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Last week I wrote about how most enterprises remain trapped on the wrong side of the “GenAI Divide;” stuck in efficiency-focused thinking rather than embracing AI’s transformational potential. Today’s post addresses the individual side of this equation: how do you position yourself to thrive rather than just survive as AI reshapes the job market?

The answer isn’t what you might expect. New research from Stanford provides a sobering wake-up call, but it also reveals a clear path forward for those willing to adapt. This blog offers that path forward. 

The Employment Reality: AI Displacement is Already Here

Erik Brynjolfsson and his Stanford colleagues just released findings in a report titled, “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” that should get the attention of every new graduate and MBA student. Using payroll data from 25 million American workers, they documented something many suspected but few had quantified: AI is already displacing workers, and it’s hitting early-career professionals hardest. 

The numbers are stark. Since late 2022, coinciding with ChatGPT’s launch, workers aged 22-25 in AI-exposed occupations like software development and customer service have experienced a 13% relative decline in employment. Meanwhile, older workers in the same roles continue to see employment growth of 6-9%.

This isn’t a future concern. It’s happening now, and it’s accelerating.

The research reveals why: AI excels at replacing codified knowledge, the formal, structured information taught in universities and business schools. But AI struggles with tacit knowledge, the intuitive understanding, contextual judgment, and practical wisdom that comes from experience. Early-career workers, who rely more heavily on codified knowledge, find themselves most vulnerable to displacement.

For MBA students and recent graduates, this creates both urgency and opportunity. The question isn’t whether AI will reshape your career, it’s whether you’ll develop the skills to shape that transformation rather than be shaped by it.

The Builder’s Framework: Three Meta-Skills for the AI Age

The solution isn’t to become more technical (though AI literacy helps). Instead, success requires developing three interconnected meta-skills that amplify human capabilities while leveraging AI’s strengths:

1. Deep Curiosity

This goes beyond intellectual interest. In the AI age, productive curiosity means persistently asking “what’s the next question?” and “how can I dig deeper?” rather than accepting first answers. When AI can provide instant analysis, your value lies in knowing what questions to ask next and which problems are worth solving.

2. Comfort with Ambiguity

Traditional business education rewards structured thinking and clear frameworks. AI thrives in these structured environments. Your competitive advantage emerges in messy, unclear situations where the path forward isn’t obvious. This means being comfortable starting without knowing the full solution and iterating based on feedback.

3. Bias to Execution

Here’s the critical shift: in the AI economy, analysis alone has diminishing value. AI can perform most analytical tasks faster and more accurately than humans. Your edge comes from moving beyond analysis to action; generating insights from data, building solutions from insights, and executing on opportunities.

This last point addresses a fundamental vulnerability I see in many business school graduates. Too many people, especially junior professionals, define their roles as analysts. Those days are ending. Analysis was never the point, it was a necessary step toward decision-making and action. AI can handle the analytical heavy lifting; you need to master what comes next.

The Builder in Action: From Insight to App in 3 Days

Let me illustrate these meta-skills in action through a recent example that perfectly captures what I mean by becoming a “builder.”

Hernan Orvalle, an MBA Fellow at Tuck’s Center for Digital Strategies who graduated in June, traveled with me to the Consumer Electronics Show (CES) in Las Vegas. During our visit to Samsung, they demonstrated an AI-powered phone app that analyzes photos of food and provides detailed nutritional information, including calories, macronutrients, dietary insights.

Most people would have observed the demo, perhaps taken notes, maybe discussed the market implications. Hernan did something different.

Within days of returning to campus, while managing full-time MBA coursework and a young family, he had built a working version of the app himself. His first prompt to ChatGPT? “How do I install Python?”

Think about what happened here:

1. Deep Curiosity: Rather than just understanding what Samsung built, Hernan wondered “How does this actually work?” and “Could I build something similar?”
2. Comfort with Ambiguity: He started from zero technical knowledge, comfortable with not knowing the answers, and worked iteratively with AI to fill knowledge gaps.
3. Bias to Execution: Instead of analyzing the market opportunity or writing a business plan, he built a prototype. The learning came through doing, not theorizing.

This represents a fundamental shift in how we need to think about capability building. We’re all builders now, but that doesn’t mean we need to become technical experts. It means we need to become comfortable working collaboratively with AI to create solutions.

The Analyst-to-Executor Transition

For MBA students, this shift from analyst to executor requires rethinking what “value creation” means in your career. In many traditional roles, producing thorough analysis was the deliverable. In the AI age, that analysis becomes table stakes; something you and AI can generate quickly together.

