Early in my career, I was often told some version of the same advice: stop overthinking, trust your intuition, move faster.

The advice was usually well-intentioned. It also described a cognitive sequence I don’t actually experience.

For me, intuition does not arrive first. It arrives last.

When I am confident about a decision, that confidence is not a gut feeling. It is the residue of having already explored the space. I need to understand the constraints, see how the system behaves under stress, identify where the edges are, and reconcile the tradeoffs. Only after that does something that feels like intuition appear.

If I skip that process, I don’t get faster. I’m guessing instead of deciding.

This took me a long time to understand, in part because the people giving me that advice were not wrong about their experience. What differs is not the presence of intuition, but when it becomes available.

For some people, much of the work happens early and invisibly. The intuition surfaces first; the structure that produced it is only exposed when something breaks. For others, the work happens up front and in the open. The structure is built explicitly, then compressed.

In both cases, the same work gets done. What differs is when it shows and who sees it.

This is why that advice was most common early in my career, before the outcomes produced by my process were visible to others. At that stage, the reasoning looked like delay. The caution looked like uncertainty.

Over time, that feedback largely disappeared. As experience accumulated, the patterns I had built explicitly began to compress and transfer. I could recognize familiar structures across different domains and apply what I had learned without rebuilding everything from scratch.

That accumulation allowed me to move faster and produce answers that looked like immediate intuition. From the outside, it appeared indistinguishable from how others described their own experience. Internally, nothing had changed—the intuition was still downstream of the work. The work had simply become fast enough to disappear.

This is where people mistake convergence of outcomes for convergence of process.

This is where large language models change something real for people who process the way I do.

Large language models do not remove the need for exploration. They remove the time penalty for doing it explicitly.

The reasoning is still mine. The tool accelerates the exploration, not the judgment.

They make it possible to traverse unfamiliar terrain, test assumptions, surface counterexamples, and build a working model fast enough that the intuition arrives before impatience sets in. The process is unchanged. What changes is the latency.

This is why the tool does not feel like a shortcut. It doesn’t ask me to act without coherence. It allows coherence to form quickly enough to meet the pace others already assume.

For the first time, people who reason this way can move at a pace that looks like decisiveness without abandoning how their judgment actually forms.

For some people, intuition is a starting point.
For others, it is an output.

Confusing the two leads us to give bad advice, misread rigor as hesitation, and filter out capable minds before their judgment has had time to become visible.

AI doesn’t change how intuition works.
It changes how long it takes to earn it.

And for people who process this way, that difference finally matters.

This past week I spent more concentrated time with the newest generation of AI models than I have in months. What struck me was not just that they are better, but where they are better. They now handle routine engineering tasks with a competence that would have seemed impossible a year ago. The more I watched them work, the more obvious it became that the tasks they excel at are the same tasks that used to form the on-ramp for new engineers. This is the visible surface layer of software development, the part above the waterline in MIT’s Iceberg Index.

What these systems still cannot reach is everything beneath that waterline. That submerged world contains the tacit knowledge, constraint navigation, history, intention, and human forces that quietly shape every real system. It holds the scars and the institutional memory that never appear in documentation but govern how things actually work.

Developers have always been described mostly by skills. You could point to languages, frameworks, and tools and build an easy mental model of who someone was. These signals were simple to compare, which is why the industry relied on them. But skills alone do not explain why certain developers become the ones the entire organization depends on. The difference has always been context.

What the models can and cannot do

The models thrive in environments that are routine, self-contained, and free of history. They write small functions. They assemble glue code. They clean up configuration. They do the kind of work that once filled the first two years of an engineering career. In this territory they operate like a competent junior developer with perfect memory.

The challenges begin where real systems live. The deeper you go, the more you find decision spaces shaped by old outages, partial migrations, forgotten constraints, shifting incentives, and compromises that were never recorded. Production systems contain interactions and path dependencies that have evolved for years. These patterns are not present in training data. They exist only in the experiences of the people who worked in the system long enough to understand it.

There is also a human operating layer that quietly directs everything. Customers influence it. Compliance obligations shape it. Old political negotiations echo through it. Even incidents from years ago leave marks in code and behavior that no documentation captures. None of this is visible to a model.

The vanishing on-ramp

As AI absorbs more of the low-context work, the early career pathway narrows. New engineers still need time inside real systems to build judgment, but the tasks that once provided this exposure are being completed before a human ever sees them. The set of small, safe tasks that helped beginners form a mental map of how systems behave is slowly disappearing.

This creates a subtle but significant problem. AI takes on the easy work. Humans are asked to handle the hard work. Yet new humans have fewer opportunities to learn the hard work, because the simple tasks that once served as scaffolding are no longer available. The distance from beginner to meaningful contributor grows longer just as the ladder is being pulled up.

