Des Traynor - Real Talk about AI and Software - [Invest Like the Best, EP.340]

My guest today is Des Traynor. Des is the co-founder and Chief Strategy Officer of Intercom - a customer service solution that helps businesses answer product questions, offer instant support, and automate sales. The business was founded in 2011 and its products operate within 25,000 businesses, including the likes of Amazon, Lyft and Atlassian. Our conversation is roughly split in half. First, we talk about AI and how it’s actually changing businesses like Intercom through products such as their OpenAI powered bot called Fin. We then talk about Des’s views as an investor, which includes an answer about software that I’ll remember for a long time. Please enjoy my conversation with Des Traynor. Listen to Founders Podcast Founders Episode 311: James Cameron For the full show notes, transcript, and links to mentioned content, check out the episode page here. ----- This episode is brought to you by Tegus. Tegus is the modern research platform for leading investors, and provider of Canalyst. Tired of calculating fully-diluted shares outstanding? Access every publicly-reported datapoint and industry-specific KPI through their database of over 4,000 drivable global models hand-built by a team of sector-focused analysts, 35+ industry comp sheets, and Excel add-ins that let you use their industry-leading data in your own spreadsheets. Tegus’ models automatically update each quarter, including hard to calculate KPIs like stock-based compensation and organic growth rates, empowering investors to bypass the friction of sourcing, building and updating models. Make efficiency your competitive advantage and take back your time today. As a listener, you can trial Canalyst by Tegus for free by visiting tegus.co/patrick. ----- Invest Like the Best is a property of Colossus, LLC. For more episodes of Invest Like the Best, visit joincolossus.com/episodes. Past guests include Tobi Lutke, Kevin Systrom, Mike Krieger, John Collison, Kat Cole, Marc Andreessen, Matthew Ball, Bill Gurley, Anu Hariharan, Ben Thompson, and many more. Stay up to date on all our podcasts by signing up to Colossus Weekly, our quick dive every Sunday highlighting the top business and investing concepts from our podcasts and the best of what we read that week. Sign up here. Follow us on Twitter: @patrick_oshag | @JoinColossus Show Notes (00:03:42) - (First question) - Des's high level thoughts on Open AI and new technology (00:07:07) - Advanced AI tech for existing businesses versus start-ups ventures (00:11:14) - Where Intercom falls on the spectrum of existing and new AI technology (00:13:16) - Training AI to be tailored to specific fields of work (00:18:25) - The natural end point is for Intercom incorporating AI (00:22:25) - Des's product philosophy behind this technology (00:24:03) - Choosing an AI provider that best suited his customer service industry needs (00:26:55) - The value comparison between using AI-led customer service versus human (00:29:18) - Why outsourcing automated data is not for everyone (00:31:15) - Des's advice for other companies beginning to integrate AI into their operations (00:36:33) - What he is excited for as an investor in this area (00:37:50) - The most common discussions he’s having about this technology (00:41:21) - The inherent risks of using AI models that are not 100% accurate (00:45:10) - Des's evolution as an investor (00:48:25) - How Intercom solved it’s AI execution problems (00:50:46) - Combating other companies that are trying to overtake the field (00:53:44) - Counter arguments for using AI technology (00:57:22) - His view on the best software out there (01:03:00) - The kindest thing anyone has ever done for him

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Patrick O'Shaughnessy?

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• 0:35Patrick O'Shaughnessy?

  Hello and welcome, everyone. I'm Patrick O'Shaughnessy and this is Invest Like the Best. This show is an open-ended exploration of markets, ideas, stories and strategies that will help you better invest both your time and your money. Invest Like the Best is part of the Colossus family of podcasts and you can access all our podcasts, including edited transcripts, show notes and other resources to keep learning at joincolossus.com.

• 1:00Speaker 01

  Patrick O'Shaughnessy is the CEO and founding partner of Positive Sum and the CEO of O'Shaughnessy Asset Management. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum or O'Shaughnessy Asset Management. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum or O'Shaughnessy Asset Management may maintain positions in the securities discussed in this podcast.

• 1:32Patrick O'Shaughnessy?

  My guest today is Des Traynor. Des is the co-founder and chief strategy officer of Intercom, a customer service solution that helps businesses answer product questions, offer instant support and automate sales. The business was founded in 2011 and their products operate with 25,000 businesses, including the likes of Amazon, Lyft and Atlassian. Our conversation is roughly split in half. First, we talk about AI and how it's actually changing businesses like Intercom through products such as their OpenAI-powered bot called Finn. We then talk about Des's views as an investor, which includes an answer about software that I'll remember for a long time. Please enjoy my conversation with Des Traynor.

• 2:12Patrick O'Shaughnessy?

  I've been so excited for this because I think it's the perfect excuse to talk to a real practitioner, builder, entrepreneur about what it's been like to feel the explosion of AI and its impact, both exciting, maybe even scary, on an existing product, an existing software product that's always been very cutting edge in terms of its architecture, et cetera, and how you approach this new opportunity slash threat. Everyone, I think, is a bomb that went off in the technology scene. And I'd love you to begin by just giving me your high level thoughts on what it's been like to see ChatGPT, GPT-4, all the open source models, all this explosion of new technology through the lens of an existing, really successful software business.

• 2:56Speaker 02

  It's been a good roller coaster, a fun one. We have dabbled in AI since 2016. We've had AI products live in market. I think the world changed with ChatGPT. Both capabilities of what AI could do changed, and also I think people's willingness to engage with a bot was a lot different after their first experience of ChatGPT. For us, I met with our head of ML, I think, the day after ChatGPT dropped, and we had a pretty long conversation where he was pretty firm that this is the single biggest transformation he's seen in AI, and he's spent his entire career in it. The more we played with it, what we realized was, hey, this thing is very conversational. It can learn, it can summarize, it can extract the main pieces, and given a query and given a set of information, it can actually propose

• 3:41Speaker 02

  an answer. And that is basically what customer support is. So the implications for customer support and intercoms were customer service platform. The implications are pretty obvious. It sounded probably more like a prophecy at the time, but I think, looking back, everyone's like, yeah, no, it's pretty obvious that the entire world of customer support was going to change. So the question was, how quickly can we do it? We downed tools, a lot of projects. It wasn't quite code red, but it was as close to a can be from an offensive point of view, was let's go hard. AI, the team worked insanely hard and we produced our first release. Well, we had our first release in beta in December. And then we talked about it publicly in January. And that was all about assisting the customer support rep because we weren't yet sure that the tech would actually be good enough to face end users.

• 4:24Speaker 02

  But we knew it was definitely good enough to augment the customer support agent. GPT four was on its way around then. As soon as we got our hands on that, that was where we were able to say, hey, we can constrain this agent to not go off topic, to not take opinions about whatever politics or to not recommend your competitors or to not do anything you wouldn't want a support rep to do. And we can also, we believe curtail the vast majority of the hallucinations by basically with the right amount of prompting. So we started to play with that. We launched that in beta in March. Then we went to general availability, I think in late May or early June. And that's been a wild ride. Just seeing how the bot almost always is outperforming what our expectations of it are in terms of like the complexity, the nuance.

• 5:08Speaker 02

  Customers will send us seven questions, not knowing they're talking to a bot. Seven questions with nested, if this, then how do I that? Then just blitz is true. Genuine conversations that might have taken a support rep an hour to aggregate all the information for Finn is just blitzing it. And there are some shocking stats. We've seen customers see fifty percent of their support volume drop. We've seen, on average, most customers who turn Finn on would know what it works, literally clicking an on button, are getting fifteen, twenty, twenty-five percent of their support volume just going away straight away. And it's just been crazy to see a product where we knew we had done all the right stuff on our side, but you're still crossing your fingers going, I hope it works well in the aviation industry.

• 5:50Speaker 02

  Sure enough, it does. So it's been wild to see it upfront and we've continued and obviously we can talk more about it. But yeah, we're now asking ourselves, what's the next tier of this? How do we make it more powerful? How do you make it interoperate with humans better, et cetera?

