How Enterprise AI Skeptics Hand Startups the Market

1. 0:00 – 2:08

   Intro

   1. HTHarj Taggar0:00

      Engineering teams at these orgs are filled with people that themselves don't actually really believe in AI, don't use code gen tools, think it's all super overhyped, or really excited when an MIT study comes out saying that it's all, like, hype and retweet it-

   2. JFJared Friedman0:12

      (laughs)

   3. HTHarj Taggar0:12

      ... and, um, and really want 'cause it's a narrative they want to believe. But the consequence of that for the companies is that they can't build the product. So if your engineers don't believe in this, then how are you gonna build a product that actually works? The knock on effect for startups then is if you can actually build something that works, the enterprises will talk to you because they have no other options; can't build it internally, can't go to an established company. Um, so the startups are actually getting, like, the shot that they never had before.

   4. JFJared Friedman0:35

      I guarantee you someone is watching this right now, and, uh, you've just horribly triggered them. Welcome back to another episode of The Light Cone. One of the things that has been really pissing me off is these AI influencers. You see them on X. You see them on YouTube. And they're claiming that 95% of AI projects are failures, and that's proof that AI is a scam. What's the real story, Jared? You actually dug in to the MIT report that these people are grifting with. What does the report actually say? What really went viral was, like, tweets about this study, and I think the tweets are actually quite misleading. Diana and I were talking to a bunch of college students recently, and they had concluded, just by reading, like, the tweet version of the study, that, like, "Oh, all these AI startups that YC is talking about, like, must not be working because the study says that they all fail." But actually, the more I read the study, the more I realized it was actually confirming a lot of the things we've talked about here on th- this podcast about what AI agents are really like in the real world and what approaches and categories are working. And so I thought it'd be interesting for us to talk about what the study really says.

   5. DHDiana Hu1:54

      Because it's a very different approach to the go-to market for all these AI solutions. It's not just standard enterprise sales. I think one of the big things that we talk a lot about is this

2. 2:08 – 3:32

   The enterprise AI adoption gap and why the failure rate is high

   1. DHDiana Hu2:08

      aspect of, um, teams, startup and founders, embedding themself into the business processes and really grokking a lot of the internal systems of record and going deep, deep, deep in the integration, which is not something that has been typically done in the SaaS world. SaaS was, like, very plug-and-play, which is different. But w- when you do succeed and plug into the systems of record, the pot of gold is actually quite big. B- but it does take a long time. We actually have a lot of examples of work with companies that have succeeded, which we can talk about later.

   2. JFJared Friedman2:40

      You had a really great way of, like, having, like, a mental model of, like, what typically happens when an enterprise tries to adopt AI and, like, why the failure rate is so high. Can you give me some intuition? (laughs) Yeah, if you think about, uh, enterprises are trying to get something done, and they've got, uh, internal IT, or sometimes when internal IT doesn't do it, they go out and they get an Ernst & Young or they get some much bigger consulting shop, a Deloitte, to come in. And, uh, it turns out if anyone has ever used internal IT systems, generally internal IT systems are bad. (laughs) And then not only that, if you decide that, you know, you can't build it in-house and you have to go to consulting, well, now you've got two problems. The output of the study is no surprise to me in that the majority of software that actually gets built in the world is very, very bad.

3. 3:32 – 4:30

   Even Apple can be bad at software

   1. JFJared Friedman3:32

      To be fair to IT consultants, um, Apple is very bad at software, you know? My favorite example is Apple, the company that can have infinite access to capital and infinite access to the smartest people in the world, all of us use, uh, iPhones. And, uh, I use the Calendar app. I think you guys do too. We use it many times per day to do our schedules. And, uh, even the Calendar app is a piece of trash. (laughs)

   2. HTHarj Taggar3:57

      (laughs)

   3. JFJared Friedman3:57

      Like, how... Oh, you know, you probably run into some sort of weird bug in that, like, almost every single day. So Apple, a company with infinite resources and infinite access to the smartest people in the world, cannot make a good calendar app. So, you know, if that's true for Apple, how could any normal company, let alone an internal IT system, let alone, like, Deloitte or Ernst & Young, like, very well-meaning people, but, like, you know, most of the time the output of something like that is bad?

