It's really not that many great local Italian places. Oh, there is a good pizza joint called Centro Pizza on Broadway. Okay. You know Broadway and Burling game. There's like the sh** street and there's the great street. Yep. Broadway has a place called Centro Pizza and they make brick oven pizza and it's f**king amazing. It's the best pizza in the peninsula I've found. Centro. It does look good. Yeah, it's pretty f**king great. Okay, there's your cold open, everybody.
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Go to lemon.io slash twist to get 15% off for the first four weeks. And 8th sleep. Good sleep is the ultimate game changer. Now you can add the pod cover to any mattress. Go to 8th sleep.com slash twist to check out the pod cover and get $150 off at checkout. All right, everybody. Welcome back to this week in Startups. My guest today is Mike Anoop. He's the co-founder president and head of labs. He had Zapier. If you don't know Zapier, I'm about to make you happier. Zapier is an amazing cool.
I discovered God, it's close to a decade ago. That helped me do really interesting automations between Google Docs, my email, you know, basic stuff. If somebody signs up for my newsletter, put them into this Google sheet. If it's somebody's in this Google sheet, pipe it into my Slack room when somebody signs up for launch fund for as an LP. And I've been doing these automations over the years.
And I train everybody on my team to learn how to use Zapier, notion, Coda, the Google Docs suite, and Zapier and all these products, because you can automate so many tests. Your partner and your co-founder Wade's been on the pod, I think twice in the past. But is this your first time on the pod? I think so. Yes. First time. Thanks for having me. Well, I just wanted to say also, congrats.
I mean, when people saw Zapier and I guess you're contemporary if this than that was a, there was like a couple of companies trying to do this. And everybody was like, yeah, that's a niche business. It is not a niche business. Explain to everybody when you started the company. And then we'll get into all this AI stuff, which is why I wanted to have you on, because AI changes everything with what you're doing.
And we're going to do a bunch of interesting demos and talk about how startups and everybody can be using AI and Zapier to plug everything together. But when did you, when did the company start? And then when did you realize that you were onto something big? And then what's the footprint of the company now? Because I hear all kinds of numbers. Like somebody told me you're making over 100 million in revenue. I don't know if that's true. But where's the company at today and where did you start?
Yeah. Well, I think it surprised me as well in terms of like, how big this is good yet? When we started it, Brian weighed my two co-founders. And we got started back in Columbia, Missouri, small college town at University of Missouri. We got started at the start of weekends. That was kind of what brought the three of us together. And we were all working with like APIs and our day jobs and side jobs.
I was like one of the early moderators and like big users of the Facebook API when it came out in like 2009, 2010. So you're all using these APIs like in contract work. And we're just doing the same things over and over again with them. And I think Brian was the one who pitched the idea at start of weekends. And the idea was like, hey, there's this huge wave of APIs. The really cool, like wouldn't be even cooler though, if like more people could actually use them.
Because you know, it sort of has to have a very technical bench to be able to take advantage of them. And that was kind of the thesis. And as we started looking online, and you know, if you sort of go online and search around for using these APIs or more commonly what you see was you search for, how do I connect these two services together? All you would find on the internet back in the 2010, 2011, 2012 era was basically developer documentation.
You know, you'd find a stack overflow link of like, oh yeah, great. Here's a bunch of code you can use to connect sales force with Gmail, for example, or high rise with, you know, the scamper or something popular tools back in that. I'm not saying base gap, yeah. Yeah, you did that same search today and like this sort of landscape of, there's also totally different. But that was sort of the sort of landscape that it looked like back then.
And I think our observation was, you know, okay, well, you know, you see all these forums where folks are almost begging the vendors for integrations. You know, you go to say the high rise forums and just see these forums threads with like hundreds of their users begging for like, hey, can you add this like a grand of XYZ integration? And it never really made sense to them to make add more than one, two, three, or four.
You know, the top requested ones just because the long tail, and it's a bit of an end squared problem, right? Every new app that gets added, there's an integration that wants to get integrated with it. And we realized, well, okay, we're probably never going to like capture, you know, the direct native integration experience. Like the vendors are going to build those directly with themselves.
But we can provide this sort of ubiquitous platform, you know, maybe we can get 5, 10% of like all of the integration markets out there because we'll be able to service the set of users that just the vendors themselves are never going to be willing to service. So that was kind of the most original thesis that like, hey, this could be more than just like, you know, a small niche, small niche SaaS company.
Over the first few years, if you got kind of started building, you know, I think one of the things that really chained like my perspective of what the business was. So I always thought for a long time, I was actually not a person of the user who was happier for the first couple of years. Like, I was building for our customers. And I always sort of saw it as kind of boring productivity software. And that was like, that's how I view this software.
You know, it's a great business like boring, boring, boring, VDB software. And what sort of started to change my mind about it was several years in, we started going to a lot of these like conferences with our users and with partners. And we started having a lot of people coming up to us and like, sort of like shouting our name like, like, giving us huge high fives and just being so effused. Like, thank you. There was like passion from the user base.
Yeah, it was, we didn't match for my number of levels. Right. And what, what, when you double clicked on that because this is really the key. You had what we call in the industry market pull. Not only where people were looking for this product. And this is beyond product market fit. You had people searching the internet. How do I integrate these two things? How do I create some glue? How do I solve this problem? And then they're so delighted. They would scream your name at a conference at you.
Yeah, yeah. It's like we're in like, that would, they'd run out of just some light. This is a great feeling. Yeah. And what it really was was like these, these, these users were not, and certainly the software can't be used this way. They were not using the software for pure like optimization, time optimization, these cases. It's not to like, hey, save me five minutes a week or save me an hour a week.
These users were like doing something that was like transformational for them, self or for their team or for their business. It was like, it was almost like a skill in mind. Like, hey, I thought I couldn't do this. And because that you're existed, I was able to do it. So, you know, you think of like the solar prer new or we're like a one or two small person business that thinks like, hey, it's out of reach for me able to build a business.
And because I actually have access to these tools, I can build an inbound lead generation through, you know, like a Google form and a lead scoring mechanism. And an outbound email thing with MailChimp. Like, I can actually do it now. Maybe I was budget constrained to be able to hire a developer. And I didn't have the skills at the time to go learn how to be an engineer to kind of stitch together all these tools myself.
