The following interview is a conversation we had with Kian Katanforoosh, CEO of Workera, on our podcast Category Visionaries. You can view the full episode here: $21 Million Raised to Build the Skills Intelligence Category

Kian Katanforoosh
Thank you for having me, Brett, and excited to chat with you. 

Brett
Yeah, no problem. So before we begin talking about what you’re building that Workera, let’s start with a quick summary of who you are and a bit more about your background. 

Kian Katanforoosh
Yeah, for sure. I’m Keon catan Farouch. I’m the CEO and Co-Founder of Orcara. I’m also a lecturer in the Department of Computer Science at Stanford University, where my focus is essentially on deep learning. And prior to that, I worked closely with Andrew Eng, who is the Co-Founder, of coursera, and a professor at Stanford as well. And I joined him in starting a company called Deep Learning AI. We were focused on democratizing access to AI education, and together we taught AI to over 3 million people around the globe. The primary feedback were receiving from learners and organizations sponsoring the classes is that there’s no shortage of educational content. There’s a notion of it now. And the limiting factor to develop a career is not content anymore. It’s understanding skills. And what skills do we have? What skills do we not have? What skills do we need for next year to achieve our goals? 

Kian Katanforoosh
And so it’s really a measurement and mentorship problem that is arising in education. And this led to the creation of Workera, which you mentioned is a skills intelligence company. 

Brett
Amazing. And can you talk us through what’s the state of AI technology today? 

Kian Katanforoosh
Well, I feel that it changes every day. Like, if you look at what happened a few weeks ago with the Chat GPT, the story I would tell now is very different from the one I would tell a month ago. But the state of AI is essentially we have a lot more compute than before. We have a lot more data being generated than before. Not only general data like text that are used to train these language models, but also extremely niche data that are solving very complex problem in the enterprise. And so a lot of people are pondering all day long over these problems. Enterprises are still hiring AI scientists and engineers. And there is an incredible shortage across the globe on AI skills, whether it is top practitioner skill building systems or literacy fluency of AI that is required now to collaborate with technical teams. 

Brett
Yeah, I’ve never seen anything go so viral like this chat GPT stuff, it was crazy. Even my mom was sharing it and asking me about it. 

Kian Katanforoosh
1 million user within five days, I think. Unheard of. Yeah. 

Brett
Now a couple of questions we’d like to ask just to better understand what makes you tick. As a Founder and entrepreneur, what CEO do you admire the most and what do you admire about them? 

Kian Katanforoosh
Man, there are so many. But I would choose Satya Nadilla, the chairman and CEO of Microsoft. I’d say I particularly look up to Satya Nadila for a few reasons. First, he comes from the world of enterprise. He was the VP of Cloud and Enterprise Group at Microsoft, which is the primary segment that my company worker serves. And so there’s a lot we learn from him and his methods. But most importantly, I think it’s his 8th year as CEO of Microsoft and he just completely reorchestrated a cultural shift there by introducing more empathy, more growth, mindset and collaboration. And I feel like the corporate culture that we remember from Microsoft has changed into one of continual learning and growth and I love that. My work is education. I’m a passionate educator and hope to be all my life a lifelong learner. And I love to see how someone manages to orchestrate a change at the scale of a giant company where now it’s more of a learn it all approach compared to a know it all approach that traditionally Microsoft had. 

Brett
Nice. I love that. And what about books? What book has had the greatest impact on you as a Founder? And this can be a business book or a personal book that just really influenced how you view the world. 

Kian Katanforoosh
On the business side, I would say there are two. There’s probably no rules. Rules. The Netflix working culture book rid Hasting and Aaron Mayer And I love the insights on high talent density, how they run a company like a professional sports team, not a family, how they lead with context, how they manage to reduce the number of controls on their employees and empower freedom. I love these ideas. On the management side, High Output Management from Andy Grove was my first management book and I love it and I still go back to it sometimes. And then I would say on the personal book, I’m a Sci-fi aficionado. So I love the three body problem from Teaching Liu and pushes me to think bigger. I don’t want to spoil the story, but it’s mind blowing. So I would recommend it to anyone who likes to think big, visionaries and who likes Sci-fi. 

Brett
No, I’ve not read any Sci-fi books yet, but people keep coming on the podcast and saying that I should. So is that the number one book you recommend starting with? 

