Shark Tank Investor Reveals Why LinkedIn Was Never Built To Solve Recruiting

with a traditional recruiting process is you get a hold of the candidate and then you have an interview with the candidate to determine whether they are telling you the truth, whether they have the skills that you need, whether they understood what the compensation you're offering, whether they have the education that you expect them to have. So you ask all of those clarifying questions to make sure it's a good filter. does all of that automatically. We allows you to avoid having to invest 3 minutes or 1 hour with a candidate to determine whether the candidate has a cultural activity because we mathematically predict that by asking the candidate questions. When you apply, we show you right away how you rank against all of the other candidates who have applied to the job opening radical transparency.

How do you prevent somebody from impersonating? You hear stories of Upwork and the North Korean hackers. I mean we've experienced it where people have falsified who they are in order to get a job. What are some steps that you've taken to go and prevent that?

So there are some which I can share.

This is who we hire the show about how the best technology companies build their teams. Today I'm joined by three times founder and CEO Alex Torra, CEO of Tory AI. Alex has been building in the space for over a decade and lives in Napa with his wife and two kids. Really excited to have you on today, Alex, and to learn more about the future of AI and recruiting and what your vision is. Thanks so much for being on Who We Hire.

Thanks a lot for the invite. It's a pleasure to be sharing our time together today. Alex, I had the chance to listen a few of your other podcasts uh going across borders and you traditionally had done a lot of business in the US. Now you've built a business around AI recruiting and specifically Latin America. What did you see in your other businesses where you said, "Wow, this is the opportunity that I want to tackle for the next 5, 10, 20 years."

Yeah. So, so first one clarification is not what we're doing is not focusing Latin America. It just happens that Latin America offers a significant arbitrage when it comes to hiring talent. So, it's natural for new platforms to allow their users to take advantage of arbitrage opportunities. So, I can dive deeper into that topic if if you like to. Now, the reason for starting this effort in particular, it's the natural evolution of the companies that I've created before. my first tech startup. I founded it alongside my wife Tanya Sapata. She was starting a voiceover career and we figure there is a good opportunity on allowing companies hiring voice actors to do it online as opposed to do it in the manual way through a casting director. And we created a company called Voice 123, which automated the process of casting voice actors. And something that used to cost $3,000 and take several weeks went on to be a free service that took only a couple of uh hours and offer better results. Then I created another company later on Calbony studio in which we did that did something similar not only for voiceovers but for many types of creative work. Uh the next step on that ladder was where we figure we can use AI and also new business models to significantly improve the way in which companies recruit talent of any type. Not only freelance but also full-time, part-time even internships, not only creative work but also everything that is regarding white color work, blue collar work and uh not only remote work but also on-site work and hybrid work. So a very broad general purpose um system which for practical purposes is a job network more than a job board because job board usually has the connotation of you posting a job and then people applying a job network is the wide spectrum in some cases you post a job and you get candidates. In some cases, the system reaches out to candidates handpicked by AI or by user but in most cases by AI to pitch them something that they are not necessarily looking for but might be interested. So it's been the natural the creation of to it's been the natural progression of my professional career if you want to think from the recruiting. Uh yes it's been I mean voiceovers was recruiting it's only that it was recruiting of a specific type of professional which is a voice right the next one is is recruiting of creative talent in general now it's just recruiting in general person so each time it's been one of two orders of magnitudes more ambitious than the than the previous one

yeah listen that you expanded to to South Africa recently so you really have gone global with with this

yeah so what happened with is uh and and the oneliner of to just for for clarity purposes it's it's the global job network. It's a new type of job network that is powered by uh what some people refer to as super intelligent AI meaning AI that can do things better not similarly but better than a human being that ends up offering a talent agent for every person out there. What do I mean with a talent agent for every person out there? the same way