So, I don't like a lot of folks that think to be able to do things with the software that, yeah, just previously felt that reach. And I think that like that feeling was what drove the, drove the passion. And I don't know, it got me way more excited about really trying to grow the business as much as you could. Yeah. And the company's now worth five billion. Yada Yada, you've raised a ton of money. You've got how many customers, how many employees ballpark?
Several hundred thousand bank customers over ten million people folks have trucked out and tried zap over the last decade. We've been around quite a while at this point. And you feel? And you've blown past a hundred million in revenue. That one was true. Yeah, the last one we shared was like 150 million. Wow. I just mind going, it took a decade or just over, I guess, right? You kind of on your 10 year, past your 10 year anniversary.
Yep. But it really took, if you look at that 10 year plus journey, at what point did you have that inflection point where, hey, this is really starting to ramp up? Because I think some people get discouraged during those first couple of years when maybe you have light product market fit. And like you said, you didn't think it was a big deal. If you could pinpoint, you know, that moment when you said, hey, you went to the conference, people started yelling at your name.
They see they weren't sure. Yeah. What moment in time was that? And then when did the business actually start to crank and make revenue? Yeah, I think the 2014 probably was around the year where we started just to get enough like recognition in the market from users and customers and partners to get that like passion and care the excitement. That was also the year that we got profitable.
So, you know, one of the other unusual things about our business is we've raised very little, central capital, only $1 million back in 2012 once the balance sheet. Since then, we basically run the business on cash from customers. So then any of those fundraising you've done is just secondary or something since then? Yeah. Yeah. We've sort of offered, we wanted to offer an equity program for everyone in the organization a couple of years ago.
So, you know, we went out to the market to get a, we've never raised my eyes. We didn't know this share price. It's actually worth it. So, we went out and actually got a share price and said, okay, now we're going to start. We can build our competition miles around that and actually offer that to everyone now going forward in the organization. And I remember Salesforce Ventures was one of the early investors. Obviously, we went to Y Combinator, another great hit by YC.
And then you just set up a secondary plan for your employees. How do you, everybody has a lot of questions about that. How do you look at executing it? You know, this employee stock option plan, equitably, fairly, keep people motivated. Yadda Yada. You have a process there. Yeah. There was, there was definitely history too. It was very interesting for us.
So when we first started the business, you know, we were three dudes from our Missouri, so we really had more of that, I guess, ethos and how we kind of ran the business, which was like sell products, you make money, you scale the business face and money make. You know, we just didn't have the like Silicon Valley like raised $100 million. That wasn't sort of our default operating tool coming into the business.
And because we were able to get profitable really early, you know, one of the things we thought to do, we actually did offer equity to early employees. Like we kind of, you know, we went to YC. So we got the like kind of traditional startup advice, like, oh, we set up an option pool and you know offer equity. So we did. And the reality of those, all those are only employees because we were hiring out of our networks when we've been remote since 2012 as well.
We were hiring out of the Midwest or hiring internationally in Europe and like, none of those early folks really value the equity part of the business back in the world. No, they've never seen anybody make money off equity. In fact, they've seen people get lied to with equity in some of those places. That would ever be worth something. So they just are like, hey, give me cash. And if you want to give me a little extra cash.
We switched to profit sharing really early on probably it was probably around 2014 when we got profitable. I think when we sort of switched over that model and said, you know, this is what our sort of teams that are telling us they want our employees are telling us they want. So like, let's talk about that instead. And it was we less overhead to for offering it because it's, you know, give give we had a global sort of employee base, just like the logistics of offering.
Perhaps sharing work or we're a lot simpler. So we actually bring that model for a really long time and up until, you know, closer to 2019 going into 2020 where you know, Zapier wasn't a lot of regard anymore like it wasn't the early days like, okay, we built a real business, you know, north of 100 million. Like Korean revenue. This is not something that's like going to go away.
So we said, all right, we want to start offering equity for everybody and get everyone a chance to sort of like participate in the upside of the business at that point. And that's where we kind of kicked off the logistics to like, okay, let's actually go try to figure out how we're going to create a secondary market. We can get a share of price for this asset figure out what it's worth build that into a sort of our compensation models.
So now we still do have like a bonus program that looks more traditional like we kind of pivoted our profit sharing to more bonus program. But we added in this sort of. Mailchimp famously, you know, did this so we call companies like this internally at our firm allocorns, you know, it's like a unicorn in a Pegasus and my joke was they fly over traditional funding rounds. Calm calm, we invested in that company when it was like a $4.5 million company and nobody would invest in it.
4dvc said no, we said yes. And then Alex and Michael came to me and they're like, oh, we're raising a little bit of money and doing a little secondary. You cool with that? I'm like, yeah, whatever. And then I'm like, yeah, it's at 250 million. And then I think the next round after that was 1.x billion. Yeah, they didn't need the money like you. They just did it off money and 37 signals was similar. What's the survey monkey was another several survey monkey and Mailchimp both did it this way.
It is possible. You raised under 2 million and you got to over 150 million in revenue. Just let that sink in. That is the definition of. I'm not like dogmatic about not raising money. No, I tend to like Zapier has done some weird stuff. We got profitably. We've been a remote company since the very beginning of the business back in 2012. I like to think of Zapier as an existence proof of alternative ways to grow and scale companies.
Now I'll also be clear, I think a lot of those things got pulled out of us rather than expressing them intentionally or proactively. We found a niche in the market that was underserved and we were able to actually do this thing. One of the things I think that's under realized about Zapier is I actually think it's one of its fundamental innovations is a bit of a business model innovation more than anything else. Explain the business model.
Yeah. Well, so in the 90s and 2000s, integration was a thing. Yeah, middleware. Microsoft is talk if you remember that tech, that tech, that product. But it's just like cost and millions of dollars to have like huge fleets of integrators basically custom engineers and IT to come in your organization like stitch all this software up together.
And our sort of I think innovation was, hey, we found a way to actually deliver the software at a usable fashion. And we found a way to reach customers through search which cost us sort of zero dollars. And so every time we're adding new integrations to the platform, which by the way are also built by majority of our partners for free. There's not a there's no money that changes hands there.
So like partners are building integrations sort of for free to get access and deliver integration of their customers. That opens up and adds new search landing pages to Zapier, which we get new customers then from Google for zero dollars effectively. So we're able to sort of find this flywheel that we were able to acquire customers for very, very low cost, which means we can deliver the software at $10 a month, $15 a month, $20 a month starting price.
