Sponsored by the 4-Step Guide to Delivering Extraordinary Software Demos that Win Deals - Click here and because we had such good response we have opened it up to make the eBook, Audiobook, and online course, more accessible by offering it all for only 5$

Sponsored by our friends at Veeam Software! Make sure to click here and get the latest and greatest data protection platform for everything from containers to your cloud!

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Julian wood is a Senior Developer Advocate for the Serverless Product Group at AWS.  We discuss lots of great stuff around the serverless platforms, transforming IT (yes Digital Transformation is real) and transitioning careers and methods from “traditional” Ops to new was of operating your environment.

Check out lots of AWS Serverless resources here at https://serverlessland.com 

Follow Julian on Twitter here:  https://twitter.com/julian_wood 

Sponsored by the 4-Step Guide to Delivering Extraordinary Software Demos that Win Deals - Click here and because we had such good response we have opened it up to make the eBook, Audiobook, and online course, more accessible by offering it all for only 5$

Sponsored by our friends at Veeam Software! Make sure to click here and get the latest and greatest data protection platform for everything from containers to your cloud!

Want to ensure your privacy is protected? I sure do. Privacy is a human right and the folks at ExpressVPN make sure of that. Head over to ExpressVPN and sign up today to protect your safety and privacy across any device, anywhere.

Over the past three decades, Don W Long has founded/co-founded six different companies his most recent which was in the top 1,000 companies in the United States in that particular industry.

He is the author of three books The Blueprint of God and the international bestselling book Sell or Don’t Eat and joins us for a discussion on his latest book God’s Blueprint for Nations and we delve into the power of mentoring and community on growing ourselves and our businesses. 

As an Amazon Associate I earn from qualifying purchases.

Get the book here:  https://amzn.to/3893xGk

Visit Don’s website here:  https://donwlong.com 

Sponsored by the 4-Step Guide to Delivering Extraordinary Software Demos that Win Deals - Click here and because we had such good response we have opened it up to make the eBook, Audiobook, and online course, more accessible by offering it all for only 5$

Sponsored by our friends at Veeam Software! Make sure to click here and get the latest and greatest data protection platform for everything from containers to your cloud!

Want to ensure your privacy is protected? I sure do. Privacy is a human right and the folks at ExpressVPN make sure of that. Head over to ExpressVPN and sign up today to protect your safety and privacy across any device, anywhere.

Warren Shaeffer is the CEO and founder of Knowable, the world’s first audio learning platform for the podcast generation.  Warren and his team are truly rethinking and reinventing learning with not only how they are delivering content but in how he shares personal and business lessons to help the future entrepreneurs build and succeed.

This was a real enjoyable and in-depth chat on the science of learning, building a startup, and making what Warren perfectly describes as nutritious content for the mind. 

Check out Knowable at https://knowable.fyi and on your mobile platform!


Hey everybody, welcome to the DiscoPosse podcast. My name is Eric, Wright. I’m going to be your host. And this is a fantastic episode by somebody who I’ve been so lucky to actually connect and then since reconnect with this has been really cool to see a lot of stuff going on in this ecosystem. So we’re going to be here in a second from Warren Schaeffer. But before we get into that, I want to give a huge shout out to our fans, friends and supporters and our sponsors, this podcast, including Veeam software.

I say this because I’m a user of the platform. I really enjoy the team and the way that they approach the challenge and dealing with the way that we have to back up and protect our data.

Now, this includes everything from your on premises, environment, physical servers, virtual servers, up to your apps, out to the cloud, and in fact, even cloud native backups beyond that, actually. What about that SaaS? Don’t forget, if you’ve got seven Office 365 Microsoft teams, it’s not actually protected.

Definitely easy to make a mistake. And in fact, let’s not even talk about ransomware and all the other craziness that can go on. So if you want to find out more about everything you need for your data protection needs, then you want to talk to the folks at VM, go to vee.am/discoposse they’ll take your right to a page and you can actually check it out for yourself again. Go to vee.am/discoposse

Also super happy to see that we’ve had great uptake from the book. This is something that I’ve built and shared out and I got really good peer review feedback and in fact I got lots of customer review feedback. So thank you very much everybody who has downloaded and bought the book. If you want to learn about the four step guide to delivering extraordinary software demos that teaches you how to better connect with people, they make sure you can engage with customers, prospective customers and how to really tell a story and get people emotionally invested in the ability for your platform or product to be able to solve a problem.

This is great for product marketers, for folks that are in technical sales. Definitely something that I’ve been very happy has had a really good response. And if you want to find out more about that, you can easily go to the landing link for that one, which is velocity closing. So we go to velocityclosing.com. It’ll take you right to the four step guide and look for some really cool, neat stuff coming up around rebranding and a neat new thing I’m doing with a company called RapidMatter.

And now let’s jump right into the Good Stuff, which is Warren Shaeffer.

So Warren is the CEO and founder of Knowable, which is an audio learning platform that is really and truly trying to reinvent and rethink the way we do learning through an audio engagement. Really, really cool. So Warren’s just a fantastic human, somebody who had a great conversation with and like I said, I’ve been lucky enough to reengage with him in a couple of other forums and stuff enjoyable on clubhouse as well as Twitter spaces recently.

So Warren is really prolific and he and his whole team are doing some really, really neat stuff. You’re going to want to check out Knowable as well. They’ve got courses from Scott Kelly. They got course from Alexis Ohanian, startup founder of Reddit, an investor. Anyways, so much to talk about it, but check it out. And here is Warren Schaeffer.

Hi, I’m Warren Shaeffer. I’m the co-founder and the CEO of Noble. We’re an audio first learning platform. You’re listening to DiscoPosse.

You’re listening to today’s days, the. And with that, we begin, Warren, thank you very much. This is definitely one of the areas that I really enjoyed being able to get into. I’ve long time it often, obviously, as a podcast or of audio, as an opportunity to get into people’s daily lives.

And when I got winds that you are going to be able to join the show. I was really excited because knowable is really cool.

But before we jump into we’ll talk about knowable. We’ll talk about a lot more. Let’s talk about you. You’ve got such an incredible storied history. But for folks that don’t already know you just give yourself a quick introduction and we’ll we’ll get rolling.

Well, first off, Eric, thanks for having me on the show. Really excited to be here. I will give you the quick background. I grew up in Southern California. I’m a first generation American. My parents immigrated a couple of years before I was born from communist Romania and landed in an Orange County of all places. And I went to Harvard for undergrad. When I was a teenager. I got introduced to audiobooks and I, I think I’d like to say before they were even cool on tape.

And and so that is what led me to eventually want to start knowable, which is an audio first learning company. As I mentioned, the top after Harvard, I spent a few years working in finance. I worked for a big bank in New York, JP Morgan, and then I worked for a private equity firm on the investing side in San Francisco. And I moved back to L.A. to be closer to family, teamed up with my current co-founder, Alex, and we ran two companies together, Nobilis, our third company.

We were a venture backed company. We launched about a year ago. And I’m happy to talk about any of it.

Yeah. And so there’s just little I could do a podcast on any one of the things you just talked about and easily fill a long and good hour, but we’ll kind of cover as much as we can.

So you you talked about your first start and your three time founder. And my guess is that there was probably even a lot more that happened before that.

But let’s talk about knowable and what you and the team are doing there, and then we’ll kind of start to go backwards in time as to what led up to that where you are today. Yeah, so as I mentioned, I’ve been lucky to work with the same co-founder across a few different businesses, and when we exited our last product, Badme, which which got a quarter of a jiffy, we thought about what we long and hard about what we wanted to do next.

And we had this list of one hundred different company ideas from everything from men’s skincare products to vinyl records to you name it.

And at the end of the day, we looked at ourselves and thought, what do we really believe is important to the world today? What’s going to sustain us for at least a decade of arguably some of the best years of our life and some of those productive years of our lives? And Alex, my co-founder, his father was a professor at Caltech. And both of us have seen firsthand how a great teacher can have a huge impact on the trajectory of somebody’s life.

And so we gravitated towards this idea of education. And there’s this great quote by H.G. Wells, who’s a sci fi writer. And in nineteen twenty nearly a century ago, he wrote that history is becoming more and more a race between education and catastrophe. And I think that’s more true than ever today. There’s so much information in the world, but a lot of people aren’t getting access to useful knowledge that they can apply in their life. So we really built we really went into the idea of this venture as what can we do in the education space?

It’s going to be interesting and actionable and helpful to the world at large. So when we looked at the space, though, we saw that pretty much every player was focused on video masterclass due to me, Skillshare, even YouTube. Right. All predicated on this idea that if you want to learn in a structured way, you have to stare at a screen in order to do so. I become a new father and my time was short and I realized I don’t have time to sit down and watch these lengthy video courses.

But I do have a lot of audio time. And same with Alex when we were both listening to a lot of podcasts and there was sort of this aha moment of, well, what if we made an audio first learning platform and we dug deeper and we realized that the number one reason that people say they listen to podcasts is because they want to learn new things. And yet there was no company which had established itself as the place for good for you audio.

So we’re building knowable to be that place.

And it’s it’s such a it’s always interesting when you hear the story and like the most people, the first thing I think is like of course, like it makes sense, but there is very much it’s a it’s a leap to get into an area because like you said, we’ve we have this sort of sensation in society.

That video is the way in which we learn we have to visually and aurally engage. But the truth as it really is, like more and more, that’s what I find.

I mean, especially I live I’ve got two young kids, and so I walk around our four and one and a half and I’ve got a 19 year old and a 17 year old. So I got a whole range.

So I don’t have the older ones. They I listen to podcast because they don’t want to talk with me and then the young ones because they can’t wait to get back.

But the really fun part of it is that I’d be putting my youngest daughter to bed and, you know, just walking around with her and I would just pop on an audio book. And I was listening to book after book over the course of a couple of months.

And and it became a real good way.

And that helped to really influence a lot of my day to day stuff because I was picking up, like you said, it’s like I was picking up lessons. I was learning about how people founded businesses and engage with people.

And it really helped to either reinforce stuff or pick up, you know, new new ways of doing things.

And I look so talk about first of all, the methodology is fantastic, right? They say audio is effectively this Gutenberg revolution now in what we can do around engaging with people.

But you’ve not only said we’re going to do this as a methodology, but you’ve really immediately reached out and got some fantastic people involved as far as the content.

So when I’m curious, sort of when you thought about yours, here’s a method, then we’ve got to think about the technology, then we’ve got to think about what we put in this platform. How did that all kind of come to fruition as you started the design phase?

Like everything, it’s a work in progress and a trial by fire. So take look, we took our best guess and really the the mission statement is let’s go and find really, really enthusiastic teachers and encourage them to share their inside knowledge, the lessons that could really change the trajectory of somebody’s life. So, for instance, we’ve got a course, our pilot course, as launch a startup, and it’s led by Alexis Ohanian, who’s the co-founder of Reddit and has gone on to become an investor at Initialize Capital and now seven, seven, six capital.

And he’s he’s really had a front seat over the past 15 years to some of the most transformative companies that have been built in our in our country and world and. What we did was we really question to say, what’s the stuff that you wish you knew when you were starting out as an entrepreneur? And and so that’s an example of somebody who we’re just so excited to have. We also have a course on speaking with confidence. And that’s led by Celeste Headlee.

She’s an NPR host and she also has one of the top ten TED talks of all time. And it’s on the subject of communication. And so she’s written books on the subject to communications, is truly lived and breathed How to be a Better Communicator. And she shared similar similar things. She shares exactly what she knows and and all the inside things that she learned over a decade plus of expertize in the field. So it’s a little bit manual in the beginning, but we’re we’re kind of slowly and gradually opening up the marketplace to allow other people to share their inside knowledge.

And, you know, the beauty of audio is that you can find an audience that there’s lower production costs. Right.

So somebody who has expertize can with the right tools, I’m sure that knowledge with a lot of people at the right time when you get into it’s funny that I’ve only just recently started to add video to the elements of the podcast purely on the YouTube side, because it’s you know, people have said it’d be kind of neat.

And, you know, I’ve made the choice to give it a whirl.

And for the most part, it’s definitely audio is is the strongest format. And it’s so much easier to do because you can just have a microphone. You don’t worry about a perfect room set up like it’s if I took a picture of the room surrounding me, it probably horrified by how it looks just because it’s I’ve got a dedicated studio, which is right next to my very small children during the day. So clearly a bad place to try and do a very tight recording, especially when you just get knocked on the door going, Daddy, why are you in there as well with young kids?

You know you know, finding time that’s dedicated is a is a challenge unto itself as it is.

Yes, certainly. It should be a noble cause just on that.

Well, and so and the other thing, too, is I I noticed that you also recently did kind of an AMA style where you’re you’re engaging with folks and and I love that you led with this thing of like, well, this could influence ultimately a course on, you know, startup founding.

And so it’s not just that you’re going with, like, pure produced content, but you have a real opportunity in that you can kind of test things out and then, you know, work it into a course from there, which is which is pretty cool. How did that one come to, you know, your mind when you were thinking, all right, let’s just let’s give this a whirl?

Yeah. I’m glad you asked about that, Eric. I actually listen to a course called Branding for Founders. That’s unknowable. And it features a couple of guys who started Robinhood Surtaxes newsletter, and they really had this statement but stuck out, which is people follow people. They don’t follow brands. Right. At the end of the day, Elon Musk has way more followers than Tesla does. And that’s just because we’re all humans.

We we care about the story of the person behind the company and said that that was sort of an aha moment for me where I realized I should really put myself out there more and and be you know, I’m so enthusiastic about what we’re building available. I use the product. I get so much value from it. And so why not share that enthusiasm directly with people? And so so I started to become more active on Twitter over the past few months.

