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Category: Startup Advice
Tags: AIHiringPivotingSalesStartups
Entities: AirbnbAlgoliaBrahante BiologicsMedplumOpenAIOptimizelyPalantirPerfect AudienceVessenceYC
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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.
When I came to YC, I think I had to untrain myself for a couple of years of like all
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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.
Welcome to another episode of Office Hours. Today we're going to respond to
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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
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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?
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.
Um so there are three ways you can do it. You can either
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build an AI software company that you sell to accountants. The second one is you can uh start your own accounting firm and it's sort of full stack or does everything or third you could 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
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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 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
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know couple months or first six months of of the time in your company. and then they try to sell that service to the accountants and they um are not supporting all the other features and all the other things the accounting firms are doing but they're doing that thing really well and that tend to work pretty well um as long as the thing
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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 companies who do that too. The biggest challenge is a lot you have to do as as as a company to do that.
you have to probably do taxes and closing the books and doing a bunch of less
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common things, but you still have to do if you want to take on that role. Um, 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.
So, if you do it this way, the thing that you would um uh track is the
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percent of the work that is automated. You want that percent to go up over time.
The third way uh is to buy an accounting firm and then try to ingest AI there. The good news is you get customers already exist.
The the the difficult thing is you're changing the culture of an existing company. If the
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bigger that company is, the harder that will be. I'm not sure I've seen a lot of companies try the third thing.
The most common one has been the the first one and the second one is like a close second. >> With the second one, you mentioned tracking how much of the work is being automated.
Do you have any like thoughts on how what they should aim for or how
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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
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thing that they have to think about every day. I would say this is where software founders sort of like are the most powerful because they can see at all 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 like right away.
Um and versus
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someone who say 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 in the that category.
The second thing I would say is um you just have to find the metric and the failure mode I've seen here is basically it works and you try to scale
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up revenue um too early. So say you automate 20% of the work and 80% sell 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 maning accounting firm with some software that's just not recommended and it's going to be
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difficult many many times and the more like at 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 nontechnical people um all they do is request things from the technical people and then you can't get anything
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So you need a certain I think forgot the percent 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. >> How about the creating a forcing
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function you have one accountant and you cannot hire more. >> Yeah.
>> Then you get more business and you have to figure out how to to scale with AI. I think your point about like having a number, I don't know how they did exactly at Airbnb, but that's like apparent and visible to people, maybe that helps kind of put a foundation on
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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. >> And if I was to say a CSA 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,
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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 CSA by a software investor?
It's like not really. That's not exactly the thing you're you're proving.
You're proving that you can actually write software to automate the bunch of the tasks. So, I rather have a slower growing firm or company where
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there is just more automation and there's a clear track record of them doing more automation or or more using more software every month. 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
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neither of the founders has a legal background. And so the way that they got started before YC was they found a large law firm in Stockholm uh that was so excited about the idea of using AI software that they let the founders work out of their office for several months
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and that's how they built their MVP 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. >> Yeah.
I think also when if you're in the first category when you sell to you sell to um say an accounting firm or law firm you want to find law firms, accounting
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firms that are with 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 in their companies. And it's hard like that's not easy to do for your first customer.
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>> It's 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.
>> Yeah. I think a common thing here is just doing really good um um sort of like as you're reaching out to customers have like a lot of pre-qualification or
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qualifying questions to 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 and 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
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into enterprise sales or drag into sales. they'll take forever and you actually don't know if if these people are going to be addicted or not.
>> All right, let's take a look at another question. Enterprise AI plays like Viva or Palunteer can take time.
There's not that many buyers. The the sales cycles can be really long.
Adoption of new
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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
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that being the initial place to go where the maybe the um the TAM can be a little bit larger, maybe where the sales cycles can be faster? Uh how should they be thinking about time to grow versus long-term defensibility?
Um I'll take a swing at this one. So, I think early on, and this is assuming that the company
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asking this question is like fairly early on, the most important thing is the pace of learning. how quickly you're you're learning what the customer wants, what the user needs, um what their problems are, what what the really um pointed pain is versus the more dull pain that they're just kind of
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tolerating. And so, um, 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
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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.