for every candidate that

but also for the company side right and I can explain on why it's important for AI to represent both sides of the spectrum at the same time as opposed to just one yeah the same way that musicians and uh and artists and uh sport people they have talent agents now with AI the idea of having every single person no matter how experienced inexperienced young whatever can have an a talent agent as well constantly helping them identify what's best for them. So that's what we are creating and when we started working it working working on it we made it market agnostic profession agnostic because the goal is to be global. So we needed something that was adaptable and very quickly the area where we got the most traction was global remote work in connecting the northern hemisphere with the southern hemisphere. Right? So today around 50% of the traction that we have in to are companies in North America and Europe recruiting talent both freelance and full-time and even internships in the southern hemisphere, South America, some Central America and big chunks of uh Europe as well. So pretty much every country is represented in our city as long as it's legal to to represent them. Of course, we don't allow people from North Korea, unfortunately, and such, but uh yeah, every country is represented.

Yeah. So, speaking of North Korea, how how do you prevent somebody from impersonating at their North like you hear stories of Upwork and you know, North Korean hackers? I mean, we've experienced where people have falsified who they are to in order to get a job. So, what are some steps that you've taken to to go and prevent that?

Uh so, there are some which I can share publicly. There are some that I cannot share publicly because the moment we share it of course then then people are going to know what they might might do. It's a never ending as with spam and other things. It's a neverending battle where you have to stay ahead ahead of the bag. For the most part it's reputational uh solution in in to people have a profile. We call them the profession their professional genome. So as opposed to other platforms where every time you apply to something new uh you are pretty much starting from scratch. Yeah,

the MD file in to essentially

yeah kind of right to learns but the MD file you have to create it manually to is more intelligent than that to learns about you not only when you apply but also when you actually don't apply to something when we invite you to something when we let you know there is this opportunity and you ignore it or when you say no I don't like it because of this every time there is an interaction we learn about you and we we start creating this this this professional genome that is not only about your experience and and resume. It's also about your professional behavior for cultural feed prediction. It's also about what you like and dislike, etc., etc. And part of that professional genome is a reputation you build by applying to jobs, right? So even if you don't get the job, we know how far along you went. We know the reasons for you to have retracted the application. We know the reasons for the company to have rejected your application. uh as well or having ruled you out or having uh for you having me number two but you didn't make it because I was a number one candidate on top of you etc. All of that information allows us to predict quite well the extent to which you're a legitimate candidate or not. Right? So just by looking at your behavior, we can make a lot of predictions. That's the number one tool that we use for predicting uh whether you are for real or not. The we have some other tools that again we can share. And very soon by the way we are going to be launching ID verification in which uh every person applying through to has to actually provide a valid ID so that we can certify that you are for real and run background checks whenever that that's relevant. A global job network where there are jobs going cross border u becomes the most important or the first place where that's that's that's critical uh to be there. So that's going to be rolled out soon to other uses.

Very exciting. And and you make a great point, right? It's cop versus robbers, hackers versus cyber security experts. This is it's never going to to end, but it sounds like none of your clients have hired anybody from North Korea, which is good. And the way you're describing this is to me it's exciting because it sounds like the original LinkedIn and and a better version of it wherever we're all confined to LinkedIn um as this professional network but a a platform that helps me and learns from me a lot along the way. I think that's that's fascinating. I didn't think about it in a perspective where I don't have to create it, right? It's learning from what I do and what I don't do. As somebody that would apply to a job, that that's really exciting. I I have a friend who used an AI service and he's somebody who would have never imagined like always has great careers, works at Fortune 100, 500 companies, but um he wasn't landing interviews and he used this AI service to to go and and land interviews and that's where he's working his current job right now. So it's uh it's definitely evolving how people are are finding their work to you know so so leading to my next question running this kind of global board like how has that changed how you hire and and who you hire.

Yeah. So I'm I'm going to I'm going to make a small clarification. Uh this is not what LinkedIn was originally meant to do. So LinkedIn has been a social network from the get-go that has a professional connotation, right? Compared to the other social networks out there, but their main goal has never been the ideal matching of candidates with job opportunities or job opportunities with candidates. LinkedIn's job board is one of the ways in which they monetize, but is not the core product that they set up to build. That's one of the reasons I started to I had a conversation around seven years ago with Jeff Winner the back then CEO of LinkedIn. He tweeted about my previous company telling how cool that company was. So I I took advantage of that tweet to engage with him. So we had an interesting conversation about the future of LinkedIn and that's when I figured when when I confirmed my my suspicion and that is LinkedIn is not trying to improve recruiting systematically because they don't see as their core product. They are simply trying to monetize with recruiting but their crowd product is their newsfeed is their social network component.

