WEBVTT 00:00:00.120 --> 00:00:04.000 every 3 months things have just kept 00:00:02.000 --> 00:00:05.240 getting progressively better and now 00:00:04.000 --> 00:00:07.480 we're at this point where we're talking 00:00:05.240 --> 00:00:09.519 about full-on vertical AI agents that 00:00:07.480 --> 00:00:11.519 are going to replace entire teams and 00:00:09.519 --> 00:00:13.559 functions and Enterprises that 00:00:11.519 --> 00:00:15.759 progression is still mind-blowing to me 00:00:13.558 --> 00:00:17.559 a lot of the foundation models are kind 00:00:15.759 --> 00:00:19.800 of coming head-to-head there used to be 00:00:17.559 --> 00:00:23.118 only one player in town with open AI but 00:00:19.800 --> 00:00:26.039 we've been seeing in the last batch this 00:00:23.118 --> 00:00:28.599 has been changing thank God it's like 00:00:26.039 --> 00:00:30.800 competition is you know the the soil for 00:00:28.599 --> 00:00:33.159 a very fertile Market Marketplace 00:00:30.800 --> 00:00:36.120 ecosystem uh for which consumers will 00:00:33.159 --> 00:00:39.159 have choice and uh Founders have a shot 00:00:36.119 --> 00:00:44.359 and that's the world I want to live 00:00:39.159 --> 00:00:47.599 [Music] 00:00:44.359 --> 00:00:50.558 in welcome to another episode of the 00:00:47.600 --> 00:00:52.920 light cone I'm Gary this is Jared Harge 00:00:50.558 --> 00:00:55.439 and Diana and collectively we funded 00:00:52.920 --> 00:00:57.399 hundreds of billions of dollars worth of 00:00:55.439 --> 00:01:01.599 startups right when they were just one 00:00:57.399 --> 00:01:04.879 or two people starting out and and today 00:01:01.600 --> 00:01:09.040 Jared is a man on fire and he's going to 00:01:04.879 --> 00:01:11.799 talk about vertical AI yes I am I am 00:01:09.040 --> 00:01:15.080 fired up about this because I think 00:01:11.799 --> 00:01:17.759 people especially startup Founders 00:01:15.079 --> 00:01:20.679 especially young ones are not fully 00:01:17.759 --> 00:01:22.478 appreciating just how big vertical AI 00:01:20.680 --> 00:01:23.680 agents are going to be it's not a new 00:01:22.478 --> 00:01:25.478 idea some people are talking about 00:01:23.680 --> 00:01:27.200 vertical AI agents we funded a bunch of 00:01:25.478 --> 00:01:28.879 them but I think the world has not 00:01:27.200 --> 00:01:31.000 caught on to just how big it's going to 00:01:28.879 --> 00:01:33.519 get and so I'm going going to make the 00:01:31.000 --> 00:01:34.599 case for why I think there are going to 00:01:33.519 --> 00:01:38.078 be 00:01:34.599 --> 00:01:40.759 $300 billion plus companies started just 00:01:38.078 --> 00:01:43.399 in this one category nice I'm going to 00:01:40.759 --> 00:01:46.718 do it by analogy with SAS and I 00:01:43.399 --> 00:01:49.439 think in a in a similar fashion people 00:01:46.718 --> 00:01:51.039 don't understand just how big SAS is 00:01:49.438 --> 00:01:53.639 because most startup Founders especially 00:01:51.040 --> 00:01:54.960 young ones tend to see the startup 00:01:53.640 --> 00:01:56.680 industry through the lens of the 00:01:54.959 --> 00:01:58.438 products that they use as a consumer and 00:01:56.680 --> 00:01:59.640 as a consumer you don't tend to use that 00:01:58.438 --> 00:02:02.639 many sass tools because they're mostly 00:01:59.640 --> 00:02:04.759 built for companies and so I think a lot 00:02:02.640 --> 00:02:06.399 of people have missed the basic point 00:02:04.759 --> 00:02:08.159 that if you just look at what Silicon 00:02:06.399 --> 00:02:10.439 Valley has been funding for the most for 00:02:08.159 --> 00:02:12.199 like for the last 20 years like we've 00:02:10.439 --> 00:02:14.120 mostly been producing SAS companies guys 00:02:12.199 --> 00:02:16.039 like that's literally been like most of 00:02:14.120 --> 00:02:18.840 what has been coming out of Silicon 00:02:16.039 --> 00:02:21.159 Valley it's over 40% of all venture 00:02:18.840 --> 00:02:23.200 capital dollars in that time period went 00:02:21.159 --> 00:02:26.318 to SAS companies and we produced over 00:02:23.199 --> 00:02:28.479 300 SAS unicorns in that 20-year time 00:02:26.318 --> 00:02:30.280 period which is way more than every 00:02:28.479 --> 00:02:32.560 other category software is pretty 00:02:30.280 --> 00:02:35.080 awesome software is pretty awesome I was 00:02:32.560 --> 00:02:37.199 thinking back to the history of this 00:02:35.080 --> 00:02:39.319 because we we always like to talk about 00:02:37.199 --> 00:02:42.878 the sort of how the how the history of 00:02:39.318 --> 00:02:45.359 Technology informs the future and um the 00:02:42.878 --> 00:02:48.639 the real Catalyst for for the SAS boom 00:02:45.360 --> 00:02:51.319 was a do you guys remember XML HTTP 00:02:48.639 --> 00:02:53.039 request oh my God like I I'd argue that 00:02:51.318 --> 00:02:57.119 that was quite literally the Catalyst 00:02:53.039 --> 00:02:59.479 for the S boom like uh Ajax Ajax yeah in 00:02:57.120 --> 00:03:01.840 2004 browsers added this JavaScript 00:02:59.479 --> 00:03:03.119 function XML HTTP request which was the 00:03:01.840 --> 00:03:04.840 missing piece that enabled you to build 00:03:03.120 --> 00:03:06.319 a rich internet application in a web 00:03:04.840 --> 00:03:08.200 browser so for the first time you could 00:03:06.318 --> 00:03:10.439 make things in websites that looked like 00:03:08.199 --> 00:03:12.798 desktop applications and then that 00:03:10.439 --> 00:03:15.919 created Google Maps and Gmail and set up 00:03:12.799 --> 00:03:18.680 this whole like SAS boom essentially the 00:03:15.919 --> 00:03:20.399 the key technology atlock was that 00:03:18.680 --> 00:03:22.360 software moved from being a thing that 00:03:20.400 --> 00:03:23.920 you got on a CD ROM and installed on 00:03:22.360 --> 00:03:25.680 your desktop to being something that you 00:03:23.919 --> 00:03:28.000 use through a website and on your phone 00:03:25.680 --> 00:03:29.400 yeah Paul Graham actually uh shares in 00:03:28.000 --> 00:03:31.759 that lineage in that he was one of the 00:03:29.400 --> 00:03:34.879 first people to realize that he could 00:03:31.759 --> 00:03:37.840 take the HTTP request and then actually 00:03:34.878 --> 00:03:40.598 hook it up to a Unix prompt and you 00:03:37.840 --> 00:03:43.239 didn't actually have to you know have a 00:03:40.598 --> 00:03:46.199 separate computer program that would 00:03:43.239 --> 00:03:48.680 change a website so via web was a online 00:03:46.199 --> 00:03:50.598 store kind of like Shopify but way back 00:03:48.680 --> 00:03:52.680 in the day yeah it was basically like 00:03:50.598 --> 00:03:55.878 the first SAS app ever like like PG 00:03:52.680 --> 00:03:57.480 actually invented SAS in like 1995 it's 00:03:55.878 --> 00:03:59.239 just that those first SAS apps kind of 00:03:57.479 --> 00:04:00.878 suck because they didn't have XML HTTP 00:03:59.239 --> 00:04:02.280 request and so every time you would like 00:04:00.878 --> 00:04:04.598 click a button you would have to reload 00:04:02.280 --> 00:04:05.840 the whole page and so it's just a shitty 00:04:04.598 --> 00:04:08.639 experience and so it didn't really catch 00:04:05.840 --> 00:04:11.319 on until 2005 when X XML HTP request 00:04:08.639 --> 00:04:14.119 white spread anyway I I see this llm 00:04:11.318 --> 00:04:16.079 thing as like actually very similar um 00:04:14.120 --> 00:04:17.918 it's like it's a new Computing Paradigm 00:04:16.079 --> 00:04:20.720 that makes it possible to just like do 00:04:17.918 --> 00:04:23.758 something fundamentally different and in 00:04:20.720 --> 00:04:25.720 2005 when cloud and mobile finally took 00:04:23.759 --> 00:04:27.680 off there is this sort of like big open 00:04:25.720 --> 00:04:29.479 question of like okay well this new 00:04:27.680 --> 00:04:32.000 technology exist what should you do with 00:04:29.478 --> 00:04:33.279 it where is the value going to acrw 00:04:32.000 --> 00:04:35.120 where are the good opportunities for 00:04:33.279 --> 00:04:36.359 startups I was going through the list of 00:04:35.120 --> 00:04:38.360 like all the billion dollar companies 00:04:36.360 --> 00:04:41.720 that were created and I kind of had this 00:04:38.360 --> 00:04:43.840 realization that um you could kind of 00:04:41.720 --> 00:04:46.440 bucket the the different paths that 00:04:43.839 --> 00:04:48.439 people took into like three buckets um 00:04:46.439 --> 00:04:50.439 there's there's a first bucket that 00:04:48.439 --> 00:04:54.399 people started with which was like I 00:04:50.439 --> 00:04:57.360 would call them Obviously good ideas 00:04:54.399 --> 00:05:00.599 that could be Mass consumer products um 00:04:57.360 --> 00:05:03.199 so that's like docs photos email 00:05:00.600 --> 00:05:05.080 calendar chat all these things that like 00:05:03.199 --> 00:05:06.840 we used to do on our desktop with that 00:05:05.079 --> 00:05:10.279 obviously could be moved to the browser 00:05:06.839 --> 00:05:12.560 and mobile and the interesting thing is 00:05:10.279 --> 00:05:15.439 zero startups won in those categories 00:05:12.560 --> 00:05:17.120 100% of the value flow to incumbents 00:05:15.439 --> 00:05:19.478 right like Google Facebook Amazon they 00:05:17.120 --> 00:05:21.478 own all all those businesses folks 00:05:19.478 --> 00:05:23.279 forget that like Google Docs wasn't the 00:05:21.478 --> 00:05:24.680 only company that tried to bring 00:05:23.279 --> 00:05:26.439 Microsoft Office online there were like 00:05:24.680 --> 00:05:27.759 30 companies that tried to bring 00:05:26.439 --> 00:05:30.439 Microsoft Office online but they all 00:05:27.759 --> 