Stanford AI Expert: 71% of People Won't Survive the AI Shift — Here's the 30-Minute Fix

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

That's Kian Katanforoosh, 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 3 moves everyone should make in 2026?

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% 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—

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?

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, 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 of hundreds of tasks is not that simple. It can take decades. 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 radiologists will go away and the drivers will go away. And then you see this meme of radiologists driving to work. Work in their, in their car.

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 older taxis. So we see the replacement happening, but how soon do you think it's going to happen for drivers, for example?

Yeah, well, you look like, you know, the, the rise of, you know, Waymo, Cruise, all these companies in the self-driving space 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 why wouldn't it be the same for the rest? I think maybe in the next decade, we're going to start 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 6 month and it hasn't.

Yeah. So we're safe for now, at least like for the next 5 years.

Generally, I think safe in a career comes down to learning velocity. It turns out to, can you reinvent yourself? You know, the, the WEF 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 2 years in tech or AI. And so you have to refresh yourself and that's what makes you safe ultimately.

Absolutely. And your data shows 71% of people misjudge their AI skill level. Can you give us some benchmarks? So what is an AI-proficient person? What does his day-to-day look like? Does he start with chatting with his AI, or is it not writing emails by yourself or having AI manage your schedule?

Yeah, I try to separate adoption of AI and proficiency. So I'll give you an example. Adoption is is like, you use AI 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.

Yeah.

That's the difference between adoption and proficiency.

Okay, what you just said makes me feel like I'm a beginner. 'Cause my prompts are really, really simple. Okay, if I want to sound like you in 90 days, what should I be doing?

So first, if you have 90 days, I would say first we need to establish the foundations. You take a few foundational classes. We can recommend some on deeplearning.ai, 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 AI, because the market is moving so fast, is that you are 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.

Can you recommend like who do you follow on X for best advice?

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. Great newsletter called The Batch. Richard Socher, you know, Yoshua Bengio, a lot of great AI scientists that, you know, people trust. 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, we would read a lot of paper that come up in Archive, the website that, you know, where papers are often published. Today there's just so much that you have to find ways to differentiate signal from noise.

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 happens every day. So, okay, we establish this, I follow the right people. What is the next step? Are there like top 3 AI apps that I should be using?

Yeah, I mean, I recommend obviously Workera for testing yourself, although it's mostly used in corporations. Other than that, DeepLearning.ai has a lot of free content out there. It's really good. You also find that the LLMs can help you learn. Like you can actually prompt the LLMs, but the bottleneck is people don't know what to ask the LLM. And that's where the assessment is so important because at some point you're going to be pretty good at AI and you're going to sort of have a wall in front of yourself. Like, what do I do next? Am I actually that good? Do I know? To give you an example, at Stanford we have, as you said, the class on campus with a lot of students and we have the class on YouTube, same content published on YouTube with a lot of views, like a lot of views. And those students would tell you that the difference between them and the Stanford kids is that it's not the material, it's that they don't know if how good they are. The Stanford 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 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 thought you were pretty good, but that's not the bar. The bar is actually higher.

Top 3 questions that you should ask yourself to kind of understand your level?

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.

When Kian says that, a lot of people have the same reaction. Okay, by that definition, I'm definitely not using AI enough yet. And honestly, for most teams, it's not a motivation problem. It's that there is no simple visual way to plug AI into the work they're already doing. Right now, AI usually lives in fragments. Cursor in one tab, cloud code in another, maybe a Copilot or an OpenAI model somewhere else. That's exactly how my team used to work too before we changed our setup. Ideas in one place, guidelines in another, code somewhere else, video editing on another platform, and almost no visibility into how all of it connects. That's why I got excited about partnering with Miro and their MCP server. MCP lets you connect your Miro canvas directly to the AI coding tools you already use. So instead of Miro being notes on the side, it becomes the central hub where your specs, diagrams, and context actually feed your agentic coding workflows. Practically, it changes two big things. First, you can take shared context— diagrams, docs, notes, system maps— and send that straight into your AI assistants to build better code. Because now Claude or cursor has actual context from your diagrams and specs, not just a prompt. Second, you can instantly visualize that code as diagrams in Miro, in a collaborative environment where the whole team can understand, comment on, and iterate together without digging through a repo. If you've been wanting to use AI more seriously at work but it's always felt abstract, fragmented, or messy, Miro's MCP makes it concrete and collaborative in a way that finally clicks. If you want to try it yourself, check out the link in the description and the MCP tutorials on Miro's YouTube channel. And now back to my conversation with Kian.