And I’ve just seen so much value from building and learning and teaching in public. And and I think when you. Bring people into your process, they become more invested and excited, and it’s just so much more. Fruitful and and cumulative when multiple people are sharing their input as you build, so, yeah, what you mentioned is yesterday I just said, hey, I’m thinking of doing this course I’m fundraising. I’ve been fortunate to raise over a million dollars from top investors in Silicon Valley.

And I want to share everything I’ve learned in this course. And again, that same premise and what do I wish I knew when I was starting out? But why don’t you join me for a live presentation? It’s going to be a dry run. It’s not going to be flawless and perfectly rehearsed. But that way I can also answer questions live. I can get I can hear what people are curious about, what people are confused about, and that will inform the course.

And also it will help me generate interest from people up top. So I’m a huge fan of this this direction. And I think it’s something we’re going to be doing a lot more of where we talk to people live and then in the process, crystallize what the course should be about.

Yeah, it’s it’s definitely such a it’s the real multimedia approach in that you can have the the structured, you know, formal, you know, like read and edited, you know, ultimate course. But then in the meantime you get the off the cuff stuff with the albums and the Ozz. But it’s a real conversation that you’re effectively being a part of.

And the fact that can be interactive as well is so cool because, you know, and it’s I I did one myself. I just did a small, you know, e-book to talk about how to better engage with people through doing technical demos. So it was a thing I hear every day. I’m Lillian calls all the time.

And I’m like, OK, look, we got a teacher in the box, right? Yeah. And so I said, OK, fine. I said I Wrage wrote the book. I spent like four days just like hammering out content. Yeah.

And then I did it and then I did the audio book for it and then I, I do now I’m actually doing these kind of in that style of like, you know, monthly Amma’s where like just invite people on and because then we can say, OK, let’s actually talk about it, because even the audio book or like the straight course, I don’t want to do a full form chorus until I know what’s actually resonating from the content.

So it’s I love that interactivity and I love that you’ve taken a real choice to to go that route, think they’re like, yeah, I love that phrase.

Wrage wrote, I am looking I, I, I felt so inspired after I did the flight yesterday and I thought, oh I really want to turn this new book and I really want to have this crystallize and have the audio format in the courts from the video format, because there are this multimedia, there’s this realization that just different people want different formats.

And so I think that I’m so curious how you to hear more about the book and where did you publish it?

Oh, yeah, that was a neat thing. So the there’s actually a platform called Sam Kart and it was funny. It was literally like, this is proof that Instagram advertising works.

And it was one of these things where it’s effectively like kind of a one page funnel style of of hosting platform takes care of everything. But they actually had a full, full course on like how to actually build a compelling, you know, the pitch, you know, how to actually build your content.

They have a really, really great course.

So I’m and I said, OK, I’m in, I’ll do this thing, you know?

And it was funny. When you go through it, you realize this methodology is obviously widely used and they’re just one place to go. So I but they actually have the hosting for it. I used a company called Beacon for the publishing, so I just wrote the content self, published their beacon and then publishes a PDF and then hosted on Sam Card. So it’s a fairly low lift.

You know, once I did it because I’ve written stuff before, it was a little easier because I knew what the editing process and what the publishing process was. So for a first timer, it would probably be a little bit more daunting. I was lucky enough to have been through the mill with O’Reilly Media and a few other publishing companies.

So but yes, it was fun because I have said, OK, what’s the what’s the short version? You know, I didn’t want to go through this like super like 200 page manual of of things. I said let’s just hammered out and make it fun.

And that’s why I like I did the audio book, too. I was like, OK, let me just read my own book. It was actually kind of funny to do this. I’m sitting there like looking at my own content, reading it back like, oh, that sounds like it’s me.

And where did that where did that where did you put the audiobook? That’s actually the next one. So I, I definitely did not research well on how to do this is the best way today. I actually just like literally send it directly to subscribers when they buy the book. It’s actually part of the bundle. So I Lilium sending empathy’s around but I’m investigating, you know where to host it because you know I’d rather have a place where they can go and then individually, you know, just grab the audio book and then get the associated PDF so you can kind of come at it whichever way you you wanted to be.

I know where you can put it. You put it unknowable. I think. I know. A good place to go. I was and it was funny, I was actually researching the like how to become a contributor and so I to be able to put my name alongside of the likes of Scott Kelly and Alexis Ohanian, I would be I would be honored to be among this crowd and and you like.

So platform is amazing. What you’re doing is really solid. Like you’re really this is a great way to give back and empower people through a format that’s is just a really great opportunity.

Obviously, you put a lot into this, it’s your third startup, so I’m going to go way back in the Wayback Machine and I’m going to imagine that you were probably a young person and you had an idea long before most people would think that it’s a good time to start a business.

No one’s a three time founder. There are 12 time founder and even venture funded.

So entrepreneurship is part of your in your bloodstream, I’m betting.

That’s right. Yeah. I mean. I as a kid, my first experience with making money was the classic lemonade stand, and we put one there was basically this one artery into this residential neighborhood and we put a lemonade stand on one side and then we realized, oh, we should have a lemonade stand on the other side so we can kick people when they come and when they go. And then we ended up, my friend and I, my friend had a younger sister and we basically employed her to run the rival lemonade stand across the street, across the artery.

And and then we just hired some more of her friends. And that was my first foray into scaling a company. And it was really exciting. And I think I’ve had that itch since since I was a kid.

I really like the Howard Schultz of lemonade stands monopolized by competing quarters. Yeah. How you’re doing. Lemonade stand on every corner. You’ve got to catch them on the driver’s side. You’ve got to catch the side. It’s all well.

And it’s it’s very interesting. And even when you talked about, you know, you’ve effectively got a founding team that that you and your your founding partner work with. And it was neat when you describe that you like we said, OK, we’re ready. I’ve got like 200 ideas. Which one should we go with? So the one thing is, it’s not about what you do. It’s about what you do. You’re always iterating. You’re always like you must always be thinking.

And then how do you actually sift it down to what, you know, needs to can actually potentially do this or to zero to one opportunity or whatever however you want to describe it?

Like, which is where do you put your focus when you have all of these ideas?

It’s a huge question. I don’t think there’s I don’t think I have a super simple answer for it, but I will I’ll take a guess at it, which is I think I’ve had the realization and probably too late in life is that you don’t actually want to focus on your solution. You want to focus on a problem that you’re passionate about solving. To that. To me, that was and that’s really the aha. Moment that for for this business was look, we don’t know exactly what the right format is for education.

The end all be all solution. Right. But we do know that we want we see an opportunity to improve, to unlock learning time for people around the world. Right. And so that problem, this idea of, oh, I want to keep learning, I want nutritious content in my brain, but I don’t have time.

I’m a busy parent. I’m a busy entrepreneur. I’m a busy nurse or doctor. Right. That that’s that feels like a really big, tangible problem. And so starting with the problem that you have and that other people have, I think that’s the truest way to to find clarity on whether it’s an idea that you want to work on. Right. So start with the problem. Don’t start with the solution. You said something really interesting, and I love this phrase, nutritious content for the brain.

That’s probably not the first time you said that. What when you thought about this, what was? Because what was the first thing you thought? This is what I would love to be a host, a platform for.

Yeah, I love there are a lot of podcasts that I love. And I went around the time when, you know, a little was birth. I was listening to a lot of how I built this episode with Guy Raz. He’s a great interviewer and show. Yeah, great show. And he interviews founders with amazing stories. But I realized that that show doesn’t teach anyone how to actually become an entrepreneur. There is no tactical advice on how do you find a co-founder?

How do you select an idea? How do you meet a venture capitalist? How do you hire a lawyer? Which lawyers you hire? There’s none of that. And so what felt missing was this useful knowledge is applicable or actionable knowledge. And that’s that to me felt urgent. And I think it comes down to that problem statement. Look, if you’re busy, you don’t have time to sift through hundreds of episodes of every podcast, even though there might be great and fun to listen to.

You want you want the essence. You want it to be boiled down into what can I take from this that I can go apply to my own life, that can change the trajectory of my life in a positive way. And it’s it’s very interesting. Yeah, and the idea of distilling down, you know, meaningful stuff and it’s in fact, one of the areas I’m looking at right now is like, how do I distill out all of these amazing lessons that have been collected over the years, you know, now in the podcast.

But there’s a lot of really cool conversation wrapped around. It won’t make it into book form, like unless you’re Kevin Smith and you go and you just take the smart cast and you put it into a written form. That’s all well and good.

But I think he had the audience and they effectively would have you know, he could have effectively published most anything and it would have been well received just because he had such a really sort of loving audience that was ready to receive any of the content.

It was great content. And he was, you know, Tim Ferriss have a similar style that let’s just transcribe what we’ve got.

But I love this idea of like founders at work. That was the other one as well, which is a great, great book. And it was effectively long form interviews that were then and the same group of questions were asked. Yeah.

And they were put into and it was that sort of style, like you said, one of it’s not just talk about the success stories, like let’s actually talk about the the ways, the methods, the lessons, like actual actionable things.

Yeah, there’s way too many like oh it just works, you know, or it was really hard to write what was really hard, what worked. I’m not I’m not richer for having listen to this thing.

You just told me that you had a problem. I don’t know how you got through it.

Yeah. Yeah. I think it’s and it’s really common, right. When at the end of the journey, it’s hard to remember all the details and and the practicalities. Right. It gets glossed over and repackaged. And that’s, you know, Hero’s Journey story that doesn’t always mirror the actual nitty gritty of, you know, what was involved. Yeah.

It’s like the Dick Wolf who’s there to the creator of all the Law and order franchises. He says, like, great drama is when you take all the boring bits out and compress the rest down to an hour.

Totally. Right. You don’t see them go to the bathroom or eat a sandwich like that just doesn’t happen. There’s no time for that.

What’s interesting is actually, if you if you watch a law and order which has done far too much of in my life, what if you look at it every time you hear the countdown is a date at the bottom on the screen.

If you actually look at it, it shows that these basically elapsed over like 18 months, which shows you really. But it’s so subdued in like subliminal almost in the way that it’s done.

You think it’s happening all at once? Yeah, like it’s happening over a week, but it’s actually OK like 11 months, right?

Every time it takes too long. Yeah, right.

There may be the first forty eight, but they don’t tell you about the other four thousand nine hundred and twenty two, which is really where the the work happens to get it right.

So the, the other interesting thing too was you ledwith talking about being a first, you know, you know, first time born, you know, in the US and and you talked about your your family’s history and where they came from since that that led the discussion and the way you described yourself.

It must be important in kind of how you see the way you come to the world. Yeah, I I think that’s a good observation. I think founders come in all shapes and forms and all ages and all backgrounds, but there’s a unifying characteristic that I’ve seen great founders have, which is that they had grit. They have this desire to overcome obstacles, and often they’ll see challenges as opportunities. I think that’s really a characteristic. I think a lot of immigrants and first generation and maybe even second Asian-Americans have that sense of grit and still where things you don’t expect things to come easy.

And and so that, I think, has shaped my lens of the world in many ways.

And certainly how I think about building a business do it’s always funny that you hear the amazing stories of our grandparents and parents who, you know, as they came to the country, you always hear these stories of literally you’re getting on a boat with like 12 dollars in their pocket. You know, people would do right now if they had 12 dollars in their pocket.

They got on a boat, they’d go to the Starbucks and they would spend eight of those 12 dollars it.

And yet it’s so it’s so interesting that, you know, when you’re close to it, you see so many of these stories.

But in the broader sense of how many people are, you know, either founders or even more so distill it down to successful founders and are able to go through that facing adversity.

And like, if you’re anybody that tells you, you know, I’ve got this great idea and I’m going to make a business out of it, most people tell you it’s a pretty fundamentally terrible idea.

Like it’s not the ideas bad, but making a business out of it is not going to be. There’s nothing easy about this.

You know, the.

Yeah, I think the that’s also why I like you said, you know, let’s actually go through some of those stories and and get further into where it works, where it doesn’t.

Yeah. So how many lessons when you were thinking entrepreneurial and then you chose you went to Harvard. When you were going to Harvard, this is also one of the other sort of like hero stories we always hear is the, you know, the founder that goes to Harvard and then leaves or whatever, like there’s all sorts of stories about, you know, whether you should or shouldn’t go to university.

You know, how does it inform your future in entrepreneurship?

I’m curious, Warren, as you went, you made the choice to go and then when you came out, you got into investments and venture. What was your mindset on either side of the university experience? I don’t think when I went to college, there was so in vogue to think about not going to college, I think that’s becoming more that’s a more recent topic of discussion. I my parents went through a lot of financial difficulty and actually ended up living with friends for four 1/2 of high school.

And I was fortunate to to win over thirty thousand dollars in scholarships and get really generous financial aid to to go to Harvard. So it was. I hadn’t really entered my mind to not go to college, and I sort of had this road map in mind, which was I knew I wanted to do something entrepreneurial or something creative or both, and but I wanted to drink and have, you know, make some money early in life, be able to invest it and compound it and then take a quote unquote, risk a little bit later.

So I don’t know exactly when that game plan got for me that I think I read a lot of books and I think I probably came across some book that basically showed me the value of compounding money. If you’re 20 versus when you’re 30. Right. You’re going to put some money in the stock market. When you’re 20 versus 30, it can be triple. You know what it’s worth when you want to retire. So that that is how I sort of hedged, I guess, my my life risk and.