00:05:33.240 lost Google one then there was a second 00:05:30.439 --> 00:05:36.000 category which was like Mass consumer 00:05:33.240 --> 00:05:39.439 ideas that were not obvious that nobody 00:05:36.000 --> 00:05:41.038 predicted um that's like uber instacart 00:05:39.439 --> 00:05:44.600 door Dash 00:05:41.038 --> 00:05:47.719 coinbase th Airbnb those ones those ones 00:05:44.600 --> 00:05:49.919 came out of left field like the the dot 00:05:47.720 --> 00:05:52.960 dot dot between XML HTTP request and 00:05:49.918 --> 00:05:54.318 Airbnb is like very not obvious yeah and 00:05:52.959 --> 00:05:55.799 so the incumbents didn't even try 00:05:54.319 --> 00:05:57.759 competing in those spaces until it was 00:05:55.800 --> 00:06:00.560 like too late and so startups are able 00:05:57.759 --> 00:06:02.560 to win there and then there's a third 00:06:00.560 --> 00:06:04.600 category which is all the B2B SAS 00:06:02.560 --> 00:06:08.120 companies and that's like 300 of them 00:06:04.600 --> 00:06:10.280 and so like Mo like by by by number of 00:06:08.120 --> 00:06:12.319 logos way more billion dollar companies 00:06:10.279 --> 00:06:14.478 were created in that third category than 00:06:12.319 --> 00:06:16.720 the first two I think one reason why 00:06:14.478 --> 00:06:19.758 that happened is like there is 00:06:16.720 --> 00:06:22.000 no like Microsoft of SAS like there is 00:06:19.759 --> 00:06:23.598 no company that somehow does like SAS 00:06:22.000 --> 00:06:25.800 for like every vertical and every 00:06:23.598 --> 00:06:27.159 product like for structural reasons it 00:06:25.800 --> 00:06:29.598 seems to be the case that like they're 00:06:27.160 --> 00:06:31.439 all different companies and that's why 00:06:29.598 --> 00:06:33.478 there so many of them I think Salesforce 00:06:31.439 --> 00:06:37.000 is probably like the first true SAS 00:06:33.478 --> 00:06:40.159 company um and i' I remember Mark benov 00:06:37.000 --> 00:06:42.560 coming to speak at YC and he tells the 00:06:40.160 --> 00:06:43.800 story as just very early on people just 00:06:42.560 --> 00:06:46.038 didn't believe you could build 00:06:43.800 --> 00:06:48.520 sophisticated Enterprise applications 00:06:46.038 --> 00:06:50.598 like over the cloud or via SAS it was 00:06:48.519 --> 00:06:51.959 just so um there was just like a 00:06:50.598 --> 00:06:53.639 perception issue right it was like no 00:06:51.959 --> 00:06:54.839 like you don't you buy like your box 00:06:53.639 --> 00:06:56.439 software and that's like the real 00:06:54.839 --> 00:06:58.119 software that you run the way we always 00:06:56.439 --> 00:07:00.079 do it it was it was quite contrar cuz 00:06:58.120 --> 00:07:02.160 the early web app sucked they were like 00:07:00.079 --> 00:07:03.719 via web where you had to be a Visionary 00:07:02.160 --> 00:07:04.800 like PG and understand that the browser 00:07:03.720 --> 00:07:06.720 was going to keep getting better and 00:07:04.800 --> 00:07:08.879 that eventually it' be good which feels 00:07:06.720 --> 00:07:10.440 like quite reminisent of today right 00:07:08.879 --> 00:07:11.720 where it's like the yeah the same thing 00:07:10.439 --> 00:07:13.199 like oh no like you won't be able to 00:07:11.720 --> 00:07:16.120 build like sophisticated Enterprise 00:07:13.199 --> 00:07:18.520 applications that use these llm or AI 00:07:16.120 --> 00:07:20.800 tools because they hallucinate or 00:07:18.519 --> 00:07:22.399 they're not perfect or they um they kind 00:07:20.800 --> 00:07:25.240 of like just toys but yeah that's like 00:07:22.399 --> 00:07:27.598 the early SAS story exactly the same and 00:07:25.240 --> 00:07:30.400 so when I think about the parallels with 00:07:27.598 --> 00:07:31.878 LMS I could easily imagine the same 00:07:30.399 --> 00:07:33.799 thing happening which is that there's a 00:07:31.879 --> 00:07:35.720 bunch of categories that are like Mass 00:07:33.800 --> 00:07:38.120 consumer applications that are obviously 00:07:35.720 --> 00:07:39.919 huge opportunities but probably the 00:07:38.120 --> 00:07:41.399 incumbents will win all of those so 00:07:39.918 --> 00:07:43.839 that's something like a per like a 00:07:41.399 --> 00:07:45.120 general purpose AI Voice Assistant that 00:07:43.839 --> 00:07:46.560 you you know you can ask it to do 00:07:45.120 --> 00:07:47.720 anything and it'll like go do that thing 00:07:46.560 --> 00:07:49.478 that's an obvious thing that should 00:07:47.720 --> 00:07:51.159 exist but like all the big players are 00:07:49.478 --> 00:07:53.120 going to be competing to be that thing 00:07:51.158 --> 00:07:55.199 right W Apple's a little slow on that 00:07:53.120 --> 00:07:58.199 one why is Siri so stupid still what 00:07:55.199 --> 00:07:59.759 year is it it makes no sense I mean it's 00:07:58.199 --> 00:08:02.800 like a count to that is like the very 00:07:59.759 --> 00:08:05.759 obvious thing is search and maybe Google 00:08:02.800 --> 00:08:08.158 will still win um on search but 00:08:05.759 --> 00:08:09.960 perplexity is definitely give them run 00:08:08.158 --> 00:08:11.639 for the money right yeah this is the 00:08:09.959 --> 00:08:12.758 classic innovators dilemma at the end of 00:08:11.639 --> 00:08:14.639 the day I mean you could argue going 00:08:12.759 --> 00:08:17.879 back to what you said about Uber or 00:08:14.639 --> 00:08:20.478 Airbnb these were actually really risky 00:08:17.879 --> 00:08:22.560 things from a regulatory standpoint so 00:08:20.478 --> 00:08:25.519 if you're Google and you have basically 00:08:22.560 --> 00:08:27.360 a guaranteed you know giant a pot of 00:08:25.519 --> 00:08:29.519 gold that you know sort of comes to you 00:08:27.360 --> 00:08:31.520 every single month like why would you 00:08:29.519 --> 00:08:33.759 endanger that pot of gold to sort of 00:08:31.519 --> 00:08:36.079 pursue these things that uh might be 00:08:33.759 --> 00:08:37.278 scary or might ruin the pot of gold I 00:08:36.080 --> 00:08:38.879 think that's I think that's like 00:08:37.278 --> 00:08:41.038 probably the primary reason why the 00:08:38.879 --> 00:08:42.479 incumbents didn't end up building those 00:08:41.038 --> 00:08:44.838 products and didn't even clone them even 00:08:42.479 --> 00:08:45.959 after they got big and it was obvious 00:08:44.839 --> 00:08:47.839 that they were going to work who will 00:08:45.958 --> 00:08:50.119 never launch an an Uber clone they never 00:08:47.839 --> 00:08:53.640 launch an Airbnb clone um I was 00:08:50.120 --> 00:08:54.799 listening to this uh talk by Travis and 00:08:53.639 --> 00:08:56.958 one of the things that he said that 00:08:54.799 --> 00:08:58.799 really stuck with me is that in the in 00:08:56.958 --> 00:08:59.799 the first years of uber he was very 00:08:58.799 --> 00:09:01.838 scared that he would was going to 00:08:59.799 --> 00:09:03.879 personally go to prison for like a long 00:09:01.839 --> 00:09:05.200 time like he was actually personally 00:09:03.879 --> 00:09:07.679 risking going to prison in order to 00:09:05.200 --> 00:09:09.600 build that company and so yeah no highly 00:09:07.679 --> 00:09:12.958 paid Google exactly was going to do that 00:09:09.600 --> 00:09:15.200 what do you think about um why the 00:09:12.958 --> 00:09:17.159 incumbents didn't go into B2B SAS is it 00:09:15.200 --> 00:09:20.000 part of the reason is that a lot of the 00:09:17.159 --> 00:09:22.519 use cases are very there's a very wide 00:09:20.000 --> 00:09:24.720 distribution I it's a great question I 00:09:22.519 --> 00:09:28.720 love to hear what you guys think my take 00:09:24.720 --> 00:09:31.000 is that it's just too hard to do that 00:09:28.720 --> 00:09:33.160 many things as a company like each B2B 00:09:31.000 --> 00:09:34.958 SAS company really requires like the 00:09:33.159 --> 00:09:37.439 people who are running the product in 00:09:34.958 --> 00:09:39.639 the business to be extremely deep in one 00:09:37.440 --> 00:09:42.120 domain and care very deeply about a lot 00:09:39.639 --> 00:09:44.000 of really obscure issues you know like 00:09:42.120 --> 00:09:45.679 take like Gusto for example like why 00:09:44.000 --> 00:09:47.120 didn't Google build a Gusto competitor 00:09:45.679 --> 00:09:48.838 well there's no one to Google who really 00:09:47.120 --> 00:09:50.959 understands payroll and has the patience 00:09:48.839 --> 00:09:52.959 to like deal with all the nuances of all 00:09:50.958 --> 00:09:55.559 these like stupid payroll regulations 00:09:52.958 --> 00:09:57.159 and like it's just like like it's just 00:09:55.559 --> 00:09:59.399 not worth it for them it's easier for 00:09:57.159 --> 00:10:02.199 them to just focus on like a few really 00:09:59.399 --> 00:10:04.159 huge categories in the B2B SAS world 00:10:02.200 --> 00:10:06.160 it's it's sort of about the unbundling 00:10:04.159 --> 00:10:09.039 bundling of software argument that comes 00:10:06.159 --> 00:10:11.399 up a lot as well I think and why didn't 00:10:09.039 --> 00:10:14.719 why did all these vertical B2B SAS 00:10:11.399 --> 00:10:18.759 products evolve versus just like Oracle 00:10:14.720 --> 00:10:21.920 or sap or um netet yeah netu just owning 00:10:18.759 --> 00:10:23.240 like everything um and I think it might 00:10:21.919 --> 00:10:25.519 be Al is another thing that's 00:10:23.240 --> 00:10:28.278 attributable to the shift to like SASS 00:10:25.519 --> 00:10:29.799 and the internet is in the old ways of 00:10:28.278 --> 00:10:31.759 selling software again like you had this 00:10:29.799 --> 00:10:34.199 box software that was really like 00:10:31.759 --> 00:10:35.919 expensive