• 6:02Patrick O'Shaughnessy?

  Do you think that this technology is actually more powerful for existing companies that have pre-built products and big data stores and big teams, et cetera, versus de novo startups that are trying to, let's say, for example, someone wanted to build a from-scratch intercom competitor starting tomorrow, and they've got all this great new technology. Often in the history of technology, if you're using new, you can counter-position using new technological platforms or whatever, and really stick it to incumbents. My experience so far has been it feels different in this context than that actually fast-moving incumbents, as long as they recognize it, are better positioned. Do you think that's right? Has it felt that way to you?

• 6:45Patrick O'Shaughnessy?

  Any big thoughts that you have on that idea?

• 6:48Speaker 02

  I've been thinking about this a lot, both from an intercom perspective, and there are a lot of people trying to do customer support startups based on OpenAI's tech at the moment. And then I've also been thinking about it as an investor as well, just looking at the startup scene, seeing what's new. The way I've concluded is, and by the way, in your example, let's just assume we'll take off the table the idea that GPT can write the code to make it the intercom competitor. Let's assume all those things are true for everyone. I think if the way in which you would rebuild the competitor, let's say I'm trying to build a competitor to intercom to make it not personal, for lack of a better word, let's pick, say, Mailchimp or some of that, an email newsletter tool, everyone understands the gist of it. The question I would have is, does the way in which you'd build the competitor,

• 7:31Speaker 02

  is it substantially different to how Mailchimp is architected? Are there some fundamental assumptions made in the codebase of Mailchimp that are now entirely invalidated? They just make no sense anymore. And if that is the case, then I think, yeah, the opportunity is with the new startup, because what Mailchimp has to do if they want to compete is actually build a whole new thing. But I think in most cases, and if we just keep pulling the email MailChimp example, if you and I say, all right, hey, Patrick, let's go build a MailChimp competitor and we're going to use AI and it's going to do AI to write your newsletters and AI to generate your designs. Brilliant. So we still have to build a massive deliverability platform. We still have to build link attribution.

• 8:13Speaker 02

  We still have to build email rendering and testing email rendering across a dozen different clients. We still probably have to build up a brand credibility and all of that stuff. And all MailChimp has to do is shout out to OpenAI for augmenting the texture writing. So in that world, I think you might be in some eighty, twenty. Eighty percent of the existing tech still stands and twenty percent is going to be new stuff coming from OpenAI or coming from Anthropic or whoever we lean on. So I don't really see an advantage now. And the calculus I actually do when I talk to companies about prospective investments or when I assess intercom threats is basically how fast can they move? Let's assume that you and me and our super hot new startup can move ten X the speed of MailChimp. And let's say we conclude it takes us three months to build all the AI stuff

• 8:57Speaker 02

  and it might take MailChimp thirty months. The question is, is twenty seven months enough for me and you to go and build every other feature in MailChimp to the same standard? And if it's not, then we're goose. We don't really have a play to make there. To give you just one counter example, let's say me and you said, hey, we're going to build a tool that it's one of these advertisement management optimization tools. You log in every day and you see what ads are working and you change your spends and you cancel some ads and tweak some ads. You could imagine how a LLM powered tool could basically optimize itself, consistently create new versions of ads, run those instead, run A B tests amongst itself and literally entirely manage your entire ad inventory

• 9:41Speaker 02

  directly without anyone ever having to log in. And in that world, I think a lot of the assumptions of the incumbent are totally invalid. They might have dashboards and reports and lovely funnels for configuring our campaigns, but it's all unnecessary. The AI is going to do it all for you. So in that world, if we're going to go after a space where, hey, we would actually build this thing substantially different where we're building it today, I think all the advantage is within the entrant. That's when it gets exciting.

• 10:07Patrick O'Shaughnessy?

  So talking again about Intercom, if with stats up to 50% of customer service requests is handled end to end without a human in the loop. That whole thing you just described is a gradient. It's never one or the other. It's somewhere on a spectrum. So where do you feel like you fall on a spectrum of this is an entirely new thing versus? Oh yeah, to build the rails underneath the delivery of this thing is like the MailChimp example. It takes four years.

• 10:34Speaker 02

  I think we're somewhere in the middle where whole workflows are removed in Intercom's case, but not the entire platforms. There are still humans doing support and they still need a pretty rich and powerful support help desk and they still need a messenger to communicate through. All of that needs to be available through APIs. You still need a knowledge base. There's a lot of other stuff that still has to be built. And then obviously Intercom also has proactive support, so there's messaging pieces as well. So I think what we have had to do is reimagine and throw away parts of Intercom that assumed, say our reporting infrastructure is very different in a world where 50% of the responses are going through AI. All of a sudden, people care a lot about the AI reporting, whereas they didn't before. Measures like first response time right now are no longer really valid because you have to subtract that AI and all that stuff.

• 11:18Speaker 02

  So I think if you can imagine of all the support work that happens, X% of it's gone. The remainder still needs a, what we would call like a classable, high-quality help desk. A good chunk of the work just disappears entirely. And then there are some features or some workflows. We'll see how we play that where we'll probably heavily augment them with AI. So you could imagine analyzing what are the most common complaints from customers today? I can imagine we'll throw a lot more AI at that feature to remove the needle and haystack approach. And actually, maybe the new version of that report is just a summary paragraph that tells you here is the biggest issues going on today. So I think that would be an example of workflow displacement. But I think in general, we believe the future of customer service will still involve humans and bots. And we care a lot about making sure that they can work together really well and they can interoperate.

• 12:02Speaker 02

  And we have this idea of a flywheel where the humans help the bots and the bots help the humans. If we're right, anyone who wants to beat us also needs to have a pretty high-quality help desk too.

• 12:11Patrick O'Shaughnessy?

  Can you help me understand the nuts and bolts of working backwards from a thing that I think everyone wants? And I'll call that thing an agent that is context specific to their business or themselves or whatever, where the feeling of chat GPT, which is so magical or G P T four is so magical, but with all the context and the knowledge and trained on me or trained on my business. And what you just said, people helping bots, bots helping people. Sounds like this is a process to take the generic, broad artificial intelligence, whichever provider you use and make it context specific or really helpful to me specifically. There's this process of retraining it or narrowly training it. So can you just literally tell me the steps of what you've learned about how that works?

• 12:55Patrick O'Shaughnessy?

  Because it seems reasonable to me that we're going in that direction. Everyone's going to have an agent that is tailored and aware of their specific circumstances and context and data and so on. And you're one of the first to actually build this process of training this thing. And I think of Finn as, okay, Finn's the intercom agent. It is a multipurpose thing, but it's very context specific to your product and your world. So what have been the steps, the mistakes, the considerations? The details of this are really interesting to me if this is where the world's going.

• 13:28Speaker 02

  There's a lot to this, but the biggest things I think we lean on OpenAI mostly to power Finn's decision making ultimately like its judgment. And also its conversational capability. So we don't have to build stuff like, hi, how are you? Oh, I'm good. How are you? All of that type of boilerplate salutation, all that OpenAI is just really good. And it's really good even when we want to get to a point where our customers can have Finn speak in their own branded tone of voice. So like a surf shop and a bank can sound appropriate for their domain. So I think our biggest challenge initially was how do we get it to stay on topic? And how do we get it to not apply knowledge that has nothing to do with your business? So if you go to any Finn instance and ask it like, who is the president of America? It actually won't attempt to answer it because there's a slippery slope.

• 14:11Speaker 02

  When you'll see a lot of people go down this slippery slope where you can force it to then take opinions that the company doesn't want to take. Getting the bot, despite all its infinite wisdom, to refuse to engage in topics that are anything uttered on this bank or this surf shop or this podcast or whatever. It's an important step. And then removing hallucinations, so it doesn't do its best to, how would you say, please the user by making shit up. They're the first two things. In practice, how does that happen? Well, we discern the most important pieces from the user. So when they come along and say, hey, how do I whatever, reset my password or open an account or whatever? Look for the guts of what they're actually asking. You perform some vector searches across all the docs that you've gathered. And that can include everything from your public knowledge base, the entire conversation that's led up until this point in the thread.

