April 27, 2023
Jason Hirshman is the Co-Founder and CTO of Uncountable where he leads product development and the engineering team. Jason was previously at Stanford University, where he received a Master’s in Computer Science and a B.S. in Mathematics. He was selected for the Stanford A.I. Innovation Group, which sought to apply machine learning in impactful ways. Jason's prior industry experience includes building software at Palantir to model data from Syrian refugee camps, leading the mentorship program at StartX, and working as an engineer at BenchPrep.
Julian: everyone. Thank you somuch for joining the Behind Company Lines podcast. Today we have JasonHirshman, Co-Founder of Uncountable. The Uncountable Platform is an enterpriseclass cloud solution for modern global R&D organizations. Jason, I'm soexcited to chat with you. As we were chatting before the episode, not only am Iexcited about your background and what you bring to the table, but alsoUncountable and really the, kind of unpacking the truth behind, this whole kindof R&D, this research and development process that other organizationsoftentimes have, many projects that they're doing at one point in time.
And I think as consumers we only see theresults of them. Maybe their papers, maybe their products, but really justcurious about what really it it takes for companies to. Identify research,develop a product, and, and then implement it into the market and seeing, whatcomplexity that's at, at a highest scale.
So really excited to chat and dive intothat. But before we get into all that good stuff, what were you doing beforeyou started the company?
Jason: Yeah, well thanks forhaving me here, Julian. Yeah, I'm definitely happy to dive in a little bit moreabout what we're doing here at Uncountable. My background is in computerscience and math.
I studied at Stanford before startingUncountable about seven years ago now. So for the last seven years we've beenworking on this company, it's been definitely a long and exciting journey. Icome from a place of really loving software development but also beingpassionate about the use of data, data analytics.
So when we started Uncountable, we werelooking to apply data management practices to some other industries beyond justtech or finance. Yeah. And basically found that the R&D space was one thatcould really use a lot better conclusions from data and where decisions aremade very consistently by leveraging data and insights from them.
But it was very hard for scientists toactually compile everything together. Yeah. So I have a lot of passion forstatistics for that type of analysis. And that's what I bring to Uncountable.
What are some things that audiences maynot be aware of in terms of companies? Using, these types of, of projects tounderstand whether it's a product or service and how are they using the data toimplement. What they're researching.
Jason: Well, I think that'sexactly right. There's a lot of focus on things like pharma, where finding thatthe next drug can be a really big deal and life-changing.
But the reality is, is that products weinteract with every day have a whole supply chain behind them, where if you'reable to advance the technologies and that supply chain, you're getting productsto market faster. And we're seeing innovations in our daily lives. So everythingfrom the paint on your walls to the batteries in your electronics, and now yourcars or even the shampoo that you're using in the shower all those come fromraw materials, chemicals, all sorts of different things that are developed by aseries of companies before they hit your shelves.
And we work with companies all acrossthe supply chain to make sure that their product development teams are able torelease products a lot faster, and they're getting better insights about whatthey can actually develop next.
Julian: Yeah, it's sofascinating to think about, the speed at which, com or platforms like yours canreally help, things go to market and really start testing in the environment.
But, even just taking a step back,setting up the environment to test, what does that look like for a lot ofcompanies, when they, before the data is even collected, is it they're usingprevious data? Are they, going through doing case studies? What types ofstructure and environments are they testing in, and what are they measuring forin particular?
Jason: Yeah. One of myfavorite parts of working on Uncountable is that our end users are really,really smart people. They're typically chemists, biologists, materialscientists who have studied for years in whatever domain they're developing ina and they're in a lab somewhere trying to figure out how they can innovate ontheir company's product lines.
And oftentimes that means taking theexisting product and swapping out a new material or ingredient. Or it meanstrying a whole new product liner platform. And oftentimes this involves daysand nights in a lab, running experiments, trying a bunch of different thingsand trying to figure out what is most profitable, what's gonna advance theproduct, the farthest, and what is the best performance or is the safest forthan consumer?
