SaaS Founder Interview with Cenk Sidar, Co-Founder & CEO of GlobalWonks

Tony Zayas 0:06
Hey, everybody, welcome to our very first episode of the tech founders Show. I’m Tony Zayas joined by Andy Halko. How’s it going, Andy?GlobalWonks

Andy Halko 0:18
It’s good. I can’t complain. I’m excited. We have a whole new show that we’re now going to be doing on Tuesdays, talking to really cool tech founders. We’ve been talking to SAS founders on Wednesdays with some great stories. And now these tech founders, we’re gonna dive in, I think a little bit into some of the really cool technology trends and what’s happening with them, which is a different spin. So pretty exciting, right?

Tony Zayas 0:44
Yeah, for sure. I’m excited. So our very first guest here, we have Cenk Sidar from GlobalWonks. Let me go ahead and bring him in here. Hey, Jake, how you doing? Hi, guys, great to see you. How are you? Very good. Thank you. We appreciate you taking the time super excited. Tell us a little bit about you know what you’re up to what GlobalWonks does, and we’ll go from there. But let’s hear about what you guys do.

Cenk Sidar 1:13
Great. Thank you. So basically, GlobalWonks is an inside software platform. So we are a platform that decision makers can access insights on a real time basis. So we have three channels, we have a marketplace, we have about 27,000 experts in 180 countries in different fields, from blockchain, to agricultural policy, to FinTech to cryptocurrencies, whatever you can think of the relevant to business and policy. So all clients can engage your platform and ask any question or challenge they have. And within minutes, their question, and the challenge is matching with the right experts on a real time basis to mobile app and or web platform. And they receive far, at least five, average eight to 10 expert insights within 24 hours, but mostly those answers coming within the first hour or so. Very cool. Yeah, it’s such

Andy Halko 2:17
a big idea. You know, what do you think about it? Like I was looking through your site and watching your video, and it just, you know, I mean, experts from all over the country, how did you get to this? You know, what were what was the origin story for you?

Cenk Sidar 2:32
Yeah, sure. My background is in business consulting and international risk. So I grew up in Istanbul, Turkey as you know, emerging markets are full of risks in free economy, politics, you know, and also running a business is much riskier in countries, that institutions are not that strong. So that’s why I started being very interested in risk. I studied business in Istanbul, Turkey, then I came here to Washington, DC in 2006, and studied international affairs and international economists. So my first job was a traditional risk advisory shop. So basically helped American businesses to go and invest or operate in emerging markets like and those years like when I first started my company in 2009, my traditional risk advisory firms are Global Advisors. Basically, those were very interesting years, the global economic crisis, the opposite bring the Greek economy collapse. So everything was about risk and how you understand and mitigate risk to operate in those high risk regions. So So the reason I started with wongs is in my previous company, basically, we were engaging local experts on a traditional utilize technology like LinkedIn, or email, or WhatsApp, or whatever you think of. But still, there was a middleman who happened to be me and my team. So there was no reason in today’s technology. Like in today’s technology world, you need a middleman. So we created a technology at GlobalWonks that was my core idea at that point, in 2017. We started working on it, we built the first marketplace in 2019. The idea was to get rid of the middleman and create a platform that you can immediately engage an expert for a question or phone call or report without needing this expensive middleman company that will charge us 2x 3x time, you know, more is basically I give that example. But we were more like, you know, direct marketplace platform for high level high end consultants that usually didn`t have platforms to participate in. So we also mean not only provided that direct access to companies or executives within large firms or medium sized companies or funds, but also we enabled a lot of independent consultants throughout the world to earn additional income, using our platform and supporting company challenges in their specific expertise matters.

Andy Halko 5:25
Yeah, that’s pretty cool. I noticed that your, your site mentions artificial intelligence and natural language processing? How are you using technologies like that to support the end outcome that your product provides