to install and you had like a 00:10:34.200 --> 00:10:38.079 whole ecosystem around it and anytime 00:10:35.919 --> 00:10:40.000 you wanted something custom like the 00:10:38.078 --> 00:10:41.679 integrators would just say oh no like we 00:10:40.000 --> 00:10:44.278 can like just build a UA custom like 00:10:41.679 --> 00:10:46.278 payroll feature or something like that 00:10:44.278 --> 00:10:48.679 and then Salesforce comes along with 00:10:46.278 --> 00:10:50.480 like a SAS solution and it just seems 00:10:48.679 --> 00:10:52.479 like it could never be as powerful or 00:10:50.480 --> 00:10:54.200 sophisticated as like the expensive 00:10:52.480 --> 00:10:56.759 Enterprise installation you just paid 00:10:54.200 --> 00:10:58.200 for but they Prov that it totally was 00:10:56.759 --> 00:11:00.278 the case and I think that just like 00:10:58.200 --> 00:11:02.600 opened the gates for all all of these 00:11:00.278 --> 00:11:04.399 like vertical sash solutions to emerge 00:11:02.600 --> 00:11:05.959 doing exactly what you're saying the 00:11:04.399 --> 00:11:08.278 other problem is that with a lot of this 00:11:05.958 --> 00:11:10.958 enterprise software if you're a user of 00:11:08.278 --> 00:11:13.600 Oracle and a netw suite because they're 00:11:10.958 --> 00:11:15.799 they have to cover so much ground the 00:11:13.600 --> 00:11:17.120 user experience is actually pretty bad 00:11:15.799 --> 00:11:19.359 they're trying to be jack of all trades 00:11:17.120 --> 00:11:21.078 but master of none yeah so ends up being 00:11:19.360 --> 00:11:24.320 a bit of a kitchen sync type of 00:11:21.078 --> 00:11:27.399 experience and this is where if you go 00:11:24.320 --> 00:11:29.560 and build a B2B SAS vertical company you 00:11:27.399 --> 00:11:31.519 could do literally a 10x better 00:11:29.559 --> 00:11:33.278 experience and more delightful because 00:11:31.519 --> 00:11:35.720 there's this Stark difference between 00:11:33.278 --> 00:11:37.600 consumer products and Enterprise user 00:11:35.720 --> 00:11:40.360 experience yeah well there's only uh 00:11:37.600 --> 00:11:45.079 what three price points in software it's 00:11:40.360 --> 00:11:48.278 uh $5 per seat $500 per seat or $55,000 00:11:45.078 --> 00:11:51.919 per seat and uh that Maps directly to 00:11:48.278 --> 00:11:54.278 Consumer SMB or Enterprise sales and 00:11:51.919 --> 00:11:56.719 then I think time in Memorial has taught 00:11:54.278 --> 00:11:58.919 us that in the past and this is less and 00:11:56.720 --> 00:12:00.000 less true uh with new software 00:11:58.919 --> 00:12:02.838 thankfully 00:12:00.000 --> 00:12:05.320 but Enterprise is terrible software 00:12:02.839 --> 00:12:08.040 because it's not the user buying it you 00:12:05.320 --> 00:12:10.639 know some high up Mucky muuk inside 00:12:08.039 --> 00:12:13.399 Fortune 1000 is the person who's getting 00:12:10.639 --> 00:12:16.399 whin and D for this you know Mega seven 00:12:13.399 --> 00:12:18.278 figure contract and you know they're 00:12:16.399 --> 00:12:20.839 going to choose something that maybe 00:12:18.278 --> 00:12:22.559 isn't that good actually for the end 00:12:20.839 --> 00:12:25.760 user the person who has to actually use 00:12:22.559 --> 00:12:28.439 the software day-to-day and um I'm sort 00:12:25.759 --> 00:12:31.958 of curious to see how this changes with 00:12:28.440 --> 00:12:33.760 llms actually I mean to date one of the 00:12:31.958 --> 00:12:35.638 more Salient things that we've seen for 00:12:33.759 --> 00:12:37.919 both SMB and enterprise software 00:12:35.639 --> 00:12:40.000 companies is that or all software 00:12:37.919 --> 00:12:42.278 companies all startups period is like 00:12:40.000 --> 00:12:44.559 you know there's a sense that as Revenue 00:12:42.278 --> 00:12:47.000 scales the number of people you have to 00:12:44.559 --> 00:12:51.159 hire scales with it and so when you look 00:12:47.000 --> 00:12:54.278 at unicorns uh even in today's YC 00:12:51.159 --> 00:12:56.198 portfolio uh it's quite routine to see a 00:12:54.278 --> 00:12:58.399 company that reached a hundred or $200 00:12:56.198 --> 00:13:02.639 million a year in Revenue but they have 00:12:58.399 --> 00:13:04.679 like 500 a th000 2,000 employees already 00:13:02.639 --> 00:13:06.278 and I'm just going to be very curious 00:13:04.679 --> 00:13:08.599 like uh even the advice that I'm 00:13:06.278 --> 00:13:11.159 starting to give companies that are you 00:13:08.600 --> 00:13:13.000 know a month or two out of the batch uh 00:13:11.159 --> 00:13:14.480 it's a it's feeling a little bit 00:13:13.000 --> 00:13:17.240 different than the kind of advice I 00:13:14.480 --> 00:13:20.079 would give last year or two years ago in 00:13:17.240 --> 00:13:23.839 the past you might say you know let me 00:13:20.078 --> 00:13:26.039 find the absolute smartest person uh in 00:13:23.839 --> 00:13:28.240 all of these other parts of the org like 00:13:26.039 --> 00:13:29.639 customer success or sales or different 00:13:28.240 --> 00:13:31.320 things like that 00:13:29.639 --> 00:13:33.919 and uh I want to find someone who I've 00:13:31.320 --> 00:13:35.320 worked with who is I know is great and 00:13:33.919 --> 00:13:37.360 then I'm going to go sit on their you 00:13:35.320 --> 00:13:39.399 know uh on their doorstep until they 00:13:37.360 --> 00:13:41.440 quit their jobs and come work for me and 00:13:39.399 --> 00:13:43.679 I want them to be someone who can you 00:13:41.440 --> 00:13:45.880 know build a team for me hire a lot of 00:13:43.679 --> 00:13:48.559 people that might still be true but I'm 00:13:45.879 --> 00:13:50.278 starting to sense that uh the Met is 00:13:48.559 --> 00:13:53.198 shifting a little bit like you actually 00:13:50.278 --> 00:13:56.078 might want to hire more really good 00:13:53.198 --> 00:13:59.120 software Engineers who understand large 00:13:56.078 --> 00:14:01.039 language models uh who can actually 00:13:59.120 --> 00:14:02.919 automate the specific things that you 00:14:01.039 --> 00:14:06.078 need that are the bottlenecks to your 00:14:02.919 --> 00:14:08.000 growth and so it might result in you 00:14:06.078 --> 00:14:10.278 know a very subtle but you know 00:14:08.000 --> 00:14:11.879 significant change in the way startups 00:14:10.278 --> 00:14:14.039 grow their businesses sort of post- 00:14:11.879 --> 00:14:16.799 product Market fit it means that I'm 00:14:14.039 --> 00:14:19.278 going to build llm systems that bring 00:14:16.799 --> 00:14:21.719 down my costs that cause me not to have 00:14:19.278 --> 00:14:22.679 to hire a thousand people I think we're 00:14:21.720 --> 00:14:24.800 right at the beginning of that 00:14:22.679 --> 00:14:26.519 Revolution right now I mean we talked 00:14:24.799 --> 00:14:29.679 about this in a previous episode we 00:14:26.519 --> 00:14:31.759 talked about there will be a future 00:14:29.679 --> 00:14:33.359 unicorn company that's only run if we 00:14:31.759 --> 00:14:36.120 take it to the limit with only 10 00:14:33.360 --> 00:14:37.480 employees that's completely plausible 00:14:36.120 --> 00:14:39.720 and they're writing the evals and the 00:14:37.480 --> 00:14:40.920 prompts does it I think what you're 00:14:39.720 --> 00:14:43.800 saying is like a trend that was already 00:14:40.919 --> 00:14:46.319 underway pre llms like I remember when I 00:14:43.799 --> 00:14:49.198 was running triple bite for example we 00:14:46.320 --> 00:14:51.920 needed to like build marketing or cust 00:14:49.198 --> 00:14:54.078 like user acquisition basically um and 00:14:51.919 --> 00:14:55.360 especially after we raised a series B 00:14:54.078 --> 00:14:57.399 the like traditional way you were 00:14:55.360 --> 00:14:59.159 supposed to do that is to like hire a 00:14:57.399 --> 00:15:01.799 marketing executive and build out like 00:14:59.159 --> 00:15:03.240 marketing team and um and just like 00:15:01.799 --> 00:15:06.159 basically spin up this machine to do 00:15:03.240 --> 00:15:10.039 like sales and marketing but I'd 00:15:06.159 --> 00:15:11.360 actually met like a y founder um Mike 00:15:10.039 --> 00:15:13.278 who was his company was basically 00:15:11.360 --> 00:15:15.278 building like a smart frying pan sounds 00:15:13.278 --> 00:15:17.480 like bizarre but like he was a MIT 00:15:15.278 --> 00:15:19.399 engineer yeah you remember this um he's 00:15:17.480 --> 00:15:21.199 an MIT engineer and to sell the smart 00:15:19.399 --> 00:15:22.399 frying pan he had to get really really 00:15:21.198 --> 00:15:25.359 good at understanding like paid 00:15:22.399 --> 00:15:26.759 advertising and um uh Google ads and 00:15:25.360 --> 00:15:28.560 just a whole bunch of stuff and so he he 00:15:26.759 --> 00:15:29.959 taken this Engineers mindset approach to 00:15:28.559 --> 00:15:32.958 it and remember just talking to him 00:15:29.958 --> 00:15:34.719 about it and realizing this would be so 00:15:32.958 --> 00:15:37.559 much better to have an MIT engineer 00:15:34.720 --> 00:15:39.079 working on like our marketing efforts 00:15:37.559 --> 00:15:41.559 than any of the marketing candidates 00:15:39.078 --> 00:15:43.599 I've spoken to and he was able to like 00:15:41.559 --> 00:15:45.159 scale us up to like we were spending 00:15:43.600 --> 00:15:47.680 like 1. like a million dollars a month 00:15:45.159 --> 00:15:49.240 on just marketing and various like 00:15:47.679 --> 00:15:51.479 campaigns and triple bite had great 00:15:49.240 --> 00:15:53.759 marketing like I remember like the Cal 00:15:51.480 --> 00:15:55.240 trained station takeover that you did 00:15:53.759 --> 00:15:57.159 all the like out