Yeah. And that let us reach a sort of set of customers and users in the world that otherwise just weren't being served historically. And so that's just that is how we make money still through today. We have software service, you know, we have starting price plans are in $20 bucks, $50 bucks, $100 goes up from there. You know, we add on layer on features around teams and companies and organizations and things like that.
We're starting to add more of a traditional sort of sales and go to market plan as well for like market customers and market customers. But by large we make money directly by selling software and users and charging for the amount of the amount of flight tasks and usage they have of Zapier. Listen, I work with super early stage companies that launch like literally year zero they haven't even incorporated yet.
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They quickly deprecated that. Jamal talked about it on all in. I think recently when they realized like, wait a second. This is like the core to the business. We don't want you having access to this Zuckerberg being savvy. And then famously now Elon and Reddit. I just had Steve Huffman on a couple of weeks ago. Or last Friday, I think actually was and he talked about them. I don't know if you saw the episode. We talked about turning off access to the API or I'm not turning it off.
Charging a reasonable fee for monetizing the API. So I'm wondering just with this collection of examples, they're all happened to be social sites, which is interesting. Yeah, that's my observation as well. Okay. LinkedIn is another one from earlier. You didn't mention that Craigslist has no API never did and will in fact sue you if you use any of their data for the riskraper.
So maybe you can talk about how you want to pick out a business example of MailChimp and Shopify also went through sort of an interesting API breakup. Oh, right. I think when they started competing with each other, you can go look at their like public blog plus around this. But yeah, effectively, MailChimp I think was starting to introduce products in the market in order to compete.
And like those some of those were going head to head with Shopify. So you know, both of them sort of mutually said, well, it's not great for my business to just be giving away value to my competitor. So like we're going to start like disallowing our use case or not, not providing the same name integration that they previously had.
And one funny outcome from that was like we had customers from both of them basically coming us and say, hey, my like vendor of choice is going to stop like supporting this native integration. Can I just use that and that led to both of shop line. Like basically just sending us a lot of their users who depended on any of the creation because we were lost or stepping as a bit of a neutral like. Well, you were speaking I typed in MailChimp Spotify API and you're the number one.
I think I'm not here because you're neutral. You're sweeten. But what do you think of this charging for the API because obviously that changes your business. You now have to I guess ask people to put in their tokens to do this. And then I guess with AI and open AI specifically, you know, they have calls and stuff like that. Does that dramatically change your business or do people just have to fill up their.
You know, like first party vendors, most software providers, actually the preferred way to go like opening I it's kind of introducing a bit of a new way to do product monetization where like. Hey, you have a direct billing relationship with open AI and if you want to use a platform product like Zapier to plug in that intelligence layer into a sort of workflow. You bring your own key. You bring your API key to Zapier.
My sense is this is actually like the smart savvy way to go about it for a lot of these products. I kind of actually wish that things like you know Twitter and all them would actually adopt more of those model words like. Okay, if I have a I'm going to establish my direct billing relationship with my sort of you know first party vendor and then allow that that user to bring their token to other tools and you just charge for the user to have access to this tools.
You can say you can say okay, well allow access to sort of the API for say Twitter in this exam. Yeah, well, Twitter apps as long as that customer is you know Twitter blue and already. For example, I think that's sort of like I don't think anybody wants to get dissimmediated and it's like why would you ever like a third party company charge on your bath anyway? I think it's. Yeah, it got kind of weird.
I think if you look at the time period where you started your company started right at the kind of end tail end of web 2.0 and the Web 2.0 movement. Really where API started in 2005 6.7.8. People were just looking people were under resource Twitter was under resource as a company they couldn't raise a lot of money they couldn't afford to have iOS developers. And you know when apps came out so that you you you all have added reddit didn't have enough money.
It was kind of free outsource development is how the community looked at it and you're like hey you make some value for yourself in the world. Don't wait any stupid and you know have at it and then the problem I guess of course becomes them when you have to go public like reddit does or Twitter has to turn a profit eventually.
You need to tighten the screws here and then you find out whoa these people were really abusing the API they were taking our data users and selling it to people and get all these kind of gray markets etc so.
I think it's actually kind of cool to for the idea that you could just fill up your card and I have a couple of startups who are doing this at opening I they fill up their card and yeah then they run it down and it's like okay I need to get more what do they call that a card that you get when you are in college and you go to the cafeteria. Whatever that card is called meal card yeah you're meal card it's kind of your meal card like just yeah points on the card get points on the card so.
I think for a lot of like me to be companies is APIs are actually in their interest right where social companies have a bigger downside that they have to protect against which is disintermediation like you know I think in Twitter's case the stories that I read on the online was you know folks basically there was like a bunch of first party.
Platform apps that were using the Twitter API they were starting to get consolidated under one owner and Twitter sort of got spooked and said well shoot we don't want like our business for an end to get distributed with our users through one owner so like we're going to tighten things down.
So if you can figure out a clever way to like protect against that outcome from happening then I think this or it's all upside from a sort of mom to they should stand by I think users at this point time around subscriptions are like used to the idea of like okay if you're going to provide me not going service there's you know there's an expectation that they're going to get.
Cost associated with that was a bit beside the privacy and reside integrations are like purely upside and there was no just a deviation or asking fact these integrations are usually really good we actually ran a bunch of studies one with type form that showed integrated users churn like 10% less. Yeah we we use super sticky the fact is we use no show we use type form and type form we love.
And a server monkey we love we love all these products if they didn't have integrations yeah we might actually use. A good product like you know Google sheets allows you to do forms we might use a pro product like Google sheets instead and just be like that's not as good but it has integration so we'll go with that right it's almost like.
You would pick we we would not use certain products certain size products if they didn't have users to the labels like we have a part quite a few years later actually say I like after I can't. Trust so I at least I know it integrates with things in like they have a sort of right mindset on that.
