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AI Is Not Going to Take Your Job (But Here’s What Will)

It’s Sunday night and you’re doomscrolling again when another headline pops up: “Company X to cut 4,000 roles as AI adoption accelerates.” It feels like it’s only a matter of time before you become the headline.

The worst part is not the fear of being fired, it’s the fog of not really knowing what to do about it while you wait for the shoe to drop.

You’ll hear it a lot: “AI won’t replace you, a person using AI will,” but what do you do with that? At the heart of AI-related anxiety is the growing suspicion that the people around you are becoming that person using AI while you are falling behind.

I'm a researcher who helps some of the largest companies in the world navigate these decisions, and this past quarter I've traveled across the US and Europe to understand where AI is actually being implemented. I want to share what I'm seeing, but first, let's talk about you, because there are three mistakes I watch people make over and over again when the ground starts shifting underneath them.

Three Mistakes People Make

Most people respond to technological disruption in the same way, but I see this playing out specifically with AI anxiety in three ways:

Denial. "My job requires human judgment. AI cannot do what I do." The reality is that nearly 30% of the Fortune 500 are now live, paying customers of a leading AI startup. These are not historically early adopter companies. If the Fortune 500 is moving this fast, the "my industry is different" defense is collapsing. Sticking your head in the ground doesn't just cut you off from the opportunity, it builds a reputation as someone unwilling to grow.

Panic-skilling. On the other end, some people start signing up for every AI course, chasing a prompt engineering certificate, learning to code at midnight, and watching endless hours of AI content. This feels productive but rarely translates into anything your employer actually values. According to the World Economic Forum’s Future of Jobs Report, 81% of employers now prioritize demonstrated work experience over a degree when evaluating candidates, and only 14% view short courses or online certificates as meaningful hiring signals on their own. The skill gap employers care about is not “can you use AI” but “can you apply AI to real business problems in your domain.” Everyone should develop a base understanding of AI, but you need to be intentional before jumping into every shiny opportunity.

Freezing. Doing nothing while recognizing that things are truly changing. This is the most common response and the costliest, because the learning curve advantage compounds. Every month you wait, the gap between you and the person who figured it out widens. Unless you plan to retire in the next five years, AI will become a critical part of your work, so it is best to deal with it head on.

Now let’s look at what’s actually happening so you can make better decisions about what to do next.

How AI Is Transforming Companies

Before AI comes for your job, it's coming for your tasks. AI is not ready to massively displace humans, at least not yet. What's playing out is a more specific and useful story if you know where to look.

The data work is being automated first. Entry-level roles have historically been allocated to the unglamorous work of cleaning, validating, and making sense of data. Questions like “is this data right” and “do we trust these numbers” are increasingly being answered by AI. One of the clearest examples is in customer support, where AI agents are already handling higher ticket volumes, improving resolution rates, and raising customer satisfaction scores, all at lower cost than the status quo. There has also been a “TikTokification” of customer signals where a product goes viral and a company’s supply chain is not ready to capitalize. AI is built to find the needle in that haystack long after your entry-level analyst has left for the day. This is one of the most immediate areas where you will see a labor impact as companies simply need to hire fewer people for this type of work.

The talent question is wide open. Regardless of what you hear online, no one really knows what AI is fully capable of yet. Leaders are desperate for people in their companies to find ways to be meaningfully better at their jobs using these tools, but this is uncharted territory for most people. The best engineers at top companies are reporting productivity gains of 10 to 20x with AI coding tools. That number resets what "good" looks like for everyone. Executives are not eager to remove people because of AI (the technology is not ready, even if they wanted to), but they are having intense conversations about upskilling their people with AI so they can leverage these tools. There will also be an expansion of scope as these tools allow people to do things like write code with natural language, even if they are not on an engineering or data team.

Agentic workflows are moving from science fiction to production. The biggest unlock is not headcount reduction. It's taking a process that has always required five people managing multiple spreadsheets and redesigning it so an AI agent handles the data gathering and pattern matching while a human applies judgment at the decision point. This puts human attention where it actually matters and lets the machine handle the parts that never needed a person in the first place. Model capabilities are improving fast, with some domains showing 20 to 30 percent jumps in benchmark performance in just four months, and every major lab is investing heavily in long-horizon agents and computer use that opens up entirely new classes of work. The most valuable person on your team will be the one who can work with these tools while applying sound business judgment and finding novel applications across the business.

You're Not as Behind as You Think

If you feel like you've missed the moment on AI, you probably haven't. We are still early. But "early" does not mean "plenty of time." This is a great graphic from Ruben Hassid over at How to AI to remind us that there is a ton of opportunity still available.

The reality is that we’re early on AI, but it can feel like you’re behind when you’re constantly scrolling on TikTok or X and following people on the bleeding edge of OpenClaw or constantly analyzing Claude’s latest feature drop.

The real shift is that AI raises the baseline expectation for how fast you learn, how fast you adapt, and how quickly you deliver value in a new context. The people who cannot keep up with that new speed or who completely disengage are the ones who become expendable, not because a bot took their job but because someone else figured out how to ramp faster, upskill continuously, and operate at a level that used to take five years of experience to reach.

The photo above is of a room full of human computers. According to NASA, this team of women had been around since the Jet Propulsion Laboratory’s beginnings in 1936 and were responsible for the number crunching of launch windows, trajectories, fuel consumption, and other calculations that helped make the U.S. space program a success.

We have moved from human computers in the 1930s to agentic AI applications in 2026, but the reality is that ambitious people have always had to navigate technological disruption. The challenge is the same as it has always been: minimize mistakes and determine where to invest your limited time, capabilities, and resources.

What to Do Next

The executives I work with are not looking for all of their people to become software engineers. They are looking for people who learn fast, can translate between the technical and the human, and reduce the time between "we have a new tool" and "we are getting value from it."

Here is how to become that person.

Step 0: Build a foundation. Start with NVIDIA CEO Jensen Huang’s “AI is a 5-Layer Cake” to understand the essential infrastructure behind AI. In parallel, bookmark Anthropic Academy and sign up for a free course like this one on AI Fluency. You can also subscribe to their AI Fluency newsletter at the bottom of the academy homepage. These are free and give you a real foundation, not just buzzwords.

Step 1: Name your 90-day problem. Every role has a version of the onboarding question: what takes too long to learn or ramp up on? Maybe it is a new system, a new client’s business, a new market, or a new regulation. Name the thing that, if you could learn it twice as fast, would make you materially more valuable. If you are still in school, pick a topic or research area and run the same process.

Step 2: Use AI to compress your learning curve right now. Instead of investing thousands of dollars in an AI certificate, take the 90-day problem you just named and use AI to cut your ramp time in half this month. Upload a complex document you have been meaning to read and have AI break it down. Build a custom prompt that helps you prep for meetings in a domain you are still learning. Use AI to simulate a conversation with a stakeholder before you have the real one. The point is not to learn AI as a skill, but to get in the practice of using AI as a tool.

Step 3: Make your learning speed visible. It is not enough to get faster. Your manager and the people above them need to see it. This matters more than you think: research from Wharton’s Ethan Mollick found that while over 40% of workers are using AI at work, most are doing it secretly because they are unsure whether revealing it will be rewarded or punished. When you compress a workflow that used to take a quarter into a month, say so. When you use AI to prepare for a meeting and it goes well, mention the prep process. You are demonstrating that you are the kind of person who figures things out fast, and that is exactly the signal that gets discussed in promotion conversations and encourages others to mention your name to their network. Most of your peers are too cautious to step into that space, which is why it is an advantage.

Step 4: Become the person who makes AI work for your team. The biggest bottleneck in AI adoption inside large companies is not the technology. It is the need for someone to sit between the tool and the team and say, “here is how this actually applies to what we do.” That person becomes indispensable, not because they are technical, but because they reduce the learning curve for everyone around them. If you can be that person, it immediately increases your opportunities for promotion, interesting projects, and influence across your organization. This is also great content to share on LinkedIn. If you need help getting started on LinkedIn, I wrote here about how to find your voice and build your network.

A Prompt You Can Use this Week

Here is a prompt you can use today with ChatGPT or Claude to start compressing your learning curve:

You are a world-class executive coach and domain expert. I work as a [YOUR ROLE] at a [TYPE OF COMPANY]. I am currently trying to get up to speed on [THE THING YOU ARE LEARNING OR RAMPING UP ON]. I have about [TIME AVAILABLE] per week to dedicate to this.

Help me build a 30-day learning sprint. I want you to:

1. Identify the 20% of knowledge in this area that drives 80% of the value in my role

2. Create a week-by-week plan that prioritizes the most high-leverage concepts first

3. For each week, suggest one concrete action I can take to apply what I learned in a visible way at work (in a meeting, in a deliverable, in a conversation with my manager)

4. At the end, give me three questions I should be able to answer confidently after 30 days that would signal to my team that I have ramped up faster than expected

Be specific to my industry and role. Do not give me generic advice.

Use this on whatever you are ramping up on right now, whether it is a new system, domain, stakeholder group, research project, or market.

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The Bottom Line

The AI conversation is going to keep getting louder. The headlines are not going to stop, but the people who build careers that hold up are not the ones who panicked the loudest or upskilled the fastest. They are the ones who figured out how to learn faster than the world changed around them.

That skill will never go out of date, regardless of the technology.

P.S. — I work with a small number of readers one-on-one to help them apply these frameworks to their own career. If you are at a moment where you need more than a newsletter, book one off or ongoing sessions here.

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If you enjoyed this, here’s what to read next

The first time I had a seat at the table deciding who gets promoted, I was surprised. I expected the conversation to be about goals and OKRs, but excellent work is table stakes. The real debate was about something else.

What became clear over the years is that promotions recognize two things:

1. Excellent work

2. Who we trust to handle what comes next

People are spending far too much time focusing on #1 as the way to get promoted when it really comes down to #2.

When someone is promoted, leaders are placing a bet that the person they're recommending will not make them look stupid amongst their peers and their boss. That means the person has to have already proven they can operate at the next level, not just perform well at the current one.

This is why so many high performers hit a ceiling they can’t explain. They’re doing everything right and watching someone else get the opportunity.

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The people who get promoted aren’t always the best performers. In fact, they’re likely top-half in performance but they’ve built relationships with the people making decisions, made their work visible to the right audiences, and positioned themselves as the obvious choice for the next opportunity. They demonstrated capability for the next level before they were given it.

The question isn’t “am I doing good work?” The question is: “what level am I operating on, and how do I demonstrate the next one?”

I built the Leadership Ladder to help you answer that question.

The (Invisible) Leadership Ladder

Level 1: Lead Yourself

Do your job well.

Leading yourself means you deliver reliably. You manage your time, your energy, and your commitments without someone standing over your shoulder. People trust you to do what you said you’d do, when you said you would do it, with excellence.

In the AI era, it also means you use AI tools to amplify your own output. Jensen Huang said it on Lex Fridman’s podcast: if he had to choose between two new graduates — one with no AI knowledge and one who’s an expert — he’d hire the AI expert every time.

• Technical Fluency at Level 1 means you’re aware of and using AI tools naturally. You know when to trust them and when to question them. You’re not afraid of the technology, and you’re not naive about it either.

• Business Judgment means you understand your role, you understand what success looks like for your team, and you make good decisions within your defined scope.

• Relational Depth at this level means people want to work with you. You're reliable, you're pleasant, and you raise the energy in the room.

At Level 1, grit matters more than genius. Most people think they’ve mastered this level, but many haven’t. If you can’t use AI to meaningfully improve your output, if people have to follow up with you to make sure things got done, or if your manager is still telling you what to do instead of trusting you to figure it out, you’re still working here.

Level 2: Lead Critical Work

Do work that impacts critical parts of your company.

Leading critical work means you own outcomes that matter beyond your own role. You're not only executing your tasks well but also taking ownership of problems that cross boundaries. You're trusted with your boss's work and leading efforts that are cross-functional.

• Technical Fluency at Level 2 is about driving productivity beyond yourself. You're the trusted human in the loop who is accelerating AI across a team or function. When an agentic system makes a bad call, the person who catches it needs to understand both the technology and the business context. That combination barely exists in the workforce right now, and it's highly coveted.

• Business Judgment means making good decisions with incomplete information. You’re not waiting for someone to tell you what to prioritize. You’ve built an ability to read a situation, understand the stakes, and act. This can’t be automated because it requires context, accountability, and the willingness to be wrong.

• Relational Depth shows up as earned trust, internally and externally. You influence people who don't report to you and raise the credibility of your team beyond its walls. You've built enough trust with the people making decisions that when a room is deciding who gets the next opportunity, your name comes up.

This is what separates the person who gets promoted from the person who gets a great performance review and nothing else.

Level 3: Lead The Organization

Shape the system that everyone else operates inside.

Leading the organization means your decisions set the conditions for hundreds or thousands of other people’s work. You’re accountable not just for outcomes, but for the structure that produces them. There are two sides to Level 3, and both have to be working.

The Internal Side: Building the Machine

Most people think leadership at this level is about strategy decks and executive presentations. It’s really about building the systems (teams, incentives, processes, and culture) that produce results when you’re not in the room.

I learned this at Amazon. The best senior leaders I worked under didn’t make better decisions than everyone else. They built organizations where good decisions happened without them. They hired people who were smarter than them in specific domains, created mechanisms that surfaced the right information at the right time, and spent their energy on the two or three bets that would change the trajectory of the business. Senior leaders are phenomenal people coordinators.

• Technical Fluency at Level 3 means you’re shaping how the organization uses technology, not just using it yourself. You’re making investment decisions about AI capabilities. You’re deciding which workflows get automated, which get augmented, and which stay human, and you’re accountable for those calls. The executives I talk to every week are making exactly these decisions right now, and most of them are flying partially blind because they don’t have enough leaders who understand both the technology and the operations.

• Business Judgment at this level is about allocating resources across competing priorities with long time horizons. You’re making trade-offs that won’t pay off for two years, and you’re building the case internally for why the organization should follow you there. This requires a combination of analytical rigor and storytelling that very few people develop.

• Relational Depth internally means you can move an organization. Not through authority, as anyone with a title can do, but through trust accumulated over time. The people three levels below you know what you stand for. Your direct reports run through walls because they believe you’ve got their back. When you reorganize a team or kill a project, people may not love the decision, but they trust the process.

The External Side: Representing the Enterprise

I once watched one of our executives resolve an urgent operations issue that our team had been navigating for days with a single text to a supplier’s CEO. That's what Level 3 relational depth looks like from the outside. Years of being credible, generous, and consistent in your industry, converted into a two-sentence text that solved in minutes what process couldn't solve in a week.

Level 3 also means you’re a face of the organization to the outside world. Customers, partners, regulators, investors, media, and talent you’re trying to recruit. You are trusted and expected to represent the company’s point of view in rooms where the stakes are real.

