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

Subscribe now

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:

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

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

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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Entry-level jobs aren’t just slowing down; they’re disappearing.

That’s not just bad news for recent grads. It’s a signal for everyone. The traditional early-career on-ramp (the system that once fed every industry) has broken. And when the base of the talent pyramid cracks, the entire structure above it shifts.

I’ve seen it firsthand while mentoring professionals at the University of Washington, many with years of experience who are already rethinking how to stay relevant.

Stanford research backs up the trend: since late 2022, employment for workers aged 22–25 in AI-exposed jobs has dropped 13%, while experienced workers in the same roles have grown 6–9%.1

And companies aren’t slowing down. The Wall Street Journal reports that Silicon Valley’s biggest players plan to pour $400 billion into AI this year, and it still might not be enough.2

The consequences are showing up everywhere.

• UPS: 48,000 job cuts

• Amazon: 14,000

• Intel: 24,000

• Microsoft: 7,000

These aren’t just cost-cutting rounds, they’re signals of how work is being redefined. Roles built purely on execution are the first to go. The valuable work isn’t disappearing; it’s shifting to people who can connect systems, not just run them.

From Execution to Orchestration

AI handles execution. Humans handle orchestration.

Execution is about doing the task. Orchestration is about seeing across systems, connecting functions, and deploying the right mix of human judgment and AI capability to solve problems that matter.

The traditional career path hasn’t vanished; it’s evolved. The question isn’t whether you can climb the ladder in your function, but whether you can see across functions to orchestrate impact.

Building An AI-Resilient Career:

1. Cross-Domain Fluency

2. AI Curiosity

3. Strategic Relationships

1. Cross-Domain Fluency

A supply chain executive recently told me something that reframed how I think about career development:

“Supply chain is a math problem. The people best positioned for success are those who have strong backgrounds in data, AI, and operations.”

Not intuition. Not relationships. Not experience alone. A math problem.

A demand planner who only knows planning can use AI to forecast better. But a planner who understands finance and operations can use AI to translate forecasts into working capital impact, identify inventory optimization opportunities, and communicate trade-offs in the CFO’s language. Same tools. Radically different impact.

This is the new advantage. AI can replicate isolated expertise. What it can’t replicate is the judgment to connect domains and orchestrate solutions.

My path from UPS to Microsoft to Amazon worked not because I climbed faster, but because I built fluency across operations, technology, and business strategy. I learned how systems connect and that’s what made me valuable as problems grew more complex.

So ask yourself:

What’s your second domain? Your third?

Not to become a generalist, but to become an orchestrator.

What to do this week:

• Identify your “math problem”: What’s a recurring decision you make by gut feel that could be solved with data or analytics? Write it down.

• Spot your adjacent domain. Which nearby field would 10x your ability to solve that problem—finance, analytics, customer experience?

• Learn one concept. Spend 30 minutes learning a key idea from that domain (“working capital,” “A/B testing,” etc.).

• Schedule one conversation. Talk to someone in that domain. Don’t ask them to teach you their job; ask how they think about the problem.

By the end of the week, you’ll have started building your second domain. Not to become an expert, but to become conversational. That’s how orchestration starts.

2. AI Curiosity

Cross-domain fluency only works if you can use the tools that bridge domains. You can’t orchestrate what you don’t understand.

In conversations with executives, one thing is clear: they’re desperate for employees eager to experiment with AI. Surveys show 84% of workers want to use AI, but 54% feel like they’re falling behind. The gap between “AI fluent” and “AI intimidated” grows every day.

Here’s the opportunity. AI doesn’t just make you faster. It makes you capable of doing work that once required an entire team.

That same demand planner who understands finance can now use AI to build scenario models linking inventory to working capital. That type of work once took multiple teams and weeks of meetings. Now it takes hours, because the orchestrator can see across domains.

Of course, AI isn’t a silver bullet. It amplifies your thinking, but it doesn’t replace judgment. But if you don’t use it, you’ll fall behind those who do.

What to do this week:

• Subscribe to AI tools. Get ChatGPT Plus or Claude Pro ($20/month). This isn’t optional anymore.

• Start small. Use it for everyday tasks—planning your weekend or simplifying an email. Comfort precedes capability.

• Apply it to your math problem. Prompt: “I’m solving [specific problem]. I understand [your domain], but not [adjacent domain]. How would someone in [adjacent domain] approach this?”

• Ask about training. Email HR or your manager: “Do we have AI usage or training programs?”

• Share one insight. By Friday, share what you learned with a colleague: “Here’s how I used AI to rethink our inventory problem from a finance lens.”

Thirty minutes a day for five days. That’s all it takes to move from AI-curious to AI-capable.

Note: Don’t put confidential or sensitive information into chatbots. If you wouldn’t post it publicly, don’t type it in.

3. Strategic Relationships

You can’t orchestrate across domains without relationships across domains.

The latest wave of layoffs isn’t just about headcount; it’s about how work gets done. And when new roles do open, 70% are filled before they’re even posted. Most managers already have a candidate in mind.

Your career is like a bike. One pedal is excelling in your current job. The other is building for your next opportunity. You need both to move forward.

Delivering great work isn’t enough when AI can replicate single-domain execution. The differentiator now is cross-functional trust.

Here’s the failure mode: A demand planner builds a brilliant AI-driven optimization model. Finance ignores it because they don’t trust supply chain to understand their constraints. No relationship means no credibility.

Orchestration fails not for lack of skill, but for lack of connection.

This isn’t networking for networking’s sake. It’s building working relationships across functions so you can deploy AI and human expertise together when complex problems arise.

I’ve written before about building a network that brings jobs to you. The principles still apply, but the stakes are higher. When jobs are scarce and AI is replicating execution skills, your cross-functional relationships become your competitive advantage.

What to do this week:

• Follow up from your cross-functional chat. Share one takeaway and how it changed your thinking. That’s how relationships become real.

• Make yourself useful. Help someone in another function—share an article, make an intro, offer a perspective. No ask, just value.

• Post what you learned. Share a short note on LinkedIn: “Used AI this week to reframe an inventory problem as a working capital opportunity.” It sparks conversation and builds visibility.

• Schedule one coffee chat. With someone in a different function you haven’t talked to in 6+ months. Ask what problems they’re solving. Listen more than you talk.

Keep both pedals moving—excel where you are, and build for what’s next.

For a deep dive on building career relationships: Build a Network That Brings Jobs to You

What This Means for Your Career

The layoffs aren’t just about cost-cutting. They’re about role transformation. The jobs disappearing are single-domain execution roles. The ones emerging require orchestration across domains.

This is what I’m telling my UW graduate students:

Don’t just deepen expertise, broaden fluency. Take the finance class even if you’re in operations. Learn data analytics even if you’re in strategy. Use AI not just within your function, but as a bridge between them.

That combination of cross-domain fluency, AI literacy, and strategic relationships is what makes you valuable.

In 12 months, there will be two types of professionals: those still competing with AI at execution, and those orchestrating AI to solve problems AI can’t see.

Fifteen years ago, I was loading trucks at 2am, wondering how to get ahead.

The tools have changed, but the principle hasn’t: learn faster than the system changes.

That’s how you build an AI-resilient career. By orchestrating what AI can’t replicate: cross-domain judgment, strategic relationships, and the ability to see what others miss.

Rooting for you,
Justin

New to Career Field Guide? Visit the Start Here guide—my best articles on career development, interview prep, and making bold career moves.

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