• 14:55Speaker 02

  So it could do things like, how do I do that? And it can infer what the data means, et cetera. So you have to perform a search of, given that this is the question, drinking in all of this context, what do we suppose is the right answer? And then how do we package that answer in a way that sounds, again, on brand and appropriate for where the conversation is at right now, depending on the tone it's taking. So that's most of the work. It's about, the vector search is really important. The knowledge sources are the single biggest variant. When we see people like amazing fin instances and like weaker fin instances, it's literally how much have you fed it? How much have you given it access to everything about this user, everything about your product, every your product docs?

• 15:33Speaker 02

  It's been impressive how Finn can even read stuff we didn't expect, be it like API docs or things like that, to actually suggest extra answers or further reading for the users. But that's a lot of what it's been about. And for us, one of our challenges was coming up with a ability to benchmark where different versions are at. Cause we can tweak and change prompting, or we can change model and point at a G P T three point five turbo versus four. And it's nuanced a lot of the time to see the differences until you spot them and then you realize, oh, this thing is actually going to go and recommend our competitor or it'll go off topic or whatever. One thing we've learned of late is there are so many people who have built the wrapper around G P T three five turbo.

• 16:14Speaker 02

  And if you just test them all by asking a really obvious question and getting a really obvious answer, you're going to conclude they're all the same bot. It is unfortunate, and I say unfortunate for us because it's in the nuance and in the more likely scenarios. That's where you realize some bots are very good and some bots are very bad. They can all say, what does X do or what does this company do? It's really when you get into specifics about like a refund request or you try and make it go off topic. That's where you see it's underperformance or you see the lack of prompting, the lack of training, et cetera. And even yesterday, we released a bot buyer's guide because we're trying to teach customers about these differences. Obviously you hate having to market nuance. We'd rather we were the only fin in town, but that's not the case. It was the case for three or four months, but now there's a lot of YC startups doing it.

• 16:58Speaker 02

  Now we're into who's got the most, who's taught this truth the most. Ultimately, I still think though, as I said earlier, the battle will really shift being who has the right platform, who has the holistic solution for customer service.

• 17:08Patrick O'Shaughnessy?

  If you think about the examples you've seen so far, some of which have crazy staggering numbers that you quoted in terms of how much of the previous product that was human focused is now end to end handled by a machine. Where do you think the natural endpoint of this is for your business? I'm curious what the adoption's been like, how many of the normal intercom customers are using Finn in some way, shape or form? So what's the friction to getting on this train? And where does it go? Is the natural endpoint that customer service is 80, 90% handled by AI and there's just the strange edge cases get spit out to a much smaller support desk? Where do you think this goes and what have been the frictions to adoption?

• 17:48Speaker 02

  The largest barrier is the people aspect. So it's customer support. Generally speaking, as a human operated industry, and if we move a button in intercom's inbox, we get our support teams get fed fire by our customers because they're like, yo, if you're going to make a single change to this inbox, we have to have an offsite to retrain our entire staff. And when you realize that could be hundreds of people, we have customers who have thousands of intercom seats. So one single change of, hey, I used to say send and close and now it's just a send. That could literally set the large intercom instance back weeks in terms of support volume. So we're very delicate about how we make these changes. The other side of that friction is our customers are very, very slow to adopt for a very good reason because they say, okay, we will try.

• 18:32Speaker 02

  So what we're seeing a lot in say Finn's case, we have many, many, many hundreds, if not thousands of Finn users spitting out tens of thousands of answers on a regular basis, et cetera. But what we're seeing is everyone wants to dip their toe. They don't want to say, let's point Finn at the entire support volume. What they say is, hey, let's turn Finn on on the weekends. Or they say things like, let's turn Finn on if and only if the question regards resetting a password or something like that. And they're doing that because they want to get a sense of how is it performing in a smaller use case? And then ultimately, we've only really been live to literally everyone, I think about eight weeks. We have a lot of planned larger migrations where our customer support teams are going to switch over to a massive amount of volume going to Finn.

• 19:16Speaker 02

  But they're busy working on their knowledge bases and they're busy working on their snippets, et cetera. The snippets being the things that Finn reads to produce answers. So I think what we're seeing is a lot of people preparing for this world. But the friction is definitely, how do I get my support team on board? How do I make sure that we're doing all the right stuff? Even in a lot of cases, all our help docs are out of date and we didn't realize it, but our customer support team were saving our ass. And now we need to update our help docs before we let Finn in. The other question you asked is where does it stop? And I think that's genuinely something we don't really know the answer to. We will very shortly have live in market this Finn generated snippets feature where Finn will read your entire conversational contacts for your business going all the way back. And it'll learn from every live conversation that happens.

• 19:59Speaker 02

  And it'll produce snippets, little nuggets of knowledge to augment its own understanding of your business along the way. Our vision for that will be that your customer support team deals with any common query they see, they should see it for the first time in the last time. They should basically answer it once and believe that they'll never see it again. Where we want them to spend their time is on high value, brand building, high urgency, high impact conversations. We want them spending time in proactive support. We want them reaching out rather like dealing with customers when stuff goes wrong. We want them reaching out to make sure that everything goes right. That's the future world we want for support. In terms of what percentage could actually of raw inbound could go true. It will vary vertical by vertical. If you think about say an e-commerce store, there's really only 10 questions you ever ask. It's where's my order? Why is it late? I want a refund. It arrived, bro.

• 20:42Speaker 02

  What's most exciting to us is what will this snippets feature do? Because you're just going to keep aggregating knowledge. If you take Intercom, we do twenty thousand support conversations a month, so about a quarter million a year. If we just look back two years, that's half a million conversations on top of our entire knowledge base and our API docs and all of our training material and our education material. It's a huge amount of information about the product for Finn to consume. I can't yet tell you where we will be, but I suspect it's still going increasing. Our own resolution rate and all of our customers' resolution rates only go upwards. And then what the question is, where does it start to asymptote? We've yet to see it, but we are only about eight weeks, so we're still at the precipice of a lot of these changes, I think.

• 21:20Patrick O'Shaughnessy?

  What is your overarching product philosophy that sits behind all this stuff? At the end of the day, this is just new technology. It's all like any technology, it's just something that lets you do something for someone else. Do you have a overarching product philosophy that is the bedrock on top of which you make all these decisions?

• 21:37Speaker 02

  Yeah, we have a customer service manifesto, which is our set of beliefs about how the world of customer service will change over the next few years. And we've had one before, but before AI, we were mostly about conversational. We pioneered the idea of the chat thing inside your product and on your website. And that was we went hard and conversational. Today, our manifesto really has four core ideas in it. The first one is bots and humans will work together. That's a very firm belief we have. We believe humans are essential. We want to supercharge humans with AI and we want AI paired chatbots to reduce a lot of the work for the humans. Our second belief is that support should be proactive and reactive, which means you should be able to get out ahead of problems. Our third is so all support needs to be conversational and omnichannel.

• 22:22Speaker 02

  So we talk to customers any way they want to talk to you, anywhere, anyway. And then lastly, all of this has to work together. So you can't try and stitch together three or four different tools. We often save customers from a world where they have a ticketing tool, a docs tool, an outbound tool, a different messaging live chat tool. And they have some Zapier powered or something, one of those cool integration powered things where they try and stitch it all together and get themselves some source of truth. But it really doesn't work very well. And we do all of that, the higher level thing is in service of an internet full of better customer service. Our mission from twenty eleven has been make internet business personal, and that's really what we're about.

• 22:59Patrick O'Shaughnessy?

  If you think about the choice between providers underneath all this, which is something I don't think you and I have talked about yet. How do you approach that problem? GPT four has, it's won Kleenex battle or something. It's the thing everyone thinks of, but the reality is there's lots of tissue providers and you know, seems there will be more every year. How do you assess these things? They're so complicated and nuanced and interesting. You mentioned Anthropic earlier. There's open source ones out of Facebook.

• 23:26Speaker 02

  Lama, this Coher.