Cenk Sidar 5:41
A great question. And so basically, what we do is we are every time a client logs in our platform and submits a question. And so let’s say the client is asking a question about real estate market in Ohio, right? In a certain city, let’s say Columbus, and when you ask the question about real estate market and the projections for next five years, we’re not pinging 27,000 experts. It’s not like a forum. Basically, our algorithm determines who knows about the specific real estate market and Columbus who work in that market who has the relevant educational and professional background and chooses top 200 people that may be relevant that question, submit a question to those people. And what happens is, so basically, there’s a lot of natural language processing, because we are analyzing every question and determining who knows about this subject in our network. And the second thing is, we also have a technology that we built, we are we have AI, engage in assessing responses we received. So we may be getting 20 30 responses, but or, based on both the size, the best responses provided, looking at the context of the response, the content of the response, the experience of people, and the previous ratings of experts. So we basically created this much more sophisticated quora oriented Hass models and people to ask questions, I mean, all clients buy credits the tokenized system, so they purchase a certain number of credits, to ask questions in the platform, and or experts or people in our platform gets paid when they go and ask you questions on real time basis. So basically, if I’m independent consultant, basically anywhere in the world, and I’m subject matter expert on any subject and I can basically answer five, six questions a day and easily make an ad $250 a day just replicating the answers I provided within the you know, the specific context, the client asked the question. So we are helping experts or independent consultants to utilize and monetize their existing knowledge, as well as helping older companies to reach out to better insights and make more informed decisions in the process of operations or investments they make. So the other way, the other way we use also the artificial intelligence, we have tons of questions asking our platform, you know, on an annual basis, so what we do is we run data analytics on this question on real time basis, and we see what topics are more relevant? Of course, we’re not looking at the specific questions the client asked. I’m talking about metadata, and in the Mize metadata, so we can easily measure Okay, there are more questions about foodtech this year compared to last year, food tech is becoming important issue, or this week, we had more questions about Tesla. So it means that there’s some market sentiment in a has been improving, or also little decreasing, depending on the sentiment of responses provided the search equations. So we are still at the early phase of this data analytics part because we don’t have you know, hundreds of thousand questions yet in the platform. Hopefully we will get one day soon. But at this point, we tried to build the meaning meaningful data sets to run analytics so AI is definitely you know, where we come in, but the most exciting thing I mean, I love that question. I love answering that because there’s a lot of hype about AI right? There’s a lot of hype about data science. Every company’s AI company now you know, you have a like a mission matching algorithm claims to be a company but I give an example, what makes us different or technologies different. We are building the optimal solution for in the knowledge economy, which is combination of artificial intelligence, human and human intelligence and the data science. So if we are missing any of this, you know, three components or solution wouldn’t work. And I think no solution will be helpful to decision makers, because in social sciences, in business, in politics, in policy, you cannot remove the human judgment from the equation, because this is not like a quant analysis of certain things without people making decisions in that kind of like situation. So you need to have a human intelligence, human judgment, part of the decision making. So we are putting the human judgment as part of the equation by engaging real people, real experts, but we augment the experience by bringing the sophisticated matching algorithm as well as NLP etc. But one other example I give you, so we are building technologies, such as providing real time analysis on major events, for example, you know, the tragic events of last night, right in Boulder, Colorado, there was a shooting, first 10 minutes of the shooting, we didn’t know what the motivation was, is a coordinated attack or, or I can give you another example of a notre Dom fire or, you know, the cutout refined parts. So if we’re leading security leader in a company, and you have people around the region, you need to know what’s happening is it, Is it like a lone wolf attack is a crazy person shooting or something that may be having other implications, you know, and longer term implications. So we build a technology. Now we’re working on with technology. Now, we are detecting this last minute breaking news. And we can immediately ask question about this subject. And that question would go to law enforcement or relevant people in a real time basis. So we can within 10 15 minutes, we can have some educated analysis around the subject, again, is a great example how we engage AI. So we look at the media, we look at the breaking news to determine what news are important for our clients, if the Minister of Finance resides in Brazil, and we have hedge fund investing in Brazil and equities, so they’re not just interested in the news that the Brazilian finance minister resigned, they’re interested on potential implications of this news, and how Brazilian financial markets will be impacted. So his or her portfolio will lose or gain value after that news. So we are creating this next generation tools for the knowledge economy, that investors and executives make more informed decisions.

Andy Halko 12:50
I’m kinda interested real quick, you know, for other founders out there in this AI data space of the value that you see in the metadata that you’re pooling, you know, is that another business model? Potentially out is, you know, this, the the questions that are being asked and the trends and, you know, the the types of answers outside of the fact of connecting the experts with people that have questions.

Cenk Sidar 13:19
Look, there are a lot of platforms that are great companies, connecting decision makers with experts, right? There are larger companies and as you know, this expert network businesses around about 2025 years, but they connect you to the person who has the insights, or he has the expertise, they don’t connect you to the expertise directly. So what we are specializing on is, basically, we don’t want you to just get connected to the person who knows about it. Because after that point, you need to three days to set up a call to set up a meeting, or ask the question to get the answer. So it’s a two three day process, at least in many other expert networks, it takes about five to 10 days, you identify the person who knows about the subject, and then you talk to that person and get the knowledge a lot. In today’s business world, you don’t have five to seven days to talk to an expert in five to seven days that matter may be irrelevant to you. So we that’s why we focus on real time insights collection. Every time you go to the platform and ask a question in 10 to 15 minutes, you have expert insights coming to your dashboard. So we don’t have any competition in this space. Because again, we built this optimal solution, combining human machine and data. And I think we will be in a great space to grow this by improving our technology, growing in network and even provide more granular insight. So even our platform Now if you ask about a specific company in Thailand and how their company’s growth projections are, I assure you, we’ll have at least 15 to 30 people who can comment smartly on their company. As we grow the network, we adding about 602,000 new experts a week. So as we get more experts on our platform, onboard more experts on the platform, and our algorithm becomes more targeted and more sophisticated, or granular two levels of answering those questions will be even better. So it’s a kind of continuous process. We are in definitely better position than we were last year. Last year. This time, we have about 5000 experts today, we have 27,000 expert, wow, we have 10 clients, we have about 54 clients plus 200 users in the platform. So we triple our revenues almost every quarter. So this is just exciting times to run a business focused on a knowledge economy, especially after I hope I can say after COVID process.

