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I'm your host, Noah Laphart, and today we welcome you back to the creation story of Archetype, part two, with Ivan and Nick. This episode is sponsored by SpeakEasy. Grow your API user adoption and improve engineering velocity with friction-free integration experiences. With Speakeasy's platform, you can now automatically generate SDKs in 10 languages and Terraform providers in minutes. Visit speakeasy.com slash codestory and generate your first SDK for free. This message is sponsored by QA Wolf.
Gets engineering teams to 80% automated end-to-end test coverage and helps them ship five times faster by reducing QA cycles from hours to minutes. With over 100 five-star reviews on G2 and customer testimonials from SalesLoft, Drada, and Autotrader, you're in good hands. Join the Wolf Pack at qawolf.com. Okay, so we talked about team. And I'm curious about where scalability came into play here, you know, and given what you guys
have built and all of your history. I know that there's some expertise and some excellence in how this was built to begin with. But what I'm curious about is if there's any interesting areas where you had to fight scalability as you grew. Tell me about that. I think there's definitely two categories of scalability we're looking at. One is, from the start, doing things that don't scale as quickly as possible so we can move forward.
And the second one is knowing of those key areas where the system and the platform and the product really needs to scale and how we think about that from the start. From the scalability side, we know that we want to build a foundation model that a customer can plug any single sensor they have into one foundation model. Being able to build Newton.
so that it supports many types of sensors or many instances of the same sensor. So in other words, a customer can plug in a hundred cameras from across an entire city or a factory for example or they can plug in cameras and radar sensors and time series sensors and lidars all into one holistic model so the model can fuse
all of these components. The ability for our architecture and our platform and our model to be able to support this type of scalable sensor fusion foundation model has been critical from the start. to go to the other bucket. We are not going to wait 30 years. until we can build this foundation model before. we start to work with customers. As we're building the foundation model, as we're training it, as we're
scaling the size of the model and the amount of data we use. We're also being very careful about which modalities we focus on first in terms of cameras and time series sensors and radars and all of these type of things. And we're really using customer demand to help us drive that.
On one axis, we are definitely thinking long term how we scale the system. On the other axis, we're being very careful on focusing on those top priority sensors so that as they come through, we can immediately start to work with customers today on their most important sensors.
And one of the useful things about the last five, 10 years of previous waves like IoT and the fourth industrial revolution, these type of things, is customers already have a large amount of sensors already in the world and they have a large amount of infrastructure already set up to stream those sensors and many customers already have large data sets of actual
sensor data from their factories or from their use cases or from their scenarios. So we can already start to leverage that and plug them up to Newton. The way I think often about scalability is that the difference between building a tech and building a product is that when you build a technology your goals
to prove that technology works. Because if technology doesn't work in one use case or one application, then there is no hope for that, right? So you just want to show the proof of existence that actually can do A or B. When you go to business technology to build a product, your goal now is completely different, is to prove that technology does not fail. And it's very challenging because there is way more modes of failure than modes of successful working.
And then you have to think about three buckets where technology can fail. It can fail during development because developers cannot be able to do this for whatever reason. It can fail during deployment. And then fail during use because some of the failure modes you did not thought about so by focusing on particular sensors for example initially we're making sure that we don't boil the ocean and we limit the amount of failure mode whether it's
So you always try to basically to limit this mode of failures by focusing on particular areas, a particular sense of data and so forth. right you have the fundamental core technology which specifically designed for scale one of the best example of technology designs for scale for scale is integrated circuits right before integrated circuits people were building computers out of the discrete components
It's fundamentally not scalable. The cost of building the next product is linear, right? The more products you build, the more expensive it to be. It cannot be this way. It has to be the opposite, right? So the more products you build, the cheaper it becomes, right? The economy of scale should work.
so economy of scale works when the technology is designed to be generalizable which means you can add new use cases for free or for very little bit of money and that's why we're excited about the foundational model approach because that demonstrated fundamental scalability patterns where the cost of adding a use case is lower than the economic value you get from the use case when it's deployed, right? So that means
The more key use cases you have, the cheaper it is to build very successful products. So that's, I think you have these two kind of like basically levers. You're building technologies fundamentally can scale, and you're also trying to limit, at least initially, to reduce more of the failures. I also should point out that scalability is not free. As you start scaling, you are compromising things. You're compromising user experiences sometimes, you're compromising on number of use cases.