• Technical Fluency externally means you can translate what your company is doing into language that customers, partners, and the market understand. You’re the person a journalist calls for a quote, or a conference organizer invites to keynote, because you have a point of view informed by real operational data, not just opinions.

• Business Judgment externally means you understand how your company fits in the broader ecosystem (competitors, regulators, market shifts) and you position accordingly. You’re shaping how your company shows up in the broader business landscape.

• Relational Depth externally means your network creates value for the organization. A phone call from you opens a door that a cold email from anyone else wouldn’t. Customers trust your word and partners want to work with you specifically. Talent joins because of your reputation. Your network is the compounding return on years of showing up the right way in your industry.

The best leaders at this level are building something that outlasts their tenure. They’re developing the Level 2 people below them, creating conditions for the next generation of leaders, and making the kind of long-horizon bets that define what an organization becomes.

Why Performance Alone Won’t Get You There

If you’ve been reading Career Field Guide, you know that a great career requires two things working together: Performance and Optionality. (If you’re new here, the One-Pedal Trap is worth reading.)

Performance is doing your current job excellently. Optionality is building the skills, relationships, visibility, and positioning that create future opportunity. Most career advice focuses only on performance, which is like riding a bicycle with one pedal.

The Leadership Ladder explains why you need both. Performance keeps you at your current level. It earns you good reviews and trust, but it doesn’t earn your promotion because promotion isn’t a reward for the past. It’s a bet on the future and the people making that bet are looking for signals of the next level.

Those signals come from Optionality. The cross-functional project you volunteered to lead. The relationship with the VP who now knows your name. The time you made a judgment call on something ambiguous and it worked. The presentation you gave to a room above your pay grade. None of those things are your job, but all of them are evidence that you can operate at the next level.

Where Are You on the Ladder?

Here’s why this matters now: AI is compressing entry-level work across every industry simultaneously. Anthropic’s labor market data shows that the rate of young workers entering AI-exposed occupations has already dropped 14% since 2022. The executives I talk to every week aren’t waiting to see how this plays out. They’re restructuring teams, hiring differently, and asking “who can operate at the next level?” The gap between what AI can theoretically do and what it’s actually doing is massive right now. When that gap closes, the people who already climbed the Ladder will have a compounding advantage that’s very hard to catch. The window to move is open, but it won’t stay open forever.

Here are three questions to get an honest self-assessment:

1. Can you use AI to produce work that’s measurably better than what you’d produce without it? Not “I’ve tried ChatGPT a few times.” Can you sit down tomorrow, use AI tools as part of your actual workflow, and produce something faster, sharper, or more complete than you could alone? If the answer is no, you haven’t finished Level 1.

2. Have you owned a project or problem that crossed beyond your defined role where you had to coordinate with people who don’t report to you, make a call without being told what to do, and take accountability for the outcome? If the answer is no, you’re performing well at Level 1 but haven’t demonstrated Level 2. That’s the gap your manager sees, even if they can’t name it.

3. If your name came up in a room where you weren’t present, would someone advocate for your promotion? Not your direct manager doing their job. Someone else — ZAa VP, a peer leader, a cross-functional partner — who would say “that person is ready.” If you don’t know the answer, or if the answer is no, your Optionality pedal isn’t turning.

Pick the question where you answered no and spend 30 minutes this week doing something about it. Open an AI tool and build something with it. Volunteer for the project no one wants. Send a message to a cross-functional partner to understand more about their role. Even better, bring these concepts to your boss in your next 1:1 and ask them which level they see you operating at and how to improve.

The career ladder used to be something you climbed by showing up and doing good work for long enough. That ladder is being replaced and the one that’s taking its place rewards people who can operate at higher levels faster, who pair technical fluency with real business judgment, and who build the kind of relationships that make other people want to bet on them.

That’s the Leadership Ladder. And right now, the barrier to climbing it has never been lower — if you’re willing to put in the work.

Start building.

Inspired to keep reading? Bookmark this for your next interview!

In my years as a hiring manager, I saw the same thing happen over and over again.

Candidates would show up fifteen minutes early, dressed sharp, and walk me through a polished summary of everything they had accomplished, then go completely blank when I asked them about our company, our team, or the actual role they were interviewing for that day.

At Amazon and Microsoft, where a few hours of research online would have told you almost everything you needed to know, this was an immediate disqualifier. In a competitive process where interviewers are actively looking for reasons to cut the list, you just can’t afford to send such a costly signal.

Skills get you in the room, but preparation is what gets you the offer.

The good news is that looking unprepared is completely avoidable. Before your next interview, you need to be able to answer three questions that every hiring manager is asking, whether they say them out loud or not. Hiring managers aren't just testing your answers. They're testing your preparation, because how you prepare for an interview is usually how you prepare for everything else.

The Three Questions

01: Why this Industry

What they're really asking: Do you understand the world this company operates in, or are you just here because we were hiring?

This question gets fumbled constantly because candidates reach for something safe rather than something true. When I was hiring for a renewable energy developer, candidate after candidate told me they wanted to help the environment. After hearing such a generic answer continually, it stopped sounding like a reason and started sounding like a lazy placeholder for deeper thought. Experienced interviewers have heard enough of those that they function almost as a red flag.

The fix is simpler than most candidates think. Spend thirty minutes before your interview reading 2-3 recent industry news pieces and identify something happening in the space that you find genuinely interesting. Something interesting enough that you could talk about it for five minutes without running out of things to say. When you can tell an interviewer something specific about the industry they’re operating in, you’ve already separated yourself from most of the candidates who walked in before you.

02: Why this Company

What they’re really asking: Did you spend time learning about us (our website, our podcast, our press releases) or are you mass-applying to every company?

Start with the company website and read it like you’re preparing for a conversation, not skimming for keywords. Understand their mission, their current priorities, anything notable in their recent news. If they have a podcast or a founder who speaks publicly, spend an hour there and you will hear things that don’t appear anywhere in the job description. Referencing those learnings in an interview signals a level of genuine interest that almost no other candidate will match. If the company is publicly listed, ask Claude or ChatGPT to summarize their last earnings call and what has recently impacted their stock price.

Research the specific team you’d be joining. At larger companies especially, understanding the team’s function and current priorities is an entirely different preparation from understanding the company broadly.

Look up your interviewers on LinkedIn as soon as you know their names. LinkedIn notifies people when you view their profile, and checking five minutes before the interview communicates exactly the kind of last-minute scramble you’re trying to avoid.

Prepare two or three questions that couldn’t have been answered by a quick Google search. The questions you ask at the end of an interview reveal as much about your preparation as anything you said during it. The ones that stick are the ones that reference something specific: a team priority, a recent company move, something from your research that you’re genuinely curious about. Interviewers remember those questions after you’ve left the room.

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03: Why this Role

What they're really asking: Can you connect your story to this job in a way that makes sense or are you just looking for any job?

Most candidates can tell you what a role does. What they can’t tell you is why they want this role specifically, and not just any role at this company.

I’ve interviewed plenty of supply chain candidates who could tell me they wanted to work in our organization. Almost none of them could tell me why sourcing over planning or logistics, and what in their background made them the right fit for that function specifically.

Before your next interview, go back to the job description and read it differently. Instead of scanning for keywords that match your resume, ask yourself why this role exists. Is it a new capability being built from scratch, a team scaling into new territory, or a function being restructured around changed business priorities? Understanding the purpose behind the posting gives you a positioning angle that generic candidates never find. More importantly, it forces you to answer the harder question: given everything you know about this role and why it exists, why are you the right person for it and not just someone with relevant experience?

Then prepare your stories. Identify five to seven specific examples from your experience that demonstrate the skills this role requires and write them out, because writing forces the clarity that mental rehearsal doesn’t. Know your results specifically since hiring managers remember numbers and forget adjectives. The difference between “we significantly improved delivery performance” and “we reduced fulfillment delays by thirty-one percent over two quarters” is the difference between a story that lands and one that evaporates. We’ll dive deeper into how to prepare these stories next week.

After the interview, follow up within twenty-four hours with a short, specific note to each person you spoke with. Reference something real from your conversation, not a summary of your qualifications. If you don’t have their emails, send the notes to your recruiter and ask them to pass them along, then connect with each interviewer on LinkedIn with a brief message. It takes fifteen minutes and most candidates don’t do it.

Using AI to Prep for Interviews

There’s a line going around that says some people use AI to improve their thinking, and some people use AI so they don’t have to think.

AI genuinely earns its place in interview prep when you use it to compress research. Feed Claude or ChatGPT a job description and ask it to surface the company’s recent strategic priorities, the industry pressures the hiring manager is likely navigating, or the questions a smart executive in that role is probably asking themselves at night. What used to take three hours of LinkedIn rabbit holes and earnings call transcripts can now take twenty minutes.

The lazier version is generating polished answers to likely interview questions and memorizing them. That works right up until your interviewer does what every good interviewer does and pushes on a decision, asks you to go deeper, or follows a thread you didn’t expect. If the answer came from AI and not from your actual experience, you’ll go quiet at exactly the wrong moment.

The rule is simple: use AI to understand the company and its context. Use your own thinking to answer questions about what you’ve done and why. Those are the only answers that hold up under pressure because they’re the only ones that actually belong to you.

The Bar Just Got Higher

AI has made applying nearly frictionless, which means hiring managers are wading through more candidates than ever, most of whom spent thirty seconds submitting a resume and assumed the interview would take care of itself.

When you finally get the interview, take the time to prepare well and then knock it out of the park.

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It was great to be at SXSW this week. It took being in the back of a Zoox autonomous vehicle to be reminded that we are living in unprecedented times.

Now, maybe more than ever, you’ve got to have a plan for your career.

Three Doors into Every Job (And Why Everyone's Crowding the Wrong One)

Right now, somewhere, a smart and hardworking person is being told their resume isn’t optimized enough. They’re paying someone to rewrite it, or watching TikToks on ATS keyword matching, or spending their Saturday writing cover letters for roles they’ll never hear back about because the hiring manager already knows who they’re going to call.

This is the junk that passes for career advice in 2026.

There are three doors into every job. Nobody tells you this, but here’s what they are, how they work, and which ones are worth your time.

The Front Door: Why the Job Posting Is a Lottery Ticket

The front door is the job posting — the lottery ticket that everyone keeps buying.

It used to be a reasonable bet, but AI and mass applying (thanks, LinkedIn) has made it nearly worthless.

When applications cost nothing to produce, everyone applies everywhere. A motivated candidate and a disengaged one submit identical-looking resumes in the same thirty seconds, and hiring managers end up drowning in hundreds thousands of polished, tailored submissions that are functionally indistinguishable from each other. The resume stopped being a signal a long time ago, and AI finished it off.

What that means practically is that spending hours improving your resume to compete through job postings is optimizing a lottery ticket. In mass-applying to hundreds of jobs, you’re not improving your odds in any meaningful way. The people who understand the other two doors are building their careers in a way that actually works.

The front door isn’t closed, it’s just the most crowded, lowest-signal entry point that exists and it’s getting worse every month.

The Side Door: The Humble Path That Actually Works

The side door is the role that gets you close to the one you actually want, even if it isn’t the one you’d choose if you had a choice.

Early in my career, my target was a corporate supply chain role at Starbucks. What I had was a job at one of their manufacturing facilities, working early shifts leading a team on the floor. Most people in that situation put their heads down, perform well, and wait for something to open up. I did something different with the interactions I was already having.

Supply chain planners, operations managers, people I exchanged emails with in the normal course of work, I started treating those transactions as the beginning of something rather than just getting the task done. After my shift, I’d send a short note:

“I’ve enjoyed working with you on this and would love to connect over coffee sometime.”

A lot of people said yes, and one of those conversations eventually led to a sponsor who opened the door to the corporate role I’d been working toward. None of it happened during work hours, but in my “off” time I could have spent doing something else.

The side door requires patience and some ego management. The role you take to get inside might feel beneath your experience, and that’s fine, because what you’re actually buying is proximity, visibility, and the chance to prove yourself to people who make real hiring decisions. Internal candidates succeed at dramatically higher rates than external applicants for the same roles because trust is already established and your name is already known. You’re not compromising on your goals by taking the side door. You’re taking the smarter path to them.

The Back Door: The Role That Finds You

The back door is the role that finds you before you go looking for it.

No application, no posting, no competing against four hundred other people. Someone already inside the organization thought of you when a need arose, reached out directly, and the conversation became an offer. This isn’t luck, even though it can feel that way from the outside. It’s the result of years of consistent, visible presence in the right circles — sharing your thinking on LinkedIn, building genuine relationships before you need them, and being useful to people in rooms you want to eventually be in yourself.

Your network works like concentric circles, and the most valuable connections are often already around you. Former colleagues, classmates, alumni networks, people you work alongside every day. You don’t need to cold-email executives to start building. You start with the people already in your orbit and work outward from there over time.

The back door is rare, but it is never accidental.

The Question You Should Be Asking Before You Need a Job

At some point you will be laid off, restructured, or simply ready to leave. When that day comes, you’ll have one of two things: a network that already knows your name, or a resume and a stack of applications.

Most people only have the resume, and not because they’re lazy or unambitious. Nobody told them the other doors existed. The career advice industry is built entirely around the front door, and the courses and services and career centers optimizing your keywords all assume that the job posting is the game. It isn’t. It’s the most competitive, lowest-return version of the game, and technology is making it harder every month.

The resume gets you a ticket to the lottery. The side door takes patience and hustle as you actively build your network. The back door takes years and the best time to start is before you need the help. Both of them build something that the front door never will, and both of them start with decisions you make long before you need a job.

You already know which one you’ve been building toward. The more useful question is what you do differently starting now.

Why Most Careers Run on One Pedal

There’s a framework I come back to constantly when I think about this. A great career runs on two pedals working together. Pedal One is performance — doing your current job well, delivering results, being someone people can count on. Pedal Two is optionality — building the skills, relationships, and visibility that create future opportunity.

Most people only focus on performance and end up stuck in the one-pedal trap. They perform, and they wait. They assume the work will speak for itself and someone is paying attention. Sometimes that’s true. More often it isn’t, and they find out the hard way in their forties when the layoff comes and the network they never built isn’t there.

The three doors are Pedal Two made concrete. The side door and the back door don’t open by accident. They open because someone spent years building relationships, showing up in the right rooms, and making themselves visible to the right people.

The resume is Pedal One on a job application. It matters, but if it’s the only thing you’re building, you’re riding a one-pedal bike and wondering why you keep falling over.

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This past week I was in Scottsdale with a room full of Fortune 500 COOs and Chief Supply Chain Officers. Executives managing billions in operations, thousands of employees, and supply chains that touch every product you’ve bought in the last week.

The conversation wasn’t abstract. Thirty years of globalization are being unwound in real time, AI is moving faster than governance can keep up, and behind closed doors, the people doing the hiring were asking the same question out loud: where are the people who can actually navigate this?