Tony Zayas 16:04
I was I was just gonna ask about that jank. Where do you see this knowledge economy going? Obviously, there were, you know, the major disruptions that COVID the pandemic trigger change business, as people do it. But I think this was headed in a direction anyway, in a lot of ways things were accelerated. Where do you see the expert, you know, kind of knowledge economy going,

Cenk Sidar 16:29
look that boom, major trends, post pandemic work. The first one is, you know, like, COVID-19, accelerated mega trend, right? We were talking about work from anywhere. I don’t like to turn work from home, because it doesn’t need to be home, you can work from anywhere. So that’s why I use the term. And I, and we were talking about it. In the past. It was a matter of next 510 years, right? But we saw the acceleration. Immediately, we were at homes, in you know, in two weeks in after that was announced as a global pandemic. So now everyone works from home, or anywhere they want. But the problem is how they collaborate, how they socialize how they get to collaborate on sophisticated manners. So I may be a McKinsey or BCG consultant working from my home or from home or wherever I decided to be at that point. But if I need to engage someone within the firm, who has experienced on some area that, you know, so I need to go and communicate with that person. And there are no tools available. So first of all, I mean, I know the first one. Most importantly, I think hybrid workforces, like collaboration in the knowledge economy, efficient technologies will play an important role. That’s why I think there’s a hyperbolic zoom or slack. But on the other hand, those technologies are great, but they’re not sophisticated tools, enable knowledge collaboration, they’re more like great video conferencing system and great communication systems, but they’re not tools that I can identify people who know about the subject in my company, I can engage the person directly. So this is one of the first trend changes of working. The second important aspect is an COVID world showed us the world The world is much more complex and complicated than we imagined. No, I mean, yeah, some people were writing about talking about it and I’m a global risk expert I wrote about pandemics etc. But even us talking about the pandemic risk didn’t imagine that you know, such a pandemic would change our lives from bottom up right that was a traumatizing experience for most of us, you know, from every perspective, but one thing we realize that a health crisis may impact anything under the sun, so, supply chain, you know, the financial markets, you know, the rise of technology companies, so, everything is kind of connected to each other, it is impossible to know all about the subject, I could be a security head or senior study consultant, you know, has all these amazing degrees or whatever, you know, make things but they will not be able to understand the you know, the scientific aspect of Coronavirus are going to impact you know the workforce or they will not understand how the supply chain security has been threatened. So, that’s why I think we live in an age that you need specific expertise is on real time basis for you to engage people and create hybrid teams. So in order to understand and mitigate risk of your supply chain from Asia to Europe. from Asia to United States used to have a logistics expert like epidote Luma, just like, you know, you need to have like the central expert, like maybe like financial analysts, you know, to look at the changing numbers of container prices over the time. So, knowledge economy, I think, becomes more and more complicated, the global economy becomes more complicated. So you need more knowledge and insights to make better decisions, and navigate through those crazy times. And I assure you, we will have similar other holistic crisis in the future that will threaten the businesses, only the businesses that are ready to understand and mitigate the risk to be able to survive.

Andy Halko 20:44
Yeah, I find that really interesting. You know, you think about data that’s out there. And there’s discrete data and silos of data. But then you say that knowledge, what’s in someone’s head, that there’s so much that’s out there, it’s, it’s hidden, it’s buried, it’s discrete, you know, and spread out and having the ability not just to pull data in, but pull knowledge in, to solve a problem, you know, is a transformational concept in the world. Because now when you’re solving a problem, you can get access to a lot of knowledge that sitting out there that you never would have been able to get access to previously.

Cenk Sidar 21:27
Here’s the most underutilized asset global knowledge, there are a lot of people who knowing a lot of stuff. And there are a lot of supply demand mismatch here. Because you don’t know who knows what, and you don’t know, if someone needs your knowledge and ready to pay thousands of dollars sometimes, because you went through the same issue that the CEO has been going through now. And your one hour advice will be word of like, you know, hundreds of thousands of dollars, if you can create the connection between the man and this what we are working on, it’s not only the external issue, though, you know, this, this, I think the more exciting part about all sorts technology, we have the marketplace model, which is, again, operating since 2019. And now we build another product called centaur. And centaur is this historic, you know, horse men mix, if you remember, you know, from the mythological books, but we call it because he’s a human machine. Combination model. So that product is now basically enables large corporations implement all technology internally, so they can ask questions internally, today employees and engage the same algorithm. So if I’m Walmart, I have 20,000 people working within Walmart, if I have a specific supply situation in one of the locations, so we are creating that technology now that I’ll be able to submit your challenge for this specific issue. And another location supply manager will be answering you within seconds without paying anything, but like Slack, like in our user subscription model. So we are working on it now. And you can tell me why slack doesn’t do this. Like it’s just a communication tool. They don’t provide you to do real time matching, they don’t have no other expertise. And we are focused on expertise and knowledge sharing, that is extremely unique in the market right now.