It's the same for a software company because it'll help them learn a
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lot faster, get better feedback, and also put themselves in a 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
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sales cycle, you're just not going to learn as quickly as the other companies that are interested in that industry. >> I would put maybe a caveat here.
I think there's two categories of companies. There's the companies like even my company Algolia that kind of like that kind of cohes over time starting with the long tail of customers and then as
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you the product matures as you can address more use cases you go up market and you end up with this multi-million dollar deals but on the other end sometimes you have a company whose the problem they are trying to solve is only an enterprise problem >> and if they are trying to to sell to
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their batch mates 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 midm 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.
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>> 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 just a much more narrow product maybe is the way to get in and have a shorter sales cycle. I would say in addition to picking the right segment
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which sounds like midm market is probably the better one or most more promising one is to qualify the people you're selling to really well like is this a lot of part of industries are trying to buy a software now enterprise mid-market small companies everybody is interested in this but you still have to qualify the individual selling to a
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person or or team you have to know that they are empowered to make the decision that they have 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
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founders think of like segment before qualification and I think sometimes just the right person is more important uh as long as they are empowered. I've seen buyers at mid-size companies move really fast if you find the right person.
But enterprise the challenge
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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 and you actually don't know in the end if it's working or not.
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.
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>> Hey, should I hire a growth hacker, a communicator, a sales, an SDR, etc., or should I try to replicate those employee by AI employee? I know there's a lot of
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solutions, but um I still not sure what's the best approach here. >> All right, what do we think about that question?
So, I I'm pretty bullish on AI sales software. Uh, but I will say that AI SDRs tend to work well when they're
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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 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
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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.
>> When I came to YC in uh eight years ago now, um I spent the last five years working on growth uh at a consumer company. And I think I had to untrain
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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 that fit great into like bigger companies, things are working.
And most of the growth advice um is the same for consumer companies and they don't really
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apply to startups. So if you if you haven't figured out things are working, if you don't have a hundreds of customers like it's unclear if it will will work and if it's working it might be working in the wrong direction sort of like it's sort of like you're you have to you do a bunch of progress that you actually doesn't teach you anything.
>> Two of the really hard questions you
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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
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SDR or an agent uh to help with that, right? There's a lot of shle work once you figure those things out and to find these people and and to get their attention, but actually figuring out how to do those things, the AI has not actually been that helpful with yet.
The the fun thing is that you can actually uh adapt that advice for the AI SDR
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companies in the sense that if they go after the people who are not able to sell their own product there is a little chance that they can do better and these customers are going to turn a lot of revenue fast but mostly churn >> and I think that has been the learnings actually the ones that I know um that
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we've some of these people is that they will probably tell you that when we sold to startups they were churn like like that is the real learning >> for them the goal is to find the the people who have a good product, who can sell it, who >> they figured out the magic trick. >> Exactly.
And then they can scale that with AI. >> Yes.
>> It's funny. It's not so different from
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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
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execution focused e 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 AISDRs and some of these new roles.
>> And I I think that founders should be curious enough to learn all of these jobs before they like scale up or really
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try to hire these teams because VP of marketing is like notoriously high churn job because and it's not because the VP of marketing folks aren't good. is because the founders have the wrong expectations of of what those people do >> and they haven't been curious enough to learn about that job and it's like a little harder to measure than
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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.
>> 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
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temporary edge or wait for the next model leap and try to make what we're doing free and more accessible? >> That's a good question.
I think there's maybe two aspects to the questions I'd like to address. The first one is these founders, they should ask themselves,
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am I doing something, building something that's going to be irrelevant once GPD5 is released or am I doing something that's going to become much better once I can leverage the new AI models? And so, of course, if you are just uh building something that solving for the
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pains that GP5 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? I would argue that um if you do
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invest on it, if you do a lot, you're going to learn a lot from the process and once the model 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 on it all, but the learnings
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are worth it. That's why you do it.