Wow. I didn't think about it like that cuz we we we gave them a ton of our money for for treating by

it's not

it's like Google and Google Adwords right so Google Adwords is not Google's core product is the search Google Adwords is the way in which they monetize right and not even right because because um LinkedIn potentially makes more money with salespeople getting leads than with recruiters actually recruiting recruiting candidates. Um, so yeah, now going back to the to to to to your question, how does that change my hiring? So it doesn't change my hiring, but it does change how people how our users end up uh hiring. And I say it doesn't change our hiring because from very early on uh from my previous companies, I already had a global remote team and uh so let me take that back. It does change the way in which we hire but only in certain aspects. So, I've been a big fan of global remote teams from the get-go and what to is enabling right now is uh making very easy for any company to do that. Right today, if you're not using TA, it's very difficult for you to simply post a job somewhere to attract the best talent globally. There's some niche job boards out there, but they're very niche and you have to pay for that. to is the only is the only company right now that allows you to post job for free that has a global reach where you don't have to ask your friends hey in which countries I can find this and and then go and post a job a job opening on that country no you can simply tell to I want the best person that is globally for this job within this budget if you have a budget already in mind and uh maybe within certain time zone if you want to have people on a on on a given uh time zone But that that's not even a requirement. And then to automatically goes and finds out um what is the best people globally for that. So so it's it's beautiful to know that when you're hiring you can find the best person for you not only based on whether the person has the skills you need but whether the person's uh goals are aligned with the mission of your company, whether the person uh professional behavior is aligned with yours. So there is a really good cultural fit etc etc. So that's that's that's very unique and before I had to do that manually right now with to not only I can do it automatically but now there are hundreds of companies doing automatically every single day. That's one something that didn't change for me but it enable other people to do it uh through to right now and the other thing is very systematically reducing interviews because to is not only about here is a job opening you apply it's about it's a job opening it is a team it is a leader here is you and we believe that there is a good match between those four items right and uh that does a lot of things one of them is allows you to avoid having to set to to invest three minutes or one hour with a candidate to determine whether the candidate has a cult fit with your team because we mathematically predict that by asking the candidate questions by asking people that have worked with the candidate questions by asking you as the company doing the hiring also questions uh and by the way you only have to answer those questions once it becomes part of your professional denom so moving forward we uh we have all of that information and the other area where our recruiting has changed drastically given because of to is because our AI does a lot of the the first interview and more that usually recruiter does, right? So uh what usually happens with uh with a traditional recruiting process is you get a hold of the candidate and then you have an interview with the candidate to determine whether they are telling you the truth, whether they have the skills that you need, whether they understood what the compensation you're offering um is, whether they have the education that you expect them to have. So you ask all of those clarifying questions to make sure it's a good filter. Tor does all of that automatically. We are performing over 5,000 AI interviews every single day today for our CL for our users.

I thought you were going to say a month every day. Five.

Okay. And either video or phone call or what do they one of the beauties is that it depends on the candidate. So there are candidates that prefer to use the web. There are candidates that prefer uh phone. There are candidates that prefer a WhatsApp interaction. And there are candidates who prefer email exchange.

You do it in their per method. Whatever they

we do and and then and then we we know how to follow follow up with with candidates to make sure that the ones that drop uh in the funnel uh uh comp have the highest chance of of coming back. And it's also very adaptive based on what we already know about a candidate, right? Because there are candidates for whom are we know a lot of information. So we don't need to ask a bunch of those questions because we already know them. uh there are some candidates that are new and then we have to find a lot of information. Some of that we find it automatically. Some of that we actually have to ask the candidate uh very directly and proactively. So it's it's highly adaptive as well so that we don't waste the time of the candidate.