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

I think when it comes to work, a lot of the value of language models is in the context. So for example, on ChatGPT, 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, whatever your style is. That's an example of context that you give to the LLM.

It's like memory, right?

Yeah, memory that you give to the LLM. Although, yeah, memory is slightly different than context. I can explain after. And at work, you sort of want your documents to be accessible to your LLM 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 XYZ, it will figure it out. So the value of the LLM increases with the amount of context it has access to at work.

Is that how proficient organizations use AI?

Yeah, I'll give you a concrete example. At Workera, we are a big Anthropic shop internally. We use a lot of Claude. All our engineers are on this version of Claude called Claude CodeMax, which is very powerful to code. And across the company, we have things that we call skills. Anthropic calls it skills, where you can think of them as files that define a certain way of doing a certain thing. Here is how we recruit at Workera, 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 XYZ? Today, because it's all coded, you don't need anymore to talk to a human. The engineer just asks the LLM, 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.

I love that.

And so it cuts communication and it's very powerful. You gain actually so much speed and create so much more time for the marketing team to think about, 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 do you check the result afterwards?

Like, oh yeah, the engineer does.

Yeah.

Okay. The engineers do.

Wow.

So now I'm very curious about your day-to-day as a founder, what has changed in the past 3 years and how you just deal with your coworkers? So you mentioned using Claude, that cuts communication. What else?

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

He's grown beyond that.

Yeah, and so right now, I think historically we've had, I don't know, 8 engineers, 1 product manager, 1 product designer. I think now it's getting way more efficient on the engineering side where you can actually probably put a team together with 2 engineers, 1 product manager, 1 product designer, and the engineers are very empowered to perform, to build everything on their own almost with some input from the other parties. And so we are seeing at Workera a lot of smaller teams, a lot of, instead of having 3 big teams, we might have 6, 7 smaller teams that have more ownership of their surface area. We have transcriptions of meetings, which is really helpful because I can remember what was the context. We use our own product in our interviewing. So there's an AI interviewer.

Oh, wow.

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

Who does your calendar? Is it AI now?

Every morning I have a briefing that my assistant builds AI systems herself, and she has a little agent, call it, or a 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.

Nice.

Pretty much, which is really helpful, you know?

Yeah. Everything that you described, if I want 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? No, you should not. And we're all like creatives and—

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 can get a lot more done with those things.

But the basics like connecting documents and— we should have done that months ago. I think it's more about agency.

It's having agency to do that.

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 3 skills that everybody should be developing. And I think you mentioned that in one of your talks, that 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 is still beyond this, this line because we're using AI as helper. But this bar is rising. What do you think? And like the way to stay beyond it and make it work for you, not control you, is to have agency. Maybe something else. What do you think?

I mean, I'd say 100% 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 skills, 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.

Still?

Yeah.

Even for someone like me who's—

I have a podcast. Yeah, I think so. 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 it doing? You have a significant advantage. You can catch the errors faster, you can iterate faster. It is hard to neglect that. And then to come to the top 3 skills, I think I'd separate in 3 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. 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.

When the machine learns by itself and just tries different things. By itself.

It learns through experience, not through example. 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 very 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.

Kian 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. 3 files that teach AI your real voice, real facts about your background, and even new 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 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. 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's your projection for the next 5 years?

So a few things I, I would say. One, people say Gen Z's— 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.

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

I don't think so. I think companies have overhired 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. They're roster managing. They're exiting people. 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 lingo. Why would Meta exit people from their metaverse team if it was AI? No, it's because they wanted to make more out of that team. And he probably thinks they can get a lot more done keeping the best people and getting them to work hard. Otherwise, you wouldn't have heard about the metaverse team exiting people. You would have heard of something else. So I think it's really performance management that is happening. 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 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. Interesting. 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 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 talent to build AI-native mindsets inside the company.

Do you think university loses its value in the next 10 years?