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I want to actually talk to you because you have some big thoughts on AI regulation will get to that in the tail end of the show in the third act but for the second act here let's go over some of the cool. Stuff people are doing on Zapier with integrations because my core and I'll let you fire up your share screen while we're talking my core premise here is there's going to be like a permanent hiring freeze at companies because everybody's getting 30% more
efficient a year using these tools at least I think and they will continue so why you add more people if writing a job wreck takes more time than writing a script and automating something so that's the core tenant I come to with this you you think that
resonates with most businesses be it when faced with hiring rack versus making things more efficient with tools like yours what you did what a lot of users want like users want automation technology to do work while they sleep like that's that is what buyers want that's the dream I don't
think we're there yet and all the use cases even even internally earlier this year in March we held a company wide hack thought we delivered the company art pencils down we're going to take an entire week and the company needs to read to keep themselves around like what is possible with this
technology what's not possible and figure our ways to work in your workflows and we've got a 20% of every individual person at Zapier 20% of all employees worked in some sort of AI into a Zapier workflow that they use far as I actually have not talked about a company that has a higher
percentage than that yet so I actually think there's amazing perhaps interesting things to learn there but you know it was you know it's essential for us and we had to do this like AI and automation are essentially synonymous I think going forward and you know Zapier's business is basically hey software that works while you sleep right yeah there's and you're referring to auto GPT or baby GPT's I guess people call these where you give a set of instructions to an AI and it
performs them over time perhaps even getting better at the task with some scripting or instructions so let's do some examples let's not too far apart from you know even how you think about what Zapier does today it's just hard to use for most people like
Zapier if you set up a zap it's a workflow it's going to do something forever and without you using the keyboard to come interact with it but it's really limited it's constraining right it's like rigid and it's also hard to set up you mentioned at the top of this podcast like hey I have to educate my new employees how do you use this technology I just not easy enough to actually like use right out of the box the penetration rate of this tech is not very deep yet so I think it's
like it's not only the right folds but it's still too hard to use and I think that's where the like technology really has a chance to shine but yeah the demos that actually have are the first one is actually they so the most interesting thing is to just turn around a chat gpp plugin that have launched back in we're one of the launch partners with open a back in back March and I'll show this off in this order we'll go through maybe a couple examples and we can
end on one of the APIs how this actually works out of that so this is a example I think how we've seen most of our users like even in some internal employees it's after adopting this stuff is you know they'll still I've heard a lot of internally were basically folks want to tabs open all day they'll have a chat gpt open one tab and zap your open the other tab a lot of reason is because the models of how the software works are like completely different I chat gpt is a
piece of software you have to interact with in order to get value of it so you know the examples one I've used myself is you know I grabbed an email from my inbox that matches a certain format and you know drafts a automatically draft a sort of reply to that okay so that you're in chat gpt 4 you're using the plugins you pick Zapier and you say hey I want to check for an email in my Gmail account and you've already authorized it to go to Gmail and now it finds the
latest email and does a reply for you yeah this one summarizes the reply and then I think if I can just you know zoom forward here what one of the actual downsides with sort of the plug and architecture on how chat gpt work trend now is everything sort of has to go through these like confirm flows which is I understand why they do it you know sort of the safety argument around it however I do think that there's probably some edge cases where we
actually take a stronger state stance than then then their platform does and like it kind of creates a weird system where like two safety systems are trying to meet the middle and it creates a really awkward user experience so I think there was a sort of more stuff we can do there but yeah here's an example where you know right the plug-ins gonna come back and grab the email response and summarize it back and yeah so this is an example where we
actually open up a tab on Zappier to give a preview of what the plug-in action is about to do I think this is an example where we can we've actually answered on safety things to like let the user explicitly know what actions are going to do and they're about to just letting them kind of roam free on Zapier on your account and now we're back inside chat gpt after the user is confirmed and pulled an email and summarizing the email and I think
that in this demo we actually even follow up and ask chat gpt hate can you sign that with with my name and rewrite it my tone and then send it through Gmail as well and you can actually fire off now from the chat gpt interface using the Zapier plug-in if you've authorized it it makes you go to that step it will actually do the send from the chat gpt interface it will in this case we're creating drafts this is kind of another one of those like probably tips I would
have for most folks that are adopting this tech is like you know the technology is really really good at drafting things to you almost want to lean into use cases where you get to get a preview of it and you can add a mark it up and have sort of control over before you press the send by yourself you absolutely can put this directly up to like sending an email directly but the one we found most folks inside Zapier adopting is you know
these flows where it goes through creating a draft for you being able to review it and approve of the porting send basically the vision for what we want to try and get this to is it feels like an off flow you know whatever whatever product you're under if you're in chat gpt or you're in any other sort of ad product you need to plug in sort of action library into it you know you click a Zapier button you say that that vendor says hey
I'd like to get access to you know your Gmail account your Salesforce account and you know your type form account and the user says you know looks like an off flow flow pop-up these are says yep that sounds good and now you're back inside the sort of first party product and you can go from there
and the initial version that we released it's like one there's one extra step which is in addition to having to sort of approve it and allow you also explicitly today have to choose which actions from those apps you want to like allow the sort of chat you be able to access to so today for example with the Gmail when you saw when we set that up you had to like say yeah okay I want to allow a chat you be access to Gmail
but I also want to have an allow to send a draft email so one extra sort of step that we have to choose those and that's somewhat of a limitation of sort of the language model technology and somewhat of a limitation of the API experience overall right now yeah it's it's there is it's a little clueless you have to log in I remember doing this you have to log into Zapier and then there's some links that you have to go to to
make sure you can search your Gmail make sure you can send from Gmail and that I'm sure will be abstracted in the coming weeks and months yeah yeah I don't disagree with you at all by the way and I think if you and I have I've looked at and sort of obsessed over some of the like usage and some of the numbers for the stuff you know I do think that a lot of the plugins and folks I've talked to the retention is is a problem right now with them you know I think if you kind of look at like
it's almost like a bit of a numbers game you know if you're sort of able to spread your user base over enough users you can sort of find a percentage of the grant find you sticking use cases to build this like chat and plug-in thing into their workflow the reality is most you people in the world haven't even worked chat to be team to the work flows yet so like no asking them to then add on a plugin that is also like you know an active development like it's I think we're still quite a
bit away before you're going to see like kind of the refinement you needed from a lot of these this like plug-in ecosystem and how even just getting the penetration of chat speaking to like legitimate like stick use cases I think is still still search for most most well you know it's it's it's going to be a slow process and then it's going to be a really fast one because once you know the first ten people in an organization figured this out and they become bionic and they're able to do
