Top 1% Career Playbook: How to Get Hired in the AI Era
23m 19s
The job market is undergoing a profound transformation driven by AI, making traditional hiring practices like resume screening obsolete. Employers no longer rely on degrees, years of experience, or static resumes—they now evaluate candidates based on real-world evidence, such as YouTube videos, public content, and hands-on experimentation. A key shift is the rise of "explorer" skills: the ability to experiment, stay curious, and adapt in uncertain environments. Technical fluency with AI tools is now a baseline requirement, even for non-technical roles, as organizations automate workflows and demand deeper integration of AI into daily operations. Domain expertise remains vital, but it must be paired with AI literacy to stay relevant. The definition of entry-level roles has also evolved, with fewer traditional junior positions available. Hiring leaders increasingly prioritize candidates who can demonstrate how they would reconstruct or reinvent a job process using AI, showcasing innovation and forward-thinking. To succeed, job seekers should begin by automating a key process, publishing their work publicly, deepening their understanding of AI tools, and maintaining a record of experiments—both successful and failed. When interviewing, candidates should focus on showing how they’d transform work processes rather than defending their past. Ultimately, AI is not replacing jobs but creating new ones, and those who embrace agility, curiosity, and visible action are best positioned to thrive in the evolving workforce.
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Breaking into the job market right now
is harder than it has been in 37 years.
Harder than during the Great Recession.
244 people apply to the average open role.
And a great resume isn't enough anymore
because a machine reads it before a person does.
If it doesn't have the right keywords,
nobody ever sees it.
And if you load it up with keywords,
it starts looking like AI wrote it
and half of hiring managers throw these out.
Some people tell you a degree still matters.
Others say it stopped mattering completely
as long as you have three specific skills.
So what is actually going on?
I asked people who run companies
employing more than 30,000 people
and every one of them decides who gets in on a daily basis.
By the end of this video,
you'll have a step-by-step plan
for getting hired in 2026.
Skills they're hiring for,
what to bring instead of a resume,
and the exact thing to say in the interview.
And as a bonus,
you get numbers from inside LinkedIn on how fast
your job is changing.
You send a resume and then you wait.
Maybe something comes back three weeks later,
usually nothing does,
and you never actually find out
what happened to it in between.
So I put that exact question to Sal Khan,
who runs Khan Academy and hires constantly.
And he was honest with me about it.
- Imagine this, someone today graduates from Stanford,
pick your major, good major, with a 3.9 GPA.
When I look at that resume, I say, okay, probably smart.
Got into Stanford, good GPA.
I don't know if those skills they learned
are gonna be directly transferable,
but there's probably someone I can work with.
Leadership skills, I don't know, looking at their GPA.
Maybe, oh yeah, okay, maybe they're the class vice president
or if they were the co-captain of the lacrosse team.
Okay, maybe they have leadership skills.
I don't know, but that's only your kind of reading.
- There's signals, not evaluation.
- And what I normally do now when I see a resume,
even someone who's been working, no matter what they write,
I look for YouTube videos of them.
I look for some evidence of like, how do they get there?
How do they communicate?
How do they, and then you try to do it in an interview,
which is just, you only have a few hours to do it.
- When we're hiring, my team is on your social media profiles
the same afternoon.
We're asking around for references
and we always end up on LinkedIn
because that's where the real picture is.
So I put the question to Ryan Roslansky,
who ran LinkedIn for six years
and took it from 700 million members to 1.3 billion.
What does LinkedIn's own data say
about where careers are going?
- Since the beginning of LinkedIn, the feature
that is requested most from members is show me
what a typical career path is supposed to look like.
LinkedIn, you have all this data.
So if I wanna become a CFO or a CEO or an accountant
or whatever, what is the path that people take?
And the reality is in the data,
there is no such thing as a linear career path.
Like it's all over the place.
So the more that people first and foremost recognize
that you have to take your career into your own hands,
there's no natural path that exists that you just get on.