of home stuff that you 00:15:55.240 --> 00:15:59.240 did it was like really high quality 00:15:57.159 --> 00:16:00.759 stuff it stuck with it you could tell 00:15:59.240 --> 00:16:03.680 was not being done by some like VP 00:16:00.759 --> 00:16:05.800 marketing person um and that was all mik 00:16:03.679 --> 00:16:07.198 and like the comment I would often get 00:16:05.799 --> 00:16:08.799 when people would ask me around that 00:16:07.198 --> 00:16:11.838 time like how big is triple bite and we 00:16:08.799 --> 00:16:12.958 were like 50 people and so much yeah 00:16:11.839 --> 00:16:14.319 yeah people be like I thought this's 00:16:12.958 --> 00:16:15.879 like hundreds of people I was like no 00:16:14.318 --> 00:16:17.639 it's all because if you put a really 00:16:15.879 --> 00:16:20.240 smart engineer on some of these like 00:16:17.639 --> 00:16:22.519 tasks they just find ways to make they 00:16:20.240 --> 00:16:23.959 find leverage and now like llms can go 00:16:22.519 --> 00:16:25.959 even Way Beyond like The Leverage you 00:16:23.958 --> 00:16:28.559 had which is pure software okay so 00:16:25.958 --> 00:16:31.359 here's my pitch for 300 vertical AI 00:16:28.559 --> 00:16:33.838 agent unicorns literally every company 00:16:31.360 --> 00:16:36.480 that is a SAS unicorn you could imagine 00:16:33.839 --> 00:16:39.480 there's a vertical AI unicorn equivalent 00:16:36.480 --> 00:16:42.240 in like some new universe cuz like most 00:16:39.480 --> 00:16:44.079 of these SAS unicorns beforehand there 00:16:42.240 --> 00:16:45.639 were some like box software company that 00:16:44.078 --> 00:16:47.198 was making the same thing that got 00:16:45.639 --> 00:16:49.000 disrupted by a SAS company and you could 00:16:47.198 --> 00:16:51.000 easily imagine the same thing happening 00:16:49.000 --> 00:16:53.360 again where now basically every every 00:16:51.000 --> 00:16:55.919 SAS company builds some software that 00:16:53.360 --> 00:16:57.199 some group of people use the vertical AI 00:16:55.919 --> 00:17:00.198 equivalent is just going to be the 00:16:57.198 --> 00:17:01.799 software plus the people in one product 00:17:00.198 --> 00:17:03.318 one thing might be just Enterprises in 00:17:01.799 --> 00:17:05.678 general right now are a little unsure 00:17:03.318 --> 00:17:07.519 about what exactly they like what agents 00:17:05.679 --> 00:17:08.880 they need and one approach I've seen 00:17:07.519 --> 00:17:11.439 from especially more experienced 00:17:08.880 --> 00:17:13.679 Founders like um Brett Taylor the CTO of 00:17:11.439 --> 00:17:15.400 Facebook started his company Sierra I 00:17:13.679 --> 00:17:17.919 don't know all the details but as far as 00:17:15.400 --> 00:17:20.798 I can tell it's essentially more like 00:17:17.919 --> 00:17:23.759 broadly about letting Enterprises like 00:17:20.798 --> 00:17:26.000 deploy these AI agents and spinning them 00:17:23.759 --> 00:17:27.480 up like custom for the Enterprise versus 00:17:26.000 --> 00:17:29.440 like oh hey we have like this specific 00:17:27.480 --> 00:17:31.759 agent to do this it's something I've 00:17:29.440 --> 00:17:33.679 seen from one of my companies called um 00:17:31.759 --> 00:17:36.720 uh Vector shift that funded about a year 00:17:33.679 --> 00:17:39.798 ago they're two really smart like 00:17:36.720 --> 00:17:41.120 Harvard computer scientists and it's a 00:17:39.798 --> 00:17:42.519 that what they found is that they're 00:17:41.119 --> 00:17:44.599 trying to build a platform to make it 00:17:42.519 --> 00:17:47.240 easy for Enterprises to build their own 00:17:44.599 --> 00:17:50.678 like use like no code or sdks to build 00:17:47.240 --> 00:17:53.038 their own like um internal llm powered 00:17:50.679 --> 00:17:54.600 agents but like Enterprises often don't 00:17:53.038 --> 00:17:57.400 know exactly what they want to use these 00:17:54.599 --> 00:17:59.759 things for and so bring it back I wonder 00:17:57.400 --> 00:18:01.080 if like in like the box software world 00:17:59.759 --> 00:18:02.759 you started off with just like a few 00:18:01.079 --> 00:18:04.399 vendors who just basically were trying 00:18:02.759 --> 00:18:06.079 to convince people to use software at 00:18:04.400 --> 00:18:08.320 all and it was just like it does 00:18:06.079 --> 00:18:09.839 everything um and then it gets more 00:18:08.319 --> 00:18:12.558 sophisticated and higher resolution and 00:18:09.839 --> 00:18:14.558 you get lots of like vertical SS players 00:18:12.558 --> 00:18:16.399 we go through that same period with llms 00:18:14.558 --> 00:18:18.240 where the early winners might just be 00:18:16.400 --> 00:18:20.120 these like general purpose hey like we 00:18:18.240 --> 00:18:22.679 like make it easy for you to do llm 00:18:20.119 --> 00:18:25.119 stuff and then it the vertical agents 00:18:22.679 --> 00:18:26.480 will come in over time or do you think 00:18:25.119 --> 00:18:28.639 there's reasons it's different now and 00:18:26.480 --> 00:18:29.400 the vertical agents will take off on day 00:18:28.640 --> 00:18:30.840 one 00:18:29.400 --> 00:18:32.840 yeah that's interesting because if you 00:18:30.839 --> 00:18:35.359 think about the history of SAS the 00:18:32.839 --> 00:18:38.480 consumer things worked first like 2005 00:18:35.359 --> 00:18:41.038 to 2010 was mostly consumer applications 00:18:38.480 --> 00:18:43.599 like email and chat and maps and people 00:18:41.038 --> 00:18:45.319 got people as individuals got used to 00:18:43.599 --> 00:18:48.079 using these tools themselves and I think 00:18:45.319 --> 00:18:49.798 that made it easier to sell SAS tools to 00:18:48.079 --> 00:18:51.798 companies because you know the same 00:18:49.798 --> 00:18:53.480 people are both employees and consumers 00:18:51.798 --> 00:18:54.879 yeah I I think the answer might just be 00:18:53.480 --> 00:18:58.279 like this is this is all just a 00:18:54.880 --> 00:19:00.360 continuation of software and just 00:18:58.279 --> 00:19:02.200 there's no reason it has to reset back 00:19:00.359 --> 00:19:05.000 like llms don't have to reset back to a 00:19:02.200 --> 00:19:07.038 few general purpose like Enterprise llm 00:19:05.000 --> 00:19:08.798 platforms doing everything because 00:19:07.038 --> 00:19:11.519 Enterprises have already been trained on 00:19:08.798 --> 00:19:14.400 like the value of Point Solutions and 00:19:11.519 --> 00:19:15.359 vertical Solutions um and like the user 00:19:14.400 --> 00:19:16.480 experience not going to be that 00:19:15.359 --> 00:19:19.879 different these things will just be a 00:19:16.480 --> 00:19:21.079 lot more powerful and so if Enterprises 00:19:19.880 --> 00:19:23.039 have already built the muscle of 00:19:21.079 --> 00:19:25.439 believing that like startups or vertical 00:19:23.038 --> 00:19:28.200 Solutions can be better than like Legacy 00:19:25.440 --> 00:19:31.240 broad platforms they are probably going 00:19:28.200 --> 00:19:33.360 to be willing to take a bet on a startup 00:19:31.240 --> 00:19:34.880 promising a very good vertical AI agent 00:19:33.359 --> 00:19:36.599 solution today and I feel like we're all 00:19:34.880 --> 00:19:39.000 seeing that in the batch now with some 00:19:36.599 --> 00:19:41.480 of our companies are getting faster 00:19:39.000 --> 00:19:44.279 Traction in Enterprises for these 00:19:41.480 --> 00:19:45.720 vertical AI agents than like we've ever 00:19:44.279 --> 00:19:47.960 seen before I think we're just early in 00:19:45.720 --> 00:19:50.798 the game right like all software sort of 00:19:47.960 --> 00:19:52.679 starts quite vertical and then as the 00:19:50.798 --> 00:19:56.279 industries actually get much more 00:19:52.679 --> 00:19:58.240 developed um then I mean I just answered 00:19:56.279 --> 00:19:59.798 my earlier question it's like you know 00:19:58.240 --> 00:20:02.640 why does company end up having a 00:19:59.798 --> 00:20:04.519 thousand employees it's actually that uh 00:20:02.640 --> 00:20:06.320 you know early early in the game 00:20:04.519 --> 00:20:08.558 everyone's making these specific point 00:20:06.319 --> 00:20:11.279 Solutions and then at some point you've 00:20:08.558 --> 00:20:13.319 got to go horizontal like you're already 00:20:11.279 --> 00:20:15.759 doing this crazy spend on sales and 00:20:13.319 --> 00:20:18.079 marketing and then the only way you can 00:20:15.759 --> 00:20:20.720 actually continue to grow once you sort 00:20:18.079 --> 00:20:22.399 of get 100% or you know some large 00:20:20.720 --> 00:20:25.480 majority of the market is you actually 00:20:22.400 --> 00:20:27.759 have to do like not just a point 00:20:25.480 --> 00:20:32.000 solution but things that sort of work 00:20:27.759 --> 00:20:33.759 together the other point of why the bull 00:20:32.000 --> 00:20:36.798 case for vertical AI agents could be 00:20:33.759 --> 00:20:39.440 even bigger than SAS is that SAS you 00:20:36.798 --> 00:20:41.558 still needed a operations team or set of 00:20:39.440 --> 00:20:43.038 people to operate the software in order 00:20:41.558 --> 00:20:44.798 to get all the workflows to be done I 00:20:43.038 --> 00:20:47.798 don't know approval workflows or you 00:20:44.798 --> 00:20:50.440 have to input the data the argument here 00:20:47.798 --> 00:20:52.000 is that you will get not only replacing 00:20:50.440 --> 00:20:54.519 all that set of SAS software so that 00:20:52.000 --> 00:20:56.400 would be like one to one mapping but is 00:20:54.519 --> 00:20:58.679 also going to eat all of the a lot of 00:20:56.400 --> 00:21:00.880 the payroll because we look a lot of the 00:20:58.679 --> 00:21:02.798 spend