Omer (00:11.840)
Welcome to another episode of the SaaS Podcast.
I'm your host, Omer Khan, and this is the show where I interview proven founders and industry experts who share their stories, strategies, and insights to help you build, launch, and grow your SaaS business.
We've got another great episode lined up for you today.
All right, today's guest is the co founder and CEO of Algorithmia, a Seattle based startup that's created an online marketplace that connects academics building powerful algorithms with app developers who can put them to use.
The company was founded in 2013 and to date has raised almost $2.55 million in funding.
So today I'd like to welcome Diego Oppenheimer.
Diego, welcome to the show.
Diego Oppenheimer (01:02.750)
Hi.
Thank you for having me.
Omer (01:04.750)
So you and I both have a common background.
We were both Microsoft guys before we went on to do other things.
Tell me a little bit about what you were doing at Microsoft.
Diego Oppenheimer (01:15.310)
Sure.
So I was actually on the Excel team, so actually building Microsoft Excel for about five and a half years.
Specifically, I was responsible for what are called the business intelligence tool or the advanced data analysis tools.
Things like pivot tables, connections to databases, connections to big data.
I was very involved in the effort of getting pictures like Power Pivot and Power Bi, kind of the first versions of those out the door during my time there.
Omer (01:44.650)
Yeah, I mean, these are the kinds of things that probably anybody who uses Excel or has used Excel at any point has probably used those features, right?
Diego Oppenheimer (01:54.360)
Yeah, I know.
It's definitely one of those things where I get to say that features that I've built have been used by a billion users across the globe.
And I'm very proud of that.
Omer (02:04.520)
That is awesome.
Okay, now I like to start by asking my guests just to try and figure out what drives and motivates them to do what they do.
So do you have a favorite success quote, something that inspires you?
Diego Oppenheimer (02:20.530)
Sure.
So I come from a family of entrepreneurs, and growing up, my father had in his desk and something that he carried throughout his while he started his own business.
This very brief quote that was always on the wall that said, no guts, no glory.
It's very simple, but I kind of think it embattles everything that entrepreneurship is about, which is if you don't put yourself out there, if you don't put everything into it, like, there's never going to be any glory with not trying.
And so I think that's kind of like the.
The guiding star for everything I've done, including this journey for me, which is, yeah, no guts, no glory.
Omer (02:57.720)
Give the audience a Better understanding of algorithmia.
Can you tell me just a little bit more about, you know, what, what is the problem that you guys are trying to solve here?
Diego Oppenheimer (03:09.080)
Sure.
So the idea actually came out of my co founder while he was in academia.
And so he was doing his in artificial intelligence.
And what he started realizing was that a lot of the work that he was doing, very, very advanced algorithmic work, things that were very applicable to the real world, were just not making it out into the hands of people who could use it.
The general academic way of the way that research is done is that they get rewarded for being cited, they get rewarded for publishing papers and talking at conferences, but they don't actually get rewarded for the.
When a company grabs an innovative algorithm and methodology and starts using it.
And that's kind of a shame because that's kind of a misalignment of how a reward systems grow for creating really impactful work.
And so out of this frustration, he started thinking and kind of sharing with me, we were friends for a while, that could we create a system where these advanced algorithms, not only across academia, but researchers, data scientists, people who are actually building these algorithms, could get them into the hands the people who would actually use them, not just talk about them, but actually put them into production systems and work with them.
And that's kind of where the idea of algorithmia was born.
Could we create a marketplace where all this work became available and consumable?
Because one of the things that we do is we actually provide them all these algorithms as a service, so they end up showing up as an API and they make them consumable and usable so that any application developer on earth could say, oh, I do want to use that advanced face detection or face recognition algorithm.
I do want to use that advanced route planning, planning algorithm in my application.
And they had a way to do that which is a little bit better than finding the obscure paper it was published in and then trying to put together code based on what was written in that paper.
Omer (05:01.380)
Right?
Yeah.
So I think there are a number of challenges in trying to run a business like that, because you're right, in the academic world, when these guys are developing these algorithms, there's no incentive to actually focus on implementation.
And as you very well described, they have a different set of motivators.
And then at the same time, it's about how do you then turn it into, how do you license it, how do you turn it into a usable piece of technology, how do you distribute it to the app developers on the other end?
And the piece, the Interesting piece here is that the fact that you said that you run it as a service.
So that must make it really much easier for developers because they don't have to get into understanding the details of an algorithm.
Right.