And I think is really, really important.
Right now, it's more important than ever though,
because skills are changing.
The types of skills that are necessary
for a specific role on LinkedIn have changed north of 25%.
You know, over the last couple of years alone,
we expect they'll change by 70% by 2030,
largely influenced by AI and new tools
and new ways of doing these professions.
So I often, when I talk to people
about what they should do with their career,
it's more about where do you wanna be in five years.
And it's more about over the next few months,
like what new skills do you wanna learn?
Because to your point, these roles are flattening,
generalists are more and more
where people are going these days.
So we always thought that the extension
of your LinkedIn profile isn't just where you went to school,
where you've worked, what skills you have,
but the ability to demonstrate the actual knowledge
that you have in your head, like by posting on LinkedIn.
- Being findable and being chosen
are two completely different things.
And the gap between them is exactly
where people get stuck for months
without ever understanding why.
Because what employers are scanning for
isn't sitting on your profile at all.
I heard a version of it from everybody I spoke to,
but Yamini Rangan is the one who laid the whole thing out
in a single answer.
She runs HubSpot, a publicly traded company,
thousands of employees,
and she names three specific things she hires for in 2026
in the era of AI.
- The way I would frame it,
we are looking for explorers.
And I'll tell you what that means.
I think there's a fundamental shift that's happening
with AI where not everybody knows exactly
how to go from point A to point B.
If you knew exactly, and think about them as map readers,
you know exactly what point A is, point B is,
you read the map because someone has figured out the path,
and you follow the directions and you get there.
That is what we were doing because you had playbooks
that worked and you were really clear
about following the playbook.
Today, with AI, there is no map.
So you have to get comfortable with being an explorer.
And there are a few skill sets that are important.
The first one is that you have to have
almost a scientist mindset.
And I look for people who are comfortable experimenting
and having a hypothesis,
proving if the hypothesis is right or wrong,
versus saying there's a set path, right?
Like experimentation, moving quickly, having a hypothesis,
improving whether you're right or wrong.
That is skill number one.
And I look for examples of when they have done it
or how they have comfort around it.
That's number one thing that I look for.
The second is just this passion and curiosity
to go deep in the work.
What is happening is that in order for AI to be effective,
you've got to be close to the ground.
You have to know what parts of the workflow are broken,
what parts of the workflow can actually get value from AI.
And you've got to get really close to the ground.
And I talk to customers, but we also do GEMBA walks.
We look at like how people are using different technology.
I'm looking for people like that,
who are very close to the ground,
close to the process, who can actually make changes.
That's the second one.
And then the third is really what I would say
as just curiosity and customer orientation.
Again, my focus has been, don't forget,
don't forget about AI.
Use it to solve something for your customers.
So can you be super curious and ask the right questions
of your customers to get the right thing?
And if you have the ability to experiment,
the ability to stay close to work,
the ability to stay curious,
then you're actually going to thrive in the world of AI.
If you are stuck with your playbook,
if you are very comfortable with going from point A to point B
in a path that someone else has prescribed,
then you're just not going to be successful
in the world of AI.
That's what I'm looking for.
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- Here is how I test for that when I'm the one hiring.
You don't have to write code.
You just have to know which tool does the thing.
It's on the list for every single role we hire for,
either the skill itself or a real appetite
to go and learn it.
For example, my YouTube producer built something this year
that a few years ago would have taken an actual engineer.
And we keep building stuff like that
because when I was hiring them,
I was looking for this itch to build something,
to automate something, and they absolutely did.
And I absolutely love that because that gives them more time
to actually think strategically and do more fun stuff.
Another guest that I wanted to highlight in this video
is Graham Lee.
He co-founded Gamma that we use all the time.
And I asked him,
do you have anyone on this team who does this best?
And by that, I mean, automating different processes.
And he didn't name an engineer at all.
- I feel like nearly all employees
are what I would still define as generalists today,
even though they spike in different areas.