Andy Halko 23:28
You talk about the value. And it makes me think of the analogy of the engineer plumber, where you know, someone’s factory is down, the plumber comes in and turns something in two minutes and says that’s $5,000 the guy knows he only spent two minutes, but he’s like, no, but I’ve been practicing practicing this for 20 years. That’s what you’re paying for. And that’s how I knew that do the right thing in two minutes. So it’s really interesting to me of that whole idea that you’re really exposing access to these folks that have that knowledge and experience. And, you know, there’s value to that. So,

Cenk Sidar 24:07
no, exactly. I mean, I think it’s a win win win, I mean, basically independent consultants in our network, not only in the US, but in many regions in the world, you know, impacted by COVID significantly, you know, many people that quit, you know, lost their jobs in you know, consulting shops or banks or you know, in other industries that have been significantly impacted by they can continue earning income being our platform, answering questions on real time basis and picking up few expert calls and writing proper reports. I mean, also the most one of the most exciting part of running a company in this space against you mentioned the knowledge is so underutilized and people are not monetizing their knowledge. Enough. So we are helping a lot of people and underrepresented groups like you know, like a woman in Middle East, you know, people working in, you know, like developing countries to earn seminar, same amount of compensation like someone in London, New York, Singapore. So also like helping, we’re also creating this equalizing force in workspace. So it doesn’t matter where you are based, again, work from anywhere, why a consultant in DC or New York gets paid more than a consultant in Nigeria, who provides same intelligence, same insight, knowledge, there is no explanation from it. Because, you know, the company shouldn’t care about my living expenses, right? is at the end is like, the efficiency. So we created this tool, and we are helping the knowledge workers to be treated and paid more fairly, you know, in global markets.

Andy Halko 26:03
Yeah, that’s really cool. I’m kind of interested about the natural language processing. So, you know, I remember here a few years ago of like, that being one of the key steps into moving into artificial intelligence, because if we could truly understand the sentiment of what people say, because everybody said things a little bit differently. And so there’s a lot of nuance though, natural language processing. I’m kind of curious if you don’t mind sharing a little bit about, you know, for our audience that may not know, what is natural language processing? And, you know, how does it impact, you know, different technologies and things like artificial intelligence?

Cenk Sidar 26:46
Sure, I can pull from the business perspective, I’m more like the business person, I’m not a scientist or data science. But what I can tell you that there are a lot of businesses, implications and benefits, I mean, AI in general, you know, when people talk about AI, they talk about automation of mundane tasks, or automation of certain jobs and job loss and potential social unrest in the future, etc. So there AI is definitely a large subject. And I’m interested in the social, political and economic implications of how AI will be shaping our lives next, you know, 5 10 25 years. But NLP is, you know, more specific technology, and we engage NLP for, you know, core platform. And what tells us is, you know, basically, let’s say, I’m asking the question about US economy, and how, you know, how macro economists look at the US economy is held in next 5 10 15 years. So what we are doing is we are basically categorizing every question coming to our platform. So we have hundreds of questions asked about US economy. As I mentioned, the average question receives eight to 10 12 responses, I think the number is like about nine or 10. It changed, but let’s say 10 responses. So it’s 10x., insights, or answers provided to each question. So technically, we can create this timelines and time series and you know, put the data and see, okay, how do macro economists view the US economy? How is their sentiment has been changing? You know, maybe, if we had, like, 300 questions last two years, and we match that questions with answers and the major events happening, and we can even augment the data set with some social media data, you know, connect that with certain, you know, major political or economic news, we will have the complete picture, how the sentiment for US economy has been changing over like, last few years, and where we are now, you know, you know, from zero to 100, are we at 75 or 60? Why did we go from 60 to 75? Up to the recent, you know, like, elections in the US, so we can provide pizza because a lot of this is solutions to such social issues. They look at the social media data, right? What are people writing about this, or they’re looking at major news, how many times the inflation issue has been covered by wall street journal or you know, Financial Times or The economy. So, we are, we can basically, look at all these things because now as other technologies, many data Science Solutions become more commodity products. So, you have access to all this open source data. You can run any data analysis, open source data, or the real value that we have, we can see how the perspective how the sentiment of macroeconomy Professor Columbia University, or Harvard or USC, changed over the last two years, and how we can quantify if we can accumulate those expert sentiments, and provide some directional analysis, to hedge funds or private equities that would like to make decisions about the investment. Again, I mean, there are a lot of like scientific like air or the size of our algorithm that we can change, we are testing different. Of course, weights and equations, we have an amazing AI team in the company that led by our head of head of engineering are not just far. But and this is a continuous thing, right? There are more tools becoming available, more data becomes available.Data is the fuel of AI, right? As we get more data, and more engagements in the platform, I think in five years, or data sets will be, you know, extremely demanded in the financial industry, because that will be the only solution that pulling human data and AI together and providing this complete holistic picture for investors.