They can just, I mean, you know, call the service few lines of code and they're done, right?
Diego Oppenheimer (05:53.000)
Absolutely.
And it's actually simpler for both sides.
So, you know, when it really comes down to the core of what we do is we operationalize algorithms.
So we've created an experience where, you know, from the supply side of this marketplace, the algorithm developers, these researchers, these academics, they come in, they upload, you know, they put their code into our system, they hit submit and that's all they need to do.
And then we've created a system where we can make these algorithms scalable, we can make them distributed, we can, you know, we can meter them, we can operationalize them, really.
And on the flip side of that, of that is that you look at the demand side and demand side now has just this one endpoint and they don't have to worry about, you know, how the algorithm was implemented.
They don't have to worry they can trust our system that we have all the security measures behind it.
They know that if they want to call this algorithm many more times, it will be scaled automatically for them.
So to a certain degree, what we do is we really give you this.
We've kind of removed the friction points between what the academics, which want to increase the impact of their work.
So they want to get it into the hands of these businesses and technology companies on the other side.
These technology companies that want to be doing the cutting edge stuff in their area, and now they have access, really by removing the friction points on both sides is how you get that service up and running.
And a win, win scenario for both sides.
Omer (07:19.800)
What's one of maybe the most popular algorithms that you guys offer today?
Diego Oppenheimer (07:26.840)
Yeah, so going along to your comment around just needing to understand how to call the API.
So text analysis and computer vision are kind of the two big areas that are really interesting today, mostly because of how easy they understand.
So text analysis tools are the ones that do.
It's in a field called natural language processing, and essentially it's being able to interpret things out of text in an automated fashion.
So, you know, computers can understand what the topic of an article is about or, you know, what the different, you know, nouns in that article are.
And so those are very easy to understand because usually the input is text and the output is going to be other text.
And so very Easy for any developer to understand without having to go really intricately into how they work.
And the same thing for computer vision, where although the algorithms are like extremely intricate and complicated, you know, if I send in an image and it tells me like, you know, kind of detects where the face is.
We do this every day.
If you think about Facebook when you're tagging your friends, you know, that algorithm of actually detecting a face on an image is actually really, really complicated.
And there's a lot of technology that goes behind it, yet we do it every day like it was nothing.
So it's very easy to understand how to use them, but they're complicated to like get them up and running yourself.
And so those are kind of for those reasons is why they end up being so popular in our service is because people know how to use them, want to use them, and they wouldn't really be able to get them up and running themselves otherwise.
Omer (08:50.380)
Do you allow third parties to submit their own algorithm as a service or is that something you and your team will build yourselves?
Diego Oppenheimer (09:01.900)
So like anybody can come in to Algorithmia and post their algorithm and publish it to our marketplace.
So anybody can do that.
You don't have to.
And then you can decide what you want to charge for it.
You can decide, you know, kind of what the permissions come with it.
You can decide even if you want it to be open source or closed source.
So that's complete control of the algorithm developer.
And it's a very, the entire model is very self serve.
And so then we display these characteristics on the other side to the demand and showcase them.
And so, you know, there's reputation behind each developer, very similar to how, you know, a GitHub or you know, some other kind of more social code building applications are in the sense that there's reputation behind it.
You can, you know, link to the paper, the professor who created the algorithm, you can link to your paper and kind of who you are and stuff like that.
Got it.
Omer (09:55.250)
Okay, so let's go back to the early days when you came up with this idea with your co founder.
Diego Oppenheimer (10:03.450)
It's.
Omer (10:03.650)
It's Kenny.
Kenny Daniel, right?
Diego Oppenheimer (10:05.490)
That's correct.
Omer (10:06.370)
So tell me about the day that you and Kenny first had this conversation about starting this business.
Diego Oppenheimer (10:13.010)
Okay, so that's actually a long, long time ago.
So Kenny and I met our freshman year in college, so long time ago.
And we kind of always had this passion for really kind of geeking out together on different topics.
It was really that came around that many, many nights of just kind of Discussing different things that interested us.
Kenny's interest was always in intelligent marketplaces.
So you could definitely see a.
A line of thinking that was perfected over years and years of undergraduate and then graduate level education and thinking about this space.
It really came to us 2008, when I finished my master.
2007, I finished my master's degree, and I had a couple months before I started working at Microsoft.
I called over to Kenny and I said, we've always talked about starting a company together.
This is the really early days.
Let's go on a trip together and kind of like, let's take some time off, let's go backpacking and let's talk.
