For twenty years, I followed a predictable script: degree, Intel, ladder. So did most of my colleagues. That script just became obsolete—not because of automation, but because of Agentic AI: autonomous systems that don't just execute tasks, but make decisions, solve problems, and amplify human judgment in ways we're only beginning to understand.

Recently, I've been stepping into a different world—startup innovation mixers where the rules are unwritten and the ladders don't exist. What I've discovered challenges everything we thought we knew about career progression in the AI era, regardless of your industry.

At a recent mixer, I met with a colleague from a prominent startup. When I asked about their biggest surprise, they didn't mention the freedom or the equity—they mentioned the 3 AM realization that there was no "expert down the hall" to validate their chip design decision. "At Intel," they said, "I had a team of world-class experts across all domains. Here, I am the expert, across multiple domains, and that's terrifying."

That gap between perception and reality? It's the defining challenge of the AI era.

You've felt this too. Maybe it's presenting a polished quarterly review while privately scrambling to understand the implications of the latest AI model your team just deployed. Or confidently leading a project meeting while wondering if you're the only one who doesn't fully grasp the new agentic workflow everyone's adopting.

My old manager had a perfect metaphor for this: a duck gliding smoothly across the water, its feet kicking furiously just beneath the surface. That image stuck with me because it captures something essential about professional life in the AI era—and it's the key to understanding what I call The Wizard's Paradox.

The Wizard's Paradox: The more powerful Agentic AI becomes at amplifying our capabilities, the more critical—and invisible—our human judgment, ethical discernment, and strategic thinking become. We appear effortlessly competent on the surface (the gliding duck), while the real work of Principled Agency happens beneath the waterline."

Most professionals are drowning because they're trying to paddle faster. The solution isn't more effort—it's learning to paddle and glide with purpose.

This week, I want to take you under the water and show you how to turn that frantic paddling into purposeful propulsion.

(Data synthesized using my customized MindStudio Deep Research Agent)

Translation: While nearly all organizations are struggling to deploy AI effectively, the professionals who are experimenting now—despite the chaos—are already pulling ahead in both speed and quality. The window for "wait and see" is closing fast.

These aren't abstract projections—they're the lived reality of professionals at companies like P&G, Intel, and across the Fortune 500. The skills that took you 20 years to master are being compressed into 5-7 year windows. And while nearly three-quarters of the workforce remains unaware of this shift, a small cohort of early experimenters is already pulling ahead, saving 5-8 hours per week through strategic AI adoption.

The semiconductor executives at SEMICON West weren't just frustrated—they were witnessing a transformation they couldn't fully articulate. One VP of Engineering told me: "I can teach someone the technical specs of our process in months. I can't teach them to see problems before they become crises."

If you're feeling a knot in your stomach right now, that's normal. The rules you've relied on for 20 years—the "expert down the hall," the predictable ladder—are dissolving.

But here's the truth: you already have the capacity to thrive in this new reality. You just need a framework to activate it.

So how do you close this gap? Not by waiting for your organization to train you. Not by hoping the "expert down the hall" will save you. You close it by developing what I call Principled Agency—the practice of acting with intention, data, and ethical grounding within your existing role.

The very definition of what makes someone valuable is shifting in the era of Agentic AI. When 95% of enterprise AI projects fail, the winners aren't those with the best technical skills—they're the ones who can identify the right problems, build coalitions across silos, and drive change with both data and empathy—exercising Principled Agency in partnership with autonomous AI systems.

Leaders at the frontier of AI have noted that the most sought-after professionals aren't just executing tasks—they're thinking in "end-to-end experiences," seeing problems systemically, and solving them proactively. These are founder traits. And they're learnable, regardless of your title.

The reason principled agency is increasingly valuable isn't pressure to constantly optimize yourself. It's that organizations desperately need people who can identify opportunities before they become crises—and most professionals don't know how.

In previous newsletters, we've explored the "Career Flywheel" and the choice between being a "Driver" or "Passenger" in the AI era. Today, we're going deeper: how do you actually practice being a Driver? How do you develop founder-level thinking without founder-level risk?

This isn't about abandoning your management hierarchy. It's about learning to operate at your growth edge—that uncomfortable space just beyond your current capability where real learning happens. Neuroscience confirms what high performers know intuitively: when you're operating at this edge with support (not chronic stress), your brain's learning circuits fire at peak efficiency.

The key is sustainable learning, not perpetual discomfort. You practice at the edge, consolidate gains, then move forward.

It's also the daily mental exercise Adam Grant describes: reframing the impostor thought of "I don't know what I'm doing" into the growth mindset of "I don't know what I'm doing yet." Grant argues these thoughts can actually enhance performance and are often a sign of hidden potential.

So how do you develop this founder-level thinking without the founder-level risk? Through a systematic approach to identifying opportunities and driving change.

I know what you're thinking: "Another framework? I'm already drowning in frameworks." Fair. But this one is different—it's not about adding more to your plate. It's about translating the work you're already doing into a language that earns executive buy-in and organizational momentum.

