Stanford AI Expert: 71% of People Won't Survive the AI Shift — Here's the 30-Minute Fix | Kian Katanforoosh, CEO Workera

Stanford AI Expert Kian Katanforoosh tested 22,000+ people on their AI skills — 71% dangerously overestimate or underestimate their level. In this interview with Marina Mogilko, Kian breaks down what separates AI adoption from proficiency, why 95% of AI agents fail in production, which skills survive the next decade, and his 90-day plan to get ahead. Kian is CEO of Workera, Stanford lecturer, and co-founder of deeplearning.ai with Andrew Ng.📌Grab your anti AI writing guide: https://siliconvalleygirl.beehiiv.com/how-to-write-with-ai?utm_source=youtube&utm_medium=video&utm_campaign=how-to-write-with-ai&utm_content=kian-katanforoosh-interview https://siliconvalleygirl.beehiiv.com/how-to-write-with-ai?utm_source=youtube&utm_medium=video&utm_campaign=how-to-write-with-ai&utm_content=kian-katanforoosh-interview 🎧 Episode with Linked CEO Ryan Roslansky about the future of work:https://open.spotify.com/episode/5sa1EeC262QyyZow3bfwYY?si=55maoBEITMWE02O8rV78nQ More from the Silicon Valley Girl: Instagram: ⁠⁠⁠⁠⁠⁠https://www.instagram.com/siliconvalleygirl/ ⁠⁠⁠⁠⁠⁠ https://www.instagram.com/siliconvalleygirl/ YouTube: ⁠⁠⁠⁠⁠⁠https://www.youtube.com/@SiliconValleyGirl⁠⁠⁠⁠⁠⁠ https://www.youtube.com/@SiliconValleyGirl LinkedIn: ⁠⁠⁠⁠⁠⁠linkedin.com/in/marinamogilko⁠⁠⁠⁠⁠ https://www.linkedin.com/in/marinamogilko/

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:01Speaker 00

  This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome? That's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration blog, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required. Compatibility and availability varies 18 plus.

• 0:26Speaker 01

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 0:55Speaker 02

  How often do you use AI? If it's not daily, I think you're generally behind.

• 1:00Marina Mogilko?

  That's Kian Katun-Fourouche, Stanford AI professor, who built one of the world's top AI education platforms with Andrew Ng. Now through his company, he's tested over a million people on their AI skills. And today, he has a step-by-step plan so you don't fall behind. What are the three moves everyone should make in 2026?

• 1:20Speaker 02

  Learn the foundations of AI. Assess yourself to make sure you're ready. Build the habits of learning. If you focus on one thing for a day, you probably are already in the top X percent of the world. For a week, nonstop, you're in the top 10%. Focus on a month, you're in the top 1%. But to be in the top 0.1%, you will have to.

• 1:39Marina Mogilko?

  Hello, everyone. Welcome back to Silicon Valley Girl and Davos. Kian, let's start with your big idea. 2026 is the year of humans. But also, we're getting a completely different narrative. You know, a new model every week, replacing jobs. How do you think we should focus on humans right now? And what is the shift?

• 2:00Speaker 02

  I think the shift that is happening is broadly due to the fact that people generally overestimate the impact of technology on the short term and underestimate what the technology can do on the long term. If you look at all the reports from Foundation Model Labs, you know, OpenAI, Anthropic and others, there's a lot of task-level reports, like AI is good at task A, task B, task C is getting automated. And actually, going from a task to some human's job changing with a job usually being made up hundreds of tasks is not that simple. It can take decades.

• 2:41Speaker 02

  And every, almost every prediction that I've seen since the launch of ChatGPT of XYZ job is going away has not happened. You know, the famous one is the radiologist will go away and the drivers will go away. And then you see this meme of radiologists driving to work in their, in their car.

• 2:59Marina Mogilko?

  You mentioned drivers. Do you have an estimate, for example? Because if you go in San Francisco, it's almost, Waymos are almost everywhere. I don't really see old, older taxis. So we see the replacement happening, but how soon do you think it's going to happen for drivers, for example?

• 3:16Speaker 02

  Yeah, well, you look like, you know, the, the rise of, you know, Waymo, Cruise, all these companies in the self-driving space, you know, really started in 2014, 2015. So we're already 11 years into them having hired tons of engineers to build that problem. So even autonomous driving has been a decade of full-on research with people working so hard. So, you know, why wouldn't it be the same for the rest? I, I think like maybe in the next decade, we're going to start, you know, seeing less voice actors, less translators, maybe customer support is going to completely change. I agree fully with that. I just think people thought it would happen within six months.

