It’s raining layoffs.
More than 100,000 U.S. jobs got cut in just the first half of 2026, and for the first time ever, “AI” was the number one reason companies gave. Oracle alone let go of tens of thousands. Block cut nearly half its company in a single day.
If you’re about to graduate into this, the headlines make it feel like the building’s already on fire.
AI got blamed for about 7% of cuts in January and 40% by May. So whether we’re on the brink of an AI job apocalypse or companies figured out that “we’re restructuring around AI” sounds better to investors than “we over-hired,” it’s a daunting time to enter the workforce.
So, how do you actually land a job and future-proof your career when you’re just getting started?
You take this solid advice from Cal Newport:
In this new economy, three groups will have a particular advantage: those who can work well and creatively with intelligent machines, those who are the best at what they do, and those with access to capital.
You probably can’t fix the money part this year. But the other two? Those are 100% in your hands.
Let’s take a look at three future-ready skills that make you the person who gets hired while everyone else is still refreshing job boards.
Future-ready skills that open doors
Develop real-world expertise at Vector (and get paid)
1. Future-proof your career by demonstrating your value
Most people can tell you what they did. Very few can tell you what it was worth to someone else.
“I was a barista.” Cool—so were a hundred other applicants.
“I ran the morning rush solo, 50+ orders an hour, and the regulars asked for me by name.” Now I can actually see something. You stay calm under pressure and people trust you. Same job, but completely different story.
This is the most useful thing you can learn before you have a “real” job, because it’s the thing that gets you one. Whoever’s hiring you—for an internship, a first role, anything—is quietly asking one question: WIIFM? How does this benefit me, my team, the company? Your job is to answer it before they have to guess.
And what they care about almost always comes down to two things: making money or saving time. (Usually money.)
Sounds cynical, but it’s just how organizations think.
The second you start seeing your own experience through that lens, you stop describing tasks and start describing value.
Watch how this works. You’re talking about the same experience, but how you describe it is completely different:
- “I ran my club’s Instagram” → “I got event attendance up about 40% by posting consistently”
- “I tutored calculus” → “I took people who were failing and got them passing. I’m good at making hard things click, fast.”
- “I had a summer sales job” → “I learned to hear ‘no’ twenty times a day and keep going. Rejection doesn’t rattle me anymore.”
None of that is lying. It’s the same duties, but you’re sharing what the other person needs to know. Think of it like marketing—except the product is your own experience, and the skill is spotting the true value behind each task.
Here’s an example of where this leads. I am a business coach for career coaches. My client rewrote their client’s portfolio and didn’t change a single project in it; they only changed how each one was explained, in terms of the impact it had. This led to a $300K+ role at DoorDash off the exact same work she’d been sitting on for months. The work was always good enough, but the framing unlocked the opportunity.
So why can’t AI just do this for you?
It can help. It’s great for turning a clunky resume line into a cleaner one.
But it can’t stand in a room, read what this specific person cares about right now, and decide which of your ten stories is the one that’ll land. That right thing at the right moment for the right person isn’t an exact formula. It’s judgment. And that judgment still belongs to you.
2. Learn to actually use AI (like, properly)
Being AI illiterate is irresponsible, bordering on professional suicide.
You must start getting used to AI tools and familiarizing yourself with how they work. Messing around with the tools themselves = the best way to learn.
Just. start. using. them.
Most people type one question and grab the first answer. That’s like using a Ferrari to drive to the mailbox. The people who pull ahead treat it like a thinking partner by handing it real context. They go back and forth until it’s actually good.
Here’s the exact process I use, and one you can steal this week:
Problem → Hypothesis → Instruction → Output → Refinement → Implementation
1. Identify a problem that you’d like to solve that AI can help with.
2. Come up with a hypothesis for what AI can help you do. This is where that meta skill of thinking about the right problems comes in. That’s something that you, as a human being, are still in charge of.
3. Instruct the AI: “Imagine you are talking to a real human assistant with an above-average IQ.” This is an exact sentence I heard from the head of prompt engineering at Anthropic.
4. You receive the output
5. Refine the output until you get it right: Never just accept the first answer you receive from an LLM and keep going until the output looks good. There’s a principle in engineering called “garbage in, garbage out.” AI is only as good as the person prompting it.
6. Implementation
Let me run it through something you’ll actually face: prepping for an internship interview.
