Pod Insights Y Combinator Podcast YC Partners Answer Your Questions | Office Hours About this episode Every founder faces moments where they’re not sure what to do next — such as how to go to market with AI products, when to pivot, and who/when to hire.
In this episode of Office Hours, YC partners Pete Koomen, Brad Flora, Nicolas Dessaigne, and Gustaf Alströmer answer real questions from founders and share stories about how great teams build conviction, learn faster, and make better decisions as they grow.
Transcript 0:00 Speaker 00 Two of the really hard questions you have to answer as a founder when you're getting started are who am I selling to? And how do I get their attention? And those are like the two big magic tricks that every founder has to pull off.
0:11 Speaker 01 When I came to YC, I think I had to untrain myself for a couple of years of like all the learnings I had and how they were not applicable to startups. If you have a lot of time to think about this question, it's probably too early.
0:27 Speaker 02 Welcome to another episode of Office Hours. Today, we're going to respond to questions from the YC community, starting with several about AI go-to-market advice. Here's the first question we're going to take a look at. If you're building an AI company in a legacy industry with a long-term vision of fully automating everything using agents or LLMs, but you can't deliver that on day one, what's the best way for you to go to market as a startup?
0:53 Speaker 01 I think that there are three types of companies. If you're going to bring AI to your legacy industries, let's take, for example, the accounting industry. So the three ways you can do it, you can either build an AI software company that you sell to accountants. The second one is you can start your own accounting firm and it's sort of full stack or does everything. Or third, you can try to buy an existing accounting firm. There are pros and cons to all three of them. The most common one is the first one. This is how most YC companies do it. They will try to understand the world of accounting. They would try to figure out what are the areas within accounting that are most valuable to go after when you're building AI software that is also reasonable to build in the first, I don't know, couple of months or first six months of the time of your company.
1:37 Speaker 01 And then they try to sell that service to the accountants and they are not supporting all the other features and all the other things that accounting firms are doing, but they're doing that thing really well. And that tend to work pretty well as long as the thing you're doing is valuable enough for them to buy. The second option is to start a new accounting firm. There are a bunch of waste companies to do that, too. The biggest challenge is the lot you have to do as a company to do that. You have to probably do taxes and closing the books and doing a bunch of less common things, but you still have to do if you want to take on that role. The challenge here is you probably have to have an accountant on staff, or maybe several, to be able to do all this stuff. And you'll have a lot of manual work.
2:17 Speaker 01 So if you do it this way, the thing that you would track is the percent of the work that is automated. You want that percent to go up over time. The third way is to buy an accounting firm and then try to ingest AI there. The good news is you get customers that already exist. The difficult thing is you're changing the culture of an existing company. If the bigger that company is, the harder that will be. I'm not sure I've seen a lot of waste companies try the third thing. The most common one has been the first one, and the second one is like a close second.
2:46 Speaker 02 With the second one, you mentioned tracking how much of the work is being automated. Do you have any thoughts on what they should aim for? Or how they can force themselves to do it? Because what I've seen is when companies have tried this, they get so bogged down in just the execution of all the work that comes in, and being a successful accounting firm in this example, that they never get around to shipping the automation, or it becomes the second thing that they have to think about every day.
3:12 Speaker 01 I would say this is where software founders are the most powerful, because they can see, look at all the works that you do, and they can try to figure out what of this work is going to be easiest to be automated right away. Versus someone who is an accountant founder, who's not a software founder, might not see that in the same way. So it helps to be a software founder in that category. The second thing I would say is you just have to find a metric. And the failure mode I've seen here is basically it works, and you try to scale up revenue too early. So say you automate 20% of the work, and 80% is still manual, and then you're trying to scale up the company. And you're hiring like 20 accountants, and then 30, and then you're actually manning an accounting firm with some software.
3:56 Speaker 01 That's just not recommended, and it's going to be difficult to manage at times. And at MB, we had this metric, which was percent of technical people that work at the company. And the reason we had the metric is At some point, you have too many non-technical people. All they do is request things from the technical people. And then you can't get anything done. So you need a certain, I think, forgot the, so it was 30% or something like that, was counted as technical. It's a framework that I find helpful because then you could make sure that the number of technical people in the company is always enough. You can continue working on automation while you're also trying to do the other stuff.
4:28 Speaker 03 How about creating a forcing function? You have one accountant and you cannot hire more.
4:33 Speaker 01 Yeah.