• 3:56Marina Mogilko?

  Yeah.

• 3:56Speaker 02

  And it hasn't.

• 3:57Marina Mogilko?

  Yeah, so we're safe for now, at least like. For the next five.

• 4:01Speaker 02

  Generally, I think safe in a career comes down to learning velocity. It turns out to can you reinvent yourself?

• 4:08Marina Mogilko?

  Yeah.

• 4:08Speaker 02

  You know, the the U F has this metric called the half life of skill that is going down, meaning on average, a skill is not useful that long. It's two years in tech or A I. And so you have to refresh yourself and that's what makes you safe ultimately.

• 4:22Marina Mogilko?

  Absolutely. And and your data shows seventy one percent of people misjudge their A I skill level. Can you give us some benchmarks? So what is an A I proficient person? What does it does day to day look like? Does he start with like chatting with his A I? Or is it, you know, not writing emails by yourself or having A I manage your schedule?

• 4:42Speaker 02

  Yeah, I tried to separate adoption of A I and proficiency. So I give you an example. Adoption is is like you use A I every day and I use it every week. You're a better adopter than I am. But turns out that if we watch you prompt engineer and me, maybe your prompts are just simple prompts. And when you look at what I'm doing, I'm doing a variety of techniques. I'm doing zero shot prompt. I'm doing few shot prompts. I'm doing a chain of thoughts. I'm doing a prompt chain that is super complex, that feeds one into another. I'm doing a retrieval augmented generation system that I built. My proficiency is higher than you.

• 5:19Marina Mogilko?

  Yeah.

• 5:19Speaker 02

  That's the difference between adoption and proficiency.

• 5:22Marina Mogilko?

  OK, what you just said makes me feel like I'm a beginner. Because my prompts are really, really simple. OK, if I want to sound like you in ninety days, what should I be doing?

• 5:31Speaker 02

  So first, if you have ninety days, I would say at first, we need to establish the foundations. You take a few foundational classes. We can recommend some on deep learning dot A I on other platforms. There's a lot of content out there, honestly, high quality. Establish the foundation. You will get to a point where what will matter the most in A I, because the market is moving so fast, is that you're plugged in the network. So what I recommend generally is you go to X, you go to Reddit, you go to some of the machine learning popular newsletters and you register to all of these.

• 6:05Marina Mogilko?

  Can you recommend like who do you who do you follow next for best advice?

• 6:09Speaker 02

  Uh, well, actually, if you go on my X and you look at who I follow, you can follow the same people. But you know, some of them are here, like Andrew Ng is a great person to follow.

• 6:18Marina Mogilko?

  Yes.

• 6:18Speaker 02

  Great newsletter called The Batch, Richard Socher, you know, Yoshua Benjou, a lot of great AI scientists that, you know, people trust.

• 6:25Marina Mogilko?

  Yeah.

• 6:26Speaker 02

  Um, and actually it allows you to cut through the noise when there's so much noise coming. Like I tell you, when I was in grad school, um, we would read a lot of people that come up in Archive, the website that, uh, you know, where papers are often published. Today, there's just so much that you have to find ways to differentiate signal from noise.

• 6:43Marina Mogilko?

  Yeah. Every time I scroll through my feed on Instagram, there's this new app and this company just changed the game in this market. Like, it happens every day.

• 6:52Speaker 02

  Yeah.

• 6:53Marina Mogilko?

  Uh, so, okay, we established this, I follow the right people. What is the next step? Are there like top three AI apps that I should be using?

• 7:00Speaker 02

  Yeah, I mean, you know, I recommend, uh, obviously Workera for testing yourself, uh, although it's mostly used in corporations. Other than that, you know, deep learning dot AI has a lot of free content out there. It's really good. You also find, uh, that the L L M's can help you learn, like you can actually prompt the L L M's. But the bottleneck is people don't know what to ask the L L M. And that's where the assessment is so important, because at some point, you're gonna be pretty good at AI and you're gonna sort of have a wall in front of yourself, like, what do I do next? Am I actually that good? Do I know? You know, to give you an example, at Stanford, we have, as you said, uh, the class on campus with a lot of students. And we have the class on YouTube, ver- same content published on YouTube with a lot of views, like a lot of views.

• 7:43Speaker 02

  And those students would tell you that the difference between them and the Stanford, uh, uh, kids is that, um, it's not the material, it's that they don't know if, how good they are. The Stanford, uh, students, they have friends at OpenAI, they have friends at Meta, they have friends at Google, they have friends, they know how good they are compared to the bar. How, how much does it take to get a job there? But if you're somewhere in the world with no ecosystem, you're not plugged in, it's really hard. And so that's where the assessment is so important. It can tell you, hey, actually, you, you thought you, you're very, pretty good, but that's not the bar. The bar is actually higher.