Andy Halko 31:26
It’s kind of interesting to me that, you know, and I don’t know if you agree with this statement, but the natural language processing is really about improving efficiency and reducing risk. Because, you know, if you’re not processing the language, and the questions, or what people are saying correctly, you’re either potentially bringing the wrong information, which could be a risk issue, or efficiency, because it takes more time for you to continue to dig and look. And so you know, the advancement of something like natural language processing, can be really powerful for any type of business, to potentially look at how do they improve efficiency or reduce risk, which I assume is how it potentially impacts your product. Because you’re getting the better answers faster, and reducing the effort of your clients.

Cenk Sidar 32:17
Again, great question, because what we’re doing now is basically, when this question is submitted on the platform, right, I probably like we have about, you’re paying about 200 experts on real time basis, we have a mobile app, they can get the notification to their mobile app or the email. So what we are doing is, of course, we’re trying to have always amazing responses and or expert network is very high quality, we have high standards, vetting and approving people on the network. But still, we build NLP power technology, so every answer coming in to the platform before those answers released to the client dashboard, is being checked automatically towards plagiarism, if someone could be pestered it from somewhere in the world. So it’s checked by grammar and typos. So if your answer has a lot of typos and grammar mistakes is not going to be released and return the content you provide. So we are working on the objective isn’t answering the question, isn’t that answering the question is repeating the question a different way. So those are, you know, project, you know, ongoing projects. Of course, we just tried to make it better. And sometimes, you know, there’s a plagiarism red flag comes up, but there’s no plagiarism, or sometimes vice versa. But or we currently we have this Mechanic Turk approach, if you know the terms like basically data, sorry, machine, human collaboration is like, you know, like, yeah, it still has a human eyes, we look at every time an answer provided and checked by or AI, we still have smart people in our team looking the question, if the AI work well, or didn’t work well, we can make the AI and the algorithm work better how the NLP works? Was it really plagiarize answer? Or was it something like, the machine got confused? Or, you know, maybe the typos are not that important at their case? The answer is amazing. But the person’s English is not great. That’s fine, because the client may be interested more in the substance rather than the look. So we are basically increasing the automation or team because in the past, we were basically our team was looking all the answers on a manual basis, like a year ago, and we had like 24/7 teams approving or rejecting responses coming. Now we automate it up to like 75 80 percent. And it will just get better as we call it more data. And we have a better understanding of or network and white labeling technology or, you know, getting some people the scores and the scores are impacting their level of approval or rejection at their point.

Andy Halko 35:04
Yeah, it’s interesting how AI work training computers to be more human. What are you gonna ask Tony?

Tony Zayas 35:12
No, I was just gonna ask Cenk about that. So along those lines, what was it like in the business earlier on when you had less data in there and less users? What were you know, what were some of the challenges you faced? Because obviously, it sounds like, the more data you have, the more input, the more you’re processing, the more it’s learning, the better it’s running. How does that work? And how did you get past those barriers up front?

Cenk Sidar 35:39
Yeah, we did a lot of trial engagements in the platform. Again, let me be clear, there were a lot of credit products in the market, like Yahoo Answers, you know, you guys remember, quora. And that all try to create a compensation model. But it’s difficult if you are open to the public. And you know, that’s why, you know, when older people ask me what we’ve been doing, and expand like we are, if LinkedIn and quora would have a baby, that will be the network pulse, because we have close network, like elite professional network, powered by quora type of engagement. Now public, though, is not for you know, the client sees equation. So in the earlier days, that was, that was a kind of experimental for many businesses to try the product. So we did provide a little trial credits, we work with some, we were lucky to have amazing investors from almost a one that are from the industries like financial, former financial executives, people who work at major consulting firms like McKinsey, Eli, Eurasia group, we have a lot of people that have experience in technology. So we were able to start testing the product and test the product market fit, again, because they change the behavior a little bit, because in the past, or even now, many businesses or many investors, and when they want to talk to an expert, the first thing they do is they call in somebody they know who may know, who may have an idea who knows about it is more sophisticated, they work with large expert network in the market, you know, like GLG, guide point officers, those companies that started earlier, so they have accounts with them, and they arrange expert network expert call, you know, using the expert networks, but we believe that’s a very slow and old fashioned way. Because again, takes time, you’re talking to one expert. And most of the time, the time you talk to the expert, the issue may be even not relevant. So we told them, Look, there’s a better way, why are you paying thousands of dollars to talk to one expert and rely on his or her expertise in that subject? Why don’t you run and we charge $300 per question is almost 1/4 of the market price for expert call is an interesting market, you know, when you talk Yeah, usually 1200 to 1500, you know, model you spend $300, ask a question. You see, as I mentioned, you know, 10 expert opinions, you can click and see their backgrounds who responded my questions. And this is a $300 risk, and it happens within 45 – 2 hours, right? And then if you read all these responses, and basically the experts don’t see each other’s responses, there is no groupthink mentality. So only you are the one as the client seeing all the responses and I can click on my dashboard, and have a real time video call in our embedded video conferencing system for $600. So in total, you spending six $900, having 10 different opinions first, analyzing all these opinions, become more educated on the subject, and then go and do the call with the one that you think is more spinning. The reason I told you, Tony, is that because in the beginning, even that makes full sense for everybody, right? Why are you telling them like, you get 10 expert opinions plus you have a phone call plus happens much faster, better experts because you decide at the end who you want to talk. And it’s still 30% cheaper than traditional solutions. Still changing the user behavior is difficult, no matter what, you know, they’re still paying not using Uber by calling the taxi on the taxi company on the corner. It doesn’t make sense. But some people don’t want to change their behavior because that’s how they’ve been doing business for the last, you know, 20 years. So that was my main challenge as a company and our sales team here. My main challenge was to educate the clients, educated decision makers they need faster most cost efficient and higher quality responses or insights when they need it internally or externally. So

