Inspired Execution

00;00;04;22 - 00;00;07;27
Chet
Diego. Welcome to the inspired Execution mini series.

00;00;08;00 - 00;00;09;23
Diego
Thanks for having me.

00;00;09;25 - 00;00;36;00
Chet
Now, you have had a phenomenal career, right? Started as a developer. You know, you were at Microsoft. You founded Algorithm Hire, and then that got acquired by did a robot. And they are they're doing some really interesting things in the space. And now you are part of a VC firm that is only focused on AI investments right through all this to your illustrious career, Right?

00;00;36;02 - 00;00;47;19
Chet
What is being like one defining moment, you know, or memory that kind of like. I was like, yeah, that was the inflection point for my career.

00;00;47;22 - 00;01;12;14
Diego
Yeah. Well, first of all, thank you for the kind words. I'm greatly exaggerated, but I. I've been appreciated. Actually, I can actually tell you, when I got into this, I actually, you know, I moved from Uruguay to the U.S. soon to go to college, and I actually started out as a mechanical engineer. And after a semester, I actually switched into information systems and data.

00;01;12;16 - 00;01;31;07
Diego
And it was specifically because, well, actually, after a year, because I did an internship with a big company. And I remember I think it was like second or third day at the job. You know, my job was to, you know, just go with one of the consultants and serve coffee right? Like, I mean, I knew nothing at that age, right, Gary?

00;01;31;09 - 00;01;48;04
Diego
Yeah. Yeah. What I'd started to do go. Go look at what's going on and learn and. And I remember this. This is going to age me a little bit, sadly, like, you'll probably know what I'm talking about, which is, you know, So they were pitching crystal reports. I don't know. Like, COG knows.

00;01;48;04 - 00;01;49;16
Chet
I know Consumer Reports really well.

00;01;49;16 - 00;02;13;11
Diego
Yeah, exactly. Exactly. And so, you know, and I remember this this the guy I was going with is the consultant. He goes in and he he he goes into this senior executive and he's like, give me like your I can remember if it was like an order like spreadsheet, it was like some data source. And he was like, I'm going to tell you something about your business that you don't know.

00;02;13;14 - 00;02;47;16
Diego
And I was like, Wow, that is some confidence, right? I can imagine going into somebody's office and just saying, I looking them straight in the face and saying, I'm going to tell you something about your business that you don't know. And he grabbed some data and he kind of like put it together in a report and kind of go by and like, kind of like mesh it around and was like, you know, did you know that your are kind of like sales in this area and you're like, not doing I can't remember exactly what the like output was, but I remember seeing there being like, I want to do this like this.

00;02;47;16 - 00;03;14;10
Diego
This is what I want to do. And like this this idea that I can affect business decisions, understand what people are working on based on the data that they're collecting and interpreting it like this is this is like it was I was just like mind blown. And it was so intense for me that like not only an internship was great, but, you know, I went back, change my career, you know, did my undergrad grad school and that in my entire career has been in data.

00;03;14;10 - 00;03;29;21
Diego
I based on that since that day. And so so that was the actual like I like defining moment of it. I was just somebody who was extremely cocky but knew what they were talking about. And it was amazing. And I was like, Why would anybody want to work on anything else?

00;03;29;24 - 00;03;58;06
Chet
Wow. That is that is so awesome. Awesome story. My God, you've you've dated both of us by talking about grocery reports of. So let's talk about the evolution of ML. You started ML Ops Company algorithm. Yeah. And you know, 2014 long before E I was a boom, right? I remember at Data Stacks, I joined in 2019 into 2020.

00;03;58;06 - 00;04;15;13
Chet
We were talking about we wanted to be a data company and we were looking at wanting to be an AI company and we started looking at this thing called MLPs. Right? Where did you get the idea of of doing MLPs and why did you think it was so important so early?

00;04;15;15 - 00;04;26;16
Diego
So so, you know, I'm very much just me. I get too tied to blame or give credit to my co-founder, whichever two you want, you want to use it. We get along great. But the.

00;04;26;16 - 00;04;27;14
Chet
Same coin.