Kian Katanforoosh
Yeah. Tree body problem. It’s a trilogy but you can start with the first one and if you like it, you can decide to read the two others. 

Brett
All right, sounds good. Well, you’re going to have angry neighbor if it’s not a good book, right? Perfect. Now let’s switch gears and let’s talk about work era and let’s go a little bit deeper into the origin story. Can you take me back to the early days of the company? 

Kian Katanforoosh
Yeah. So, as I said, the reality is were looking at this ocean of content that is emerging with people around the world, telling us, students who are taking my Stanford classes but not being at Stanford on YouTube, I would ask them, what’s the difference between you and a Stanford student? And they would say, we have access to the same courses. So that’s not the difference. The real difference is we are not surrounded by mentors that can evaluate us, guide us, tell us what to learn, and that makes a huge difference. And so the question was, how can we build a mentor for the world? Like literally a mentor that scales at the quality of top professors in the world, not only for students, but also for employees of enterprises. And there is a field called psychometrics, or the science of measurement that is super old, super rigorous, statistically heavy. 

Kian Katanforoosh
And in today’s world, with the data sets that we can collect, we can really go ten X beyond what has been done in psychometrics. And in fact, if you want an example, today Workera has a technology that can measure people around the world. We have millions of data points, and by measuring peoples around the world across hundreds, thousands of skills, we understand the cross correlation between skills. So if I know, Brett, that you can do two times two equal four, I can infer that you also can do two plus two equal four. You can probably do two minus two equal four. You may or may not be able to do square root of nine equals three, but at least based one measurement, I can infer tens of skills around it. And so today you can measure someone on maybe 20 skills infer 200, 500 skills, which is mind blowing at a level of accuracy that is unheard of. 

Kian Katanforoosh
And so this generates a lot of data in the enterprise. That data is used to coach employees, give them real time feedback on what are their gaps, what are their strengths. Personalized learning. By choosing the right recommendation, you don’t want to take a course that is covering skills you already have. You want to spend your time on the areas that match your goals for your career. You can also start matching people to projects based on their skills measurement. You can match people to mentors. You can essentially plan your workforce ahead of time at an aggregate level. And this is what we call skills intelligence. Does that make sense? 

Brett
Yeah, that does make sense. And I’d love to zoom in there on skills intelligence. So is skills intelligence your market category or how are you thinking about market categories here? 

Kian Katanforoosh
I’d say there’s a new category emerging called Workforce Intelligence. Talent Intelligence. Skills Intelligence. If you attend an HR Tech or a Net Tech conference, everybody’s talking about workforce intelligence nowadays and a lot of companies are starting to rebrand solutions toward that. But the way really we look at ourselves in the category is there’s a lot of companies that are aggregating data so they have job descriptions and resumes and course completions and other signals that may be self reported, like performance reviews from managers or self reviews by employees. And all of these are used to then infer the skills of an employee. But unfortunately, all this data is noisy. And so where Kerasit is one layer below where we are not an aggregator of data, we are a generator of accurate and granular signal that can then be used in order to coach people or mentor them. 

Kian Katanforoosh
And so that’s what we call skills intelligence. Really? 

Brett
And what kind of activities are you doing to shape that category and to really try to drive that category around your perspective? 

Kian Katanforoosh
Yeah, we’re doing all sorts of content around what we believe is the market today, where it is going. We talk about skills inferences, we talk about skills intelligence technologies. We also provide many of our testing capabilities for free online, for people to try it out, to test it, to give feedback. And then we also build, of course, case studies with a lot of our customers so that other enterprises can also learn how skilled intelligence have allowed players in their industry to run an effective workforce. 

Brett
And what’s the go to market motion look like for you then? Is this PLG or is it top down enterprise sales or a mix of both? 

Kian Katanforoosh
Yeah, it’s primarily top down enterprise sales land and expand where we would start with a group in an enterprise and then expand rapidly toward the larger group. But there’s also some early PLG where several of our customers were inbounds and they were actually users that referred the product to their manager, the manager who referred it to the VP or the sea level and then created a larger expansion within the group. 

Brett
And as you’re having those sales conversations, is there a specific point that you really see an AHA moment where they just get it, their eyes light up and they really understand the value that you can bring? 