for companies big chunk is still a 00:21:00.880 --> 00:21:04.200 payroll and software's Tiny exactly they 00:21:02.798 --> 00:21:06.000 spend way more on employees than they do 00:21:04.200 --> 00:21:07.480 on software so it'll be these smaller 00:21:06.000 --> 00:21:11.640 companies that way more efficient that 00:21:07.480 --> 00:21:14.440 need way less humans to do random data 00:21:11.640 --> 00:21:16.200 entry or approvals or click the software 00:21:14.440 --> 00:21:18.080 I agree I think it's very possible the 00:21:16.200 --> 00:21:20.480 vertical equivalence will be 10 times as 00:21:18.079 --> 00:21:22.399 large as the SAS company that they are 00:21:20.480 --> 00:21:24.120 disrupting I mean there there's two case 00:21:22.400 --> 00:21:26.440 it could be that the vertical Point 00:21:24.119 --> 00:21:28.639 solution could be just big enough and 00:21:26.440 --> 00:21:30.440 you don't need to do that bro breath 00:21:28.640 --> 00:21:32.440 thing right it that could be a nice 00:21:30.440 --> 00:21:34.400 scenario should we give some examples I 00:21:32.440 --> 00:21:37.038 feel like we've all been working with so 00:21:34.400 --> 00:21:38.640 many vertical AI agent companies we've 00:21:37.038 --> 00:21:41.480 got like news from the 00:21:38.640 --> 00:21:44.000 front how it's actually going well your 00:21:41.480 --> 00:21:46.519 former uh head of product Aaron Cannon 00:21:44.000 --> 00:21:48.880 is working on a YC company called outset 00:21:46.519 --> 00:21:51.960 that I worked with and uh basically 00:21:48.880 --> 00:21:54.120 they're taking llms uh to the surveys 00:21:51.960 --> 00:21:56.558 and qual Trix space so qual Trix is 00:21:54.119 --> 00:21:58.918 almost certainly not really going to 00:21:56.558 --> 00:22:00.798 build the best of breed uh large 00:21:58.919 --> 00:22:02.400 language model with reasoning and then 00:22:00.798 --> 00:22:04.720 the funny thing about surveys is you 00:22:02.400 --> 00:22:06.600 know who's it actually for it's for 00:22:04.720 --> 00:22:08.600 people who run products for marketing 00:22:06.599 --> 00:22:10.319 teams it's for people who are trying to 00:22:08.599 --> 00:22:12.719 make sense of like what do our customers 00:22:10.319 --> 00:22:15.480 actually want and what are surveys like 00:22:12.720 --> 00:22:18.120 guess what that's language so um and 00:22:15.480 --> 00:22:20.240 then I feel like these types of 00:22:18.119 --> 00:22:23.639 businesses um actually have to thread 00:22:20.240 --> 00:22:27.278 this needle um because Enterprise and 00:22:23.640 --> 00:22:29.440 SMB software often is sold based on a 00:22:27.278 --> 00:22:32.400 particular person who who is the key 00:22:29.440 --> 00:22:34.798 decision maker and um you have to go 00:22:32.400 --> 00:22:36.320 high enough in the organization so that 00:22:34.798 --> 00:22:38.558 the people you're selling to are not 00:22:36.319 --> 00:22:40.200 afraid that their whole their job Andor 00:22:38.558 --> 00:22:42.759 their whole team's job is going to go 00:22:40.200 --> 00:22:45.038 away totally that's kind of the move 00:22:42.759 --> 00:22:46.278 that I seen that a lot of companies that 00:22:45.038 --> 00:22:48.400 sell need to do because if you're going 00:22:46.278 --> 00:22:50.720 to go and sell to the team that's going 00:22:48.400 --> 00:22:53.759 to get replaced by AI they're going to 00:22:50.720 --> 00:22:56.600 sabotage it man it just does not work so 00:22:53.759 --> 00:22:58.079 I think this is an interesting way that 00:22:56.599 --> 00:22:59.839 a lot of these are top down and you have 00:22:58.079 --> 00:23:02.480 to go through at some point even get the 00:22:59.839 --> 00:23:04.759 CEO to sign off on it a company I'm 00:23:02.480 --> 00:23:06.798 working with u MCH that's sort of 00:23:04.759 --> 00:23:08.000 essentially an AI agent but for at least 00:23:06.798 --> 00:23:11.158 where they're starting is like QA 00:23:08.000 --> 00:23:13.079 testing um they're getting really great 00:23:11.159 --> 00:23:15.760 traction right now and it's interesting 00:23:13.079 --> 00:23:17.960 because you remember a decade ago um why 00:23:15.759 --> 00:23:21.079 can't we worked with rainforest QA like 00:23:17.960 --> 00:23:24.519 rainforest was a QA as a service company 00:23:21.079 --> 00:23:26.399 and that they had this exact tension of 00:23:24.519 --> 00:23:29.558 where they couldn't actually replace 00:23:26.400 --> 00:23:31.600 your QA team and so they needed to build 00:23:29.558 --> 00:23:33.440 software that made the QA te more like 00:23:31.599 --> 00:23:34.639 efficient but really that obviously 00:23:33.440 --> 00:23:35.840 meant trying to replace as many of them 00:23:34.640 --> 00:23:39.000 as possible they couldn't replace the 00:23:35.839 --> 00:23:40.798 whole team and so they were always on 00:23:39.000 --> 00:23:42.319 the sort of like tight rope between 00:23:40.798 --> 00:23:43.759 trying to sell the software to like the 00:23:42.319 --> 00:23:47.079 head of engineering as like this will 00:23:43.759 --> 00:23:48.400 mean you'll need less QA people and 00:23:47.079 --> 00:23:49.960 great but then you also have to go sell 00:23:48.400 --> 00:23:51.400 that to the QA team who don't want to be 00:23:49.960 --> 00:23:53.278 replaced and so I think that was always 00:23:51.400 --> 00:23:56.200 like a friction for that business for 00:23:53.278 --> 00:23:59.038 how it could like scale and grow but now 00:23:56.200 --> 00:24:01.080 like mtic with AI can actually just 00:23:59.038 --> 00:24:02.960 replace the QA people so their pitch is 00:24:01.079 --> 00:24:04.319 not oh this like makes your QA people 00:24:02.960 --> 00:24:06.400 faster it's like this just means you 00:24:04.319 --> 00:24:07.918 don't need a QA team at all so they can 00:24:06.400 --> 00:24:09.960 just focus the sell onto like 00:24:07.919 --> 00:24:12.120 engineering and Engineering doesn't need 00:24:09.960 --> 00:24:14.440 buying from QA at this point and you can 00:24:12.119 --> 00:24:16.000 also go in I mean to start with you can 00:24:14.440 --> 00:24:18.080 go and sell to companies that don't even 00:24:16.000 --> 00:24:19.640 have big QA teams at the moment they 00:24:18.079 --> 00:24:21.000 just use something like mtic and then it 00:24:19.640 --> 00:24:22.120 will just like keep scaling with them 00:24:21.000 --> 00:24:25.480 scaling and they'll just never build a 00:24:22.119 --> 00:24:26.959 QA team ever yes that is a real life 00:24:25.480 --> 00:24:28.759 case study of what Diana was saying 00:24:26.960 --> 00:24:30.000 about why these vertical AI agent 00:24:28.759 --> 00:24:31.960 companies going to be 10 times as big as 00:24:30.000 --> 00:24:33.880 the SAS companies yeah I'm seeing this 00:24:31.960 --> 00:24:35.440 interesting now um like in recruiting 00:24:33.880 --> 00:24:38.720 too I had this exact same issue with 00:24:35.440 --> 00:24:40.278 triple vet where to build the software 00:24:38.720 --> 00:24:41.720 um to build software that makes it easy 00:24:40.278 --> 00:24:43.278 to like screen and hire software 00:24:41.720 --> 00:24:45.200 Engineers you need buying from both the 00:24:43.278 --> 00:24:47.000 engineering team that they're joining 00:24:45.200 --> 00:24:48.159 but also the recruiting team and 00:24:47.000 --> 00:24:49.519 effectively the software we were 00:24:48.159 --> 00:24:51.480 building was trying to replace the 00:24:49.519 --> 00:24:54.720 recruiters but we couldn't completely 00:24:51.480 --> 00:24:57.960 replace the recruiters but now with NYC 00:24:54.720 --> 00:24:59.278 and so the recruiters were always like 00:24:57.960 --> 00:25:01.000 oppos 00:24:59.278 --> 00:25:03.038 opposing it CU it was a threat to them 00:25:01.000 --> 00:25:06.440 yeah so it just always like friction on 00:25:03.038 --> 00:25:08.319 like how um on like how far you can get 00:25:06.440 --> 00:25:11.360 when the customer you're trying to sell 00:25:08.319 --> 00:25:13.918 to is worried about being replaced um 00:25:11.359 --> 00:25:15.959 but yeah I think now it's still early 00:25:13.919 --> 00:25:18.720 days but now with AI you can build 00:25:15.960 --> 00:25:20.319 things that do the whole stack like of 00:25:18.720 --> 00:25:22.120 recruiting we have a company we worked 00:25:20.319 --> 00:25:23.639 with last batch like Nico work with them 00:25:22.119 --> 00:25:25.239 a priora which is actually just doing 00:25:23.640 --> 00:25:27.240 like the full like technical screen the 00:25:25.240 --> 00:25:29.200 full initial recruiter screen and 00:25:27.240 --> 00:25:31.480 getting great traction so I think as 00:25:29.200 --> 00:25:32.679 those things keep going like they won't 00:25:31.480 --> 00:25:33.798 have they have the same thing you won't 00:25:32.679 --> 00:25:35.480 have the friction of oh I need to 00:25:33.798 --> 00:25:37.639 convince recruiters to use this you're 00:25:35.480 --> 00:25:39.480 probably just like not build a 00:25:37.640 --> 00:25:43.679 recruiting team in the same way that you 00:25:39.480 --> 00:25:46.440 used to I mean other example is even for 00:25:43.679 --> 00:25:49.240 de tool companies they have to do a lot 00:25:46.440 --> 00:25:51.558 of a developer support and I work with 00:25:49.240 --> 00:25:54.240 this company called cap. AI that 00:25:51.558 --> 00:25:58.599 basically buildt one of the best chat 00:25:54.240 --> 00:26:01.679 Bots that responds to a lot of the a lot 00:25:58.599 --> 00:26:03.879 of the technical details that are hard 00:26:01.679 --> 00:26:05.480 to answer and I think a lot of the 00:26:03.880 --> 00:26:08.440 companies that started