And that's a lot of work goes into everyproduct that gets released and we at Uncountable are trying to help them figureout what to try next. Which experiments are gonna have the most value in how tokind of best use their own time and the resources of their lab.
Julian: And what were theyusing beforehand?
Were they using one system that's alegacy product? Were they using multiple systems to try to not only collectdata, but also, analyze it and, and create predictions off of it? What werethey using before Uncountable?
Jason: Yeah, like so manyprofessions, the R&D world was really much on Excel and PowerPoint.
You would have your own spreadsheetwhere you put all the data in that you're collecting at the lab bench. And thenyou'd present it to your colleagues via PowerPoint presentation. And that'strue whether or not you're in academia or a really large organization. Yeah.But the problem is, is that at the industrial scale where you might have a teamof 200 plus scientists developing a number of different product lines,everyone's working on very similar products, even if they're approaching itfrom different angles.
And moreover, there needs to be moresharing of knowledge in a company setting. Okay. And so we want to really makesure that people are unlocking the data so that it no longer lives in aspreadsheet and is in a centralized system that everyone can access. We workwith companies that have labs in North America, Europe, and Asia, all workingon very similar problems.
And if they can't access what the otheris working on, they can end up running redundant experiments or trying thingsthat another lab is already determined, won't be successful. And so making surethat there's a consistent system for people to access is really crucial inthese industries, whether it's.
A chemicals company or a form of acompany or a materials company.
Julian: And were those, Theprevious experiments, just thinking about, the amount of headache it would beto do something that's already been done and well tested. Is there acentralized database for that or are people scrounging, publications from,Google Scholar or what in particular?
Are they going to find the informationprior to see if, if something's been tested?
Jason: Yeah. So the, thenormal process here is you go to school, you get a a really advanced degree inthis type of science, and then you go to industry and find that you need evenmore of an education, that you need a lot of experience with a particularproduct area, a particular chemical area to really be effective.
Yeah. We've even had scientists describethis whole process to us as learning a system of black magic that, like theR&D process feels magical. Yeah. Because a scientist with 30 years ofexperience can go in and know exactly what to do in a situation. But theproblem is, is that if it takes 30 years to train a scientist to be effective,you're probably not moving fast enough, right?
You want someone to be able to join yourlab and right away be able to push out that next product line. And the only waythat can happen is by trying to take that 30 years of insight that's insomeone's head and actually institutionalize it and have it somewhere in adatabase where that you can learn from it quickly.
And so for us, we really want toactually remove the magic. We want it to be a much more disciplined scientificprocess after all of this science than to be some sort of magical experiencethat requires, 30 years of expertise before someone could be effective.
Julian: Yeah, and I'm thinkingabout, without a system like this, what, what are, are, are scientiststypically, missing conclusions or correlations between data points?
Obviously, they're doing the best theycan, but, I think once we find, some of the sort, I'm sure we fixate and maybego on a trail of thought, but there might be something in the data that, thatwould maybe take us on a different direction. How much do you think, I, I don'tknow if you have a percentage are, are missing in terms of the relationshipbetween data and also what's the typical delay before, a company likeUncountable comes in with a really sophisticated product to be able toactually, give you the insights in a more rapid rate?
What's the average delay and, and whatpercentage of, information are we missing in, in the legacy products?
Jason: Yeah, I, I think thething to emphasize here is that we don't make these scientists moresophisticated. They're, they're already really smart, really well educated. Butwhat we do is put more information at their fingertips.
Mm-hmm. And so you hit it on nail, whichis like when you have information like regulatory data about what ingredientsare acceptable, what raw materials are acceptable in different countries, youcan run these experiments more quickly because you're more confident thatyou're testing something that's actually applicable to whatever product you'retrying to release.
And, and that's a really hard thing to,to get right. And we wanna really help scientists be able to do that.
Julian: Yeah. And o obviouslythe, there's this, oh, there's always a skeptic in me, but thinking about justlike all the, information around say, studies that don't actually have provenresults and they're a little bit extrapolated from, what's actually there.