interesting things with their gmail box like say where the people that I was you know emailing with back in 2012 to 2020 that I'm no longer emailing with summarize my conversations with them and then suggest which one you know some emails to catch up with them and like
well that's going to be super powerful or like you know your venture capitalist hey what founders was I talking to ten years ago what are they doing now it's like whoo like this is going to lead to being able to do things that would take so much time nobody would ever even consider doing them like I you would have to hire a full time person to go through my emails from you know the 2010 to 2020 period find every founder put them into a Google sheet and then look up on their
website and see which ones are still at the same company well it's like yeah well and it works and it works so I know super powerful we sort of see that exact same thing that's the message you know we delivered internally which is how we got so many folks that have started it out in real use cases is you know hey there this is like a chance to go learn the technology the future is not going to be like and I can't really start to think of any sort of technological
like you know displaced jobs in a quarter but over the course of two three years five years like you're going to see folks that they're excited because they understand and you can have like interested in the job market who natively know this stuff especially if you look at the adoption rates of like chance of t in education coming like all those folks graduating through college and entering the job market like they're going to have a skill set that I think a lot of
that's probably probably going to have that's going to be a leg up for a lot of them any other language models now built into Zapier and have integrations Google bar yeah we have well no like chat to be where we have a plug and launch jet this is nothing to announce at this point yeah we do have all of the way more language models actually built into Zapier sort of first party so because this is the kind of the brain vendor about it right when we actually went to go
the chat to people again one of the reasons we built that was you know we saw this huge influx of AI apps launching on Zapier we had like hugging face and human loop bro sort of getting built on Zapier and we sort of realized like oh wow there's this huge explosion of AI products that's happening the market that are not going to get on Zapier and wanted to offer an API to them to be able to bring Zapier's integration platform into their products just felt like first thing we've ever
actually launched a public API a bit of an almost embarrassing point that we're 10 years in it's like we're finally now just launching a public API that other sort of vendors can can pull in but we felt like it was sort of what needed to happen at this point given the pace of like products we're getting released but in the more traditional way where you go to Zapier.com and you're building workflow you know we have Anthropics now with cloud is on Zapier we have the Google
the bar version we've got opening as integration as well so you can build those in more traditional workflows but I do think some of the more exciting interesting ones are like the paradigm shifts where you have like a completely different you know front end interface for how you build and use this stuff.
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it seems to me like nine nine had a ten times somebody gives me examples of where this is going LinkedIn comes into play how do you think about LinkedIn and how do they operate with Zapier at this time yeah I'm trying to remember it was it was five plus years ago I think when they went through their API sort of tightening phase they did pretty open API at the time and then they started tightened it down a guy wrote a lot of their like like automatic message sending stuff
they like contact scraping stuff they started segmented out in the there I think it was around the time where they really wanted to go after recruiters is there like kind of key customer buyer and so they kind of shaped all of their API usage around that persona and kind of just said
you know what all other uses were just kind of delete and get rid of we don't care about them yeah the one the most of our customers and user wanted was the things like lead hydration right where I could take an email address and go like get information about that lead
particularly for like marketing flows you talk about hey it got a you know say a founder of contact form that you have a type form and you get an email address and you want to like automatically pull in a bunch of information so you don't ask the founder to like type in the resume or whatever so folks were using things like that for those use cases and the reality is the market now is kind of like address the gap you know there's a billion different sort of lead hydration companies that also
scrape public information and you know wouldn't be surprised just some of that originally did come from LinkedIn but you know they had that big publics in case they had a big public's rating place with a company based in Israel that they lost yeah it was like back and forth they won the first one they lost yeah I'm sure with the current likes status of all is well I mean the the great irony of this is be they tighten their grip and said hey you can't do
certain things and so what that does is since there's a need like we saw with Napster back in the day if people want something you know whether it's a TV show music or to enrich a lead and rich an email and hydration I've never heard that it's a great term you don't get an email and then get the person's title and where they work previously it's really smart then some gray market company doing gray hat stuff is going to do it and they're
going to scrape all of LinkedIn and then they're going to back into it and you I know this because I've had so many companies do this with Facebook data LinkedIn data and then legal letters get sent and the only people who get really impacted are the good actors who want to play by the rules and then the people who get who benefit they get punished and the people who get rewarded are the gray hats and the black hats
who are going to just scrape the information offshore and they don't answer to anybody so yeah yeah that's kind of a bit sort of the quote you know the mask or our users and customers too I would almost every time there's an API application or terms of service change or something big like those of the folks getting affected and by large amounts Zapier's customers are very small businesses thinking like one
to four-sized teams and companies. Talk to me about like the large enterprises is there anybody who's taken Zapier to like a large organization and coordinated it and do you have that feature because right now I have my team using it but I don't know if we have a central relationship with you. We're like building some offerings here we're trying to figure out how to do this basically.
That's sort of the TLDR we do have some like examples Netflix is probably one of the larger ones where you know they'd have a very IT-forward perspective where you know the IT organization basically has an identity of saying our job is to make you more efficient, more productive like we get that's why we get paid that's why we're sort of here versus you know a lot of more traditional old school mid-market plus companies where you know hey they're
like technology side of the house might be looking at the rest of the business is I am a cross-center I'm about protection, I'm about control, I'm on risk management those are just very fundamentally different perspectives I think when you start talking about like introducing new technology like language models and like a out of mission to business process.
So we are seeing more and more of like IT support is thinking this way and we're starting to put together some like packages and software and services around actually basically doing what we did internally which is like I actually think we have some expertise now at figuring out how do we actually adopt AI use cases into real workflows that do allow folks to get legitimate time back and a lot of them to move on higher value activities and use cases and work flows and jobs
and actually make like deploy that into the organization and that's like the common thing that I get asked whenever folks come up to me and talk to me these days I don't have to say I stuff is like how are you guys actually using it what do you see your users actually use it for because I think a lot of folks are like it's still the tech there's broad awareness for what language models and AI can do at this point I don't think there's broad penetration for it actually into real use cases yet.
So talk to me about how you look at I guess everybody's got an opinion on this but AI regulation I saw some of your tweets. Do you think that this is moving so fast that there's going to be significant negative consequences for humanity? Do you think we need to slow it down or do you think we need some thoughtful regulation how do you look at this because open AI. I'll speak like most of what I know.
Certainly I think there's some interesting philosophical things we could jam on I don't feel equipped to have that argument or even debate at this point but I can speak to what I know which is Zapier we have like millions of legitimate useful workflows and automation that we've users have set up over the last decade.