These compromises are where I think you have a lot of arguments and heated discussions in any companies you work. What are we going to leave out, right? Why it's important, right? how we can get data to prove that we're making rational decisions and not just throwing things against the wall and see what's going to stick. That's why you need some research of the market. And that's where you actually, what Nick was mentioned about, it's super important to go to customers early.
need the signals from the real world is that when you're building something for scale, start cutting off some use cases, start focusing to reduce failure mode, focusing on generalizability particular mode, that's where you need the actual feedback from the market. So as you step out on the balcony and you look across all that you've built thus far with Archetype, what are you most proud of?
I think it's really hard to choose. You have five children, which one do you love most, right? Peter or Anne? I don't know which one of them. I... For me, what's interesting and what's most exciting, what I'm very excited about is how we were able to connect the foundation models, the need for the platform to deploy them, and the interaction models we created. in the company which we call lenses and the way we built it was organically in the product most of the stuff we're using is not new
You can't invent everything from scratch, right? We're using a lot of existing algorithms. We're looking at the internet, which was difficult before us, all the cloud computers, like all the tools. We're standing on the shoulders of giants, right? But not everything those giants built for problems you're trying to solve. You have to create sometimes new solutions.
And for example, what we discovered very early, both from an intuitive point of view, but talking to our customers, there's this concept of agents. It doesn't really work in kind of AI we are building, which is... suggested to amplify human capabilities, give them superpowers, use AI as an amazing intellectual booster and give you new perception to see these new sensors and see the world in different color.
The agent just doesn't work really well there because agents are just autonomy and some sort of funny paperclip talking to you and telling you what needs to be done. We find people rejecting it fundamentally. So the concept of lens, which is like a glass or this magical object which breaks the lines in a multiple of color, an experiment of Newton's.
The metaphor of the lens as a way to interact with the AI, I think I'm quite proud of. And it's happened organically, which means we start using technology, we start building it, and start trying to how we can communicate these capabilities of the people to the people.
information around the world is condensed to most important things, right? And you design lens and talk about physical lens to show information in the most probable way. It focuses on the very small objects, on the very far objects, on the blue objects, on the red objects.
We see the AI as a kind of a new lens for people to see the world through. A short explanation is our customers, at least, it's really sticky. So people really like it and like it more than agents. Because whenever we give them this metaphor... It gives you immediately the language to think about what it could do and what it could not do.
So you don't need to go through a lot of explanations. Think about the lens. You can focus it, you can direct it, you can stack them together and so forth. That's what I thought was a very bright idea and throws a path against other companies to do this kind of work.
two things I'm most proud of, or at least two big bets we've made that I think we can really start to pay off when we see the trend. The first one is, two years ago when we decided what this company is going to do, we made this bet on physical AI about how we were all excited about what it would mean to bring AI into the physical world and take all the advances and foundation models and everything else that was happening at the time.
and really apply it to the area that we all love and be working on. because while everyone else was thinking about chatbots and everything else, and at least in the early days, like this is even before VLMs came out. We had a hard time to explain to people what it would mean to even connect a camera up to a foundation model, never mind hundreds of sensors, right? It took quite a while to explain that at the time of chatbots, right?
And then VLMs came out and then it was a little bit easier. And now physically, people still just think about robots and self-driving cars. It still takes a little bit of work to make them see it goes beyond that. And there's so many applications in the physical world.
Making that bet early and going with our gut and all five of the founders have been working in different parts of this puzzle throughout our careers and trusting that intuition. I'm proud that we made that bet and that at least we can see the path of where this might go. While making that bet, I'm really proud of the progress the team has made on a very small team.
with a relatively small AI budget. And we've been able to build early foundation models and test them with customers and actually be able to show for these very noisy, complex customer problems, we can solve this. still very early days and we have a lot to going back to the scaling question we have a lot to do to really scale this but
The progress of actually being able to take Newton, train our own foundation model, apply it to real customers' problems across multiple disciplines. It's really exciting to see this come together and where this is going to go. Let's flip the script a little bit. Tell me about a mistake you made and how you and your team responded to it. The philosophy of failure is that it's okay to fail.
And trying to avoid this failure is the biggest failure. It's not okay not to know why you failed. And I think one of the things which is, so let's call it no blame culture, where... It doesn't matter who made a failure. At some point, we don't try to point a finger on a failure, but we need to know why that happened. So we need to know what was a mistake and how we can fix it in the future.
This is, I think, the fundamental philosophy we've been building back in company, but also when we work together. We always were like, okay, everybody's going to screw up. Everybody. It's going to be a big screw up, a small screw up, it doesn't matter.
we try to avoid them is going to lead to bigger failures and that he cannot will not be able to take risks and if you don't take risks you end up being with something very mediocre you have to take risks and that means you have to be comfortable with failure How much failure? That's a different question. If there is some failure, in most cases, hopefully it's a failure process. So you should be able to go and work out what the process was.