using them they 00:26:05.480 --> 00:26:10.319 actually ended up having Dev rail teams 00:26:08.440 --> 00:26:12.720 that are a lot smaller because it 00:26:10.319 --> 00:26:14.678 ingests a lot of uh the developer 00:26:12.720 --> 00:26:17.919 documentations even the YouTube videos 00:26:14.679 --> 00:26:19.360 that Dev tools put up and even a lot of 00:26:17.919 --> 00:26:22.200 the chat history so it just keeps 00:26:19.359 --> 00:26:23.759 getting better and better and it's like 00:26:22.200 --> 00:26:26.000 gives really good answers actually it's 00:26:23.759 --> 00:26:28.640 one one of the best I've seen yeah I 00:26:26.000 --> 00:26:30.240 also worked with a customer support like 00:26:28.640 --> 00:26:32.600 an AI customer support agent company 00:26:30.240 --> 00:26:36.798 called Power help well actually we both 00:26:32.599 --> 00:26:39.558 did um last batch and I learned a couple 00:26:36.798 --> 00:26:42.720 interesting things from parel um the 00:26:39.558 --> 00:26:45.200 first is customer like AI agents for 00:26:42.720 --> 00:26:46.720 customer support was like the category 00:26:45.200 --> 00:26:48.399 that's like famously crowded where 00:26:46.720 --> 00:26:49.798 there's like supposedly like you know a 00:26:48.398 --> 00:26:52.278 100 of them and if you go and you Google 00:26:49.798 --> 00:26:54.679 like AI customer support agent you'll 00:26:52.278 --> 00:26:55.960 get like a 100 results on Google um but 00:26:54.679 --> 00:26:57.640 what I learned through working with 00:26:55.960 --> 00:27:00.759 parel is like it's actually kind of 00:26:57.640 --> 00:27:03.240 like like almost all of those 00:27:00.759 --> 00:27:05.599 companies are doing very simple like 00:27:03.240 --> 00:27:07.798 zero shot llm prompting that can't 00:27:05.599 --> 00:27:08.918 actually replace a real customer support 00:27:07.798 --> 00:27:10.839 team that does a lot of really 00:27:08.919 --> 00:27:12.759 complicated workflows it just kind of 00:27:10.839 --> 00:27:14.720 makes for like a nice demo like to 00:27:12.759 --> 00:27:16.480 actually replace a customer support team 00:27:14.720 --> 00:27:18.000 for like an at Scale company that has 00:27:16.480 --> 00:27:19.519 like 100 customer support reps that do 00:27:18.000 --> 00:27:21.038 lots of complicated things every day you 00:27:19.519 --> 00:27:22.558 need like really complicated software 00:27:21.038 --> 00:27:24.720 that does all the stuff that like Jake 00:27:22.558 --> 00:27:26.158 heler was talking about and there's 00:27:24.720 --> 00:27:27.720 there were only like three or four 00:27:26.159 --> 00:27:28.720 companies that were even attempting to 00:27:27.720 --> 00:27:30.720 do that 00:27:28.720 --> 00:27:32.120 and cumulative they had cumulatively 00:27:30.720 --> 00:27:33.640 they had like less than 1% Market 00:27:32.119 --> 00:27:35.719 penetration and so the market was just 00:27:33.640 --> 00:27:37.640 completely open I could also see that 00:27:35.720 --> 00:27:40.319 being another case of um hyper 00:27:37.640 --> 00:27:42.720 specialization or hyper verticalization 00:27:40.319 --> 00:27:45.240 like there's not going to be I mean 00:27:42.720 --> 00:27:47.640 maybe eventually there could be a single 00:27:45.240 --> 00:27:50.839 general purpose customer support agent 00:27:47.640 --> 00:27:52.480 software company but we're like in in 00:27:50.839 --> 00:27:54.278 you know that that'll be like a eighth 00:27:52.480 --> 00:27:56.558 or ninth inning kind of thing and we're 00:27:54.278 --> 00:27:58.278 literally in the first inning so you 00:27:56.558 --> 00:28:00.879 know instead you know you're going to 00:27:58.278 --> 00:28:03.440 have companies like gig ml that you know 00:28:00.880 --> 00:28:06.399 it's doing it for zepto doing 30,000 00:28:03.440 --> 00:28:08.840 tickets uh every single day and 00:28:06.398 --> 00:28:11.719 replacing a team of a thousand people 00:28:08.839 --> 00:28:14.000 and but it's very specific and it has 00:28:11.720 --> 00:28:16.240 you know it's not a general purpose demo 00:28:14.000 --> 00:28:19.159 Weare kind of thing like it's 10,000 00:28:16.240 --> 00:28:22.759 test cases in a very detailed uh eval 00:28:19.159 --> 00:28:25.760 set that you know is basically just for 00:28:22.759 --> 00:28:28.119 zepto and things like zepto yeah uh but 00:28:25.759 --> 00:28:30.158 if you are you know any of the other 00:28:28.119 --> 00:28:32.359 Marketplace companies you're probably 00:28:30.159 --> 00:28:34.799 going to use it cuz like that's a very 00:28:32.359 --> 00:28:36.759 well-defined kind of marketplace that's 00:28:34.798 --> 00:28:38.319 you know instant delivery Marketplace I 00:28:36.759 --> 00:28:40.679 think this is the kind of dynamic that 00:28:38.319 --> 00:28:42.720 led there to be like $300 billion do SAS 00:28:40.679 --> 00:28:44.559 companies rather than like one like1 00:28:42.720 --> 00:28:45.919 trillion do like meta SAS thing that 00:28:44.558 --> 00:28:47.440 provides all the software for the world 00:28:45.919 --> 00:28:50.240 it's just like the customers just 00:28:47.440 --> 00:28:51.679 require really heavily like tailored 00:28:50.240 --> 00:28:53.159 Solutions and it's hard to build one 00:28:51.679 --> 00:28:54.399 that like works for every everyone 00:28:53.159 --> 00:28:55.720 exactly I mean we already gave three 00:28:54.398 --> 00:28:57.359 examples of customer support but there 00:28:55.720 --> 00:28:59.798 are very different verticals it's like 00:28:57.359 --> 00:29:01.158 de tool comp need very different kind of 00:28:59.798 --> 00:29:04.158 support that you need and the training 00:29:01.159 --> 00:29:06.200 set to marketplaces very different right 00:29:04.159 --> 00:29:08.080 yeah I guess whether you have agents or 00:29:06.200 --> 00:29:10.360 real human beings working for you you 00:29:08.079 --> 00:29:13.240 end up with the same problem which is 00:29:10.359 --> 00:29:15.918 every company bumps up against CO's 00:29:13.240 --> 00:29:18.720 theory of the firm which says that any 00:29:15.919 --> 00:29:21.559 given firm will grow only so much to the 00:29:18.720 --> 00:29:23.798 point where it uh becomes inefficient to 00:29:21.558 --> 00:29:27.440 be larger than that and then that's why 00:29:23.798 --> 00:29:29.558 they sort of networks and ecosystems and 00:29:27.440 --> 00:29:31.519 you know a full blown economy you know 00:29:29.558 --> 00:29:34.079 like every firm will sort of specialize 00:29:31.519 --> 00:29:36.880 to do what it is particularly good at 00:29:34.079 --> 00:29:38.599 and then the limits the outer limits of 00:29:36.880 --> 00:29:42.880 what those firms can be it's actually 00:29:38.599 --> 00:29:44.480 based on uh your ability as a manager so 00:29:42.880 --> 00:29:46.799 yeah that that part a little bit breaks 00:29:44.480 --> 00:29:49.159 my brain because you know when we spend 00:29:46.798 --> 00:29:50.879 time with Parker Conrad at ripling uh 00:29:49.159 --> 00:29:52.760 one of his favorite points is actually 00:29:50.880 --> 00:29:55.278 well you know everyone's very obsessed 00:29:52.759 --> 00:29:57.798 with with the fact that the rocks can 00:29:55.278 --> 00:29:59.720 talk and you know maybe they can draw 00:29:57.798 --> 00:30:02.839 but the more interesting thing for him 00:29:59.720 --> 00:30:04.000 you know running HR It software that uh 00:30:02.839 --> 00:30:05.720 you know he spends a lot of time 00:30:04.000 --> 00:30:07.919 thinking about HR like actually the 00:30:05.720 --> 00:30:11.038 coolest thing about the LMS is that the 00:30:07.919 --> 00:30:13.720 rocks can read and from his perspective 00:30:11.038 --> 00:30:16.679 like you he's I think he has 3,000 00:30:13.720 --> 00:30:19.000 employees he still runs payroll for all 00:30:16.679 --> 00:30:20.320 3,000 employees through Rippling so I 00:30:19.000 --> 00:30:22.679 think he spends a lot of time thinking 00:30:20.319 --> 00:30:26.278 about like how can one person extend 00:30:22.679 --> 00:30:27.720 their ability as a manager and uh I 00:30:26.278 --> 00:30:28.519 think we're going to see a lot more 00:30:27.720 --> 00:30:31.278 there 00:30:28.519 --> 00:30:34.278 that would be an a reverse argument that 00:30:31.278 --> 00:30:37.278 if we're at this moment where uh tools 00:30:34.278 --> 00:30:40.720 for managers and CEOs are going to get 00:30:37.278 --> 00:30:42.119 much more powerful um oh it could it 00:30:40.720 --> 00:30:44.000 could it could increase the scale of the 00:30:42.119 --> 00:30:45.439 firm that you can run right and that's 00:30:44.000 --> 00:30:47.480 certainly what ripling is trying to do 00:30:45.440 --> 00:30:49.600 like he's attempting to build this like 00:30:47.480 --> 00:30:51.319 Suite of HR tools where if he wins he's 00:30:49.599 --> 00:30:53.119 going to eat a whole bunch of billion 00:30:51.319 --> 00:30:54.678 dollar SAS companies and like one one 00:30:53.119 --> 00:30:57.239 giant company it's very interesting 00:30:54.679 --> 00:30:59.159 point Gary I think what made me think 00:30:57.240 --> 00:31:03.120 about this is that with having all these 00:30:59.159 --> 00:31:06.159 AI SAS tools it's going to give the 00:31:03.119 --> 00:31:08.359 ability to all these leaders and all 00:31:06.159 --> 00:31:10.120 these Orcs to basically open the 00:31:08.359 --> 00:31:12.359 aperture of the context window of how 00:31:10.119 --> 00:31:14.599 much information they can parse because 00:31:12.359 --> 00:31:16.479 there a limit of how much uh humans we 00:31:14.599 --> 00:31:17.759 can have meaningful relationship there's 00:31:16.480 --> 00:31:20.360 like the