AI process: job interview example
Problem: You landed the interview. You’ve got three days. You want to walk in sounding like you already get the company — not like you skimmed their homepage on the way there.
Hypothesis: Maybe AI can take me way past “tell me about yourself,” if I give it enough real context about this specific job.
Instruction: Open Claude. Paste in the actual job description, your resume, and the company’s most recent news or product launch. Then: “You’re the hiring manager for this role. Based on this job description and what this company clearly cares about right now, what are the 10 questions you’d most likely ask me—and which weak spots in my resume would you poke at?”
Output: It hands you a scarily accurate list. Now you’re not guessing what’s coming.
Refinement: Don’t stop there. “For the three questions I’m weakest on, here are 3 things I’ve done that are relevant: [enter here]. Help me build answers with the STAR method using my actual experience.” You’ll likely need to push back, add more details, or correct the LLM; don’t let it inflate or downplay your skills or experience. Push it to help you refine your answers for an interview setting.
Implementation: You run through it out loud the night before. You walk in, and half the questions are ones you already rehearsed.
That’s the whole move: Share detailed information and paint the full picture, keep pushing until it’s good, and put it into practice. Same process works for debate preparation, a scholarship essay, a group project nobody else wants to lead, even something like figuring out what to major in.
A friend of mine did a version of this and said the actual interview felt like the second time he’d had the conversation. That’s the edge.
Here’s what this looks like in a business context:
AI process: entrepreneur example
Problem: In my business, people would book a sales call and show up “just curious”—no intention of actually deciding anything. Wasted their time and mine.
Hypothesis: I saw a video that said you could fix this by emailing people between the moment they book and the call itself. These emails should answer FAQs, handle objections, and get them info up front so they arrive ready to make a decision. What if Claude could help me write all of those emails?
Instruction: I dropped 20 real sales-call transcripts into Claude, plus my program materials and the video itself, and started with: “Go through these transcripts and pull the top 10 FAQs, objections, and need-to-know items people have before they buy.” Then I pushed: “The video says to send 12 emails. Based on those objections and what you know about my program, give me the 12 best email ideas and outline how you’d write each one.” Then: “Now write them. Here are examples of emails I’ve written before so you match my voice.”
Output: It spat out 12 emails.
Refinement: I spent about an hour editing them into something that sounded like me.
Implementation: I loaded them into my email tool that afternoon, and moved on. The very next sales call lasted ten minutes—because the person showed up with their real questions already answered and a decision basically made.
Later I mentioned this to a guy who runs sales at an eight-figure company. His jaw dropped. It had never occurred to him to do it. There are opportunities to use AI everywhere, but almost nobody thinks in these loops yet. Learn to, and you’re practicing one of the most in-demand AI skills in 2026.
For more future-ready skills, check out these articles:
The three levels of AI thinking
Someone I work with told me: “I’d never even think to use AI the way you do.”
Despite access, AI literacy is low. I’m not doing anything technical, but I DO think in systems.
When you’re thinking about how to future-proof your career, it’s not whether you use AI, because everyone does. It’s how well you use it. That’s one of the most future-ready skills you can build right now. Most people are stuck at Level 1; climb to Level 3 and you lap them.
Level 1 → The Search Engine
You use AI the way you use Google. One question, one answer—check.
“Summarize this reading.”
“Write my cover letter.”
“Give me a study guide for this exam.”
You take the first thing it hands you and move on. It’s better than nothing, but you’re leaving most of the value on the table by treating a ridiculously powerful tool like a fancy search bar.
Level 2 → The Conversation
You start treating it like a thinking partner.
Let’s say you’ve got a big presentation coming up like a final project, or you’re pitching an idea to a club or a professor.
Instead of asking for a summary, you paste in what you’ve got and say: “Here’s my material, here’s who I’m presenting to and what they care about. Help me frame this so it actually lands.”
Then you push back on the first draft. You add more context. You say, “That sounds too stiff—the way I talk is more conversational.” Keep building on each answer until it sounds like something you’d actually say. This is where the real value lives (and where most people never get).
Level 3 → The System Builder
You take a single conversation and turn it into something you can reuse.
The person who did a Level 2 back-and-forth to write one cover letter asks the extra question: “Can you turn this into a template I can tweak for every application?”
Now every application takes ten minutes instead of an hour. The person who used AI to study for one exam builds a “Quiz me on this until I can’t get it wrong” system and runs it for every class after that.