Some engineer pushed some configuration change and I broke the production system. Okay. That's a failure of process. That's not the engineer's fault, right? That's because you've got the right test or the right rule of life procedure or the right QA. Ideally, you want to get a good enough process in play. where there's enough scope that people can actually then go and take pretty big risks and there's a safety net for them that doesn't cause problems.
I think like Avana has this thing where the difference between a 2x idea and a 10x idea, it's nonlinear, right? It's definitely more work to do something that 10x versus 2x. But it's not a linear amount of work, right? So like this concept of
throwing the fishing line out as far as you can and then dragging it in. Maybe you don't catch the thing you thought you were going to catch, but you probably are going to catch something a lot more exciting than you would if you just threw it three feet in front of you. And if we go out with this objective of trying to make... incremental progress you may still feel on your goal of incremental progress right but it's almost guaranteed that you're not going to get something that's
exciting are going to move the needle, right? If you go and try to do something a lot more ambitious, maybe you fail in the original goal, but maybe you actually discover something along the line, which is a lot more interesting and has a much bigger... commercial footprint or a much bigger impact right so i think like this innovation risk i think is something that our team has done a lot of
At a certain point, you then need to transfer that innovation risk into production success, right? And at that point, then that's where the process comes in and making sure the system doesn't go down for a customer or making sure that a launch is successful or all of these things, right?
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This will be fun. Tell me what the future looks like for Archetype, for Newton, for where you're headed. And we touched on it a little bit in the earlier questions, but I want to give full space for the future vision. What was exciting with us and with what we're doing with Archetype. And then while immediate product which we're building is going to be focused on the solving specific customer problems efficiently and at scale.
What we see emerging from our work is less of a lens or agent-style solution bag of tricks. but we see this as emerging as a new kind of computational platform a general computing platform because what we're building right now if you look at the shape of things coming together It has all the elements of general computer as imagined by Neumann. but implemented completely differently to where the central computing unit is an AI model.
And you don't program it in a computing language, but you program it in a human language. If you look at how right now we're thinking about Newton's system, it has two pieces. It's a model of the physical world, a physical world model which we built.
another semantic model of the world which is humans model of the world and they talk to each other and then programming is going to be in the future where you don't write in a machine code or write differential equations of the physical systems This requires people to understand and write and everything like that. But you will be explaining the model what you need directly through the natural language in designing formats of the output and designing formats of the input.
And then the model will be, it will be like working with a human who will be like asking questions like, what do you mean here? What do you mean here? Can you add more detail here? It's going to be questions of building the future tools and software. It's going to be more of the partnership between you and this artificial intelligence system, which will guide you through this building application process. It's going to be all done using human language.
Maybe with some formal structures or formalisms. But fundamentally, we see Newton emerging as a general-purpose computing platform. It's a new form of computer, and that's actually really exciting, I think. We are not creating a better way to solve existing problems or solve new problems.
We're inventing all together a new kind of computer. It goes back to Steve Jobs when he was talking about what he was building a bicycle for the mind. I think this describes how we think about the computing as well.
but it's not like a bicycle it's like a space rocket for your mind it takes you out of the human earth gravity to the universe I'm particularly excited about this because one of the reasons that we are excited to bring AI into the real world is to use it as a way to help amplify what humans in the real world are capable of.
This ability for any user through natural language to be able to almost program these very sophisticated AI models that are able to suck in all the sensor data and they could potentially have actuators so they can control things in the world. and they can maybe see things on the micro scale or on the macro scale that a human can.
as a tool for humans to amplify their intelligence or their ability or what they're literally capable of perceiving in the world, right? Because maybe a single human is not able to observe. data through a city, for example, right? But by using AI as a tool, it allows what humans can do with that tool. And the fact that Ivan is saying that, no, you don't need to go into this tool and program it in assembly, right?
or program it in pytorch right you don't need to be an expert ai developer to build it you can actually just tell the tool what you want in human language and have a dialogue with it and that's where this concept of maybe the assistance and the chatbot and the agents and the lenses all comes together as one system, right? Where you actually have the lenses in the world analyzing things. All of these paradigms can work together, right? They're not mutually exclusive.
But the ability to give this powerful system to any user, not just machine learning engineers, never mind software engineers, like anyone can use it. I think that's really exciting in terms of the direction that the model can go and the platform and the product in the future. This message is sponsored by QA Wolf. If slow QA processes bottleneck your software engineering team and you're releasing slower because of it, you need a solution.