Kian Katanforoosh
Yeah, definitely. So the product has two sides. There’s the employee side and there’s the admin or leader or manager side. On the employee side, it’s really after you take the tests and you get all that feedback at a granular level, it’s mind blowing. It’s like, wow, within this domain there’s so many skills I was not aware of and now I can work on them. And on top of that, Workera has found the best content out there for me. So I don’t need to think about where to study and it’s so granular, and the granularity makes it meaningful. Like if you take a python on Workera is not a skill, it’s 89 skills, which is so granular on the leader side or manager side. When you launch a program like that, within a month you can expect almost all the employees part of the group to have completed their initial baseline, and then they’re on a continuous learning plan where they’re measured on a continual basis. 

Kian Katanforoosh
But within a month you get full of dashboards and insights on where are our gaps, how can we marry our hiring strategy with our upskilling strategy? How can we coach our employees to deliver on projects, how can we match them to these projects in a quantitative manner? And so it’s really the AHA moment when you start seeing all that data come to fruition for your enterprise. 

Brett
Makes a lot of sense. And for shaping this category, what’s your approach with analysts? Do you view firms like Gartner and Forrester as critical to these efforts? Or what are your thoughts there on analyst firms? 

Kian Katanforoosh
Yeah, we have not engaged with Gartner and Forrester quite yet. I think they’re important ones, especially in the enterprise segment. We are on G two, we have reviews on G Two, very positive ones. I think next year will be a good time to start engaging. And I feel that the market category, workforce intelligence, is still emerging, but it’s emerging very rapidly. If you look at some of the top analysts in HR tech, josh Berstein Red Thread, they’re talking more and more about workforce intelligence today. 

Brett
Makes a lot of sense. And looking at the website, you have some really impressive logos. You have Merck, you have Samsung, you have accenture. What was it like landing those logos? And as a startup, it’s always very tricky from a trust and credibility perspective to get these big name brands to trust you and to take a chance on you. So what did you do right? What did you get right that really allowed them or made them feel comfortable enough to make that bet that what your platform can do is possible? 

Kian Katanforoosh
Yeah, that’s a great question. Just context. We’re serving roughly two dozen enterprises. We’re a very horizontal business. We serve every major industry. And what we did right, I think, is we nailed down the pain point. When you’re a VP or a C level there and you have tens of thousands of employees under you have no idea what skills you have in house. You have no vocabulary around skills, you have no standardization. And so work here just makes sense. We provide you the ontology that can map to your strategic initiatives and projects. And then I say there’s a few things that we did right, including meeting all the highest psychometric standards. It is really easy to build a quiz that is not valid, but it’s really hard to build a psychometrically sound assessment. And so when you come and you demo a platform that has all these standards met, it’s inherently enterprise grade. 

Kian Katanforoosh
We also have all the integration capabilities that are expected in the enterprise. We pull data, we push data, we integrate with their ecosystem, with their HRIS, with their LMS, with their LXP, all their talent ecosystem and learning ecosystem, if you will. And we also pass all the security grades which help in the enterprise stock two and so on. And then what else comes to mind? I would also say that for some of the industries we serve, like financial services, we are able to meet all these requirements. And the first one was painful. But now that we’ve done all the work to meet them, it’s much easier to appeal to other enterprises and scale and expand within those enterprises. 

Brett
And for worker, who is it displacing or who is it disrupting? What does the status quo look like for your customers if they’re not using you? Are they just using internal systems? Is there a legacy provider who’s controlled the market for a long time? What does that look like? Who owns the line item today? Or is there no line item for. 

Kian Katanforoosh
Something like this yet? Yeah, we’re not replacing anything per se. I would say the situation is typically enterprises over the last ten years have bought a lot of content. They may have bought Coursera, Udemy, LinkedIn learning mix of them usually, and they’re seeing that content sitting underutilized because employees are saying, my manager gave me a course that is too easy or too hard for me, or it’s not relevant, or there’s too many courses in that platform, I don’t know where to start. And so you want to make the most out of this content and you need an objective measurements layer. And so your only option today without Workera is typically to refer to a large professional services firm that will come, we’ll send you an army of consultants, may help you understand and produce a PowerPoint that tells you what your workforce is and where it will be. 

Kian Katanforoosh
But our solution is kind of revolutionizing that aspect of workforce intelligence where you can get a software solution that scales across as many people as you want in the enterprise. It’s a continuous pulse over your capabilities. And so it sits really in the middle of your talent ecosystem, pushing the data that can increase the engagement of other systems. 

Brett
And do you just plan to continue to go deeper and deeper into enterprise? Or would you eventually go down market and go to mid market and SMBs? 