So, you know, for instance,
our head of design, exceptionally talented,
designer, very visual,
But he also knows how to code and he was coding even before like, you know, Vibe coding was more accessible to most designers.
But the fact that he can now ship something like end-to-end, test it, like get early versions into the hands of users, that's a superpower, right?
And so he can cross domains where, you know, traditionally it'd be a designer or a UX researcher that does a little bit, some of this like early, you know, sort of investigation or like early research.
He can do that himself, develop the early prototype, ship the early prototype, get something into the hands of users to actually give feedback on and oftentimes then ship something into production, like almost like completely end-to-end.
That just wasn't really possible before.
But for us, like we try to look for that, the spirit of like a generalist across every function, whether it's, you know, sales, marketing, engineering, product design.
And I think that allows you to have a relatively lean team that can, again, like play across multiple domains at any given point in time.
Right.
And I think in general, that just means a team that can be pretty collaborative too, because you have deeper empathy for what each function can bring to the table.
And like when you're bringing something to an engineer, for instance, you've already done so much work to think through like, okay, how do I make this right so the handoff is as seamless as possible?
These things I think are hard to quantify, but when you have a team of generalists, there is some magic in like working together.
And you're probably thinking, so I keep doing my current job perfectly.
And at the same time, I teach myself technical skill for a job.
I don't even have yet.
That's two jobs and almost nobody has room for two jobs.
So I asked Aaron Levy how technical you actually have to be.
He's been running Box, again, a publicly traded company for 20 years, and he hires across marketing, sales, finance, engineering, all of it.
He drew a very specific line.
And then he told me exactly which tools to open first.
When you're hiring now, how is it different from hiring five years ago?
What are you looking for in a candidate?
So I think right now is a great time to be going deep.
You don't have to like, it doesn't mean like you have to like be vibe coding all the time and building entire products, but you should try and really understand what is the agent doing?
How does it work?
How does MCP work?
How do CLIs work?
How do skills work?
And getting really well versed in that.
The people that are doing that will have a huge leg up in the next kind of three to five years because all of these companies will be hiring for people that can do that within their workflows.
So we're definitely looking for people that whose technical acumen is growing, whose AI sort of savviness and fluency is growing.
You want to be able to do that.
You want to be using these tools, you know, in your, in your free time as much as possible.
So you again, understand kind of how they're working and what's going on.
At the same time, I don't think a lot of the, I think it actually still matters that you have like some degree of domain expertise.
Like you're really good at marketing.
You understand what customers want.
You're really good at selling.
You're good at product management and, and interviewing customers and, and assessing markets.
Like those are the, these like timeless sort of, you know,
skills that transcend any kind of technology revolution.
And so AI is just a way of, of augmenting those domain skills.
So there's in, in some respects, any role that we're hiring for marketing, sales, finance, engineering, et cetera, we need all of those domain skills, but also we need you to now be increasingly kind of AI fluent or a little bit more technical.
Uh, can you recommend top three apps that people should be using?
Um, you know, pro pro I mean, it probably won't be much of a surprise.
I would, I would download codex.
I would download Claude, uh,
even for non-technical.
Non-technical people.
Yeah.
A hundred percent.
Well, especially partly is, is because codex is becoming more, uh, inclined, uh, toward knowledge, work use cases.
And what should they be doing with it?
Like automate a process within their automated process, give it just a crazy problems and see what happens.
Like, uh, go do this research in this market.
Um, you know, wire up multiple MCP servers to data sources you have.
Um, so you understand how, how does it work?
Like, how is it queering that, that other system?
How does it, how is it accessing my email?
Like, like, oh, scary.
Oh.
Uh, no, actually I understand it now.
Like, like get, get a sense of how that all kind of is working together.
So I think just any one of the top AI tools for productivity, maybe for coding, uh, is a good way to get started and it'll already get you like 90% of the way there.
So codex.
Uh, probably Claude cowork perplexity.