I've distilled this into a four-step framework: PADE

1. Problem Clarity
Define the business challenge in terms that matter—revenue impact, competitive risk, operational efficiency. Not "we need better tools," but "we're losing $2M annually to manual processes that AI could automate." This clarity is the first step, whether you're a founder or an IC.

2. Alignment (Strategic)
Demonstrate how your proposal advances existing organizational priorities. You're not asking permission to pursue your passion project—you're offering a solution to their stated goals. This is how you transform personal initiative into organizational momentum.

3. Data-Driven ROI
Show how your initiative self-funds within 12-18 months. Include the costs others overlook. Executives want to see the math. In semiconductors, this might mean showing how AI-driven yield optimization could self-fund within 12 months by reducing scrap by just 2%. A concrete, measurable outcome.

4. Ethical Foundation
Maintain a strong moral compass. The best intrapreneurs ask not just "Can we do this?" but "Should we do this?" When proposing automation, they proactively address workforce impact and retraining—not because HR asked, but because it's right. This isn't corporate politics—it's how you build trust and long-term organizational momentum.

The PADE framework is your antidote to "waiting for permission." It's how you translate the "bias for action" into an enterprise-safe format. It's the practical step that turns your pilot experiment into a data-driven business case that leadership can scrutinize and, ultimately, support. You aren't just identifying a problem—you're delivering a pre-built solution that aligns with the company's goals.

This is the work.

Here's what this looks like in practice:

Sarah, a process engineer at a semiconductor fab, identified a recurring yield issue that cost her line $50K monthly. Instead of waiting for management to notice, she applied PADE:

Within 30 days, she had budget approval and a cross-functional team. Six months later, the fix was saving $600K annually—and Sarah had been promoted to lead a new AI-driven quality initiative.

This is what it looks like to be a "Driver" rather than a "Passenger"—practicing principled agency in real time.

It's the "give it forward" ethos that drives high-velocity teams. As John C. Maxwell noted, "One is too small a number to achieve greatness." Communities and networks compound individual effort. But awareness alone isn't enough. You need to practice the mindset on something small first.

Here's your assignment—and I want to hear about it:

Choose one problem at work that you've been waiting for someone else to solve. Not a massive transformation—something small:

Write it down using this format: "We're losing [X time/money/opportunity] because [specific problem]."

Want to stress-test your thinking before the real meeting? Here's how I do it every week:

Use this AI prompt to Red-Team your approach:

"Act as a skeptical CFO. I'm proposing [your initiative]. Challenge my business case by asking about: (1) financial viability and ROI timeline, (2) strategic alignment with existing priorities, (3) hidden costs I might have overlooked, and (4) potential unintended consequences. Be direct and tough."

💡 Pro Tip: I use this technique to Red-Team this newsletter every week—it helps me balance rigor with readability. It's a simple, powerful way to use AI as a partner to help you see what's happening just beneath the surface—transforming intuition into intention. The same principle applies to any business case: stress-test your thinking before the real meeting.

Do something toward addressing it—without asking for permission first.

Capture your learning using the 3 R's:

These three R's—Resistance, Revelation, Refinement—are how you build your founder muscle memory. They're also how you start to see patterns in your own behavior and the systems around you. Over time, this practice becomes second nature.

Once you've practiced that bias for action on something small, then ask yourself Graham Weaver's question: "What would my goal be if I knew I couldn't fail?" Write it down. That's your North Star.

Here's the truth: the gap between where you are and that goal isn't as wide as you think. It's not about having all the answers. It's about having the courage to start paddling beneath the surface—to do the unseen work that makes the graceful glide possible.

Remember: The Right Questions are better than Right Answers.

The "Pro Tip" above isn't just about stress-testing your argument—it's about cultivating the habit of strategic inquiry. When you learn to ask better questions, you develop the founder-level thinking that makes you indispensable.

And you don't have to do it alone.

I want to hear about your experiments with Principled Agency:

📊 Share on LinkedIn with #PrincipledAgency—I'm tracking these stories and will feature the most compelling examples in next week's newsletter.

💬 Reply directly to this email—I read every response, and your insights often shape future topics. (Seriously. Last month, three reader emails became the foundation for the "Career Flywheel" framework.)

🎯 Challenge my logic—If you think I'm wrong about something, tell me why. Every time I've been wrong, I've learned something new. (And I've been wrong plenty.)

Next week, we'll explore how collective intelligence amplifies individual agency, and why your network is your greatest AI advantage. Plus, I'll share what I learned from your PADE experiments this week—including the mistakes, breakthroughs, and surprising patterns that emerged.

This week’s issue was a philosophical one, built on the shoulders of giants. For those who want to explore the "mood board" that inspired these ideas, here are the key sources.

💡 Pro Tip: Overwhelmed by the reading list? Copy any of these URLs into Claude or ChatGPT and ask: "Summarize the key arguments and provide three actionable takeaways I can implement this week." AI is your research assistant—use it.

·         Until next week—keep paddling beneath the surface.

·         Joseph

P.S. Remember: The gap between where you are and your North Star isn't as wide as you think. Start with one PADE experiment this week—that's all it takes to begin paddling with purpose beneath the surface.