Is this, in a way the having the abilityto not only have more sound conclusions or, or, or, or potential, confoundingoutcomes or and is it a way to kind of minimize the risk of, say, having, datathat's, say misinformation or things along that nature because of the, the waythe network works and kind of validates the, the information or the, the studythat you just kind of com completed.
Jason: Yeah, no, I, I thinkit's really important that you're able to learn from your, your colleagues sortof mistakes and failures. Yeah. As much as your own. So it's not only aboutgetting more conclusions from your own data, but seeing a broader array ofinformation that you can learn from. And so really it that helps the analysis,right?
So if I have twice as much data to learnfrom it's not necessarily about creating a more advanced conclusion, but. MaybeI'm coming to a better conclusion because I have more information to work with.Yeah. And that's a, a big part of it for us. When companies use Uncountable forseveral months or even several years now, what we see is their ability to actquickly on information and have the same opinion shared across the lab is muchgreater.
And the visibility that an executive atthat company has into what's happening in the R&D environment is also a lotbetter. So we actually can work and collaborate with a marketing team who wantsto understand what's possible in the R&D environment, what would bepossible for our next product release.
And so you can start to connect togetherdifferent parts of the organization.
Julian: It's so fascinatingthinking about, what technology allows you to do that. And before I go into,what you've kind of implemented and, and Uncountable to be able to kind of notonly define data, label it, but also read it and, and, and make conclusions offof it.
Describe the user journey a little bitfor our audience. So, you're a company or organization, you wanna startresearch and development into a particular sector, whether it's a campaign orwhether it's a product, whether it's a chemical. What is my typical, userexperience with Uncountable and.
How can I get the most out of theproduct if I'm, thinking about using for my next R&D project?
Jason: Yeah. So if I'm anR&D team considering using Uncountable my, our first question to that teamwould be kinda, where is your experiment store today? So someone's at a labbench mixing together a bunch of chemicals or raw materials.
What happens to a record of that data?Oftentimes it goes into a physical lab notebook or a spreadsheet. Or somethingthat's inaccessible to the rest of the team. Yeah. And so we go and say, well,how would you like that to be stored such that other people can interpret yourinformation? And when we kind of survey a bunch of team members and try tostandardize the way that information is stored so that everyone can understandeach other's results then we go and configure the Uncountable system to, toreflect that standardization and have people start to record that data in oursystem.
Now, in addition to that, we try tofacilitate communication in the lab. So if I'm going and requesting that, oneof my technicians go and runs an experiment, that request happens inUncountable. So you can effectively prioritize the different requests that arebeing made optimize the use of lab resources and actually track the resultsthat are coming in.
So I'd go into Uncountable, make arequest based on my experiment, and then go and actually receive those resultsa couple of days later. So everyone in the lab environment has their ownaccountants logging in to see what's happening in the lab and be able toanalyze that data.
Julian: It's amazing to thinkabout the, the amount of collaboration that goes into using the platform and,and, and, when you're distributing the results, kind of who's involved in theprocess and what's, like the typical use case that Uncountable sees.
Obviously it, it seems like, a lot oforganizations use it, but who optimizes the best at this point? And I guessalso who are you particularly, who's under utilizing the platform?
Jason: Yeah, that's a greatquestion. So, we work with a, a wide variety of customers. We have over 75customers actively using our product.
And that's across a pretty good amountof different industries. Everything from, paint companies chemicals some lifescience organizations and even food companies that are making all sorts of, ofdifferent consumer products. So it's really important to us that we continue tolearn and get different perspectives from a wide variety of R&Dorganizations.
Yeah. R&D as a whole is a very kindof secretive discipline. Knowing what goes into a product is sort of the keysto the innovation of a company. If you understand, the Koch formula you're,you're probably one of the very few actually trusted with that in theirorganization.
And as an a software company, we'reactually given a lot of very sensitive data. And so our priority as a companyis keeping that very safe and secure. At the same time, we're also giveninsight into how these R&D organizations work. What are the problemsthey're thinking about? What are the inefficiencies in how they do things?