It’s telling in the way you described it as well, that you drink before you take on risk, and that’s another successful founders sort of lesson in what I’ve talked to a lot of folks is that most of founding and operating a company is spent in continuously drinking the environment.

That’s right. Yeah. And the faster you can drink, the better. And this is something that it’s so hard. And even for me and my staff to continue learn, this is and this is going back to why build a course in public. Right. You want to get validation super fast. Before I spend six months making a course on raising some money, let me see if anybody even cares if I’m getting free. And it’s like, will anyone join?

And, you know, look, 250 people signed up. And that was a really strong signal to me, like, wow, there’s real demand here for this topic so that the faster you can be risk, the better, whether it’s financially or intellectually. I think that’s a great heuristic to look for. And obviously, you were definitely in the risk and risk business when you got into the investment side coming at a school, so taking on risk at scale, you jumped right in.

You know what? What was that early part of it, you know, as you now could take the successes of what you’re effectively what your family gave you, you know, this this heart to do this thing. And now here you are. You can you can said you won, you worked to get scholarships. You got through this. Now, you know, the side must have been a pretty proud thing, but also a fairly daunting task to take on.

Right. That you are suddenly OK. We did it. Now what what was that like on the other side now when you got into the investment world?

Yeah. So when you say I did it, are you referring to getting a job in finance or what?

Yeah. Yeah, just like that. You literally pushed right through versus you know, you know, you didn’t take a journeyman’s job and then just choose to ride it out until retirement.

You you went right into an area where you you could pretty much affect the outcome, which is again, sort of a bold, you know, choice in that you knew you were taking on risk.

But, you know, you obviously had both sides of it. In your mind.

Are you saying the decision to leave finance, to go work and you and your finances, as did that route, like you got right into the game? Oh, yeah, money.

I mean, I think, you know, I. I really again, it goes back to this idea of drinking to take risk. I really thought, well, this is a great opportunity to learn some hard skills, no matter what finances is the underbelly of any venture. So and coming out of a liberal arts college, I thought it’d be great to have that practical, tangible skill of knowing how to financial model and understand thinking about investing and really what investing is.

It’s maximizing rewards and minimizing risks. And and when you extrapolate that out of finance, you realize that basically every decision is an investment decision. You have a limited amount of time and attention and you have to decide where to invest it. Right. And the people who think about investing their time versus spending their time, those are the people who end up being really successful because those little micro decisions compound. Right. So if you spend time, you’re basically doing something where you’re not learning, you’re not adding value to anyone else.

You’re not really treating yourself your body in a healthy way. That’s time spent. If you’re investing time, you’re there reading or learning. You’re writing, you’re teaching, you’re meeting somebody new. Those are things that accumulate and compound. So, I mean, I think you go back to your question. The risky thing to me, I realized, was. And I got this because I read a lot of books was ending up at the end of life and and wishing I had taken done something else and tried.

And this is such a cliche now, but it’s so important that people know that most people on their death bed, they regret the things they didn’t do, the things that they didn’t try. And so I knew that I would regret not taking a chance and that I would actually be risky not to take a quote unquote risk by going and trying something different. And like that, I like that thought process, it’s it’s tough for us, I think, as humans to look far forward and then regret is such a driver.

You know, you look at all of the work around the heuristics and behavioral economics, and one of the biggest drivers is regret. You know, we will make decisions.

We will do make choices wholly based on our sense of potential regret, more so than the value that we would get at it. But we are much more driven by the wish that we had something, which is why when you make bets and you know, you look at the sort of works of conmen and Tversky, yeah, so much of it was wrapped around this pervasive effect that Regrette has on human decision making.

Yeah, I mean, the whole field of behavioral economics that they pioneered is so powerful. And once you once you become aware of these cognitive limitations, you can work around them. Right. Because I don’t like loss aversion and sunk costs and confirmation bias. I mean, that’s a that’s a great book. Or I mean, I read the Michael Lewis Oh, The Undoing project. That is so good. Yeah. Story.

I wept like a baby at the end of it when when I won’t give the spoiler but is just such a beautiful human story.


Oh yeah.

I’ve, I studied it was a fine as I had studied a lot of of cornerman Tversky and sort of pinker and a lot of I was very deep into that game on self study for a long time. Yeah. I when I got the Michael Lewis book I was like, oh wow, this totally makes sense now.

Such a profound, just emotional story about how they approached it.

And it’s funny that, you know, they were it didn’t make sense that it even happen that the fact that here we are, a behavioral psychologist that would win a Nobel Prize for economics.

And yet we think it’s standard game these days, right?

Totally. Yeah. I was everyone. It was up until then it was, oh, people are rational. Oh, no, no. We’re very far from it.

Fairly far from it. In fact, we’re predictably irrational.

Oh, so funny now. This is interesting as well, you know, there’s a lot of sort of timing that works out and being able to bring a business, you know, and obviously the technology’s there to do this and knowable has such a great opportunity.

So bringing knowledge and, you know, nutritive brain injections effectively in an attention economy, you know, how do you approach things knowing that you are effectively fighting for a cohesive time blocks in somebody’s day? Great question.

Yeah. Our our vision statement, our why the reason for being a company is we want to live in a world. I want to live in a world where daily actionable learning is as addictive as social media is today. And so we know that, look, social media is so addictive. It is it’s weaponized distraction. And we want to push the bounds of of how people spend and invest their time, because it’s so easy for people to spend their time on social media to just end up scrolling.

And, you know, there’s a lot of benefits to the platform, but so many people just get sucked in and end up consuming and and not taking knowledge and applying it and actually improving their lives. So we think a lot about how do we use those behavioral psychology hacks that social media platforms are using to make mobile more addictive. And look, we’re in the early days of our product development, but we think things like streak’s and habits and social pressure, that’s all getting built into the product to really create this atmosphere of, hey, when I go to knowable, I know that I’m doing something good for myself and it’s also fun and feel good.

I get that reward right. You probably read some of the habit books like Atomic Habits are Right. Expect to have a reward is so important to talk to creating a habit. And so that’s something that we’re working on, building more into the product so that you’re not just doing it for them, but for the knowledge sake, but because there is some productize gamification and makes you feel invested and good in the process. Yeah.

And the the the real truth of the they call it the click were like it’s the thing that just gets triggered, you know, of like these are their behavioral things that are baked into us and using it for positive results, which is which is interesting.

Like they said when we describe it, we talk about time spent in social media, how much time you’re spending in social media. No one says how much time are you investing in social media?

They know it is spending purely I mean, look, it’s like any tool, right? You know, a hammer can be used for a nail or it can be used for for for.

More or less the purpose. So it depends how you use it. I mean, I think there are ways to invest time in social media, but I think a lot of people cross over the border pretty fast and pretty far. And so spending their time.

Yeah. What’s interesting, too, is as a student, at least in reading of it, I didn’t actually attend Stanford, but of B.J. Fogg and sort of this whole idea of persuasive computing. I studied that for quite a while. And then, you know, you see the stuff that’s Tristan Harris is doing around human computing and you ethics in A.I. The tough part was that more people will use B.J. Fogg’s material as the wrong side of the hammer then than the right side, which is unfortunate.

You know, especially when you see kids like you’ve got little kids, you see them like I can watch my my youngest son pick up an iPad and just tap know what to do like they built it makes complete sense to him. Yeah.

And then along comes an ad and like so the funny thing is I taught him to skip commercials real quick.


What like the fact that he just sits through it now and it’s like yeah.

How did. I don’t know how this happened.

It used to be a lot of smart people applying that those behavioral. Inefficiencies. Yeah. If only they would build enterprise software that way, then it may actually get adopted. Well, yeah, I think that I think that is happening.

I do think we are kind. Funny you mention that, right? With Noble, we’re seeing a lot of opportunities on the enterprise side where, hey, we can employees. You know, L.A. is really important for four big organizations today. L.A. stands for learning and development and and upskilling. But look, if you ask somebody to sit and watch a training video at their computer that’s competing with their work time, but if you give somebody something that they can listen to when they go for a walk or on their commute, that that opens up a lot of enterprise time.

So. So that’s something that we’re thinking about actively, too, of how do we actually make workforces better? How can we create a diversity and inclusion course that makes you really want to listen and make a difference?

Yeah, and I think using those techniques in any way in the learning development space, especially in the enterprise, like because we all know everybody who’s listening to this, who’s works for a company, and you get that annual it’s like, oh, it’s the compliance training ranty, anti money laundering training. Right.

And there’s like you just horrified with the fact you have a waste like five hours of your day and you’re like fast forwarding as much as you can, and they make it so that you can’t fast forward, you know, but they have to actually make it engaging.


And make it worth their time. And that’s effectively what you’re doing, is you’re giving them a sense of worth. Yeah. For having done it.

That’s right. Yeah. I think there’s certainly room for improvement there. Now, the the things that you. You’re doing it definitely is a very positive spin in so much of what you do, and it’s like it’s natural, even when you tried to describe a bad thing you could do with a hammer, you really got stuck.

And it’s so cool because. You it must be, again, sort of like baked into you that what’s the most positive thing I could do as a result of what I’m about to do? Yeah, I think, you know, it’s funny, I just started reading the autobiography of Benjamin Franklin, and it’s I it’s amazing. This guy just truly the the OG of self-improvement and positive thinking. And I think there’s so much power to it. And it really is just what filter to you apply to the world, because you can see things in many different ways.

And I do think one of the benefits are argument brains is that we have the ability to decide how we react to things. Right. There’s a there is a a control mechanism between stimulus and response. So I think the more we can view things to the positive lens and see problems as opportunities, that the better will all be.

They had tried to get in front of the old amygdala hijack, as they call it, right? Yeah, right. Whether brand useful, sometimes very useful, but oftentimes gets in the way and modern society.

Yeah. And it’s it’s very interesting that I’ve even through my team at work, we’ve gotten we’ve had to make a lot of really heavy decisions lately. And it’s it creates some rather animated group discussions. And I say that like, you know, ten year version ago of me would have said like some really crazy arguments.

But they really, truly are animated discussions. And we’ve become as we age, I think, much better at like recognizing when.

So what I’m hearing you saying is this and when you start to respond to things, it’s very different than, know, 20 year old me would have been like, look and like my favorite thing to be with, say, like if you’re writing an email and it sounds like you’re going to begin with, look, comma, you’re going to want to get up and walk around for a bit before you finish that sentence.

That’s right. Yeah. You don’t send emails when you’re emotional. A hundred percent. I think that I mentioned this course unknowable called Speak with confidence. And Celeste Headlee, one of the other experts in that court is Julian Treasure. This guy has 80 million views across his TED talks on the subject of communication. And he’s written this book basically about how to listen. And there’s this big realization of most people when they’re in a conversation, they’re listening to respond.

They’re not listening to understand. Right, and what you just did, what you said is so great, which is repeat back, kind of paraphrase back what somebody says because it helps you actually understand what they said. It moves it from the one part of the brain to the other. And then it helps them also feel heard, which is fundamentally what most people want. They want to feel heard, appreciated and understood. And so I think improving as a listener is is a huge way to become a better communicator and conversationalist and learner and person and friend and partner.

And that’s something that I still still work on. I’ll give you one take. We have another course that’s led by an improv comedian named Will Hines. He’s part of the Upright Citizens Brigade, which is the Amy Poehler start, as we did, of course, in partnership with them on improv. And and I had the chance to interview him after the course was done. And he shared this great bit of wisdom, which I really liked, which is after somebody finishes talking horse for half a second.

Before you respond, because it gives your brain time to actually think about what they said and then allows you some time to think about how to reply so that you’re not spending the whole time when they’re talking, wondering, OK, let me here’s what I want to add to this. Yes, that’s we most of the time is more like your turn to talk, your turn to talk. My turn to talk to your dog eat dog. It’s like.

It’s like what am I going to say? I can’t wait to tell him about this thing I did was like, would you do improv?

My fear I messed around with with a group of folks and we they they were doing improv stuff and I got to jump in for a while and it was fun. And the whole thing is like, yes. And right now it’s like the whole idea of like carrying this forward. And I would just for fun, just coming to say like, no.

Yeah. Just completely shut it down. It’s funny, but the way in which you effectively pick up the conversation, you have the chance to either. Yes. And carry forward or, you know, and even like you said. When you listen and you want to say something back to somebody, tell them what you heard and because the way in which you say it is important, it’s not just that.

What you’re saying is what I’m hearing you say is, yes, totally.

It sounds like to me it feels like. Right. You’ve put it through your own personal lens. It’s yeah. I found this very helpful for for marital conversations. I have we have three kids under four, and so we’re short on time.

And so we really have to get better at how we talk with each other and really listen, because it’s so easy to just. Try to be understood and not try to understand what’s the default and it’s a continuing thing, like even therapists need therapists, like it’s not like no one’s no one’s perfect that the stuff we’re always trying to figure it out. And it’s harder on some days than others and. Yeah, but I think.

This is where this kind of growth mindset versus fixed mindset, I think it sounds like that’s what you’re alluding to, right, is this idea of, well, it’s a skill that you can get better at. You don’t aligning.

A lot of people approach certain things like relationships as insider works or doesn’t. It’s binary. And don’t take on the idea of, oh, I can improve at this. Right. I can I can learn to manage this relationship better. And that growth mindset, I think, is really powerful. And the more people have it, I think the better our society will be.

Yeah, there’s there’s a lot of things, stuff like journaling, future authoring stuff where you’re effectively envisioning, you know, kind of what’s what’s ahead. And then you it helps you to actually define it, because when you’re working it out in a session, you’re physically writing it out. It does imprint it effectively on your mind that, like, this is a goal.