That question has a direct answer and if you’re building your career, it’s the most important thing you can understand right now.

The Market in Splitting in Two

There’s two competing versions of what’s happening for people in the job market right now:

1. Catastrophe: AI is coming for everything, your degree is worthless, learn a trade or drive for Uber (until they’re all autonomous too).

2. Optimism: Don’t worry, new jobs always emerge. Technology creates more than it destroys. It will be fine.

Neither is quite right. The more accurate version is that the job market is genuinely difficult1 and a specific kind of person is thriving.

Hiring for college graduates is down 16% compared to last year and 44% below 2022 levels2. Companies are pausing hiring not because they don’t need output, but because AI and digitization is making it cheaper to get output without headcount.

At the same time, according to The Wall Street Journal, base salaries for AI-skilled workers with zero to three years of experience grew 12% from 2024 to 2025, the largest gain of any experience group. At companies like Scale AI and Databricks, 22-year-olds are making $200K base before equity. Databricks CEO Ali Ghodsi told the Journal: “Under 25, you can be making a million.3”

The market isn’t collapsing: It’s bifurcating and the divide isn’t really about age or even technical skill, it’s about something harder to name and harder to fake.

What the people doing the hiring decisions are actually saying

Every executive I spoke with in Scottsdale is wrestling with the same gap.

They can’t find people who are technically fluent and have real business judgment. They also can’t find experienced operators who are genuinely excited about embracing new AI tools. The person who sits comfortably in both places barely exists.

This isn't a small problem. When an agentic system makes a bad call, it can ripple into hundreds of millions of dollars of disruption before anyone catches it. The human in the loop isn't a formality; they're the last line of defense. That person needs to understand the technology well enough to question it, and the business well enough to know when something is wrong.

That person is extraordinarily rare, every executive I know is looking for them, and you can become that person: the full-stack worker.

The Full-Stack Worker

A full-stack engineer works across an entire system, not just one layer. The Full-Stack Worker is that idea applied to being human at work.

Three qualities that function as a system with each layer making the others more powerful:

1. Technical Fluency. Not necessarily a CS degree, but genuine comfort with AI tools, data, and the systems shaping your industry. Enough to use them, question them, and improve them. The bar here is curiosity more than credentials, but those who go technically deep will be rewarded with pay and opportunities.

2. Business Judgment. The ability to read a situation, understand what’s actually at stake, and make a call when the data is ambiguous, which it always is. This is what executives mean when they say they can’t find good people. Judgment can’t be automated because it requires context, stakes, and accountability.

3. Relational depth and grit. The ability to make people want to work with you, follow you, and bet on you combined with the stubbornness to do hard things without being asked. Not charm as a substitute for competence, but as a multiplier of it.

An AI agent can execute, generate, and optimize. It cannot walk into a room, make a judgment call under pressure, and make people want to follow them. That’s still a human thing. In 2026, make it your thing.

🤖 Bookmark this article for a step-by-step guide to build AI fluency and create portfolio-ready proof you can bring to your next interview or annual review.

The Window is Open

Nobody has ten years of AI experience yet. The ceiling on how fast you can close the gap on a 45-year-old with decades of institutional knowledge has never been lower. Most Fortune 500 companies are still figuring out how to move past AI pilots, which means the people who can bridge technical fluency, business judgment, and relational depth are needed right now, not in five years.

This window won’t stay open indefinitely. The people who get after it in the next two or three years will have a compounding advantage that’s very hard to close later.

The executives in that room in Scottsdale are looking for someone. That someone could be you. Start building.

Career Field Guide is a newsletter for early-career professionals building AI-resilient careers. If this resonated, forward it to someone who needs it or subscribe below.

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Week 4 of a four-part series on building career resilience.

• Week 1: You vs the Robot

• Week 2: “I’m Comfortable With AI” Is the New “I Know Excel”

• Week 3: Stop Waiting to Be Discovered

• Week 4: Developing Cross-Domain Fluency

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I was recently in Paris for work and was reminded how disorienting it is to move through a place where you do not speak the language. Even small tasks, like ordering a coffee, suddenly require a lot of effort when you can’t easily communicate in a common language.

Inside companies, cross-functional work often works the same way. Each department speaks its own strategic language, so when different teams work on the same project they end up talking past one another. Progress, therefore, depends on the people who can translate between them.

When I joined the Amazon team building Project Leo, I might as well have been in Paris. Sitting with engineers planning component procurement, staring at intricate design documents, and watching CAD files update in real time, it quickly dawned on me that I needed to learn their language or the project would stall. In other words, I had to become the Rosetta Stone who could translate between supply chain and engineering.

The shrinking value of one good skill

AI is getting excellent at executing tasks. What it still struggles with is asking the right question and making sense of the answer in context.

The analyst who built better Excel models than anyone on the team lost that edge the day everyone got Copilot. The same thing is happening across every domain, faster than most people realize.

This is already happening at the organizational level. Jason Calacanis — one of the besties on the All-In podcast — published a post last month announcing that his firm won’t hire another human employee for at least a year or two. His AI agents handle 20% of the firm’s tasks and improve 10% every week. His conclusion: adding a human to the team has become a “luxury spend.”

He also noted that at his firm, the roles of product manager, UX designer, and developer are merging. One person with the right AI tools now does the work of all three. The specialist who mastered one of those roles has a problem. The person who understands all three has an opportunity.

What AI still cannot do is move between domains. It can’t understand the incentives of a finance team well enough to know which supply chain tradeoffs they’ll accept. It can’t translate what an engineering team is building into what a sales team can actually promise. It can’t sit in a room where operations, product, and customers are all pulling in different directions and find a path forward.

That’s cross-domain fluency. And the research backs it up.

David Epstein spent years studying the world’s top performers across sports, science, and business. His finding, published in Range is that in complex, unpredictable fields (which is every field now), generalists consistently outperform specialists. A study of nearly 400 MBA graduates found that those with broader cross-functional backgrounds received more job offers than their specialist peers and in some cases earned up to $48,000 more.

McKinsey’s research on the emerging agentic organization goes further. The most valuable human workers in an AI-first company, they found, are what they call “M-shaped supervisors” or people with broad fluency across multiple domains who can orchestrate AI agents and human teams simultaneously. The World Economic Forum estimates that nearly 40% of current skillsets will be overhauled or outdated by 2030.

The specialist isn’t disappearing, but the specialist who can only operate inside one domain is quickly losing value.

The two traps

Most people pick one of two losing strategies.

The first is specializing and staying there. Depth feels safe and allows many to get promoted early in their careers, but this is where many stop developing range. AI is now compressing the value of their single-domain skill, the same way it compressed the value of knowing more Excel than anyone else in the room. By the time the ceiling appears, it’s too late to start building fluency elsewhere.

The second is knowing a little about everything and being indispensable to no one. This is the person who can speak every team’s language well enough to seem valuable but isn’t the person anyone calls when something actually has to get done. This person is classic style over substance and may build some credibility early on but is revealed as lacking depth over time.

To be successful in an AI-first world, you need a better strategy. You should develop one primary domain with real depth, plus one adjacent domain with working fluency. Enough to understand how decisions get made on the other side of the table, and enough to be taken seriously when you’re sitting at it.

Which adjacency to pick

Your best adjacency is usually the domain that controls whether your work gets funded, approved, or acted on.

• Engineering: product or finance — the people who decide what gets built and whether it’s worth building.

• Data science: communication and storytelling, because insights that don’t move decisions don’t move organizations.

• Marketing: sales or operations, because promises need to be deliverable.

• Supply chain or Operations: finance, because every operational decision eventually becomes a business case, and the people who can build that case themselves are the ones who get resources.

• HR: technology, because the future of that function is building systems, not administering processes.

• Design: sales or market research, because design decisions backed by data analysis become strategic defensible investments.

You don’t need to become an expert. You need to know enough to ask smart questions, understand the answers, and translate between your world and theirs.

What about AI skills?

Regardless of your domain + adjacency pairing, everyone should be building AI fluency in parallel. Here’s a step-by-step guide to start building basic AI fluency in 30 days.

Three things to do this week

• Tonight: Name your adjacency. Write down one sentence: “My primary domain is [X]. The adjacent domain I need to develop fluency in is [Y].” If you’re not sure, ask yourself who says yes or no to what you’re trying to do. That’s your adjacency.

• This week: Have one real conversation. Find one person in that domain and ask for 30 minutes. The message: “I’m trying to understand how [their function] actually works in practice. Would you be willing to meet for 15 minutes (coffee or call) so that I can ask 3-5 questions?” When you’re there, ask them: “What’s a decision you make regularly that people outside your function almost never understand correctly?” Don’t sell yourself. Just use the time to learn and follow up with a thank you email or text.

• This month: Get in a cross-functional room. There is a project happening in your organization right now that spans your domain and your target adjacency. Volunteer for it. You don’t need to be the expert; you just need to be in the room. Fluency doesn’t develop by reading about it. It develops by having to use it under real conditions, when something is actually at stake.

The compounding effect

Over the last four weeks, we’ve discussed how AI fluency amplifies your performance, strong relationships build career optionality, and cross-domain fluency multiplies both.

In the years ahead, every company will have specialists and AI tools. The advantage will belong to the people who become the Rosetta Stone between them.

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Week 3 of a four-part series on building career resilience.

• Week 1: You vs the Robot

• Week 2: “I’m Comfortable With AI” Is the New “I Know Excel”

• Week 3: Stop Waiting to Be Discovered

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The Job Application Process is Broken

AI didn’t just change how we write resumes, it changed the economics of hiring. For $20 a month, anyone can generate a clean, well-written, ATS-optimized resume and a tailored cover letter for every job they apply to. The marginal cost of sending one more application is effectively zero, so people don’t send five applications anymore. They send fifty or hundreds.

That may feel empowering for candidates, but it’s a supply shock from the hiring side. When everyone can bypass the qualification process and look qualified on paper, resumes stop being a signal and start being noise. Hiring teams respond the only way they can: they stop relying on the application process and start relying on their people to recommend candidates.

Last week, I argued that AI fluency is the skill that gets you in the room. Wall Street Journal shared this week that grads with AI skills are in high demand. That said, it is increasingly true that a human is what will actually get you in front of a hiring team.

This week, we’ll unpack how to build strong relationships that open doors for you throughout your career.

The Math Nobody Wants to Hear

If you graduate with 200 people who share your major, you don’t have 200 peers. You have 199 near-substitutes. Add in the impact of globalization and the power of AI tools, it’s become clear that most college degrees have become commodities to the point that many are debating whether a college degree is still worth it at all.

Generative AI has created a market where:

• The cost of applying to a job is near zero

• Everyone’s resume and cover letter looks essentially the same

• The volume of applicants per role has exploded

Imagine a hiring manager posts a role on Monday morning. By Tuesday, they have 1,000 applications. Nine hundred of the resumes are clean, well-written, and “tailored.” Two hundred are genuinely good. Fifty are great. Five come with a trusted internal referral or a note from someone they respect. Guess which people are going to get the first interviews.

As Scott Galloway illustrates above, most hiring decisions are effectively made before the job posting ever goes live. 70% already have an in with the hiring team and the job posting is often just the compliance layer on top of a decision that’s already forming.

If no one is willing to vouch for you, you’re competing for the leftover 30% of roles in the most crowded, AI-saturated, and commoditized part of the market. That’s not a winning strategy.

The Career Bicycle That Might Save You

I want to introduce you to my Career Bicycle Framework™. Think of your career like a bicycle that runs on two pedals: Performance, the work that earns you a paycheck, and Optionality, the relationships that earn you a future.

Most early-career professionals obsess over Pedal One. That makes sense because performance is concrete, shows up in reviews, and gets you promoted.

Pedal Two is what gives your career resilience. It’s what creates opportunities when your team gets cut, your company reorganizes, or your manager changes. It’s what turns a cold job search into a warm introduction. It’s also what leads to once-in-a-lifetime opportunities that you may not be fully qualified for yet.

Performance, by itself, is becoming more commoditized over time as people leverage LLMs throughout their workday. Trust, reputation, and human endorsement are becoming more valuable.

• Pedal One = Keeps You Employed

• Pedal Two = Keeps you Employable

The Two Traps Early-Career Professionals Fall Into

In the first decade of your career, I see the same two mistakes over and over.

Trap #1: Waiting

Let’s call her Mary.

Mary is great at her job. She’s competent, reliable, and consistently delivers strong work. She tells herself things like, “If I just keep performing, I’ll get noticed,” or “I’m not really into networking, I just want to do great work.”

Mary believes the system will eventually reward her and while sometimes it might, often it does not.

When a reorg hits, or a manager leaves, or her team gets cut, Mary discovers something uncomfortable: almost all of her career equity is locked inside her current org chart. Outside her team, very few people know her work and even less would go to bat for her.

She wasn’t wrong to focus on performance (Pedal One), but she was wrong to think it was enough to accelerate a great career.

Trap #2: Spraying

Now meet John.

John is charismatic and ambitious. He believes volume is the game. When he sees a role posted on LinkedIn, he immediately messages the hiring manager or recruiter with a short note and his resume. Sometimes he references the wrong role and it’s clear most of his messages are written by AI, but he thinks this is all part of network building.

In reality, John’s burning social capital before he ever builds any and annoying a lot of people in the process. People don’t remember strangers and they definitely don’t vouch for them.

Both Mary and John may occasionally get lucky, but neither is building a system that compounds over time.

How to Actually Build Strong Relationships

Start Early (and Make It Boring)

Most people only start “networking” when they’re laid off or desperate for a job. That’s exactly when it feels transactional, awkward, and self-serving. People can feel that energy from a mile away.

The best relationships are built when you don’t need anything. You need to treat Pedal Two activities as a normal, continuous part of your career.

Start close:

• Book regular 1:1s with coworkers (even if it’s 1x per quarter)

• Build relationships across functions

• Default to yes for coffee, lunch, and happy hour invites

Then widen the circle:

• Clean up your LinkedIn

• Engage thoughtfully online with people in your field

• Have one real conversation a month with someone outside your company

This positions you to build strong internal and external relationships, which are critical to building your career over the long term.

2. Put Yourself Where Opportunity Can Happen

When I was at Starbucks, I started in manufacturing but wanted to move into corporate roles. That meant finishing a 10-hour shift south of Seattle and then driving north to the corporate office just to have coffee with people doing the jobs I wanted one day.

This was inconvenient and exhausting, but it eventually worked. Those conversations led to a corporate role and multiple mentors, some of whom still shape my career today.

I also moved my entire family from Seattle, WA to Austin, TX for a career opportunity that provided outsized impact and scope, relative to my previous experience. Not every action needs to be as dramatic, but you do have to put yourself in places that provide opportunity.

You can start online, but the strongest ties are built over coffee, meals, projects, and shared context. Put yourself in rooms, cities, and conversations where the kind of work you want is actually happening.