00;04;27;17 - 00;04;54;24
Diego
Yeah. Yeah, exactly. Exactly. So, you know, one of the things you learn about is like, you know, being the absolute first to something is like, has its challenges, you know, and, and so I think, you know, but a little bit more concrete I was working on. So when I worked at Microsoft, I was lucky enough that I actually worked on the on the V1 team that created Power BI and the engine behind Power BI and kind of like the analytics engine behind that.

00;04;54;27 - 00;05;20;05
Diego
And one of the things that was popping up, I was a I was a product program manager and Excel was, you know, we were building a lot of descriptive analytics tooling and for the first time predictive analytics were starting to kind of like pop up like, you know, something very basic, like we're going to go build a trend line, we're going to go kind of like figure out like, you know, kind of like that next step.

00;05;20;07 - 00;05;35;25
Diego
And one of the features that I ended up creating and owning and kind of developing for it for for Microsoft was something called automatic pivot tables and automatic pivot tables. If you go into Excel and kind of like click on it.

00;05;35;25 - 00;05;38;00
Chet
So it's like a big frickin feature.

00;05;38;00 - 00;05;59;12
Diego
Just yeah, And it was exciting and it was exciting about that point was that, you know, we it was the first time I got exposed to grabbing essentially academic work, right? Was Microsoft Research. Right? So people at Microsoft Research were working on these kind of like data pilot automation and stuff like that. And I was like, Hey, this is amazing.

00;05;59;12 - 00;06;19;12
Diego
We want this in Excel. Like, this is this is like exactly what we want to ship. And, you know, we have a billion users and like, who wouldn't want to ship that? And they're like, Oh yeah, here you go. Here's some like, you know, code and you can just like put it in. And that was a first time where they're like, well, this is like written in like MATLAB and like I'm supposed to ship this to like a billion users.

00;06;19;14 - 00;06;41;26
Diego
Like, that's insane. Like, that's never going to happen. Like, how do you like that? This just won't work right? And, and it remember at a time my my co-founder, who we've been friends for years, he had been constantly being like machine learning is the future like people don't understand like, you know, he's he was doing his PhD in AI and and he's like and it's so hard.

00;06;41;27 - 00;07;03;25
Diego
He's like, I'm so frustrated because it's so hard to put anything like it's all academic code and nobody's like, hardening it, which means nobody will be able to use it, right? Nobody will be able to. And, you know, it was the first time that you are essentially dealing with the term, you know, like you're moving from deterministic to probabilistic code and it breaks.

00;07;03;25 - 00;07;32;27
Diego
Yeah, right. Like that was it. And so the thesis behind algorithm was the future of code is probabilistic and a bunch of what breaks and like we probably should go fix the things that break and so some things still work right You can still check in your code to get you know to get and you can still do, you know, connectivity into different data sources, but like a bunch of things, everything from how do you deploy it, how do you run inference to how do you, you know, kind of like monitor for errors, all this stuff like.

00;07;33;04 - 00;07;48;10
Diego
And so we essentially kind of like the idea really was the future is going to be machine learning and we probably should go fix some of the kind of like traditional software engineering components that just break down when the code turns probabilistic.

00;07;48;12 - 00;07;59;23
Chet
And so so basically your take was that there is an SDLC, a software development lifecycle. What does that look like in the HTML world? And let's go and actually create that.

00;07;59;26 - 00;08;17;11
Diego
Yeah, right. That was that was really kind of like the, you know, the super not, you know, we it's a new and you know this well like you know it's a startup right So twist and turn in like you know wrong corner and like you hit it right but like yes if you abstract you know you look at eight years from the outside, like that's what it looks like.

00;08;17;13 - 00;08;18;20
Diego
Yeah.

00;08;18;22 - 00;08;42;24
Chet
You know, it's funny. I will we'll talk about this in a second, but tell me if you had to if you had to do this again. Now generating by being as big it as it is, would you do it or would you say, no, The game has changed significantly that I will not do a Gen I ops?

00;08;42;26 - 00;08;43;00
Diego
Yeah.

00;08;43;03 - 00;08;57;14
Chet
Or or do you know, I heard about a company that said they said they're doing vector ops and I'm like, I, it's a little early for that, right? I mean let's at least get a few apps out there for the next two years. Right? But would you do that now with you only experience?