   4. HTHarj Taggar4:29

      I think

4. 4:30 – 11:08

   Why getting enterprise software to actually work is so hard

   1. HTHarj Taggar4:30

      a lot of what goes on is that in the big enterprises to really deploy sophisticated software, it usually has to be used by multiple teams across the org. And so big enterprises, like, there's just always gonna be, like, political battles and turf wars and various things going on. And so part of the reason I think these enterprises go to consultants is that you can go to an Ernst & Young and get them to, like, meet with, like, the data science team, the customer support team, the, like, IT team, and, like, write up a bunch of docs about what everyone wants and sort of almost play, like, some sort of mediator role of, "Hey, like, here's kind of what we're aligned on, and here's, like, the spec that will work for everyone." The challenge is, like, then you actually have to... Which I think is valuable, but then for the next step is to actually, like, implement that, and at which point the consultants don't have, like, the technical expertise to build the software. And then often in the enterprise, even if they have an internal software team, like, the systems are just, like, so old and, like, siloed that you actually need both, like, the external consultancy expertise to bring everyone together but then also the software expertise to actually build the systems.

   2. JFJared Friedman5:31

      And is the thing that you end up with at the end, like, basically like a camel? You know, a horse designed by a-

   3. HTHarj Taggar5:35

      Yeah.

   4. JFJared Friedman5:35

      ... committee?

   5. HTHarj Taggar5:36

      I mean, I think Ja- Jared and I actually, um, now Summer 2022, a while ago worked with a company called Tactile that's building sort of like a high level, like, a business decision engine for banks in particular. So it does things like in real time can help them go through, like, uh, KYC and AML to figure out, um, someone who's applied for a loan, for example, and then instantly figure out, "Oh, yeah," like, "does this person have the right credit? Do they meet the right business rules?" And do that, like, you know, millions of times per day at scale. The banks themselves, uh, like Citibank and JP Morgan, have tried to build this kind of software themselves, and it's ta- in each case it's taken years, three to five years and tens of millions of dollars, to actually get this implemented, whereas Tactile was able to build a rest API that makes decisions in real time. You can plug the latest AI models into it, uh, and they've been able to do that all for a fraction of the budget and in way less time.

   6. JFJared Friedman6:29

      And there's a company that I worked with called Greenlight that also sells AI systems to banks, and they were telling me a story that is exactly along the lines of what Gary was talking about, where there was a bank that they were trying to sell to.

   7. GTGarry Tan6:42

      ... and the deal fell through because the bank had an existing relationship with Ernst & Young, who apparently builds all the software for the bank, which is apparently not that uncommon. And they were like, "Well, you know, we trust our vendor, Ernst & Young. We've been working with them for years. They say that they're going to build this AI system."

   8. JFJared Friedman6:56

      And that's where they got it all wrong. (laughs)

   9. GTGarry Tan6:57

      (laughs) Yeah, and so Ernst & Young spends a year trying to build this AI system, it doesn't work at all, and the bank comes back to Greenlight and is like, "Uh, actually could you guys build this for us?" And Greenlight now has their system, like, fully deployed at the bank and it's actually working. An interesting thing about the report is that of the products that they surveyed, two-thirds of them were projects where the enterprise built an internal software project or did it with the help of a consulting agency, and only one-third was ones where they bought a product from an outside agency like a Greenlight. And so enterprises are mostly trying to build things in-house, but the success rate of the ones where the enterprise went with an outside vendor, like a Greenlight or a Tactile, was much higher than the success rate of when they tried to build stuff themselves.

   10. JFJared Friedman7:42

       I mean, why do you think this is? Like, you know, I certainly have my theories, but you know, going back to the Apple thing, my sense is that there actually just aren't that many people who, uh, are polymaths enough to be good at product and good at engineering to make things that actually work. Like, there are lots of people out there who are really, really good engineers, but you know, maybe they're just in, like, the coding cave all day and they can't relate to, you know, someone working at a bank 'cause they just, like, don't step outside of their coding cave. And then way over on the other end, like, you know, I, that sort of goes back to the user. Like, why doesn't the user just do this? There's some evidence that maybe they will, you know? There are all these examples of, uh, people, like, you know, Varun Mohan from Windsurf mentioned that back when he was, uh, working on that with his sales leader, who might not have a engineering degree, they used Windsurf to create their own tools. So way out at, like, the 150 IQ type, uh, organizations in the world, this is already happening. But for now, like, a lot of the people who really understand the domain, they can't code, or they don't understand tech, or they can't, you know, do design a product and ship it. So you know, for now, there's just this startup-shaped hole in basically, uh, every process or every sort of annoying system that should exist that doesn't exist yet.