Do Cool Stuff (in Public)

Visibility and action create a surface area for luck and opportunities. Build cool things, share what you’re learning, get curious with AI, and create products or tools that show how you build, not just what you’ve been assigned at work.

You don’t need to be impressive. It’s far more important to 1) get started on something and 2) build momentum as you iterate and make it a habit. You can start with any of the below:

• A simple newsletter

• A GitHub project

• A YouTube channel

• A short LinkedIn post explaining something you just learned

People are drawn to momentum. They remember people who are working on cool stuff, and, over time, this creates something far more powerful than a resume: a reputation and network that you can rely on for decades.

Time to Take Action

AI didn’t make relationships “nice to have,” it made them the moat for your career.

This week, do three things:

1. Book one conversation with someone you don’t need (yet).

2. Share one small piece of work in public.

3. Help one person with no immediate upside for you.

Next week, I’ll close this series by unpacking the final pillar of career resilience: how to develop cross-domain fluency. This will cover the ability to move between domains, translate between worlds, and stay valuable even as roles, tools, and org charts keep changing. Subscribe below to get the article in your inbox next Thursday!

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Week 2 of a four-part series on building career resilience.

• Week 1: You vs the Robot

• Week 2: “I’m Comfortable With AI” Is the New “I Know Excel”

• Week 3: Stop Waiting to Be Discovered

• Week 4: Developing Cross-Domain Fluency

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What COOs Actually Want From the Next Generation

I spent 15 years as an operator before I started advising the people who run large organizations. Now I sit across from COOs and Chief Supply Chain Officers asking what they can’t find in the next generation of talent.

Three capabilities come up every time: AI fluency, relationship depth, and the ability to think across domains. This series is my attempt to translate what they’re asking for into a playbook you can actually use.

The Opportunity Nobody Is Capturing

Only 6% of organizations have turned AI into measurable business impact. Meanwhile, 42% abandoned most of their AI initiatives this year. The bottleneck was never the tool—it was the judgment of the people using it. That gap is where your career opportunity lives.

The Productivity Gap is Already Happening

In a PwC survey of nearly 50,000 workers,1 daily AI users were dramatically outpacing their peers: 92% reported productivity gains (versus 58% for infrequent users), 58% felt more job secure (versus 36%), and 52% saw salary increases (versus 32%). That 30 minutes a day compounds to roughly 130 hours a year—time power-users reinvest into higher-visibility work and the contributions that lead to faster promotions. The difference isn’t talent or technical background, i’s habit. And habit is learnable.

“Try ChatGPT!” is not a Strategy

Using AI to rewrite your resume or summarize an article is the equivalent of using Excel to make a grocery list. It works, but you’re nowhere near the real capability of the tool.

Learning science calls this the experience trap — confusing familiarity with mastery. You can use ChatGPT every day for a year and still be a novice if you’re always asking the same kinds of questions the same way. What changes the trajectory is structured experimentation with increasing stakes—and producing something you can show.

This 30-day challenge is designed so you can start tonight with ten minutes and zero background in AI.

The Career Bicycle in Action

If you've been following along, you know the Career Bicycle runs on two pedals: Performance (what you can do) and Optionality (who knows you can do it). Most people treat AI learning as a solo, private activity and get stuck in the one-pedal trap.

This challenge is designed to turn both simultaneously. Your learning becomes visible, which means it compounds beyond the skill itself.

The payoff is that in 4 weeks (or less) you can develop genuine AI skill and have multiple interview- or review-ready artifacts that you can add to your portfolio.

Week 1: Stress-Test the Tool

Goal: Understand where AI is strong, where it confidently falls apart, and build the verification muscle to tell the difference.

In 2024, 39% of AI-powered customer service bots were pulled back or reworked because they were confidently giving customers wrong information. Air Canada got forced by a tribunal to honor a discount after its AI chatbot invented a bereavement fare policy that didn’t exist.2 A lawyer submitted court filings citing legal cases that AI fabricated out of thin air—twice, in separate cases.3

Companies need people who can catch these mistakes before they become expensive. That skill—verification—is one of the most valuable and underrated things you can walk into an interview with.

The Exercise:

• Pick one AI tool (Claude, ChatGPT, or Gemini) and subscribe for the premium version (download on your phone for ease of access)

• Register for Claude 101 (free) or watch the video below to the building blocks of prompt basics and AI Fluency.

• Then, as the week progresses, deliberately try to break the model in your domain. Give the model questions you already know the answers to, feed it contradictory constraints, ask about edge cases, and grade its responses.

Start here - copy this prompt:

“I’m a [your field] professional. Explain [core concept in your domain] as if you were briefing a hiring manager. After your explanation, list the three most common mistakes people make when applying this concept in practice.”

Read the response, grade it, and then follow up:

“What might be wrong or incomplete about the explanation you just gave me? Where would an expert in this field push back?”

That second prompt is the one that builds real skill. You’re training yourself to interrogate AI output—the exact judgment that employers are paying a premium for.

Your artifact: A one-page “AI Blind Spots in [Your Field]” document. List three to five specific examples of where AI got it wrong and what the correct answer is. This becomes an interview talking point and you can post it on LinkedIn tonight after even one round of this exercise.

Week 2: Build a Repeatable Workflow

Goal: Move from one-off prompts to a repeatable human+AI process you can describe, repeat, and improve.

The gap between AI users and AI power-users are systems and processes for partnering with models. Interviewers increasingly ask candidates how they'd use AI in the role. Having a real workflow to describe is the difference between a generic answer and a memorable one.

The Exercise:

• Check out the Claude for Work course to get a sense for the difference between personal vs professional capabilities that LLMs offer.

• Pick one recurring deliverable from your work or coursework (a weekly report, competitive analysis, or project update).

Then run this four-step loop:

1. You define the problem and constraints (what question are we answering, for whom, with what limitations)

2. AI generates a first draft using a detailed prompt that includes context, audience, and format

3. You evaluate, correct, and refine—this is where your judgment lives

4. AI iterates based on your feedback, now it’s working with your expertise, not replacing it

Use this prompt to start:

“I need to create a [deliverable type] for [audience]. The goal is [specific objective]. Key constraints: [time, data available, scope]. Please draft a first version, and flag any assumptions you’re making so I can correct them.”

That last line—“flag any assumptions”—is critical. It forces the AI to surface where it’s guessing, which gives you clear targets for applying your judgment. Run this loop on the same type of deliverable at least three times this week, improving your prompt each time.

Your artifact: A before/after comparison. The AI's first output next to your final version, with annotations on what you changed and why. This demonstrates exactly the kind of judgment AI cannot replicate.

Week 3: Build Something Real

Goal: Produce a professional-quality deliverable that demonstrates AI-augmented thinking.

Anyone can write "comfortable with AI" on a resume. Very few can show a concrete example like AI-generated analysis with their judgment layered visibly on top.

The exercise: Using the workflow from Week 2, produce something relevant to the job or field you're targeting. If you've never built a deliverable like this before, ask AI to show you three examples of the format, then use the strongest as your template.

• Business or strategy: A competitive landscape analysis of a company you’d want to work for.

• Engineering or data: A technical comparison of approaches to a problem in your field.

• Marketing or communications: An audience analysis and content strategy for a real brand.

• Operations or supply chain: A process improvement proposal grounded in implementation realities.

The key: don’t hide the AI. Explicitly show where AI contributed and where your judgment improved the output. Include a brief "How I Built This" section: two paragraphs on your human+AI process. That transparency is itself a signal of sophistication.

Your artifact: A polished deliverable you can share on LinkedIn, bring to an interview, or add to a portfolio. When an interviewer asks, "How do you use AI?", you now have a 60-second answer backed by proof.

Week 4: Teach What You Learned

Goal: Convert your learning into relationships and visibility—Pedal Two.

Harvard researcher Amy Edmondson found that the activities most associated with genuine skill development are sharing information, talking about errors, and experimenting in the open. Teaching doesn't just help others; it rewires your own understanding. It's also how you close the loop on the Career Bicycle.

If you do Weeks 1–3 privately, you've only turned Pedal One. Week 4 is where the skill becomes leverage.

The exercise:

• Write one LinkedIn post about something specific you learned. Not “AI is amazing”—something concrete: “I spent a week testing AI on demand forecasting problems. Here’s the one type of question it consistently gets wrong and why that matters.”

• Share your portfolio piece with a brief explanation of your process.

• Have one conversation with someone in your network (a boss, professor, or coworker) about what you built. Ask what they think.

Your artifact: The post itself, the engagement it generates, and ideally a conversation that wouldn't have happened otherwise. That's how learning becomes leverage.

If you need help setting up your profile or engaging on LinkedIn, bookmark this article for later: “I Don’t Have Anything Valuable to Say on LinkedIn.”

Your Move

You don’t need permission to start. You don’t need a budget, a manager’s approval, or a technical background.

You need one AI tool, one hour a day, and the willingness to document what you learn.

Four weeks from now, you'll have something most candidates don't: proof that you know how to work with AI, not just use it. The interface skill is table stakes, but the judgment behind it: knowing what to ask, recognizing when the output is wrong, and contextualizing results against your domain expertise, is what this challenge actually builds.

You've read enough. The only question left is whether you'll do something with it. Open a tab, paste the prompt, and see what you find. Four weeks from now, you'll know exactly what you're capable of—and so will everyone watching!

Next week we will cover Capability 2 — Build Strong Relationships. We’ll unpack how to build a professional network that actually works, especially when you’re starting from zero. Subscribe below to get the article in your inbox next Thursday!

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Inspired to keep reading?

Week 1 of a four-part series on building career resilience.

• Week 1: You vs the Robot

• Week 2: “I’m Comfortable With AI” Is the New “I Know Excel”

• Week 3: Stop Waiting to Be Discovered

• Week 4: Developing Cross-Domain Fluency

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The Mood of the Market

When I asked students what worried them most about entering the job market right now, the answers were blunt and consistent:

• Too many applicants, not enough roles (43%)

• Lack of real-world experience (33%)

• Not knowing what companies actually want (19%)

• AI replacing the work I’m trained to do (5%)

At the same time, 78% reported using AI tools daily in coursework or job searching. That contrast matters as AI isn’t theoretical to this generation, it’s already embedded in how they work. The concern is not whether AI will affect employment, but how individuals should approach career planning in response.

The Core Shift: From Job Titles to Capabilities

The labor market feels unstable because it’s re-pricing work faster than people can adapt. AI compresses execution, which means value no longer comes from doing tasks but from:

1. Asking the right questions

2. Directing systems (human + machine)

3. Verifying and improving outcomes

This is why resumes feel less effective and referrals matter more. Companies aren’t hiring for static skills, but for people who can navigate ambiguity.

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3 Capabilities to Build Career Resiliency

I shared with the graduate students three capabilities that all of us need to build in order to have a resilient career in the age of AI.

Capability 1: Get AI Curious (Not AI Afraid)

AI anxiety often shows up as a single thought: AI will be able to do my job.

Although job roles include technical tasks such as typing speed and task volume that AI can replicate, the value of human work extends beyond the mechanical actions machines can imitate. As execution becomes commoditized, careers will be built on the ability to exercise judgment and handle ambiguity, including defining the right problem, selecting an appropriate approach, weighing tradeoffs, and taking responsibility for outcomes.

What “AI Curious” Actually Means

Being AI curious is not about becoming a machine learning engineer. It’s about learning how to:

• Experiment with 1–2 core models regularly

• Understand how models break, hallucinate, and fail

• Building workflows where AI drafts and you decide

In practice:

• Let AI write the first version

• You refine, contextualize, and validate

This mirrors how senior professionals already work with junior teammates. AI is becoming your newest (very fast) coworker.

Key insight from the room: STEM students already use AI daily, but mostly tactically. The gap is moving from usage to leverage.

Capability 2: Build Strong Relationships

One slide landed harder than any AI stat:

“70% of roles are filled through referrals and networks, not applications”-Scott Galloway

This isn’t new, but it’s more punishing now. When AI increases applicant volume, companies rely more heavily on trust signals and relationships become an easy filter.

The Career-as-a-Bicycle Framework

Think of your career as a bike.

• Pedal One: Performance Today

  • Do good work

  • Be reliable

  • Deliver results

    • Provides pay and promotions today

• Pedal Two: Options Tomorrow

  • Relationships

  • Visibility

  • Optionality

    • Provides network and opportunities tomorrow

If you only pedal performance, your career stalls when conditions change. Relationships are what allow you to keep momentum and maintain resiliency amidst disruptions and layoffs.

What Relationship-Building Looks Like (Practically)

Not networking events or transactional coffee chats.

Instead:

• Optimize your LinkedIn and engage weekly

• Share what you’re learning (projects, GitHub, reflections)

• Be a connector: introduce people without asking for anything

Students often say: “I don’t have anything valuable to share yet,” but I’ve previously written how easy it is to setup your LinkedIn, build you network, and start adding value.

Capability 3: Develop Cross-Domain Fluency

AI is improving at a breakneck pace, but it still struggles with asking the right question and verifying the results, a helpful framework from Erik Brynjolfsson at Stanford.

AI is getting increasingly excellent at the ‘Execute The Task’ part of the formula so the most resilient careers will be filled by people who can build strong judgement across multiple domains:

• A primary domain (engineering, biology, analytics, design)

• A secondary domain (business process, product, UX, operations)

This is how generalists beat specialists with AI.

Examples of High-Leverage Pairings

• Engineering + Product thinking

• Data science + storytelling

• Domain science + customer insight

• Technical depth + change management

You don’t need mastery in both. You need fluency.

How to Build It

1. Pick an adjacent domain (finance, supply chain, UX)

2. Talk to someone who works there—ask how decisions are made

3. Tackle a cross-functional project in class or at work

Cross-domain fluency makes you harder to replace because AI struggles with context-switching across incentives.

Career Fragility vs. Career Resilience

Careers become fragile when:

• Identity is tied to a single tool

• Skills are optimized for one workflow

• Value comes only from execution

Careers become resilient when:

• You can reassemble skills in new contexts

• You build social and informational leverage

• You treat AI as infrastructure, not competition

Your job is not to predict the future, but to stay adaptable inside it. This helps you to avoid the one-pedal trap of focusing on performance to the detriment of building a strong network that can support you amidst the inevitable job layoffs and disruptions that will occur in the coming years.

The Bottom Line

The students were right to be uneasy. The market is tighter. AI is moving fast.

But the path forward isn’t panic or perfection, it’s capability-building across three areas.

Follow along for the next three weeks as we unpack how to build these capabilities.

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Excited to keep reading? Below are popular posts from the archives:

Anthropic CEO Dario Amodei published an essay renewing his warning about the economic impacts of AI:

New technologies often bring labor market shocks, and in the past humans have always recovered from them, but I am concerned that this is because these previous shocks affected only a small fraction of the full possible range of human abilities, leaving room for humans to expand to new tasks. AI will have effects that are much broader and occur much faster, and therefore I worry it will be much more challenging to make things work out well.