And it’s it’s kind of neat. And that’s a thought question for you one. Where how do you go from idea to eye?

We’re going to do more with this idea, what’s that process look like as an example?

So as it relates to startups, there’s this common commonly thrown around acronym, which is minimum viable product and the. Basic concept of it is, is that you want to learn as fast as you can whether anyone is going to care about what your idea is, what your solution is. Right. So, again, I’m going to repeat it, start with the problem. Start with a group of people who have a problem and figure out, OK. Is this solution going to solve their problem or are they going to?

Is it sufficiently different or better for them to care? And when they pay me money, or would somebody else pay me money at some point if enough people used this solution that I’m putting up with the world and you want to find that out as soon as possible. So rather than waiting six years and making your perfect dating app that you just tried manually to figure out, OK, can I connect to people and are they going to care? Right.

I’m using my quote unquote algorithm. Can I connect to people. Right. And and so so you really want to do things that in the beginning that don’t necessarily scale and that’s OK. But you’re looking for a signal. You’re looking for a sign that you’ve made something and somebody else cares about it. And the best signal in my mind is. You make something, you give it to somebody. And they share it with somebody else unsolicited, right, that’s such a clear sign of, oh, wow, I made something useful.

That’s that’s what you’re looking for. So you read a book and you give it to one person and they give it to five more people. That’s that’s that signal, yeah.

And take on the reading side of it, and, you know, if I look at what I really love about your platform and your content approach is that when I get an audio book, it’s read by a professional reader, like a professional voice actor, and even you get to know them when you listen to enough of them, like, oh, yeah, I don’t know their names, but I’m like, I remember another book that they read.


So what I love is that when you’re doing a course by Scott Kelly, it’s Scott Kelly.

It’s not Scott Kelly wrote this and then a professional voice actor is hammering out in a in a small, soundproof room. You are you’re getting the real deal. All of your content creators are they create the content. They voice the content.

That’s right. So cool. Right. There’s nothing better than when you get an audio book that’s read by the author, especially when they’re a personality, you know, because, you know, they invested the time to do it.

Yeah. And it feels that feels more like you’re actually sitting in the room with them.

And so I’m so I’m so on the same page. I think we’re not not everyone agrees with us there. I think there are some people who just prefer the professional narrator. And but I am totally in the camp of I want to hear Jerry Seinfeld read Jerry Seinfeld’s book. I want that voice. They don’t want some random person who doesn’t appreciate comedy. Reading that book, you hear professional voice actor.

So what’s the deal with you?

Like, no, it’s got to be his thing, right? Yeah. Yeah, there’s definitely there’s there’s so many nuances that and it’s funny at the end of it to you very much feel a part of their story. Totally.

Yeah. You feel like you just spend time with them. I mean they’ve been in your brain, they’re so intimate. It’s in your ear canal. Right. And they’ve been whispering sweet nothings. You’ve been walking and driving for the world.

Yeah, but what does blow it up, though, is that I’ve definitely gotten to the point where I listen to a lot of books at like one point seven, when you hear somebody talk and they sound like they’re talking very like you don’t sound like that at all.

Like button. Yeah. Yeah.

Because you say yes. So we’re going to to talk about this because you’re just trying to get it in as quickly as possible.

That’s the downside to, you know, that long, really long form book content is that when you see one in, you’re like 17 hours, you’re looking down into this like I’m not ready if I know if I’m ready to take this one on.

But luckily, I think like Netflix opening up the idea of like episodic content, being able to consume in batches or across time, like there’s no right way to consume episodic content.

And then this audio content follows the same format, like in the 70s, later told us that no one will go more than thirty minutes and retain interest in the program.

Right. When our programs was like, I don’t know if this is a good idea. You know, people love Lucille Ball, but I know they’re going to love an hour of her. They do love seventeen straight hours of her. If it truth was all right, if we actually let them try it so, so cool that we can be now.

You can, you can go you can get a knowable you know. Of course underway. Yeah. Take a break.

You can. That’s right. Yeah. Kind of at your at your own pace. And you know there’s certainly been times where I had a long drive and I was so grateful to have a seventeen hour audio book right today. I don’t I’m not commuting so I don’t want but I want the short three version. Yeah.

And this is the interesting thing is you’ve, you know, been, you know, the platform, it’s been been going for a while. Did you see sort of a marked difference in in patterns of utilization as we saw the world kind of shift the way we operated?

Yeah, I think I think it has halted some of the acceleration and audio consumption. Certainly, I know for myself, because I’m not reading and listening to less audio just now, I think a lot of people are in the same boat, but I think that it will come back. I do think that people will also just get screened for TI. And and going back to this idea of how what we really want to inculcate is this idea that noble is something that you can go take on a on a break.

Right. You can go for a walk or a run and put on your headphones and know that you’re going to listen to something that has a potential to a lesson that can change your life for the better that we can. So I think that we’ll see new behaviors and patterns emerge.

And I think that is the. That’s what I love about what you’re doing, and like I said, when it comes out in so much of what you the way you even describe it, you can just tell it’s in there. It’s like we want to know what’s a meaningful, positive impact that I can have as a result of the content. This is it’s going to be fluff.

I mean, good on right, everybody. There’s Harlequin romances for a reason, right? Somebody somebody out there loves that side of the story. Yeah, fantastic.

But, you know, we have such a great opportunity and especially when we’re in this real sort of challenge, detention economy, when we don’t have time away, like you said, that, you know, you realize we all despised commuting, but once we learned how to do things while we were commuting. That was a real opportunity sometimes to learn, to read or even just detach and going for a run. So I’m a cyclist and I love one of my favorite things is that the moment that I put my leg over the crossbar and I clip in my pedals and I start turning the pedals, I’m immediately in a different mindset because I know for the next four hours I have no way that I can type.

I have no way that I listen.

I have no I’m literally it’s me and an introspective mind for four hours, which can be a really dangerous place depending on what your mindset is.

But it’s it’s this beautiful sort of forcing function. Like when I get in a plane, that was my thing. When I get into a plane, people are like, you know, what I hate is that the Internet on planes is really terrible.

I’m like, you know what I, I love, but I’ve never found out because I, I when I’m on a plane. Yeah, it’s me. White noise. I can actually do other things.

Yeah, yeah, yeah. I’m the same, I’m the same way. I think that downtime is really important and it’s just forcing yourself or putting yourself in situations where you, you don’t have the willpower to resist the weapons of mass destruction. That’s right out of the smart, smart way to design environment.

You talked about UCB and I know from reading a bit about you were in that you you took a little time on the microphone yourself on on improv and stand up, OK, talk about minimum viable product and finding a product market fit making material, you know, two minutes on a microphone at an open mic feels like an eternity.


So cool. But it’s like, oh so what what drove you to take a run at that and what was the experience like.

I have always I’ve always been a fan of humor. I think humor is such a great tool in the chest of life and it feels like people have a great sense of humor. They’re able to. Go over the bumps of life so much better and easier and right and so I’ve just always appreciated comedians because my parents are immigrants. I think they let me watch a lot more TV than I probably should have. And I watch. And then when I was a kid.

And so that really. Influenced my my style of humor. I think I know what I think I’m squarely medium funny. Sometimes I can can make someone laugh and most of the time it’s it’s a mess. So I but it was something that scared me. And I really like this idea of doing things that scare me, because often on the other side of that is a sense of growth. And so I agree. I think it’s really scary to go up on stage and just have a microphone and talk to a roomful of strangers and try to get laughter.

But I think it’s also such an exciting format because the feedback is so objective. Right. And a world where there’s a lot of subjective value. Right. You don’t know. Well, how how good am I? Did I do a good job or are people just being nice, having being able to say something and then hear whether or not somebody laughs or not, you know, whether how you perform. And so I think that objective feedback is also exhilarating and exciting.

And a bit of advice I got on standup was talk about the stuff that you’re embarrassed about, because that’s the stuff that it’s honest. And most people will relate to it in some way or at least a certain subset of people relate to it.

And I think that’s humor is usually a byproduct of a sense of relief. Right. This idea of like, oh, you have that, OK, and that’s it. Yeah.

Yeah, it is. Because you effectively you’re just like anything, you know, when you’re you’re laying yourself bare. Right. You are going on a stage to present your you’re going to do stand up your you’re surrendering yourself to the audience for that period of time and hoping that they will carry you through this ride. And it’s very much an interactive experience, which is what’s funny. People always ask me, like it’s like I’ve been lucky enough through the course of my career to do a lot of, like public speaking and getting some large audience.

I used to be in a band years ago, so I was fairly familiar with being on a stage, but it still scares me all the time. And because you don’t know, it’s it’s a dynamic experience.

And I would and I would describe it and I help people with coaching for public speaking.

And I would say I call it listening to five thousand people at a time, because what you’re doing is you’re effectively letting them guide your talk. You may already have some kind of points you want to hit. Right.

But you have to let them steer you. You’re looking in the audience. You’re seeing the person in the front row who’s like leaning back and chuckling.

When you say a joke and you’re looking at the person who’s in the third row, who’s looking down in their phone all like from the moment you start, like, OK, I’m not watching that person for guys because they are not invested in it. Yeah, but it’s the audience themselves, you know, and at standup especially, they’re already invested in a way to the upcoming hour. Right. Five minutes, whatever, because they’ve made a point. I’m in this chair.

Yeah, I’m giving you a chance, kid.

So let’s see what you got.

Totally. I like that listening to find finance some people at the same time. That’s a good way of thinking about it in the end.

Is this as well as well, even the hour like when you see a comedian and they do an hour, what I see is eleven months of somebody doing five minute bits and two minute bits, like just testing it out and working it out and that real like they truly call it working out material.

It’s a gym membership, you know, going every day and trying it out and you’re testing in small rooms and on your family and friends wherever.


And eventually you glued together stuff that’s in a flow that’s in an hour.

It’s so when you. You know, when you’re a people based business, it sounds to me like that’s been a focus of you, is that you’ve always looked at a personal effect as an outcome of anything that you create. Absolutely, yeah, we are all west of us. So I think keeping that in mind and thinking about how do you want someone to feel, what’s the outcome that you want when they use your your product or service, I think is as a great.

System level way to think about what you’re working on. And sadly, it’s more rare sometimes than than we realize. I mean, I don’t think it truly is like we hear, of course, some of the tough stories and like it. I mean, there’s obviously, like you said, when you get into venture where people say like, oh, yeah, like congratulations on your venture funding, you almost want to say like, no, no, no.

Means to an end. Yeah, yeah. That is a person who’s betting on you to get an outsized return on their investment. And you’ve got the timer is ticking now.

Yes. Yes. It’s definitely a double edged sword, for lack of a better phrase. I think that. Yeah, I think a lot of entrepreneurs confuse raising money with succeeding at a business, and I you know, really the goal is to not have to raise money, right? The goal is you have a business. The people they love so much are paying you and you’re funding through your customers. But, you know, there are certainly advantages to funding.

And I don’t want to I don’t want to dismiss it totally or be ungrateful to work with great investors, which, you know, I certainly am. But but there are there are downsides and it is a calculated risk like anything else.

Yeah. And it’s there definitely is, as you say, sort of this idea of like a revenue funded business is fantastic. But of course, there’s going to be limitations.

There’s going to be advantages, disadvantages. And. Yes.

And, you know, like in your folks that you’ve worked with, you’ve chosen people that have been successful. They’ve got a high bar for their ethics to the way that they approach it, which is important as well. Right.

Because there are a lot of places that people could go to get money that are purely, you know, just accounting driven.

And so I like that in looking through sort of your investors and the folks that you work with in the market, I can appreciate their approach to it because I’ve seen, you know, their impact on the world and is really we are trying to do good things, you know, through technology and such. Yeah, absolutely, I think that in general, finding partners who are playing a long game is so valuable, who are playing multiple rounds because the firm, they care about, the reputation they cared about doing, supporting businesses that are doing good and aren’t just totally bottom line focused.

Right. Because they know that their only funding businesses that hurt people that long term, their value will diminish. So so I think fine people who play the long game, whether it’s a financial partner or a business partner, a life partner. When you chose to start your own venture and get into funding it and such, obviously your I should say I, I shouldn’t say obviously I shouldn’t lead with that.

Like, it would seem to me that your history in finance would have helped you to inform how to approach that part of the business building. How important was it that you had been intimate with the industry of finance as you went to actually raise funding? I think I think it’s helpful to know your customer and whatever situation you’re in, and so when you’re trying to capitalize your business, what you’re effectively doing is you’re selling equity. So it is by definition, you’re doing it.

It’s a sales process. And one of the best things that the best sales people do is they have empathy for their counterparty. And so really putting yourself in the shoes of the investors is helpful because you understand what are their needs and how can I fulfill them. So I did this presentation yesterday. I’m doing this course on fundraising. And my second tip is basically know your customer and know what venture investors want and how their math works. And the way their math works is they’re really trying to find the next Facebook, the next Pinterest and Snapchat, the next multibillion dollar company, because that is what really drives the returns.

There’s a Scott Kapoor is a partner at Andreessen Horowitz, put out a book recently called The Secrets of Sandhill Road. And he has this chart in there which shows that 60 percent of the returns for venture investors come from six percent of the deals. So when you’re pitching to investors, you really want to pitch this idea that you can be one of those six percent companies, that you’re going to be that company that can really drive outsized returns. And knowing that and having empathy for what the investors needs and wants are puts you in a better position to find a partner who you’re going to to jive with.

Yeah, that’s actually such a good read that will scare you out of ever going to where adventure because you realize how much is behind the scenes of the age of the funds, the the percentage of returns that have already come back in.