Kian Katanforoosh
Yeah, that’s a good question. Next year we’ll be highly focused on enterprise. We do have new features that are going to come and potentially a team plan coming out toward the end of next year or the year after, in order to allow either managers in enterprises to use Workera for their teams or SMBs mid market to start leveraging our systems. 

Brett
Nice. Very cool. And I know we touched on that at the start about the state of AI, but I’d love to talk about the state of buzz and hype. Every startup these days seems to have AI on their domain and I see you have that as well. And that’s become kind of standard practice, I think, with a lot of companies. And I imagine it’s very hard to stand out with all of that noise around AI and what’s possible. Surely your background has to help there and you have a lot of credibility in AI, but how do you separate what you’re doing from all of the buzz and hype and market abuse that is happening with AI in general? 

Kian Katanforoosh
Yeah, it’s inevitable. I was looking when generative AI became trendy this year, you were seeing all the startups that were branded NLP to rebrand generative AI, which is interesting, but I understand it. I just think that when evaluating a startup or a solution or you want to make sure that you understand what data it’s been trained on and what measures have been used to evaluate the model and that’s really what AI is. But you can call pretty much anything AI from a linear regression to a hardcore transformer architecture model. I don’t think you’re a fancier if you use a transformer than if you use a linear regression. I’m particularly excited about AI applications that are more niche. I think that some of the big companies have done a great job for more general applications like Chat, GPT and that will help the community heavily. But when you look at niche applications, it’s very interesting to see when they claim that they’re doing AI, what data do they have? 

Kian Katanforoosh
Do they have any data that is proprietary that nobody else has? Because if they do, just say crap data in, crap data out. Well, the reverse is very true as well. If you have a unique data set that nobody else has, you can build a model that will create unique outputs. And so, to me, more than the buzzworld AI, the most important is what data is behind the scene and what application is it leveraging? 

Brett
And is there like, a framework or a set of questions that someone should ask if they’re trying to evaluate if the AI is real or if it’s just being used for a marketing buzzword? Like, let’s say, for example, I was going to do angel investment in a startup. They have AI in their domain, AI is all over their website. What questions would I ask to make sure that it’s real and make sure that it’s something that’s truly different and unique? 

Kian Katanforoosh
Well, ask for a demo. Ask for a demo. And if they don’t have a product demo, ask for a jupyter notebook demo. I feel that data scientists are really capable of putting that together. And then I would look at what’s the input of your model, what’s the output of your model, the architecture, if they’re comfortable talking about it, is an interesting conversation. And then I would try to find the gaps. Or there is a concept called error analysis in AI, which is you look at your model and you look, where is it failing on, which data points it’s failing? And then you try to troubleshoot it based on what it’s failing on. Well, I would think a great question to ask is, have you run rare error analysis and what have you learned from it? And then you start knowing how much in depth have they measured their model or evaluated their model. 

Kian Katanforoosh
You also want to make sure that the way they’ve measured their model has been done properly from the perspective of a training set versus a validation or a test set, where they are not measuring their model on the data that was trained on. And even when you ask for the demo, they may give you a data point as input that the model has been trained on. You may want to suggest what input the model may take and see if the model still reacts appropriately. 

Brett
Nice. That’s super helpful. Super helpful guidance for me and I think for anyone listening in, and helpful to cut through all the noise with AI. Last couple of questions here for you. As I’m sure you’ve experienced, bringing an innovative idea to market is never easy. What would you say has been your greatest challenge so far, and how do you overcome that challenge? 

Kian Katanforoosh
Technology challenge. I said earlier, it’s easy to build a quiz. Everybody can build a quiz, but it’s hard to build a rigorous assessment that meets the psychometric standards. So that’s on its own, a challenge that is ongoing, and we’re pushing the boundaries on that front. The second piece is I believe people are the most important assets of the company. And I’ve made a few hiring mistakes in the past, getting better at it. I recommend the book Hire With Your Head from Lou Adler, which is a very good one, to become more rigorous around hiring, especially for some key executive roles. And today I try to get more data points. I try to do more back channeling understanding what this person has done in the past and who they worked with and what’s their reputation, and also using our own product to hire. We ask most of our hire to take the worker assessment. 