Level 3 thinkers always ask one more thing after they get a good answer: “How do I turn this into something I can use again?” That’s the question that separates people who use AI from people who leverage it.
If you want a head start on this kind of thinking, read “Thinking in Systems.” Not just for your career—for your life. Systems thinking is counterintuitive, and getting good at it will set you apart.
Once you get good at explaining your value and using AI at Level 3, you’re already ahead of almost everyone. The ability to do both makes you genuinely rare. Add the last skill (the one we’re about to get into), and you’re in a category almost nobody occupies.
3. Build THIS and future-proof your career for good
This one skill has done more for me than any other. It’s brought job offers and freelance work to me instead of me chasing them. It’s connected me with mentors and people I’d never have met otherwise. And yes, it’s made me real money.
I’m talking about building a personal brand. I know it might feel like cringey self-promotion. But it’s really just learning to talk about the things you’re good at, the things that fascinate you, and the stuff you’re working on—in public, consistently, for a long time.
It’s all about demonstrating your thinking out loud.
Take Katelyn Burgeon, who writes about marketing online. She recently landed a $430,000 job offer, because, in her words, “I show up on the internet, think in public, and promote my businesses.”
Now imagine doing that for your own career.
Instead of only firing off applications for data analyst jobs, post a breakdown of a public company’s earnings report on LinkedIn—what the numbers actually mean, what you noticed, what you’d do if you were on their team.
Instead of just applying for email marketing roles, break down the funnels of creators you admire. (Daniel Bustamante does exactly this, on top of his full-time email marketing job—worth a follow.)
Instead of just applying for design jobs, do a mock redesign of a company you love. A 17-year-old I know did this with Alex Hormozi’s brand—Hormozi replied, it kicked off a wave of interest, and that teenager has since built a seven-figure business off the momentum.
See the pattern? You stop asking for permission to do the work and just start doing it in public.
That does two things at once: It pulls opportunities toward you, and it quietly builds the option to run your own thing someday, which is its own kind of security.
Personal branding is a whole skill in itself, and one of the most future-ready ones you can pick up. If you want a primer, start here:
Human ingenuity will always win
Anthropic—the company behind Claude, which is, in my opinion, the best writing tool out there—is posting $400K job offers for storytelling and writing roles. So is OpenAI.
What does that tell us?
That the skills that win in 2026 and beyond aren’t about technical execution anymore. AI can code. It can analyze huge data sets. It can do stuff.
It’s not so great at nuance.
An LLM is never going to come up with an ad campaign like “1,000 songs in your pocket.” Only an exceptional human being like Steve Jobs, who knows how to think in stories, can do something like that.
I have a theory about why AI can never fully replace us, one that points straight to the skills that’ll matter most.
Your brain is wired into an actual nervous system. That system is connected to others, including the one in your gut, where hundreds of millions of neurons are firing. Trusting your gut isn’t a metaphor; it’s a real, physical response you get from having a body. Stack that on top of everything you’ve lived—your memories, what you’ve seen, everything you’ve ever consumed—and you get something AI will never truly replicate:
Taste.
AI can’t just KNOW what great looks like.
But you can.
That’s taste—human judgment, the ability to read a room and know something in your bones. It’s not just for creative work. The same lived experience that gives you taste is what powers every human skill AI struggles with…things like knowing what someone actually needs, adapting when a plan falls apart, connecting with a person in real time.
Which means the three skills we talked about aren’t separate from taste. They’re the same muscle, used in different rooms.
And none of them are things you’re born with. They’re built. In front of real people, where the stakes are real.
Which brings me to the one underrated experience that builds them all at once: learning to sell.
Why? Because…
- Selling is Skill #1 in real time. You can’t do it without learning to translate what you offer into what the other person actually cares about (exactly what you need to do to get hired).
- Selling sharpens Level 3 AI thinking. Great selling is diagnosing the real obstacle in front of you and adapting on the fly, which is the same critical thinking that helps you problem-solve with AI
- Selling requires putting yourself out there. Speaking clearly, listening actively, holding a room—that’s the foundation for everything Skill #3 is built on, and you only get it from putting up reps.
The market isn’t broken. It’s just different. It rewards a combination of future-ready skills most people won’t build, because they’re uncomfortable and slow. You have to be OK being bad before you’re good.
But if you can push through?
You stop waiting for opportunities. You create them.
Ready to start future-proofing your career today? Request an interview.