Yeah, I've seen some arguments on the candidate side where oh it's not a good experience but then a lot of other candidates go and say it's an amazing experience because you get feedback, right? How many recruiters have gone and ghosted candidates after with no feedback? But when you have this system, it's it's um unbiased feedback based on data and and it helps you improve and you actually get it. So I think overall it creates a better experience. What you know, how have you been defending it? No, I mean I think that every single person that has complained about the experience of to uh potentially has a good reason to complain because uh it's a product that we did not wait for it to be complete before we put it out there. Great.

Right. We started the development of Tore in 2019. That's when we started doing experiments. We had the first version up and running for candidates around 2022. uh and little by little we have been opening the private beta. So to has been out there as a public as an open tool that anyone can use uh shy of a year uh over a year um shy no year and four months, five months and still that was a version that we were not necessarily the proudest of was a version that had many bugs but we needed that to be out there for us to be to learn to know what we needed to to adapt. So many people for example um complained that in some aspects we were asking a lot of questions before they knew whether they were a match or not and yes now we have learned and we have improved so that with fewer questions we can make better better predictions. Uh but we wanted to give you right away feedback so that you knew whether you were a match for the job opening or not and how you rank against other candidates which is something also unique to to when you apply. We show you right away how you rank against all of the other candidates who have applied to the job opening. radical transparency as you mentioned so that you learn right but also u there are thousands of ways in which a person can for example upload a resume to to so that we use it as your initial input and for some people unfortunately the formatting they use had bugs especially if LinkedIn it's a it's a it's a wall garden when you download your resume from LinkedIn and you upload it somewhere else is usually a pardon the the my my French is a cluster right so we had to learn how to how to how to deal with that when people wanted to use the LinkedIn profile for something so we could deliver a good experience. So all of that takes time u what I'm confident uh because we measure it is that every time the satisfaction level of the users goes up the NPS goes up what people experience in the platform improves and for as long as we continue making progress every single week we are in good hands. So yes, for well today when when a candidate applies, they go through an AI screening uh process again whichever method they they prefer and that might take some time. It depends on the job. There are jobs that have a lot of requirements for which a lot of the questions might take 15 minutes for you to answer. There are jobs that have very few requirements uh or where we already know about you and with one click you apply and that's it. we let you right know yeah you're a match and uh if you're highly ranked and this is how you rank then there's a high likelihood that the company's going to be uh looking at your application

that's amazing so I I've had this vision where I guess you're building this where um at least one side a fair 5,000 interviews a day that's incredible so the like interviewing sucks right like if you're a candidate and also if you're a hiring manager a lot of the times it would be amazing for AI to solve that that problem and I imagine a world where a candidate's only going to interview once and they interview once with your AI that that hiring manager interviews once with the AI and then the AIS just interview with each other until they find a perfect match. Like what do you see in 10 years? How will how will Tori evolve 10 years from now um with the rapid innovation of AI

right now? the state-ofthe-art general with uh with AI and recruitment is you have AI that represents candidates uh and uh they are applying automatically to a 100 job openings per day or so and then you have AI that represents companies that are then dealing with all of those uh automated applications trying to identify the ones that are actually valuable uh and so the ones that were manual that are valuable and uh then screen candidates and by the way it's expensive on both sides and uh it's far from uh being fast because AI because of the way it works is slow is trying to do what a human would usually do with the limitations of a human only that in an in an automated in an automated way what we are building at to is a centralized AI that understands what the candidate wants and understand what the company also needs and can do better match significantly faster. Um, if you put two AIs to chat among themselves to determine if a candidate might be a good match is easily a two to three minute conversation between them in the best case scenario and it's not accurate because they are not necessarily speaking with the same factors or the same elements in mind in terms of what uh it needs to be for a match to happen. And it is very likely they are going to be lacking information anyway to make a good prediction. In the case of Tore, we are analyzing the same 100 factors plus on both sides and we do it in less than 100 nanoseconds per per match. Wow.