None of us know the true impact of AI, but it’s critical to recognize that we are in another significant economic shift that may dwarf what occurred throughout the Industrial Revolution.

Amidst the noise, I want to help you think about the skills you should be focused on to be prepared in this age of AI.

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Bad Career Advice

Career advice has been remarkably consistent over the last decade: Learn to code, gain technical skills, and engage primarily in STEM disciplines. The advice was not wrong, but it is being exposed in the age of AI. According to OpenAI CEO Sam Altman, “AI can [now] perform the work of entry-level employees.”1 Meanwhile, Anthropic revealed that Claude writes 90% of its own company code.2

If AI can write code, analyze date, and generate reports then what exactly should you be learning to do in 2026?

What Harvard Discovered in 1980

Economist David Deming at Harvard has been tracking something fascinating: how the labor market values different combinations of skills over time. His research3 reveals a pattern that most career advice completely ignores.

Since 1980:

• Jobs requiring high math skills AND high social skills grew by 7.2 percentage points (with wages up 26%)

• Jobs requiring ONLY high math skills (but low social skills) actually shrank by 3.3 percentage points

• Jobs requiring only social skills stayed flat

The jobs that required strong technical skills without strong people skills have been declining for four decades.

It’s clear that winners in the economy have not been the most technical, but the ones who combined technical capability with human connection.

How AI Makes This Worse and Better

Erik Brynjolfsson at Stanford frames this beautifully. He argues that almost every valuable task breaks down into three phases:

1. Asking the right question (defining the problem)

2. Execution (carrying out the steps)

3. Evaluation (verifying results and refining)

"As execution becomes commoditized, the bottleneck—and the value—shifts to asking the right questions and evaluating results."

Brynjolfsson argues that AI is getting “astonishingly good” at Step 2 (Execution) so the critical skills to develop are asking the right questions and evaluating the results. Workers, he argues, are becoming “Chief Question Officers.”4

Your primary job is not doing the work, but having the judgement to know what to ask, why it matters, and whether the AI actually succeeded.

The Three Skills That Matter Now

Three capabilities consistently emerge as the skills that protect and accelerate careers in the AI economy.

1. AI Fluency (Not AI Expertise)

Jensen Huang, CEO of Nvidia, put it plainly at Davos5: “Learning to work with AI will soon be as fundamental as learning to manage people. Prompting, supervising, and evaluating AI systems will become core skills across industries.”

LinkedIn’s data confirms this. “AI Literacy” is the #1 skill on the rise, defined not as machine learning engineering, but as effectively using tools like ChatGPT and GitHub Copilot. The premium goes to people who can leverage AI as a force multiplier, not people who can explain how transformers work.

2. Domain Judgment

Domain judgment is the pattern recognition that tells you what “good” looks like and it’s one of the hardest for AI to replicate. It’s knowing which customer complaint signals a systemic problem versus a one-off issue or recognizing when a financial model’s assumptions don’t match reality. Broadly, it’s the instinct that something is off before you can articulate why.

The Economist recently highlighted the paradox facing entry-level workers: if AI handles the grunt work that used to teach judgment, how do you develop it? Their answer: the smartest companies are redesigning junior roles around higher-order tasks, such as client interaction, negotiation, and exposure to decision-making.6

The implication for your career: seek roles where you’re evaluated on outcomes, not tasks. Find mentors who can transfer tacit knowledge and build judgment through exposure, not just execution.

3. Human Connection

Scott Galloway shared a stat that should change how you think about job searching: “Google puts out a job opening, they get 200 CVs within eight minutes. They limit it down to the 20 most qualified. Seventy percent of the time, the person they pick is someone who has an internal advocate.”7

In a world where AI can help anyone look qualified on paper, relationships become the differentiator. The highest-value people in organizations are those that can bridge technical and business domains.

This is one of the reasons that you should always go to the happy hour, find opportunities to connect with your coworkers, and start building your network on LinkedIn.

The Career Bicycle Connection

If you’ve been following Career Field Guide, you know I think about careers as a bicycle where one pedal is focused on Performance (excelling at your current role) and the other on Optionality (building capabilities that transfer across jobs, companies, and industries).

The three skills I’ve outlined (AI fluency, domain judgment, and human connection) are critical ‘Pedal Two’ investments. They’re portable, compound over time, and protect you when industries shift or companies restructure.

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Building these skills isn’t just about surviving the AI transition, it’s about learning to position yourself for the opportunities that emerge on the other side.

My Challenge to You

This week, audit your skill development. Are you focused on learning tools that AI might master next year? Or are you building judgment that takes years to develop and relationships that can’t be automated?

If you’re early-career: Stop optimizing for tasks. Start optimizing for exposure. Seek roles that put you in rooms where decisions are made, even if you’re just observing. The judgment you absorb is worth more than the spreadsheets you build.

If you’re mid-career: Your domain expertise is more valuable than you think, but only if you can translate it. Learn enough about AI tools to spot where they can augment your judgment (start with generative AI) and become the person who bridges the technical and the strategic.

If you’re leading others: The companies that win will be the ones that redesign entry-level roles for judgment-building, not task-completion. As McKinsey’s Bob Sternfels said at CES: organizations are growing client-facing roles by 25% while cutting back-office roles by the same amount8. The question is which side of that equation your team is on.

The skill everyone’s getting wrong isn’t technical versus soft. It’s thinking you only need one. The future belongs to people who can do both and who build relationships with others who can vouch for that rare combination.

Justin

I taught my oldest son to ride his bike in a small park in Seattle. He pushed off hard, focused on one pedal, and kept tipping over—exhausted and frustrated.

From his perspective, he was doing everything right and still not moving.

A lot of careers look exactly like that.

“Every job will be changed.”

Jensen Huang sounded optimistic about AI recently at CSIS, but most Americans are not.1 Whether it’s the more than 1.1 million job cuts in 20252 or the drumbeat of AI-driven displacement, the result is the same: people are scared.

I argued that strong performance at work is no longer protection from layoffs and the response confirmed what many people already feel: fear, confusion, and no clear career strategy in the age of AI.

The most vulnerable workers in the AI transition are not low performers. They’re high performers with no external leverage.

Why High Performers Get Stuck

Over the past few years, tens of thousands of people woke up to an email or text message telling them their job was gone. The surprise wasn’t the scale of the layoffs, but who was laid off.

Those impacted learned two things:

1. Being great at your job is not the same thing as building a resilient career.

2. People who rebounded quickly had strong networks outside their company.

The One-Pedal Trap

Most successful careers follow the same pattern: years of becoming exceptional at the job, earning mastery, promotions, and praise inside the company. In reality, high performers are in a more precarious position than they realize:

• Their professional network rarely extends beyond current colleagues

• They lack relationships with people who could readily hire them elsewhere

• Their skills are tied to proprietary systems unique to their employer

When change hits—a reorg, a layoff, or new leadership—the very traits that drove their success become liabilities.

I call this the One-Pedal Trap. You rode so hard on performance that you forgot the other pedal existed.

The Two-Pedal Framework

After watching thousands of people struggle after layoffs, a clear pattern emerged: some people could maintain career momentum, while others could not. The difference wasn’t talent, but how they had been using the two pedals of their career.

A career works like a bicycle. One pedal drives performance today. The other builds strategic growth for tomorrow.

🚴Pedal One: Performance

The work you do every day

This is your current job: execution, reliability, results. It’s how you earn trust and build your reputation. You need to perform well, deliver outcomes, and build credibility with your manager and team. This is what gets you promoted, paid, and praised today.

🚴Pedal Two: Optionality

The work you do on your long-term direction

This is the strategic side of your career: skills, positioning, relationships, visibility, and clarity about what comes next. This is what protects you and creates opportunity tomorrow.

Optionality doesn’t only mean “post on LinkedIn.” It can mean one coffee a month with someone two titles ahead of you at a different company, or deliberately owning a cross-functional project that creates portable signal outside your current org.

Most people focus almost entirely on the first pedal. They work hard, deliver, and stay busy. They assume that strong performance today will automatically create opportunity tomorrow, but it rarely works that way.

The Pedal Ratio

Here’s what most people miss: you don’t need to push both pedals with equal force. You need to be intentional about your Pedal Ratio.

Early in a role, you earn the right to build externally by delivering internally first. No one wants to grab coffee with someone who hasn’t done anything yet. Performance buys credibility, and credibility is the currency that makes Pedal Two work. When you’re new or early in your career, you’re 80/20 (almost all performance), proving you belong.

The danger is never changing that ratio. The person who was 80/20 in year one and is still 80/20 in year eight has built a career entirely dependent on one company’s continued appreciation, which increases career risk.

When you’re established and maturing in your career, you shift to 60/40, then 50/50. The shift to 50/50 isn’t about checking out. It’s about building a career that compounds in two directions at once—one inside your company, one outside of it.

And if you’re sensing instability? That’s when you flip aggressively. When you sense instability—a reorg rumor, a leadership change, an industry shift—you might flip to 30/70, investing heavily in optionality while you still have a paycheck. A 30/70 ratio while you still have a paycheck, a title, and access is infinitely easier than trying to build a network from scratch with a LinkedIn headline that says, “Open to Work.”

The best time to shift your ratio is before it’s forced on you.

How I Learned This the Hard Way

At Starbucks, I only knew Pedal One existed.

I worked my way up from leading a plant team to running complex planning exercises at corporate. I was heads-down, executing, and proving I belonged, but my entire career lived inside the company.

After I joined Microsoft, I sat through a training session on building your network and one of the focuses was getting started on LinkedIn.

That night, I looked at the profiles of a few senior leaders and saw that they weren’t just accomplished internally. They were shaping industry conversations and building relationships with people they’d never worked with directly.

Their careers existed outside the company, not just inside it.

That’s when it clicked: a resilient career requires both pedals moving at once, performance today and optionality for what comes next.

Don’t Crash Your Bike

Both pedals have traps.

⚠️The Performance-Only Cyclist

Profile:

• High performer, reliable, exceeding expectations.

• Loved by managers.

• Network is almost entirely current colleagues.

• Few recent interviews and little external signal.

• Busy on Teams, quiet on texts.

Result: When layoffs hit, they’re forced to take the first offer and they don’t have options.

⚠️The Optionality-Only Cyclist

The opposite. They’re everywhere.

Profile:

• Constant LinkedIn activity

• Coffee chats booked weeks out

• Heavy focus on personal brand

• Mediocre execution

• Regularly passed over for promotion

Result: When they try to activate their network, they learn a hard truth: people refer performers, not self-promoters.

Which Pedal Are You Neglecting?

If you’re neglecting Performance:

• When did you last ship something your manager bragged about?

• Are you known for results, or just for being visible?

If you’re neglecting Optionality:

• If you were laid off, how many people outside your company would take your call?

• What skills are you building that would transfer to another company?

Most people don’t crash because they stop pedaling, they crash because they ignore imbalance.

The Uncomfortable Truth

Your job doesn’t love you back. Your company will make the decision that’s right for the company. That’s not evil, it’s just how organizations work. The goal is not to care less about your job. It’s to care enough about your future to stop betting it on a single employer.

The answer isn’t to stop performing. The answer is to stop only performing.

You don’t control the environment. You control your exposure to it.

Building a Resilient Career

Wherever you are in your career, this is the question to ask: Which pedal am I neglecting?

If you’re 25 and heads-down proving yourself, great. Keep pedaling on performance, earn trust, and build credibility, but spend at least 20% of your energy developing relationships and skills that exist outside your company’s walls.

If you’re 40 and realizing you have little optionality, it’s not too late. Start now: update your LinkedIn, reconnect with former colleagues, and develop a skill that isn’t tied to your current role.

The best time to build your network was ten years ago, but the second-best time is today.

Rooting for you,

Justin

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📌: Share this article with someone who’s grinding hard at work but might be neglecting their second pedal.

Inspired to keep reading:

I met with a mentee last week who shared the below:

I know I should build my presence on LinkedIn, but I don’t have anything valuable to say and don’t really have the time or know how to do it.

The Motivation Question: Why LinkedIn

Look—if you don’t feel motivated to do something, then you won’t do it. I know this is super deep stuff, but you should start with whether you actually want to build a presence on LinkedIn. If you don’t, then don’t do it. Figure out another way to network, such as industry meetups, connecting on TikTok/Insta with people, or posting on X.

Three reasons why I build my network (primarily) on LinkedIn:

• LinkedIn is built for career networking. People are (generally) not posting about personal life, politics, or their pets so it’s less cringe when you post your work wins, milestones, and career-related accomplishments because people expect (and celebrate) it.

• It’s a less awkward way to stay in touch with people. As of this article, I have 5.7K followers and 5.5K connections on LinkedIn. There is no way I would have asked for 10K+ people’s phone numbers or emails. LinkedIn allows me to keep in touch with people, ask for their advice, and share with I’m working on. I think of LinkedIn as my personal assistant who allows me to reach out or stay in touch at a moment’s notice.

• There is a correlation between your network and opportunities in your career. Opportunities ultimately come from people. Unless you went to an ivy league or got an internship at Goldman, building a network is the primary way that you will get opportunities in life and LinkedIn makes it easy to build those connections.

Still not sure? Start small and see what happens. Here’s how…

Step 1: Build the Network

1. Follow/Connect: Add people who are already in your network and those you meet or find interesting. Unless you want to be talking into the void, you have to start with connecting with people on LinkedIn. There are two ways to get connected to someone on LinkedIn: Connect and Follow.

Connect: If you click this, they receive a notification and have to Accept or Decline your connection request. TL/DR: connection requests max out at a certain point, so some people are touchier with who they connect with—don’t overthink it. If they are at your company, someone who knows you, or you’ve partnered with them then connect.

Follow: If you click this, they receive a notification that you followed them, but there is no approval process on their end. This allows you to see all of their posts and you can interact with it on the main feed. I recommend you follow a ton of people, including thought leaders, authors, business leaders, and anyone you find inspiring.

TL/DR: Connect: we know each other, let’s keep in touch. Follow: you don’t know me, but I think you’re cool and want to learn from you.

Step 2: Show Up in the Network

1. 5-10 Minutes per Week: Post thoughtful/non-AI-generated comments on others’ LinkedIn posts. Imagine that every single post on the main LinkedIn feed is someone talking directly to you (1:1 IRL) and then your comment is how you would respond. If you’re talking to someone at dinner, you’re not going to listen to them, think deeply about it, pull up ChatGPT, ask them to refine your response, and then reply. You’re just going to talk—do this for 5 minutes daily or a few minutes multiple times per week and you are on your journey to building your LinkedIn presence. When you comment, others see it, see that you are engaged in interesting discussions, and respect you a bit more.