Let's obviously enjoy the exploration and the traveling and all that stuff, because we were friends.
But I think it would be a good time of really getting unobstructed time to let ideas flow.
And so this is early 2008.
We actually went backpacking through Australia and New Zealand for a couple of months and really started developing the idea there.
We had a notebook and we had a laptop in a tent, and we kind of started building out the idea and discussing what a computation marketplace would look like and what a algorithm marketplace might look like and kind of developing those ideas.
And those ideas went, you know, we evolved them more and more and more over the years, but.
And it really wasn't until 2013, you know, so many years later, where we really thought we had a grasp on what the problem was and where, you know, where we could bring a lot of value by creating an actual marketplace around algorithms as a service.
The first marketplace for algorithms as a service.
Omer (12:13.490)
Okay, cool.
Okay, so 2013, you feel like you've got.
You've got a good handle on the problem.
How did you guys get started with the business?
What was the first thing that you did?
Diego Oppenheimer (12:24.610)
So a lot of investigation.
Right?
And so the first thing was said is like, you know, our hypothesis was that there was a need for this marketplace.
So the first thing we did is we figured out, yes, there was a ton of supply.
We know that there was over, you know, we started doing research.
Over 10,000 papers a year are published in computer science around algorithms alone.
We knew that there was a big supply, and we'd seen it because Kenny was academic and his lab and his partners and all the conferences he was going to, clear distinction there, that this was something that academics were yearning for to have a platform to increase their impact on the other side.
We said, okay, so there's a supply side.
We started looking for the demand side.
It was Actually, through my work at Microsoft that, you know, the demand side really kind of popped up.
One of the things that we needed to do on the Excel team was kind of figure out there's features, kind of automatic pivot tables and a couple other features that we created that they require some deep algorithmic thinking to be able to kind of understand the data and build the feature around it.
And although we had some extremely talented application developers on the Excel team, we didn't really have algorithm developers.
It just wasn't a skill set that was particularly there.
And so we would go to Microsoft Research and at Microsoft Research, $7 billion research center, we would start looking there.
And after months of searching and talking to people, finally I get contacted with this algorithm developer there and he tells me that he's been working on the exact feature that I wanted for six years and it's in Excel already.
And we had no idea.
And so knowing that Microsoft is actually one of the best companies out there to commercialize their research and seeing how miserably that was breaking, you know, that failure right there, I was like, wow, okay.
And so that situation very, very similar happened like two, three more times.
And so that was kind of another validation there of like, you know what this is like, clearly a problem.
Like even in a company that is kind of designed for having research center push to, you know, push their work to get implemented, it's failing.
And even when it's like a very, very obvious transition, like the same product and exactly the same feature you want.
And so a lot of validation along those lines were done.
We started talking to people in the space, heads of data science at different big companies and saying, what are the problems that you're having?
Why are you not using, why is a team of 60 data scientists at X Fortune 500 company not using the state of the art in terms of algorithms?
And they started telling us, well, because it's not easy enough, because the risk reward ratio is off, because we don't have time, all these factors ended up building into, okay, so we know there's a demand for it.
And like these are the things that the demand is saying, like we need to address it being very, very easy to use.
We need to make trial and error very easy.
We need to make it very obvious and easy to discover.
We need the explanations to be there.
So a lot of these things essentially like our feature list was being built for us by kind of listening to, you know, why they wouldn't use the state of the art research that's coming out of these universities.
And so that's what we spent months in the beginning of really collecting that data and understanding it and writing it down and kind of prototyping what that might look like.
Omer (15:48.150)
When you were talking to these guys, were you pitching the idea of what would become algorithmia, or were you trying to focus purely on the problem and just understanding why things were the way they were?
Diego Oppenheimer (16:06.680)
No, we were trying to focus on the problem for the most part.
You know, we had this idea that we wanted.
We were still kind of like, naive in the sense that, like, don't talk about your idea or, you know, somebody's going to steal it.
And, you know, halfway through that process, realized the ideas are dumb.
A dozen, it doesn't really matter.
You could be screaming it at the top of your lungs on a rooftop.
And as long as you know whoever was going to win is the one who's going to have the best execution.
Omer (16:31.320)
I wouldn't call that naive.
I'd call that actually smart.
Because one of the mistakes that I see often happening is people get so hung up on the idea that they go out there trying to get validation for their idea instead of validating that there's actually a problem that needs to be solved.