Andy Halko 40:11
exactly getting easier now for you is to explain and educate on the benefit and value. Has that evolved a lot?

Cenk Sidar 40:21
Sure. Yeah, I think. So I’m not very hands on with ourselves, you know, process right now we have a great sales team here in DC in New York. So they are focused on, you know, selling, selling the service to multiple, you know, like, you know, clients for the marketplace. But it’s getting easier. I think people of course, as time passes younger, and more tech savvy people taking higher also is easier to work with people who understands the importance of speed, the importance of, you know, engagement and cost efficient ways. But the best way for us and our technology sales team to explain and educate is providing free credits, if you’re in a position, sometimes, Hey, get this to three credits as to three questions on the platform, we pay for it. And we guarantee after this experience, we will never go back to a traditional solution, because it just doesn’t make sense.

Tony Zayas 41:22
It’s really cool. So just to chat a bit about go back to AI for a second. Right. So I think this is a fascinating conversation topic for anybody to talk about, regardless of how much you know, or how little you know, I would love to hear from your perspective, someone that’s, you know, working with this. They’re about AI and machine learning and things like that.

Cenk Sidar 41:49
Sure, look. I’m always a big fan of technology. I’m an early adopter to many different technologies. But so I understand the optimist and pessimist view about the future of AI, right? Is AI going to help humanity or is going to harm wherever you stand. So, it is like a tough subject, because like, in the past, when technology revolutions happened, right, when mass production started happening in late, you know, late 18th century, a lot of people lost jobs, but they created also more jobs, you know, people became factory factory workers, that work in certain areas. So, so like, ai always causes job losses, and some, in some cases, job creation. But I think I’m a little bit more in the pessimist side, in terms of job creation, led by AI, because AI will only create high value jobs in the market. And unfortunately, large portions of the world are not ready to work on this, you know, high value jobs, such as, like, you know, drone technologies, and, you know, like operating drones, or, you know, like being AI data scientists, I mean, how many data scientists meaning not that many of you had to hire a lot of factory workers in the 18th century 19th century. So, I’m more worried about increasing social inequality, because I think more people in the US and other parts of the world will be facing difficulties to earn income, you know, it becomes more and more difficult in 14th century and the you are able to buy you know, this glass from country x and sell it on you know, country y with a profit margin, because there was the arbitrage factor that was easy to you know, make money bring trade, if you carry a product from location A to location B because there wasn’t that much communication or the price, you know, price transparency, pricing, transparency, those issues. Now, everything is real time is almost impossible to do arbitrage in physical goods and services. It will be more and more difficult for typical undergraduate. You know, graduates trying to college graduates try to get jobs, because, you know, they will not be that much, right. accountants, you know, most of the accounting and auditing will be done by the AI, maybe some high level touch will be done by the head of the accounting firm, right? The same the journalism, of course, there’s a human aspects still need it, but I think the percentage going down to 10 15%. And that will cause some real serious source of problems. And we start seeing that like, last 10 15 years, I think in many countries now we have increasing populism, like, increasing, like, you know, authoritarianism in the world. And unfortunate those kind of issues would only feel the anger and hatred of some people who got isolated from the workforce. I mean, think about, like, 90s, right. I mean, if you will be based in Pennsylvania, or Ohio work as a textile factory, I mean, you lost your job. Because, you know, free trade, you know, maybe like, you know, NAFTA, you know, 94, all this stuff. But it wasn’t the decision of Bill Clinton at that point who, oh, I want to live in jobs in Pennsylvania, and close the textile factories and export this job. That was global threat. And global dynamics, the same thing is no one’s fault, that AI is being built, you cannot stop those changes, you cannot, you know, stop those transformation its gonna happen, impossible to stop that transformation. So that’s why we need to learn how to live in the AI age and how to make ourselves relevant in the future of the AI. But the most important risk is, unfortunately, not easy for masses to do that, you know. So I’m, so I think they will, that will be more concerned about the politics next 10 25 years, how we keep those people part of the economy, right? Are we talking about UBI universal basic income? Are we just gonna send them checks every month, just to be, you know, you know, happy participants to the global economy that are kind of unfortunate. surpress some countries already start doing that. I think some countries will start thinking I mean, some technologies like Andrew Yang is big on that idea. I think he’s very, right. I mean, he sees the risk coming. So in my opinion, if the politics can`t resolve that issue, we will be facing significant uncertainties and risk factors next 25 years, that may cause some domestic and global conflict, because the bulk populations still increasing, and you know, you have to feed those people, and you have to make sure the economy is embracing to them. And also talk about think about the large, like Frank, I mean, you know, Facebook, Amazon, you know, these companies, I mean, they need people to be part of the economy, you know, only rich people like ordering groceries from Amazon or getting Ubers I mean, ultra rich people having their, you know, drivers or they find somewhere private or something, but, you know, the Amazon needs middle income to survive middle income people to survive. And this why, even though they are, I think, focused on automation, and, you know, they’re responsible for job losses, etc. At some point, I think they will be the ones maybe funding and financing incentives, like UBI keep their growth going, because otherwise they’re not going to survive survive either.