2. 15 Minutes per Week: Post an interesting article related to career or industry on your own LinkedIn profile. This means that you’re actually reading interesting things related to your career, but this is a pretty easy one. Read an industry publication (Google: top news for XYZ industry or XYZ role), think about/form a thought, and then post it. It can be as simple as sharing the link, a brief thought, “Really inspired/interested/encouraged by this article last week,” or “This changed my mind about how I approach X/Y/Z.” Don’t overthink it—the cringe feeling creeping up your body is called climbing cringe mountain, but you have to get over it and just do it. Also, do this step in combination with step 1 for maximum impact.

It’s Really That Easy?

Yup—it’s really that easy. Keep Connecting or Following people that you meet at college, work, conferences, events, happy hours, and throughout work travel. Every person you add on LinkedIn is increasing the surface area of you getting lucky and finding an incredible career opportunity.

That’s It, seriously. Already doing this and want to go deeper?

Here are three ways to level up once you’ve built the habit:

1. Use the Featured section of your profile to post cool stuff you’re working on. If you’re at a big company and then post about something you’ve helped deliver, then add the link of the article to your Featured section (this helps recruiters and hiring managers know what you’ve done). Below is the section from my Linkedin.

   

2. Write an article highlighting your work, career-related things you’re thinking about, or where you see the world moving. LinkedIn has a built-in article and newsletter feature so you can create these without having to setup anything on Substack or your own site.

   

3. Comment on a LinkedIn news article. Click on any of the stories in the LinkedIn News section of your main feed, click Share in the top right of the article, and create your own post or comment on someone else’s thoughts. On the DL, LinkedIn’s own editorial people review the activity in this section of the site and may feature your work if it gains traction or is particularly thoughtful.

It’s a snowy day in Austin, TX so I’m going to wrap it up, but you can do this! Start small, build your network, and before you know you’ll have a valuable community of people that you can rely on throughout your working life.

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Inspired to keep improving your career:

25,000 Agents and Counting…

The CEO of McKinsey shared at CES that they now count 25,000 AI agents in their headcount1. These workers don’t sit in meetings, ask for promotions, or complain about the coffee, but they are doing a lot of the work previously reserved for entry-level employees.

The stakes are clear: Agents are eliminating the work that shaped the first years of a worker's experience—the same work that college career centers tell graduating students will be their foot in the door.

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2026: The Year Agentic Goes Mainstream

We have strong conviction that AI agents will change how we all work and live…there will be billions of these agents, across every company and in every imaginable field…many of these agents have yet to be built, but make no mistake, they’re coming, and coming fast.

We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs. It’s hard to know exactly where this nets out over time, but in the next few years, we expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.

Andy Jassy, CEO of Amazon2

This is corporate speak for “there will be blood.” Amazon is investing tens of billions of dollars into AI infrastructure because it believes that it will yield an outsized financial return, a significant portion of which will be their ability to reduce their headcount and manage agents vs humans.

The Work That’s Disappearing

The data confirms what hiring managers already know: young workers are being priced out of entry-level roles.

Since 2021, hiring of workers aged 22-25 has dropped 20% in software development and 13% in customer service - the two fields with the highest AI exposure. Meanwhile, hiring of workers 31+ has grown or held steady. This isn’t a recession pattern where everyone suffers equally. Unfortunately for younger workers, this trend is a structural displacement of the bottom rung of the career ladder.

Conversely, the most experienced workers are fine. They have context, relationships, and judgment that agents can’t replicate yet. But the work that creates that experience—the grunt work, the repetitive tasks, the “figure it out” projects that teach you how things work—that’s what agents are consuming first.

If you're under 30 and trying to break into these fields, the game has fundamentally changed. The internships and entry-level roles your professors and parents describe may no longer exist by the time you're ready to apply for them.

When Entry-Level Work Disappears, So Does the Learning

When I joined Microsoft’s Azure supply chain team, a significant portion of my early work was building Excel-based planning tools, compiling KPI dashboards, and creating decks with data analysis and recommendations for leadership. This work wasn’t glamorous, but it was essential to the business and to my development. The work taught me how our supply chain actually functioned, which metrics mattered and why, how different stakeholders interpreted the same data differently, and what “good” analysis looked like versus what just looked impressive.

Today, an AI agent could handle 60-70% of what I did in my first 18 months, including pulling data from multiple systems, generating variance analysis, creating visualizations, even drafting initial recommendations based on historical patterns, faster and more consistently than I ever could.

Here’s the problem: You can’t learn to direct work you’ve never done. The new supply chain analyst gets hired to ‘manage AI tools and provide strategic recommendations,’ but how do you know if the agent’s demand forecast is reasonable if you’ve never built one manually? You can’t develop judgment about what good looks like without first doing the work that creates that judgment.

Companies are hiring people to oversee AI doing entry-level work, which means we're creating a generation of "managers" who've never been managed, "strategists" who've never executed strategy, and "analysts" who've never analyzed anything from scratch—the exact experience that would teach them what good looks like.

What Actually Matters Now (And What to Do About It)

The initial labor reduction is reality and we’ll see continued reductions in entry-level roles in the next few years, particularly in functions where the work is repeatable, digital, and doesn’t require physical presence. New jobs will emerge simultaneously, as companies realize they need people who can bridge the gap between human judgment and AI capability.

If you're graduating into this market, you have every right to be frustrated. You're being asked to compete for disappearing jobs while building judgment through work that no longer exists. That frustration is valid, but it won't protect you. The people who survive this transition will be the ones who build business and technical skills, regardless of their role. Here are three no-regret actions to take:

1. Engage with AI as a tool, not as a threat or a mystery. This doesn’t mean becoming a machine learning engineer, but you must develop a comfort with experimentation. Learn to debug prompts, recognize when output is garbage, understand the constraints of what these systems can and cannot do. The supply chain analyst who spends weekends testing ChatGPT’s ability to generate demand scenarios learns more about both AI capabilities and demand planning than the one waiting for formal training.

2. Become bilingual in technical and business language. You need to understand enough about how systems work to spot opportunity and risk, to have credible conversations with the people building these tools, and to translate between what the business needs and what’s technically possible. This doesn’t require a computer science degree, but it does require curiosity about how things actually work and a willingness to sit in meetings where you don’t understand every term, and the patience to ask questions until you do.

3. Chase project exposure over job titles. The emerging roles: prompt engineer, AI training specialist, agent orchestration manager (or whatever we’re calling them next month). In that these roles didn’t exist two years ago, the people doing them now didn’t wait for a job description to appear on LinkedIn to enter into the field. Instead, they volunteered to help IT test the new planning agent, asked to shadow the team implementing the customer service automation, or made themselves useful to the groups building these solutions. The early adopters are riding the first edge of the wave because they learned the emerging technology even when it wasn’t their “job” because they understood that proximity to AI teaches you more in three months than waiting for assignments teaches you in three years.

Do you wish you’d learned to code back when the internet was simple HTML pages and Flash players? This is that moment—again. Right now, companies are figuring out AI integration in real time. They’re deploying agents without clear success metrics, running into failure modes they didn’t anticipate, and realizing—often too late—that no one actually knows how to manage the transition.

AI technology is moving faster than organizational understanding and that gap is the opportunity window. Relevant expertise is still operationally scarce and every industry needs people who can evaluate where agents work, where they break, and how to design workflows that blend human judgment with AI execution. That window won’t stay open forever, but it’s open now, and it rewards speed over credentials.

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Those 25,000 agents at McKinsey still need managing, training, debugging, and strategic direction. The people who learn how to do that well will be the ones who built the career ladders that replace the ones we lost.

P.S. If you're a college career counselor, MBA program director, or corporate L&D leader trying to prepare your people for this transition, I'd welcome a conversation. You can find me on LinkedIn or reach out at justin.gillebo@gmail.com

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Lebron James was drafted first overall in the 2003 NBA Draft at just 18 years old. On day one of his career, he was expected to raise the floor of a struggling franchise and help them win immediately. Fortunately for us, you and I are not Lebron James.

I thought I needed to be excellent at everything on day one. I remember the pressure I felt showing up to my desk at Starbucks in my first corporate role. The first-time imposter syndrome hits differently when you’re a first-generation college student whose family is more familiar with hard work than navigating a corporate campus.

Early in your career, it’s easy to believe that opportunity goes to the most exceptional person in the room: the fastest learner, the smartest voice, the one who nails everything on day one, but that’s wrong. Maybe this perspective is born out of the education system where students are expected to know the answers and are graded on their performance, but that model doesn’t carry over from school to work.

Managers do not expect interns or early-to-mid career professionals to be exceptional on day one. Even senior executives are given structured onboarding plans, so they don’t break things before they understand them.

In most roles, opportunity doesn’t flow to brilliance; it flows to reliability.

If you want more opportunities at work, do three things consistently:

1. Excellent at the basics

2. Enjoyable as a colleague

3. Someone your boss can depend on

Opportunity compounds around people who make work easier for others and get better without needing attention.

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A Day in the Life of Your Manager

If you want to succeed at work, one of your primary jobs is to make your manager’s life easier.

Most managers are not spending their days doing deep, focused work. They’re moving between back-to-back meetings, juggling competing priorities, and translating vague executive direction into something the team can actually execute. Their attention is fragmented which is why you want them to be encouraged and energized when they see your name on their calendar or pass you in the hall.

The people who get the best projects, most flexibility, and stronger sponsorship at work tend to share a set of unglamorous traits. Typically, it’s not because they’re the most talented but because they reduce uncertainty for the person accountable for the outcome. If you create great work inconsistently and unreliably, you become more of a headache than star.

Before reading further, take a moment to score yourself from 1–5 on each of the three traits from your manager's perspective:

1. Excellent at the basics

2. Enjoyable as a colleague

3. Someone your boss can depend on

Excellent at the Basics

Your work holds up without supervision

One of the strongest early-career people I ever managed struggled mightily with public speaking, but when something was assigned to them, it came back complete, on time, and at a high level of quality. I trusted them to deliver without creating more work for everyone else.

Practically, this means closing loops, hitting timelines, and catching small issues before they turn into bigger ones. When your name is on something, your manager shouldn’t feel the need to double-check or chase.

This kind of excellence is quiet and unglamorous, but it earns autonomy. People who are excellent at the basics get more freedom not because they ask for it, but because their work consistently holds up when no one is watching.

When this person later asked for extended remote work over the summer, I didn’t hesitate because I knew the work would be done the same way. Regardless of what HR policies may suggest, flexibility doesn’t come from a policy. It comes from trust.

Enjoyable as a Colleague

People want to work with you

When you’re being interviewed, hiring managers are quietly weighing three things: can this person do the job, will they help take us to the next level, and do I actually want to spend time with them on a work trip.

You spend 40–60 hours a week with your colleagues. Over time, managers don’t just assign work based on skill, they choose for who they want more of in their day.

Being enjoyable to work with doesn’t mean being funny or extroverted, but it does mean reducing emotional friction. Here’s what that looks like in practice:

• When feedback arrives, the enjoyable colleague says “Let me think about that and get back to you” instead of immediately defending their approach. They treat feedback as information, not an attack.

• When a meeting is spiraling, they’re the person who says “I can take that” rather than explaining why something isn’t their responsibility. They make the path forward clearer, not more complicated.

• When stress is high, they don’t amplify it. They stay steady when others are spinning, which makes them the person managers want in the room when things get hard. If they flag a problem, they also bring a solution.

One of the harder lessons I’ve learned over my career is that driving results isn’t the only goal. How you get the work done matters just as much, especially as responsibility grows because when work gets intense, people gravitate toward colleagues who make it feel lighter rather than heavier. Those are the people who get pulled into better projects, given the benefit of the doubt, and trusted when things get messy.

Someone Your Boss Can Depend On

Managers know what they’re getting.

I once managed someone who, on their best weeks, was high energy, fast, and knocked assignments out of the park. I also managed someone who was defensive, missed details, and needed constant reminders to get work across the finish line. Unfortunately, this wasn’t two different people. It was the same person, depending on the week.

From a manager’s perspective, that volatility is expensive. Even strong results lose value when they’re unpredictable, because it becomes harder to plan around you. Work quality is a piece of the puzzle, but it’s also emotional stability, attitude, and how you interact with coworkers or clients.

Everyone has an off day occasionally, but managers gravitate toward people who show up the same way because consistency makes everything else easier to manage. Early in your career, steady progress beats occasional brilliance. Consistency earns trust, larger responsibilities, and eventually the freedom to take bigger swings.

Managers can’t bet bigger on people they can’t plan around.

Next Steps

You don’t need to be like Lebron. Success can look like Tim Duncan—consistent on the fundamentals and quietly reliable. The people who get more opportunities aren't exceptional on day 1, they're reliable on day 100.

Getting more opportunity isn’t about trying to be exceptional at everything all at once. It’s about earning trust in the moments that matter: being excellent at the basics, enjoyable as a colleague, and someone your boss can depend on.

What’s one specific behavior you’ll change this week to close that gap?

To help you move from assessment to action, I’ve created a worksheet that walks you through:

1. Assessing your strengths and current patterns of reliability

2. Identifying specific behaviors to adjust

3. Applying those changes in a practical, immediate way

Set aside 15 minutes to work through it. The people who get more opportunities aren't always the smartest in the room, they're the ones their managers can count on when it matters.

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If you’re inspired to keep reading, here are popular posts:

Every role you accept optimizes for something and costs you something. I didn’t understand this at the time, but I can trace my own blind spot back to the moment I finally earned a role at Amazon.

Live to Work or Work to Live

I remember how proud I was to get my Amazon employee badge. I had just joined a world-class team and I eagerly shared the update on LinkedIn. Finally becoming an Amazonian felt like validation of all the work I had put in and proof that I was winning the career game.

Then an older mentor left a comment:

“Congrats, Justin! Just don’t forget to work to live too!”

The advice felt trite and ill-fitting for a moment when I was celebrating a big win. My only focus was how to keep climbing the ladder and I dismissed the message as something people say who’ve already won.

It took me years to realize that “work to live” wasn’t a dismissal of ambition—it was a warning about unchosen tradeoffs. Careers aren’t just shaped by companies, roles, and pay, but by what we quietly give up to get them.

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The Question You Should Be Asking

Every season of your life imposes constraints on what you can and should optimize for in your career. Most people, however, spend their time fixating on roles and companies while ignoring the more consequential tradeoff decisions underneath them. The real question you should ask is: What am I optimizing for in my career and what am I giving up as a result?

In practice, most people chase the shiny object or borrow their parent, spouse, or company’s vision for their career without considering what they want to make a priority.

I’ve summarized four career priorities below, including when it likely makes sense for you to pursue it and what tradeoffs it will require. If you want to dive deeper, there is a worksheet linked at the bottom of this post that will allow you to reflect and start making your priority intentional to start the new year.