And we're able to really compile a setof tools. That are helpful for all the companies we work with. Yeah. So ratherthan approaching R&D as sort of a secretive discipline, we want to approachit as a set of scientific principles or procedures that are widely applicableno matter what type of product you're building.
Yeah. And go beyond just, this is whatI'm doing in this experiment, to, this is how experiments should be rungenerally. And this is how to think about data organization. And a lot of thestuff just isn't shared because of the, the secrecy involved in the R&Dprocess.
Julian: Yeah. And is a lot ofthat secrecy just, trying to not, have, have proprietary information shared or,industry secrets.
How, how important is, not sharing thosepieces of information versus sharing kind of the procedures that, that allowyou to at least uncover similar types of data to at least, compare and contrastyour findings to the, I mean, Obviously, I think the founder in me is like,tell everybody everything.
So we know everything we can build coolstuff all, all at once. But that's not necessarily the perfect world, you know?
Jason: Well, one of the mostcommon questions we get is, do you share data among your customers? And theclear answer is no. We never combine data sets. Every customer lives in a silo.
And that's because we want to reallytreat every single piece of data we get from customer as securely and safely aspossible. So there's never co-mingling of data. Regardless though, what we dolearn are things about how the R&D process actually happens in each ofthese lab environments.
How do scientists and technicianscommunicate? What types of things go in their, bodies of their emails when theyrequest experiment? How do we kinda best handle those types of processes? Andthose are really shared across our organizations and aren't the secret part of,of R&D. But, but certainly keeping this information safe and secure is oneof the main functions that we have as a company.
We tell our engineers that we need tomake sure that the way we store data Uncountable is, 10 to a hundred times moresecure than the way our customers even store data because it, it really is kindof a pillar of our organization and our software.
Julian: Yeah. Yeah. And I cansee, That the importance of that is kind of, you live and die by the, theability to collect this, keep it private and redistribute it very efficiently,very effectively, and but I'm also curious in terms of, I don't talk to a lotof founders who, go through user experience with the lab group.
And I'm curious, do you, did you buildtowards the user experience on the lab, for, for, for lab technicians to useand kind of go upmarket? Or did you go to larger organizations to kind of,communicate the value of having a platform and go kind of a top down approach?What was most effective for you and has it changed at all?
Jason: Yeah. One of the waysthat we compete is by really delivering an enterprise class solution. There arelots of preexisting products on the market to handle electronic lab notebooks,lab information management systems that really serve smaller groups ofscientists, and we want to be that enterprise solution.
But as a consequence, the person, payingthe, the check and or signing the check and the person. Using the software arevery, very different. One cares a lot about the executive visibility theability to make sure that as a whole the R&D organization is runningefficiently and the person using it, who's typically a scientist or a techniciancares about how do I make sure I get this done faster today?
And I want this to be as easy to use aspossible. Oftentimes as easy as Excel is. And those are, are very differentneeds and I think every enterprise software organization. Has that same kind ofset of problems. Yeah. For us, the way we dealt with it is by making sure thatwe're adding value to scientists and their everyday work.
Really trying to understand what takesthem time today when they're sort of recording data or compiling data that wecan make really, really fast. Because we know we're never gonna be as flexibleas an Excel spreadsheet. So we have to kind of win them over in different ways.Yeah, and the way we win the executives over is by really making sure that wecan institutionalize and standardize their data and give them the leverage theyneed to be able to have broader insights about the R&D process.
And every company today wants to be ableto use, data analytics, ai, machine learning to drive insights, but you simplycan't do that unless you have the data somewhere in a centralized fashion.
Julian: Yeah. And thinkingabout obvi, jumping to the AI component, what is the, the, advancement of thattechnology at least recently kind of allowed your company to do?
Or, or is it, were you at, itscapabilities previously and, and now that it's popularized, now you're throwingit on everything for everybody to, SEO back search, you what, what's kind ofbeen the relationship with the technology and has it advanced recently to beable to offer more capabilities that your response to.