Yeah, yeah, I think so. I think unless you're a top-tier university where you have 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 the university, you think about a bundle. Like universities have content, mentorship, research, blah, blah, blah, you know, and that bundle will for sure change. I think it's just, it's going to be a different offer. Maybe it's not going to be 4-year bachelor's degree, 2-year master's. It's going to share. 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 educators being trained than the amount of jobs available after they graduate. You don't want a society that has that. You want a society that has zero skills gap. 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 problem, but I think universities could be better at it.

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

Super hard.

To have a program that's established.

One model is universities focus 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. 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 on board an employee. And instead of them becoming a partner at a consulting firm in 7 years, they become in 6 months.

Yeah.

And that would be ideal, I think.

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

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.

I love that. This is exactly how universities should be working, right? Not only now, but also like 20 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 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?

Yeah, yeah, for sure. 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 how hard it is. And that's why MIT's study said only 5% 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 driving license, essentially a certificate for the year. 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. 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. Yeah, 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 200 and you got 150 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 we review within 4 business days and respond to the person. We've upgraded your score and we've corrected the agents. 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 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. We realized that we started, we were like, everything has to be stochastic, meaning sort of non-deterministic. And then 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 the multiple choice question. And take my time to check A. That 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.

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 pave the right path for that agent?

I think it's like, so companies now have these agent marketplaces. 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 who get connected 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.

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?

Yeah, I think there will be more companies.

Is it going to help even out the market?

Yes, I think so. I think there will be more entrepreneurship. There will be more small businesses. 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 6 hours. In 6 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. 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? The bar is high. Yes, it's easy to build a simple signature tool or calendar scheduling, but 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 50% better for the cost of switching to be worth it for a user. 50% better. And on top of that, you will have to make sure it keeps being 50% better.

Yeah, and you do the right marketing as well.

So I don't buy this idea of personal software. I don't buy that people are going to build their Calendly and they're going to build blah, blah, blah. I think some company will build a Calendly that is 50% better than Calendly that is AI native. And everybody will use that agent. And because you don't want to, you don't have the time. We don't have the time to build our personal software and maintain it. So I don't know. I think it's just marketing campaigns.

Yes, totally makes sense in the next 5 years. We just don't know what's going to happen in 10 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, an AI tool has all of the legal knowledge. It's just so much better than anything.

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

Yeah.

So I don't buy that there will be—

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

It will be. It will be. It will be.

It will probably be one of the top 3 or 4 though.

I don't know. 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 innovation in that niche. 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 the UX right, they get the lingo right. These things are very important at the end of the day.

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 demand is more than supply, ship it from China automatically and just sell it. It makes me a little sad. But from what you said, because it takes a human to constantly improve something and think about the details and innovate.

And that's the defensibility is not the software. It's not going to be the code because that's easy. It's the expertise that you put into it.

and the, the founder energy.

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

I love it. Okay. For everyone who is listening, our audience is 25 to 40 years old. They all want to become better in the age of AI, build something. What are the 3 moves that they should make in 2026?

You know, learn the foundations of AI, assess yourself to make sure you're ready. Build the habits of learning. Like every day when you wake up, take 5 minutes, read the X posts of the people that you trust in the space. And it turns out, you won't feel better after a week, but you will feel a lot better after a year. You'll feel like you're probably at the cutting edge. Someone said, I saw like, if you focus on one thing for a day, you probably are already in the top, you know, X% of the world in that thing. If you focus on it 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 build that habit and follow it for 5, 10 years. And you might be the top 0.1% at what you're trying to do.

I love that. I also like your point about joining a hub because this helps you evaluate yourself against other people and like compare notes and learn from each other. Maybe start locally. And then change groups.

Yeah, I think especially if you're early in your career today, hubs have significant advantages because AI started in Silicon Valley, pretty much, I guess, the new wave of AI agents. 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. 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 compared to the rest. But I think that in the next 5, 10 years horizon, 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.

Local hubs.

Exactly. And that's what happened in the dot-com when engineer, software engineering 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.

Yeah, that's amazing. Thank you so much for this conversation. I love podcasts. When, after the podcast, I'm going to just text my team. We're going to build the Claude thing. We're going to make sure we have all of the documents, everything synced. Thank you so much. Love this feeling. Let's get to work.

It was fun.

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 at Davos with Ryan Roslansky. 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 on my channel and I'll see you very soon. Bye.