4 Career Priorities

You can touch all four over a lifetime, but you can only optimize for one at a time.

Before you think about your next role, it’s worth naming which of these priorities you’re actually optimizing for right now.

1) Opportunity

Optimizing for a promotion, visibility or network growth.

When It Makes Sense: You’re early in your career or deliberately focused on expanding your reputation, network, or skills to take advantage of a unique opportunity. It may be that you want to work at a great company on the rise or choose a role that will catapult you to a new level of excellence. Molly Graham calls this a J-curve career move.

The Tradeoff: At Microsoft, I saw many people optimizing for opportunity. The issue is that these people build credibility inside their company, but weak networks outside of it. As your reputation increases, you become increasingly dependent on your manager, organization, and salary so pursuing a passion project or taking a parallel role (even if it better fits your skills or interests) is increasingly prohibitive. These opportunities are often highly demanding, so you have to be willing to pay the price of sacrificing personal time and flexibility for the opportunity.

2) Income

Chasing higher compensation or asymmetric upside.

When It Makes Sense: You’re intentionally building financial runway or at a place in life where sacrificing time for increasing financial reserves makes sense. It also may be the case that you’re required to pursue a higher income due to cost of living or personal obligations (paying off student loan debt, caring for an aged family member).

The Tradeoff: At Amazon, I watched (and experienced) how easily stress and long hours became normalized. Once equity starts vesting and lifestyle adjusts, it’s tough to choose a different role or take a step back when it means economic loss. Also, leadership is generally aware of how much everyone is paid, and higher pay often comes with higher expectations of being “always on” and available. Golden handcuffs can lead to a high personal cost to health, family, and loved ones. As Jon Bellion sung, “I know a lot of CEOs with billions and resentful kids, yikes.”

3) Flexibility

Choosing a job or company with options around hours, location, and remote work.

When It Makes Sense: You’re in a season when optimizing around family, health, or experiences is most important. If you have young kids and are in a two-income household, this priority can offer a lot of benefit for one or both partners. Alternatively, if you want to take 1-2 years and travel or experience the world, then a high flexibility role can provide a lot of value.

The Tradeoff: You’re willing to be the first to be laid off, the last to be promoted, and are in a place where flexibility is prized over higher earning or career development. Bluntly, this priority is riskier for people who are not yet mid-to-senior in their career and would limit this priority to 1-2 years for younger generations who want to grow their career over a long-time horizon. For the vast majority of people, early momentum still requires time, proximity, and sustained effort.

4) Stability

Reducing volatility, protecting against downside, and choosing a steady company/role.

When It Makes Sense: You’re deliberately prioritizing stability over growth to support caregiving, education, recovery, or a planned transition. It may also make sense if you are not looking to build your career but prefer a safe job where you can work 9-5, leave for the day, and not carry work home with you.

The Tradeoff: If you’re reading Career Field Guide, I’m going to assume that a small minority of you are going to prioritize this area. That said, stability trades optionality for predictability. In seasons of personal tumult or high obligations (death in the family, pursuing graduate school, or partner with a high-risk job), it can help to not have to think often about work-related stress. Also, if you’re nearing retirement, a stable job can keep you engaged while minimizing career-building expectations. That said, for early and mid-career professionals, stability can stagnate the development of new skills and make it tougher to take advantage of future opportunities.

What Are You Prioritizing and Does it Make Sense?

Clarity in your career comes when you name what you’re optimizing for and accept what that choice costs.

The goal isn’t to choose the "universally-correct” priority (there isn’t one) or to assume that the same priority must apply for an entire lifetime. The goal is to choose the right one for this season, on purpose, rather than inheriting it by default.

Put a meeting on your calendar right now for when you can take 30 minutes to work through this short worksheet and think through the season you’re in, what you’re actually optimizing for, and what you’re knowingly giving up as a result. While there’s no right answer, the goal is that you’re making these decisions on purpose. This type of work and alignment sets the foundation for a meaningful and rewarding career.

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Related posts if you want to keep reading:

Last month I spent a few weeks in London for my first visit to the Zero100 office. My family joined me over Thanksgiving and during that week I saw my sons in a new light. I was there for work and they were tourists, but I realized that one day they will be in my shoes.

As they raced up the stairs of a red double-decker bus and looked up at Christmas lights glowing over Regent Street, they seemed older and younger at the same time. Something about being far from home made their growth impossible to miss. Being there with them made me think about the world they’re growing up into and the kind of advice that will actually matter when they begin their own careers someday.

Below are five lessons I’m sharing with them this holiday season.

1. Be Curious

Like London, the world moves fast. This past year alone we saw massive jumps in satellite deployment, humanoid robotics, TikTok ecommerce, and agentic AI.

The pace of change will only accelerate in 2026. No one will be able to keep up with every advancement, but the people who stay curious and keep learning will always find their way. Curiosity is the only skill that compounds for a lifetime.

Satya Nadella said it best:

Many who know me say I am also defined by my curiosity and thirst for learning. I buy more books than I can finish. I sign up for more online courses than I can complete. I fundamentally believe that if you are not learning new things, you stop doing great and useful things. So family, curiosity and hunger for knowledge all define me.1

Curiosity keeps you relevant. More importantly, it keeps you alive to the world.

2. Be a Relationship Builder

One thing you notice in London is how many worlds fit into one city. I lost count of the number of languages I heard on the Tube, in cafes, and in conversations between friends walking by.

Technical skills matter, but career success is shaped by a long series of people who teach you something, help you grow, or open a door you didn’t know existed. Relationships, not resumes, are the real capital of work.

If you can be the person who builds relationships and connects people across cultures, companies, departments, and teams, you will rarely be without opportunity. Interesting work tends to find the people who make others feel seen.

As I’ve written before on networking: be helpful, genuinely care about people, and always go to the happy hour.

3. Be a Light

During my trip to London, I took a weekend train up to Lille, a bustling university city in northern France. As I crossed the countryside, snow began to fall.

Cities can feel cold and anonymous, especially when you don’t know the language, but small acts stand out: a woman who held the cafe door for me, the barista who brought me the Wi-Fi password in English after noticing my confusion, the gracious waitress who patiently took my order in broken French. Your character is most visible when the world around you is moving too fast to notice itself.

In work and in life, be the person who creates warmth. People remember light. They follow it. And in moments of uncertainty, they rely on it more than they’ll ever say.

4. Be Optimistic

There are real reasons to worry about the future: layoffs, the rise of AI, political and economic instability. But all of that faded the moment I saw my boys press their faces to the bus windows, pointing out every Christmas light display.

It reminded me that even in uncertain times, people still build beauty. They still imagine something better and bring it to life.

The world is changed by people who believe progress is possible. Not because they are naive, but because optimism makes action possible. There is wisdom in maturity, but we could all benefit from maintaining a childlike optimism that we have the power to care for one another and to use our careers to create good in the world.

5. Be Practical

Traveling with kids teaches you flexibility quickly. After a long day, we boarded the wrong train (twice) with very tired children who just wanted to get back to the flat. But as in life, you adapt.

Optimism does not mean naiveté. The company that asks you for two weeks’ notice can become the same company that lays you off by text2. We all want the world to be better, but we have to play the chessboard in front of us. Learn how the game works. Understand the incentives, the players, the terrain. Wish for better, but operate with clarity.

Walking through London with my sons and watching them explore, adapt, and light up with curiosity reminded me that the world they’re inheriting will be unpredictable, but not unkind. These five principles won’t guarantee them a perfect path, but they will help them navigate anything with integrity, courage, and joy. And in many ways, they’re the reason I started Career Field Guide in the first place.

Rooting for you,

Justin

If these ideas resonate, I hope you’ll join me in 2026 as I post weekly essays to build resilient, meaningful careers in a world that won’t stop changing. Career Field Guide is for anyone navigating the future of work with curiosity, courage, and hope.

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High performers across every industry are learning the same lesson at the same time: the rules they built their careers on quietly changed.

For decades, ambitious operators worked under an implicit understanding: performance bought protection. Strong ratings, critical initiatives, and exceeding expectations were the currency that purchased job security. Layoffs happened to underperformers, to other functions, and to companies that made bad bets.

That logic no longer holds.

In 2025 alone, U.S. employers have announced more than 1.1 million job cuts,1 the highest level since the pandemic shock of 2020. Tech, finance, retail, and professional services are all shedding roles at once. What’s different this time is not just the scale. It’s who is being cut.

Increasingly, it’s high performers in the middle and upper tiers of organizations.

Over the past fifteen years leading supply chain and operations teams across Fortune 500 companies, I’ve seen where headcount decisions actually get made. I’ve watched the language shift from discussing labor as a talent strategy to discussing it as a balance-sheet variable. When labor becomes a balance-sheet variable, it stops being treated as a long-term asset and starts being managed like any other cost that can be raised or cut to protect short-term financial performance. This is why so many people who did everything “right” are now learning an uncomfortable truth: performance is no longer protection.

This is Career Fragility, the structural exposure that high performers now face when companies treat employees as options rather than assets.

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Career Fragility: The Psychological Contract That Broke

For decades, the psychological contract between knowledge workers and firms was built on a stable exchange. You provided consistency and upward-sloping performance, and in return, companies provided relative predictable progression and insulation from shocks. That model made sense in a world where capital was expensive, coordination was slow, technology scaled linearly, and growth was constrained by human throughput. Millions of careers were built on this quiet logic. What ended this contract was not a shift in corporate ethics but a full repricing of the economics underneath it.

From Assets to Options: The Mental Model Shift

Companies used to treat experienced employees as long-duration assets, valuable investments to protect, develop, and retain. The switching costs were high, the knowledge was sticky, and the planning horizons were long enough to justify patience. They now increasingly treat them as options. An option is not something you commit to protect indefinitely. It is something you hold because it gives you flexibility under uncertainty. It is valuable precisely because you can exercise it—or let it expire—depending on how conditions evolve.

When AI compresses the cost of cognitive output, when economic cycles trigger faster resets, and when capital markets reward headcount discipline, the calculus changes. AI compresses the cost of cognitive output by making thinking-based work faster, cheaper, and less dependent on large teams. The old assumption, that investing in your people always pays off over time, now competes with a new assumption: that flexibility itself is the asset worth protecting.

The implications are profound. Under the asset model, the rational move was to retain strong performers through downturns because the cost of rebuilding institutional knowledge exceeded the cost of carrying them. Under the option model, the question becomes: does keeping this role open future possibilities, or does it constrain them? That’s a much more volatile calculation. And it can flip against you even when you’re performing well.

Three Forces Made This Inevitable

1. AI is collapsing the cost of white-collar output. Research from the Brookings Institution2 finds that more than 30% of U.S. workers could see at least half of their occupational tasks disrupted by generative AI, including high-skill cognitive roles long considered “safe.” The baseline of what one operator plus tools can produce is rising structurally. For firms, this shifts the headcount calculus. Fewer people may now be required to achieve the same output.

2. Economic slowdowns now trigger faster, harder resets. When economic pressure arrives, firms are no longer relying on hiring freezes or bonus compression. They are executing headcount resets. Through October 2025, announced U.S. job cuts exceeded 1.099 million3, the highest level for that period since 2020. The severity and speed matter deeply for anyone relying on loyalty or track record as insulation.

3. Capital markets now hardwire labor as a margin lever. On earnings calls, labor is increasingly discussed alongside cloud spend, real-estate commitments, and SG&A normalization. In both public markets and private-equity-backed firms, workforce reductions are framed as margin-defense measures, not long-term capability investments.

When these three forces combine, the old link between performance and protection breaks mechanically.

This Is Not Your Failure

If you are reading this with a knot in your stomach, that response is entirely reasonable. Many professionals, especially those early in their careers, have built their futures on the belief that sustained excellence would produce long-term stability. For much of the past century, this implicit promise guided earlier generations as they made decisions about family life, home ownership, and retirement planning.

Although frustration is an understandable initial response, remaining anchored in it won’t lead to a constructive outcome. People who fare well in periods of structural change tend to be those who recognize the shift early and adjust their strategy. A willingness to adapt matters more now than holding on to an older idea of what’s fair, and it gives you a much better chance not only to navigate the changes ahead but also to turn them into real opportunities.

Entrepreneurship Is the Behavioral Response, Not a Cultural Trend

As institutional career sponsorship weakens, people respond rationally.

For the first time in decades, millions of workers are being pushed, not pulled, into independent and portfolio-style work. This is why, as hiring cools, rates of entrepreneurship rise. Not because millions of workers suddenly discovered a passion for founder life, but because the firm is no longer a reliable risk absorber of personal career risk.

This is not a creator boom. It is a balance-sheet shift.

When institutions stop underwriting careers, individuals are forced to self-insure. Founders need customers. Independents need distribution. Fractional leaders need deal flow. Advisors need both trust and visibility.

All of that is network-first economics. In a network-first economy, access to opportunity flows through relationships and trust before it flows through formal job structures.

What we are watching is the early formation of a parallel labor market that runs alongside traditional employment rather than beneath it.

What This Means for You

Single-employer careers are quietly becoming single-point-of-failure careers. When all of your reputation, relationships, and work product live inside one organization’s systems, you are structurally easy to erase.

This is why network optionality now protects careers in a way performance alone no longer can.

Loyalty has become asymmetric. Employees are still expected to commit fully, even as firms no longer reciprocate with long-horizon security. Promotions are increasingly defensive rather than developmental. And personal risk has moved down the org chart. Volatility that used to be absorbed by capital now sits with individuals and families.

What we’re describing has a name: Career Fragility. It’s not a personal failure. It’s a structural condition. And it requires a structural response.

The New Rules

If performance now functions as a qualifier rather than a shield, four things matter more than ever.

1. Build in public inside your domain.

   Private excellence that never leaves your organization has shrinking economic protection.

2. Become useful outside your org chart.
   If all your value exists inside one company’s systems, it is easy to erase.

3. Convert relationships into shared work.
   Projects, advisory roles, writing, teaching, and operating collaborations create compounding network equity.

4. Anchor your reputation in judgment, not output.
   Output is increasingly automatable. Judgment is not.

Networking used to accelerate careers. It now also stabilizes them.

A Warning for the New Year

The most exposed professionals in the next decade will not be the lowest performers. They will be the most loyal single-employer optimizers in a world that no longer sponsors single-employer safety.

Your role belongs to your company and your paycheck still comes from your company, but your future is being shaped elsewhere, whether you are participating in that reality or not.

Your First Move in January

I’ll write soon about what ‘building your network’ actually means in an era of Career Fragility and the difference between visibility that looks good and embedded relationships that actually protect you.

But if you take one thing from this piece, take one action this month: help one peer at another company, advise one small team, join one working group. Do it before urgency forces your hand.

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The entire point is to be embedded in opportunity before you need it.

Rooting for you,

Justin

Motivated to develop your career? Subscribe to Career Field Guide for insider stories and frameworks to help you build resilience into your career.