Andy Halko 48:14
Yeah, the, the concept in artificial intelligence that really always catches me is the singularity. So this thought process that machine learning and AI hit a point, the singularity where they become as intelligent and, you know, powerful as humans. And the idea is that at that point, because they can make, they can move so fast and make decisions and so fast that knowledge, invention, discovery will skyrocket. You know, for years, we’ve kind of increased our knowledge, and our like discovery, and it’s always been kind of a semi flat line. But that point where we hit the singularity, and AI truly becomes like, skyrockets up. I mean, have you heard of that concept before and

Cenk Sidar 49:08
Skywalker? I mean, definitely, I think. I mean, that is a risk. And that’s a potential opportunity, right? I mean, then maybe some of the major problems of humanity, maybe result through the enhance cognitive and you know, like, you know, knowledge created by AI. I think one of the great example is IBM Watson. Right. Watson has been like working on college like salt resolving a lot of like health related issues. I think many of these big data and AI technologies were utilize in inventing the vaccine, you know, in data analytics, especially. So that’s why probably we came up with an approved vaccine within less in a year. So I mean, there’s no, like, again, I mean, it’s not a like a black and white issue, I think I agree with you, there will be a lot of augmenting the human knowledge and human understanding of universe may open many doors to all of us. But on the other hand, all these advances will also make people, humans obsolete and irrelevant. And unneeded, you know, that maybe you don’t need 20 scientists working on coming up with a vaccine, and you made this machine. So

Andy Halko 50:40
I mean, I agree with you, it’s such an interesting thing for society. Because, I mean, at one point, there was a shift, where people had farms and they handle, you know, they were very siloed that they, you know, had their own animals in their farms and these other pieces, but then we went to trade. But do you know, and that was a big shift, where people now did one thing, and they sold it to another, that if AI really becomes this thing that is inventing, you know, I hear the concept with a singularity is that space travel is that they’re not the computers will not only, you know, figured out the discoveries, but they’ll also be able to print the engines, and they’ll literally be able to do the whole process themselves. You know, and does that change our society that we aren’t, you know, we go to that next shift where it isn’t about working and trade, it’s about something completely different that we don’t see. I mean, I know we’re talking 40 50 years. But, you know, it’s an interesting to think of, and my kids might be around to see it. So.

Cenk Sidar 51:48
I mean, I have a seven and five year olds, and I’m really worried. I mean, how they’re going to make their living in 30 40 50 years now, what profession they should choose. I mean, not everyone can become like, data scientists or cybersecurity expert, or, you know, maybe, I mean, I think they will be like very, I mean, but then we should think about everything, like we should reconsider typical university education, I mean, I need to go like, for your college and pay, like, $150,000, I don’t think so I can probably get the same education. You know, using, you know, this all open source educational materials available everywhere on the internet, where you’re gonna pay, you know, like MBA degree to sit in my in front of my zoom screen now. And, you know, like, and pay $150,000 for your health. Whereas I could use that money for something, you know,

Andy Halko 52:44
millions of philosophers and artists is basically what it’s going to come down to

Cenk Sidar 52:48
look, I mean, that’s the most important interesting thing. I think there is a saying, you know, there are, there are decades, nothing happens. And they are dates, decades happen. So we live in a days decades happen. Yeah. So all these things that we’ve been talking about, you know, five years ago, more like in a futuristic point of view became today’s reality now, and that trend will be just accelerating in five years, 10 years, again, it’s difficult to, you know, make predictions about where the world and the business growth has been going. But one thing that I’m certain that’s why in my, in my time, and my energy, building, global warming is human judgment will still stay relevant. I mean, even if we have all these amazing things, building AI, there will be some humans building those, and in some social science related stuff, you know, in political decisions, again, like, machine cannot make a decision about launching UBI for the masses, or not universal basic income, is a human decision, you know, probably doesn’t even make sense why the machine will think why we are giving free money to all these people, because the machine would mess the humanitarian side of things, because you know, the system will not be sustainable, if you don’t provide living to this masters. And

Andy Halko 54:13
like, you know, I will have to disagree with you a little bit. Because it’s like my argument with my wife, sometimes about self driving cars, you know, that you know, who’s gonna be better, but I trust a car and a computer much more than the person that’s trying to text on their phone or put their makeup on in their car. And, you know, for universal basic income, is there a chance that I trust the computer to have the data and the processing and be able to, you know, understand the thousands of scenarios and outcomes that can happen and make a decision off of it versus, you know, politicians who are, I mean, that’s the interesting thing about AI is you know, what, Is there at what point do humans get a trust factor for what computers tell us to do? Because at some point, their decisions might be better than, you know, like self driving cars, that decision of how they accelerate or make a turn or do anything else might be better than what a human can do.