• 23:28Patrick O'Shaughnessy?

  Yeah, there's all these cool things happening. So talk us through that part of all this. If I think about Azure versus AWS, you can build your software on any of the big three cloud providers or something with little differences. Is it the same story here? Is it just subtle differences or is there a clear leadership from OpenAI or someone else?

• 23:47Speaker 02

  From our perspective, I suspect it'll end up a lot like the Azure versus Microsoft type thing. Certainly it's trending that way. We started with OpenAI because they were first out of the gates and we've been partners with them for quite a while. And we've had that early access to previous versions of all this tech. They do seem to be leading the way too. GPT four, when they released it was definitely a head and shoulders above everything else that was out there. So I think we're going to partner with whoever we think is going to give us most access to the best tech. The reason we change will be probably more of either somebody else has better tech, which has yet to happen. Or it'll be like some later day optimization of, hey, you could imagine something like Anthropic is available in an EU instance of Amazon. And we can't get GPT over there, so let's swap or let's.

• 24:33Speaker 02

  You can imagine some version like that where we do it for business reasons. So we'd either, if we were to go and chase anyone else, it probably either like accessibility or availability reasons. It could be tech reasons, we just haven't seen it yet. The last one where I just, I'd be surprised if it shakes out this way is just price. So GPT four is expensive and as a result, Finn is perceived to be expensive. We charge 99 cent resolution. It's way cheaper than you'd pay a human, but way more than you'd guess because we get an awful lot of people will just say, but it's just an API call. Haven't you charged that much money? The reality is that's what OpenAI charged. So that's why, that's how it checks out that way. But I could imagine if prices continue to run hot and people start to release substantially cheaper versions. There are genuinely features that would be prohibitively expensive for us to build today.

• 25:15Speaker 02

  To give you a simple example, summarize every conversation in real time as they happen. We have five, six hundred million conversations a month. That would bankrupt us. However, if somebody gave us a substantially cheaper version, all of a sudden that's back on the table. There are pricing implications here. We haven't bumped into them yet, and right now we're still in the innovating and pioneering phase. We're trying to do as much cool stuff as we can, so it doesn't feel like we're yet in the mode to optimize. But I will say, I'm very, I take a lot of comfort in the fact that there's so many strong competitors here. It tells me that price will go down, availability will go up, and the competition to improve the tech will be pretty high.

• 25:50Patrick O'Shaughnessy?

  Ninety nine cents per resolution. How does that stack up to the cost on average across intercoms customers for a human-led resolution, do you think?

• 25:58Speaker 02

  So the first variable is, is your support done by a citizen of the United States working in San Francisco, California? Or is it outsourced to an agency? And if so, where is that agency located? We've never seen anyone get it substantially cheaper than those ninety nine cents. There is a nuance to this. If the answer to the question is no, then an agent can do sixty no's in an hour, and you're probably not paying them sixty dollars an hour. But that's rarely the case. Most of the time, it is a lot of time taken to onboard agents, train them up, get them to be able to deal with their complexity, get them to manage multiple back and forths. But for sure, some people will show me an example and say, that's definitely not worth ninety nine cents. And that's true. We don't know if or are I would or not to think is worth answering until we answer it.

• 26:41Speaker 02

  But in general, we see most support reps paid somewhere between like the floor here would be, I don't know, eight, nine, ten dollars an hour or something like that. We haven't ever outsourced to extreme low cost providing areas. We've never outsourced support at all. But I'm sure someone will tell me you can get it for like two dollars or three dollars an hour. Well, let's see. But in all these cases, I think most of our customers who are B two B tech companies, generally speaking, they're paying more for their actual support team. And then the other aspect that people often forget is, there's a behavioral difference between a user getting an answer in zero seconds versus in seven minutes. So if the question was, hey, I've just signed into Asana and I wanted to know how to create a project. If you answer that question immediately, they go and create the project and they continue to expand and growth goes up.

• 27:26Speaker 02

  If you answer that question eleven minutes later, they're on a different tab signing up for a different project. So there is value in instant support that goes beyond simply job done.

• 27:35Patrick O'Shaughnessy?

  Yeah, it's totally fascinating. Obviously, you would expect the 99 cent thing to come down. And also, the customer support agent sitting in a call center just all of a sudden feels like dystopian or something. That it's just a job that, but for the most valuable conversations, should be automated on top of a knowledge store that a company has. And I wonder why there hasn't been a company that does this for everyone. So you talked about your problems, like, okay, we've got to solve, you got to make it not answer irrelevant questions and start having opinions and hallucinating and all this stuff. And then we need to train it on the customer's knowledge store. There's a process that you've built that lets your customers work. Why isn't there a company that you could just hire to do all that for you, given that it seems like everyone's going to want one of these agents in their business?

• 28:21Patrick O'Shaughnessy?

  What matters is the data. It's not, it seems to me like what's valuable is the companies that have great data, not the flow. So why isn't there just a provider that just does that part for everybody?

• 28:32Speaker 02

  You know that phrase that people are too optimistic about what's possible in the short term and too pessimistic about what's possible in the long term? That's very much how I see the commentary around AI. If you said, all right, drink in all the S1s that are published every year and produce stock tips or something like that. The drinking in piece isn't actually hard. It's hard to find a moister. I think basically what is proprietary and easy to differentiate on is either your data store or the actual workflows that you build on top of the LLM's interpretation of the data. So it is definitely easy to point an LLM at a large source of information and even pointed out, I said, like through something like Pinecone, pointed out a vector search across that. So we can filter it down and really get to the specific bits you want to see.

• 29:16Speaker 02

  What's genuinely hard is making it useful, making it do something that's actually displaces a large amount of work. And if you take an area like finance or legal, I think the threshold or the requirements you'd have are pretty high in terms of trustworthiness. And I think the challenge would be being able to make a bold enough claim that you can stand over to say, hey, this thing will not get your conclusions wrong. If you could package that up and then integrate it into an existing workflow, I think you would actually have a great opportunity. But I think simply the ability to filter information and consume it and then given this thing answer this question. That's not the hard bit. The hard bit is make that into a product. We're touching on the difference between a feature and a product, if you know what I mean. The ability to consume information and spit back some stuff is a feature.

• 29:59Speaker 02

  The product around it is reporting, accuracy, collaboration, all of the other shit that actually makes it into something that a business would adopt. That's, I still think, a challenge that you have to take on if you do a startup in this space.

• 30:10Patrick O'Shaughnessy?

  You said earlier that this was on the borderline of being a red alert or something for intercom. Talk about how that gets managed inside of a company. I imagine there's lots of companies that are looking at this as both an existential risk and opportunity. So what do you think you did well, poorly? What advice would you give other people facing down this seismic change? In terms of just managing and steering an existing company through it?

• 30:35Speaker 02

  I think the thing we got most correct, and I put a lot of credit to this, although he won't take it, I want our CEO, was just the speed of decision making. I think it's very, very easy for a company to say, hey, it looks like this open AI stuff is going to be a thing. Let's form a small cross-functional team and we'll do an investigation and we'll get a readout from them in three or four weeks. And then we'll finish out the quarter's roadmap as we planned and then we'll take stock and see where we're at. And I think that's probably the norm behavior for a lot of companies is to work in that methodical way. And that's not what we did. What we actually did practically was abandon multiple projects, deleted multiple roadmaps for our automated support group, for our ML team, for our inbox team.

• 31:17Speaker 02

  Literally bend everything they were working on and worked on this thing instead. So startups often talk about how important speed is or whatever. And I certainly do this. I often feel when I'm talking to founders that I've invested in, whatever, I'm always trying to say it's not their speed. It's your speed. How quickly are you going to make this decision to unlock them? If they know you take a week for a decision, they're not going to come to you quickly with stuff. So in this exact example, Owen was like, let's go. We weren't going to wait for the data. We weren't going to wait for the evidence. We weren't going to wait for our competitors to say that they're also looking at this. We were just, it's go time. So I think that is probably the single biggest unlock. We were able to fire at a startup pistol that quickly. As in, I think like fourteen hours after ChatGPT dropped, we were able to go. And we started working on it. And then we probably also had a small advantage. We'd been dabbling in AI for years.