Jason: Yeah, so I, I thinkwhat the recent advances AI have shown is that the applications of AI are a lotbroader than people previously realized. But the kind of the core technologyhas always worked by, the more information you can give it, the better it does.Yeah. And in the case of the R&D environment, that information comes in theform of experiments.
So what we do for companies is providethem AI superpowers by having all that information in one place. No matter whatmo types of models you want to use or type AI you want to use, or if you wannaleverage platforms from Microsoft or Amazon, you really need your datatogether. We talk with data scientists all the time at these companies that aresort of frustrated by the fact that there isn't data that they can use fortheir models.
Yeah. They'll get a one really cleandata set from a project, analyze it, and then sort of be done because they justdon't have a second one. And we don't want that to be the case. We want everyexperiment that happens in the lab to keep feeding into a larger and largerdata set. Yeah. Some of the recent advancements really show the degree to whichyou can use natural language to sort of instruct a system or to engage withdata.
And what we're hoping is that as thoseadvances continue to happen, and to be on the kind of the cutting edge of beingable to take natural language and instruct a computer to analyze a data set forme, or filter my data in some way, And Uncountable. We've already developed thetechnology to be able to build a machine learning model in just a few clicksbased on data.
We've always thought that the way youdeliver machine learning is by not assuming that you're working with a traineddata scientist by assuming you're working with someone who's passionate abouttheir data, but maybe hasn't had that training. And that's always beenimportant to us. We've always tried to build tools that a non statistician canuse, A non-data scientist.
Julian: Yeah. And, and to, towhat point, I guess to what involvement does a scientist have? Are they,inputting everything manually and, and the platform's kind of indexing it andallowing you to kind of build those insights, kind of collecting the data? Or haveyou built kind of some sophisticated where almost like plugins to where otherinstruments can plug into and start collecting data?
Where are you in that process? And doyou, do you kind of plan on involving, I guess, more automated collection ofdata, Than you do now?
Jason: Yeah, I think there'san overall goal here of letting the scientists do what they do best, which isanalyze data and plan experiments. The scientific method is very much aboutcreate a hypothesis and then go test it.
It's not about go and spend an hour withan Excel spreadsheet. And. Our goal is really to, to get to that as much aspossible, which means trying to take some of the burden away from scientists interms of the, the data collection, some, some of that automatic kind ofpipelining that you're talking about.
So we work with, the IT teams at ourcustomers to try to set up automatic transfers from equipment. We have a seriesof high throughput experimentation tools to be able to analyze larger sets ofdata that's more frequently being collected in a modern lab environment. Andcertainly work with labs that are on the forefront of automation.
So there's a lot of really coolcollaborations that we're doing there, and we certainly see a future wherescientists can spend more time thinking and less time sort of manipulatingspreadsheets.
Julian: Yeah. Tell us a littlebit about the traction. How many, clients do you have kind of currently usingthe platform, and what's particularly exciting about the outcomes that they'veseen in terms of maybe, speed and completion or understanding of theirexperiments that they're running, but would love to hear, the amount of clientsand, and some, some positive outcomes that they're seeing.
Jason: Yeah, so we have over75 customers using our product and we on a regular basis are seeing. Thatnumber increase pretty rapidly. Yeah. And that becomes from the fact that ourproduct keeps getting easier and easier to use and we keep seeing more and moreuse cases for it. We sell to such a wide variety of industries at this pointthat we're constantly seeing new opportunities.
Yeah. The success stories for us looklike the standardization of data in these types of environments where we haveexecutives telling us that their R&D teams are better able to tackle newproduct development problems than they have ever been before. We work with,pain companies that are putting all their data in our system as well as, a foodcompany that might actually work with us to make sure that all their qualitycontrol and management data is in our system.
And so it's a really VA wide variety ofuse cases at this point. And we're constantly looking for more.
Julian: Yeah. And thinkingabout both external and internal, being that, obviously R&D doesn't seem tohave stopped, but do you kind of foresee any potential, like risks in thefuture or even now in terms of, what would kind of impact you moving forwardand continue to scale and grow your company?
What risks kind of do you see outthere?
