know, backed by this company, their goal 745 00:41:42,570 --> 00:41:46,020 S2: is to be acquired by a bigger company. That means 746 00:41:46,020 --> 00:41:48,630 S2: if I invest in this now, it may not be 747 00:41:48,630 --> 00:41:51,030 S2: here in four years, three years or whatever, whatever contract 748 00:41:51,030 --> 00:41:52,590 S2: they're trying to get me to sign. So like, I 749 00:41:52,590 --> 00:41:55,290 S2: kind of need to think about that, um, based on 750 00:41:55,290 --> 00:41:59,440 S2: kind of where it's come from. Um. But it's. I 751 00:41:59,440 --> 00:42:01,570 S2: agree that it'll be easier to get in a startup, 752 00:42:01,570 --> 00:42:03,340 S2: it'll be easier to get a lot more traction and 753 00:42:03,340 --> 00:42:06,550 S2: actually get paying customers, especially if you then go back 754 00:42:06,550 --> 00:42:08,859 S2: to like using AI to enhance what you know or like, 755 00:42:08,860 --> 00:42:11,080 S2: fill in the gaps of what you can't, you know, 756 00:42:11,140 --> 00:42:14,049 S2: do where you couldn't have done before is easily like 757 00:42:14,050 --> 00:42:16,240 S2: it's yeah, it's it's going to it's going to skyrocket. 758 00:42:17,690 --> 00:42:23,089 S1: Yeah, I'm really excited for it. Well, um, this has 759 00:42:23,090 --> 00:42:26,540 S1: been fantastic. So tell us, um, in addition to your 760 00:42:26,540 --> 00:42:31,279 S1: return on security, which is the newsletter and the overall brand, uh, 761 00:42:31,280 --> 00:42:33,259 S1: what are what other stuff do you have brewing? Do 762 00:42:33,260 --> 00:42:37,009 S1: you have like, uh, reports coming out, new research, like products, 763 00:42:37,010 --> 00:42:39,049 S1: anything we should be aware of? 764 00:42:39,590 --> 00:42:40,250 S3: Yeah, I've been. 765 00:42:40,250 --> 00:42:42,350 S2: Trying to, uh, write a bit more on the blog 766 00:42:42,350 --> 00:42:43,880 S2: this year. Try to just try to get more of 767 00:42:43,880 --> 00:42:46,700 S2: my thoughts out there as possible. Um, I've kind of 768 00:42:46,700 --> 00:42:48,980 S2: gone back to doing kind of monthly, and I'm going 769 00:42:48,980 --> 00:42:51,260 S2: to be releasing a quarterly roundups as well, because I 770 00:42:51,260 --> 00:42:54,200 S2: want to be able to track these things over time. Um, like, 771 00:42:54,200 --> 00:42:57,140 S2: I'm big on kind of showing like look backs and saying, like, 772 00:42:57,140 --> 00:42:58,640 S2: this is where we were last year this time, or 773 00:42:58,640 --> 00:43:00,920 S2: this is where we were last month this time to 774 00:43:00,920 --> 00:43:04,190 S2: kind of help understand trends. Um, so I'm just kind 775 00:43:04,190 --> 00:43:06,890 S2: of really increasing the output of because I want to 776 00:43:06,890 --> 00:43:09,650 S2: be a true resource. I want to be like the 538, uh, 777 00:43:09,650 --> 00:43:11,089 S2: I want people to be able to use it and 778 00:43:11,090 --> 00:43:16,280 S2: actually consume it. And, um, I also plan to do 779 00:43:16,280 --> 00:43:18,739 S2: I'd like to open source the data and have somebody, 780 00:43:18,739 --> 00:43:20,930 S2: some data science people say, like, all right, tell me 781 00:43:20,930 --> 00:43:22,430 S2: what you can come up with. Like what? What is 782 00:43:22,430 --> 00:43:25,700 S2: interesting like either, you know, run some models on it, 783 00:43:25,700 --> 00:43:28,580 S2: run something and let's let's see what, what ideas you 784 00:43:28,580 --> 00:43:32,270 S2: come up with, um, and what kind of trends you 785 00:43:32,270 --> 00:43:34,940 S2: can uncover from a data science perspective. Um, so I 786 00:43:34,940 --> 00:43:36,320 S2: think that's pretty fun. 787 00:43:36,320 --> 00:43:36,710 S3: Yeah. 788 00:43:36,710 --> 00:43:39,620 S1: Um, yeah. Yeah, I want to mention that that that's 789 00:43:39,620 --> 00:43:43,250 S1: something that's super awesome about what you do. You have 790 00:43:43,250 --> 00:43:46,190 S1: the data. You've been collecting this data in a very 791 00:43:46,190 --> 00:43:53,580 S1: sort of manicured and, um. Detailed way in rigorous way 792 00:43:53,580 --> 00:43:55,500 S1: this