• 32:02Speaker 02

  We have a live product called Resolution Bot, et cetera. So anyway, my advice to other companies, I think if we go back to the topic I said about the ratio of in your new world, how important is AI? And how important is everything you've already built? I think you need to work out if there are core product assumptions that are just no longer valid. You need to down everything. Nothing else matters. If you get this wrong, nothing else matters. And if you get this right, nothing else matters. It's the only thing. I think if it's not that, is it, hey, there's significant workflows we can just cut out and rebuild that use AI. So like reporting or whether it's image creation or something like that. Then I would say like, it's almost too late if you're trying to be the first in your category to say we have blah, but with AI.

• 32:44Speaker 02

  I think everyone's over this idea of we also have a lightning bolt in our UI that if you click, it can invent some text. It's a done deal at this stage. You now need to look at, in my opinion, proper end to end workflow automations. I would start fixing my gaze a lot more on what pieces of work can we remove entirely? Cause that's where the actual formula changes. We often talk internally about the formula of support, which is number of inbound times. The amount of time it takes to solve a conversation divided by amount of support reps and all that. We look at all that. If you can just chomp out a massive amount of one of the numbers in that formula, that changes the optics of the business entirely. And I would encourage other startup founders to say like, of all the tasks that have to happen in our product.

• 33:26Speaker 02

  Which can we remove as much of as possible, take away so much of the what would now be seen as undifferentiated heavy lifting? Where can we just delete it? And that's how you have a winning product.

• 33:35Patrick O'Shaughnessy?

  That's interesting. So if you think about the many of these things where it's, I'll call it copilot for X, Y, and Z, I think that you're saying something very different. Don't build another copilot. Everyone's doing that. Everyone's adding AI to their existing thing. Instead, say, what part of the customer's workflow can we just literally kill? Just have go away completely without them? We almost don't want customers to interact with AI. We just want things to happen as a result of AI that they don't even think about.

• 34:03Speaker 02

  That's the highest order goal you can go for. And there are cases where you can achieve that end to end pure automation.

• 34:11Patrick O'Shaughnessy?

  I mean, you've done it.

• 34:12Speaker 02

  Yeah, exactly. I wouldn't say that the copilot style things are useful. If you take, say, I will take GitHub Copilot, the most famous of them. It turns out finishing the sentence in code is something every programmer does dozens of times a day. So if you look at how often does this thing happen? And how long does the thing take? If you start writing a for loop in code, sure, it doesn't take a lot of time for you to finish the bracketing and all that stuff. But you might do that whatever 20 times a day. So being able to hit tab instead of having to type anything, there is a, if you just multiply the two things at frequency times time spent, you'll actually get a sense of where the value is. A non-AI example, this is like spell checking or something like that. It's just okay, people type it all the time. They have to go back and fix. If you can do it for them, that's better. I think you can look at it from that perspective, but I just think a lot of startups

• 34:55Speaker 02

  tend to go for the copilot thing because bluntly, I think it's the easiest thing to do. Hey, write an idea for a blog post and we'll suggest the opening paragraph. Whoopty doo. It's not really a huge thing. What might be a huge thing is find it the most common SEO terms that we're not ranking for and suggest articles we should write and write the articles and go to Dolly and create the images and then suggest them back and then criticize them. And then now I sit down in front of 12 suggested articles that are fully specked out. Now I'm starting to think, huh, this could be replacing a workflow. Everything else is just a little bit of nicety along the way, I think.

• 35:29Patrick O'Shaughnessy?

  As an investor, how much have you seen that is exciting in this spirit?

• 35:34Speaker 02

  I would have to say I see much more of the, what I would call like the easiest API call type AI. That is honestly, right now, the majority of what I see is not amazing. It's very much just, we also know how to summarize a piece of text or change the tone of a piece of text or whatever. And that could be like, we're a sales tool, but we can guess the opening introduction in your paragraph, whatever. And I think those things are basically dead in the water. I think stuff like, say, what Rewind AI are doing, I think is far more powerful. Drinking in a massive amount of data and then giving me a natural query engine on top of it. I think that's like really exciting. I think some of the visual stuff I've seen, be it like mid journey or be like, there's one I've invested in called Kettle,

• 36:18Speaker 02

  where like, again, you just describe the visual of what you want and they produce a vector. And what's really cool about a vector is you can actually tweak it yourself then if it's not exactly what you wanted. I've seen really good examples of that type of thing. But I do believe that we have yet to get to the bottom of where the AI, the high order stuff I'm describing where it's a complete automation. I think a lot of that's still to come. The question for me is, will it come from the incumbents or not? That's still, I think, an open question in a lot of these industries.

• 36:46Patrick O'Shaughnessy?

  When you're having conversations with peers at other software companies, you and I are introduced by John Collison at Stripe. What are the most common discussions, interesting discussions that you're having about all of this that's unfolding?

• 37:00Speaker 02

  The biggest question I always try to zoom in on is, I know AI is really important and it's core to this new workflow that you're going to automate or whatever. Talk to me about the rest of the products that you're building around it. And this sounds like it's maybe perhaps not the sexiest answer to give you. But I think a lot of folks have forgotten that you still have to build world-class software these days. And genuinely, it's not, I think you said previously on Twitter, something to the lines like strategies for amateurs and executions for winners. And I think having a cool idea for an AI feature is genuinely can be unique. And maybe you spotted the capability, you just haven't. But in most cases, you still have to go and build an incredible piece of software around it. So you might have a unique twist on how project management should be done.

• 37:44Speaker 02

  And it involves a bit of AI and that's awesome. But you still need to have a PM tool that's as good as linear. And that's still going to be a huge amount of work. So what I try to do is shift the conversation to there just to make sure that there's actually something behind this. Cause my fear in so many of these AI cases is that we're all just pinging text over to OpenAI. And these prompts and these bits of text, they might be proprietary. You might have a bit of an edge there. You might do some clever shit client side before it goes over there. But it can't be that you're the only person who's worked out the right prompt. If you're investing in something that is built on AI, the thing that you're doing still has to be pretty brilliant, which might mean really good integrations, really rich platform, beautiful UI, et cetera.

• 38:28Speaker 02

  The only other area I'd say I've had some interesting discussions in is, and again, I don't know if this will fall into incumbents benefit or startups benefit. Is this idea of chat driven UI? So I'm sure there is at least one piece of enterprise bloat where you use in your day to day life that is just awful. For me, it might be, say, Workday or Coupa or one of those tools where the tool is just so deeply complicated. And alls I wanted to do was file an expense or request a day off or something like that. And the next thing you know, I've nine tabs open and I've got three dropdown menus and I'm still none the wiser. I really, really in those tools want to press command J, book as October seventeenth return or something like that. I think chat UI will be a massive impact to that whole industry where the way I describe this is

• 39:11Speaker 02

  when the user knows what they want to do, but they don't know how to do it in your insanely powerful or complicated, to be complimentary. When the user knows what they want to do, but they don't know how to achieve it. That's when chat UI is really going to take off. And I don't know if you saw equals the spreadsheet tool. Yeah, a little bit, not much. So equals is basically a spreadsheet tool. One of the things they have built in is AI and another thing they have is live data sources. But the cool thing I like about the way in which they've used AI is I don't know Excel query language very well. And with equals, I don't need to. I just say, sum up all of these before that were expensed before January seventeenth and multiply it by the CPC return and it works it out. So you can literally write Excel queries in natural language. You can also write SQL queries in natural language too.

• 39:54Speaker 02

  So what that does, from my point of view, that has made the spreadsheet an infinitely more accessible tool to millions of people who never were going to learn Excel. And I think that's where you can blow up your total addressable market just through the magic of a chat UI. So I think that's another area of excitement for me where when I see a sustainable advantage that the chat UI would open up. That's another thing that gets me excited.

• 40:16Patrick O'Shaughnessy?