   11. DHDiana Hu9:07

       It's a very r- rare breed of skill sets where they have a lot of the extreme, up-to-date, latest and greatest AI understanding and product taste, and at the same time, to some extent, a lot of the kind of humanity in a sense, to understand all the human processes to then grok those into, into a product. And I think there's a different permutation of what you both have mentioned around, uh, companies going after consultants as a solution, as a vendor. There's this company that Jared and I were group partners with, Castle.AI. They're also selling to banks. There's a, there's a theme here.

   12. GTGarry Tan9:44

       (laughs)

   13. DHDiana Hu9:44

       They're basically building a AI mortgage servicer. There's a lot of, uh, vendors around that have been around for, like, decades with very old system and they're catching on as well. They know that their lunch is going to get eaten, these vendors, and they are adding AI on top of it. And what happens when Castle goes into all these sales conversations with the banks, they have to do a bake-off with the current incumbent solution. And turns out what they learn, the, the banks still do it because they trust the vendor, they've been around with them for a long time, not, not just a consultant, but a, like a regular old-school s- software system. And funny thing is, a lot of times these products are very subpar. The particular customers they work with, they, like, closed off the bake-off because it's like, "Wow, the, this vendor solution was just AI slapped on top of it." So it's such thing as, like, it could be AI on the vendor's side and they add it, but it's lacking this aspect of being really native from the beginning and having that really good taste on the product. And this is how Castle has closed some of these large banks, which is impressive, just one year after the batch now.

   14. JFJared Friedman10:54

       YC's next batch is now taking applications. Got a startup in you? Apply at ycombinator.com/apply. It's never too early, and filling out the app will level up your idea. Okay, back to the video.

5. 11:08 – 13:39

   The Reducto case study

   1. GTGarry Tan11:08

      And Ayanna, you have another example of a company-

   2. DHDiana Hu11:11

      Mm-hmm.

   3. GTGarry Tan11:11

      ... speaking of Apple, that actually-

   4. DHDiana Hu11:13

      Oh no. (laughs)

   5. GTGarry Tan11:13

      ... sold to a fang company that- (sighs)

   6. DHDiana Hu11:15

      Oh yeah.

   7. GTGarry Tan11:16

      ... had tried to implement an internal solution and had it not worked. M- maybe you can tell us about that.

   8. DHDiana Hu11:22

      This is a very impressive case study. The company is called Reducto. They just announced their series B recently, and they actually closed a fang company 154 days after the batch, which is, I haven't seen that happen. And this big fang company reached out to them because they did a YC launch.

   9. JFJared Friedman11:42

      Hmm.

   10. GTGarry Tan11:43

       Hmm.

   11. DHDiana Hu11:43

       That's how they found them. So our launches-

   12. JFJared Friedman11:45

       Hmm.

   13. DHDiana Hu11:45

       ... get people watching them, and they reached out. It's like, "Oh, this is interesting. We'd love to try it," and we've been working on a solution. Turns out this particular company has been trying to... What, what Reducto does is, uh, document processing for AI, and this company has been having a lot of internal systems and build internal solutions for years to run a lot of the operations. And a lot of the solution, they try, they tried open source, they tried AWS Tesseract, all sorts of OCR solutions, and they were not cutting the mark. And this is where product encel- excellence really got Reducto to win the deal and be a pretty big one. But the thing about this one that still took time to go through it... I mean, so Reducto had to compete with internal team and they had to have a lot of finesse to navigate a lot of the politics, which is actually one of the aspects that the MIT paper does talk. So we do agree with that. There's still a lot of work to get there, uh, but they got it done. And it's still, like, hard, but at the end of it, they do have this awesome deal with them and they've been live in production for more than...... a year or two now.

   14. JFJared Friedman12:53

       Yeah, what was the secret to avoid, uh, pissing off the wrong people, you know, still be in the running-

   15. DHDiana Hu13:00

       (laughs)

   16. JFJared Friedman13:00

       ... and, uh, eventually win?

   17. DHDiana Hu13:02

       This is where you do things that don't scale. One of the things that they did is they became really good friends with the champion and really building friendships with them. And they saw that, "Oh, there's these really smart kids, and I want to take a shot on them." And is, uh, this is what I think a lot of the story around YC founders selling to big enterprises. I think there's something about this ambition and really optimism from founders that is contagious that really gets people excited. It's like, this is a bit of a boring problem to, like, process documents, but you're super jazzed about it, and, "I'll give you a shot." And then when they do, they surpass a lot of the expectations, and it's

6. 13:39 – 14:39

   The type of enterprise employee you should find as a founder

   1. DHDiana Hu13:39

      cool.