We had these experiences this year with cloud sonnet right when they 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 we see >> cogen is a great example like all this
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cogen tool like they were barely working >> and with the new models >> magic. >> 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
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lot of times when we think about traction, it's, oh, things aren't working. Uh, 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? >> Probably the the most difficult situation you can be in, right?
If it's
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working, it's working. Easy.
Not working. Obviously, you have to change something.
I can think of one of my companies who went through that exact journey. Uh, fire call.
So fire call is a way for companies to uh it's open opensource product that can help a lot of companies to extract data to extract information from any website super
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successful right now a lot of AI agent customers and so on but before that they were working on another product called mandible and I think that when they actually pivoted they already had hundreds of thousands of dollars of AR so significant traction like not like
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just uh you know 200 bucks actually customers and big logos too. Uh at the time they were doing some Q&A on top of documentations and I think in their case what they saw is that their growth was
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relatively slow. uh they were seeing all of this need uh from the market on the side and what happened is that as part of building Mando they ended up working on a crawler because they didn't have they couldn't find any tool out there that could solve
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their own problem so they built it for themselves and as they were chatting with other founders they realized that everyone every AI agentic company needed that uh that calling function and they ended up realizing that their product the nichy thing inside the bigger
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mandable thing uh was actually way more valuable that mandable was >> and so they experimented a little. It's not like they moved from idea A to idea B like overnight.
They experimented with that uh component uh and uh and it took
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off and uh and rapidly 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. Was there like a moment or an observation that they had that led them to observe that
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this is the the subset of our product that we should bring to the four? >> I don't think it's a you know a formula.
I don't think there is an algorithm. Hey, if A and B then do that.
I think it's more like a a deep conviction you build for yourself coming from a lot of
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conversations you're going to have. I mean in a way that happened to us at Agolia when u 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 mandible but like a few thousand dollar a month and
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we kind of like get that feeling that it was very hard sales like things were slow people were not really value val valuing the product and at some point we're like okay like it's we could build a a lifestyle company but it's not what we want to do here uh let's change something uh that was easier for us
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because >> lower revenue but for Mandable I think it's more like it's a 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. >> I love something that you said there the question of like are people really valuing the product?
I think that um
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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
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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 it's that's great fuel to encourage people to dig deeper and find a better version. Um, an example that that I worked with was a Greile from the
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winter for 24 batch. When we funded them, they had a few thousand dollars of MR and they were feeling awesome about this, right?
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
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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
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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
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product. >> One more thing I think about pivoting is that it's a very vulnerable step in a company.
U it's a it's it's one of those moments where companies fold and they just give up and be like ah we've tried two three things we're just going to give up. Nothing is working.
And when you are asking yourself the question,
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should we pivot? You h 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 to poured your uh months or years into something is not working and now you have to do something else.
So you have to first be
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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 the conviction, you have to have the energy, and I think there's there's frameworks that we can help with uh on on how to do it, 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
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it's very hard for us. A lot of founders come to us and be like, is this is a better idea or this is a better idea?
And 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 out and validate the ones that you think are the best ones.
The harder one
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with pivoting on the framework side is to be like here's a 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 demor like demoralized and be like well then I don't want to work on anything because like the one idea we had wasn't good you don't think it's good uh
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>> or user Gustaf doesn't think it's good >> or like or or just like the feedback we got from people is that this isn't good so it's much better when you pivot to have a range of different ideas exploring so so like you can find conviction I run 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.
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Also relates to sort of like finding the motivation. >> 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 in MR and they've been working super hard on sales and it just wasn't turning into anything real.
And they ended up
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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
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indicator that maybe you should pivot is you just stop believing that what you're working on is going to work out. >> All right.
Thank you so much for that question. >> YC's next batch is now taking applications.
Got a startup in you? Apply at y combinator.com/apply.
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It's never too early and filling out the app will level up your idea. Okay, back to the video.
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? It's a
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hard question to answer because you don't actually know the 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
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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 uh knowing all the answers in the moment of asking the question.
>> It comes back to what we're saying about conviction. >> Like I think the good idea maybe the one
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where you have a little bit of revenue. Yeah.