It is an incredible set of mathematics and and behaviors that will drive how an investor will comment at a particular point in time.

So it’s it is a very, very, very well done. And I love how transparent Scott was as part of the discussion, because it’s not often that people kind of share the math and the process behind the math.

Right. Which is which is pretty wild. I agree.

So for folks that want to find out more and I will make sure they do, I will be we’ll talk beyond here about, you know, how I can help to expand the brand. You know, Noble is just so fantastic. And I appreciate you taking the time with me today. Warren, this is a real pleasure.

And you’re you’re doing good things. And like I said, it’s sadly, it’s is more rare than than it should be. But it’s so nice when you see people that are becoming successful as a result of the good things they’re doing. And it’s well deserved.

Thank you so much for the kind words. I really enjoyed the conversation. It’s nice. It’s really energizing and fun to talk with someone who is so well rounded and so interested in and learning. And I’m sure we get to talk for multiple hours easily. And I look forward to continuing the conversation.

We’ll have to have we’ll do a knowable course on how to have a two way conversation on financing and venture capital. But it is this was really, really cool.

So, again, for folks, if they want to connect with you online, Warren, what’s the best way that they can reach out?

The best way to find me is on Twitter. My handle is at @wwshaef and you can also find knowable at knowable.fyi and the best places to find me and no perfect.

Yeah, I’ll make sure we have links in the show notes as well.

And, and I’ll, I’ll definitely be albe before long before we got to today, I wanted to test her to talk about things, you know, through the course of the discussion. But I’m definitely I’m keen, you know, if it’s valuable, I’d love to contribute content towards the platform. It’s just such a such a great opportunity. So absolutely.

Yeah. Let’s talk more about your book, because I think that’s a really easy way to get started. So we should have an offline conversation about that.

We’re in good shape. Warren, thank you very much for spending the time today. And I’ll say happy twenty, twenty one. We’re we’re we’re we’re around the corner on a lot of things and we’ve got some some good stuff ahead.

And for folks, definitely, if you want to hear great stuff like this, head over to knowable because you get to hear it from the from the creator’s voice, which is in my my view, one of the best ways to learn.

Sponsored by the 4-Step Guide to Delivering Extraordinary Software Demos that Win Deals - Click here and because we had such good response we have opened it up to make the eBook, Audiobook, and online course, more accessible by offering it all for only 5$

Sponsored by our friends at Veeam Software! Make sure to click here and get the latest and greatest data protection platform for everything from containers to your cloud!

Want to ensure your privacy is protected? I sure do. Privacy is a human right and the folks at ExpressVPN make sure of that. Head over to ExpressVPN and sign up today to protect your safety and privacy across any device, anywhere.

Luis Ceze is a computer architect and co-founder and CEO of OctoML. I do research in the intersection of computer systems architecture, programming languages, machine learning and biology. 

OctoML is doing some very cool things about demecratizing ML and transforming how ML models are optimized and made secure for deployment. Luis shares a lot of great info on the foundations of ML, ethics of data, and how he builds a team.

Check out OctoML online at https://octoml.ai

Watch the Video Version of our podcast with Luis Ceze of OctoML


Oh, yeah. Welcome, everybody, to the Disco Posse podcast. My name is Eric Wright and be your host. And this is a really fun episode if you’re digging machine learning then look no further.

You’re in for a great conversation. Before we get started, though, I want to make sure I give a huge shout out to all the great supporters and fans and friends of the show. This episode is brought to you by our favorite and good friends over at Veeam software.

This is everything you need for your data protection needs. I trust this company with my data, my identity. My goodness, whether it’s on the cloud, whether it’s on premises, whether it’s in using cloud native and the new stuff they’re doing with their recent purchase of a company called Kastin and Integration. Really cool stuff.

Whether you want to automate and orchestrate the entire kit from end to end for full business continuity and disaster recovery with Veeam availability orchestrator, you name it, Vimes got all sorts of goodness for you. If you want to go check it out and you can easily go to vee.am/discoposse and also let us know that you came from ol’ DiscoPosse’s podcast.

It’s kind of cool, but the Veeam family, it’s hard to say the Veeam family are extremely cool in that they’ve been great supporters. I love the platform. I love the team. And in fact, like if you actually go back in our archives, you can hear Danny Allen, who’s a fellow Canadian fellow cyclist and also a really fantastic human who’s the CTO over at Veeam. I was really lucky to have Danny on, but at any rate, go check it out.

Please do.

I definitely believe in their platform, in their product go to vee.am/discoposse

This is also brought to you by the four step guide to delivering extraordinary software demos that win deals.

This is something that I decided to build myself because what I found is that I’ve continuously involved in sort of sales processes and in listening to folks that are struggling with being able to connect with people, whether it’s in product marketing, product management, sales, technical sales. So what I did was I took all the lessons that I’ve captured myself and from my peers and compress them into a very easy to consume concise book. It’s called The Four Step Guide to delivering extraordinary software demos.

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Plus there’s an audio book, a course and I do regular AMAs for folks that that buy the package. So go to velocity closing dotcom and you can actually download the whole kit right out of the gate today.

With that, we’re going to jump right into the episode. This is Luis Ceze, who’s a fantastic person who I was so happy. He’s the CEO and co-founder of Octo ML.

Not only have they got the really cool thing, they called the optimizer, which is a fantastic name for a product, but they’re doing some really neat stuff around democratizing and making highly performing an insecure machine, learning models.

Really, really cool. So check it out. Plus, the Beast talks a lot about building the business, the educational impact of where technology and is so much cool stuff.

Anyways, I hope you enjoy the show as much as I did. Hi, this is Luis. I am a co-founder and CEO at OctoML and a professor of computer science in Washington, and you’re listening to the DiscoPosse podcast.

You’re innocent till the days go by the. So this is fantastic. I do want to very quickly introduce you as your company is doing some really neat stuff.

And of course, I say this as a as a precursor to what you’re going to tell us or for the people that are listening. We hear ML/AI and it becomes like this wash that it’s assumed that it’s like, you know, they always say no one believes what’s actually going on.

I’ve dug in and I’m excited about what you and the team are doing. So I wanted to lay this of this.

You have really are solving a very genuine and interesting challenge. And I can’t wait to kind of figure out how you got to solve these problems.

So anyways, me take it away.

Let’s let’s introduce you to the audience and talk about where where you’re from and how you got to begin the OctoML story.

That sounds good. Yeah. So I have a technical background. So most of my you know, I guess intellectually active life has been in computer architecture, programing languages and compilers. You know, I’ve my speech to the University of Illinois. I spent time at IBM Research before then working on large scale supercomputers like bludging, you know, primarily applied to life sciences problems. And at the University of Washington, where I’ve been almost for 14 years now, it’s kind of crazy to think about my research career.

There has been focus on what we call the intersection of new applications, new kinds of hardware and everything in between, you know, copilots programing languages and and so on. In about five or six years ago or so, we started looking at the problem of, well, the opportunity. I would say that based on the observation that machine learning is getting popular super fast. Right. Because, you know, machine learning allows us to solve interesting problems for things that we don’t know how to write direct code for.

Like, for example, if you think about how you can write an algorithm to find cats in the photograph, it’s really hard to to write the direct code for that. But, you know, the machine learning allows us to infer a program, learn a model from data and examples. Right. So this proof has proven to be really powerful and machine learning is permeating every single application we use today. Right. So but anyway, so six years or so ago, we started thinking about, well, there’s a variety of machine learning models that people care about for computer vision, natural language processing, you know, all the time, series predictions and so on, and a variety of harder targets that you want to run these models to.

This includes CPU’s, GPS and then accelerators and FPGA and DSD and all sorts of compute engines that have been growing really fast. So you had this interesting cross product.

If you have lots of models and lots of hardware, and how do you actually get them to run? Well, where you need them to run, that includes the cloud, the edge, you know, implantable devices, you know, with smart cameras, all of these things. Right. So and one thing that’s interesting to note in this context about machine learning models as computer programs is that they’re very sensitive to performance and they’re very computer hungry, the memory hungry, their bandwidth hungry.

So they need lots of data. They need lots of compute, therefore, making them perform the way you want them to perform, to be able to run fast enough and or use, you know, a reasonable amount of energy when being executed requires quite a bit of tuning your performance. Right. So that means that if you look at the machine learning models are deployed today, they’re highly dependent on hardware vendor specific software stacks like Nvidia with their GPS has cooled down and included a Khuda stack.

You know, ARM has compute led, Intel has indicated and you know, and then software at the height of it is have their own software stack in general. So this is also not ideal because then that means that from somebody who wants to deploy machine learning models, they need to understand ahead of time where they’re going to deploy, how they can deploy and use some custom tools that typically aren’t super easy to use. And that might not even be a software stack for the hardware that you care about.

That works well. Right. So long story short, the research that we started with a version of six years ago was to try and create a common layer that maps the high level frameworks that people use. Think of the data scientists use, like Tensor Flow, PI talks and so on, or numpty and bridge that to a higher targets in an automatic way. So we don’t have to worry about how are you going to deploy it, create this clean, open uniform layer that automates the process of getting your models from data scientists to production?

Well, this seems like yeah, it seems like a good idea and people would agree. But there’s a lot of challenges there, right? Because the way machine learning models are applied to. They rely on hand tuned, low level optimizations of code that really means like understanding the model, understanding the hardware and tuning the low level codes to make sure that you make the most out of that hardware. Right. So that takes a tremendous amount of work.

It’s not sustainable. So the research question that we start exploring was, can we use machine learning to optimize that process? So essentially use machine learning to make machine learning faster on your chosen hardware. And that’s what that was the that was how the Tensor Virtual Machine project was born. So we started this project six years ago, five, six years ago. And fast forward to today. It’s top level Apache Foundation Software Foundation project called Apache TVM has been adopted by all of the major players in AML, including the Amazon, Microsoft, Facebook and so on.

It’s supported by all of the major hardware vendors. It is actually the de facto open standard for deploying models on a bunch of hybrid targets. That is open source, right? So, so armed, for example, adopted to VMD as the official software stack. So AMD is building with we talk to him about, you know, support for AMD CPU’s and chips on on Apache to VM and then other companies like Xilinx, which makes upgrades and a bunch of other nascent hardware companies are using Apache to VM as their preferred software stack.

And just one final sentence, and I know this has been going on, but I just thought know there’s no there’s no rapide way like this.

This is a super important understanding of how we got to even the start line, which is even before where we are today.

Right, right. Yeah. So anyways, and then TVM has been adopted both by end users and and hardware vendors. And the way to think about EVM in one sentence essentially is this compiler time system to form this common layer across all sorts of hardware. And think of it as a 21st century operating system for machine learning models that runs in all different hardware. Right. So that’s Apache to it has almost 500 contributors from all over the world, has been adopted, as I said, by all the major players in the industry.

And we formed talk to him about a year and a half ago to continue investing into PVM, all of the core people around Apache to VMware, co-founder of the company. So these are three PhDs in Washington. And another co-founder, Jason Knight, was head of software products. And Intel left until the time to join the company. So I’ll come out today. It’s about 40 people. We our mission is to build this machine learning acceleration platform to enable anyone that a very automatic way to get the models deployed in a hardware that they want without having to fiddle or with, you know, different software specs or having to tune low level code to deploy your model.

Really, we are about automation and democratizing access to efficient machine learning because the tools today require quite a bit of various skills. So. And I think that’s where we really want to begin, is that every, you know, abstractions are generally done because it allows for obviously diversity of platforms above and below the line.

Wherever that abstraction layer is, the appropriate abstraction is a fantastic place where platform begins.

Then even further up is how do you organize a commercial entity that can create an additional value.

Even beyond that is a is really amazing because, you know, especially in a niche area like this where you look at look at the folks that are contributing to TVM and to who are obviously well down the road, you know, people are thinking that smell is coming like it’s already here in America anyway.

But so beyond the abstraction now, there’s that optimization which makes it you know, we’ll talk about the optimal approach to it. Maybe give a sense of what does a non optimized machine learning model do relative to an optimized one, because I think this is it’s hard for people that don’t get it to understand this.

Yeah, great scale.

I love that question, Erica. So so the UN optimized version typically means that you have you get a machine learning model and you run it through, say, tensor flow defaults, deployment model or Piotrek, and you choose the CPU or GPU. And most of the time what you get is not deployment ready because it’s not fast enough or uses too much memory or doesn’t make the most of the hardware and so on, or you don’t get the throughput that you want.

Or if you’re deploying the cloud, it’s too expensive because it uses a lot of compute. So now if you run that through TVM and just what it will do is it gets that model and then generates an executable that’s tuned for that specific hybrid target that you that you’re going to deploy it into essentially generates custom code, uses its machine learning magic that we can get into if you want. But basically to find the best way of compiling your model into your hardware targets, to make the most out of your hardware resources.

And the performance gains can be anywhere from two or three acts all the way to 30, 40 X. Right. So we have so if you look at our conference, for example, we had a conference in December for the past three years. There are cases of folks showing that there were up to one hundred up to eighty five X better performance. And we talked about anything above 10 X.. It’s not a nice to have it’s an enabler. Like if you make something five 10x better, you enable something that wasn’t possible before because it’s just too slow or too costly.

And that’s the level of performance gain that we’re talking about here. So and this can translate into enabling anybody that before was too slow to deploy. Now you can deploy it. That reduces costs in the cloud because 10x faster means 10x cheaper to run in the cloud and so on.

When this it also helps to answer the myth. I would believe that there is a hardware specific machine learning unit.

Well, there are obviously hardware specific iterations.