Like these are some just easy ones to just get started with and you'll have a good sense of kind of what the market looks like.
There is one more side of this market I want to talk about, and that's junior roles, job openings in the U S fell to 6.5 million at the end of
last year, the lowest level since 2020 and 43% of recent graduates are now working in jobs.
They don't require the degree they paid for.
So the bottom rung has genuinely got harder to reach.
But the more of those conversations I had, the more it looked like what's actually shrinking is the junior role.
As we used to define it, the version that was basically a list of tasks, somebody handed to you, Luana Lopez, Lara co-founded Cal sheet, a prediction market whose entire business is running the world.
It's not just about the numbers and pricing, what happens next.
So basically they're predicting all types of things and nobody has better tools for seeing where all of this is going.
How are you hiring these days?
How has it changed from the last year?
We like being very lean.
Uh, so we were one 70 people, um, at the moment and people that work very well, like our sheet, they are very low ego and willing to learn a lot.
I think we're very direct culture.
We really like being efficient with time.
So that means like feedback is like, I don't like something you did.
I'll tell you right now.
And I'll be honest about it.
And you have to be, uh,
in that kind of like cultural side is very important for us.
But realistically, the two things that matter the most is just working really hard and having like a commitment to, to work above everything else.
I, when I say commitment to work is more about, um, when we ask you to do something and we trust you with something, we can trust that it's going to be done great.
It's not about number of hours.
It's not about these things.
It's about AI as well.
It is.
But so in the engineering side, in the engineering interview, we put a lot of time into it and kind of like, now you can use AI in the interviews and it's completely
fine.
Um, and, um, and all of that, and actually in a lot of the systems review that we do interviews on systems review or like previous project review is kind of a big component of that because now a lot of the things that we used to look at like two years before of like, oh, can someone actually do this or do that?
But now like, whatever, like that's just not relevant.
We are actually talking about, um, in design.
Now I told you that we were trying to get more and more on the figuring out how to use AI in a better way in design, in our design interviews, we're starting to be like, has this person use a lot of AI?
So it's spreading to design.
What about knowledge work?
Less so we need to do one thing actually, funnily enough in the legal team, we're starting to do that a lot too, to be like, cause we have so many cases and litigation.
We're trying, we're starting to be a lot more like, how have you used AI for this?
How would you use AI for that?
It's less about, um, and it kind of adds, goes back to the willingness to learn.
I think it's less about them having the answers or having used it to do something amazing before, but more like, are they willing to do it?
Because we have again.
Like our engineering team, what we're doing is that we're kind of putting them that the AI group in like design, and then they're going to go into legal and try to kind of like, how do we help them to do it?
And we just want people to be open-minded.
And then, and the answer is like what they used to, how they used to work is not the way that we're going to work at Kaoshi and the world's going to do.
And we just need them to be open-minded and have like low ego to figure out like, oh, this thing that I thought I was very good at is actually, I don't need to do anymore.
When we were hiring a social media manager this year, every single question asked was some version of the same thing.
How would you answer?
How would you automate this?
Where does AI go into this process?
And honestly, I don't need anyone to have the right answer.
If somebody says, I'm not sure, but here is how I test it.
Here are the two things I try first.
That's exactly what I want to hear because in the 10 seconds, it takes them to say it.
I can see the entire shape of how they think.
Connor Grannon told me something very close to that.
- Doesn't that just break your heart when people are struggling to find work, right?
And with AI, I think that we're in a zone right now where the opportunity is absolutely colossal for this reason.
It requires, understanding AI and really using AI well, is an incredible tool because you don't have to learn anything.
You don't need to be a coder.
You don't need to be anything.
Literally, all you need to be able to do is talk like a human and everybody can do that.
That's number one.
But number two, and this is the really important thing.
It's not enough to just go into a job interview and say, I know how to use AI because everybody can say that.
And it's sort of like Excel thing.