whole thing with the D Mar 00:31:17.759 --> 00:31:22.879 number it's about 300 people that you 00:31:20.359 --> 00:31:25.719 150 that you can have a meaningful 00:31:22.880 --> 00:31:27.960 relationship with but with AI because 00:31:25.720 --> 00:31:30.159 all of these rocks now can read I think 00:31:27.960 --> 00:31:32.319 think we will be able to extend that 00:31:30.159 --> 00:31:35.159 dumbar limit that we have yeah I think 00:31:32.319 --> 00:31:38.558 uh Flo crell had this interesting post 00:31:35.159 --> 00:31:41.480 on Twitter that went viral around um I 00:31:38.558 --> 00:31:44.240 think someone had made a voice chat like 00:31:41.480 --> 00:31:46.360 just weekend project as a CEO but it 00:31:44.240 --> 00:31:50.278 would call uh all 00:31:46.359 --> 00:31:52.439 1,500 of their employees yeah and uh you 00:31:50.278 --> 00:31:53.798 know it was you know very short call 00:31:52.440 --> 00:31:56.840 like kind of sounded like it was from 00:31:53.798 --> 00:31:59.480 the CEO just asking kind of personally I 00:31:56.839 --> 00:32:02.558 mean it sort of reminds of um that scene 00:31:59.480 --> 00:32:04.079 in her where it zooms out and uh 00:32:02.558 --> 00:32:06.558 actually you know you're following the 00:32:04.079 --> 00:32:09.278 experience of one person using the her 00:32:06.558 --> 00:32:11.240 OS but actually that her OSS is actually 00:32:09.278 --> 00:32:13.038 speaking to 15 you know thousands or 00:32:11.240 --> 00:32:16.880 tens of thousands of people all at one 00:32:13.038 --> 00:32:19.398 time how many others 00:32:16.880 --> 00:32:21.760 8,316 yeah I mean large language models 00:32:19.398 --> 00:32:25.398 can talk and can have conversations and 00:32:21.759 --> 00:32:28.200 then to what extent can uh you know this 00:32:25.398 --> 00:32:31.000 power actually extend the capability of 00:32:28.200 --> 00:32:33.798 one or a few people to uh understand 00:32:31.000 --> 00:32:34.919 what's going on I I heard about that yuk 00:32:33.798 --> 00:32:36.278 it got it definitely got me thinking 00:32:34.919 --> 00:32:37.759 because as understood the product is 00:32:36.278 --> 00:32:39.599 something like it just it will call up 00:32:37.759 --> 00:32:41.558 all your employees and then your 00:32:39.599 --> 00:32:43.398 employees can just like ramble about 00:32:41.558 --> 00:32:45.119 what they've been doing and it will just 00:32:43.398 --> 00:32:46.839 extract the meaning out of it and give 00:32:45.119 --> 00:32:48.879 the CEO of like a like bullet point 00:32:46.839 --> 00:32:50.359 summary of here the most important stuff 00:32:48.880 --> 00:32:51.639 and there were a bunch of like SAS 00:32:50.359 --> 00:32:54.798 companies that attempted to do these 00:32:51.638 --> 00:32:56.558 sort of like weekly pulse Pulses from 00:32:54.798 --> 00:32:58.720 employees using like traditional SAS 00:32:56.558 --> 00:33:00.519 software but like that version is is 00:32:58.720 --> 00:33:02.600 literally a 100 times better than the 00:33:00.519 --> 00:33:07.159 pre- elm version of this idea but I 00:33:02.599 --> 00:33:08.959 wonder with like that particular tool um 00:33:07.159 --> 00:33:11.399 just like it's not it's going Beyond 00:33:08.960 --> 00:33:12.679 just like reading and summarizing like 00:33:11.398 --> 00:33:14.439 this this is the argument of like if 00:33:12.679 --> 00:33:16.080 writing is thinking then like there's 00:33:14.440 --> 00:33:19.080 actually just a huge amount 00:33:16.079 --> 00:33:20.439 of work that's involved in the effort of 00:33:19.079 --> 00:33:22.319 figuring out like who's an effective 00:33:20.440 --> 00:33:24.278 communicator and like what are the most 00:33:22.319 --> 00:33:25.960 important things to be like what what 00:33:24.278 --> 00:33:28.159 are the key things to be focused on as a 00:33:25.960 --> 00:33:30.200 company and I just wonder if that at 00:33:28.159 --> 00:33:31.720 some point do the llms do like they go 00:33:30.200 --> 00:33:33.720 beyond just like summarizing and reading 00:33:31.720 --> 00:33:37.159 and doing actual thinking at which point 00:33:33.720 --> 00:33:39.919 like who's actually running the the 00:33:37.159 --> 00:33:41.720 organization an interesting 00:33:39.919 --> 00:33:43.200 thought I guess the other thing that's 00:33:41.720 --> 00:33:45.919 kind of interesting about how Parker 00:33:43.200 --> 00:33:47.639 Conrad's thinking about it is um I found 00:33:45.919 --> 00:33:49.880 out about this recently off a an 00:33:47.638 --> 00:33:51.759 interview with Matt mcginness his coo 00:33:49.880 --> 00:33:54.720 that uh there are more than a hundred 00:33:51.759 --> 00:33:57.398 Founders who work at ripling now as sort 00:33:54.720 --> 00:34:00.120 of specific people who run like an 00:33:57.398 --> 00:34:02.158 entire SAS vertical inside Rippling it's 00:34:00.119 --> 00:34:03.918 super cool the way he's built the team 00:34:02.159 --> 00:34:04.880 har probably knows a lot about it 00:34:03.919 --> 00:34:06.919 because you've done a bunch of 00:34:04.880 --> 00:34:10.440 interviews with him um yeah I mean it's 00:34:06.919 --> 00:34:14.039 definitely very focused on uh recruiting 00:34:10.440 --> 00:34:17.720 Founders and I mean Parker like Rippling 00:34:14.039 --> 00:34:20.960 is essentially the the case against 00:34:17.719 --> 00:34:23.158 vertical like verticalization trying to 00:34:20.960 --> 00:34:23.159 uh 00:34:26.719 --> 00:34:31.078 horizontalization like lots of value and 00:34:29.239 --> 00:34:33.078 he wants to recruit Founders and teams 00:34:31.079 --> 00:34:34.599 that build on top of the platform like 00:34:33.079 --> 00:34:36.280 it's almost a little bit more sort of 00:34:34.599 --> 00:34:39.240 like amazones whereas like shared 00:34:36.280 --> 00:34:40.760 infrastructure um yeah I think every 00:34:39.239 --> 00:34:42.598 product that they've released I mean 00:34:40.760 --> 00:34:45.000 things like time tracking and whatnot I 00:34:42.599 --> 00:34:46.960 mean basically they launch a thing and 00:34:45.000 --> 00:34:49.480 it hits like multi-millions of dollars 00:34:46.960 --> 00:34:50.960 in ARR on day one of launching and 00:34:49.480 --> 00:34:53.119 that's exactly what we were talking 00:34:50.960 --> 00:34:55.760 about earlier like once you once you 00:34:53.119 --> 00:34:56.960 have a vertical once you have a tow hold 00:34:55.760 --> 00:34:58.920 what you're saying is Well I have to 00:34:56.960 --> 00:35:01.800 spend this money on sales and marketing 00:34:58.920 --> 00:35:05.358 anyway can I uh you know basically get 00:35:01.800 --> 00:35:06.920 higher LTV and hold my CAC constant and 00:35:05.358 --> 00:35:09.078 uh that's sort of what you if you look 00:35:06.920 --> 00:35:10.760 at all the top uh software companies 00:35:09.079 --> 00:35:12.079 today it's like that's what Oracle is 00:35:10.760 --> 00:35:14.440 that's what Microsoft is that's what 00:35:12.079 --> 00:35:17.039 Salesforce is ripling knock on wood 00:35:14.440 --> 00:35:20.280 going to be the next but um it's it's an 00:35:17.039 --> 00:35:22.719 interesting alternative to uh going from 00:35:20.280 --> 00:35:23.720 zero to one totally on your own do you 00:35:22.719 --> 00:35:25.279 guys want to talk about some of the 00:35:23.719 --> 00:35:27.799 voice companies that we have I think 00:35:25.280 --> 00:35:30.040 that's like an interesting like sub 00:35:27.800 --> 00:35:32.519 category of this of this stuff is like 00:35:30.039 --> 00:35:35.759 really blowing up now I have a company 00:35:32.519 --> 00:35:37.079 that I work with called Salient that 00:35:35.760 --> 00:35:40.200 basically 00:35:37.079 --> 00:35:42.760 does AI voice calling to automate a lot 00:35:40.199 --> 00:35:44.759 of that collection in the auto Ling 00:35:42.760 --> 00:35:46.400 space which tradition so they like call 00:35:44.760 --> 00:35:50.079 up people and they're like hey you owe 00:35:46.400 --> 00:35:52.599 $1,000 on your car yeah which actually 00:35:50.079 --> 00:35:54.519 up with that actually this kind of job 00:35:52.599 --> 00:35:57.760 is one of those butter passing job it 00:35:54.519 --> 00:35:59.559 kind of sucks because a lot of uh these 00:35:57.760 --> 00:36:01.839 low wage workers work in all these call 00:35:59.559 --> 00:36:04.480 centers and it's like a terrible boring 00:36:01.838 --> 00:36:05.838 job so very high churn and giant 00:36:04.480 --> 00:36:07.440 headcount to run these because there's 00:36:05.838 --> 00:36:10.039 just so many accounts with these banks 00:36:07.440 --> 00:36:14.480 that have to do that and this is a 00:36:10.039 --> 00:36:17.199 perfect task that AI could automate and 00:36:14.480 --> 00:36:19.519 what Salient has done is has been able 00:36:17.199 --> 00:36:21.159 to actually get very very accurate and 00:36:19.519 --> 00:36:23.199 it has been going live with a lot of big 00:36:21.159 --> 00:36:25.279 Banks which is super exciting and this 00:36:23.199 --> 00:36:28.000 was a company from last year and 00:36:25.280 --> 00:36:29.960 demonstrating that that part of it that 00:36:28.000 --> 00:36:32.079 they were able to get in because they 00:36:29.960 --> 00:36:33.880 sold through top down I guess the space 00:36:32.079 --> 00:36:35.760 feels like it's moving very quickly and 00:36:33.880 --> 00:36:38.318 that we have incredible companies that 00:36:35.760 --> 00:36:40.839 are voice infra companies like vapy and 00:36:38.318 --> 00:36:43.679 then people can sort of get started 00:36:40.838 --> 00:36:45.880 right away and Retail also I mean these 00:36:43.679 --> 00:36:48.358 companies that have reached pretty fast 