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who is your heroes. Do you have heroes? Remember there is no more heroes anymore, who knows, as a famous song goes. I'll talk about historical figures. One of the people who I was always very inspired by was Claude Shannon, who is inventor of information theory. He did more than inventor of information theory. He created computer games and did a lot of robotics work.
This is a power of imagination and be able to take these very imaginative ideas, but convert them into the hard language of math and make them amazingly applicable. For me, it was always, I thought, that's really amazing. He doesn't get much recognition for what he's done, but I think his impact on our lives is probably one of the biggest impacts across everybody. He still has the best master's thesis, I think, that's ever been written and that ever will be written.
Time to time I go back and look at it and normalize my own ability when I read that. These giants... I think the interesting for them was that not just necessarily they were super smart and did this amazing work, but they were not afraid to tackle large ideas and they did not have a fear of dreaming big. Every great company, the people who started them, they share the same quality. I work for Sony, for example, and I read Morita's
memoirs, and they were in Tokyo after the Second World War, which completely was destroyed, and they were thinking about making a consumer electronic company. It's amazing. The power of imagination and spirit is incredible. Walt Disney is a company I work with and it's also pretty incredible like how he was able to give this power of imagination so that's historical figures I think for me
I always look at these giants and the main quality they have in all these fields of endeavor. They were not afraid to think big. They were not shy about it. And from the contemporary figures, actually, my co-founders probably is the biggest influence on me right now. I'm learning a lot from them. And I think the culture we have in the company is about open debate and conversation.
I've been wrong many times and I've been wrong sometimes so I think Having this open intellectual kind of space where we can talk about this is, for me, one of the most important things because it challenges my understanding of things and lets me grow. The one person who I've met in person and instantly changed how I think about technology is probably Megan Smith, who, when I met her,
Probably 2011, 2012. At that time, I was a postdoc back in MIT, and she went to MIT, and she was heavily connected with the Media Lab, and she was visiting. I got to meet her for five minutes or something and talk to her. But it's like one of those people where when I talk with her... Instantly I could tell like how phenomenal she was and I was very impressed with how excited she is about technology. by building exciting systems and things that are much, much bigger than an algorithm or a model.
a small project or something right and after that she ended up becoming like the cto for the us under obama and some phenomenal things since then And after meeting her, I was like, whoa, this person's amazing. Who is this? And I looked at what she'd done beforehand, and it was like, oh, wow, this person is phenomenal, right? So I think in terms of...
That was probably a five or 10-minute conversation I had with her over drinks in one of the MIT meetup things. I was like, wow, this person's phenomenal. Another person I look up to a lot, who I met once at Google, but never got to work directly with Jeff, is Jeff Dean. I think, for me, his ability to lead very large engineering organizations.
to be a driver in AI and machine learning. Throughout his career, he's built so many fundamental systems. He still codes almost every day, if not at least once a week.
His ability to drive and build like... fundamental technology that most of the internet is relying on at this point along with Sanjay and others I respect that a lot I respect his ability to be a leader to be a phenomenal engineer to be a phenomenal computer scientist to drive and ship and build these very powerful ai systems yeah i think that for me is a good model in terms of it is possible to do all of those things
Not to compare myself to Jeff, but it's a good example that it is possible to be an SVP or a VP or a very senior leader and still touch code or still have a senior position in developing algorithms and things. So that's exciting for me. I see a future where I'm not just stuck in meetings all the time in the future. That's great.
Guys, last question. So you're getting on a plane and you're sitting next to a young entrepreneur who's built the next big thing. They're jazzed about it. They can't wait to show it off to the world and can't wait to show it off to you right there on the plane. What advice do you give them, having gone down this road a bit?
I don't think there's one advice fits all. I think it's very difficult to give advices because you read some advice from very famous people sometimes on the internet. The most important thing is to believe in yourself. Great, thank you. That's very helpful. So how I can turn... What, anything else? Believe in yourself?
and that's what happens when you try to give general advices because at the end it's true if you don't believe in yourself you don't stick to your guns and try to things moving forward i don't think anything is going to happen these are necessary conditions but not sufficient To be successful, I think there are a lot of necessary conditions, but sufficient conditions are more as important as necessary conditions.