4:33 Speaker 03 Then you get more business and you have to figure out how to scale with AI.
4:37 Speaker 02 I think your point about like having a number, I don't know how they did it exactly at Airbnb, but that's like apparent and visible to people. Maybe that helps kind of put a foundation on the culture of the business of like, this is really important to us. All of us are looking at this. We want this number to go up.
4:52 Speaker 01 And if I was to say a C R A investor and that would mean one of these companies, I actually care more about the trajectory of the automation rate than the overall revenue. Because like, yeah, there's many accounting firms out there. If you start an accounting firm, you make a lot of revenue. Like, does that mean you can raise a C R A by software investors? Like, not really. That's not exactly the thing you're proving. You're proving that you can actually write software to automate the much of the tasks. So I'd rather have a slower growing firm or company where there is just more automation and there's a clear track record of them doing more automation or using more software every month.
5:24 Speaker 00 I worked with a company in the previous batch, the spring batch, which was just now wrapping up called Vessence that are building software for lawyers. And neither of the founders has a legal background. And so the way that they got started before Y C was they found a large law firm in Stockholm that was so excited about the idea of using AI software that they let the founders work out of their office for several months. And that's how they built their M V P, which seems like a really great path for founders that are excited to work in a legacy industry, but don't have as much personal exposure to it.
5:58 Speaker 01 Yeah, I think also if in the first category, when you sell to, you sell to, say, an accounting firm or a law firm, you want to find law firms, accounting firms that are where the founder or decision makers really, really bought into helping you and getting things done. And sort of like they themselves are empowered and incentivized to increase the use of software in their companies. And it's hard, like that's not easy to do for your first customer.
6:21 Speaker 00 It's like the stage even before the early adopters in the crossing the chasm model. It's like the ones who will adopt it before it even exists.
6:28 Speaker 01 Yeah. I think a common thing here is just doing really good. So like as you're reaching out to customers, have like a lot of pre qualification or qualifying questions that sort of qualify someone in or out from being that person. Like, do you know how someone who's going to be highly incentivized, kind of excited about doing new software, an early adopter, can you figure out how the pattern of those people look like early on? I think that's pretty important because otherwise you can drag into enterprise sales or drag into sales, it'll take forever and you actually don't know if this people are going to be adequate or not.
6:58 Speaker 02 All right, let's take a look at another question. Enterprise AI plays like Viva or Palantir can take time. There's not that many buyers, the sale cycles can be really long, adoption of new products can take a great deal of time in those markets. Meanwhile, investors are impatient for growth. They want to invest in companies that are growing very quickly. In today's AI gold rush market, should companies start out in the mid-market? Should they think about that being the initial place to go where the, maybe the TAM can be a little bit larger, maybe where the sales cycles can be faster? How should they be thinking about time to grow versus long-term defensibility? I'll take a swing at this one. So I think early on, and this is assuming that the company asking this question is
7:43 Speaker 02 like fairly early on, the most important thing is the pace of learning. How quickly you're learning what the customer wants, what the user needs, what their problems are, what the really pointed pain is versus the more dull pain that they're just kind of tolerating. And so oftentimes when I meet with a company that may want to go super enterprise from the get-go, which these enterprise software companies are not so different from like the moonshot space things we fund sometimes, where, you know, putting a satellite in orbit is not so different from landing like a half million dollar ARR contract with a big company. And so if you kind of think about it that way, you know, the advice to the space companies is always, well, let's find something smaller that we can wrap our hands around and be really successful with.
8:26 Speaker 02 It's the same for a software company because it'll help them learn a lot faster, get better feedback, and also put themselves in a better posture as a startup to make changes, iterate, ship product, talk to users and so forth. Whereas if you go straight after the big guys, unless you have some special in from the get-go that'll help you short circuit that sales cycle. You're just not going to learn as quickly as the other companies that are interested in that industry.
8:50 Speaker 03 I would maybe a caveat here. I think there is two categories of companies. There's the companies like even my company, Algolia, that kind of like that kind of grows over time, starting with the long tail of customers. And then as you, the product matures, as you can address more use cases, you go up market and you end up with this multimillion dollar deals. But on the other hand, sometimes you have a company whose the problems they are trying to solve is only an enterprise problem. If they are trying to sell to their batchmates or small startups, I mean, they don't have the problem. So they don't have a choice. They have to go to start high. Now there is like difference between mid-market and enterprise. That's very fair. And probably you want to go after the smallest company that has the problem you try to solve.