I know that I also know that it is way too hard for most users to use that even today I know you gave us lots of a few sprays at top parents that I love that we use the reality of those most users it's not we fought for a decade on trying to make Zapier is enough to use for the sort of traditional you know professional does not know how to code does not know it's not technical.
However we still the long way to go and we fought in that problem for so long that I think we are reaching some limits of the paradigm of traditional software to actually put like workflow and automation into the hands of like end users.
I think this is a language technology first thing I've really seen I think offers a step function not just like an acceleration but some of the things that actually like a step function and an adoption rate of how many business users and users can actually set up and use more technical concepts things like automation.
Most of the people use app here even though they might not call themselves technical they're still builders are right they have that that sort of like identity or like I'm going to go create something.
I think this where this like language of knowledge helps it drives down the barrier to creation by by just a ton so you know I get excited for first and foremost I get really excited about the idea of like oh wow well we have like 10 million people who've like tried Zapier maybe this could get us to 100 like folks who've like tried and used automation successfully and I think that's like a really positive thing and especially if we model all the use cases on like what people are using happy before that's all great you know I just want to make more of those people and.
I think I think there's a chance to at this point so first and foremost that's kind of where my head goes first is like I think the technology transformation particularly the pro-seamer you be business work of automation space and it's not just time saving you know these are legitimate like it's it is like unlocking technology for a lot of users around what they can actually use language models automation to do.
It's not things that they weren't otherwise doing when they open source side I do think like so we haven't talked about this much basically I actually get I know you introduced me as sort of the president of the company I gave up my exact title last year last summer in July I put the exacting I went to Brian away my co-founders and I said I think we have I got to go on in this language model ML AI stuff we got to learn what this is going to do.
So Brian said he was going to do the same thing around the same time so both of both of them and I basically said we're going to get rid of our exacting roles and we're just going to go full time and focus on research and engineering for AI particularly in the context of source app here. So back to the laboratory. Yeah basically out of the exact suite no more fancy bathrooms back down to the garage right back to the garage quite literally.
So like I do think like you really do have to go hands on to learn what's like possible to check my look around I think one of the coolest things is when I like look around it like you know my peer group of founders you know folks have been around for 10 to plus years all we're doing something similar we're just like going back and actually getting hands on the technology and I think you have to to learn what possible.
Yeah and I actually think Zapier plays a role here too because I think this point about you have to go hands on to learn what it can do and what it can't do. It is well beyond like I'm going to open up a GitHub repo and download code locally and run it. Things like Zapier can be in gateways for a lot of users to discover what is possible and what's not the same way that the chat you see is offering a view of like what's possible what's not.
You know we have users that are like basically experimenting a trial through trial and error with workflows and zaps and plugging you know a reasoning engine you know language model into the middle of a workflow to say lead score or you know draft reply to you know a message that I received or draft a pull request that I received. Or you know score the geartickets or summarize customer feedback and like dump it out into a Slack channel.
There's a lot of trial experimentation around and I think those users are figuring out what it can't do. At the same time right they're figuring out I shouldn't just automatically send email. No it's not ready for that. Yeah be careful. I shouldn't insert this into an HR hiring decision where I'm not going to review the decision. Definitely not. Yeah so like I have a lot of trust when I look at our users of how they do their own experimentations define what it's good and bad for like.
I think we got to put that experimentation mindset in more hands that's why I get really excited about the one source thing about. Hey the more we sort of can get this technology to fuse and a more individual hands at the end of the day I think it's going to allow. More people in the world to understand what it's good at what it's bad at and like calibrate and I don't know I have I have a large. A high degree of trust I think in sort of folks ability to.
Figure out that and navigate that chart like you know deal with the hands of the technology as long as you give them enough time to. That's where my maybe the philosophical back comes on it's like okay if you really can like. Sort of drop a like I don't know some sort of step function technology change in a month and like. Okay maybe there's like a moment where there's like enough disruption there it's worth asking the question.
But like based on everything I've seen the last 12 months of language models that that is not what we're dealing with. What we're dealing with is more hey there's something that would take you a hundred hours might take you one hour or some that took you 10 hours might take you one hour or. I still got to learn how to do it. I'm not going to do it. Which is really important I think.
Yeah but you do agree that this is going to make companies massively more efficient and you're going to need much much fewer people. To do much more. I mean it's hard for me not to agree with that statement in the limited edition. Yeah based on where folks want this technology to go like I've seen it that exists. Yeah there's a huge demand for it. Yeah. And so then the question becomes you know we as technologists looking at society.
Are left to wonder are there still problems to solve because if a 10 person team can. Do the work of a 20 person team they could solve twice as many problems. It's not like there are not a long list of problems to still be solved in humanity. And so that's where I look at it. I mean the sort of classic use case probably even weighed my show this way and the show is like the pipist example. Or even the word computer. It used to be a profession in itself back in the 50s and 60s.
Typhus was a profession like. Yeah. I got trained in middle school how to type on a keyboard right. I was just having this. I wrote in my I started doing some email newsletters again. And I was like you know there used to be I started my career as a PC support specialist. What a PC support specialist did was they set up your computer. They upgraded the memory they put in a larger hard drive they set up your ethernet hard.
And then they sat there for two days installing software on your computer using CD roms. All those technical engineers took your job Jason where they made the beautiful iOS onboarding flow and you get a new phone. Exactly. And now it's like yeah you don't need a PC support specialist to come and set up your computer and your Microsoft office for you. You can simply. Uh oh here's my sub stack. Thank you.
Um, I guess there's a section and the you know then when I saw you know startups and when you were setting up your startup you were right at the point of cloud computing. So did you rack your own service for Zapier and have a code. We know we did not line node. If you remember the name. The node. Yeah. Great. I saw a box on there somewhere. But yeah. Uh, and. And it was very first thought service is. Yeah. They were the pioneers right.
So you you you were the first generation of startup founders to not have to go order PCs and build a rack and find a co location facility rent space go down to the co low facility. Have a sys admin and you're you might not have even had us to sad men or somebody at your co location facility. The generation right before you if you were working at flicker or a Facebook you were racking servers. And you had two or three people on your team who were managing that for you.