Each model, each data set based on scale, size use, you know, there’s a lot of factors that even the most perfectly designed physical unit with a broad set of use, whatever is going to be, whatever the right combination of things, may not be appropriate for every model.


So this is an beyond this is not like there’s like a really good gaming laptop and a really good you know, machine learning is at any it doesn’t take long before you get to the scale of using machine learning before even a machine learning node is not optimized for your particular model.

Absolutely. As another way of saying that, too, is that even if you have fantastic cadre, you know, and numerous resources, if you don’t have good software to make use of it, you know, it’s just no good for you. The question is, you know, it takes quite a bit of work for you to massage your model to make the most out of a hardware target. Right. And it doesn’t mean that all heart attacks will be appropriate for all models, but by and large, it’s dependent on very fairly low level, sophisticated engineering required to get there.

So and that’s what we all about. Automating a doctorate now. So you.

You have me curious and I’m going to ask you to go down the rabbit hole right away. How do you possibly at a code level through software to models on the fly based on hardware?

This is like my I’m lighting up at the idea of, like, getting Texaco’s you need to because I would love for folks to really get a sense of.

Yeah. Where those challenge is being solved.

Great coup. Absolutely. So let me just start once upon a time now. I’m not going to be that long. No, but I mean, well, you know, fundamentally, machine learning models, by and large, are a sequence of linear algebra operations. Think of it as multiplying a multidimensional data structure, but not think of it as a matrix matrix, vector multiplication, matrix, matrix duplication. But sometimes more than two dimensions would be imagine a three dimensional matrix to call the tensor.

Right. So in general, like a generalization of that, it’s about another 10. It’s a lot of linear algebra operations. Right. So now these are very performance sensitive because they depend on how you lay out the data structure in memory because it affects your memory. How can your cash behavior that depends which instructions you’re going to use in your process. Because different processes are diffuse. They have different instructions that are more appropriate than others. Like instead of doing a scalar where you multiply one number by a single, the nobody could use a vector structure which which applies to vectors at a time.

And there’s so many ways there’s literally millions, potentially billions of ways of compiling the same program into the same hardware. But among the billions of possibilities, some of them are vastly faster than others. So what do you have to do is just search, right? So given a program that’s your your and now model and give the higher target and there are billions of ways in which you can compile those, how do you pick the fastest one? OK, so now to answer your question directly, how do we use an amount for that search?

Well, the brute force way and I’d say the less smart way of doing this would be to try all 10 orbiters of possibilities. But the problem there is that you don’t have time. Imagine making a variant of the code, compiling, running it, even just that, even if it takes a second each, you’re talking about centuries of computes to actually you talk about centuries worth of time to actually find what’s the best program. Where Emelle comes into play is that as part of how TVM operates, TVM starts up when you create a new harder target, you runs a bunch of little experiments that builds a machine learning model of how the hardware itself behaves.

So this machine learning model is then used to do a very fast search among all the possibilities in which you are going to compile your models how to target among all of those possibilities, which one is likely to be the fastest one? And that can be vastly faster. Think of it a hundred million times faster than trying each one of them. So now you enable this ability of navigating the space of configurations in ways in which you can optimize the model and then choose the best one.

OK, so now a machine learning model has a combination of these. So we just apply this subsequently of every layer of a model and then we compose and see how they compose and run it through the prediction. And then again, we validate like are we doing a good job in the way we do? That is by doing the full computation and running of performance tests and comparing are we doing better? Yes. And we keep the search going. Does that does that give you a general idea of how to do that?

It does.

And this is the. It’s the interesting challenge that we have with anything that’s any long running process, even if, like, just think of just traditional batch computing where where folks live, a massive long batch.

And at some point your you know, let’s just you know, for folks remember the days of the overnight jobs, right. So they’d have some four hour batch that would run. And you’re five hours in and something’s wrong. And there’s the difficulty of assessing.

If I stop now, optimize correct code, do something and then rerun, is it more worthwhile to do so versus just letting it run out? And it’s going to take twice as long as I expected. Like, that’s a relative number that I think a lot of folks would remember, even if it’s like a five minute script, if it takes five minutes and it should take 30 seconds, you know, makes sense.

But like the the scale at which you’re talking about, number one, to the initial problem of like where we’re going to go and use a model against a mass data set, it’s going to take.

Potentially hours, days, whatever is going to be significant. But then to run scenario, run that scenario. Repeatedly before triggering it effectively defined the most optimal place in which the host exactly is in fact identifiability to the right.

Yes, I think you could run those running, running parallel simulation modeling.

This is you anybody would think, oh, of course, where you’re going to use machine learning, like, well, you’ve now got an inception problem, right? Your effect, you have to do something that’s incredibly complex. To solve an even more complex problem, but. It seems untenable for people to imagine that because this could be done, so this is why this is this is like, yeah, it is how we do it, that we use this machine and others to make it.

So now we actually let me let me go and ask a question and ask her, like, what do we offer as a company? What is what is the commercial story here? Right. So anyone TVM is opensource, right? Anyone can just go to a package to PVM GitHub repo, download the code and run it. But Psyllium takes, you know, to set it up because he has to set up harder targets. And then you have to collect these machine learning models that predict how the harder behaves.

And and, you know, it is a sophisticated tool that it works really well. But you do it does it does require quite a bit of lifting to to get going in the context of of an end user. Right. Well, we did talk to a man who has built a platform called the OCTA Mizer, which is a fully hosted software as a service offering up to Veniam that automates the whole thing that has a really nice graphical user interface.

We can upload models, choose your harder targets, click the magic button, optimize, and then a few hours, maybe a day later, you get an executable just deeply optimized for your your higher target of choice. And the way that this is different than the experience using Cavium, as I said, it’s much easier. You don’t have to stall and do anything. There’s no no code required. And you just literally upload the model, choose the target and download it, or you can use an API.

But also the optimizer has no models and has pre a preset set of these hardware targets. Optimizer is built on machine learning that are ready to go for use, who don’t have to go and collect yourselves just because you can be protected from days using the optimizer when this is what I think is incredible.

And I spoke with somebody very recently and we had we just we’re just I’m enthralled with this idea of where we are today. And, you know, now at twenty twenty one, as we record this, it’s like.

The accessibility of both models and training data to like, if you wanted to try and get into the business of machine learning, just even to dabble with it, to get the hardware, to be able to have some data, to be able to the like the one or one layer of machine learning was very low level, like very simplified, and there was no access to go beyond and really test it. So now because like what you’ve got with the optimizer, like I said, you’re shipping stuff that’s there and it’s ready to go, so you don’t even need to then wear it like so like those first steps are incredibly challenging.

And and this is what I want to impress upon people that there’s effectively no reason why you wouldn’t just get started because it’s been done for you and it’s accessible to you now. Like, it’s it’s a wondrous time where we can do these things, because for all of the things that people are worried about, you know, one, I don’t understand complex mathematics. So how can I do with machine learning? Well, it’s not necessarily.

Not exactly. It’s about abstracting those away. Right. Right. And secondarily, how do I know how do I learn to trust what machine learning does? The only way to do it is to get in and see it. It’s a weird because machine learning has this really odd thing.

Even when we talk about A.I., sometimes it’s I describe it as like the scene from The Matrix when, you know, when when the Oracle system, you don’t worry about the face. And then he says, what? And he turns around, he knocks a base off the table and she says, what a really make your noodle is. If you would actually would have done that if I hadn’t told you about it.

And when we explained machine learning and like what you get, like you said, how do you find a picture of a cat? How do you tell the difference between a blueberry muffin and a Pomeranian? Like there’s all of these things where. There’s people don’t trust the outcome because they saw a meme about it one day, but you can dove in. You can test it out. You can put data through it. You can see Output’s.

It’s it’s there today because of what you and the team and what what the community is doing around this stuff, which is pretty amazing, right?

Yeah. And I want to pull that thread for just a minute on how the machine learning models, which, you know, there’s a whole sub field of machine learning, which is about explainable A.I. or excitable machine learning models to get people to trust and more. But I would even start by saying that how do we trust software to let’s forget about machine learning. Let’s just think about software the way we trusted by saying, like, we’ve put this much time testing it and you will have some confidence that it’s likely to work on the scenarios where your users care about.

We do not do formal verification of all software today. You don’t want to formally verify Excel or Oracle or Microsoft score. Basically, you just if you test it extensively and then you have confidence that it behaves the way you expect it to behave, and then you put a checkmark in any ship it machine learning models, they are that way too.

You have a training session, you have a test set, you train it, you test, and then you can do all sorts of ways of actually get more serious to the test. But, you know, that is going to work within the set of inputs that he was, you know, certified for, tested for. It works well, right. So, yes. So then you can go all into to this a huge fun discussion that we could have at some point.

Probably not. Now on how you explain to humans what is it in a machine that humans would trust them better.

Right. So and yeah. So that might involve compromising performance. It could be you might want to choose a model that’s not just as fast, but at least when you look at internally it works. You can explain to humans it might be useful for, say, medical diagnostics where you want a doctor to see, like, you know, this kind of like generally looks right to the decision tree. Here it is. Right, right. So then we can help with those cases, too, with integrating the optimizer, because if you choose to use a model that’s not as fast just because it’s more potentially trustworthy, we can help you recoup performance by giving you a highly optimized version.

And this is where, you know, I would say that the people that realize the difficulty that they’re facing, I’d like to get like, how do we get better at machine learning? You brought up the most perfect point. We don’t we just broadly trust software as if it’s like if it’s linear in its ability to scale. We were like, oh, I can almost run as fast as the machine. So they’ve we’ve just it kind of we grew up with it.

So we don’t distrust it as much as we we don’t necessarily trust it, but we don’t distrust it. Machine learning and quantum and the idea of being able to scale far beyond human capability. There’s this really odd. Case where the distrust is greater than the trust. And even though there’s no fun, I mean, this is I mean, effectively, it’s a lot of the core and the fundamentals of like behavioral psychology, you know, because of the way that we we place bets, the way we we think about, you know, outcomes versus efforts.

It is really funny or peculiar, I should say, you know, to see how people behave, but yet when they see the outcomes, like you said, they’d be like, oh, OK, now that’s fine.

I make sense, but. When you go one step further, which is especially the folks that are going to be, you know, customers and folks that you’re talking to. They’re further along where they know the risk of, you know, yeah, and the benefits outweigh. Yes. So they but the benefits to outweigh but the benefits outweigh the risks. Right, exactly. And also, I mean, trust the kind of stuff that it’s I guess it’s a kind of property kind of feeling that it takes a while to build, but it’s very easy to lose.

Right. So it takes a lot of work to build trust. And it means that investing, learning how you can live with it for a while and it works really well. But then you make a small change because models evolve fast and then that one breaks and he makes you lose some trust in it. But, you know, that’s just part of how it is.

And I feel like given the strides made in machine learning, research and getting models to be more trustworthy, more explainable together with all of the machine learning systems work, which is all we focus on in making these models perform and run well in the real world, I feel like very, very quickly going to we’re going to trust them just as much as we trust software and, you know, things that are really transformational to our lives, like self-driving cars, like automated diagnostics, like, you know, using A.I. designs, drugs and therapies and diagnostics is just such a special for us that the the progress that and the impact it has on human life is so far beyond the risks that he can cause, in my opinion.

You know, this may be philosophical, but I do think that in this case, the benefits far outweigh the risks.

So I’d be curious, especially because you’re obviously very close to it near you. You were you’re doing this in academia as well as in business. So you’re really tackling it on two streams, which is always amazing.

And I think that’s where a lot of the stuff comes from. But in fact, a lot of technology, amazing technology startups have been founded from academia and made their way into commercial business. And then those folks maybe get into venture capital. And it’s neat to see this progression.

But, you know, there are very few people that most people know.

And I wherever the descriptors of most or many, but who they could look to in and get that first understanding of the impact and importance of machine learning on society. Obviously, one that I know off the top of my head, of course, Cassi Korsakov.

She’s with Google and a fantastic person that truly does a lot to sort of share. The human side of of the value of machine learning and, you know, it’s neat to see those stories. So I’m curious, Lewis, who in your peer group and yourself included, like, how do you how do you get people involved and interested in the potential that we have as a society because of machine learning?

Yeah, great, great question. The way I think we get people interested and excited about is just by continuing to show the kind of problems that we can solve, the kind of new applications that we can build with with machine learning. Right. So let me let me take a recent example, seeing all of the progress going on on this large language models based on or three, for example. I mean, the ability the ability of summarizing text is fantastic with generating new tax is great to help you draft these technologies.

Just seem like magic. They work really, really well. And and I think that has the potential to amplify ability to understand large bodies of tasks of texts. Right.

So, for example, some of my colleagues and friends at AC2 here in Seattle had been working on these tools that help one understand how bodies of knowledge in a specific field. They’ve done this for covid recently, for example. I think it’s just really amazing applications that can capture the imagination and have a direct impact right now that really gets people more excited about it. I’m not sure that’s what you’re asking. No, I think it’s all about showing great.

But then, you know, just seeing the. So that’s one of them. The other one seeing the I know that we’re so far away from fully autonomous vehicles, but just seeing the kind of things that are ever more accessible electric vehicles from big ones like, for example, Tesla. I’ve that a model, a model three can do Real-Time Computer Vision and build a 3D model of the world around it. And you see, you know, the cars and people crossing the streets and then, you know, like this thing that is happening all the time.

It’s like, oh, this is a model. The car is actually agrees with them. Just as you get exposed to this, you get people more and more and more and realize how how exciting this is. So think about the applications that it enables.