9:32 Speaker 02 Or sometimes it's reducing the scope dramatically. Maybe you can land with like one or two users that work at the enterprise and get something that's useful. It's just a much more narrow product, maybe is the way to get in and have a shorter sales cycle.
9:46 Speaker 01 I would say in addition to picking the right segment, which sounds like mid-market is probably the better one or most more promising one, is to qualify the people you're selling to really well. Like it's this, a lot of part of industries are trying to buy AI software now. Enterprise, mid-market, small companies, everybody is interested in this. But you still have to qualify the individual, selling to a person or a team. You have to know that they are empowered to make the decision, that they have incentives to make these decisions and the consequence of buying the software. And you have to be able to meet them and kind of talk to them and like make sure you have like an actual interaction with them. A lot of founders, so I think of like segment before qualification. And I think sometimes just the right person is more important, as long as they are empowered.
10:30 Speaker 01 I've seen buyers at mid-sized companies move really fast if you find the right person. But enterprise, the challenge, like you said, is like feedback cycle is slow. They make slow decisions and then founders can get bogged down in like a multi-month. Like sales cycle, you actually don't know in the end if it's working or not.
10:45 Speaker 02 Thank you so much for that question. For our next question, the founder actually submitted this over video. So let's take a listen to that.
10:51 Speaker 04 Hey, should I hire a growth hacker, a communicator, a sales, an SDR, et cetera? Or should I try to replicate those employee by AI employee? I know there's a lot of solutions, but I'm still not sure what's the best approach here.
11:11 Speaker 02 All right, what do we think about that question?
11:13 Speaker 00 So I'm pretty bullish on AI sales software. But I will say that AI SDRs tend to work well when they're plugged into a sales process that's already working well. And where I haven't seen them work well is when founders sort of turn to an AI SDR as the solution of last resort, where they're just totally unable to sell their product. And they think maybe AI can solve this problem for me. And I haven't seen that work. I think the hard work of figuring out how to sell the product is still very much on the founder.
11:45 Speaker 01 When I came to Y C eight years ago now, I spent the last five years working on growth at a consumer company. And I think I had to untrain myself for a couple of years of like all the learnings I had and how they were not applicable to startups. And I think a lot of the tools people are building, it fit great into like bigger companies as things are working. And most of the growth advice is the same for consumer companies. And they don't really apply to startups. So if you haven't figured out things are working, if you don't have hundreds of customers, like it's unclear if it will work. And if it's working, it might be working in the wrong direction. Sort of like it's sort of like you have to, you do a bunch of progress that you actually doesn't teach you anything.
12:26 Speaker 00 Two of the really hard questions you have to answer as a founder when you're getting started are, who am I selling to? And how do I get their attention? And those are like the two big magic tricks that every founder has to pull off in sales. And once you know the answers, it's, it's a lot easier to point an AI SDR or an agent to help with that, right? There's a lot of schlepp work. Once you figure those things out and to find these people and to get their attention.
12:51 Speaker 01 But actually figuring out how to do those things, the AI has not actually been that helpful with yet.
12:55 Speaker 03 The funny thing is that you can actually adapt that advice for the AI ISDR companies. In the sense that if they go after the people who are not able to sell their own product, so there's a little chance that they can do better. And these customers are going to churn. A lot of revenue fast, but mostly churn.
13:13 Speaker 01 And I think that has been the learnings, actually. The ones that I know, that we've funded some of these people, is that they will probably tell you that when we sold to startups, they were churn. Like that is the real learning that they have.
13:23 Speaker 03 For them, the goal is to find the people who have a good product, who can sell it.
13:27 Speaker 02 Who have figured out the magic trick.
13:29 Speaker 03 Exactly. And then they can scale that with AI.
13:31 Speaker 02 Yes. It's funny, it's not so different from the advice I think we've been giving founders forever about hiring the first salesperson, which is it's almost always too early to hire them unless the founder has already figured all this stuff out, how to get attention, what are the objections we're going to get? And so the same advice of like, you want to hire a salesperson when it's an execution-focused experience and they're just kind of running the playbook that you've already put together. It's probably that times 10 with these new AI ISDRs and some of these new roles.