• 8:16Speaker 00

  This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome, that's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration block, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses setup required. Compatibility and availability varies 18 plus.

• 8:41Speaker 01

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 9:11Marina Mogilko?

  Top three questions that you should ask yourself to kind of understand your level.

• 9:15Speaker 02

  How often do you use AI? You know, if it's not daily, I think you're generally behind right now. That's a simple one. The other one is, you know, think about 10 products that use AI that you encounter in your daily life. Can you come up with 10 products? You know, and some people would realize like, actually, I don't realize where is AI? Is it here? Is it there? I don't know. I don't have this ability to like identify AI. You're probably behind.

• 9:44Marina Mogilko?

  If I want to start using AI for work, what questions should I be asking myself?

• 9:49Speaker 02

  I think when it comes to work, a lot of the value of language models is in the context. So for example, on chat GPT, there is this feature that allows you to give custom instructions to the model. So, hi, my name is Kian. I'm XYZ. I like to speak in English or in whatever language. And I like to be concise or I like to. You know, whatever your style is. Um, that's example of context that you give to the L L M.

• 10:18Marina Mogilko?

  It's like memory, right?

• 10:19Speaker 02

  Yeah, memory that you give to the L L M. Um, although, yeah, ma- memory is slightly different in context. I can, I can explain after, but the, um, you know, and at work, you sort of want your documents to be accessible to your L L M if possible. You want your custom instructions to be accessible. You even want the custom instruction of your coworkers. So that when you talk about your coworkers or you're trying to send an email to X, Y, Z, it will figure it out. So the value of the L L M increases with the amount of context it has access to at work.

• 10:47Marina Mogilko?

  Is that how proficient organizations use A I?

• 10:50Speaker 02

  Yeah, I'll give you a concrete example. So uh, at, at work era, we, we are a big, uh, anthropic shop internally. We use a lot of clouds. Uh, all our engineers are on this version of cloud called Cloud Code Max, uh, which, um, is very powerful to code. And across the company, we have things that we call skills. Anthropic calls it skills. Where, where you can think of them as files that define a certain way of doing a certain thing. Like, here is how we recruit at work era, or here is our brand guidelines. This is the font we use. This is how we speak. These are the color palettes that you can use. Before, if an engineer wanted to build a website, they would have to call the marketing team at the end and say, can you review the font? Can you review the alignment? Can you review X, Y, Z?

• 11:36Speaker 02

  Today, because it's all coded, you don't need anymore to talk to a human. The engineer just asks the L L M, can you just verify that the copywriting is correct? The color palette is right? And they know that the marketing team has maintained that code.

• 11:50Marina Mogilko?

  I love that.

• 11:51Speaker 02

  And so it cuts communication and it's very powerful. You gain actually so much speed and, uh, creates so much more time for the marketing team to think about, uh, do we need to change our fonts? Do we need, rather than like every day, talk to an engineer and say, no, change that font, change that font. So.

• 12:08Marina Mogilko?

  Do you check the results afterwards? Like, oh.

• 12:10Speaker 02

  Yeah, the engineer does.

• 12:11Marina Mogilko?

  Yeah, okay.

• 12:12Speaker 02

  The engineers do.

• 12:12Marina Mogilko?

  Wow. So now I'm very curious about your day to day as a founder. What has changed in the past three years and how you just deal with your coworkers? So you mentioned using cloud. That cuts communication. What else?

• 12:26Speaker 02

  I would say one thing that has changed is we are getting flatter as an organization, which means we have, for example, our head of AI decided to become an IC, an individual contributor from a manager role. And that didn't used to happen before. And he's doing great as an individual contributor. And he feels more productive and he feels like he's back close to the machine. And I think that's a trend that we're gonna see a lot. The second aspect is, so in tech, you have this ratio of within a perfect team. How many engineers do you have? How many product managers do you have? How many product designers? Historically, you would have, I don't know, some Jeff Bezos calls it the two pizza team. The team has to be able to eat two pizzas.

• 13:10Speaker 02

  If it's more than two pizzas, the team is too big, basically.

• 13:13Marina Mogilko?

  He's grown beyond that.