Your value lies in what happens next:

• Converting analysis into actionable insights
• Building solutions based on those insights
• Executing on opportunities the analysis reveals
• Making judgment calls when data is incomplete or conflicting

This doesn’t mean abandoning analytical rigor. It means treating analysis as input to action rather than output in itself. The human-AI collaboration happens when you use AI to overcome analysis paralysis; moving from data analysis to asking follow-up questions, exploring scenarios, and iterating toward solutions.

Navigating Organizational Reality

My previous blog post highlighted how enterprises are divided between those embracing AI transformation and those stuck in efficiency thinking. As an individual, you need to be strategic about where you build your career.

Some organizations will resist the shift toward execution-oriented roles, preferring traditional analytical deliverables. Others will embrace the builder mentality and provide environments where you can develop these meta-skills. Part of career planning in the AI age means seeking out (or helping create) cultures that support this transition.

This might mean:

• Choosing employers based on their AI maturity, not just brand prestige
• Seeking roles that reward iteration and experimentation
• Working with managers who understand that failure is part of the building process
• Building internal tools and prototypes even when it’s not explicitly in your job description

Building Your Builder Portfolio

Unlike technical skills, these meta-skills resist traditional measurement. You can’t get a certification in “comfort with ambiguity.” Instead, you need to demonstrate these capabilities through what you’ve built.

Start creating a portfolio of projects that showcase your ability to:

• Identify interesting problems worth solving
• Work iteratively with AI to develop solutions
• Execute despite incomplete information
• Learn from failure and iterate quickly

These don’t need to be commercial products. Build internal tools for your current employer, prototype solutions to problems you observe, create proof-of-concepts that demonstrate new approaches. The key is showing that you can move from “what is” to “what could be.”

Practical Exercises for Skill Development

Curiosity Cultivation:

• Weekly “Why?” Sessions: Pick one process in your current role and spend 30 minutes asking “why does this work this way?” Use AI to explore alternative approaches.
• Problem Collection: Maintain a running list of inefficiencies or frustrations you observe. Practice articulating why these problems matter and who they affect.

Ambiguity Tolerance Training:

• Start-Before-You-Know Challenges: Each month, begin a project without knowing how to complete it. Use AI as a collaborative partner to figure out next steps.
• Incomplete Information Decisions: Practice making recommendations with 70% of the information you’d prefer to have. Use AI to help explore scenarios and edge cases.

Execution Bias Development:

• Build Something Weekly: Dedicate time each week to creating something: a simple tool, a prototype, a process improvement. Start with AI assistance and move toward implementation.
• Action Accountability: For every analysis you complete, force yourself to identify and take at least one concrete next step within 48 hours.

The Mindset Shift: From Task Owner to Purpose Owner

Perhaps the most fundamental change required is shifting from owning specific tasks to owning the underlying purpose of your work. Traditional job descriptions focus on activities, such as, “analyze market data,” “prepare reports,” “manage vendor relationships.” In the AI age, you need to think in terms of outcomes and impacts.

Instead of “I analyze market data,” think “I help the company understand market opportunities and make better strategic decisions.” Instead of “I prepare reports,” think “I ensure stakeholders have the insights they need to take action.”

This connects to the first principles thinking I’ve used with undergraduate students. When you understand the fundamental purpose behind your work, you can adapt your methods as tools evolve. When you own just the tasks, you become vulnerable to replacement.

The Path Forward: Choose Building Over Waiting

The Brynjolfsson research is sobering, but it’s not deterministic. The 13% employment decline affects workers who remained in traditional analytical roles. The data doesn’t capture what happens to early-career professionals who develop builder capabilities and migrate to organizations embracing AI transformation.

You have a choice: wait for institutions and employers to catch up to the AI transformation, or start building the skills that will position you to lead it. The MBA students and recent graduates who thrive in the next decade will be those who develop comfort with uncertainty, master human-AI collaboration, and focus on execution rather than just analysis.

Start building. Start now. Your career depends not on what you know, but on what you can create with what you’re learning.

Discussion Question: What’s one project you could start this week to practice moving from analysis to execution? Share your ideas, and what you built from them, in the comments.

This is the second post in a series on AI strategy for business leaders. Read the first post on [Beyond Task Replacement: Why Your AI Strategy Needs First Principles Thinking] and watch for upcoming posts on AI adoption patterns and leadership skills for the AI economy.