So I think what you guys did is something that I think more people should be doing, which is just really keep digging until you really get a good, you know, a really solid grasp on what the problem is.
And, And I guess keep asking, you know, I heard you say why a lot of times, and it's like, keep asking why until you really get to the root cause of it.
So, okay, so you guys have got a, you know, a pretty good handle now.
And you, you mentioned something about prototyping.
So were you.
Were you still at Microsoft when you were doing this research or had you left and what was going on?
Diego Oppenheimer (17:28.260)
So, you know, as soon as we started kind of taking this seriously, I, you know, asked.
Microsoft have this thing that called moonlighting they allow you to do as long as it doesn't affect your normal job.
And it's not really competitive.
And it wasn't competitive to what I was doing.
So I was spending nights doing this, not to the happiness of my wife, but a lot of, you know, like, I would, you know, I was sending out a lot of emails, trying to get conversations started with people in the data world.
And, and a lot of this was kind of like planning and talking.
Kenny's a night owl, so it was kind of easy in that sense.
Get online with him at like 10pm and go till very early hours in the morning, kind of discussing and kind of planning these things out, we went through a couple of iterations of the product that were like, quite honestly, just didn't work out at all.
Initially we thought that we were going to be an algorithm optimization platform where we were going to have defined problems and people were going to submit their algorithms against that problem and see who had the best algorithm.
And then immediately with that submission, we would turn that into an API endpoint.
And so you would have the best algorithms for each problem.
And that was actually the first iteration of what we built.
And it was terrible.
Not terrible.
I mean, it was a really interesting product, but it was riddled with problems and wasn't really solving a business case.
And we very quickly realized that we weren't going to be able to expand on that.
And so kind of being alert to what's not working and being willing to kind of kill off things that weren't working, I think played in our favor in those days, I guess.
Omer (19:08.060)
You left Microsoft When?
In August 2013.
So that, I guess was the time where you decided that you were going to make a leap and do this thing full time.
Did you guys decide to look for funding right away then, or did that happen a little later?
Diego Oppenheimer (19:27.050)
No, we weren't ready at that point.
We had been talking that we would kind of continue doing moonlighting.
So Kenny was also at a different job at that point.
He was working at a different startup.
We decided that we would push that boundary as far as we could.
And at the time In August of 2013, we had already started writing code.
We were both writing code now.
It was taking a lot of time.
And when, I mean a lot of time, it means like I was going to Microsoft from 9 to 5, coming home, going to the gym, and then sitting down at like 8 or 9pm and like coding until 4 or 5 in the morning and then restarting the next day.
That got just to a point where we thought that to be able to continue exploring algorithmia and to be able to really dedicate time to it, like it couldn't be part time.
And on top of that, if you, if we wanted to raise money, which, you know, we were starting to think about, no investor angel, anybody is really going to give you money while you're working part time.
It just doesn't work that way.
They need to know that their money is being.
It's such a risky proposition to them that they need to know that at least you are committed 100% behind their money.
And so that was pretty obvious to us in that sense.
And so we said, well, one of the things needs to.
My wife came to me and essentially is like, okay, you can't do both of these things anymore.
It's kind of ridiculous.
Pick one.
And nicely enough, I got to pick the one I actually wanted to work on, which was algorithm at the time.
Omer (20:52.260)
Very cool.
Okay, so how long did it take you guys?
Or tell me about how far you got with the product and the business before you decided you were going to get funding.
Diego Oppenheimer (21:03.440)
So we took our, you know, we had a beta program, we had launched a MVP with a very capital M at the time.
And you know, based on that, we were looking at, you know, we had gotten our first couple customers, we had users, we had gotten press at the time.
We got a couple of lucky break in the sense that we were exploring a space that was so new that people were starting to list and we got a little bit of mind share that way and managed to pick up our first couple hundred beta testers after some blog posts that we did.
And that allowed us to iterate a lot on the product over and over and over again.
And we didn't really feel like we didn't start the process of proper funding process until March of 2014.
And it still took a while, right?
Again, like very, very new product, just two guys working on it, kind of out there thought, you know, thinking.
And so it was definitely not a.
It wasn't a slam.
Like I didn't walk into, you know, a VC office and just get and leave with a check on the first try, let's put it that way.
Omer (22:17.390)
So had either of you run a business before?
Diego Oppenheimer (22:19.670)
I had started a small startup in college and I had also had experience working as part of different types of businesses for a long time, but neither of us had, at this scale, like really started something where we plan to get this big.