Uh so we are talking about not one, not two, not three but four five orders of magnitude uh better. Now all of that sounds fancy but but what's the end uh the end result? It's a centralized matching system. Think of a of a stock exchange where a job comes in and uh it automatically identify the best set of candidates and even the best candidate for that job or a candidate comes in and in real time the system identifies the best job opportunity for that candidate to take advantage of right away. And again, it doesn't have to be full-time. It's also freelance like 40% of what we do is freelance jobs today. So, but I am envision being that NASDAQ of jobs of the future. I love it. It's already happening uh right now. This is the real time activity of to this is not an animation, sorry, a recording. This is actually happening right now in real time. Here on the left hand side, we see users. So people updating their genome, answering an AI interview. They are checking out job openings. They're replying to messages sent by Emma. Emma is our AI talent agent. And here on the right hand side you see what our AI is doing which is from inviting candidates to recalculating the ranking of candidates for giving job openings to messaging uh candidates asking questions to candidates uh as well following up with candidates etc etc. All of this is happening in in real time. And while we use LLMs, what generative AI uh there are many other types of AI that we're using in here to be able to do this at a massive scale with uh price consciousness in mind so that we can offer this for free. It totally is free for users. How we monetize with some premium functionalities but by default it is free for everyone. they get full access to uh to the network and and we can do it because because uh we are not simply throwing LLMs to solve the problem. We are really optimizing for real time matching in here that is highly accurate.

Wow, thanks for sharing that. That that is incredible. That is a a ton of activity. So yeah, hundreds are going through while we have this podcast. the you you said it would take two to three minutes for the different AIs to go and and talk to each other and then share some context regarding um a candidate, but they they would still not have all the context. There's different sides of, you know, the the hiring theory. Some really believe in behavioral and cognitive assessments, uh, personality assessments where other data shows that those things don't really make a a big difference. uh what uh where where do you you stand and how do you develop your your software to um maybe account for preference of of the the user more or are you really pushing your agenda on hey we know what what hiring like how hiring works and you should follow this process. So there are many many tools out there that that tell you oh we are the company that you can use to determine the personality traits of the individual right and you should hire based on that and there are others that do other type of of skill testing and they tell you you should hire based on this etc etc but the reality is that it's not one factor there are so when we started to one of the first things we did is we sat down not with hundreds but with thousands of recruits ters, hiring managers and entrepreneurs. And we asked them, "What are the most important things you keep in mind when recruiting someone for different types of roles?" And then we also observe their behavior to identify the ones that they took into account but they did not explicitly mention because there are many that are explicit but many that are implicit and even uh uh unconscious. And we identified 112 different factors uh that for different roles and for different people have different priorities but one way or another most factors are involved on that decision making and we prioritize them and we started working on those. Right now we have deployed over 80 of those factors in to we still have 30 or so to go and every month we release one or two more to make the matching improve more and more to because one of our goals is to is to is to is to offer the best predictive model ever to exist for determining if a person is going to be a good match for a job or not. And indeed there are some of those factors or some of those factors are related to psychometrics the behavior of of the person. So we invested a significant amount of time on that area. Uh some of the explicit uh factors that people told us were related to what we usually chat uh uh or talk about as personal traits. Right? So the first thing we did is we identified which are the most scientifically accurate personality traits uh systems out there and how can we use that to match the person with the job. So we ended up first testing uh with what's known as the big five model. Um we expanded to the exac model which is the big five plus the honesty spectrum in there. uh we develop all of the UX to be able to capture information relatively quickly uh from a person and from a company refers to that we released it and then we realized that doesn't work it doesn't it it didn't just just the personality alone of an individual the personality traits end up not have end up being a a factor but a factor with very little prediction capabilities very little weight so we're shocked because like everyone and their grandma right in HR and says no personality traits and such. Well, what we figure is that most candidates end up self- filtering themselves from jobs for which their personality is not a match.

Wow.

So, and the most Oh, that you had to eat it to prove it.