13:58 Speaker 01 And I think that founders should be curious enough to learn all of these jobs before they like scale up or really try to hire these teams because VP of Marketing is like notoriously high churn job. Because it's not because the VP of Marketing folks aren't good, it's because the founders have the wrong expectations of what those people do. And they haven't been curious enough to learn about that job. And it's like, well, harder to measure than engineering or something else. So I'm a huge fan of like founders being curious and really trying to learn the job first before they hire a bunch of people.
14:29 Speaker 02 That's a great question. Thanks for asking that. Okay, here's our next question. Should we, startup founders, spend money aggressively now to gain a temporary edge? Or wait for the next model leap? And try to make what we're doing free and more accessible?
14:45 Speaker 03 That's a good question. I think there's maybe two aspects to the question that I'd like to address. The first one is, as these founders, they should ask themselves. Am I doing something, building something that's going to be irrelevant once G P T five is released? Or am I doing something that's going to become much better once I can leverage the new A I models? And so, of course, if you are just building something that's solving for the pains that G P T five is not yet solving, it's probably a bad idea. Now, let's say you are in that second category and the improvement of models are just going to make your own product better. Should you wait or should you invest and work on it?
15:25 Speaker 03 I would argue that if you do invest on it, if you do, you're going to learn a lot from the process. And once the models are going to be ready, you plug them and your product is going to be much better day one. So indeed, you have maybe wasted, I don't think that's the right word, but maybe overspent a little, but the learnings are worth it. That's why you do it.
15:43 Speaker 01 We had these experiences this year with Cloud Sonnet, right? When that model came out, a lot of companies that were building, say, internal tools were like suddenly working and they weren't really working before. So we've already seen this before.
15:55 Speaker 03 Cogen is a great example, like all these Cogen tools, like they were barely working. Yeah. And with the new models, magic.
16:03 Speaker 02 All right. Thanks for that question. All right. For our next questions, we're going to talk a bit about pivoting. We got several questions from founders about that. First up, when should you consider pivoting if you've got some traction? A lot of times when we think about traction, it's, oh, things aren't working. You're stuck. We need to pivot. But what if you have some traction, but it's not strong enough? And you wonder about that. When should you consider pivoting in that situation?
16:26 Speaker 03 Probably the most difficult situation you can be in, right? If it's working, it's working, easy. Not working, obviously you have to change something. I can see one of my companies who went through that exact journey, Firecall. So Firecall is a way for companies to, it's a simple, open source product that can help a lot of companies to extract data, to extract information from any website. Super successful right now, a lot of A I agent customers and so on. But before that, they were working on another product called Mendable. And I think that when they actually pivoted, they already had hundreds of thousands of dollars of ARR. So significant traction, like not like just, you know, 200 bucks, actually real customers and big logos too.
17:09 Speaker 03 At the time, they were doing some Q and A on top of documentations. And I think in their case, what they saw is that their growth was relatively slow. They were seeing all of this need from the market on the side. And what happened is that as part of building Mandible, they ended up working on the crawler. Because they didn't have, they couldn't find any tool out there that could solve their own problem. So they built it for themselves. And as they were chatting with other founders, they realized that everyone, every AI agenty company needed that crawling function. And they ended up realizing that their product, the niche thing inside the bigger Mandible thing,
17:54 Speaker 03 was actually way more valuable than Mandible was. And so they experimented a little, it's not like they moved from idea A to idea B overnight. They experimented with that component and it took off. And rapidly they decided, okay, that's the company. Big leap of faith, like hundreds of thousands of revenue, then you start from scratch. But it worked out so well for them.
18:20 Speaker 02 Was there like a moment or an observation that they had that led them to observe that this is the subset of our product that we should bring to the fore?
18:29 Speaker 03 I don't think it's a, you know, formula. I don't think there is an algorithm, hey, if A and B's and do that. I think it's more like a deep conviction you build for yourself, coming from a lot of conversations you are going to have. I mean, in a way that happened to us at Agolia, when we started our very first product was a An SDK that people could embed directly in their mobile app, like on device. And we got some revenue, like nothing compared to Mendable, but like a few thousand dollars a month. And we kind of like get that feeling that it was very hard sales. Like things were slow, people were not really valuing the product. And at some point we realized, okay, like it's, we could be the lifestyle company, but it's not what we want to do here. Let's change something.
19:14 Speaker 03 That was easier for us because lower revenue. But for Mendable, I think it's more like it's a company, it's founders that are very well connected with a lot of peers. And I think that's what gave them that internal conviction to actually change something.