Kian Katanforoosh
And so that gives us another data point. I’ve also been fortunate to bring a lot of advisors in some of the key hires, and oftentimes I think when I was younger, I did not realize the perspective that someone who’s done it before, who’s been in that role before, can bring. And today I really value that type of input before making a hiring decision. 

Brett
Very interesting. And how important do you think it is today for a tech Founder of a fast growing, soon to be unicorn. How important do you think it is to be in San Francisco or Silicon Valley? 

Kian Katanforoosh
First, I love Silicon Valley. I owe a lot to Silicon Valley. But I would say that while it is still the best place in the world, in my opinion, to start a company, by far, other places are also catching up, meaning you can still found a company very well, and there’s plenty of examples around the world, and it’s due to partly democratization of education and content. You have so much content on how to raise a seed round, how to raise an A round, how to raise a B round, which was not the case many years ago. You have the remote aspect of things which allow you to attract people. There’s actually a lot of machine learning engineers if you want to build an AI systems in the Bay Area, obviously, and they’re top notch, but you have other hubs that have been growing, montreal, even New York, London. 

Kian Katanforoosh
You find a lot of data scientists and machine learning engineers there a lot more than before. And I would say even with the democratization of education and mentorship, you have people that are extremely talented all over the world. And so, per se, you do not need to have an office in San Francisco. And in fact, I would say Workera. We have no office. We’re completely remote, and we’re in 22 countries today, and San Francisco is not our biggest region anymore, which I think is interesting. 

Brett
And were you tempted, though, as a Founder, to leave San Francisco and move to Miami, move to Austin, or what made you choose to stay in SF. 

Kian Katanforoosh
The community of mentors. A lot of our investors are here. A lot of my personal mentors are here. And the community, just a lot of my friends and people I hang out with are here. And so it makes sense for me to stay here. I would also say that San Francisco is a good place to focus. Honestly. There is not as many things to do if you want, as some other places like New York or Miami, I believe. And that allows you to stay focused on what you care about. So it depends on the time of your life. But I enjoy San Francisco nowadays. 

Brett
Nice. Always good to hear. I feel like that’s a controversial take these days. A lot of people are very anti San Francisco, so always like hearing from those who still believe in it. 

Kian Katanforoosh
Yeah. 

Brett
Now, last question here. If we zoom out into the future, what’s the three year vision for a worker? 

Kian Katanforoosh
Well, first, we’re developing really a talent operating system in the enterprise where my belief and I’m a super soccer fan and it’s timely with the World Cup, but I don’t know if you are Brett, but if you are a club in the British Premier League today, like Manchester City, you cannot compete unless you have analytics. And in fact, Pep Guardiola, the manager, knows perfectly what speed Riyad Mares goes at and what’s his best foot and what chances he has to score a goal from a certain angle. They probably know all of that. And they measure it every single day. Or every single week. And I think these same concepts of running professional sports team with analytics is going to make it to the enterprise. It’s just a matter of time and we want to play a part into it and allow it to happen for the better of everyone. 

Kian Katanforoosh
Coach people. And so this is really building the talent operating system in the enterprise. Now as we grow our clientele and we’re able to bring more value to enterprises. Our dream is really to build a world where I want to say career and employment decisions are not based on personal network, but are based on skills. And in fact, today you have our freemium, where a lot of people around the world are taking our tests on a weekly basis. On a daily basis. And they are able to get their feedback. And soon we hope to serve them career opportunities in the enterprises that we serve. That’s really a world where at any point in time, you can measure the supply and demand of skills. You can accelerate progress. If Elon Musk needs 20,000 thermodynamicists next week, we can find them because we have measurements at a global scale. 

Kian Katanforoosh
And we can give the opportunity to the people who are the closest skill wise to this opportunity. And I think it will make a beautiful meritocracy. And we’re looking forward to it. 

Brett
Nice. Well, that’s amazing. And that’s certainly an exciting vision for the future. So unfortunately, we’re up on time. So we’re going to have to wrap here before we do. If people want to follow along with your journey as you build, where’s the best place for them to go? 

Kian Katanforoosh
You can find me on LinkedIn and connect. I also have a Twitter account, Keancatan, and you can follow Workera. I’ll give updates on that front as well. But thank you very much for having me. Brett yeah, no problem. 

Brett
Thanks for joining us. And thanks for sharing this vision again. It’s super exciting and look forward to seeing you execute on this vision. 

Kian Katanforoosh
Thank you. 

Brett
All right. Keep in touch. Our.