00;08;57;16 - 00;09;21;15
Diego
So So I think so. So the answer is probably yes. But I don't know if there is a like, you know, I'm still deep in the like. Is it that different? Like there is stuff that's different, right? But like, you know, like I was kind of joking around where it's like, you know, you know, to me, like people start calling it all ops and I'm like, you know, it's, you know, like to me this is still ML ops.

00;09;21;18 - 00;09;41;10
Diego
You know, if you need a new market map, that's fine. But like, like let's start actually, like looking at and, you know, some things did change, right? Like you needed different hardware or more hardware, but like, some components change, but not like that much from the, like workflow and process. And we're still using a lot of the same tooling.

00;09;41;12 - 00;10;06;29
Diego
And so like, do you answer your question about would I go into it like I'm actively working with companies in that space? So I'm like a huge believer. I think the future of software is, you know, hey, I can maybe, you know, and, you know, and like, I think now it's like, well, duh. But like, I've been saying that for ten years, so I get a little bit of credit for, for, for that for sure.

00;10;07;02 - 00;10;30;26
Diego
Sure. And, you know, to me, the like, it's always about like, what is stopping us from getting X, Y, Z into production. So, you know, the one thing that I always really enjoyed about the kind of like by sector was like the value where the value I created was really like very clear, right? Which was yeah, between the chair and the screen.

00;10;30;26 - 00;10;55;20
Diego
Right. They looked at it, they looked at something, they made an analysis and they made a business decision. That's where the value happens, right? So all the data sources, the databases, the you know, all the all the displays and dashboards, all of that is in service of somebody interpreting data and, you know, kind of like making that into a decision that would affect the business, you know, hopefully go up or go down whatever, you know, direction you want it to go.

00;10;55;23 - 00;11;14;18
Diego
In the world of machine learning, it's always about like kind of like, hey, where do we get to that prediction inference point, right, where we're going to either make a, you know, make a prediction, create a something like, you know, automate a workflow. So like those value points to get to them. Like there's a lot of complexity in the ops to get there.

00;11;14;18 - 00;11;20;15
Diego
And so and the thing in service of that I think is they are were the mission to attack I will.

00;11;20;17 - 00;11;44;10
Chet
I will take a little coin just for the just for our listeners. I'll take a little contrarian view. Of course I liked I really liked the web and I really like mobile and I really like the way Cloud developed, right? It was all focused on the kinds of apps that people would build. Not they, they were not very focused on DevOps and data engineering and ML ops and things like that.

00;11;44;10 - 00;12;06;21
Chet
I think I think somehow I feel that we really need to create space for people to do experimentation now, and I think you will not disagree with me there, but what you are working on now on let's talk it, let's call it, let's call it Jenny I Ops. I just want to just put it as a big thing, not Al-Alam, because you know, jenny, i apps are a lot bigger than L.A.

00;12;06;22 - 00;12;32;17
Chet
Lems. And as you do this new ops piece, you're building companies that that are that are going to ship features next year that people will use in 25 and 26 and 27. And i think a lot of people get confused in the market where they start thinking they need the ops stuff now. No use opening. I use Gemini if you want, go and use Lang chan, use Lamar index, slap some stuff together.

00;12;32;19 - 00;12;42;17
Chet
You know, use user database. Right. My that's my point going experiment like crazy because all the industry is robust enough that the technology is coming. Is that fair?

00;12;42;19 - 00;13;09;16
Diego
Yeah, I think it's fair. And actually I don't think it's even that contrarian to to what I said. So like, you know, one of the things that I think is, you know, I kind of talked about what, you know, if you had a pitched, you know, a startup 20 years ago, you know, you would be like, oh, I need money for servers and like, I need to, like, run this thing in like, bovine, like and now and then, you know, what happened with the cloud providers was like anybody with a credit card, right?

00;13;09;17 - 00;13;33;25
Diego
Could get up and running over a weekend. Right. And so like the cost of getting started prototyping, getting an MVP like whatever that running. So that is never that has not existed in AI until now like we just you know, one could argue like Chhatrapati was the catalyst, right? Like that really opened the mind. But like ultimately, like we suddenly have a, you know, a, you know.