So our startup and university was a small algorithmic trading company and there's five of us and we actually sold the company not for anything worth really commenting on.
So we had gotten that experience.
I had raised a little bit of money from angels, had done a lot of business development, so I had gotten a little bit of my chops there, but not really.
That was.
This was kind of like a first go for both of us at this scale.
Omer (22:59.920)
And when you said you had some early customers, were you charging for the product at that point?
Diego Oppenheimer (23:05.120)
Yes.
So we had a couple of, you know, first customers where, you know, we were, you know, to a certain degree just giving up.
You know, we were working with them and they're, you know, to get stuff that they wanted on our.
On our platform.
Platform and be able to run it.
And it really looked a lot more like a very, very, very discounting, discounted consulting gig, which they were getting a very, very cheap price for.
But, you know, it allowed us to, like, kind of explore talking to customers and bringing them on the platform and have them explore it and give us feedback.
And so we had gotten a couple of those, but nothing, like, really significant.
We had made a couple of dollars, you know, before raising any money.
Omer (23:47.010)
So when you said discounted consulting gig, it was because you guys.
It was very high touch.
You guys were putting a lot of time into helping these guys solve the problem, but you were pricing it more like it was a product.
Diego Oppenheimer (24:00.290)
Correct?
Right.
Okay, correct.
Exactly.
It was one of those things where, you know, they say things do things that don't scale, like, what's this?
The ultimate not scaling thing.
But it allowed us, you know, the way that we saw it, the way that we interpreted it, is we were getting a minimal amount of money.
We were getting paid for the privilege to get a lot of time with potential customers and really listening to them and what they wanted and how they wanted to build it and what they were looking for, and really kind of building out from there what the friction points.
These first couple ones that we had, even though they, like, essentially, you know, I don't want to say randomized, because they're not really randomized, but they took a lot of time dealing with, really led us to the next couple of steps with the company.
So well worth it.
Omer (24:48.930)
When you went full time working on this business, what kind of Runway did you guys have for yourselves?
Diego Oppenheimer (24:55.570)
You know, I came from a, like, finance background to a certain degree I worked in.
And so I had planned out a year without a salary.
A year without a salary is what, as far as we could take it in some.
And the clock started ticking in November of 2013.
And so that was essentially where we said with Kenny, we said, look, we can both afford a year of doing this.
We're lucky enough that we come in an industry where the opportunity cost is extremely low in the sense that we're not really risking not having jobs in a year.
We really needed to quit on this and go find a different job.
Like, we could do that.
So very unique position.
You know, that's not something that I. I'm very appreciative of having been in that position.
And we're lucky enough in the world of software that that's the case today.
It Obviously does not translate to every industry in that way, but, yeah, so about a year is really where we.
Where we had set the mark.
Omer (25:50.100)
Okay, so you said.
I think you said in March 2014, you guys decided to get funding.
Can you tell me about the process that you went through?
Because you'd mentioned earlier, before we started recording that, but it was a little different.
Diego Oppenheimer (26:05.580)
Yeah.
So given that we were an algorithm company, it made sense to write code to try to do at least as much of the fundraising for us as possible.
And so a lot of the things that we did was actually use resources like AngelList and LinkedIn and our other connections like Facebook and stuff like that to try to understand what investors were interested in this space, but also which ones we were most directly connected to.
And, you know, and so a lot of this time was spent, you know, me writing these fairly complex Excel spreadsheets that were pulling data from the.
These different sources and then kind of rating the investors by, you know, how likely or not they should be.
Very similar to how, like, kind of business development folks do their world where, like, you know, they kind of do these ratings into, like, best fit, not best fit, and kind of how they're going to approach it.
And then once I kind of have had this mega spreadsheet of investors based on location and interest and what they had invested in previously, and, like, where did we consider that they had invested in maybe a competitor?
I grabbed that list and kind of like grabbed the top 60 and flipped it and started from the bottom.
So the least fit being the ones we started with because we figured that our pitch was going to be awful at that time and that we were going to learn on the way, and we were going to get questions and all these smart VCs, an angel asking us really good questions, and we were like, halfway through the list, you'll probably have gotten all the questions, and at that point, you'll know how to answer them.
And so once you're halfway through the list, and this is our theory was now you're going to be spitting off answers to those really hard questions without hesitation.
And so we kind of designed that process in a very, very programmatic way, which was.
Which ended up being successful for us.
That said, you know, it's a sample set of one, so, you know, I can't really.