Uh, yeah, linoe dot com slash twist and get a 500 all credit. I forgot. I was like, they were. They're actually easier. So thanks for the plug. I do think one of their if I can add one other point of regulation type side. I do think one side thing that I think is. I, I, I'm fearful this is how it's going to play out. I'm not sure what the check back in in six months, 12 months and see if this is true.
But I, I am the little disappointed that I think the way that most of the research around AI and. LLM's heading is towards more closed. Companies like they're not being as forthright and we could like forthright and sharing of. Basically that's like the technology, the progress, the architecture. Like they're, we're kind of getting into the space where people are realizing how much value is in. I think the research and they're just a lot more close. Pulling up the ladder behind the ship.
I do think is going to the, that's my like that's almost my like sort of anti like acceleration viewpoint. Like I think there is a, there is a path where actually progress slows down for a little bit of time right now. Because we're either sort of one approaching some of the asymptote limits of what we're going to exploit out of transformers and language language model. The kind of current architectures we have. And to like more research is getting sort of closed up.
So it's not as much open sharing not as much progress. And as you know, like reason open and exist basically is because of some of the progress that came out of. Sort of public sharing from another. Competitive company. Yeah, Google. So I do think that's a bummer. That is such a weird move that they went from opening eye to closed day on. They literally took the reason they existed and reversed it. They're like this technology is too powerful.
For everybody to not have a saying it and for it not to be transparent. Then they got a couple years in like you know what? This technology is so powerful. It's too powerful for everybody to know how it works. And I am 100% in agreement with you. Do you think that leads to the development of your. I like fundamental to I completely trust like Sam and Greg. Like sure as stewards of technology. I can't think of two of the better people that I would like try to put in charge of that problem.
But like if I look at the second order effects of what that then leads to. Like it does. I am a little worried that it does lead to like more closed up nature. We're going to see less progress. We're going to see less sharing. We're going to see less like technology getting pushed to the edges. And those things. The good news and all of that is it seems like. The open source community and the open source models are advancing much faster. I don't even saw that Google memo.
But there was a Google engineer who's like, listen. At the pace that open source, the open source community is rocking on this. They're going to just beat us and we don't have a mode. And either it's open AI. So. It's almost like they're squeezing. Too hard. And that's leading to people saying, you know what? I don't want to build on a closed system, which you know. You may call it open AI, but I don't want to have the risk factor. Of working with open AI. So I'll. Look at some alternatives and.
I mean, I just open a more than anyone has pushed for the technology to be developed in public. So I will make the argument in their favor. I know that I'm sort of. You know, poking it a few things in decisions I've made. But like. I also think we wouldn't be sitting around this conversation. Zampi would not have shifted its viewpoint. No, they have. They've had. I mean, they've. They've sent mixed messages to the market. Yeah, I think so. Yeah. So it's like.
The largest message is we have a product that you can use for free. And I do think that that has changed a lot of folks' opinions around what's possible. And they keep. And you look at the API every six months. They seem to just drop the API price 90%. Right? They've done that. I think you can bet on like cost going down. I think you can bet on context windows going up. I don't think you can. There's not like a smooth ramp on architecture improvements though.
I do think that that is like one thing I've been. First thing spending more and more time on lately is. Explain that to a layperson. Yeah. So. I don't prefer going to be an expert in this either. So. Sure. But I'll try. I'll try to sort of my best interpretation of what I understand about sort of transformers of the fundamental level is. This is basically an architecture that was not necessarily. It was like published from the paper Google back in 2017.
That paper was the continuation of actually quite a long journey of research as well around. When I think originally started around translation like literally translating a sentence from one language say English to French, right? And the first sort of deep neural networks that did this were sort of constrained. They kind of fixed the amount of characters that you could translate from X to Y. And that led to the invention of this.
And the technique called attention, which allowed you to have variable length inputs and outputs so you could. You know, the word in English is different length in the word in French. So you could actually kind of deal with that problem. And this led that like attention mechanism was like its own neural net at one point.
And the sort of infamous paper attention is all you need was the dropping of one of these like answering or recurrent neural networks because it wasn't like an important part of building a transformer really simplified the architecture stack. And it that architecture stack also happened to be one that really ran in parallel, which fits sort of the GPU scaling curves that we've sort of seen for the last decade.
So those kind of two things kind of in parallel allowed to invent progress around GPT, you know, one, two, three, and now four. And sort of so on from that. So but we have like. There has not been at least a like well established alternative to architecture. All of the like a products progress research paper you're seeing like a lot of the momentum and sort of attention is really shifted into what can you build on top of language models? Right.
Like what can you do with a language model that has this like it seemingly capability of reasoning right this capability of to use this generality around being able to generate content like what can you what can you do with that. You know, I would have actually up into last summer I would have said there's like I'm like 99.9% sure large language models are not on the critical path to something like a GI that like we should see the level of generality of intelligence.
I I've decayed that prediction to call it 80 85% and the reason for that is there are some things that you can do with like GPT for right now that no one's practicing. And because it's too slow for the language model to generate tokens like you have to let these language models think out loud and they improve their performance.
You know the classic example is the thing that actually inspired me to go all in on a I last summer, which was the let's think step by step paper that came out last January. And this was a technique that some researchers found where if you put let's think of step by step at the top of your prompt in the next is the exact same question again.
The language model actually boosts performance because it gives it time to generate tokens almost like an internal model of whether you're thinking about you're letting the model think out loud for what it should do. And then letting it reflect over those tokens that's bad out to generate it's like actual next action. There's some like performance eval's that went from like 30 35% of like 80 90% crazy step function increases.
It's really weird if you literally say to the chat GPT let's try that again. And can you try to find me three more and you just keep doing that you get to like the six or seven back and forth and it's like yeah I got you your answer and I did it right and it's like. It's a good thing to you some partial right correctness is like another big channel you've been thought that internally.
Anyway, so there's these use cases that like can demonstrate greatness in certain scenarios oftentimes the unreliable like example you just mentioned where you had to ask it six times and it got one out of six right.
Okay, the question is how do we like figure out which one is right more consistently yeah where the second one is these really deep reasoning chains where you can actually let the models continue its like thought process this is the like auto GPT style stuff where you can let the model reason through like a tree based search of reasoning for almost 30 minutes 45 minutes an hour in cases where we had demos run in last fall and it can get to the right answer.