19:26 Speaker 02 I love something that you said there, the question of like, are people really valuing the product? I think that times when I've been in office hours and maybe sometimes we fund people and they already have a little bit of traction. And they come in and as we talk to them and kind of learn about how they talk to their users, we find out that they're not talking to their users or they don't really know much about them. And so there's, you know, kind of a gentle urging the founders, maybe not so gentle sometimes, to go and find out how much they value the product. And oftentimes they don't value the product very much. And so that's great fuel to encourage people to dig deeper and find a better version. An example that I worked with was Greptile from the winter 24 batch. When we funded them, they had a few thousand dollars of MRR and they were feeling awesome about this, right?
20:10 Speaker 02 They went from zero, they had dozens of customers paying them, the numbers were going up all the time. And I just kept urging them to go talk to the users and try to find out which ones really valued the product, like you said. And I remember they asked straight up in an office hour, shouldn't we just grow this number? Like just, let's just make number go up. Why do we need to do all these, these interviews? And after they talked to enough folks, they realized, oh, like no, no one person is saying the same thing about our product. There's, it's just kind of a disorganized relationship between what we're making and how people are interacting with it. And we need to get it more organized and really focus on one or two of these. And they've been able to do that and grow, but it's about, are they valuing the product?
20:52 Speaker 01 One more thing I think about pivoting is that it's a very vulnerable step in a company. It's, it's one of those moments where companies fold and they just give up. Be like, ah, we've tried two, three things, we're just gonna give up, nothing is working. And when you are asking yourself the question, should we pivot? You have to also be pretty certain that you have the energy to do the pivot. Because the pivot is a, is a hard one, because you have some existing stuff that you built, you poured your months or years into something, it's not working, and now you have to do something else. So you have to first be like, okay, I have the energy to keep going and trying to do this. And it'll be an uncertain time for a while. So you have to build a conviction, you have to have the energy. And I think there's, there's frameworks that we can help with on, on how to do it.
21:33 Speaker 01 But at the end of the day, it's sort of like, it's easy to give advice in the pivot, but you're literally starting from scratch. And it's very hard for us, a lot of founders come to us and be like, this is a bad idea, or this is a bad idea. And, and I'm like, yeah, if you give me seven ideas, I will give you my subjective opinion of which one is better. But I don't know, I'm not the customer of any of them, and you should go and validate the ones that you think are the best ones. The harder one with pivoting on the framework side is to be like, here is our new idea. And they come with one idea. And if I say, no, I think here are all the reasons why it's a bad idea, then get founders to get demoralized and be like, well, then I don't want to work on anything. Cause like, the one idea we had wasn't good, you don't think it's good.
22:10 Speaker 02 Our user Gustav doesn't think it's good.
22:12 Speaker 01 Yeah, or like, or, or just like, the feedback of the Gotha people is that this isn't good. So it's much better when you're pivoting to have a range of different ideas exploring. So, so like, you can find conviction around something and be fine with throwing out a few of them. So that's one framework, because it relates to sort of like finding a better idea, also relates to sort of like finding the motivation.
22:29 Speaker 00 I worked with a company in the fall batch that spent the entire batch sort of wondering, should we pivot or should we not? And they had a few thousand dollars in MRR and they'd been working super hard on sales and it just wasn't turning into anything real. And they ended up pivoting right after the batch ended. To a like open source billing framework called Autumn. And they have far less traction now than they did in terms of dollars. But what they do have now is conviction and you can just hear it in their voice. And that's like, it feels like the higher order bit where the actual leading indicator that maybe you should pivot is you just stop believing that what you're working on is going to work out.
23:04 Speaker 02 All right. Thank you so much for that question.
23:06 Speaker 05 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.
23:20 Speaker 02 Okay, for our next question, a founder asked, when should you kill a good startup idea to find a great one? And furthermore, like, what is a great startup idea anyways?
23:30 Speaker 01 It's a hard question to answer because you don't actually know that whether it's good or great in that moment usually. Like, you don't know if a great startup idea is great until you've truly gotten people to give you the feedback to say that it's great. So it's just hard to know that. And I think it's more like a hypothetical question, assuming you have all the answers at the time. And to me pivoting is a process, it's like frameworks and process and you do work. It's not like knowing all the answers in the moment of asking the question.
23:57 Speaker 03 It comes back to what we're saying about conviction. Like, I think the good idea may be the one where you have a little bit of revenue. But, you know, maybe that's nice to have. And the great ideas, the ones that you're so convinced because you get these customers like who need you every day to solve a real pain.