I don't know if it's repeatable.
It's just, you know, we.
When we tell that story, people are.
Have always kind of been fascinated because it's been very different from how they have approached fundraising.
Omer (28:18.490)
I Would expect nothing less from a former Excel data guy.
Diego Oppenheimer (28:24.010)
Yeah, there's something to be said about that, right?
Omer (28:27.050)
Yeah.
And I think that's really clever about flipping the list and kind of going, well, those guys are going to reject us anyway, so let's use that as a great opportunity to refine our pitch for the guys that matter.
So how much did you initially raise in that round?
Diego Oppenheimer (28:46.110)
So we have only raised one round.
Essentially.
We had a convertible note that we opened and that turned into a proper round.
So 2.045 was the total.
2.545 million is the total that we raised in one go.
And that's all the money we've raised up until date.
Omer (29:02.130)
How did that change things for you?
What is different about the business now?
Diego Oppenheimer (29:07.010)
Well, you know, up until that point there was just two of us.
Right.
And so that was, you know, it was very limiting.
So I guess the first thing that, you know, it was allowed us to do is hire a team, which has been like a fascinating journey, even up until now, convincing people about your idea and getting them passionate, but, like, more importantly, actually convincing people that they own this to.
And seeing the passion that they put behind it and like, how much they care about the product to the point of like, you know, getting mad when the product gets criticized.
And like, you know, for us it's kind of obvious that was our baby, but, like, being able to that, seeing that translated into a full team caring so much about the same product that, like, you know, was a notebook, you know, between me and Kenny a couple years back, that's been by far, like, not only the most rewarding, but also like the most surprising.
And like, still, I'm in awe every day.
You know, I walk into the office every day and like, you know, all these conversations and talking to our teammates and seeing how passionate they are is really what, you know, it's a big motivator for me to come back every day.
Omer (30:14.150)
What's the size of the team right now?
Diego Oppenheimer (30:16.230)
So we're approaching 9, so.
Yeah, so still sub 10 very small.
Omer (30:21.120)
And are you going to keep it like that for a while or do you have plans to sort of go further?
Diego Oppenheimer (30:25.440)
Yeah, we currently have four open positions, so we'll probably get to the 12, 13 mark and be there for a while.
There's kind of these jumps in startups.
You go from the sub 10 to the sub 20 and then the next one's like sub 50 and.
And each one of those, a very, very different set of challenges, a very, very different set of growing pains.
Well, as you start establishing yourself as more and more company, I don't get to meetings aren't just like what used to be when I would just call Kenny at 1 o' clock in the morning and be like, hey, can we talk about this?
So those things kind of come with a, with that territory, but it's really, I mean, it's been fascinating to me
Omer (31:14.600)
looking back at the journey that you, you've taken over the last couple of years.
If you were doing this again, what, what are some of the things that you would want to do differently?
Diego Oppenheimer (31:24.920)
So looking at things that we want to do differently, you know, I feel like we made, I mean, as expected, kind of a lot of mistakes along the way.
Everything from the way we were doing interviews, some hiring decisions, some financing decisions that I would have just, you know, rather make decisions.
Like, I think the biggest thing for, like the biggest lesson learned is the importance of speed and just really, you know, coming down on decisions quickly and not letting them.
And like, this is kind of like one of the biggest transition like happens when you go from a company like Microsoft to a startup where at Microsoft you could kind of dilly dally on a decision for a couple months and kind of see how it plays out.
And in a startup you just can't.
You have to make that decision quickly and move on and be wrong.
Right.
Like, make the decision, mess it up, know that you messed it up, never make that decision again.
But like that entire process needs to be compressed in, you know, instant, not, you know, over the course of six months.
Omer (32:32.530)
Yeah, I think it's a very hard transition to make.
And I think not everyone can make that leap, leap from sort of that Microsoft type culture to a startup world because they are so different.
And I think at Microsoft, everything, you sort of start everything by thinking about making it scalable.
Right.
I mean, when you were working on Excel, I think, you know, earlier, I think you mentioned, you know, you had a billion users.
Right.
So it's a completely different set of challenges and a mindset that you have to take to approaching those, those problems.
What does, what does, what does the future for algorithm arrhythmia look like?
I mean, what sort of is it, what are you guys working on right now that you're excited about?
And is this something that you can share in terms of where you're headed with the business?
Diego Oppenheimer (33:16.850)
Absolutely.
I mean, we're excited just based on the amount of growth that we're seeing in terms of people using our product.