00;13;33;28 - 00;13;54;24
Diego
ML models where single task completers right up until these kind of foundational models have kind of came out and got popular and access to AI and machine learning was fairly complex. And so you did need the ops, you did need like, you know, to be able to get up and running. You needed your, your things now it's exactly it.

00;13;54;25 - 00;14;16;26
Diego
Now, I would argue like just slapping, you know, an opening API into your application will not get you, it will get you an experiment and it will get you a great prototype and a great demo. It will not get you a product. And so there is still kind of like the, you know, a pretty deep level of, okay, what are the new problems?

00;14;16;28 - 00;14;18;21
Diego
Right? And like, I'm warming.

00;14;18;21 - 00;14;19;29
Chet
Up after you experiment is.

00;14;19;29 - 00;14;37;12
Diego
Exactly, exactly. And you know, I'm very biased because, you know, I co-founded a company recently and kind of like the risk and safety, but like, you know, yeah, one of the things that's like, you know, I talk about a lot is that you know, these models because they are generic task completers Yeah, that is their power and their risk, right?

00;14;37;12 - 00;14;54;05
Diego
They can do stuff and maybe they don't want it to do a bunch of stuff. So I need to put guardrails around them. And so like now there's and that's kind of an ops operation, It's an application ops operation where it's like, Hey, I can't let this thing go off the rails and do what I don't want it to do because it won't complete my product workflow.

00;14;54;08 - 00;15;00;26
Diego
And so I think the the office is still necessary because that's what gets our software that Skippable.

00;15;00;28 - 00;15;01;09
Chet
Yes.

00;15;01;09 - 00;15;06;12
Diego
But it's where that ops is is shifted.

00;15;06;15 - 00;15;28;01
Chet
Yes, for sure. And my take is a lot of people think about the ops before they actually do the experiment. And my take is let the experiments happen while they are people thinking about the ops. And I absolutely agree. So let's talk about let's talk about guardrails idea. You just founded this company. We are You and I just enter into an elevator.

00;15;28;04 - 00;15;33;17
Chet
You punch for I punch seven. We've got 20 seconds. What's the pitch?

00;15;33;19 - 00;16;02;27
Diego
So one of the great things around these like models is they can do a lot of things. One of the problems with these models is they can do to a lot of things. And so how do you actually make sure that the output of these machine learning models is what you want, right, from a safety perspective, from a risk perspective that they're operating against your product principles and that you can have some level of guarantees, right, that is going to behave in a certain way.

00;16;02;27 - 00;16;20;10
Diego
So guardrails, air dot com is a open source library that allows you to build railing, literal railing against, you know, and guards around, you know, the outputs of our labs to keep them on track and terms of like the behaviors that you want. So that's that.

00;16;20;12 - 00;16;44;10
Chet
That is awesome. I will I'm sure a bunch of our listeners are going to go in. Definitely check it out. So you are now an investor. We were talking about this before we started the podcast, which is, you know, but you are you're you continue to feel like you're a builder, right? You're you're incubating companies, you are helping them grow things like that.

00;16;44;10 - 00;16;59;25
Chet
So tell us a little bit about how how your how factory is doing it differently and then what is what is the most exciting trend that, you know, what what are you most excited about in 2024?

00;16;59;27 - 00;17;29;24
Diego
Right. So, yes, a factory was founded by two operators, one of them, Christopher Wray, who's a professor over at Stanford who's had multiple I start ups and founded multiple AI startups. And the idea was to create a kind of venture like studio where we could, based on research and I research, kind of like build out what the future of AI should look like or, you know, maybe mold.

00;17;29;24 - 00;17;52;16
Diego
Why, you know, what we you know what, what we want to look. And so the idea was we can look at what's kind of occurring around the corner from a research perspective and be very, very academic based. Understand what you know, what's a product, what you know, I say, what's the technology, what's a feature, what's the product, what the company and and try to make that determination.

00;17;52;16 - 00;18;15;11
Diego
So my day to day is very much focused on like going through with our portfolio companies that journey of understanding, you know, or even pre portfolio. So Gödel's is a good example where Shriya, the CEO, she's this seemingly capable EML engineer and she came up with this problem, right. Which is like, well, when I came up with it, like she, she got obsessed with this problem.