Everybody uses it, but people are using it to make a grocery list or something like that, not to transform, you know, how a hedge fund operates.
The holy grails.
Can you invent the work of everybody around you?
So if you go into a job interview, and this is what I tell everybody to do, go into a job interview prepared with what they do, what are the, like, they'll have given you a sense of what you should do.
So go in there and say like, hey, here's my domain expertise, but let me just show you what I would do with AI and how I would reinvent this process because folks, and you know this too, right?
Anyone can do this with Claude Gemini ChatGPT.
You don't have to come up with it.
You just have to monitor and steer it.
- Yeah.
- And say, here's how I would change this role to even potentially put me out of a job, right?
I mean, senior leaders would love that, but here's the really holy grail.
This is the kicker on all this.
This is the kicker, such a ChatGPT thing to say, my gosh, I take it back.
But here's the really important thing, say, and here's how the team can use this in a very different way.
Here's the exact workflow.
Here's what I would change.
Here's how I would think about it because we want to upskill a team.
You don't want to just hire somebody to come in and do AI differently.
You want somebody to come in and change the way it's done around that person.
- Yeah.
- That's the grail.
- One thing before we keep going, if you want to stay updated on all the things these founders say here,
we take the use cases and prompts from every conversation and put them into my newsletter called Future Proof.
And of course, we also put my own beautiful use cases there.
For example, we'll put together the seven skills that came up again and again,
the ones people will need in the next few years.
These are the skills that my team and I are also using daily.
Subscribe through the link in the description.
You'll get that one, and you can also read our
previous newsletters and see what you missed.
Everything in this video comes back to being visible somewhere.
And it works in both directions because
the only reason you get to hear any of this is that founders now show up on podcasts and on
socials. 10 years ago, I had a really hard time explaining what I was doing in Silicon Valley.
Today, founders come here and tell you exactly who they hire. So if you're job hunting right now,
leave a line about yourself in the comments. People really do read them. And I know that a
lot of my guests read the comments after the podcast goes live. This is a moment in time
where even though in some ways it's hard to get a job, it's also a place, we're in a moment where
it's actually still easy to differentiate yourself. Like today, if I put on a job application that I
have a webpage, people are like, big deal. But in 1996, if you said, I have a webpage, people are
like, ooh, let's hire this person. They know what's about to happen. Now you can say, I come
with a hundred agents. I come with a hundred agents. Or why don't you interview my agents
first? Or do you have an agent that would like to talk to mine? Or here's my portfolio of AI
generated art.
Or here are my workflows that I've automated. Or here's a Loom video I created of a day in my
life where I have automated my life with agents. If you know anyone like that, I would be interested
in hiring them for Khan Academy tomorrow. But all in all, here's what surprised me once I put
all of this together. Not one of them told me that a resume was the thing that got somebody
hired. None of them pointed out a degree either, or at years of experience. Every single person
I asked described somebody whose work they had already seen before the interview.
But none of this can wait until the week you start applying. So here are five things,
and you can begin all of them this month. First, take one process inside your job you
already have and rebuild it with AI. Like, properly. Until it genuinely saves time.
That's your demo, and that's the single most useful thing you can do right now.
Second, write about it publicly. Like, come on, I've been telling everyone to start being active
on socials many, many years ago. Now, with LinkedIn, I think the awkwardness of it goes away
because Instagram is more like style, day-to-day. YouTube is long form. Latent is where your
professional content lives. Because every single one of those people goes looking for evidence of
you before the interview, and your posts on X and LinkedIn are the ones that keep working while you
sleep. Third, try to go one level deeper, technically, that your role strictly requires.
The way Aaron described it, just understanding how the tools actually work underneath.
Fourth, keep two or three real examples of experiments you run, including the ones that fail,
because people like Yamini, who runs HubSpot, are going to ask you exactly for that.
And fifth, when you finally get into the room, don't spend any time defending your resume. Spend
it showing how you would rebuild a process. Because the whole narrative inside companies now
is, let's make our company suitable for agents, let's make sure things that can be automated are
automated, so you have to be the one presenting this opportunity for them.