  How do you think about the risk side of this? You mentioned earlier finance or banking or maybe healthcare, places where certainty is more important than in other places. And in Excel, I guess it's the same thing. If this is lower stakes or approximations are fine, then that sounds amazing. But if this needs to be precisely right, then great, I'm glad I can write the query for me, but then I still got to go double check that the query is doing the right thing. And in customer service, I'm sure there's elements of this too, where some things are really easy to handle. You're just pointing someone at the right document or something, but sometimes it's criticality rises and the cost of error goes up. And then you start, you start to then worry about these models.

• 40:55Patrick O'Shaughnessy?

  So how do you think about tail risk of a big complex thing that we don't really know how it's working, but it just works most of the time, but we need a hundred percent of the time.

• 41:05Speaker 02

  Yeah, and to make it more complicated, because you've unlocked a wider addressable market, the person will not identify the mistake. I won't know if there's a bug in the Excel stuff because I didn't know what it's supposed to look like in the first place. I think this is a genuine risk. So the answer I'm supposed to give you is, and that's why there's always going to be a human in the loop when you turn this around. So rather than doing it for the Dez's of the world, you do it for the person who actually builds spreadsheets for Dez and instead you speed them up. And I think that works to some degree, but we even see this, say assistive driving AI where the person has their hands on the wheel, but they're not paying anywhere near a amount the same of attention as they used to. Because they know the car is actually driving itself. So you actually have this challenge of when criticality is high, should AI basically be left out? Do we need to have a higher threshold?

• 41:48Speaker 02

  How do you define that threshold when it is purely generative? It's hard to say. I can tell you what we do in Intercom is we have a separate product to Fin called Custom Answers. And what Custom Answers does is more of, how would you say, an old school, traditional AI existed before November. And in that case, its behavior is quite different. It works something like if this query looks like it's relating to an important topic, let's say refund or locked out of my counter authentication, then So that's your AI there. It's just like if fuzzy logic matching then and then we have a very specific answer. And what we do is we use custom answers to target really specific things where we have a precise set of steps. And we actually can't afford to have generative conclusions. We actually need to control every word that is said.

• 42:33Speaker 02

  A lot of our customers use Finn and this together. And they just effectively splice out stuff that sounds incredibly important and let Finn take care of the rest. But even at that, there's still gonna be a weakness there somewhere. If you keep scratching and sniffing, you'll find something that actually turned out to be more important on its standard. I think this is the fuzzy world we're all heading into where things will be possibly more wrong than they used to be. But they'll happen in real time as opposed to taking days, weeks, months, depending on the task being optimized. I do suspect this is why you won't necessarily see GPT used in doctors diagnosis apps. They might speed up the doctor, but they'll still need to be human in the loop. And we just have to hope that say the driver in the AI augmented car, that they're still fully switched on to what's actually going on. And they're not just

• 43:17Speaker 02

  copying and pasting shit out of chat GPT or whatever. But I think this is a new world we're entering into. There's no denying it. There are gates and there are railings, but there are no, I don't think any guarantees. It's hard to take a new technology into society and prevent all its downsides.

• 43:31Patrick O'Shaughnessy?

  As an investor, I'm really curious how you are, whether it's different or the same as you've always viewed this as an investor. For the first time we talked, you had some really interesting thoughts on just the things you look for in young companies. Even since Intercom started, the friction and expectation for starting companies has changed. There's so many of them. Y C batches are so much bigger. If there's a whiff of an opportunity, all of a sudden you get twenty people leaving their jobs to start a company. It's amazing. It's great. The world benefits from this for sure, but I think it makes investing harder. Especially given sometimes the prices are quite high. How have you evolved as an investor? What are the things you're looking for?

• 44:09Patrick O'Shaughnessy?

  Any philosophical changes over your investing career? And then I'll obviously talk specifically about the change that L L M has had on that too.

• 44:17Speaker 02

  Early days, I used to get fooled a lot by just a great looking product. I mean, fooled is probably the wrong word, because some of them actually worked out pretty well. But just, I thought if you could build software, that that was enough almost. And in some cases, some of my earliest investments, that actually turned out to be enough of a judgment. But not, certainly in later years, beautiful UI and stunning landing pages became more commoditized, or like maybe more easy to do for people. And as a result, it was probably easier to fool me at least, but I suspect probably easier to fool half the industry. So I think I've learnt now that a product can be a really, really nice, beautiful execution, and perhaps still just not work out. And I actually had one of those recently.

• 44:57Speaker 02

  It was a meeting, like a Zoom competitor, that was built with AI inside it for identifying action items and all that stuff, based on what people were saying in the call, and it would produce meeting notes afterwards. And it was incredible, it would produce a highlights reel of the conversations. You could zoom into the exact, specific moments that mattered, et cetera. It sounds cool. Problem is, no one wants to pay for that on top of hanging out, it's on top of Zoom, full stop. But the product, if you saw the product today, you'd be like, that's insanely brilliant. It's awesome. It looks great, it works great, I've used it many times, et cetera. But if you try and take it to a company and say, well, you pay seven dollars a head for this on top of your Zoom fees, and you're already probably buying the G Suite anyway. The answer just basically says no. So that's one thing that I've just become more wise to, which is, and it sounds so stupidly obvious when I say it like that.

• 45:39Speaker 02

  But the route to market times the user's propensity to put their hand in their pocket is a real thing. And in those cases, Zoom already has the route to market, and G Suite is already there with a pseudo monopoly on all things productivity inside a company. Another change I've just been trying to distill of late has been, I find myself asking startups the same question a lot lately, which is, what is the single thing you can say that is unique to you, valuable to your user, true and simple? And all four things matter, and most people fail on one of the four. It's either too complicated to pitch, or other people can say it, or your users don't give a shit, or it's not true. And that has been a really impressive. And I often say this, if it's a random company I barely know, or a lighter week intro.

• 46:24Speaker 02

  I often say it as a pre qualifier to actually bothering to take a call, or even sometimes reading a pitch deck. And that's a maturity I didn't have. I would have heard the pitch five years ago and gotten really caught up in how nice their product looked and all that. Whereas now I find if you fail at that hurdle, the chances of you being a really successful business, the outsized outcome that makes angel investing make sense is pretty slim. And then the third one, I just continue to beat a drum about is just execution. And I won't say too much about it because I know you agree. If your idea is any good, you should assume it will become commonplace and everyone will have that idea because they're going to see your version of it. And then everyone's going to try and do it. And if someone does a lot better than you or can do it the same as you, but faster, you're going to lose.

• 47:01Speaker 02

  It won't be close. You will definitely lose. First really doesn't mean a whole lot unless you helps you build a bit of a brand maybe, but you'll get outpaced pretty quickly. Once somebody has a feature you don't. So I care a lot about do defenders get that? And if the founding team isn't super technical, as in they can't design, build, execute their own software. Then I always worry about that because they often don't.

• 47:20Patrick O'Shaughnessy?

  How did Intercom itself solve that execution problem? Because I think relatively early on, people realized, oh yeah, this is a thing. This is a good idea that there's a simple digital way to interact and connect with my customer in the space that they are. I'm assuming, I don't know much about Intercom's early history, but I'm assuming that lots of people tried to do this and you won. So is the simple answer just execution? You just went faster and basically followed the advice you just gave about investing, but for building?

• 47:50Speaker 02

  I'd love to say yes, but it would sound arrogant. I will say like we had a shitload of competitors, copycats, people who were just liberal right click view source. Let's have some of that messenger code. And there are still many, there are people who literally just copy our source on a regular basis. I think ultimately what our customers know is that if you want the latest and greatest Intercom, this company that has it. And I think a bet on Intercom is a bet on the persistent position as the people who are doing the newest stuff best. And that's why it surprised basically none of our customers when we launched AI shit, because the tone we heard back from a lot of them was like, of course you guys who go first. For a lot of our customers, that's why they sign up. And we talk a lot about this. We even publish, if you go to intercom.com slash changes, you'll see the rate of release from our product team.