• 13:15Speaker 02

  Yeah, and so right now, I think historically, we've had, I don't know, eight engineers, one product manager, one product designer. I think now it's getting way more efficient on the engineering side, where you can actually probably put the team together with two engineers, one product manager, one product designer. And the engineers are very empowered to perform, to build everything on their own almost with some input from, you know, the other parties. And so we are seeing at Workera a lot of smaller teams. A lot of, you know, instead of having three big teams, we might have six, seven smaller teams that have more ownership of their surface area. We have, you know, transcriptions of meetings, which is really helpful because I can remember, you know, what was the context?

• 13:57Speaker 02

  You know, we use our own product in our interviewing, so there's an AI interviewer.

• 14:02Marina Mogilko?

  Oh, wow.

• 14:02Speaker 02

  I think we just make all these tools accessible to our workforce and we make sure they adopt it very frequently.

• 14:10Marina Mogilko?

  Who does your calendar? Is it AI now?

• 14:13Speaker 02

  Every morning I have a briefing that my, so my assistant builds AI systems herself. And she has a little, you know, agent call it or workflow that tracks my calendar and tracks what I know or what past conversations I've had. And every morning I get a briefing automatically in Slack that tells me, this is where you need to be and this is what you need to know.

• 14:37Marina Mogilko?

  Nice.

• 14:37Speaker 02

  Pretty much, which is really helpful, you know.

• 14:39Marina Mogilko?

  Yeah, everything that you described, if I wanted the same in my company, do you think I need to hire someone who's more AI native? Or my team can just handle it?

• 14:47Speaker 02

  No, you should not.

• 14:47Marina Mogilko?

  Creatives and.

• 14:48Speaker 02

  Yeah, I think you should start yourself. It all starts by yourself. So I think you should try it yourself and you will actually figure out that you can get a lot done by yourself. And you're already very proficient, so it will be easier probably for you. If you want to get in the technical realm, yeah, you will need someone more technical. You need someone who has coded in the past, you know, it's like you can get a lot more done.

• 15:10Marina Mogilko?

  But the basics like connecting documents and we should have done that.

• 15:15Speaker 02

  I think it's more about agency, it's having agency to do that.

• 15:19Marina Mogilko?

  And that's agency. I'm glad that you mentioned it because I was thinking a lot being here in Davos, everyone's talking about AI. I was thinking about top three skills that everybody should be developing. And I think you mentioned that in one of your talks, there's some skills that die out really fast and some skills that just stay with you. They have more longevity and I think agency is something that, you know, if we imagine AI is this bar, it's already telling some people what to do, like they're kind of below AI, like if you work in customer support, right? You just prompt something and you read it out loud. Most of us are still beyond this, this line because we're, we're using AI as helper. But this bar is rising. What do you think?

• 16:00Marina Mogilko?

  And like the way to stay beyond it and make AI work for you, not control you is to have agency. Maybe something else. What do you think?

• 16:08Speaker 02

  I mean, I'd say hundred percent agency is a durable skill. We feel it durable as in it will be useful even 10 years from now. It's very important. There's a lot more durable skill, critical thinking, problem solving, effective communication. I think AI literacy is a durable skill. People will need it for a long time. Coding, I think is a very important durable skill.

• 16:28Marina Mogilko?

  Hmm, still, even for someone like me who's a podcaster.

• 16:32Speaker 02

  I think, I think so. I, I don't think you'll have to learn syntax, like you don't need to know how to code manually. But if you can tell if the coding agent is, what is, what is it doing? You have a significant advantage. You can catch the errors faster, you can iterate faster. It is hard to negligate that. And then to come to the top three skills, I think like I'd separate in three groups, so. For technical folks, very technical folks, like foundational model level. Right now, companies are fighting for talent that can do reasoning, that can build reasoning loops and reasoning models. There's very few people in the world that can do it, and they're very, very valuable. The second one that's underrated, forgotten sometimes is distributed computing.

• 17:17Speaker 02

  There's not that many people that can build clusters, that can train models on massive clusters. It is very complicated. It requires a combination of math skills, linear algebra, electrical engineering. It's very, very complicated. And those are hardcore engineers, very valuable. And then the third one is reinforcement learning. So in AI, when you look at a model, it usually goes through different phases of training, like pre-training and post-training. People that have, and at some point in the, sometimes pre-training, sometimes post-training, there are certain techniques from the world of reinforcement learning. That's why the idea is like AlphaGo or chess. Those games that you've seen AI play better, they're based on reinforcement learning methods.

• 18:00Marina Mogilko?

  When the machine learns by itself and tries different things.

• 18:03Speaker 02

  It learns through experience, not through examples.

• 18:06Marina Mogilko?