Yeah. The most the most obvious one was sales. Well, if you're introverted, it's very very very very unlikely that you're going to apply to a sales jobs to a sales job. So uh yes it's the percent were simply confirming something that candies were already doing. So it wasn't adding a lot. So but we knew there was something else in there and uh and that's one of the areas where we identified that there are some other things related behavior of candidates that companies were taking into account but they were not being very explicit about it in the recruitment process. And that's professional culture what what some companies refer to as cultural fit which usually they define it with does the candidate has the values that we have etc. So it was a very subjective ephemereral uh uh uh definition depending on the person that you ask but we did a lot of research on that. We came across the work of a professor of Stanford back in the day Berkeley uh called Charles O'Reilly had created an instrument a psychometric instrument called the OCP the organizational cultural profile uh and that ended up leading us to what we have um today. So what we identified is that it's not about the profession the the personality traits of the person. It's about the professional behavior traits of the person and the professional behavior traits of the leader that the person is going to have in the company.

Y'all really have thought through every component of this and given it so much attention and detail and and I can see the passion behind it. It's so it's so exciting. Um you said you have 80 out of the you know 112 that uh are out there in the last year. Have you really expedited pushing it out with AI and uh you know using cloud code or what are y'all using internally to get features out faster and and continue level?

Yeah, every everything on on the under design. I mean AI has evolved uh uh significantly during the previous four years and uh so it's it's models. So we use AI not only for coding, we use AI for understanding what users uh want. We use AI for extracting important information about a candidate to speed up the job searches. We use AI to extract important information about a job opening and a company to speed up candidate searches as well. We use AI to interact with you to send messages to users to customize messages. So yeah AI all over the spectrum and the models have changed. We were using um for example for one process in particular we're using Gemini up until two weeks ago in which we switched to deepse because deepsec is offering better performance and lower cost with better predictability uh etc etc. So yeah, we we are we consume every single model out there not only the leading edge ones but also the more stable ones the older ones and uh but and we have systems in place that are constantly testing which are the best performing models whatever we need so that we can continue improving costs and improving also the experience of user.

Well love it. So you have a dedicated person that's going testing different models um and and finding which one is going to be the best for

we have dedicated AI that is doing that work.

Love it. And then who's managing the AI?

Uh every person. So we have a team of technical product managers. We used to have uh up until two years ago we had what called the squats or POS. We had a product designer, we had a QA person, we had a tech lead, we had engineers. Um, now every person in the team is a technical product manager that manages AI.

Wow. So, you don't have those individual roles anymore. You have one person and AI and

that that's incredible. So, you've you've been able to evolve your team um like that. It's it's not like it's it's necessarily replacing jobs. of creating different jobs that are more fun and challenging and you know can keep people around longer is what what we've seen um we've seen a lot of folks also rather than hire new developers just spend that in in tokens is that do do you see yourself going that route more keeping a smaller company and and using more AI agents versus um having more headcount Yeah.

Uh yeah, my I I think that that well this does not apply to most companies. I think that leading edge companies are need to move fast and uh what slows down the speed of innovation in a company. It's the amount of time you have to invest getting people to communicate and align work with each other. So the smaller the team, the faster each person per capita gets to be, right? And there is a point at which adding more people actually make everything slower uh as a whole. Not only per capita but as a whole. So my prediction is that the best the fastest teams are going to be teams where there is a flat structure with one person leading a bunch of people but those people having no one reporting to them uh for the most for the most part and that's the structure that I'm trying to set up in. So at the end of the day, I believe that as we grow to be a unicorn or the ka unicorn, we're likely going to end up staying around 20 people with them reporting to me. Y

I love it. There's, you know, there's stories about this, right? in research where some companies to be productive after 150 people they would create a new building and they wouldn't even go in the same building as

um the the rest to keep that that tribal mentality and that there's been a lot of pressure from boards to increase ARR per employee is that so are you creating that because you just want to move fast or are you also getting pressure from investors um and you know um you know outside forces that really push you.

