A Product Market Fit Show | Startup Podcast for Founders
00:01:13 · Dylan, Pablo Srugo
[00:01:13] S0: I knew that I loved startups and working on startups because I'd actually started a company when I was in college. It was a terrible company, terrible idea. That's where I learned how I learned how to program because I built everything for that startup. After we shut the startup down and we were working on in college, I just knew that I loved software development. I loved building things. Creating a startup is the ultimate task of building something because you're just constantly building. In 2015, ‘16, I moved out to San Francisco and I took a job as a machine learning engineer at Cisco. I was working on natural language processing and understanding systems. It was around that time that the Amazon Echo came out. I remember that just felt like such a futuristic experience. I still remember the commercial. My immediate reaction was no way that actually works. These people are just sitting on their couch and asking for this song to play and some speakers hidden somewhere. I bought an Echo and I just remember feeling so blown away by how well it worked, how futuristic it felt. I would constantly test it. I would be rooms away with the shower on and the sink on and the TV on and see if I could still get the Alexa to play this song or answer my question. It worked and it was just so crazy and so cool what a good experience felt like, because prior to that, I mean still today, which is crazy, you talk into your phone sometimes… [00:02:45] S1: It's terrible. Yeah, I don't understand what's going on.
00:02:45 · Pablo Srugo, Dylan
[00:02:45] S1: It's terrible. Yeah, I don't understand what's going on. [00:02:49] S0: I got really into this whole idea of natural language interfaces, especially over voice. Then I also totally saw where machine learning was headed. [00:02:59] S1: You saw this Gen AI, I mean, maybe you didn't, let's say, predicted, but you already felt things were going to go there. [00:03:05] S0: Totally, because classical machine learning, you really had to build these task-specific models, do this handcrafted feature engineering for vision, for speech, for natural language were performing pretty well. They were very much the v0 and you just saw, okay, if the ceiling's nowhere in sight and these models are going to get bigger, they're going to train on more data, compute is getting better. [00:03:34] S1: Was that generally accepted? Because that's still pretty early. I mean, obviously AI's been a buzzword for a while and it's gone through its own hype cycle. I'm just trying to remember back to like 2015, 2016, deep learning obviously was a thing, but would you say it was generally accepted back then that that was going to be the thing or was that a bit of an insight on your part? [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.
00:03:51 · 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.