>> But you know maybe that's a nice to have and the great idea is the ones that you're so convinced because you get these customers like who need you every day to solve a real pain. in my early days of tinkering with startup ideas.
I had many great ideas, but they weren't so great after all because I wasn't
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actually building anything or weren't showing it to any any customers. So, in the end, they weren't great.
>> 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
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technically they're bad startup ideas. Anything that's not a great startup idea is is 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 a rock and you're like
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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 um what's the what's the wackiest version of this we can build in a couple weeks
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and see how people react to it. and just always testing for signs of of greatness >> 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's going to tell you anything else.
>> Yeah, >> I think this is a really important
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question. I was asked this morning um 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 tiptop best performing founders during the batch and the
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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 and and making sure that they're on that path and working on something that could get really big.
>> And they probably don't think it's great. They don't say that for a long
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time. Yeah.
Like like I have a great idea is not something that founders who have great ideas say generally. 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 that way.
All right, thanks for that question. For our next
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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
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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?
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
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it's hard, like the bar is so high, nobody try and nobody does it. If you have the 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 experiences badge that was I
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mean crazy enlightening for me at that team that was uh building some software for science things that was kind of working but not exciting that much like the market was not taking and then uh and then they pivoted to a company
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called Brahante Biologics and they are building microactories to manufacture drugs in a in small volume and the more we discuss about it you could see their their eyes light up. You could see how convinced they were that it was the right thing to build.
And each time we
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were discussing about it, we were seeing all the the challenges padding up one after the other on the tech side, on the regulatory side, like so many things to uh to overcome and you could see like yes, let's do this. Like the world's
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needed, we should definitely do it. I mean, I love that energy.
uh and that area that is probably one of the most difficult I've seen a company uh try to tackle but if it if it works it's going to change the world. >> Yeah.
>> What if this software difficult idea like like it's like it would take you
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know for a fact it'll take you and your co-founded six months to build it like how would you approach that? >> 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
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real-time bidding platform that was connected to all the ad exchanges and all these integrations and 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
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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
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people to do the really hard technical stuff. So we kind of you know were able to chop it chop it out into pieces that we could tackle that way.
You have to be careful like that's a question that is a little dangerous because some people could use that as an excuse >> uh to just work on their idea for six
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months. >> That's true.
and stay kind of like in their garage or somewhere. It kind of happened to us at Agolia.
I mean 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 agree we were lucky that
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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 about their problem like living their life. And so I would do that much better now.
Uh so yeah, as long as you don't use
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that as an excuse to not speak with your customers. >> At Optimizely, uh 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
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an AB 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 uh started with just the
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simplest thing which was a little it was a 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 and get some
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consulting contracts to build AB tests for for other companies. And so that was how we ended up talking to customers was we just built the jankiest possible version for us before building the public version.
>> At least you knew where like what you're building and why. >> Yeah.
It turned us into our own users. I
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mean it was super useful. 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 founders's early employees. When's the right time to do that?
And how do you
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know? And how do you tell?
>> If you have a lot of time to think about this question, it's probably too early. 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
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>> that you can't even find a slot in your calendar to do an interview with a candidate. So when 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 employees have to
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or co-ounder have to work way out of your normal schedule to even accomplish the things you're currently doing. then it's the the right time to have started.
Now the question is like how easy it because it takes a while, right? So um that's probably too late in a sense um because you have probably three months
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before that person is starting and then things going to break even further. So like what do you guys think?
It's like an it's an early indicator of that moment is that there's a specific thing in the company that's breaking or about to break. It's either engineering or it's sales or or onboarding.
It's like specific things are breaking and if you
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have early indicators that they're breaking and you have to be honest with yourself. Are these early indicators or are they um just my hopes that they're going to be early indicators.
You have to be honest with yourself this is actually going to happen. Um that sounds like the right time to start interviewing at least or or putting out job wrecks.
The hardest thing about startups is that it is really hard to
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hire. You're not particularly competitive as a as a startup that just have like two founders and 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. They you can already kind of convince them to
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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 you hire 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 is less of an issue because they're they're more standing by than you might believe than
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like an a later hire and you have to hire cold from from from some some other way. >> I'm trying to remember my experience at there was kind of like the different uh phase of the company.