  Yeah.

• 18:06Speaker 02

  And that skill is also very valuable. So that's the technical tier. In the tier, applied tier, I would say forward deployed engineering is very popular. Meaning if you can also do business and be technical at the same time, that combination is rare. And then for day-to-day life, I think identifying AI, being able to use it natively is the most popular skill for general awareness.

• 18:28Marina Mogilko?

  Ian just talked about how most people use AI every day, but their prompts are still super basic. Take my example. For months, I was struggling with AI writing. It just didn't sound like me. It used the wrong words. It used the wrong tone. It invented facts and overall sounded like AI. So I decided to build a system. Three files that teach AI your real voice, real facts about your background and even your phrases you'd never say. And this transformed my entire workflow because I can now write better LinkedIn posts. I can now write better emails and come up with better ideas. All of these files are free for my newsletter subscribers. There is a link in the description. Go ahead, download those files, and they come with an instruction on how to teach your AI to speak like you.

• 19:13Marina Mogilko?

  The technology is amazing. Start using it in a proper way. The link is in the description. So do you see jobs market going down at all? Or what was your projection for the next five years?

• 19:25Speaker 02

  So a few things, I I would say, one, people say Gen Z is, there's no job for Gen Z's. We've heard that over the last couple of years. I think last year was definitely the hardest I've seen for university grads.

• 19:37Marina Mogilko?

  But was it about AI? Because a lot of people.

• 19:39Speaker 02

  I don't think so.

• 19:40Marina Mogilko?

  Yeah.

• 19:40Speaker 02

  I I think like.

• 19:41Marina Mogilko?

  Over hiring during COVID?

• 19:41Speaker 02

  I think companies have over hired during COVID. And now they're saying AI is automating our stuff because it makes the stock go up. The truth is they're performance managing a lot, their roster managing, they're exaying people. And and maybe there's a little bit of that job is not as important as it used to be. But there's a lot of like, we want to keep our best people and they hide it behind the AI lingua. You know, why would Meta exit people from their Metaverse team if it was AI? You know, no, it's because they wanted to make more out of that team. And he he probably thinks they can get a lot more done keeping the best people and getting them to work hard. You know, otherwise you wouldn't have heard about the Metaverse team exiting people. You would have heard of something else. So I think I think it's really performance management that is happening.

• 20:27Speaker 02

  And I think they don't find enough AI native talent. The reason Gen Z has struggled to find jobs in the last year is that there's just not enough AI native talent in the markets. There are still just pockets that are in hubs. And if you're in the hub as a Gen Z, actually, you you you you can do fairly well today. There's good offers, there's good opportunities. When you're outside of the hub, it's very hard. It's much more difficult. So long story short, what I think is going to happen is over time, companies are going to figure out how to update their workflows. So yes, you will see productivity go up and you will see a lot of movement internally. I think we're going to see more internal mobility than we've ever seen in our life.

• 21:06Marina Mogilko?

  Interesting.

• 21:07Speaker 02

  It will be very common for you to start in the marketing team and go to the sales team, start in the sales team and go to the HRBP team. Whenever, you know, you need to move, that's the movement inside the company is going to grow. The company's total headcount, I think, is going to decrease. I think on average, companies are going to be slightly smaller, but it's not going to be a massive cut. It's going to be, you know, every year, maybe they don't backfill people who retire. They just don't hire more, you know? Or if someone leaves, they probably try to do a cultural refresh by bringing AI native talent that is. Coming out of universities. And at the same time, they invest in their talents to build AI native mindset inside the company.

• 21:47Marina Mogilko?

  Do you think university loses its value in the next ten years?

• 21:52Speaker 02

  Yeah, yeah, I think so. I think unless you're a top tier university, where you have a brand defensibility. People don't join for the content, they join for the network, the brands, the being surrounded by people that work hard, that are ambitious. Those will not lose their values. So when you think about university, you think about a bundle, like universities have content, mentorship, research, blah, blah, blah, you know. And that bundle will for sure change.

• 22:20Marina Mogilko?

  Yeah.

• 22:21Speaker 02

  I think it's just, it's gonna be a different offer. Maybe it's not gonna be four year bachelor's degree, two years masters, it's gonna change. I think one of the weaknesses of universities today is the mismatch of the job market skills needed. Like you have too many universities that still teach skills that you won't need. You know, I come from France and I recall when I was a student, we had double the amount of physical educator being trained and the amount of jobs available after they graduate. You don't want a society that has that, you want a society that has a zero skills gap. At all points, the people that are joining the job market have the exact skills that the market needs. It's not an easy problems, but I think universities could be better at it.