We just recently hit a milestone for us which was, we have over 14,000 developers on the platform.
And this is a goal that we thought was going to take a couple of years to achieve and it's been months.
So very, very excited about that, definitely.
And seeing what people are building with our product.
That's every single time that somebody kind of sends us a demo or sends us, hey, I built this into my application.
I'm using this algorithm that's been fantastic.
We partner with universities and so we've partnered with the first couple of universities and we have a whole other batch of these universities in the pipeline that should be closing soon.
And just seeing how our brand and our product is expanding is really what's next and what we're fostering a lot of.
So up until now, it's been a lot of internal building.
We had essentially the last 12 months have been get a reliable system up and running.
You know, when I talked about like that capital mvp, you know, like that needed to be transformed into a product.
And that was a monumental effort on part of our team to be able from like, you know, last September, October, we started hiring the first couple of engineers to where we are today to now, we actually have a released, stable, reliable platform that people can run their businesses on.
And now it's a question of turning that around now and going and reaching those people and bringing them in and doing a lot of the efforts of, you know, that evangelism and bringing more and more users in and increasing their dependency on our platform.
And so it's a, it's, it's, it's been, it's an inflection point for us in kind of the direction that we have been working on, which was very focused internally to.
Now we're going to start, you know, we've started focusing externally.
Omer (35:13.820)
Well, I think it's a fascinating business what you got and the problem that you guys are solving here because potentially it, it gets really sort of bleeding edge solutions into the hands of app developers a lot faster, a lot earlier than maybe what they're able to do today.
And the model that you guys have makes it super easy for them to also be able to implement that into their own products.
So I'm really, really fascinated by where you guys are headed with this.
All right, so it's time for our lightning round.
I'm going to ask you a series of questions and like you to just answer them as quickly as you can.
You ready?
Diego Oppenheimer (35:54.570)
Sounds good.
Omer (35:55.370)
What's the best piece of business advice that you ever received?
Diego Oppenheimer (36:00.090)
Treat secretaries, executive assistants and office managers like they run their place because they actually do.
Omer (36:05.130)
Because they do.
Diego Oppenheimer (36:05.970)
Yeah.
Omer (36:07.130)
What book would you recommend to our audience and why?
Diego Oppenheimer (36:10.730)
I'm going to cheat.
I'm going to say two.
The first one is Delivering Happiness by Tani Se, the founder of Zappos.
Everything you need to know about customer service and how to treat customers is beautifully set up in that book.
And on the slightly more morbid side of things, the Hard thing about Hard Things by Ben Horowitz, where it's like a very good perspective on everything that can go wrong and how perseverance is kind of how you have to push through it as an interpreter.
Omer (36:39.870)
Yeah, both are great books.
And the thing about Ben's book is that, I mean, who would want to run a startup after you read that book?
Diego Oppenheimer (36:49.880)
I think that should be a test, right?
I mean, it should be like, read this book.
And if you're still like, I was depressed after reading that book, I was discussing with my wife and she's like, I was kind of explaining it to her and she's like, why are you doing this?
But yeah, it was actually, you know, after reading, I was like, okay, I get it.
Like, yes, that's why we do this.
It's the ultimate challenge.
Omer (37:13.810)
What's one attribute or characteristic in your mind of a successful entrepreneur?
Diego Oppenheimer (37:18.770)
To be shameless.
And not in the sense of, obviously you want to be honest and have good morals, but what I mean by shameless is really, nothing's beneath you.
You hear about all these great entrepreneurs.
They were the janitors at their offices.
They were the ones going to get coffee for their teams.
To really be successful, I think you just have to really, really be shameless and do whatever it takes.
Omer (37:40.940)
What's your favorite personal productivity tool or habit?
Diego Oppenheimer (37:45.500)
Inbox Zero.
At Microsoft, you would get drowned in emails all the time, and it would be like 50% of your time would be reading email.
So one of the things that we did at Algorithm is we pretty much don't use email at all.
Everything's on live chat.
But on top of that, I maintain inbox zero on a 24.
Sometimes slips on a little bit more hour basis to, you know, just rapidly going through it and making sure that, like, the only things that say in my inbox are important.
Omer (38:12.630)
Very cool.
What's a.
Another business idea that you.
You maybe have, you'd love to pursue if you had the extra time?
Diego Oppenheimer (38:20.630)
I think the world of organizing, logistics and distributing, like, you know, if you think about all these, like, Uber for something companies that are popping up, there's a logistics problem behind there and how to do deliveries, how to create coordinate drivers, how to do.
