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Hello, fellow humans!

I recently listened to an AI advocate talk about how he uses AI in his workflow, and he said his biggest use of AI agents is automating work that otherwise would not get done. I couldn’t help but wonder: if the work wasn’t getting done, how valuable was it really? It’s an uncomfortable conversation, but part of the AI strategy we need to ask ourselves is, are we using it to accelerate work that doesn’t need to exist in the first place?

It is exciting to be able to automate execution and craft processes that can run without your intervention, and it feels productive. Look at all this work the robot is doing for me! But that thinking misses a more fundamental shift: the real value isn't in doing jobs faster—it's in developing the distinctly human capabilities that determine which jobs matter at all. People who master these aren't necessarily the ones with the most technical skills, but those skills can become your career accelerant. Otherwise, you might be optimizing your way into irrelevance.

I’ve been trying to understand which human skills are most irreplaceable by AI and how we can invest in them as we develop this relationship to AI, and I’ve found a really helpful mental model from John Vervaeke talking about what AI could never be. The People Space makes the case that AI augmentation makes human roles richer and organizations more resilient.

In all the panicking about automation and job loss, the people who are thriving right now are the ones who master what philosopher Vervaeke calls the "knowing" that AI fundamentally cannot replicate.

Let me show you exactly what that means—and more importantly, how to develop these skills starting today.

I put “know” in quotes because LLMs don’t know anything; they are probability machines that pick the next most likely token or word based on their training. People have been trying to find the percentage of total human knowledge that is digitized and available to AI for training, and while they vary, most estimates fall between 10-15% with only 5% of it structured in a way that makes it searchable. It’s important to understand that AI lives in a relatively small universe. But within that universe, AI is exceptional at two types of knowing:

Retrieving basic information is so fast and easy that doing it well is a foundational skill for almost any job these days. If your job primarily involves retrieving information or following established procedures, you're already feeling the pressure.

The flip side of that is the opportunity, because AI completely fails at two other critical forms of knowing, and this is where you should invest your time and effort.

Wisdom isn't about being smart. It's about overcoming foolishness.

Vervaeke defines wisdom as the capacity to overcome self-deception and avoid foolish decisions. In practical terms, this means knowing when to trust AI's output and when to pause, reframe, and think differently.

The valuable skill here is bounded rationality and self-correction—understanding when, where, how, and to what degree to use logic and AI recommendations.

Your action item: Treat every AI output as a high-probability suggestion, not a final answer. Your job is applying non-propositional knowing to assess the ethical, contextual, and long-term wisdom of what the AI recommends.

Ask yourself:

Discernment is about defining what matters—not just processing what exists.

AI excels at relevance realization—filtering massive amounts of information and predicting patterns. But it cannot determine what should be relevant based on human values, long-term consequences, and contextual nuance.

The critical skill here is salience landscaping—perceiving your environment and determining which elements are important based on context, values, and long-term goals. This means deciding which facts matter most right now, in this specific situation, for these particular stakeholders.

Your action item: Define the problem, not just the output. Use AI to generate and process options rapidly, but focus your human effort on correctly framing the problem and selecting the right goal. Leverage AI's speed to test multiple perspectives and filter out low-value information—but you set the criteria.

Here's where it gets practical. Vervaeke's framework identifies four types of knowing, two of which are inherently human-centric and resistant to AI replication:

Let me break down what this actually means for your daily work.

AI can't experience what it's like to be in your customer's shoes, your colleague's position, or your stakeholder's context. It processes patterns but doesn't have situated, embodied experience.

This is your edge.

When you apply knowledge with awareness of how a decision feels to others or how it will be perceived in a specific context, you're using perspectival knowing. This is why the best product managers, consultants, and leaders will always be human—they understand not just what the data says, but how it will land with real people in real situations.

Develop this skill by:

This is the deepest form of knowing—understanding how you and your environment co-shape each other. It's about identity, flow state, and meaning.

AI can optimize your workflow, but it cannot determine what makes your work meaningful to you. It cannot foster the sense of belonging and purpose that makes you resilient and engaged.

This matters because: jobs enhanced by AI should free you to spend more time in flow, feeling deeply connected to the purpose of your work. If AI automation is just making you more efficient at meaningless tasks, you're using it wrong.

Develop this skill by:

1. Shift Focus from Content to Context

Since AI handles informational content, your value lies in understanding the social, political, and emotional context in which that information must be applied. Stop competing on knowledge breadth. Start dominating on contextual depth.

2. Cultivate Flow and Belonging

A job enhanced by AI should increase your time in flow state—that feeling of being fully absorbed and energized by your work. If it's not, you're optimizing the wrong things. Focus on practices that increase participatory knowing and deepen your connection to the purpose of your work.

3. Hone Meta-Cognition

Practice "seeing how you see." Become aware of your own cognitive biases and self-deception patterns. This meta-awareness is the mechanism of human wisdom that ensures AI tools are being used for constructive, not foolish, ends.

Regularly ask yourself:

4. Differentiate Your Information Diet

To overcome cognitive rigidity and groupthink—which feed self-deception—consciously seek out diverse sources and perspectives. This is essential for enhancing discernment and challenging the default suggestions of an AI trained on centralized data.

AI tools tend toward consensus and mainstream patterns. Your competitive advantage lies in synthesizing diverse, even contradictory perspectives that AI training data might underrepresent.

The future of work is about humans who can leverage AI versus humans who can't, but that has nothing to do with technical skill—it's wisdom, discernment, perspectival awareness, and participatory knowing. These are trainable capacities that nearly everyone can nurture, develop, and grow; they’re not innate gifts.

Start developing them now. Your career may depend on it.