• 22:59Marina Mogilko?

  Yeah, and it's really hard for universities to do that, right?

• 23:02Speaker 02

  Super hard.

• 23:02Marina Mogilko?

  You have a program that's established.

• 23:04Speaker 02

  One model is universities focused on durable skills. And then companies build the capabilities to teach perishable skills. So for example, the problem is reasoning. Reasoning, the people who know reasoning, they're PhD students from the top AI labs in the world, that's where they come from. So it is coming from universities generally. Ideally, you would want all universities to give you AI native talents. They, everyone who graduates has amazing AI skills. They're not specialized in a specific area, but they have great durable skills. Join the company and the company has somehow a stack, an HR and learning stack that can take onboard an employee and instead of them becoming a partner at a consulting firm in seven years, they become in six months.

• 23:45Marina Mogilko?

  Yeah.

• 23:46Speaker 02

  And that would be ideal.

• 23:47Marina Mogilko?

  And that's what you do at Workera, right?

• 23:48Speaker 02

  Yeah, we help a lot of companies do that, we do part of this problem, but the general idea is durable skills taught at school, perishable skills taught at the company.

• 23:57Marina Mogilko?

  I love that. This is exactly how universities should be working, right? Not only now, but also like twenty years ago, because skills keep changing. I think in Workera, you have AI agents, right? That work in production and a lot of companies are failing to build those AI agents. Also, we tried, like in my company, we have a media company, we're not that technical. But from what I see, agents sound great. But then in real world, it's still like a set of steps that they're following and you still need a lot of human work. Can you tell me why in your company they're working and they're not working for a lot of other companies?

• 24:32Speaker 02

  Yeah, yeah, for sure. I I think it is very, very hard to put an agent in production. People don't realize that. A demo is not a production agent. You have demos are so easy to do now. You see so many of them. If you can tell the difference between a demo and a production system, then you know what how hard it is. And that's why MIT's a study said only five percent of agents work in production. So I'll give you some examples. The reason, I think, so we've done large deployments. One of the companies that is here, Bill McDermott, the CEO of ServiceNow is here. ServiceNow uses Workera enterprise-wide, so everybody is being measured, mentored, skills gap identified, and they get sort of an AI drivering license, essentially, a certificate for the year.

• 25:16Speaker 02

  That agent has been deployed very large scale. For this to happen, you need, there's so many things that can go wrong. OpenAI can fail. What do you do? We have a model routing layer that allows us to route immediately to the next best model.

• 25:33Speaker 02

  Translation, people have different languages. It's not as easy as just saying, oh, tell, do the assessment in Japanese. It's not at all as easy. If a Japanese person looks at that, they would say it has a lot of cultural gaps. It is not culturally intelligent. So it's so much hard work in there. The agent has to be connected to the UI and somehow the agent misses a button. It just doesn't see it and then you're stuck. Oh, the agent actually scored you very unfairly. Your score should have been two hundred and you got one fifty and you don't agree with it. Well, we have a feature that allows the person to say, I think the agent was wrong. And then you send a human expert in the loop that's. Read review within four business days and respond to the person. We've upgraded your score and we've corrected the agent.

• 26:19Speaker 02

  And when you do that across thousands and thousands of people, well, of course, the agent gets better over time. And yeah, the first deployment is a mess. The second one is a little bit less of a mess. And you know, at some point, you just build that muscle of looking between the lines and in the details, because that's what matters. In a lot of cases, we even removed AI. Like, we realized that, you know, we started, we were like, everything has to be stochastic, meaning, you know, sort of non-deterministic. And then, you know, we got some feedback and users said, no, actually, I really like when part of the experience is deterministic, where I don't need to be real time talking to the AI interviewer, because it stresses me out. I want to take my pause and I want to be able to look at a multiple choice question and take my time to check A.

• 27:04Speaker 02

  That's not, you know, doesn't need like agentic AI. And so we had to decide where do we do deterministic and where do we do stochastic? Because stochastic allows you to understand the reasoning of the person. You have a live conversation with an agent, you can dig deeper in their thoughts, but it's not always the right solution.

• 27:22Speaker 00

  This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome, that's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50-page restoration block, or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up, required compatibility and availability varies, 18 plus.

• 27:47Speaker 01

  This episode is brought to you by Indeed. You're ready to level up your business, but the pressure to find the right person to make that happen is intense. Indeed sponsored jobs can help carry the load. It boosts your job post to help it reach quality talent fast. Cut through the chaos with Indeed sponsored jobs. Get a $75 sponsored job credit at indeed.com/podcast. Terms and conditions apply. Hiring now? Then this is a job for Indeed sponsored jobs.