   2. HTHarj Taggar13:39

      I've heard it's a, it's actually a sort of a particular archetype of big company employee. It's someone that really wants to do a startup or has always sort of had dreams of a startup, but it's not, they're not actually ever gonna do it, they're too risk-averse. And so they can kind of live vicariously through an exciting startup with founders that they get along with. And if you find someone like that to be your champion, it's like they want you to succeed because they're gonna feel like they're on the startup journey as well.

   3. DHDiana Hu14:02

      I think so. I think it's finding more people like that that want to nurture that inner child that had this dream of startups but didn't get to do it.

   4. JFJared Friedman14:08

      What's funny is this is a good example of, uh, when you meet, uh, especially young founders, often they try to, like, dress up. Like, they'll just dress up in a suit, and they, like, copy Microsoft's homepage or something. And they shouldn't do that. Like, they should just try to be a little bit more authentic. Like, it's actually fine to be a startup. Like, it's important to come off as smart and with it, but you do not need to copy the formalism of, you know, sort of wearing a suit or the equivalent of that in, like, your interactions with people.

7. 14:39 – 15:25

   Meet founders who’ve been acquired by enterprises

   1. HTHarj Taggar14:39

      Another good tactic is to find, uh, founders whose companies were acquired by big companies and get them to be your champion. With Triplebyte, we essentially, we were able to work with Apple, and there were, like, almost no recruiting companies working with Apple. And that was all because of their, uh, a YC company, Q, started by Robbie Walker and Danny Yost, actually, um, that had been acquired by Apple, and then they helped us get in there. And then I actually, I remember we got, like, a pilot with Oracle through a founder who had sold his company to Oracle and was just pushing for them to hire better engineers and helped us through procurement and gave us all the internal politics and step-by-step playbook to get the pilot going.

   2. DHDiana Hu15:17

      I think that's the special thing about being here in Silicon Valley, is this, uh, pay-it-forward aspect that I think it, you cannot measure in a study.

   3. GTGarry Tan15:24

      One

8. 15:25 – 19:40

   Enterprise/startup tension and symbiosis

   1. GTGarry Tan15:25

      of the other interesting things from, from, from this paper that's also, I think, like, a very optimistic point that got lost in the, in the, like, tweet version of this paper is that, like, there's overwhelming demand from enterprises to adopt AI, and they're way more willing to take bets on new startups. So, you know, all these, all these, all these tricks are helpful, but I, I do think it's probably much easier to sell to a FAANG company now, some AI agent, than it was back when you were running Triplebyte.

   2. HTHarj Taggar15:51

      But it sort of ties back into this original study. Something we, we've talked about and maybe tweeted about is that I think the enterprises would certainly prefer to buy these solutions from established software companies, even established startups, like late-stage startups that have been around for a while and have lots of funding and feel less risky, but they fundamentally can't build the products. And I think many of the YC partners feel that a lot of the time, it's just because the engineering teams at these orgs are filled with people that themselves don't actually really believe in AI, don't use code gen tools, don't think, think it's all super overhyped. They're really excited when an MIT study comes out saying that it's all, like, hype and retweet it and, um-

   3. JFJared Friedman16:29

      (laughs)

   4. HTHarj Taggar16:29

      ... and really want 'cause it's a narrative they want to believe. But the consequence of that for the companies is that they can't build the product. So if your engineers don't believe in this, then how are you gonna build a product that actually works? The knock on effect for startups then is if you can actually build something that works, the enterprises will talk to you because they have no other options. Can't build it internally, can't go to an established company. Um, so the startups are actually getting, like, the shot that they never had before.

   5. JFJared Friedman16:50

      I guarantee you someone's watching this right now, and, uh, you've just horribly triggered them.

   6. HTHarj Taggar16:55

      Oh yeah, no-

   7. DHDiana Hu16:56

      (laughs)

   8. HTHarj Taggar16:56

      ... I tw- I t- I, I tweeted this, and I got lots of angry emails (laughs) .

   9. DHDiana Hu16:59

      (laughs)

   10. HTHarj Taggar17:00

       Sorry, lots of angry retweets.

   11. JFJared Friedman17:00

       Someone out there right now is going, "Ugh."