• 48:33Speaker 02

  We're just consistently grinding, making things better. I think I've heard it glibly referred to as like the gingerbread man strategy, which is run, run as fast as you can. You can't catch me.

• 48:44Speaker 02

  That's our thing. You can copy and you can copy, but it's going to be consistently yesterday's technology tomorrow. And that's not what customers want. They want the best stuff out there. But I think we do talk a lot about speed internally. If you talk to any of the intercom folks, still be like, they're sick hearing about it. And it's not just speeding as encoded really hard, hands on the keyboards type of stuff. It's speed is in processes, decision making, et cetera. I think for what it's worth, a pet theory has all SaaS is basically UI on top of databases. So everything is copyable pretty quickly. The goal is to get a position that looks daunting to copy. If you're trying to build a project manager tool and you're saying, hey, let's rip off linear, you're going, oh, well, there goes the first two and a half years of our roadmap. It's a tough one to take on. Same with Stripe, and I'd hope same with intercom, whatever. You just look at me like, shit, we really

• 49:29Speaker 02

  have to build all that. We're going to be at this quite a long time. And by the time we've done it, those guys move fast. So by the time we get to where they are today, they'll be long gone. And that's the hope that you can build, but just raw product momentum as a moat, if you will.

• 49:41Patrick O'Shaughnessy?

  Do you worry about, I guess I'll call it the Red Queen effect of that style of building relative to, I'm trying to think of an extreme example, like Visa or something, which notoriously is an extremely good business because they built an unassailable competitive position, but there's no speed at Visa. If anything, it's better to have more bureaucracy, like, don't screw up this position that we've carved out for ourselves. There's no product velocity at Visa. Running faster than everybody seems like a great way to win, and I think it is. But do you think it's necessary but not sufficient? And ultimately, you need to get yourself in a Visa-like position of, even if you wanted to copy this, and you also moved really, really fast and on unlimited resources, there's something about it you

• 50:22Patrick O'Shaughnessy?

  just can't even copy. Whether that's, this is like classic moat question. How do you think about that for intercom?

• 50:29Speaker 02

  I think this to me is honestly where a brand comes in. And that's ultimately, you need to build, and we're in the middle of undergoing a pretty extensive distillation and rebrand, if you like, of what intercom is all about. But I think you need to transfer the energy that's felt in a product momentum into being a sustainable brand position. To ultimately get to a place where it's just, why wouldn't you just use Intercom? That's, I think, the best way to turn a dominant product position into a sustainable thing where you become the, it would just seem odd to not use Slack or Stripe or Figma or Intercom, whatever. It's just, why wouldn't you? And I think because all those tools you have to assume will get caught eventually because in IDN or something like that, will, whatever Stripe releases now, they'll have it in two years. So there will be some eventual,

• 51:13Speaker 02

  there is not infinite runway in all feature sets. Eventually you start releasing shit just for the sake of it, and that's a dangerous place to be. So the goal as you're doing this is to transfer the credibility that exists provably in your software into the brand and ultimately capture hearts and minds from that point of view. And then there are other like more tactical things, community, evangelism, advocacy, making sure you reward the people who use your product a lot. Then there's the technical version of that, which is integrations and interoperability. Do you fit much better in the tech stack than everyone else does? Do you assign good partnerships and good data share agreements? If you use Intercom plus GitHub, Jira, it works really well together. And if you try and use new app with the same, it'll be harder for them to get the same co-promotion, the same partner status, whatever. So there's other aspects to it as well.

• 51:56Speaker 02

  But I think job one is honestly have the best product and the best way to do that is to move really, really fast. Job two is to transfer it out into being a brand position rather than a technical position. And then job three, I think, is to then expand your tentacles into everything. As I said, the hearts and minds of your customers, partners, et cetera.

• 52:10Patrick O'Shaughnessy?

  One of the prevailing ideas in the business world is that the pure software business is the ultimate in business because it has very high margins, it can have very high retention. Sometimes doesn't necessarily require a ton of upfront capital to get going. It's this beautiful thing. If you're on like a debate team or something and I force you to take the opposite side to say, actually, like, here's the bad things about software businesses. Here's why it's not a panacea. Here's why you should consider building something other than software. What would be your debate points? And I ask this as somebody, obviously, that's done this, that's succeeded in doing this at a company that's grown very large. What would be your contra points in that debate?

• 52:49Speaker 02

  I think the two things come to mind, obviously, because the debate I'd prepare, but two immediate things come to mind. One is there are very few durable modes in software. So whatever it is you have, it's almost by definition going to be copied. Someone will infer the database structure and right click, copy the UI, and all of a sudden they have the goods to your product. So that's a challenge and we don't really play the patents game. We don't really play the exclusive partnership game in software in general. We generally tend to put it on the internet for everyone. So whatever your piece of software is, assume everyone's coming for it. And then also the second piece, which is just as important. Software has a really, it's very perishable.

• 53:30Speaker 02

  What it took to be best in class in project management, even just three short years ago, is nowhere near good enough for today's standards. And there are not a lot of categories where you can have a great product and also know that it's going to be dated as hell in thirty six months. But software is one of them, so you have to continually reinvest to maintain your position. That's the first, if you compare that with other areas, other non-software areas you might go into, you'd find industries that don't have those true traits of insane perishability and lack of a mode of any sort, really. The third one is more of a, perhaps a nuanced point, but there's a tension in software in general, which is the ideal software is one line of code that everyone uses in the exact same way. And it's got a massive margin on it and needs no maintenance.

• 54:15Speaker 02

  But in practice, in order to get a second customer, you usually need a second line of code and the thousandth customer needs another line of code. And if you're chasing an actual, trying to grow a business, the more customers you're trying to attract, usually the more at least settings and preferences that you have to add so that you work a different way for enterprise than you do for startup or whatever permissions. All that shit, which means the product just keeps getting bigger and bigger. And if you want to have a large market, you generally tend to have to adopt loads of different styles of workflows. And that means loads of different code and that means more complicated UI. So in essence, for a product to be big and successful, it has to get worse. You get worse for any individual, but better from a market capture point of view.

• 54:57Speaker 02

  So you're constantly trying to find a sweet spot along the collision of those two lines. How big is the market and how simple can the product be? And what you'll find is to maintain a large product, because in a very product with a large, totally addressable market, you usually have to have a lot of software. And bearing in mind, as we just agreed, that software is both not protected by any mode and it's going to age out pretty badly, pretty quickly. So you have this tension of the need to continually reinvest. And now we reinvest lots because it turns out to go for a large market, we've built a shitload of software. And that is just a difference. This bottle of Coke is the same one that Barack Obama would drink and the same one that Bill Gates would drink. And I'm drinking it and some rando on the street would drink it too.

• 55:39Speaker 02

  That's not the case in software. If we want to say, hey, we do ticket tracking or we do customer support or whatever. The Y C startup, the fifty person company, the five hundred and five thousand or the fifty thousand there. It's a whole different ballgame at every level. Yet if you say we want to be it for number one for the entire industry. That's a lot of if statements to deal with all those workflows and every one of those if statements has a team of engineers and P M's and designers behind it. So you have that tension of market size versus product quality. And it's a hard puzzle to solve. And again, there are other industries in this case Coca Cola or whatever, where they don't actually have to play that game. So that's why they're being on software to invalidate my own career.

• 56:15Patrick O'Shaughnessy?

  It's an amazing, amazing answer and list. Does it stand to reason then that the best software ever is Bitcoin?

• 56:22Speaker 02

  Oh, possibly. I actually, there are a few software products I've seen, to give you a shitty example, just say like the Notes app on iOS. Where actually I know mega core people and I know aged old grandfathers or whatever, and they all use notes. So there are some products that cut through and just say, we are one thing and we're that one thing for everyone and you all like it. And I think that's really, really cool. Another example of will be say bearer.app, just a really nice note taking tool that I use. Where they have just one product for everyone, but everyone uses the product and everyone uses it roughly the same way. So you get a lot closer to the idea of liquid profit because they just need to build one thing for one person and it works really, really well.

• 56:58Patrick O'Shaughnessy?