I don't uh I don't have a board in in T and that's uh on purpose, right? I I I think that I'm an investor myself. I think that investors play uh a good part on in the ecosystem per se, but but I think that uh the ones that should be calling the shot in terms of how the company is structured are the CEOs of the company and their team, not not the investors. As as an investor, as a shareholder, I think that our responsibility is to identify who the CEO of a company should be and then back that person up with whatever tools they need. is not to tell the person how they need to be structuring the company, how to be how they should be structuring the the team. Uh and similarly, I don't let my investors uh tell me what I have to do, suggest what I have to do, advice on what I have to do. Yes, tell me what I have to do. I think that's that's detrimental for the success of the company.

Do do you ever go and seek advice from from them on different areas on on

Always all the time. Life is too short to make the mistakes that others already make. Uh I don't mind making mistakes for as long as those mistakes are fresh new uh no one has done before.

Love it. The uh so you know as we start wrapping up here um it sounds like you've built a really like mission driven company. It's going it you you're focused on long-term gains. Um you're talking about stock exchanges. How how do you keep bringing missional aligned folks? And you mentioned it a couple times in um you know the Tory profiles where it's cultural values. It's a vibe check it arguably one of the hardest parts um to to have a board a job board or even recruiters and and hiring managers screened for. So, how do you continue to bring in those folks that that see the long-term vision and and um want to be alongside you in one of those 20?

The so on that on that we have actually data not only about our company but all of the users of to the more social or environmental impact the company has the easier it is for that company to recruit. It's it's it's it's very it's one factor that that is very clear. Um if you have a company uh that is extracting value from society and that's it, it's significantly more difficult for you to recruit. You have to pay more. You have to work harder at recruiting candidates. If you're a company that lives the world better for your kids, it's much easier for you to to attract people. you don't even have to pay salaries as high as those other other companies. Now, the reason I started to is because u I'm very passionate about helping people find job opportunities. And the first company I mentioned at the beginning of the podcast, W123, one of the reasons we started it is because we figure that back in the day, what W123 did is is allowed the voice of an industry to switch being meritocratic regardless of where you were uh located. And uh it had a huge impact on that industry. Uh but it led me to visualize how good technology could be used uh when used properly uh in terms of helping people find the job that they deserve and finding and helping companies find the talent that the best talent that they can they can hire. So this is to is the ultimate expression of that mission of that desire. my most human talent goes to waste because it's never exposed to the right opportunities and that's what we are changing. We want to make sure that every single job opening is considered for every single person and we do the best possible match so that no one out there ends up not not being able to maximize their full potential because no one ever told them about the best of opportunity that there was uh out there for them. Uh that's a mission that is relatively easy to get behind of and especially with this day and age of AI where things are changing so fast so so drastically and we starting to migrate to a human touch economy um that mission becomes more important than than ever before and uh so it's yeah it's unfortunate it's not difficult for me to to identify talent and when you have the possibility of finding the best talent on on earth regardless of where they are. Then uh it becomes even easier to find to find that talent and attracted to to the company

and you get to use your product. Uh so that that helps as well.

Yep. Yep. Yep.

Amazing. Uh Alex Fenegger, where can folks find you? Uh so to self-service I know many people when they when they when they hear about what we do say okay can we schedule account to to get started so you can demo the product whatever no it's free go to toai and and use it like you can post a job in in two minutes you can start doing a head hunting with AI in 60 seconds as well if you're a candidate same thing so it's it's self-service is very easy to use again it's it's free me uh individually you can find me on the Twitter Uh you can find me on Instagram, LinkedIn, shadowban me by the way. They know what we are up to. So uh I do post there, but my post get hidden uh pretty much automatically by LinkedIn. So I don't suggest uh uh following LinkedIn on anything that has to do any person that is doing recruitment uh u innovation because LinkedIn doesn't like that. Um but uh I have a newsletter that you can sign up to at me.gra.com togra.com my my last name as well and my email it's uh alexanderto.ai. Happy to to help any any person listening to this podcast.

Fantastic, Alex. Thanks for being on. See y'all next Tuesday.