whole time. So you have this data set and 793 00:43:55,500 --> 00:43:59,250 S1: you put out different products around it. Um, I love 794 00:43:59,250 --> 00:44:03,050 S1: the idea of when I go to the site. Um, 795 00:44:03,050 --> 00:44:05,810 S1: I would love like a dashboard that is very much 796 00:44:05,810 --> 00:44:10,010 S1: like FiveThirtyEight, which FiveThirtyEight really only spins up for, like 797 00:44:10,010 --> 00:44:13,160 S1: big elections. I'm sure it's getting big right now, but like, 798 00:44:13,160 --> 00:44:18,070 S1: I would just like to. Get quick narratives like, remember 799 00:44:18,070 --> 00:44:21,100 S1: the thing we talked about, okay, what was 2021? What 800 00:44:21,100 --> 00:44:25,600 S1: was 2022? So what I would really love is like, 801 00:44:25,690 --> 00:44:29,890 S1: just explain the whole thing. Starting five years before Covid, 802 00:44:29,890 --> 00:44:32,710 S1: the thing that you did, you said, look, we have 803 00:44:32,710 --> 00:44:35,890 S1: this build up of general economics. Then we had Covid 804 00:44:35,890 --> 00:44:38,080 S1: happen or whatever. If I could get that in a 805 00:44:38,080 --> 00:44:42,910 S1: paragraph or one sentence or a full page, imagine dragging 806 00:44:42,910 --> 00:44:44,860 S1: this kind of related to the thing I'm getting ready 807 00:44:44,860 --> 00:44:50,080 S1: to release. Imagine dragging your explanation from one sentence of 808 00:44:50,080 --> 00:44:53,560 S1: 15 words all the way to a full page of analysis, 809 00:44:53,560 --> 00:44:57,219 S1: but it's exactly the same thing that you described, right? 810 00:44:57,219 --> 00:45:02,770 S1: So it's like, give me the the TLDR of the 811 00:45:02,770 --> 00:45:07,000 S1: entire security space. Yeah. Um, and then you have different 812 00:45:07,000 --> 00:45:09,879 S1: graphs there, right? It's like, show me VC performance. Show 813 00:45:09,880 --> 00:45:12,520 S1: me the number of startups, show me like whatever. And 814 00:45:12,520 --> 00:45:14,470 S1: I feel like that's what I'm getting from you in 815 00:45:14,469 --> 00:45:17,710 S1: your newsletters. So I feel like that could be like. 816 00:45:18,430 --> 00:45:21,670 S1: Definitely like a future direction for you with like, this 817 00:45:21,670 --> 00:45:25,570 S1: really rich front page dashboard. And it's just like it's 818 00:45:25,570 --> 00:45:29,930 S1: like a. It's like a universal dashboard to just state 819 00:45:29,930 --> 00:45:30,830 S1: of the Union. 820 00:45:31,250 --> 00:45:32,600 S3: Yeah, I love that idea. 821 00:45:34,110 --> 00:45:36,149 S2: I love that idea too, because I kind of like, 822 00:45:36,150 --> 00:45:37,530 S2: I think about what I do is like, almost like 823 00:45:37,530 --> 00:45:40,110 S2: a public utility. I'm like, I want you to use it. 824 00:45:40,110 --> 00:45:41,669 S1: Like it is. It absolutely is. 825 00:45:41,670 --> 00:45:43,680 S3: Already I love it, I love it. 826 00:45:43,680 --> 00:45:45,960 S2: That's it's I love that idea too. I'm definitely gonna 827 00:45:45,960 --> 00:45:47,520 S2: noodle on that one and try to figure out how 828 00:45:47,520 --> 00:45:51,540 S2: to how to do it. Because I want to share more. I, 829 00:45:51,540 --> 00:45:54,120 S2: I've made so many like great like connections, and I've 830 00:45:54,120 --> 00:45:55,710 S2: learned so much just by doing this. And so I 831 00:45:55,710 --> 00:45:56,759 S2: want to be able to like kind of share it 832 00:45:56,760 --> 00:45:58,049 S2: back as much as possible. 833 00:45:58,380 --> 00:46:01,590 S1: Yeah. Well, I encourage everyone to go check you out. 834 00:46:01,590 --> 00:46:06,270 S1: It's our return on security. Absolutely. The the best and 835 00:46:06,270 --> 00:46:09,870 S1: really the only newsletter in that space. Just absolutely crushing it. 836 00:46:09,870 --> 00:46:12,390 S1: Can't wait to see what you do next. And thanks 837 00:46:12,390 --> 00:46:13,140 S1: for coming on. 838 00:46:13,530 --> 00:46:15,390 S2: Awesome. Thanks so much for having me. It was great talk. 839 00:46:15,390 --> 00:46:15,870 S3: All right. 840 00:46:15,870 --> 00:46:17,310 S1: Take care. Thanks.