   12. HTHarj Taggar17:03

       (laughs)

   13. DHDiana Hu17:04

       (laughs)

   14. JFJared Friedman17:04

       And the message for you guys out there, the irony is all you have to do is literally just try it. Like, if you code and you're a great engineer or even an okay engineer, honestly, if you just try this stuff and get really good at it and, you know, give it a shot. Like, it's not like a, "I try it once, and it screwed up a variable name, and now I'm mad, and I'll never use it again," right? It's actually, like, invest into a real project. It doesn't have to be your main work. It could just be a side project. Do something super fun. We literally had a, um, a VibeCoding dad's night about a month ago. And, you know, people who are not even technical... We had, like, a landlord who was making a VibeCode thing for their tenants so that they could, like, see if they had paid their rent or something. It's like, you will be amazed. And so the people who, like, feel this, it's like, just give it a shot 'cause you, you know, you are sort of the perfect people in the world to use these tools. And even if... I mean, honestly, it turns, you know, 10X engineers into 100X engineers, and it turns 1X engineers into 10X engineers. I mean, I, it's, that's, like, such a gift, but it requires an overcoming of, like, this very real emotion that's inside of people.

   15. HTHarj Taggar18:16

       Yeah, the, the other instance over the last week where I've just seen this sort of, like, the, the people waiting for the "it's overhyped" narrative was after that Andrej Karpathy Talkish interview.

   16. DHDiana Hu18:25

       Oh, yes.

   17. HTHarj Taggar18:25

       Did you guys see that?

   18. JFJared Friedman18:26

       Yes. Yeah, I told you 10 years.

   19. HTHarj Taggar18:28

       (laughs) Yeah, I saw, I read the tweet. The, the tweets were essentially, "Oh," like, you know, "Karpathy says agents are overhyped and can't do the work." So then I listened to the interview, and it's like the point he's making is you can't just, like, give an agent a prompt and expect it to do everything perfectly the first time. Like, you still actually have to do lots of work to provide the right data and do all the correct context and actually do the evals and all, like, the actual tooling. And my interpretation of that was that's, like, a fantastic opportunity for startups and anyone who can build software.

   20. JFJared Friedman18:55

       Fantastic. (laughs)

   21. HTHarj Taggar18:55

       (laughs) Like, there's just, like, tons of stuff that's still yet to build. And I just found it, like, an interesting Rorschach test almost of it's like, if you fundamentally want to believe that everything is overhyped, you're gonna read into that that, "Oh, yeah, like, look, like, AI expert confirms it's overhyped." But if you listen to actually what he's saying, it, there's, like, tons of opportunity to build really great tooling. It's like, it's, these things are a tool, and you just have to help them work better versus expect that they're all just gonna...... be absolute magic and work without any help.

   22. JFJared Friedman19:23

       Yeah. Well, I think the exciting thing is basically there's just a lot of op- opportunity to rebuild all these systems to be AI native because software needs to be completely be rewritten to work with AI, which is really just lots of opportunities for, for founders, which is

9. 19:40 – 21:43

   Outro

   1. JFJared Friedman19:40

      cool.

   2. GTGarry Tan19:40

      Here's one other point from the study that I, I also thought was really interesting in terms of, like, why enterprise is such a big opportunity for startups. I'll actually r- read this quote, this is from some enterprise buyer person, "We're currently evaluating five different gen AI solutions, but once we've invested time in training a system, the switch in costs will become prohibitive."

   3. JFJared Friedman20:00

      That's the CIO of a $5 billion financial services firm.

   4. GTGarry Tan20:04

      Yeah. (laughs) Right?

   5. JFJared Friedman20:04

      Fantastic. That sounds like a moat to me.

   6. GTGarry Tan20:07

      Right? (laughs) Exactly.

   7. JFJared Friedman20:08

      Yeah.

   8. GTGarry Tan20:08

      I hadn't heard such a direct quote from, like, a legit enterprise buyer about that before. So, all these people who are worried that these, like, ChatGPT wrappers won't have moats, like, that's the moat.

   9. JFJared Friedman20:18

      So, uh, there you have it, the AI doomer influencers have been m- leading you astray. AI is hard to actually implement and it turns out, it's so hard to implement that only 5% of the time it actually works. But it also turns out that if you're a startup founder and you're a really good one, at YC the acceptance rate is under 1% now. So, we gave you a whole bunch of examples of people who are in that 1% who then went on to be a part of, like, probably that top 1% of implementations that actually work because some of the smartest, best product people, engineers, uh, are actually focused on it. Ultimately, it's about people who really, really great at technology but also are polymaths. They're, you know, understand other people, can understand what that, uh, bank, you know, $5 billion fintech CIO really, really wants. That's the good news. You should not look at these stats and say, "I could never be a part of that 5%." If you're actually really, really good, you absolutely can be and we have so many examples of that at YC. So, with that, we'll see you guys next time. (upbeat music)

Episode duration: 21:43