00:36:45.880 --> 00:36:50.838 scale just because it's one of the more 00:36:48.358 --> 00:36:53.119 exciting like mindblowing things that 00:36:50.838 --> 00:36:56.400 you can get up and running within I mean 00:36:53.119 --> 00:36:58.880 literally the course of hours um and 00:36:56.400 --> 00:37:00.119 then some of the question that you know 00:36:58.880 --> 00:37:02.358 remains unanswered and we hope they 00:37:00.119 --> 00:37:04.880 figure it out is how do you hold on to 00:37:02.358 --> 00:37:08.318 them especially as you uh run into 00:37:04.880 --> 00:37:11.559 things like the new open AI voice apis 00:37:08.318 --> 00:37:13.239 um you know do you go direct like you 00:37:11.559 --> 00:37:16.199 Pro it's probably way more work to try 00:37:13.239 --> 00:37:19.039 to use the underlying apis off the bat 00:37:16.199 --> 00:37:21.279 but these uh platforms are clearly low 00:37:19.039 --> 00:37:23.159 bar and then the question is can you 00:37:21.280 --> 00:37:25.440 keep raising the ceiling so that you can 00:37:23.159 --> 00:37:27.118 hold on to customers forever har you 00:37:25.440 --> 00:37:29.400 were making an interesting point earlier 00:37:27.119 --> 00:37:31.880 about like how the apps that people have 00:37:29.400 --> 00:37:34.000 built on top of LMS has changed from 00:37:31.880 --> 00:37:35.400 like early 2023 when it started until 00:37:34.000 --> 00:37:36.719 now voice which we were just talking 00:37:35.400 --> 00:37:38.880 about as a great example of this I think 00:37:36.719 --> 00:37:41.279 even if you went 6 months back it felt 00:37:38.880 --> 00:37:43.599 like the voices were not realistic 00:37:41.280 --> 00:37:45.560 enough yet the latency was too high like 00:37:43.599 --> 00:37:48.359 there was it felt like we were probably 00:37:45.559 --> 00:37:51.159 a ways off having AI voice apps that 00:37:48.358 --> 00:37:53.358 could meaningfully like replace like 00:37:51.159 --> 00:37:57.078 humans calling people up and like here 00:37:53.358 --> 00:38:00.519 we are and yeah I was just zooming out 00:37:57.079 --> 00:38:03.920 thinking back to the first YC batch 00:38:00.519 --> 00:38:05.119 where llm powered apps first came in was 00:38:03.920 --> 00:38:08.760 probably winter 00:38:05.119 --> 00:38:11.000 2023 you know almost 2 years ago now and 00:38:08.760 --> 00:38:13.560 the apps were essentially just things 00:38:11.000 --> 00:38:15.480 that spat out some text and not even 00:38:13.559 --> 00:38:17.759 like perfect T Rockit talk that's about 00:38:15.480 --> 00:38:20.679 it yeah sort of more like copy editing 00:38:17.760 --> 00:38:22.839 marketing edit email edits it was just a 00:38:20.679 --> 00:38:24.598 kind of more like just like incremental 00:38:22.838 --> 00:38:26.159 yeah like I I had a company I mean the 00:38:24.599 --> 00:38:28.519 one that sticks in my head is a company 00:38:26.159 --> 00:38:30.358 Speedy brand and all what they did is 00:38:28.519 --> 00:38:32.920 make it very easy for like a small 00:38:30.358 --> 00:38:35.199 business to just generate a Blog and 00:38:32.920 --> 00:38:37.519 spit out content marketing um it's like 00:38:35.199 --> 00:38:40.759 very obvious idea and it wasn't perfect 00:38:37.519 --> 00:38:41.838 but it was pretty cool at the time and 00:38:40.760 --> 00:38:43.319 that's what we've talked about a bunch 00:38:41.838 --> 00:38:45.159 of the show but that's like the chat gbt 00:38:43.318 --> 00:38:47.519 raer turned out around that time hey 00:38:45.159 --> 00:38:49.480 like this is what an llm app looks like 00:38:47.519 --> 00:38:52.199 it's just a chat GPT rapper it does very 00:38:49.480 --> 00:38:53.639 basic spits out some text like it's 00:38:52.199 --> 00:38:56.439 going to get crushed by openi in the 00:38:53.639 --> 00:38:58.960 next release like and it did yeah well I 00:38:56.440 --> 00:39:01.000 I I don't know if that one did but but 00:38:58.960 --> 00:39:02.838 the that that first that first wave of 00:39:01.000 --> 00:39:06.039 llm apps mostly did get crushed by the 00:39:02.838 --> 00:39:07.759 next wave of GPT I feel like we've had 00:39:06.039 --> 00:39:09.400 this sort of boiling of the Frog effect 00:39:07.760 --> 00:39:11.240 where from our perspect it's sort of 00:39:09.400 --> 00:39:13.480 like every three months things have just 00:39:11.239 --> 00:39:14.598 kept getting progressively better and 00:39:13.480 --> 00:39:17.119 now we're at this point where we're 00:39:14.599 --> 00:39:19.160 talking about like full-on vertical AI 00:39:17.119 --> 00:39:22.119 agents that are going to replace entire 00:39:19.159 --> 00:39:23.838 teams and functions and Enterprises um 00:39:22.119 --> 00:39:26.400 and just that progression is still 00:39:23.838 --> 00:39:28.358 mindblowing to me like with two years in 00:39:26.400 --> 00:39:30.400 which is still relatively early and the 00:39:28.358 --> 00:39:33.358 rate of progress is just like unlike 00:39:30.400 --> 00:39:34.760 anything we've seen before and I think 00:39:33.358 --> 00:39:37.480 what's interesting to see is we 00:39:34.760 --> 00:39:39.760 discussed this in the last episode is a 00:39:37.480 --> 00:39:41.400 lot of the foundation models are kind of 00:39:39.760 --> 00:39:43.680 coming head-to-head there used to be 00:39:41.400 --> 00:39:46.960 only one player in town with open AI but 00:39:43.679 --> 00:39:48.598 we've been seeing in the last batch this 00:39:46.960 --> 00:39:51.400 has been changing Claude is a huge 00:39:48.599 --> 00:39:53.960 Contender thank God it's like 00:39:51.400 --> 00:39:57.400 competition is you know the the soil for 00:39:53.960 --> 00:40:00.079 a very fertile Marketplace ecosystem uh 00:39:57.400 --> 00:40:02.079 for which consumers will have choice and 00:40:00.079 --> 00:40:04.200 uh Founders have a shot and that's the 00:40:02.079 --> 00:40:06.200 world I want to live in so people are 00:40:04.199 --> 00:40:08.559 watching and thinking about starting a 00:40:06.199 --> 00:40:11.519 startup or maybe have already started 00:40:08.559 --> 00:40:13.639 and uh they're hearing all of this how 00:40:11.519 --> 00:40:16.519 do you know what the right vertical is 00:40:13.639 --> 00:40:19.920 for you you got to find 00:40:16.519 --> 00:40:21.559 some boring repeative admin work 00:40:19.920 --> 00:40:23.800 somewhere and that seems to be like the 00:40:21.559 --> 00:40:26.159 common threat across all of the stuff is 00:40:23.800 --> 00:40:29.720 if you can find a boring repetitive 00:40:26.159 --> 00:40:33.078 admin task um there is likely going to 00:40:29.719 --> 00:40:35.439 be a billion dollar AI agent startup if 00:40:33.079 --> 00:40:37.039 you keep digging deep enough into it but 00:40:35.440 --> 00:40:39.159 it sounds like you should go after 00:40:37.039 --> 00:40:42.199 something that you directly have some 00:40:39.159 --> 00:40:44.078 sort of experience or relationship to 00:40:42.199 --> 00:40:45.480 that is a common like there there's 00:40:44.079 --> 00:40:47.720 definitely a Common Thread I've seen in 00:40:45.480 --> 00:40:49.358 the companies that are that I'm seeing 00:40:47.719 --> 00:40:50.639 promis with and another one just pops 00:40:49.358 --> 00:40:52.480 into my head sweet spot I think I 00:40:50.639 --> 00:40:54.159 mentioned on this before like they're 00:40:52.480 --> 00:40:56.358 basically building an AI agent to bid on 00:40:54.159 --> 00:40:58.519 government contracts and the way they 00:40:56.358 --> 00:40:59.920 found that idea and a year ago was they 00:40:58.519 --> 00:41:01.239 just had a friend whose full-time job 00:40:59.920 --> 00:41:03.039 was to sit there on like a government 00:41:01.239 --> 00:41:05.358 website like refreshing the page like 00:41:03.039 --> 00:41:07.119 looking for new proposals to bid on and 00:41:05.358 --> 00:41:08.519 they they were pivoting they're like ah 00:41:07.119 --> 00:41:10.838 like that seems like something an llm 00:41:08.519 --> 00:41:12.480 could do um a company from a recent 00:41:10.838 --> 00:41:13.880 batch which pivoted into a new idea 00:41:12.480 --> 00:41:15.240 that's getting great traction like 00:41:13.880 --> 00:41:17.000 they're basically building an AI agent 00:41:15.239 --> 00:41:18.759 to do um process like medical billing 00:41:17.000 --> 00:41:21.119 for dental clinics and the way they 00:41:18.760 --> 00:41:22.800 found the idea was um one of the 00:41:21.119 --> 00:41:24.039 founders mother is a dentist and so he 00:41:22.800 --> 00:41:25.760 just decided to go to work with her for 00:41:24.039 --> 00:41:27.759 a day and just sit there seeing what she 00:41:25.760 --> 00:41:29.280 did and she's like oh like all of that 00:41:27.760 --> 00:41:31.040 like processing claim seems like really 00:41:29.280 --> 00:41:32.240 boring like an llm should totally be 00:41:31.039 --> 00:41:34.960 able to do that and he just started 00:41:32.239 --> 00:41:36.879 writing software for like his mother's 00:41:34.960 --> 00:41:40.079 dental clinic so I guess I mean in 00:41:36.880 --> 00:41:41.559 robotics the classic Maxim is uh you the 00:41:40.079 --> 00:41:42.720 robots that are going to be profitable 00:41:41.559 --> 00:41:47.799 and that are going to work are going to 00:41:42.719 --> 00:41:50.719 be um dirty and dangerous jobs and in 00:41:47.800 --> 00:41:53.960 this case for vertical SAS look for 00:41:50.719 --> 00:41:56.399 boring butter passing 00:41:53.960 --> 00:41:58.599 jobs well with that we're out of time 00:41:56.400 --> 00:42:01.130 for today we'll catch you on the light 00:41:58.599 --> 00:42:12.449 cone next time 00:42:01.130 --> 00:42:12.449 [Music]