24:13 Speaker 01 In my early days of tinkering with startup ideas, I had many great ideas. But they weren't so great enough there all because I wasn't actually building anything or weren't showing it to any customers. So in the end, they weren't great.
24:23 Speaker 02 I kind of wonder if there is such a thing as a good startup idea. Like, I feel sometimes like they fall into two buckets. There's great ideas and then everything else, which like isn't going to yield a huge company, which so technically they're bad startup ideas. Anything that's not a great startup idea is the opposite, which would be bad. And so I think, you know, if you're like, oh, this is a good startup idea, you need to really be like pushing on it for signs of greatness. You know, like you find a rock and you're like scrubbing it to see if there's a diamond in there. And if you're just like, look at my rock, look at my rock all the time, it's not enough. You need to really like put it through its paces and that's like being aggressive on like sales, it's being aggressive on what's the what's the wackiest version of this we can build in a couple of weeks and see how people react to it and just always testing for signs
25:08 Speaker 02 of greatness.
25:09 Speaker 03 And be honest with yourself. Like it's easy to believe something is great, but actually like you believe so much it's great without any confirmation. You don't want to listen to anyone who is going to tell you anything else.
25:19 Speaker 02 Yeah, I think this is a really important question. I was asked this morning, I did a phone call to make an offer to do the summer batch with a founder and it was great. All those calls are always really fun. And they asked me afterwards, what is the difference between like the tip top best performing founders during the batch and the others? I thought about it for a second and the thing that came to mind was the really great people are obsessed and really focused on finding a great startup idea and making sure that they're on that path and working on something that could get really big.
25:49 Speaker 01 And they probably don't think it's great. They don't say that for a long time.
25:52 Speaker 02 Yeah.
25:53 Speaker 01 Like, like I have a great idea is not something that founders who have great ideas say generally.
25:59 Speaker 02 Right. The canonical Steve Jobs thing is like, here's a dopey idea, right? He would say that even though maybe he thought it was great, but yeah, they don't talk about it that way. All right. Thanks for that question. For our next question, we're going to talk about technical challenge. So is it ever wise to pivot away from an idea because it's proving too technically difficult to build? For example, maybe the idea is validated from talking with potential buyers and you think the product would sell really well if you can just build it and it works well, but you can't seem to get it to that threshold just yet. How should founders think about that situation?
26:30 Speaker 03 Well, actually it's the opposite. Like if something is really hard on the technical side, I mean, I think that's an even better idea. Like nobody else is going to try, right? If it's hard, like the bar is so high, nobody try and nobody does it. If you have the courage, the courage to actually do it, if you have the skills to do it, I mean, that's the best idea for you ever. So definitely go after it. I had this experience this batch that was a, I mean, crazy enlightening for me as a team that was building some software for
27:02 Speaker 03 Science things that was kind of working but not exciting that much like the market was not taking. And then it pivoted to a company called Bramante Biologics. And they are building micro factories to manufacture drugs in a in small volume. And the more we discussed about it, you could see their eyes light up. You could see how convinced they were that it was the right thing to build. And each time we were discussing about it, we were seeing all the challenges panning up. One after the other on the tech side, on the regulatory side, like so many things to to overcome. And you could see like, yes, let's do this. Like the world's needed, we should definitely do it. I mean, I love that energy.
27:47 Speaker 03 And that is probably one of the most difficult I've seen a company try to tackle. But if it works, it's going to change the world.
27:54 Speaker 01 What if it's a software difficult idea like it's like it would take, you know, for a fact it'll take you and your co-founder six months to build it. Like, how would you push that?
28:03 Speaker 02 I think sometimes when you run into that, you can think about ways to reduce the scope a little bit. With my own company, Perfect Audience, from years ago, we knew that we needed to build our own real time bidding platform that was connected to all the ad exchanges and all these integrations. And it was very overwhelming. We didn't even have any idea how to build it. So first, we found another company that had one of those products and an API. And we built a great front end for it, like the best front end for controlling these types of things that anyone had made and took that to market. We built like a custom billing system and that got us a bunch of users and helped us like get going and get in motion. And eventually we had the understanding and the wherewithal and the connections to then go hire the people to do the really hard technical stuff.
28:47 Speaker 02 So we kind of, you know, were able to chop it chop it out into pieces that we could tackle that way.
28:52 Speaker 03 You have to be careful, like that's a question that is a little dangerous because some people could use that as an excuse to just work on their idea for six months.