00;18;15;13 - 00;18;36;02
Diego
And so even previous to the launching of the open source package, you know, we spent weeks talking about what might that look like and then, you know, build the package and then founded the company and started the company and stuff like that. And so like that, that kind of like workflow is kind of how we, you know, we think about building kind of the, you know, the future of companies.

00;18;36;05 - 00;19;02;21
Diego
Another pretty, you know, kind of like I'm not at this point today famous companies coming together today, which is also kind of like incubated inside, you know, inside factory which is, you know, right now kind of like a big deal in the world of open source machine learning and AI engineer. And so our goal here, you know, from our perspective, from the you know, from the partners perspective, is exactly that.

00;19;02;21 - 00;19;35;00
Diego
Like, how do we how do we kind of have a disciplined approach to kind of like create, you know, you know, shaping the future of AI, you know, from a from a company building perspective. So that was the first part of the question. Sorry. When long you talked about like kind of like what, excited? So, you know, I think, you know, the the exciting thing to me now is that, you know, foundational models have really opened like, like my opinion, you know, a positive Pandora's box of imagination.

00;19;35;02 - 00;20;05;05
Diego
And it's one of these like to me like this, like kind of like we will look back in time and say this was a kind of like mass shift in terms of, you know, like what the you know, in the world of technology, right? In the same way where like the first OSes came out, the Internet came out like where what we're seeing right now in my opinion, is pretty like, let's fit this new thing to what we already knew.

00;20;05;08 - 00;20;24;15
Diego
And the next stage is like the next level of interfaces, like the next kind of like, you know, so, so so what we're seeing right now still is like in that and I don't mean this in a demeaning way because I actually think there's like a million businesses that are fantastic where you literally can slap an API with a AI onto a business and it's going to be spectacular.

00;20;24;18 - 00;20;51;19
Diego
But like there's a whole new world of interfaces, of abstractions, of, of, of, of human computer interactions that are about to open up based on the idea that now we can express in our own natural language with computers. Yeah. And you know, so we're moving from this like, you know, punch card to code to like moving mouse in your moving mice and keyboards.

00;20;51;20 - 00;21;09;28
Diego
Now, you know, we're interacting in our, you know, in our human language with computers and they're interacting with us. And this is opening a whole new level of interfaces and workflows. And I think it's going to change. You know, what excites me is that I don't know what's on the other side of this, but like, it's it's a whole new world and it's exciting.

00;21;10;00 - 00;21;28;14
Diego
And so I think, like that's the the those new experiences are the ones that I think are are close, right where we're seeing them happen in life. And I think for me personally, this is the greatest technological advance I'll see in my lifetime. And so it's it's cool to be part of it.

00;21;28;17 - 00;21;49;28
Chet
For for for sure. By the way, there's this there's no shame in beautiful haikus, right? There's there's absolutely there's nothing there's there's nothing there's no shame there. But I read something the other day, which is somebody said that they brainstorm with chat, you know, and this was actually a Google employee that actually said they actually do it at Bard.

00;21;49;28 - 00;22;07;09
Chet
But, you know, you keep it on. You keep talking like you're brainstorming and it's recording everything. And then you say, summarize for me and you can go deeper and say, do some research on this. So it's kind of a it's kind of a cool thing. You know, you're changing the way you work, but you're also changing the way you think, right?

00;22;07;09 - 00;22;24;27
Chet
And a lot of people don't spend a lot of time about that. It's not just changing the way you work. It's also changing the way you think. And that's your point, right? Because your when you when you went from when you went from punch cards to a do a graphical user interface into the green screen, you actually had this you know, you had this beautiful thing.

00;22;24;27 - 00;22;46;09
Chet
You actually changed the way you work, right? And so it made them it made a difference. So I'm super by the way, I would agree 100%. This is going to be the most excited, most excitement that anybody with here and anybody will feel in this lifetime. Right. It is going to this is this is going to be the biggest industrial revolution that mankind has ever gone through.

00;22;46;12 - 00;23;09;02
Chet
Right. And we will, given our history, you know, as as a human race. Right, we will emerge really victorious. Right. It'll be ups and downs. But the the I loved what Jensen from Nvidia said. Right. Which is when's the last time where a company became more productive. They did layoffs. Yeah. Never. Whenever they get more productive, they hire more people.