I call it the Amazon Fresh Problem.
I think there's something fascinating about.
There's an industry that hasn't really been shaken up yet.
It's the same players, the large players are dhl, ups, et cetera.
And then the smaller players are these logistics company software that's been the same software since the 90s and really needs to shake up.
Omer (38:56.050)
Yeah.
I think Amazon just announced that they were getting into the restaurant delivery business.
Diego Oppenheimer (39:00.290)
Yeah.
Omer (39:01.330)
But I mean, that doesn't surprise me.
Amazon's trying to get into air everything.
Right.
So.
All right, what's an interesting or fun fact about you that most people don't know?
Diego Oppenheimer (39:10.630)
So I grew up in South America, so I'm really, really passionate about meat and barbecuing.
I butcher my own meat.
Omer (39:18.710)
Wow.
Diego Oppenheimer (39:19.430)
And so I've done a lot of classes around butchering and it's like something I enjoy doing and it's actually a lot cheaper.
And so, yeah, I do that.
And one, you know, kind of one day the idea is to retire and build out a little barbecue joint where me and my wife will manage.
Omer (39:37.770)
Wow.
Where in South America are you from?
Diego Oppenheimer (39:40.410)
Uruguay.
Omer (39:41.930)
Or isn't it Uruguay?
Diego Oppenheimer (39:43.610)
Yep.
I mean, Uruguay.
Omer (39:45.690)
I got to pronounce it correctly.
Diego Oppenheimer (39:46.970)
Yeah.
Well, in Spanish it's Uruguay.
So like, that's why I.
Omer (39:50.730)
So, so what do you do?
Like, you could, there's like farms run in the Seattle area or something that you just go to.
And yeah, most farms catch an animal.
Diego Oppenheimer (39:58.580)
Yeah, it's a little bit less, you know, wild west than that.
But most farms will allow you to like, you know, you can, you know, when they, when they have calves or anything like that, you can kind of buy, you can buy, you know, pre.
Buy the kind of animal and ask them, you know, they'll, you know, once, once it's time for, you know, for them to be produced, then you can like, you know, buy kind of like in larger pieces of the, of the animal and you can break it down yourself.
It becomes a lot more, you know, you get grass fed homegrown animals that you understand the whole life cycle it's gone through and that it's been treated properly.
And so there's a lot of, like, there's a lot of goodness to kind of understanding where your meat comes from.
Omer (40:40.710)
Yeah, no, I've never, I've never got as far as even thinking about butchering my own meat.
But I think one of the things that I do love about this part of the world where we live in is that that there's so much food that you can get that you can trace back to, like, local farms and places like that.
And.
I don't know, but that just.
I don't know, it gives me.
Makes me feel better, I guess, when I eat that stuff.
All right, and finally, what is one of your most important passions outside of your work?
Diego Oppenheimer (41:09.430)
So I'd say, like, I really like the idea.
And this is actually something being, like, kind of recently.
So I have two nieces.
I've been really on this thing.
We're about, like, getting STEM education for girls and getting more girls into technology has been, like, a recent passion of mine.
I came from a background with very strong personalities, female personalities, in my household.
My mom ran.
You know, she was a president of her marketing at a large company.
I saw the, you know, the kind of.
The passion there and like, the whole.
This whole movement around not having enough women and technology has been kind of, I don't know, struck a chord with me.
And so it's something I've been literally passionate, trying to be more and more involved in.
Omer (41:52.370)
That's great.
That was really good.
Cool.
Diego, it's been a pleasure.
You know, I really appreciate you taking the time to do this and sharing your story about algorithmia and what you guys are doing.
And I had fun chatting as well.
So now if folks want to get in touch with you or find about more about algorithmia, what's the best way for them to do that?
Diego Oppenheimer (42:13.090)
So my email is diegoalgoryhythmia.com and if not on Twitter, my handle is D O P P E N H E. Cool.
Omer (42:23.770)
And they can go to algorithmia.com if they want to find out more about the product.
Diego Oppenheimer (42:30.010)
Exactly.
Omer (42:31.130)
Cool.
Thanks, man.
This has been a pleasure, and I'll let you get back to work, I guess.
Diego Oppenheimer (42:35.530)
Thank you very much.
It's been a pleasure to be on the.
On the podcast.
Thank you for having me.
Omer (42:38.890)
Cheers.