29:00 Speaker 01 That's true.
29:01 Speaker 03 And stay kind of like in their garage or somewhere. It kind of happened to us at Agolia. I mean, it took us six months to get the product in a good shape because it was like, like low level, like, like search engine, actually pretty difficult technically to build. And we, I would argue, we were lucky that it actually ended up working really well. But if I were to go back, I would spend so much more time with customers. Even if the product is not ready, you can learn so much from learning what's their problem, like living their life. And so I would do that much better now. So yeah, as long as you don't use that as an excuse to not speak with your customers.
29:37 Speaker 00 At Optimizely, the hardest part of building our first product was building this website editor that would work with any other website and would allow a non-technical person to go in and build an A/B test without writing code. And that ended up taking us at least six months, I think, to build. But the way that we cut the problem down into something smaller was we built the first version for ourselves. So my co-founder Dan and I started with just the simplest thing, which was a little bookmarklet, actually, that would pop open a little text field on any website that either he or I could use to just write JavaScript manually that would get run on the website. And that was just enough for us to go in
30:21 Speaker 00 and get some consulting contracts to build A/B tests for other companies. And so that was how we ended up talking to customers, was we just build the jankiest possible version for us before building the public version.
30:33 Speaker 03 At least you knew where, like what you're building and why.
30:36 Speaker 00 Yeah, it turned us into our own users. I mean, it was super useful.
30:39 Speaker 02 All right, thank you for that question. All right, so for our next question, a founder asks, "What are some guidelines or metrics to know whether to start hiring for your startup?" And I assume this means like hiring people beyond the founder's early employees. When's the right time to do that? And how do you know and how do you tell?
30:56 Speaker 01 If you have a lot of time to think about this question, it's probably too early.
31:02 Speaker 01 If this is something that comes to mind every day for you, it's probably too early because it's the right time to hire when like things are so busy that you can't even find a slot in your calendar to do an interview with a candidate. So when things are coming at you because things are starting to work. You're sort of like at the breaking point. Things are breaking. Like, you have to work way out of like the normal schedule, or your employees have to, or your co-founder have to work way out of your normal schedule to even accomplish the things you're currently doing. Then it's the right time to have started. Now, the question is like, how easy it? Because it takes a while, right? So, that's probably too late in a sense. Because you have probably three months before that person is starting. And then things are going to break even further. So like, what do you guys think?
31:46 Speaker 01 It's like, is an early indicator of that moment? Is there's a specific thing in the company that's breaking or about to break? It's either engineering, or it's sales, or onboarding. It's like, specific things are breaking. And if you have early indicators that they're breaking, and you have to be honest with yourself, are these early indicators? Or are they just my hopes? That they're going to be early indicators? You have to be honest with yourself that this is actually going to happen. That sounds like the right time to start interviewing at least. Or putting at job recs. The hardest thing about startups is that it is really hard to hire. You're not particularly competitive as a startup. They just have like, two founders and a few customers. So, it's not going to happen right away. You're going to start with your personal network. And often, the first couple of hires are people that you, they already know your thing.
32:29 Speaker 01 You can only kind of convince them to come in and meet with you. You think you can convince them to join you because they already trust you. So, a lot of these early hires, aren't people they hired cold anyway? They're people that sort of like, already know it. Which actually, I think, if I think about the question of early indicators, maybe it's less of an issue because they're more standing by than you might believe, than like, a later hire and you have to hire cold from some other way.
32:52 Speaker 03 I'm trying to remember my experience at Agolia. It was kind of like the different phase of the company. Initially, we hired just a few people. Like, the right thing to do as you're looking for product-market fit. And then we got product-market fit. And then we never hired fast enough.
33:07 Speaker 01 Yeah.
33:08 Speaker 03 And then we were way larger. And then we were like, oh, we have hired too many people. So, there's these three phases. Like, the pre-product-market fit. Okay, let's, only a few people. And then, and then we waited too long to actually hire. And we ended up like, we had like, nine people, 1.2 million AR, before AI.
33:26 Speaker 03 And our life was a nightmare. But when I remember that time, I remember about it like very fondly. It was probably the best time with the company still, even if it was so hard.
33:36 Speaker 01 It seems like hiring is one of those things that you can grab on to and be like, "My company is successful because I've hired people." And it's a dangerous thing to start thinking about hiring as a success metric. Hiring is not a success metric at all. It's sort of like a way to not go under or have a function in a company fail.