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I was in a London hotel room after my first week at Zero100 when I turned on BBC and realized how narrow my news consumption had become. Almost everything I followed was centered on the United States.

I grew up working class in the Pacific Northwest. My worldview was shaped by what I could see directly in front of me: the warehouse floor, the factory line, the distribution center. Sitting in that hotel room, I understood something uncomfortable. The world I advise global executives about is far larger than my information diet suggested. My perspective needed to expand faster than it currently was.

That realization connected to something that has carried me across four industries and fifteen years of operations leadership. Learning speed determines career trajectory.

But here is the deeper truth. Learning fast and learning deep are not the same thing. You need both. Learning fast helps you navigate change. Learning deep builds expertise that lasts.

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This distinction shapes how I approach every new domain, every career pivot, and every client engagement. Here is the system I rely on.

1. Build a Deliberately Diverse Information Diet

Most professionals optimize for speed over diversity. They follow the same sources, consume the same narratives, and develop the same blind spots. Then they are surprised when market shifts catch them off guard.

The executives I advise face this at organizational scale. The professionals I mentor face it individually. The pattern is identical. Similar inputs create predictable thinking.

After that night in London, I rebuilt my information architecture around a simple idea. Choose sources that disagree with each other and cover different parts of the world.

Here is what I follow now:

• For markets and demographics: The Prof G Pod with Scott Galloway and Ed Elson. Broad, data driven, willing to challenge assumptions.

• For international perspective: BBC News. When I travel, I watch local news to understand what matters to people in that region, not just to the U.S. market.

• For technology and capital formation: The All In Podcast. Four investors with different worldviews debating the same topics. The disagreement is the point.

• For depth: I rotate between The Economist, Wall Street Journal, and New York Times. Each one provides a different lens on global context, business mechanics, and cultural dynamics.

• What I removed: Anything optimized for engagement over insight. That eliminated most social media and the majority of newsletters that simply repackage other people’s thinking.

Better inputs sharpen judgment. Global inputs extend your horizon.

But consumption alone does not create learning. You need a mechanism to synthesize.

To build this capability:

• Select three sources from different worldviews

• Choose two publications you will read consistently

• Add one international or regional source

• Schedule time to consume them

• Remove sources that make you feel informed without changing how you think

2. Create a Weekly Learning Loop

The fastest learners are not the ones who consume the most content. They are the ones who synthesize consistently.

I use the same learning loop across every industry shift and role transition. It works because it forces application, not just accumulation.

• Sense: Scan widely for early signals. I capture anything surprising, contradictory, or repeating across sources.

• Filter: Decide what might matter. My test is simple. Does this affect my industry. Does it surprise me. Does it repeat across unrelated sources.

• Connect: Link patterns across technology, behavior, and incentives. This is where insight emerges.

• Rerank: At week’s end, review what repeated and what shifted your understanding.

• Act: Apply one insight the following week. This step matters most. Learning without application is just expensive entertainment.

Here is what this looked like last month.

I captured twelve insights on AI implementation across enterprises. Two survived the filter. First, companies struggle more with change management than with technology. Second, successful implementations start with narrow use cases, not large transformation programs.

That pattern showed up in manufacturing, logistics, and financial services. Different industries, identical constraint.

I applied it immediately. A client asked about scaling their AI investment across planning functions. Instead of recommending a broad roadmap, I suggested they start with one planning process, prove value, and expand. That recommendation came directly from synthesizing multiple weak signals into one strong pattern.

Synthesis compounds into judgment. Judgment compounds into decisions others trust.

To build this capability:

• Capture ten insights weekly

• Highlight the two that pass your filter

• Link them to your work in one sentence

• Apply one insight within seven days

• Track what worked and what did not

Time investment: about ninety minutes weekly.

3. Learn by Immersion When It Matters

Fast learning helps you navigate breadth. Deep learning builds authority.

When I need to understand a domain that is unfamiliar, I use a principle from manufacturing. Go to the Gemba. In Japanese, ‘Gemba’ means the actual place. It is the idea that you cannot understand a system from conference rooms or slide decks. You have to go where the work happens.

As my boss loved to say, you cannot solve factory problems from a board room.

At UPS, I started in the warehouse. At Starbucks, I ran distribution operations and worked directly with store teams. At Microsoft Azure and Amazon AWS, I visited supplier facilities and manufacturing lines to understand infrastructure decisions. At Amazon’s Project Kuiper, I sat with RF engineers and studied how they designed component level tradeoffs and produced Computer Added Designs (CAD).

When I moved into renewable energy, I spent time on solar farms with field operators. Not to become an electrician, but to understand where theory meets reality.

Immersion collapses the gap between concept and constraint. It exposes how a system actually works. It builds credibility with practitioners who know immediately whether you are operating from theory or experience.

This applies far beyond operations. When I advise executives on AI strategy, I spend time with the teams implementing it. I look at their workflows, their data pipelines, and their change management constraints. That ground truth shapes everything I recommend.

My process:

• Build a first principles mental model

• Immerse deliberately by visiting the work

• Update the model in real time

• Reinforce with structured learning afterward

Recent example. In my first weeks at Zero100, I needed to understand how Chief Supply Chain Officers make technology investment decisions. Instead of reading analyst reports, I spoke directly with executive leaders across industries about their evaluation criteria, budget constraints, and organizational dynamics.

Those conversations revealed something reports often miss. Technology decisions are rarely about technology. They are about readiness, risk tolerance, and expectations from the Board. That insight now shapes my research.

To build this capability:

• Identify one domain to understand deeply in the next ninety days

• Go where the work happens

• Prepare five basic questions

• Document assumptions before and after immersion

• Return to more formal learning with your new context

Immersion does not scale, so reserve it for domains where credibility truly matters.

Why This Matters Now

I have worked across industries that could not be more different. Retail, cloud infrastructure, satellite manufacturing, renewable energy, and enterprise research. The companies, technologies, and business models all changed.

The skill that made every transition possible stayed the same: I learned faster than the role required.

Learning speed is not about knowing everything. It is about choosing better inputs, synthesizing faster, and applying insights sooner than others.

Careers compound when you do two things well. Learn fast enough to navigate change and learn deep enough to build expertise that outlasts it.

Learning fast made my transitions possible. Learning deep made them meaningful.

If you are navigating a career transition, evaluating a new industry, or building credibility in an unfamiliar domain, focus less on consuming more information and more on learning better.

Rooting for you,

Justin

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Early in my career, I made a mistake so embarrassing that everyone in the room noticed except me.

It was one of my first broader staff meetings at Microsoft, my first time inside a real big tech conference room. My background up to that point had been warehouses, factories, and night shifts. I knew how to run a floor. I did not know how to read rooms filled with senior leaders wearing Patagonia vests and discussing quarterly priorities.

So I did what anyone does when they do not know the rules: I guessed.

I showed up early, walked into a large conference room, and grabbed an open seat at the head of the table. It seemed logical. It was open, it had a good view of the screens, and no one was there yet. I did not realize that this was the seat people informally held for the executive who led our organization.

I had no idea. None.

A few minutes later, the executive walked in. He paused behind me. Then he stood next to me. Then he leaned in slightly. All subtle cues that I should move.

And I did not notice any of them.

I was not being bold or trying to make a statement. I was simply unaware. I did not understand how these rooms worked. I knew the rules of a warehouse floor. I did not know the unwritten rules of a corporate conference room.

After the meeting, one of our senior leaders pulled me aside. He did not embarrass me. He did not speak to me like I was in trouble. He simply said, quietly and directly:

“Just so you know, that seat is usually where our exec sits. When he stood near you he was giving you a chance to move. It is totally fine. You will get used to this.”

I will never forget how calm he was. He treated it as a learning moment, not a moment of judgment. His approach earned my trust for years.

The executive whose seat I took eventually became an important mentor. But in the moment I wanted to sink through the floor.

And I learned something I still carry with me.

That moment crystallized something I had felt but could not yet name: corporate environments run on invisible rules, and nobody explains them until you break one.

Presence, awareness, and perception are skills just as much as coding, understanding financial markets, or marketing. None of them are automatic, you learn them through real and—sometimes painful—experience. People early in their career are often not taught this, especially first generation professionals or anyone coming from hands on roles. You do not understand the social rules of corporate life until you trip over a few of them.

After that meeting, I started paying attention more intentionally. I noted where leaders sat and how they entered rooms. I noticed that our VP always arrived exactly two minutes after meetings started, not late enough to disrupt, but late enough that everyone was already seated. I realized that it wasn’t carelessness, it was deliberate.

I listened to how they handled small talk, how they behaved at happy hours, and how they presented themselves on work trips. I observed when they spoke and when they stayed quiet. What they wore and why. What seemed normal and what seemed unusual.

I was not trying to become someone else. I was trying to understand the environment well enough to figure out how I wanted to show up.

Here is a cheat code for your career: Watch how people who have earned respect navigate the culture. Not to become them, but to understand the rhythm of the environment so you can build your own style.

Here are a few lessons I wish someone had given me earlier:

1. You will screw up. Publicly.

It is unavoidable. Do not let it define you.

2. Say sorry once and then move on.

Over-apologizing creates more awkwardness than the original mistake.

3. Let people tease you about harmless mistakes.

When someone ribs you about sitting in the wrong seat or mispronouncing a colleague’s name, lean into it. Self-deprecating humor builds trust and belonging. But learn the difference between teasing that includes you and mockery that diminishes you. The first brings you closer to the team. The second pushes you out.

4. Before work trips or offsites, ask questions.

What is the dress code?

Is there anything I should know about ordering a drink?

Is there any guidance around expectations?

It is not naive. It is proactive. The people who have been there before want to help you. Let them.

5. Read your company’s policies.

Things like reimbursements, gifts, alcohol, and travel expectations are small details that can prevent big mistakes. Err on the side of conservative actions (not having another drink, not taking a gift, not reimbursing unnecessary items).

6. Presence is quiet and perception is subtle.

Learn to read cues. Who sits where. Who has influence. Who speaks last. Who people look toward for reactions.

These are the details that separate people who stay stuck from people who accelerate.

And the most important part. Give yourself grace.

That Microsoft meeting was uncomfortable and humbling. I wanted to disappear. But it made me better. It taught me to observe, pause, and understand the room before jumping in.

Your career will be full of moments like this. Do not avoid them. Do not overthink them. Learn from them.

They become stories you carry forward. And years later, you laugh about them with the people who helped you grow.

Rooting for you,

Justin

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Imagine spending months planning your dream surfing trip…only to discover the ocean is flat.

You can paddle out and exhaust yourself trying to create momentum or sit on the beach wondering what went wrong. Either way, you miss the point of surfing.

Most people plan their careers the same way.

They pick the right “board” — a degree, a resume, a company — but never ask if there’s a wave worth riding.

That’s why some careers compound effortlessly while others stall despite hard work.

Why the Wave Matters

When I worked in supply chain at Starbucks Coffee Company, I saw how operational innovation could win in a crowded market. But retail growth was slow in the low single digits each year. I was surfing, but only on a small ripple.

When I moved into cloud technology, first at Microsoft Azure and then at Amazon AWS, it felt like stepping into a completely different ocean. The current was stronger, faster, full of energy. The industry was growing more than 30% a year, and with it came rapid promotions, new roles, and opportunities I had never seen in retail.

Same skills. Same work ethic. Just a bigger wave.

Your skills, network, and effort matter, but the right industry amplifies all of them. If you want to accelerate your career, you must surf the biggest available wave, not merely pick a nice surfboard.

The Pattern Behind Every Industry Wave

• 1890s-1980s: Sears rode the catalog and department store wave.

• 1910s-1980s: Ford drives mass production and automobile growth.

• 1970s-2000s: Walmart upended Sears with discount retail at-scale.

• 1990s-2000s: Microsoft rode the personal computer wave.

• 1990s-Present: Amazon innovated on e-commerce, then launched AWS.

• 2020s-Present: OpenAI and Anthropic are riding the frontier-model of AI.

These companies all still exist, but their true dominance came during specific industry waves. For people working within these companies and industries, these periods opened huge opportunities for workers to gain valuable skills and grow alongside their industry’s momentum. Sears employees in 1990 worked just as hard as Sears employees in 1950, but their career trajectories had different results. One group rode the wave; the other missed it.

Most people don’t recognize the wave until it’s already passed. The people who build wealth and accelerate their careers are the ones to spot waves early and position themselves to ride them.

You cannot outwork a shrinking wave. But if you catch a growing one, even average paddling takes you farther.

That’s the real secret of strategic industry selection.

So how do you read the surf before paddling out? These four factors act as your industry radar.

4-Factor Framework to Choosing Your Industry Wave

Once you know waves matter, the next step is finding the right one to ride. Here’s how to read the surf before you paddle out:

Factor 1: Growth Magnitude
How fast is the industry expanding? A fast-rising wave creates new roles and faster promotion paths. Cloud technology grew at more than 30 percent a year, far outpacing retail’s single-digit growth. Look for industries with momentum and scale.

How to Assess: Search for “industry growth rate” or “market size” reports, investor presentations, or hiring trends on LinkedIn and Indeed.

Factor 2: Structural Duration
Will this growth last? Walmart’s discount-retail wave sustained decades of expansion. A microtrend might vanish in a few years. Choose waves with long structural tailwinds, not fleeting hype.

How to Assess: Look for signs of steady investment (venture funding or R&D), consistent year-over-year growth, or durable consumer behavior changes.

Factor 3: Skill-Fit Leverage
Can your existing skills and network apply to this new wave, or would you need to start over? Moving from retail to cloud technology worked because the role stayed similar, even though the industry changed. The best waves multiply what you already know.

How to Assess: Review job descriptions in that industry and highlight overlapping skills. Talk to people working there to see what transfers easily and where you might need to learn more.

Factor 4: Ecosystem Momentum
Is the industry spawning new business models, adjacent roles, or partner ecosystems—or is it isolated? Look for ripple effects: new companies, new job types, new geographies.

How to Assess: Watch for clues like new startups being funded, conferences forming, or universities adding new programs. These are early signs of an industry building lasting infrastructure.

Use these four factors as your industry radar.

Ask yourself:

1. Where are the industries growing 20–30 percent or more each year?

2. Which sectors seem small today but are quietly building momentum?

3. How can I align my skills to surf one of those waves?

What to Do Next

1. Identify three industries that genuinely interest you (the waves you might want to ride).

2. Score each one on the four factors above (high, medium, or low).

3. Rank them and choose the industry with the highest combined score.

4. Then pick companies and roles within that industry, not the other way around.

If you plan only for the role or the company, you might end up paddling in flat water. If you plan for the wave and then choose your board, you set yourself up to ride momentum instead of fighting it.

Rooting for you,

Justin

If this helped you, forward it to someone still paddling in flat water.

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