Initially we hired just a few people like the right thing to do as as you're looking for product market fit
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>> and then we got product market fit and then we never hired fast enough. >> Yeah.
Mhm. >> And then we were way larger and then we were like, oh, we have hired too many people.
Kind of these three phases like the pre-product market fit. Okay, let's only a few people and then uh and then we waited too long to actually hire and
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we ended up like we are like nine people 1.2 million hour >> before AI and uh and like our life was a nightmare. But when I uh remember that time, I remember about it like very fondly.
It was probably the best time of the company still even if it was so
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hard. It seems like hiring is one of those things that uh 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 a way to not go under or have
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a functioning company fail. >> The thing that's changed now, we see so many companies being proud to reach some kind of revenue with people as possible.
That was not the case 10 years ago. >> It's a meme now.
It wasn't It wasn't a meme >> 10 years ago. It was like no employees was the the metric people were.
>> We have multiple white companies who say
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we want to be a billion dollar >> 10 person company and we have six slots less left. >> We're far from the days of I need to hire enough engineers that my I can flip this to Facebook for a certain amount of money.
Like that's gone. >> When I have founders that are working in the batch who ask if they should hire um
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almost always the answer is no, right? Um founders will make the mistake of thinking we'll speed them up but in reality it ends up doing the opposite.
Um, but the exception of that rule, uh, I call these opportunistic hires where it's your smartest friend happens to have graduated last month, right? Uh, or
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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.
Uh, and you bring them on because it happens to be the right moment. >> Yeah.
And I would just qualify and say that those opportunistic hires are great when there's a superlative involved like
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smartest friend, best, greatest, when it's um, you know, worked at big company X that is impressive or something like that. Like those are not opportunistic hires.
Those are bad hires. >> Like if you think they were great but you're not super sure.
>> Yeah, >> that's a little dangerous.
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>> 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 SAS product?
What are some advantages and drawbacks of open source? >> That's an interesting question.
Like we've worked with a lot of open source companies that we see, but most of them
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are dev tools because that's very common go to market when you're selling to developers who really care about their products being open source. They can look at the code, they trust them, and it's easier also when you yourself a developers because your your customers are kind of like the same people, the
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same persona as you are. I do think however that it's sometimes useful um for enterprise 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
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was really about creating the trust at their customer like in these enterprises and shortening the sales cycle by maybe a year for this such a kind of product because it was open source because there these enterprises could could trust
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So even like beyond the dev tool approach being open source was super useful to them. They were not chasing stars or chasing a huge community just using it as a as an aspect of their sales cycle.
20 is another one where they are doing an open source CRM. So
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same thing in a way CRM is pure SAS 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 in the code if necessary. They they'll never do that but just the level
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of trust that it generates. >> Knowing that they can is enough.
>> Knowing that they can is enough and also it helps like with some city things like compliance stuff like if you open source you can sell cost. So no question about like sending your data to some random startup on the cloud.
So all of these reasons are good reason for like SAS
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product to open source. Another thing is that it's going to become kind of like the main go to market for SAS products, but I think it makes sense for some difficult sales where there's a lot of concerns about privacy and sensitive data.
>> Cell phoning in in the world of AI seems
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more common than maybe in the world of SAS. Yeah, it's all about like are you okay to share your private data with a third party >> and uh if people don't share like don't trust open AI are not going to trust the small startups you're starting.
>> Yeah, that is interesting. I feel like
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years ago pre everyone working with different AI products and building AI products the um request from a customer to self-host the product or give it was treated as like oh no that's impossible like what a crazy ask um we we can't do
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this whereas now we have a lot of small startups that find ways to do it quickly and efficiently even doesn't even come up in office hours sometimes they say oh they asked for this so we built it and now they're now they're running it locally and it's And I think that's I think that's progress. >> I mean, there's some drawbacks, too.
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Like the the the self hosting comes at a cost. >> 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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