A Product Market Fit Show | Startup Podcast for Founders
00:03:51 · Dylan, Pablo Srugo
[00:03:51] S0: It was definitely… I don't know if mainstream is the right word, because for example, I remember going to the first TensorFlow meetup down at Google's headquarters in Mountain View or wherever Google's headquarters are. That was in 2016, 2015. There was already this ecosystem around TensorFlow, which was the deep learning library of choice at the time. [00:04:15] S1: It had some believers but wasn't necessarily a mainstream thing yet, but you were one of those, let's say, early believers. [00:04:21] S0: The models, they had a ton of potential and it was still so early, but the actual accuracy of these models was still not that good, right? Even speech to text models that were deep learning based in 2016, 2017, the error rate of those models compared to the error rate now, I mean, it has dramatically improved. The first model I ever trained for speech to text was on 10,000 hours of audio data. Then now the model that we're going to release soon, that's trained on 12.5 million hours of audio data. It's on hundreds of TPUs. Just the scale has increased so much, and as a result, the accuracy and the capability and the robustness of the models that you can create now has improved so much. Now it is mainstream. Everyone's working on it. Every company is trying to implement AI or think about AI as part of their strategy, but back then, it was only the early adopters that were actually putting this stuff into production.
00:04:21 · Dylan, Pablo Srugo
[00:04:21] S0: The models, they had a ton of potential and it was still so early, but the actual accuracy of these models was still not that good, right? Even speech to text models that were deep learning based in 2016, 2017, the error rate of those models compared to the error rate now, I mean, it has dramatically improved. The first model I ever trained for speech to text was on 10,000 hours of audio data. Then now the model that we're going to release soon, that's trained on 12.5 million hours of audio data. It's on hundreds of TPUs. Just the scale has increased so much, and as a result, the accuracy and the capability and the robustness of the models that you can create now has improved so much. Now it is mainstream. Everyone's working on it. Every company is trying to implement AI or think about AI as part of their strategy, but back then, it was only the early adopters that were actually putting this stuff into production. [00:05:19] S1: Did you, back then, was it always your idea to be this layer that others would build on top of or did you think through, because let's say, arguably if you have the best speech to text, maybe you need to just build an app to do transcription and you offer transcription. Did you think through that?
00:05:19 · Pablo Srugo, Dylan
[00:05:19] S1: Did you, back then, was it always your idea to be this layer that others would build on top of or did you think through, because let's say, arguably if you have the best speech to text, maybe you need to just build an app to do transcription and you offer transcription. Did you think through that? [00:05:34] S0: As a developer, I was always so inspired by the iconic developer companies like Twilio, like Stripe. There was a company at the time in 2015, ‘16, when all this was formulating called wit.ai, I don’t know if you've heard of them. They were acquired by Facebook. They're a small company, but they built this developer platform for building these text-based natural language interfaces. You could train it to understand, hey, I want to set an alarm. AI would take that prompt and would turn it into this structured JSON that your app could operate on. If someone said, “Hey, I want to set my alarm for 7 p.m.” they would spit out a JSON it was like action. [00:06:16] S1: Right, so it’s text to code, yeah.
00:06:16 · Pablo Srugo, Dylan
[00:06:16] S1: Right, so it’s text to code, yeah. [00:06:18] S0: Exactly, and the community around it was so cool and people were building so many cool things with it. I say that because for me, building a developer platform was always something, as a developer, I was really passionate about. The goal really when we started the company was let's build really advanced new models for speech tasks, speech to text, speaker diarization, speech understanding, and let's make them available through a developer platform that is super easy to use, that has great docs, that you can get started on for free, and that can scale with you if you deploy it at really large scale, even if you're a big enterprise organization, but let's build that platform that anyone can just pick up and use, whether you're a college student or a developer at a Fortune 500. Democratizing a really powerful and cool tech, whether it's the ability to process a credit card or send a text message or do something with an AI model, as a developer, I feel like unleashes the potential of this tech to so many people. There's entire businesses that have been built on Twilio as a result, but it just shows when you make this technology just really accessible and easy, people will unleash their creativity. I wanted to work in that space.
00:06:18 · Dylan, Pablo Srugo
[00:06:18] S0: Exactly, and the community around it was so cool and people were building so many cool things with it. I say that because for me, building a developer platform was always something, as a developer, I was really passionate about. The goal really when we started the company was let's build really advanced new models for speech tasks, speech to text, speaker diarization, speech understanding, and let's make them available through a developer platform that is super easy to use, that has great docs, that you can get started on for free, and that can scale with you if you deploy it at really large scale, even if you're a big enterprise organization, but let's build that platform that anyone can just pick up and use, whether you're a college student or a developer at a Fortune 500. Democratizing a really powerful and cool tech, whether it's the ability to process a credit card or send a text message or do something with an AI model, as a developer, I feel like unleashes the potential of this tech to so many people. There's entire businesses that have been built on Twilio as a result, but it just shows when you make this technology just really accessible and easy, people will unleash their creativity. I wanted to work in that space. [00:07:32] S1: At this time, you're in California, you're working at Cisco as a brand name company, you're a machine learning developer. You're, I'm assuming, making a pretty good salary. When do you decide to take that leap of faith? How far along do you get on Assembly AI before you quit that full-time job and just go all in?
00:07:32 · Pablo Srugo, Dylan
[00:07:32] S1: At this time, you're in California, you're working at Cisco as a brand name company, you're a machine learning developer. You're, I'm assuming, making a pretty good salary. When do you decide to take that leap of faith? How far along do you get on Assembly AI before you quit that full-time job and just go all in? [00:07:49] S0: It is funny because I worked on a startup in college and then I just did software development as a contractor for two years. Then I took this job at Cisco and then I started Assembly. I feel like the time at Cisco was almost this paid vacation, so to speak. It was active, maybe, no, that's not the right word. It's active rest mode. You know when you're working out you don't want to sprint but… [00:08:11] S1: Yeah, you do stuff, not too much struggling, just enough that it gives you a little bit of… [00:08:16] S0: I went to the gym every day at 5 p.m. I was in amazing shape. I was reading. I was learning things in my spare time. I just felt bored because nothing I was working on just felt like it had impact. I wanted to feel like what I was doing was having impact. [00:08:37] S1: Was that your mindset? Through it, through that time at Cisco, you were like, this is my in-between. I'm going to find a startup. I'm going to find a problem I really care about and work on it. Was that set in your mind that you found something again or not really?