Cenk Sidar 55:20
I think there’s a lot of conversation about AI ethics and AI moral. I mean, think about a drone, right? I mean, if you’re, if you are, you know, flying over a certain enemy zone, in you know, like, you know, is it like a pilot, and you see a target, but then you see five kids playing around the house, or you may decide not to hit the target, because you’re gonna kill five little kids playing on the target, right? There’s a human judgment, there’s a human decision. But if you’re drone, flying, you’re focused on your mission, and you’re going to bump the hell out of that place. Because you don’t, you don’t have any humanitarian or you know, like a moral compass. So those things are, but you can also train the drone, don’t shoot the target, by people with little kids playing

Andy Halko 56:14
what I read about, you know, our self driving cars is that there were programmers that had to figure out in scenarios who dies, you know, if, if it’s coming up to a bridge, and there’s a woman walking across one part, and then there’s a family, you know, an older woman on one side, on the other, it’s his family with young kids, or you can veer the car off and kill the people in the car. You know, what does the AI choose? Does it kill the old lady? Because she doesn’t have as much time left? Does it kill the family? Does it kill the people in the car? And some programmer has to kind of think that through in those scenarios right now to determine.

Cenk Sidar 56:57
Exactly, but also take from that, then that will be institutional decision, right? I mean, then all the Tesla cars have priorities in their algorithms who will be killed in certain situations, there will be a lot of points contradicting or disagreeing with that decision by the human makes that decision is a human’s decision at the certain situation and will be less questioned. So, I mean, I don’t know how easy it is to choose, you know, to kill an old woman or a young kid, you know, there are different views to I mean, you know, different views.

Andy Halko 57:33
That the AI space is such an interesting one, and it touches everything today, you know, but I think that’s the amazing thing, what you’re doing is, you know, your part of AI is bringing together all this knowledge and expertise, and this human element of it for us, you know, in this near term future.

Cenk Sidar 57:51
Yeah, exciting field. I’m, I’m very happy that I’m, you know, being part of this, because, again, not from the just, you know, corporate role perspective, but also enabling many independent and in some cases, like, you know, isolated, isolated knowledge economy workers, and provide them a fair ground earning similar levels of same levels of income with their counterparts in developed countries. Because, again, today, location doesn’t mean anything, right, is a good thing as a bad thing. So, I was talking to somebody from my home country, like Turkey, that he was interested in going to Stanford computer science, studying computer science at Stanford. But technically, anyone who wants to take all this computer science classes at Stanford, can take those classes now online, right? And I went to like, grad school and I want to call, you know, like, I know, the value, the most important thing about the graduate schools or colleges is the relationships you build, right? You get to know people, you go to MBA, you get to know the future SVP of a major corporation and then you collaborate at some point, you know, you help him or she helps you, whatever, you know, the deal is, but if you don’t have that anymore, if no one is gonna go back to building and also think about the buildings and buildings are so such a waste of resources. I mean, any think about any building with 5000 employees, or 10,000 employees Think about it, he why headquarters or right, Coca Cola headquarters in Atlanta, wherever you use that buildings for 10, maximum 12 hours, 12 hours of the day, and five days of the week, but you have security there, you heat that building, you provide electricity, the schools are open in certain months of the year, but you need to maintain this. So why are we wasting resources of the merit and maybe it doesn’t make sense and if I don’t go to school building, why I’m gonna go to Stanford, I can just take classes online from people that I may get this classes. So I think you live very, I mean, you know, there’s another Chinese proverb saying, I wish you live in interesting times or something, you know, this kind of I don’t know, is a curse, or a good thing. But we definitely live in interesting times and we don’t know what you’re gonna bring to us.

Tony Zayas 1:00:17
Yeah. Well, fascinating stuff Cenk. Before we, before we end this today’s show, where can where can people who are watching find out more about you and GlobalWonks

Cenk Sidar 1:00:30
like GlobalWonks.com is our basic domain platform for independent consultant or interested in register and, you know, start monetizing their income, they can go and sign up through the GlobalWonks.com to sign up page. Again, the same thing for the clients, they can just set up the account to do everything or they can email us the email address on the system. Personally, I’m available on all social media platforms, LinkedIn, Twitter, Facebook, my first and last name Cenk Sidar. Happy to have a dialogue with anybody interested in the subjects and the future of the knowledge economy. And there was a pleasure to chat with the guys amazing conversation today. Thank you.

Tony Zayas 1:01:13
This was awesome. Thank you so much. Everyone tuning in, everyone. Take care. Have a great week. Bye bye. Bye, everybody.

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