• 28:17Marina Mogilko?

  Wow, so from what you're describing, it feels like in order to deploy an AI agent in your company, you need a very technical person who can do the right reasoning and ask and like pave the right path for that agent.

• 28:28Speaker 02

  I think it's like, so companies now have these agent marketplaces, like you can go on their internal platform and create an agent with a prompt. That is very different than building an agenty company where the bar is just super high. So for example, if you want to create a bot on Slack that reads a channel and summarizes it for you every day, you don't need a team that is technical. You now can have someone go on the marketplace of agents, hook it, connect it to Slack and tell it what to do, it will do it. But we're building an AI agent that is supposed to be the best in the world at measuring someone's skills to give them feedback. That's a different problem. You can't get it wrong. The bar is extremely high. And there you need a research team, you need an applied team, you need a product team.

• 29:13Marina Mogilko?

  And talking about jobs, I feel like we need more and more people these days because of all of the tools, all of the opportunities that open up. But do you think there will be more companies? Because it's easier to start a company.

• 29:23Speaker 02

  Yeah, I think there will be more companies.

• 29:24Marina Mogilko?

  Is it going to help even out the market?

• 29:27Speaker 02

  Yes, I think so. I think there will be more entrepreneurship, there will be more small businesses. You know, last year I saw on X, some of these vibe coding tools, I'm not going to say which one, people would know, did a marketing campaign saying, oh, one of our users rebuilt Calendly and rebuilt DocuSign in six hours, in six hours. Where is that product? Who has used it? Nobody has ever used that product. Nobody has ever seen it. It's probably not even maintained anymore. Because what makes Calendly and DocuSign, by the way, opened a new office in San Francisco and they're growing, you know? So what's interesting is if you don't have the best product, if you're not significantly better than DocuSign, why would I change to your product?

• 30:12Speaker 02

  The bar is high. Yes, it's easy to build a simple signature tool or calendar scheduling, but your Calendly is very actually powerful. It has so many features. And so the only way to replace that is if actually you build a product, the product is not only as good, but actually Maybe fifty percent better for the for the cost of switching to be worth it for a user. Fifty percent better. And on top of that, you will have to make sure it keeps being fifty percent better.

• 30:35Marina Mogilko?

  Yeah, into the right marketing as well.

• 30:37Speaker 02

  So I don't buy this idea that of personal software. I don't buy that people are gonna build their Calendly and they're gonna build blah, blah, blah. I think some company will build a Calendly that is fifty percent better than Calendly that is AI native. And everybody will use that agents. And because you don't wanna, you you don't have the time, we don't have the time to build our personal software and maintain it, you know. So I don't know. I think it's just marketing campaigns.

• 31:02Marina Mogilko?

  Yes, totally makes sense. In the next five years, we just don't know what's gonna happen in ten years when AI is so good. And it just gets all the knowledge, like, I don't know, I'm thinking about the lawyers who are using AI. Like AI, an AI tool has all the legal knowledge, it's just so much better than anything.

• 31:17Speaker 02

  For sure. I agree it will be an AI agentic tool. I just don't think there will be hundreds of them. I think people will use the best.

• 31:24Marina Mogilko?

  Yeah, yeah, yeah.

• 31:25Speaker 02

  So I don't buy that there will be.

• 31:26Marina Mogilko?

  But it it will be one major company, don't you think?

• 31:29Speaker 02

  It will be, it will be, it it will be.

• 31:30Marina Mogilko?

  Probably be one of the top three or four.

• 31:32Speaker 02

  I I don't know, ma- ma- but you look like Calendly has built an amazing business. There is a feature that is the exact replica of Calendly in Google. Exactly. So how did they build that business? Because there's still a need for like innovation in that niche, you know. I don't think we will be using thousands of agents in the future, like you and I. I think we will be using a smaller number that are specialized. And the teams behind it make them consistently better, continuously better. Not only it will have the ability to teach itself, but there will be a user feedback loop. So that they get the UI right, they get UX right, they get the lingo right, you know. These things are very important at the end of the day.

• 32:12Marina Mogilko?

  Yeah, it sounds very positive for entrepreneurship, because sometimes as an entrepreneur, when I think about AI. If AI can identify the problem, like when it comes to Amazon Marketplace, for example, identify the product where um demand is more than supply. Ship it from China automatically and just sell it. Uh, it makes me a little sad, but from what you said. Because it's it takes a human to constantly improve something and think about the details and innovate.