I really hope this was useful, and I don't know if you're seeing this, but what I'm seeing from my
perspective is that, I don't know if you're seeing this, but what I'm seeing from my perspective is,
is that AI is creating more jobs, more new jobs, than it's taking away. So it is a social elevator
for a lot of people who've been waiting for the next opportunity. And if you are the one who wants
to jump on the social elevator, please subscribe to this channel, because I'm sharing a lot of these
conversations about getting hired and advancing your career or starting a business with AI. Don't
forget to subscribe, and I'll see you soon. And by the way, leave a comment, I'm gonna read them too.
Podcast Summary
Key Points:
Employers are shifting from resume-based hiring to evaluating real-world skills, evidence, and demonstrated experience through platforms like LinkedIn and YouTube.
A fundamental shift is occurring
Hiring leaders prioritize "explorer" mindset—people who experiment, test hypotheses, and stay close to the work—over rigid, playbook-driven roles.
Technical fluency with AI tools (e.g., Copilot, Claude, Perplexity) is now essential, even for non-technical roles, as teams increasingly rely on AI to automate workflows.
Domain expertise (e.g., marketing, sales, product) remains valuable, but it must be paired with AI literacy to remain relevant in evolving job markets.
The definition of a "junior role" is shrinking; entry-level positions are more competitive, and many graduates now work in non-degree-required roles.
Hiring now emphasizes evidence of work—such as AI-powered process experiments, prototypes, or public content—over resumes or degrees.
Candidates who can demonstrate how they would reinvent a job process using AI are more likely to stand out and be hired in 2026 and beyond.
Summary:
The job market is undergoing a profound transformation driven by AI, making traditional hiring practices like resume screening obsolete. Employers no longer rely on degrees, years of experience, or static resumes—they now evaluate candidates based on real-world evidence, such as YouTube videos, public content, and hands-on experimentation. A key shift is the rise of "explorer" skills: the ability to experiment, stay curious, and adapt in uncertain environments.
Technical fluency with AI tools is now a baseline requirement, even for non-technical roles, as organizations automate workflows and demand deeper integration of AI into daily operations. Domain expertise remains vital, but it must be paired with AI literacy to stay relevant. The definition of entry-level roles has also evolved, with fewer traditional junior positions available.
Hiring leaders increasingly prioritize candidates who can demonstrate how they would reconstruct or reinvent a job process using AI, showcasing innovation and forward-thinking. To succeed, job seekers should begin by automating a key process, publishing their work publicly, deepening their understanding of AI tools, and maintaining a record of experiments—both successful and failed. When interviewing, candidates should focus on showing how they’d transform work processes rather than defending their past.
Ultimately, AI is not replacing jobs but creating new ones, and those who embrace agility, curiosity, and visible action are best positioned to thrive in the evolving workforce.
FAQs
Companies are looking for explorers with a scientist mindset, curiosity, and the ability to experiment. Candidates should show passion for deep work, be close to the ground in their processes, and have a strong curiosity about customer needs.
Resumes are often scanned by machines first, and without the right keywords, they go unseen. A resume alone doesn’t show how someone thinks or solves problems—employers now look for evidence of real work and thought processes.
Show how you’d use AI to reinvent a process or improve a role. Highlight real examples of experimentation, automation, or problem-solving. Employers value how you think, not just what you’ve done.
Hiring managers often review LinkedIn profiles the same day as the interview. Social media shows how you communicate, solve problems, and think—providing a real picture beyond a resume or degree.
Yes, but it’s not about coding. Understanding how AI tools, CLIs, or MCPs work is increasingly valuable. Being technically fluent helps you adapt, automate, and innovate in any role.
AI fluency means understanding how AI tools work, knowing where they fit in workflows, and being willing to experiment. It’s about using AI to solve real problems, not just completing basic tasks.
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