The Path Less Traveled
By J.T. Cooper
There’s a fundamental misunderstanding about what the AI revolution means. Most people think the competition centers on who can prompt or code better, who codes faster, who learns the latest tools, and who knows the right models. This is surface-level thinking. The real competition operates on an entirely different plane.
Here’s the principle that changes everything: The one thing AI cannot learn is the reasoning it cannot observe.
Think of that principle as the blueprint for opportunity in the AI era. Let me explain why.
The Invisible Store of Knowledge
AI has consumed an extraordinary amount of human knowledge. Every book, every documentation page, every tutorial, every StackOverflow thread, every GitHub repository, every podcast transcript, every Wikipedia entry—all of it ingested, indexed, and learned. Large language models have effectively swallowed the documented internet.
But one category of knowledge remains completely invisible to these systems: the decisions humans make that they never verbalize. Your unconscious reasoning. Your intuition. Your private heuristics. Your shortcuts. Your mental models. Your gut filters. Your silent judgments. None of this appears in training data because it exists entirely inside human minds, unrecorded and uncaptured.
AI is blind to what it cannot see. This creates an asymmetry that most people have completely overlooked.
What the Researchers Know
The public narrative suggests AI will replace programmers, designers, doctors, and lawyers. Meanwhile, inside the labs at OpenAI, Anthropic, and Google DeepMind, researchers understand something quite different: AI models are only as smart as the humans who explain their thinking.
The most sophisticated models consistently fail on edge cases, ambiguity, messy real-world constraints, competing priorities, ethical trade-offs, fuzzy judgment, context-switching, and rare domain scenarios. The reason is straightforward: none of these situations has been adequately documented online. Humans don’t typically write out their reasoning step by step—they simply act on their accumulated experience. AI cannot learn from actions that were never explained.
This isn’t a temporary limitation that will disappear with more compute or better architectures. It’s a structural gap between observable data and unobservable cognition.
The Value of Embodied Intelligence
Consider what a senior engineer does when they sense a bug before seeing it. A designer might feel that a layout is somehow wrong. A nurse can instantly recognize when a patient is becoming unstable despite seemingly normal vital signs. A skilled salesperson reads the micro-tension in a client’s tone, while an investor recognizes evasiveness in a founder’s answer. A veteran negotiator spots a crack in the deal, and a chef knows a dish has gone wrong just from the smell.
None of this knowledge exists online. None of it has been labeled. None of it exists as text. It’s embodied cognition, the kind of intelligence humans acquire through actual living and repeated pattern-matching in complex environments.
Here’s the critical insight: AI cannot surpass humans in domains where humans have never externalized the rules. Reasoning, not skills, resumes, or project portfolios, is becoming the rarest asset in the economy.
The Amplification Opportunity
A small number of people have already recognized the strategic play. They take the reasoning they’ve internalized over years or decades and convert it into something explicit: prompts, workflows, decision trees, heuristics, evaluation criteria, and systematic instructions. In other words, they transfer their intuition into a machine-readable blueprint.
Note for the cynical: We will discuss why this is different from expert systems in a follow-up article.
When they succeed at this translation, AI amplifies their capabilities far beyond what any human could achieve through traditional scaling. These people become consultants who never sleep, product teams of one, agencies with zero employees, founders without staff, analysts with perfect recall, creators with unlimited production capacity.
They’ve discovered a fundamental principle: If you can explain your thinking to a machine, the machine will powerfully augment you.
The Misplaced Panic
Most people are anxiously focused on learning Python, mastering LangChain, writing better prompts, or building agents. They’re missing the actual opportunity. Your internal reasoning—the part of your mind you assume is “nothing special”—is exactly the thing AI cannot steal, scrape, or replicate.
AI is making certain things abundant: content generation, code production, design iteration, routine automation. The only resource becoming genuinely scarce is authentic human judgment. You already possess this. You simply haven’t recognized it as an asset.
This is the blind spot. This is what almost nobody discusses. And this is why understanding it matters so much: You don’t need to learn how to think like AI. You need to learn how to make AI think a little more like you.
The Coming Divergence
Within a decade, society will divide into two distinct groups. The first group will let AI replace their thinking—they’ll become passive consumers of AI-generated outputs, gradually losing the ability to evaluate quality or exercise independent judgment. The second group will teach AI their thinking—they’ll become creators of systems that encode their accumulated wisdom and amplify their decision-making capabilities.
The gap between these groups will exceed the gap between computer users and non-users in the 1990s. It will be a difference not of access but of agency.
Most people will never realize they already possess the one thing AI desperately needs and cannot autonomously generate: the unspoken intelligence inside them. But once you see this principle, you can’t unsee it. And that awareness alone changes your position in the emerging economy.
Note: I’m working on the following expansions and elaborations to this piece:
1. Concrete Case Studies Section: Add a detailed section with 3-4 specific examples of people who have successfully externalized their intuition. Show the before/after—what they did manually, how they translated it, what the multiplier effect looked like. This would ground the abstract concepts in reality.
2. Methodology Chapter: Develop a practical framework for how actually to externalize intuition. What questions should people ask themselves? What techniques work for capturing tacit knowledge? How do you test whether your externalization is accurate? This could include:
- Recognition vs. recall exercises
- Decision journals and pattern extraction
- Constraint mapping
- Exception analysis (when does your intuition fail?)
- Peer validation techniques
3. Domain-Specific Deep Dives: Take 2-3 professional domains (maybe medicine, software architecture, and creative direction) and show specifically what invisible knowledge exists in each and how it could be externalized. This would help readers see the pattern in their own field.
4. The Education Angle: Connect this to our AI literacy work. How does teaching people to externalize intuition relate to democratizing AI? Is this something that should be part of community education? Could the learning community of practice model help people develop this skill collectively?
5. Historical Parallels For better historical context, add a section comparing this moment to other technology transitions where tacit knowledge became explicit (the printing press and oral traditions, photography and observational skills, calculators and mental math). What can we learn from how those transitions played out?
6. The Dark Side Address potential concerns candidly: Could externalizing intuition lead to people losing their own judgment capabilities? What happens when flawed intuition gets amplified? How do we maintain critical thinking while scaling through AI? I never shy away from examining both sides.
7. The Collaboration Model: Explore how teams and organizations might collectively externalize their institutional knowledge. This could connect to our AI Literacy community of practice work—maybe groups can help each other articulate what they know implicitly.
8. Measurement and Validation: How do you know if your externalized intuition is good? What tests or benchmarks exist? This would add rigor to what might otherwise seem subjective.
9. The Learning Curve Some people will be naturally better at externalizing intuition than others. What determines this ability? Can it be taught? What are the prerequisite skills? This could help readers self-assess and know where to start.
10. Economic Implications: Develop the scarcity argument more fully. If authentic human judgment becomes a scarce resource, what does that mean for compensation, career development, and organizational structure? Paint a clearer picture of what the economy looks like when this plays out.