And then a final one. It’s more academic. What’s becoming more top of mind today that I find particularly exciting and happens to be related to one of my personal intellectual passions of, you know, molecular biology and life sciences. I think that nature is a boundless source of two things, you know, mechanisms that we can use and molecules that can go and used to do beaches and things. And then second is all sorts of interesting problems that you can use a and the mouth to understand, you know, how nature works.

And it has tremendous impact on on understanding life and on understanding disease and understanding new therapies and so on. And there’s some things I think it’s fair to say that the strides that we’ve made in understanding, you know, gene regulatory networks and understanding, you know, a lot of life sciences processes would not have been possible without machine learning.

And right, so and. Yeah, so this has an incredible effect today, like, you know, how we can design a vaccine super fast, how can actually test it super fast? How can actually understanding do DNA sequencing of of of different people understanding? What is it? How did it correlate with things that you observed? I mean, this all boils down to it is enabled by conventional processes, largely based on machine learning.

And that’s one of the most you know, I don’t have the numbers handy, but I you know, I know it’s a good example to use, but as far as like the the the economies that we’ve achieved of time and scale is, you know, look at sequencing DNA, both the physical exertion required to do so on like hardware, the time and the cost in 20 years time or 10 years time even what it doesn’t take long to go back and see.

It was thousands of dollars in order to and and, you know, the amount of time required to do so versus now it is pennies on the dollar in effect relative to what the cost was not too many years ago, but.

Absolutely, yeah. And they should mention as one of the one of the research areas that I’m still active is on essentially using DNA for data storage, which would involve writing DNA and reading DNA sequencing. And this relied on on the progress of DNA. So I watch these trends very closely. And just to put numbers there, we’re really talking about, you know, the first human genome, the sequence. It was a huge landmark a couple of decades ago, actually cost over a billion dollars.

And today you can do a full you can do a full genome sequencing of, you know, under a thousand dollars, which is just you talking about a million faud, literally a million fold decrease, a million acts, decrease in cost. And then the amount of and this is all, by the way, enabled not only by no better understanding how, you know, it’s, of course, the genius idea of the next generation sequencing. But from there to today, a lot of it is really advances in computing infrastructure because it’s very complex, intensive advance in imaging technologies and optics.

Right. So and advances in machine learning, decoding very faint signals to read the letters that are in the DNA sequences, just. Yeah, all roads on the backs of Moore’s Law plus, you know, computing. That’s right.

Well, it’s interesting to see you as we come through. There’s a beautiful sort of readiness that’s arrived of all of these criteria. Right.

Like you said, you know, computational power, the understated scientific understanding, all of these things, they they move enough in effectively like a horse race beside each other.

And when one crosses the line, the rest cross very shortly after because one effectively carries the other. And there is this merger of things that has to occur to get then from there exponential increase in capabilities.

And we’ve seen so much recently and we as humans, we far overused the phrase exponential.

Right. People like us. And there’s a literally I talked with Joe back to you ahead of the founder of a company called Quant Gene.

And and he talked we talked a lot about that. And that’s but that’s their whole thing, is they’re using quantum computing and genome sequencing to find. Better ways to detect every kind of cancer, he says, but 10, 20 years ago, you would have a team of scientists and entire research area that’s focused solely on researching one type mapping, one type of cancer. And now, because of the ability in quantum computing, the ability we have in hardware, software and people and understanding, they can seek every possible type of cancer collectively through the research they’re doing.

And this is really like first principles like this is exponential growth in what we can do as an outcome because of the technology that we’ve enabled.

So what you’ve done and what you and the industry in your peer group and all of us are they’re doing is. Using first principles to do to set the stage for. An unlimited amount of new first principles thinking, going to do fantastic things, yeah, it’s a great point. And the way out outside this conversation back to what OCTA Mountain does is there are a lot of problems today or opportunities today, specifically in life sciences. For example, if you’re doing deep learning over genomic data that, you know, it’ll be without significant optimization would be beyond the reach of most people talking about problems that could literally take millions of dollars worth of compute cycles in in in cloud services.

If you could if you make that 50 X faster, the problem that takes billions of dollars cost in the tens of thousands of hundreds of dollars, which something that now becomes feasible and is also something that we’re very excited about this. You know, what we are doing is because not only do we make it more accessible to enable applications that we are doing today and make them faster and more responsive, but also the kind of optimization degree that we offer could enable things that would be beyond the reach of many today in application areas that are more custom, like, for example, what is life sciences when I think it’s one one great example.

So, yeah, and I think this is the fantastic opportunity that you have got now for your current and future customers is that it’s no longer about baseline achievement, but we can immediately begin to think of optimization versus that wasn’t accessible before. That just wasn’t it was just a matter of can we do it? And now it’s can we do this and are we doing it in the most effective and optimized manner.

Right. Yeah. And which which is often necessary like so to actually make it. Let me let me give you a without disclosing anything sensitive. You know, we’re being we’ve been working with customers that both deployed AML at scale and the edge and the cloud on the edge side. Think of it as if you had the machine learning model that helps you, that helps you extract help you understand the scene so you can replace objects in real time, say, for video chat, for example.

And then you have a you have that app running all sorts of events and devices like, know, different types of laptops, PCs or tablets or phones and so on. Once you have a model like that, what you have to do, what you have to do today to deploy it is by every single time you had to go and optimize and make sure that this is run fast enough on this and on on this ABC and any that different modeling that that is like, you know, it’s just really the unsurmountable of but not automating all of that, you know, which is what we do with the optimizer is something that is enabling, you know, the evolution of these applications.

And on the cloud side, you know, if you’re doing things like, you know, computer vision over large collections of of of images or video and to a large scale. So this could cost, you know, an incredible amount of money. If you don’t optimize writes, it means that you until you hits a certain cost target, you can’t you can’t even for companies that have deep pockets, that’s so significant what we’re talking about here.

So and it becomes the interesting conundrum of in order to test to see that your your model is effective and how long it’s going to take to run and what the optimization opportunities may be for it, you run it against your data set, but if you run it repeatedly against the same data set, it’s actually goes counter to the value that is dangerous if you continue to run like you’re not going to get expected results. And it may sort of skew some results if you send exactly the same data through exactly the same model over and over again.

Because they.

You do it again. Yeah, yeah, yeah. But usually it’s a wee.

So so that’s why effectively people are probably going to sort of throw up their hands and say, hey, you know, at least we know it works. We don’t know that it could run faster. So there was sort of an unfortunate acceptance up until, you know, what you’re bringing to the market that there just was it was just the cost of doing business in Emelle. Right. And that doesn’t need to be the case anymore, does it need to be.

Exactly. Does need to be the case. And these tools and he doesn’t need to be the case for as many possible users as far as as we possibly can. That’s why we strive for really easy to use and really making the level of abstraction much higher. So instead of you having to bear a super talented software engineer with a data scientist to go and do these things, going to be able to have the data scientists themselves to just go and use a tool that subsumes the need of having to work closely with this engineering team to deploy it.

Right. So, yeah.

Yeah. Well, yes, this is the thing of. We can now actually get positive business and societal outcomes instead of just technological outcomes.

I think one of my favorite things I remember Peter Tiel, he refers to he says we’ve we were trying to get Star Trek, but all we got was the Star Trek computer.

We didn’t get the tricorder. We didn’t get the transplant. We didn’t get the other things. All we got was the the computer that, you know, and and in fact, that’s the dangerous place to rely on. You know, we need to do things with these things. And this is why we are now at the point where we can really do amazing things.

Absolutely. And especially if you are a scientist. Right.

So I’m actually curious, Louise, what is a data scientists? Because I started to get different pictures of what that person is today, so if I’m an organization that I’m looking to hire a data scientist, what’s that profile of a person look like?

I’m curious in your experiences, given that you’re obviously very close to the field.

Yeah, no, that’s that’s a great question. Also, it’s a great question. And there’s just so many possibilities here. I tell it, say it really it really depends on what kind of problem we try to solve after data. Scientists tends to specialize in different kinds of data rights for different kinds of models. I would say that we should approach our, you know, see what kind of data you have to probably try to solve and go after.

Data scientist had zero domain experience because if you have some domain experience, you tend to get a lot better, you know, more predictive models and a lot better analysis out of the data that you that you have. What I think you actually focus people that say that the folks and people understand the problem domain and understand is the you know, the core tools in machine learning and data and analytics and statistics. Right. To go and work with your data now to go full circle.

Now, what I think is harder is trying to find a data scientist that can do that and also can do all of the complicated, ugly software tricks and they have to do to actually get get the model to or get the results to be usable as an end product. This is almost impossible to find somebody like that. This is why, you know, when we do, because somewhere early on in the life of the company, we’re doing some interviews to see what is it that we will be going after.

The number one pain points that we heard from folks that were running these things is that, well, you know, we have great data scientists and we’ve been doing better because the tools for the science are getting better and, you know, and there’s more. But now we have to go compare them was with very rare software engineering skills. And that’s what breaks the whole magic tear, because now you have the data and the data scientists just don’t have the rest of the resources to go and make their output be useful.

That’s where that’s where we started. Like, let’s just go to zero in on let’s automate the process of what gets out of the hands of data scientists and what should be the deployable module and get that gap and cover that with, you know, very sophisticated automation that uses machine learning. That’s really what the optimizer does. Right.

So first of all, my favorite name on Earth of a platform optimizer.

Sounds cool. I’m glad you like it. We love it. Yeah, the optimizer is definitely yeah. Every time I say like, it makes me smile. I’ve been saying this for over a year now and so I love. Thank you. Thank you for that Eric. So I hope I answered your question, but yeah. So how are you. Data scientist is I’m glad the tools are getting better, but it’s just so dependent on what kind of problem you want to solve that.

Yeah, it’s really about people understand the problem domain.

So it’s it’ll be interesting to see because I think we face right now as a society and businesses and governments is the sense that you’ve got to wait for the next.

We have to wait for the next batch of students to come up through the education system with access to the tools. So you have an eight to ten year cycle before people are actually able to do. And and in that amount of time, since we have so fundamentally change now, we don’t have to wait for that. We can we can train people in place. We can up level people where they’re at, through software, through technology, through capabilities.

It’s yeah, it’s an interesting point. I’m not sure if that’s where you’re going. And so it is a complete tangent here, but I think it’s fascinating to think about the role of A.I. and now in machine learning, it’s actually in educating humans. Right. So right. So there’s like ways of using AML to generate problem sets for kids to learn the ways of evaluating their kids. Yeah, so. And using that to actually train engineers. Right.

So the the potential for this stuff is just it is wondrous.

You know, there’s obviously there’s and I’ve talked with a few folks about some of the challenges around the ethics and biases.

And I and definitely I think what it’s superimportant, extremely important and tough on him.

I know I’ll ask you this kind of in your let me lean on your academia side, because I especially, as you put it, my Professor Hattab, my professor, had said you’re you’re very you probably that’s probably an area that where it gets dealt with or questioned the most. Is it through academia? We’re studying, you know, what are the potential like in business? It’s more like how do you, you know, broadly get this out in the world.

But we are finding through, you know, through thinking groups and through, you know, think tanks and through universities and the academia, like we are now at the study phase or continue to be when we’ll be for a long time in the study phase of.

How do we make sure? That we are as best as possible using these tools and this data. You know, it’s a real conundrum, because if it if it’s a representation of society, how much do we steer it in order to get what we hope to get out of it? Versus if a machine learning model gives you an output and we should there’s a reason it came up with that output we made up, trust it or understand it or maybe not like it, but it’s more like looking at how it got there than trying to, you know, stand at the output phase and then try and steer it towards a belief or an opinion, which is.

Yeah, well, this is a great question, super deep. And again, it could be a topic of a long conversation, but I would say that. Right. No, no, no.

But I’m I’m happy to offer some some thoughts here, because I do have colleagues and friends that I think about this for a good chunk of their waking hours, so. First of all, I mean, absolutely, we have to be mindful of biases in in machine learning, especially because of machine learning being dependent on training data. We need to make sure that the data is representative of a broad set of uses that’s actually equitable across all of the stakeholders in how this model is deployed and is aspects of the model architecture that should be training should be developed in the loop assuming.

And I think that comes fundamentally from having a diverse team. Right. So if you have a diverse team of people that are working on there’s a diverse engineering team or diverse team of of of data scientists as a team, that actually doing this naturally will point out deficiencies in training data and the architecture of the models. Just so with that as the people aspect here, that if we talk about machines doing more and more things, you have machines, you have people designing machines and designing engines that these people themselves need to be there.

Is this why I’m a firm believer of extremely diverse things? I’ve done that in academic teams that I’ve that I’ve built and know, pay a lot of attention to that at Octonal as well. That’s one thing.

And then the second thing is just through education. Right. So we have to keep bringing up these aspects of of of bias and make sure that he works for all the stakeholders, not just the machine learning, by the way, but in any engineering discipline. So there’s a friend of mine that once gave a talk and you to put his name here, but so about bias in machine learning. And he started with a great example. They might have heard that one of the very first photographic films that you heard that story before, photographic films that Kodak that essentially we’re talking about the chemical engineering thing like you designed the chemistry of our bodies and the way they designed the photosensitive material.

They realized that the way they were judging whether it was good or not was by, you know, checking this against a certain set of people with a different skin color, with a specific skin color. That means that if you actually use this in other skin color would just not work at all, which is not look right. And it was the case. So that means that they were they were biased. They set a great example that bias in how we evaluate whether something is ready or not for all the stakeholders is just not applied to machinery.

But any engineering discipline in this case, I thought was a really great one because it talked about something that, you know, on the order of a century old. Right. So it is just and then the way the tone of the film was and good for all of the callers. And it actually showed, you know, as one historical aspect of that. And that’s that’s true. You know, how you design you know how this affects building architecture.