33:53 Speaker 03 I think that's changed now. We see so many companies being proud to reach some kind of revenue with as few people as possible.
34:00 Speaker 02 It's cool.
34:00 Speaker 03 That was not the case 10 years ago.
34:01 Speaker 02 It's a meme now. It wasn't a meme.
34:03 Speaker 03 10 years ago, it was like number of employees was the metric people were.
34:06 Speaker 01 We have multiple ways to companies to say we want to be a billion dollar, 10 person company, and we have six slots less left.
34:12 Speaker 02 We're far from the days of I need to hire enough engineers that I can flip this to Facebook for a certain amount of money. That's gone.
34:20 Speaker 00 When I have founders that are working in the batch who ask if they should hire, almost always the answer is no, right? Founders will make the mistake of thinking it will speed them up, but in reality, it ends up doing the opposite. But the exception to that rule, I call these opportunistic hires, where it's your smartest friend happens to have graduated last month, right? Or left his or her job or whatever. And you know that they, you know they're going to work well with the team. You know that they're incredibly good. And you bring them on because it happens to be the right moment.
34:53 Speaker 02 Yeah, and I would just qualify and say that those opportunistic hires are great when there's a superlative involved, like smartest friend, best, greatest. When it's, you know, worked at big company X that is impressive or something like that. Like, those are not opportunistic hires. Those are bad hires.
35:09 Speaker 01 Like, if you think they were great, but you're not to be sure, that's still dangerous.
35:14 Speaker 02 Yes, probably not. All right, thank you for that question. All right, here's one last question. When is it a good idea to open source an enterprise SaaS product? What are some advantages and drawbacks of open source?
35:26 Speaker 03 I think that's an interesting question. Like, we've worked with a lot of open source companies that way, see. But most of them are DevTools. Because that's very common go-to-market when you're selling to developers who really care about their product being open source. Can look at the code, they trust them. And it's easier also when you're sell for developers because your customers are kind of like the same people, the same persona as you are. I do think, however, that it's sometimes useful for enterprise. I have that company in mind, Medplum, who is building an open source EHR. And I think for them, being open source was not about the go-to-market in sense of like selling to developers. It was really about creating the trust at their customers, like in these enterprises. And shortening the sales cycle by maybe a year
36:10 Speaker 03 for such a kind of product because it was open source, because these enterprises could trust them. So even like beyond the DevTools approach, being open source was super useful to them. They were not chasing stars or chasing a huge community, just using it as an aspect of their sales cycle. 20 is another one, where they are doing an open source CRM. So same thing in a way. CRM is pure SaaS product, not targeting developers at all. And still, some people may want to use that product because they can expand it, because they can trust it, because they can dig into code if necessary. They never do that, but just the level of trust that it generates.
36:49 Speaker 02 Knowing that they can is enough.
36:50 Speaker 03 Knowing that they can is enough. And also it helps with some silly things like compliance stuff. Like, if you're open source, you can sell a host. So no question about sending your data to some random startup on the cloud. So all of these reasons are good reasons for SaaS products to open source. And I think that it's going to become kind of like the main go-to-market for SaaS products. But I think it makes sense for some difficult sales, where there's a lot of concerns about privacy and sensitive data.
37:18 Speaker 02 Self-hosting in the world of AI seems more common than maybe in the world of SaaS.
37:22 Speaker 03 Yeah, it's all about like, are you okay to share your private data with a third party? And if people don't share, like, don't trust OpenAI, they're not going to trust the small startups just starting.
37:35 Speaker 02 Yeah, that is interesting. I feel like years ago, pre-everyone working with different AI products and building AI products, the request from a customer to self-host the product, or it was treated as like, "Oh, no, that's impossible. Like, what a crazy ask. We can't do this." Whereas now, we have a lot of small startups to find ways to do it quickly and efficiently. Even, it doesn't even come up in Office Hours sometimes. They say, "Oh, they asked for this, so we built it." And now they're running it locally, and it's great. And I think that's progress.
38:08 Speaker 03 I mean, there's some drawbacks too, like the self-hosting comes at a cost.
38:12 Speaker 02 That's right. Clear cost. You have to charge a very high price for it. That's right, absolutely. Thank you so much for your questions. If you'd like to have one of your questions addressed in a future episode of Office Hours, leave it in the comments. Thanks. We'll see you on the next episode.
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