Jason: Yeah, I mean, I thinkfor us it's about really staying dynamic and making sure that we're able toaddress a wide variety of problems across a wide variety of industries.Scientific development's never changed. The ability to collect different typesof data is always evolving, and so we wanna make sure that our air platform'skind of flexible, customizable enough to adapt to, to changing circumstances.
Over time we've certainly sold to alarge variety of different industries. Industries like the battery industrycontinue to evolve really, really rapidly. We're seeing a lot of traction inmodern food industries for kind of artificial meats and milks and that type ofthing. So really being able to address whatever new products come out is reallyimportant to us.
Julian: Yeah. And ifeverything goes well, what's the long-term vision?
Jason: We wanna have as muchof an impact as possible in the industrial kind of scientific process. So whenyou are working as a scientist in the industry are you recording data in abetter way? And whether that's unUncountable or. Through a system that weinfluence.
It's just really important to us thatthat data is being collected in a way that people can actually use it andreally has a longer useful life. If you run an experiment today and then yousort of forget about it a month from now and it gets locked away in a Excelspreadsheet on a SharePoint there's not much useful value to that work that youdid.
We wanna make sure that a scientist twoyears from now can actually reference that experiment and use it as part oftheir analysis.
Julian: Yeah. How often areexperiments not referenced? I'm just, I could, I could see like, at least inmy, index of, files and information, things that just sit dormant, but areuseful today, is that happening often with these R&D teams? Just because ofthe, obviously it's, it's not their capabilities, it's the technology that,that they have access to. Yeah. How much is being missed in thoseexperiments?
Jason: Yeah. I would say thatthere's even kind of a worse problem, which is that sometimes an individualscientist, Doesn't remember the circumstances for a particular experiment ayear or two after they ran it.
So oftentimes they won't actually trustthat data and will actually go and replicate the experiment just to make surethat that result was right. And, and that's because of this lack ofstandardization. You might not record all of the necessary parameters. Youmight have been making assumptions about what your standard protocol looks likeat any given time and not remember that your s o P changed a year later.
And so it's really important to havesome structure backing your data just so that you can trust it let alone beable to reference and find it.
Julian: Yeah, I like this nextsection I call my founder faq. So I'm gonna hit you with some rapid firequestions and see where we go. So, first question I always like to open it upwith is, what's particularly hard about your job?
Jason: Yeah, so I runengineering and product for Uncountable. On the engineering side, the, thehardest thing that I do is, is hire grade engineers. I'm really proud of theteam that we've built so far. But we're always looking to, to add to it andreally add people that are passionate about building a really kind of strongproduct.
Mm-hmm. In addition to solving hardengineering challenges. Us and I find that recruiting and retaining greatpeople is my everyday challenge. I'm really passionate about trying to find thebest and make sure that we can continue to grow the engineers that we have. Wepour a lot of time and thought into how we can make engineering Uncountable agood experience, and more importantly, really challenge the engineers that wehave.
We want everyone walking away fromworking at Uncountable. Saying Uncountable was one of the hardest places Iworked, but I grew and learned the most from working there. Yeah, on theproduct side, the hardest thing I have to do is balance the needs and demandsof our 75 plus customers, is really making sure that as an enterprise productwe can create a really customizable product while still creating great userexperience and continuously simplifying the product.
And I engage our engineers on a dailybasis and sort of challenge them to, to figure out how we can add 10 newfeatures and also simplify the user interface. And have to continuously do thatweek over week.
Julian: Yeah, thinking about,the interview process and, and hiring talent. So many founders, I mean, that'sthe biggest, well, one of the three biggest headaches, right?
It's like building product, fundraisingand hiring people and retaining them. Like, those are the buckets that, thatyou lose a lot of sleep, earn a lot of gray hairs about how do you, what arethe questions that you ask to identify, the right talent for your team? Andwhat's the structure that, that you've held being that it's so important tokind of standardize how people are intake through your, your interview processand evaluated and.
And added to your team, what, what,what, I don't know if it's a secret sauce or something like that, but what,what's been valuable and successful for you to, to bring on, Great people?