But it's so slow and so expensive like literally cost probably a thousand dollars to run that like yeah search you're only going to do it for use cases where the product experience is like okay if it's offline completely synchronous like the shoe jar attached because like models are expensive so well I mean find me a stock to short and explain your these examples yeah.
Okay, I need to process you know million records of data and I'm okay if it takes three days that's fine just like yeah it was another good use case. So there are examples of like cases where the model just can do better than how they're getting practiced because generally with like consumer facing person or facing products latency matters a lot reliability matters a lot so like all these product builders are self included or chopping off use cases that are slower expensive.
And as the sort of cost curve comes down as the context window goes up I there could be some interesting techniques around reasoning that are just out of reach from a sort of keeping from a not a capability from a cost in like performance standpoint that might become in reach and you know maybe you're way to build an access.
So having these auto GPT's talked to multiple language models and having dueling language models where they analyze each others data and they start talking to each other that kind of feels it's a different type of singularity but it certainly feels promising if you were to say hey I'm looking for socks to short please go to five language models and ask them about you know the stocks that are most shorted right now and then put together a thesis based on their five and you start having.
The chat all different GPT models around the world working on the same problem and then some of them asking reinforcement questions to each other I mean this is you know what the rumors of like the GPT for our texture have you read about those no time we're describing us effectively the rumor and I'm confirmed as far as I know but around how GPT for our texture works which is essentially they have eat different.
You know attention heads or a sort of model heads that are all trained on different subsets of their e-vow and they're all 200 billion parameter individual models and they essentially like do a mixing mechanism where they run the input three to one and they mix the output together from the log props and use that to generate the final token so not you realize from what you're describing where you have like it's like a mixture of experts I think is how they would describe it each head is an expert of a different type of reasoning or different type of input problem and you try to correct.
Right because then yeah and then I don't even know if those are verticalize experts I mean who knows how they chop those up is like one expert because it's actually come in it sucks that this is close right that seems so interesting I would love to read about that I think that could probably accelerate progress in some way the fact that there's only a couple of hundred people in the world they probably know the real answer is probably because they also have some exposure based on who trained that data.
So let's say one of them is like this is Wikipedia, Reddit, Kora and Twitter data and it's you know consumer fit it's a crowdsourced information this is the SEC academia you know the New York Times Wall Street Journal and like a professional you know quote unquote professional these are this is a journalist answering the question from the journalist framework based on the Wall Street Journal Washington Post Persona's and data sets.
So or informed by those and if they were to actually say that then you would be able to make the case of well hey you're literally picking your expertise based on data sets and that's probably why they circled that would be an interesting reason to circle the
circle that you see creative about what data they use I think they'll consider that pretty proprietary I actually think this is a problem that goes away for long run not because of like some cultural acceptance of this fact but more about I think the amount of data to actually need to do training systems just gets dramatic the lower through moral innovation.
I mean there's some existence here like I actually think there's a lot of really good reason to go back all the way back down to like fun and do like an architecture search essentially why did you can we know two existence groups in the world of emergent reasoning intelligence behavior one is humans right discovered through sort of genetic evolution over billions of years and large language models which were are invented over our core running on our own silicon algorithms you know sort of
that's what we wanted the fact that n equals to their suggest that like oh wow there's so many more you run the probability like you would be shocked if like it's not that n equals to around the textures that let in like the transform architecture is nowhere near what you see anywhere model that's sort of human brain completely wildly different in how they work so like it's suggest that there's probably more the thing with this interesting white humans is how comparatively little
examples they need and trained data they need in order to be sort of generally until we see human seem to be born with some of neat amount of like a build capabilities or abilities very strange like like following pattern recognition there's some like things that show really really in childhood that like they never get trained on fear of like fear of predators like we actually understand predators in some way natively like you were
grown if you were and I think they've done this with studies like you you don't need to have seen a shark coming at you to know you're about to fucking die I don't pretend to be a scientist but like there's just like some pretty compelling like like I say existence proof examples where
oh okay yeah the they're likely are way more architectures out here that we should go search for and like I think a really good constraint function to go to an architecture search would be to say let's gain the amount of sample data that we put into this
architecture search so we can find like architecture that are just way more cost factor cost vision and perform I could talk to you for hours and we've talked for now Mike you got to promise me you'll come back maybe like six months from now I think this is moving so fast I'm going to
make an executive decision here and two things since you and I are you know in close proximity to each other number one we got to get some ramen and then number or a lobster sandwich and then number two you got to come back on the show in six months yeah thanks so much for
office recordings we can do one person to I know I'm literally looking for I'm selling our office in the city and I'm setting up our incubator and accelerator somewhere in like San Mateo area and when I get that we're going to have an in-person studio again and we'll do a
live version of this where we get like audience questions and stuff like that so I'm trying to find a theater like I want to get like a theater or like a warehouse space where I could have like 50 people come in a more raw like kind of customize yourself I like a raw yeah I hate
these fancy space I a lot of people have an email I got a fancy space for you over here I got a fancy space over here and El Camino come to the restaurant just shut down basically is I'm hearing that's what I'm looking for is I I'm looking for like a shutdown restaurant like an old Mexican join with a parking lot or something where I can have found a Friday's people have drinks and then I can have you and I just sit and wrap out about stuff so those two things will be on the
docket and thanks for making Zapier it's just such a great product and it's made much easier warm welcome nice and sure you know what makes you happier which I came up with that you guys advertise on the pod years ago and I'm like how do you actually pronounce this and I think I was the one who came up with Zapier makes you happier and so we have a lot of time we ended up in the flutter of the website too I think it still might be somewhere in the about I might have I might have been the
origin story of that I'm not sure if I was or you're marketing because like I use it's still a thing you know yeah everyone is pronouncing Zapier Zapier Zapier what we used to do we used to joke Zapier anyway what is that yeah exactly good is that beer dot com such twisters I think the landing pages still up and we'll see you all next time bye bye on behalf of the producers and the partnership team thank you for listening to
episode 1769 we'd like to take one more time to thank our partners and broker use code twist to get an extra 10% off insurance at embroker dot com slash twist lemon dot IO get 15% off your first four weeks of developer time at lemon dot IO slash twist and eight sleep go to eight sleep dot com
slash twist the check with the pod cover and get a hundred fifty dollars off at check out if you were looking to become a partner of this week in startups you can email Hannah at Hannah at launch dot com that's Hannah at launch dot com thanks for listening