00;23;09;04 - 00;23;29;15
Chet
Right. So let's increase productivity. Let's come up with new amazing new revenue models, amazing new ways of doing business and interacting with people. And everything will start taking care of itself, right? So I have a very optimistic view of this because I really do think that, you know, people with A.I., you know, humans with A.I. will actually be the ones that will change.

00;23;29;15 - 00;23;34;08
Chet
And I don't think it's that far away because unlike every other wave, this is going to accelerate.

00;23;34;11 - 00;23;56;04
Diego
Yeah. Look, I'm I'm I'm very much like I said this at an on a talk I did the other day and like I have no stock in Openai so like I'm not suggesting but I'm saying like if you have any of your readers, our listeners here, like who have not interacted with some of these foundational models like this would be like the first thing you do, like you should drop everything you do after this, like a requirement.

00;23;56;06 - 00;23;56;13
Diego
Yeah.

00;23;56;13 - 00;24;00;10
Chet
And just if you have not played with Jeopardy, you should not listen to the point.

00;24;00;10 - 00;24;32;13
Diego
Yeah. And it's and it's amazing. I use it every day. Yeah. I had multiple alarms in my workflows on a daily basis. Everything from helping me write product specs to questioning my product specs to helping do research. Like I do feel ten productivity because of the, you know, the kind of like assistant like it's like the kind of the promise of a AI or, you know, some people I'd called it like augmented intelligence instead of artificial.

00;24;32;13 - 00;24;52;15
Diego
And it's really true. Like I do feel augmented. I feel I can do in 2 hours what I used to do in six. I can I can be more thorough, I can have better insights and like, why wouldn't you want to do that for yourself? Right. Right. Just from a self growth perspective, like, I think it's you know, it's kind of like you're tricking yourself by not working with this stuff.

00;24;52;17 - 00;24;52;28
Diego
Oh.

00;24;53;00 - 00;25;14;20
Chet
This is awesome. We could we could we're definitely going to meet offline, but it seems like we to have a podcast and talk about this for like another hour. Yeah, I mean, I'm so Diego, I'm going to go to the rapidfire part and so quick questions, quick answers. What's a problem humanity is facing that you want A.I. to solve first?

00;25;14;22 - 00;25;31;04
Diego
I think quicker ways of doing retraining of of humans is really something that I can be extremely good at and is very necessary because we're about to enter into a pretty aggressive creative destruction period thanks to this technology.

00;25;31;06 - 00;25;39;21
Chet
That's awesome. What's one thing in your daily life that you want A.I. to automate.

00;25;39;24 - 00;25;45;29
Diego
Anything that has to do with scheduling? I am so bad at it.

00;25;46;02 - 00;25;52;17
Chet
Something unrelated to A.I. that you're passionate about cooking.

00;25;52;19 - 00;25;58;09
Diego
I love cooking and I don't think I'm ever going to get in the way of that. It's. It's art.

00;25;58;11 - 00;26;04;19
Chet
Yeah, it is art, but it does actually put a really good recipe together and makes you order it from DoorDash. Sure. I'm Instacart.

00;26;04;24 - 00;26;16;15
Diego
Sure. But I bet that the process of you experimenting and the suggestions might come from them, but like the taste and the kind of like the workflow, it's, it's, you know, it's closer to the art and science.

00;26;16;17 - 00;26;19;00
Chet
What's the, what's the go to dish.

00;26;19;03 - 00;26;27;11
Diego
My go to. Okay. So I grew up in South America. So like I am like, like anything on a grill is like, you know, kind of like religion to me.

00;26;27;13 - 00;26;39;04
Chet
Yeah, That's awesome. One word you would use to describe the best tech leaders. One word.

00;26;39;07 - 00;26;42;20
Diego
Persistent.

00;26;42;22 - 00;26;49;29
Chet
One word to describe how you feel about the future of A.I..

00;26;50;01 - 00;26;51;22
Diego
Amazed.

00;26;51;24 - 00;27;07;21
Chet
Yeah. This is awesome, Diego. It is been an absolute blast. I think our our listeners are going to love this episode. I really appreciate you taking the time and I look forward to doing this again, but also spending some time and hanging out with you.

00;27;07;27 - 00;27;09;18
Diego
Thank you again for having me. Appreciate it.