Jason: Yeah, I mean, I dofind the process to be particularly rewarding, so, well, more than a headache.I do think that it's actually a, a very interesting and rewarding butchallenging process to, to really find those people for the team.
And to me, we, we try to answer twoquestions about each of our candidates. The first is, can you do the work?Right. Do you have the, the technical competence, the intelligence the abilityto go in and do a great job? And the second is, are you a fit for theorganization? And, and the fit part is really where we try to differentiate.
We think of fit not only as what can youbring that's sort of special and different that can help our team, but alsowhat can we bring to you? Is there a story we can tell about why Uncountable isthe next role in your career path? What are you gonna learn here that you'renot gonna learn elsewhere?
What type of flexibility can we giveyou? That not every company can, and we have some great candidates that comethrough the door that are fantastic engineers, but we really can't tell a storyfor why we're the right role for them. And, and we can pass on a, a candidatefor, for that reason alone.
Because ultimately if we can't convinceourselves that this is the right role for you, it's gonna be hard for you two,a year into the role to say yes. I'm still happy working at Uncountable. And sofor us it's really about challenging the candidate and saying, what do you wantto get out of the next couple years?
What do you want to get better at? Isthere a different type of role you see in your future? Kind of what type ofengineer do you want to be in five years? Yeah. And we're trying to ask thosequestions kind of early and often in the interview process to figure out ifit's a fit.
Julian: Yeah. Thinking aboutjust like external factors that impact companies, what have you seen maybe eventhe last two, three years, four or five in regards to external, whether it's Ilegislation or interest in companies looking to invest in researching a certainparticular aspect.
One thing that comes to mind is like theinflation reduction Act is, moving people towards buying electronic vehiclesand things that are more sustainable and, and things like that. I, I'm assumingimpact the research that companies do. Whether including that, but whatexternal factors kind of, motivate companies to start researching in new areasthat aren't particularly something that they've done before?
Jason: Yeah. So they'redefinitely, definitely a number of them. The, the biggest one is kind of macrotechnology trends. We're seeing a big push towards greener technologies. Forexample, the battery industry is seeing a big resurgence. But another is sortof like supply chain shocks.
Mm-hmm. And this can happen in a numberof ways. I mean, especially over covid. Everyone is starting to expect theirsupply chains and look for areas where they were dependent on certain countriesor certain areas. And we help companies try to find substitutable rawmaterials. And companies really use Uncountable as a source to try to analyzetheir dependencies on different materials.
And you also see regulations coming up.So when the EU passes a new initiative to make products greener or healthierit's really important that companies can respond to that quickly and put outthe changes that are necessary to get them into compliance. So we help a numberof companies make their products more sustainable to be in line with thosetypes of regulations.
Julian: Yeah, it's sofascinating thinking about, how much that pushes, companies in a differentdirection. And and, and one thing that also comes to mind is like, what, what'ssomething that hasn't been tested on your platform that you're kind of hopingfor, waiting for, or curious about see to see its capabilities and what,Uncountable can really bring for the, bring to the table in terms of theresults and reliability.
Anything come to mind?
Jason: Well, I think the, theimagination of every lab director is a fully automated lab. Yeah, one where youput in, this is the experiment I wanna run, and then the sample gets, goes inand it comes out with the results. And that means that it's moving around anautonomous way that the machines are working autonomously and the data ispushing back to the cloud all on its own.
We have companies that are at theforefront of those types of innovations that are building autonomous labsthemselves or building parts of them. And the, the thing that everyone wants tosee is sort of the end to end process there. If you put in a description ofwhat you want from a product and then the system goes and tries to basicallyoptimize it for you.
And we have helped with many differentparts of that process, including the recommendation of what experiment to trynext. But really getting to that end-to-end vision I think is what everyone isgoing to be working towards over the next decade.
Julian: Yeah. Yeah. Obviouslyyou've been entrenched in this for I think seven years, what you said, withresearch and, and building the company.
But I'm gonna hit you with this questionanyways. If, if you weren't working on Uncountable, what would you be doingotherwise?