  Maybe Craigslist belongs on the list or something.

• 57:01Speaker 02

  Yeah, for sure. I think there's a lot to be said about something like Craigslist in that regard. And they dodged a lot of bullets by keeping it simple. And the flip side is imagine if Craigslist had VCs. Imagine the ways in which it would have gone wrong. Because they would have been pushed for growth, pushed for growth. They would have added features. They would have gone through pointless redesigns and rebrands and all that. And they probably would have honestly lost their way somehow. So I can't comment on Bitcoin specifically. I always feel that just I'm undergone in terms of knowledge. But I do think a simple thing that loads will use the same way is a really nice place to be. But I can't help but feel the wolves would be constantly at the door trying to copy exactly your thing because it looks so easy.

• 57:36Patrick O'Shaughnessy?

  Oh, what's interesting is when you think about the different counterexamples, maybe Twitter is an interesting one here. Not that it's ever been a great business, but it's more or less been the same. The product velocity is not high, so the value is the network. Or maybe the other examples would be vertical market operating system software where the software also sucks there. But the data store that gets dumped into this thing is so valuable that no one bothers to switch because oh, my whole business is built on this thing. So maybe your point is right about, you know, all software is just a database and some UI on top. And you should think more about the database either as a network of people or critical information that a business is storing inside of it. And that's it. You just invest in one of those two things and avoid everything else.

• 58:19Patrick O'Shaughnessy?

  Avoid workflow, avoid nice UIs. Like you said, it's a fascinating question.

• 58:24Speaker 02

  It is. It's unfortunately when we have to spend our lives trying to wrestle it. But yeah, I don't want the wrong way to press all this as guys thinks UI doesn't matter. It absolutely is not the case. It just does not think neat UI is a sustainable position. And to make it sustainable, you have to be maniacally investing in it all the time to keep it as the Michelin star project management app or whatever it is in order to stay at the top.

• 58:45Patrick O'Shaughnessy?

  You have other worldviews that would be spiciest at a dinner table conversation or something to rile people up?

• 58:51Speaker 02

  Geez, yeah, I have a whole folder of things that Des can't tweet.

• 58:54Patrick O'Shaughnessy?

  Give us one or two.

• 58:56Speaker 02

  Here's a hot take right now that I've seen a lot of my own portfolio go through. And it's not a positive one, but I'm sure you see a lot of investment reports that go like this. Say we're a series A or series B. We've managed burn, we've executed a riff. We now have 74 months of runway and we feel really good about that. And what I translate that to is I am going to piss away six years and two months of the best productive years of my entire career. Pushing this boat up a hill to see if it gets to the top and it's not going to. And honestly, I want to reach out to the founders and connect to them on a human level to say, hey, neither I nor I don't think many of your VCs who are actually good humans want to see you do that.

• 59:37Speaker 02

  I think you should set yourself a time limit of like six months or a year to get this thing growing. And if it doesn't, wrap it up. It's not because I want the money back. It really isn't. It's just, it's such a waste of human capital. I think what happens in an existential crisis is people try to preserve their life. And you have to realize your life is not your startup's life. And there's a line between them and the worst thing that happens to you isn't that you go out of business. The worst thing is that you stay in business banging your head off a wall.

• 1:00:05Patrick O'Shaughnessy?

  Waste your time in your life.

• 1:00:07Speaker 02

  And in both cases, I'm not seeing my money back, but at least one of them, you're not emptying the best years of your career. And I think that's probably one where even as I say it now, it sounds still too blunt. But the point I don't think is talked about enough right now. There are a lot of companies that are dead by definition, based on their last round or their last two rounds valuations. And honestly, I don't think that suits anyone. I think even from a venture capital perspective, I think they'd rather take the scraps or the pennies on the dollar back. They'd probably back the founder again, just under different market conditions. Twenty twenty one was a hell of a drug. We all lost a run of ourselves and that's okay. You don't have to pay the full price of that. The last thing I always say to people is you have to measure the ROI of the time you spend based on the future expected value of it.

• 1:00:50Speaker 02

  So go on a rager with your friends and wake up hungover and that's fine. As long as you feel like those friendships and those memories are going to be useful to you in the future. Great. Similarly, whatever you're doing with your business, think about it from a point of view of when you're like fifty or take your age and double it or whatever. Will you value the things you have been doing right now? And a lot of this is my way to get people to stop scrolling Instagram or TikTok, whatever. Cause this is not a useful use of time. But I think it's quite easy. I say this because I probably regret most of my twenties in this regard, but it's quite easy to go through life. Just too much in the moment. So you forget to actually think about the compound interest of the time you're spending. And I think by the time you realize that that shit matters, you're like thirty. And I'm haunted by this quote that is, I read it somewhere. Inside every eighty year old is an eighteen year old wondering what the fuck just happened.

• 1:01:35Speaker 02

  And I think that's something that pawns my mind. Yeah, it is scary because I'm forty two. So I'm already starting to wonder what happened.

• 1:01:43Patrick O'Shaughnessy?

  I've so enjoyed talking to you from the early days of when you were conceptualizing Finn and it's been such an interesting example for me to watch of really talented team and company approaching something new that could be this disruptive innovation story, but in many cases, yours included. I don't know. It's always different and it's always interesting to see how companies handle this. I've so enjoyed our conversations in this one, too. I asked the same traditional closing question of everybody and I'm bummed we're out of time. What is the kindest thing that anyone's ever done for you?

• 1:02:11Speaker 02

  I'll try and stay emotionally neutral as I tell this story. I wrote about this story on my blog, but when I was growing up, it was nineteen eighties Dublin, not a very rich place. My dad had left home, my mom was left. I'm the youngest of seven children and I had three older brothers, three older sisters. And in nineteen eighty eight, when I was seven years old, I basically was obsessed with this computer called the Amiga five hundred by Commodore. I don't know if you remember it. And one of my friends, when I was just seemed like the coolest thing ever. And I just wouldn't shut up about it. Clearly, I just wouldn't shut up about it. And I guess I was still just at that awkward age where I was too young to work out why I couldn't have one. Because basically, my friend had one, therefore it was gettable. So why didn't I have one? And I guess my woman didn't know how to explain. Purnas nineteen eighties Dublin divorce, well divorce wasn't even welcome in Ireland at that time. So it was just, why is this woman not got her husband anymore?

• 1:02:57Speaker 02

  That was how Catholic Ireland would have seen it. And then one day for my birthday, I think it was my ninth birthday. I'm not even that sure. I think it was my ninth birthday. It showed up. And at the time, I was really thankful. But it was only really when I was like twenty five or something like that, that the actual penny dropped off. Like, how the hell did she pull that off? And because of genuinely, I can give you a full lineal history because of the Amiga five hundred. I learned Amiga Workbench. I learned Amos. I learned how to program bits of memory. I enrolled in computer science. I met a guy, we started a blog. I met Owen, who's the CEO of Intercom. I met my wife at the same meetup that Owen had organized where I met him for the first time. The entire history of my career goes back to that one machine. I still don't really know how she got the money to get it for me, but she did. And that was the kind of thing.

• 1:03:43Speaker 02

  I only hoped I could pay her back, but she passed away unfortunately. So whatever costs like seven hundred Irish pounds, which is probably like a grand or something like that in dollars. I would have loved to have, now that I've got some money, I would have loved to have paid it back, but that opportunity was never presented.

• 1:03:56Patrick O'Shaughnessy?

  It makes me, it's really interesting to think about what equivalent thing could you do to unlock a path like that for somebody else?

• 1:04:04Speaker 02

  I think about that a lot.

• 1:04:05Patrick O'Shaughnessy?

  I think that is an incredible question to think about. And probably more often than not, it's something that's maybe doable. It's the thought about what it could be and then going and doing it is a magical, wonderful, awesome story. I've done a lot of these, I think around four hundred or something, and I haven't heard a story quite like that in response to the question. So what an awesome place to close. Thank you so much for your time.

• 1:04:27Speaker 02

  Thank you, Patrick.

• 1:04:29Patrick O'Shaughnessy?

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