My basic advice would be the same things we just mentioned today during the interview. Think Big, Think Global by Act Local. Making sure you have a big idea which can scale, but you have a MVP which you can ship and talk to customers as soon as possible. It's very practical kind of advice. If you don't have a big idea, you'll be competing in a very crowded field very quickly because people have
Incremental ideas, you're not the only one who are going to have it. There are 3,000 people in the world, or 5,000, no, 100,000 people in the world who are going to have exactly the same idea. You need to have a longer vision, a little bit less approachable, like we had with physical AI. It gave us such a huge advantage.
but you can't be just a visionary talk about big ideas what would be your least complex piece you can ship as quickly as possible and then you need to talk to real people to get to talk to real people as quickly as you can I would probably give a different advice to design or engineering or another kind of person but entrepreneur that would be my advice. One big piece of advice, particularly to an entrepreneur or a young technology leader, would be to go with your gut.
Because I've seen this a few times in my career in terms of at a certain point, you start to become the expert in an area. And you talk with other people in the field who may be experts in their area. and they give you feedback and sometimes you tell them your idea and they're like that's crazy like why would you want to do that But actually, if you are the expert, sometimes your gut intuition can be correct. And I think knowing when you should listen to someone else.
is maybe more senior than you or more of an expert in a different area and when you should be like yes that's good advice but it doesn't match my use kit A specific example of this is when Yvonne and I were back in Google in the early days, 2015, 2016, At this point, deep learning was definitely a thing. It was before TensorFlow would come out. There was no such thing as TensorFlow Micro or anything running on embedded devices. At that point, Google was a cloud server company.
And Ivan and I were going around senior leadership and talking to people about putting this tiny little radar sensor. One day it's going to be in a phone or it's going to be in a watch or it's going to be in your kitchen speaker and it's going to understand you. And we want to put our deep learning models in the phone. We want to put our deep learning models in the watch or in the speaker.
And Nick laughed at us, some of the senior people I talked with. They were like, Nick, you're a Google guy. Why are you not putting the data to the cloud? Why are you not running the models on the server? All of this stuff. and of course we couldn't do that because it would completely break the interaction it would be a three to four to five second delay of you performing a gesture and waiting for an answer coming back like we would have never have shipped anything
We pushed very hard to develop technology that would let us deploy machine learning models on embedded firmware in kilobytes or megabytes of memory. And we were able to do this successfully. and it was a few years before tensorflow and all of these type of things that then came and then everyone was like yeah of course we're going to put our machine learning models on our phone or of course we're going to
run our machine learning models on a fitbit or things like this here right so this has happened a few times in terms of being at the kind of tip of the spear or at the top of the wave you can see things that maybe other people can't and if you're an expert enough in it you should just go with your gut and make a bet You might be wrong, but if you definitely follow the herd, then you're always going to be second.
The interesting thing is that you don't need to be an exact expert to understand where things are going and often experts do not see that. because experts is very narrow. I remember when we were at Google, one of the things we built together as a team was Project Soli, which is, as Nick was mentioning, a small micro radar. At that point, this technology did not exist, period. There was no consumer-grade radar that you can put on a phone and get it done there, right?
And I'm not an expert on radar, but I know the properties of the radar. I knew the first of the principles. And if you look for a sensor, that can allow to sense the world in a privacy-secure way, can fit into the package. It doesn't need to have an opening, a hole to look through like a camera, which is solid state, which means it's cheap and can be manufactured at scale.
If you put all these components, you can understand that radar is actually really exciting technology, even if you don't know how it works. Basic idea, electromagnetic waves going in, bouncing back and so on and so forth.
But when I talk to some of the experts, one of the senior leaders at the end of the meeting, he's like, why I'm sitting here? This is completely idiotic. And he left in the middle of the meeting. And nevertheless, you don't get discouraged, as Nick said. And if you get discouraged by everybody, experts' opinion. You will never get anything. When an expert says that something is possible, they're usually right. When experts say that something is impossible, they're very often wrong.
This dynamics is very powerful. Both excellent pieces of advice. And the alignment on your advice too is probably a great reflection of your partnership and with all of your other co-founders as well. So excited to have shared that. Really appreciate you both being on the show and telling the creation story of Archetype. Thank you for being on the show. Thank you, Noah. Thank you for the invitation. And this concludes another chapter of Code Story.
Code Story is hosted and produced by Noah Laphart. Be sure to subscribe on Apple Podcasts, Spotify, or the podcasting app of your choice. And when you get a chance, leave us a review. Both things help us out tremendously. And thanks again for listening. It seems like everyone is either starting a side hustle or becoming their own boss. And you know what they're hearing a lot? It's the sound of another sale on Shopify. The all-in-one commerce platform to start, run and grow your business.
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