• 32:36Speaker 02

  And that's the the defensibility is not the software. It's not gonna be the code, because that's easy. It's the expertise that it put into it.

• 32:44Marina Mogilko?

  And the founder.

• 32:45Speaker 02

  The user feedback, the agency of the founding team, you know, things like that matter more. And that's what makes them win, yeah.

• 32:51Marina Mogilko?

  I love it. Okay, for everyone who's listening, our audience is twenty five to forty years old. They all want to become better in the age of AI, build something. What are the three moves that they should make in two thousand twenty six?

• 33:05Speaker 02

  You know, learn the foundations of AI. Assess yourself to make sure you're ready. Build the habits of learning, like every, every day when you wake up, take five minutes. Read the X posts of the people that you trust in the space. And it turns out, you know, you, you won't feel better after a week, but you will feel a lot better after a year. You'll feel like you're at the, you're probably at the cutting edge. You know, some, some, someone said, I saw like. You know, if you focus on one thing for a, a day, you probably are already in the top, you know, X percent of the world in that thing. If you focus on it for a week, nonstop, you're in the top ten percent. Focus on a month, you're in the top one percent.

• 33:46Speaker 02

  But to be in the top zero point one percent, you will have to build that habit and follow it for five, ten years. And you might be the top zero point one percent at what you're trying to do.

• 33:54Marina Mogilko?

  I love that. I also like your point about joining a hub. Cause this helps you evaluate yourself.

• 34:00Speaker 02

  Yeah.

• 34:01Marina Mogilko?

  Uh, against other people, like compare notes and learn from each other. Maybe start locally and then, you know, change groups.

• 34:08Speaker 02

  Yeah, I think especially if you're early in your career. Today hubs have significant advantages because, um, so. You know, AI started in the Silicon Valley, uh, pretty much the, the, I guess, the, the new wave of AI agents. Um, so companies came. So there was more opportunities, so more people came, because more people came, more companies came. And now if you're in San Francisco, you don't even need to put an effort to learn what's happening in AI. I go out at the dinner, uh, we talk about voice AI somehow. People talk about Tesla autonomous autopilots. You just learn constantly because you're in the hub. I think in the next few years, it will be like that. The hubs are way advanced, uh, compared to the rest. But I think that in the next, you know, five, ten years horizon.

• 34:52Speaker 02

  You know, people will get slightly older, they would want to build families, they will leave the hubs, a lot of them, they will take that knowledge with them, they will probably start building somewhere else.

• 34:59Marina Mogilko?

  Local hubs.

• 35:00Speaker 02

  Exactly, and there, that's what happened in the dot com when engineer, software engineer was concentrated and a few years later, actually it became democratized because of online learning, because of access to information, but also because a lot of these experts moved elsewhere. And I think the same thing will happen in 10 years, even outside the hubs, you will find great AI native micro hubs or local communities.

• 35:21Marina Mogilko?

  Yeah, that's amazing. Thank you so much for this conversation. I love podcasts. When, after the podcast, I'm gonna just text my team, we're gonna build the Claude thing, we're gonna make sure we have all of the documents, everything synced. Thank you so much. Love this feeling. Let's get to work. Thank you. Kian is the reason why I came back home from Davos and started implementing Claude across everything that I do. We set up multiple Claude projects for all the social media that we're running. And honestly, it's been so transformational. Another conversation that's been really transformational was my conversation that I recorded Davos with Ryan Ruslinski. So if you're all about AI, if you're interested, what's happening to jobs and how you can get a better job by posting on LinkedIn, watch that episode. It's live

• 36:06Marina Mogilko?

  on my channel and I'll see you very soon.

• 36:08Speaker 04

  Now at Kroger, your points add up to more ways to save every trip. Use them your way to take dollars off groceries at checkout or save at the pump. You can even save up to $35 on your next fill-up. Same points, more choices. And with low prices on fresh, quality food, plus digital and personalized offers tailored to how you shop, your dollar goes further. Redeem your points online or in the app. Kroger, fresh for everyone. Restrictions apply. See site or store for details.

• 36:39Speaker 05

  When you spend enough time around great companies, you notice that the best ones treat trust as a growth strategy, not a box to check. Vanta helps companies build and prove trust from day one. Its Agentic Trust Platform connects compliance, risk, and trust at enterprise scale. Vanta delivers a 526% return on investment over three years and pays for itself in just three months. Learn why over 16,000 of the fastest-growing companies use Vanta at vanta.com.

• 37:09Marina Mogilko?

  Bye.