There’s is it’s like a lot of things that humans use should have this thinking, not just machine learning. Right. Just that machine learning gets that extra aspect, because right now it’s enabling applications that it’s not a machine that gets extra attention on this because of how their applications is changing our lives super fast today, but also because so sensitive to data litigation is so fast that, you know, leads to a lot of misfortunes and, you know, let’s say missed opportunities to make it better early on in its.

There’s so much positive, but unfortunately, what will happen is the one the one negative story will be the one that becomes the focus. Quite often it’s like with anything. It was interesting. I was at a an event a couple of years ago says that it’s almost feel like it’s been that long since we’ve been at in-person events.

And it was a Canadian insurance company that had created their own their own call centers with EHI machine learning, all the stuff. And they basically fed it every single call that they’ve ever had taken with a customer service call and and trains this. And then they finally this was the moment where they said it as the to answer the next call. And it took the call and it dealt with with the person and they said it goes all the way through. And like, obviously they’re listening and monitoring like what’s let’s see how it behaves and it gets all the way to the end, solves the person’s problem in a perfect human sounding voice and gets all the way to the end.

And and this is the closing of the call. Then the machine says, is there anything else that I can help you with today?

And they said, yes, they stopped and looked at each other the like.

We that’s never been in a training manual. It’s never been there’s nothing that tells it to do that. But through all of the different calls and all the different ascertains that this was the best way. And they said then what was even funnier was the response. The person says, no, thank you, but I just want to thank you, especially because it’s so nice to talk to a human for a change right now.

I love that. Yeah, that’s it. That’s it. But this is the. There there’s going to be a beautiful call, like an augmented world, where we can leverage machine learning in these capabilities with like natural language processing and all these different things, we can use that like here.

But companies that are using it to detect, you know, emotional changes in people’s voices and they’re using effect to detect changes in their behaviors that, you know, could be for people that are at risk of suicide or there’s, you know, so there’s so many incredibly positive things.

And this is why, like I said, when so we have a friend in common, Amber Roland, who is, you know, you know, through your you she helps you with your PR and just fantastic human.

And she’s done a ton of stuff, you know, introduce me to the great people as well over time. And she’s like, every time I talk to her, it’s just like this, like, oh, yeah, here’s the human side. And she’s going to introduce me to people that are doing. Big things in when she said, I want you to talk to these folks, to talk to them. I had to race to the reply and say, I’m so glad that you did.


Now I know, of course, because like you mentioned, it’s tough. This is the tough part.

It’s hard to have hero customer stories because a lot of the customers you have, obviously, there’s going to be sensitivity and there’s and you’re ready, you know, in the in the birth of the company.

But you know what is maybe another quick example of a real human outcome that you’ve been able to see come to life. Well, yeah, great, so that we have several of them, right, so we. Let me let me if I can just pick up into, like, what kind of customers we work with today. Right. We have two categories of customers, one hour in which you learn the end users. These are companies that deploy that have products that use machine learning both on the edge and any of the cloud without getting into specifics.

I think of it as enabling much more natural user interfaces. I’d say that this is has, you know, a human outcome, because if you actually enable a new way of using voice, basing their faces in in very cheap, low, low end devices, you can buy them into more, more user scenarios and therefore have both add added convenience to people that are able and also add that ability to people that do not have, you know, that that are potentially disabled.

Right. So let’s say that is like a really nice outcome of just enabling more intelligence and intelligence. Think the edge is something that we have customers that we have enabled to do so. But customers are just machine learning and users and then also enabling hardware vendors that do not did not have a solid software stack to make their hardware useful for machine learning and then enable them to to to do so. But I’d say that in general, like the impact on human life, what we do is, again, one, enabling applications that weren’t possible before in terms of telling you the edge and also enabling these large scale compute problems that could be related to, say, life sciences, you know, that would not be accessible without the level optimizations that we provide.

So that’s how we got really proud of what we do in terms of the and, you know, and the impact in human life is we didn’t have any applications and even things that would be possible before. So.

Well, the the thing that I I try to remind people, too, is, you know, when we look at phases of of adoption and real life, if we look at sort of the hype lifecycle of so many things and we talk about edge computing for a long time and people still sort of struggle with what it means, but in effect, the the phone, you know, in a way, the phone you hold in your hand while it is a computer that’s stronger than the computer that since the first humans to the moon, it is an, in effect, an edge device.

Edge devices aren’t just raspberry pis that are glued to the side of a cell phone tower. They’re going to be computing. They’re distributed with different physical capabilities, different memory, different storage, different network, different CPU. And this is when. The ability to use decentralization, it will this is the again, exponential effect is that we can rather than taking collecting the data, they’re stream it back to central storage processing, essentially streaming it back the amount of bandwidth.

It’s it’s untenable. Right. And this is why being able to do processing and machine learning at the Edge is an amazing leap in. And what we need to do.

And this is what hammers home the value of what you’re doing, because there is no way that the model you’re going to run centrally is going to be run the same way at the edge of the hydra’s different.

Everything is. Yeah. So I love. Yeah, you said it exactly right. And I just said one more potentially overly dramatic point here, that which is speed of light is limited and light is fast, but you cannot make it faster. You know, if you had to actually have to go and you know, the speed of light is a limitation in in wireless, in any communication. Right. So not to justify this in any any communication.

So that means that some things fundamentally have to do at a very short physical distance to actually enable low latency and not having to rely on a long, long range infrastructure. This all of the hopes that he has to jump. So being able to compute and the edge has this fundamental enabling, like back by, you know, hard laws of physics that you must run this locally advisee continuous application. Right.

So, yeah, it also just enables low power, right? Yeah.

There’s this is the reason why people hate Bitcoin, not just because of most of the people that got in early and got rich was because of the physical impact it has on compute requirement.

And so there’s always this comparison of like, oh, you know, for every bitcoin you mine, it will basically you could power a city for a year or whatever it’s going to be had.

But this that is that’s a sort of a mythical historical thing. But beyond Bitcoin, when we look at. Yeah, using block chain, using machine learning all of these things to be able to do them on lower power, diverse hardware platforms. Yeah.

This is this is the Gutenberg Revolution of Machine Learning.

Wow. Thank you. All right. That was beautiful. Take the good. Agreed. Yeah.

And also to free people from having to even think about how they can deploy models because it’s just so that course like can even as I development to knowing how you going to be used. But how do you know. I mean there’s so many just think about mobile phones like, you know, there’s literally 200 different Android phones. So how are you going to tune for every single one of them right now? It’s just like this, a very small example when I just think about it came out as soon as he could run to the phone grid and a camera could run on a smart trained on a smart device and on the smartwatch and all of these things, just not having to worry about where it runs, could enable a whole wave of innovation.


So, yeah, this is so you must be excited. To be able to be both, you know, in academia and watching this world evolve and now you can very literally create the future through what you’re enabling at Octo Amelle, this is how good does it feel when you when you began this journey?

It’s got to be challenging.

And I say this like, obviously there’s no easy path to entrepreneurship and.

Yeah, well, thank you I for that question, because I used to present to Forsys how lucky I feel to have the team that we have. And I think that has one of the reasons that I think we have such a fantastic team is because of our connection to academia and the fact that we are a company that has a bottom line to to it’s you know, we have investors, we have customers have employees. And luckily, when we are in a very good position and that means that we’re not we’re not a research group.

Right. But we have a lot of we are really pushing the pushing the state of the art because we are a deep technology company. Right. So we are enabled by the fact that we had people with the products, that we build everything. But the fact that we actually had people that think on the frontiers of what’s possible with machine learning, like using machine learning to make machine learning better. And the connection to academia, I think is is really important and extremely synergistics and I would say essential to us because we are connected to the latest and greatest in machine learning models and the latest and greatest understanding of where even the hardware industry is going and what’s possible there, but also as a source of talents.

Right. So our company has incredible, incredible, incredible talent. We have more than a dozen PhDs in the team and a team of 40. Not that, you know, it’s just about that. But everyone is great. But I’m just saying that just showed the level that we are operating here in terms of pushing state of the art that we have a lot of people that, you know, operate like software engineers and making a product, but they all have a research mentality and research background and always think about how is it that how can I do something better than was done before?

Because that’s how a lot of folks have done research, you might think. Right.

So that’s and that’s very fortunate. Yeah. Yeah.

It’s always that tough metric when you like it. And I believe everyone should be proud especially to say, like, you know, we have a number of PhDs at it. At my own company. We have the same thing we talk about sometimes.

And it feels odd sometimes to say it depending on what the context. But the truth is, what you just said is that there are a group of people who chose to go above and beyond in order to advance something that had been done before that could be done better. And then when you bring a specialty machine learning of all the technologies and the things that we’re doing in the world right now. This needs those advantages for sure, thinkers who are willing to do what they did before and as a group, as a collective, and it’s also important that you don’t have one PhD because then having multiple.

Thinkers that way, people who’ve lived that life, they have the ability to use critical thinking as a group. To aim for the best outcome, not the right answer, the best outcome, and yet as humans, especially as entrepreneurs, we often get stuck with.

I’ve got the right answer and I’ve just got to teach the world that versus let’s as a group work with our customers in the community and the world and academia and come up with the the best outcome because it will be surpassed in future.

Absolutely absurd. Yeah, no, I love that that comment. And one thing I wanted to add there is, is that, you know, the path to impact and the time to impact the machine learning model in machine, any progress. And General is extremely short and a grand scheme of things. You’re talking about something that was in the academic world. People write papers about, you know, in January of a year could be by, you know, by by the end of the debate admitted that same you could be in production by people using it, like just this kind of like unheard of writing in terms of scientific disciplines, writing academic papers about and that having impact on people’s lives in new products within months.

Not not we’re not talking about years or decades, which is a typical thing that in a lot of disciplines you think about advances in life sciences, but at times it has an impact on diagnostics or or it’s just a long time like the future. Same thing in physics and chemistry. So I think many people I think generate for something that’s in production in March. You know that, right? So having this title of what the researchers and getting to see is really important.

And I think it’s a beautiful opportunity. Like, I love that people are coming because the dangerous thing is that if it only lives in academia and never makes it, if the same people that build, you know, take the concepts to the next level, don’t get a chance to actually be a part of the implementation of them, how do the how do we learn, you know, other than waiting for the next academic to come along and evaluate and analyze?

And like you said in the past, it would be a decade before you would see the results, you know, necessarily now that you can literally in academia work towards a goal, do you achieve your plan, evaluate, take the hypothesis, and now you can actually enact that hypothesis.

And as a commercial business, I think this is really, really cool.

Yeah, thank you. I completely agree. I couldn’t agree more.

So so, you know, one, before we close up, Lewis, I’d love to hear your thought. Eighteen year old Louis Sasi decided he was going to school, No one.

Did you imagine you were going to go to school as long as you did? When did you build your plan and when did that? When did when did today become part of that plan?

Well, that’s said you give me the goosebumps here. So just a quick personal company. I grew up in Brazil. I went to engineering school in Brazil when I was 18. I was an electrical engineering student at the University of Sao Paulo. You know, at that time, I, I definitely like I really liked research, was involved in some research, but honestly never thought that first I’ll become a professor, that is all. And then even though I would say that I had thought about starting companies at that time but never ended up not doing it because of those, I got into the academic world and research and, you know, left to Brazil to go to IBM Research to work on this machine that was working life science.

And after that I went to so that was very, you know, taking the next and the next opportunity. So where did that plan come together? I don’t think there was. I don’t think I was ever a point where the whole plan came together was I’ll follow, you know, the flow.

But I always had the North Star that what gets me up in the morning is intellectual excitement and working with people that I can learn from and admire. And, you know, academia is great for that. And at Oxford now, it’s been great for that, too, because, you know, it’s been a different dream to have this kind of team they were able to build here.

So I I hope that we find more losers in this world. You took kind of. Thank you. Well, thank you for the conversation. There’s been a lot of fun, and I hope to chat with you again.

So, yeah, absolutely. I’ll be excited to watch the growth of the team, the organization, your customer base here. Some of the stories. We’ll get caught up again in future.

All obviously of links down in the show, notes for folks that want to find where if they if folks want to contact you directly, Luis’, obviously they can go to Octoml.ai, can we have that? But what they want to reach out to you directly, what’s the best way to do so.

Yes, you can just go ahead Lewis at OK, time out. Right. I listen to him outright. You trust me and I’ll come back to you. Looking forward to hearing from your audience.

I also want to congratulate you and thank you for being an amazing intellectual who doesn’t use their university address when they run a company.

It’s I know there’s a beautiful pride in the Stanford edu or the University of Washington that it’s always amazed me to see someone use like a three year CEO of a company and they still use their university email as their contact.

And like you, you should be proud of everything is OK to email is the thing to be proud of everything.

You’ve got to take it down to be proud of. But thank you.

Yes. I’m very, very proud of our time out for sure. Yeah. This email address will be the only to now. We’ll be there for a long time. So this email address will be valid for a very, very long time. I’m very proud of it.

So judging by you and your team, I very firmly believe it will be so. Thank you very much for the time today, Louise.

Thank you. Thank you again, Eric. Wow, there was there’s a lot of fun.

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Thomas Graf is the CTO and co-founder of Isovalent as well as a long time open source contributor to numerous projects. Linux kernel, BPF, networking, containers, security. He also previously Linux kernel developer at Red Hat and shares a ton of great info on the challengs of security and networking in Linux and Kubernetes as well as how he and his team have built a business on open source technology.

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