Jason: Yeah well there'sobviously a lot of really exciting technology areas. What I'm passionate aboutwhen my two co-founders are passionate about is really building a greatorganization and continuing to scale a company.
So for us it's really about thechallenges that come from trying to scale out this product trying to continueto grow an organization. We currently have 40 employees working for us and areconstantly looking to, to grow the team. So no matter kind of what I'm workingon, I think those are the challenges that motivate me and I would be passionateabout.
What I bring to the table is a lot ofexperience with data analysis, machine learning, statistics so something in theAI field which certainly is not dying off anytime soon. Would be where I'mat.
Julian: Yeah. I, I'd like toask this question too, is what's something that you, you're good at now as afounder that you wish you were better at earlier on?
Jason: I, I think it's reallyunderstanding how engineers can grow in their careers and the right way tomotivate different types of people. One of the things that I see is that peopleoftentimes don't put enough energy into setting goals for themselves and. Wereally work with each of our engineers to identify what they want to learnevery year, what they want to get better at.
And oftentimes just asking the rightquestions is enough there. But really trying to get better at understandingwhen people could use a push, when, to ask them how they're feeling about thetype of growth they're experiencing is something that I continue to get betterat.
Julian: Yeah. Love that. I alwayslike to ask this question cuz I love how founders extract knowledge out ofanything that they ingest, whether it's early in your career or now, what booksor people have influenced you the most?
Jason: Yeah, that's a greatquestion. Well, I, I think certainly a, a lot of what I learned. And auniversity has influenced what I'm doing here.
I used work at start, which is aStanford startup accelerator where I got to work with a lot of companies at theearly stages. And there's nothing quite like being able to see 20 differentcompanies try to solve a very similar set of challenges as they get off theground. And the reality is, is that that early stage of going from zero to oneis really, really hard and always feels unique.
But there's a lot of pattern matchingyou can do to try to figure out kind of the types of common problems. And thething that I didn't realize and I'm now realizing is that going from zero toone doesn't stop. Once you build a 40 person company like we have today, we're,we're constantly looking at launching new types of product lines, trying tofigure out how we can extend our product.
And every day there's sort of a new zeroto one challenge in sometimes a small and sometimes a big way. And so reallythose lessons I learned from working with a lot of different startups helped mea lot there. And I'm sure it's the type of thing that your podcast listenersare trying to do themselves.
Yeah. And so constantly trying to learnfrom other company's examples.
Julian: Yeah, I love that. AndI know we're coming to the end of the show, so I'd like to make sure we didn'tleave anything on the table. Jason, is there any question I didn't ask you thatI should have or anything that we didn't talk about that you wanted to chatabout?
One last moment here. Did we leaveanything on the table?
Jason: I don't think so.Thank you for the, the really interesting set of questions and for, forengaging with me today here. And I'm looking forward to listener comments aswell.
Julian: Amazing. Jason, lastlittle bit is where can we find you as a founder?
Give us all your plugs as an audience,your LinkedIns, your Twitters, anything and anywhere we can support not onlyyou, but also the product and really get involved in the event.
Jason: Yeah, absolutely.Well, I'm happy to connect with people over LinkedIn. My name's JasonHirschman. My email address is email@example.com
I respond to all sorts of, of questionsand ask for help there. And, and mainly we're looking for, for great engineersgreat product people, people that, that want to come and join a company that'sbeen growing consistently the last seven years and tackling a lot of reallyhard problems. So feel free to reach out if this is sounds interesting.
You can also find more information onour website, Uncountable.com. We have a whole page about how we approachinterviewing, hiring, and how we really think about growing the team. So Iencourage you to check that out.
Julian: Amazing. Jason, it'sbeen such a pleasure. Not only learning about your experience and what got youhere, but also what the, the capabilities of Uncountable are.
Not only what it's doing now forcompanies and labs and organizations, but also what the future capabilities areand how it continues to improve. So, all in all, we appreciate you being on theshow today, and thank you so much for being on Behind. Company Lines.
Jason: Awesome. Well, thankyou for having me.