AI can help with simulated practice. A motivated learner can now ask a model to recreate plausible outages, messy migrations, ambiguous requirements, or conflicting constraints. These simulations resemble real scenarios closely enough to be useful. For people with curiosity and drive, this is a powerful supplement to traditional experience.

But a simulation is not the same as lived exposure. It does not restore the proving ground. It does not give someone the slow accumulation of judgment that comes from touching a system over time. The skill curve can accelerate, yet the opportunities to prove mastery shrink. We will need more developers, not fewer, but the pathway into the profession is becoming more difficult to follow.

What remains human

As skills become easier to acquire and easier to automate, the importance of context grows. Contextual judgment allows someone to understand why an architecture looks the way it does, how decisions ripple through a system, where the hidden dependencies live, and how history explains the odd behaviors that would otherwise be dismissed as bugs. These insights develop slowly. They come from exposure to the real thing.

There is also a form of entrepreneurial capability that stands out among strong engineers. It is the ability to make decisions that span technical concerns, organizational dynamics, customer needs, and long-term consequences, often without complete information. It is the ability to reason across constraints and understand how tradeoffs echo through time. This capability is uniquely high-context and uniquely human.

At the more granular level, some work is inherently easier to automate. Common patterns with clear boundaries are natural territory for models. Rare or historically shaped tasks are not. Anything requiring whole-system awareness remains stubbornly human. This aligns with predictions from economic and AI research: visible tasks are automated first, while invisible tasks persist.

The vanishing on-ramp sits directly at this intersection. AI is consuming the visible work while the invisible work becomes more important and harder for new engineers to access.

What we must build next

If the future is going to function, we need new mechanisms for developing context. That may mean rethinking apprenticeships, creating ways for beginners to interact with real systems earlier, or designing workflows that preserve learning opportunities rather than eliminating them. Senior engineers will not only need to solve difficult problems but will also need to create the conditions for others to eventually do the same.

AI is changing the shape of engineering. It is not eliminating developers, but it is transforming how people become developers. It removes the visible tasks and leaves behind the invisible ones. The work that remains is the work that depends on context, judgment, and the slow accumulation of lived understanding.

Those qualities have always been the real source of engineering wisdom. The difference now is that we can no longer pretend otherwise.

This shift requires us to change how we evaluate talent. We can no longer define engineers by the visible stack they use. We must define them by the invisible context they carry.

I have been working on a framework to map this shift]. It attempts to distinguish between the skills AI can replicate today (common domains, low complexity) and the judgment it cannot (entrepreneurial capability, systems awareness).

At breakfast the other day, I was thinking about those old analogy questions: “Hot is to cold as light is to ___?” My kids would roll their eyes. They feel like relics from standardized tests.

But those questions were really metacognitive exercises. You had to recognize the relationship between the first pair (opposites) and apply that pattern to find the answer (dark). You had to think about how you were thinking.

I was thinking about what changes when reasoning becomes abundant and cheap. It hit me that this skill, thinking about how you think, becomes the scarcest resource.

Learning From Nature

A few years ago, we moved near a lake. Once we moved in we noticed deer visiting an empty lot next to us that had turned into a field of wildflowers. A doe would bring her fawn and, with patient movements, teach it where to find clover, when to freeze at a scent, and where to drink. It was wordless instruction: demonstration and imitation. Watch, try, fail, try again. The air would still, the morning light just breaking over the field. Over time, that fawn grew up and brought its own young to the same spot. The cycle continued until the lot was finally developed and they stopped coming.

That made me think about how humans externalized learning in ways no other species has. The deer’s knowledge would die with her or pass only to her offspring. Humans figured out how to make knowledge persist and spread beyond direct contact and beyond a single lifetime.

We started with opposable thumbs. That physical adaptation let us manipulate tools precisely enough to mark surfaces, to write. Writing captured thought outside of memory. For the first time, an idea could outlive the person who had it. Knowledge became persistent across time and transferable without physical proximity. But writing had limits. Each copy required a scribe and hours of work, so knowledge stayed localized.

Then came printing. Gutenberg’s press changed the economics. What took months by hand took hours on a press. The cost of reproducing knowledge collapsed, and books became locally abundant. Shipping and trade moved that knowledge farther, and the internet eventually collapsed distance altogether. Local knowledge became globally accessible.

Now we have LLMs. They do not just expose knowledge. They translate it across levels of understanding. The same information can meet a five-year-old asking about photosynthesis, a graduate student studying chlorophyll, and a biochemist examining reaction pathways. Each explanation is tuned to the learner’s mental model. They also make knowledge discoverable in new ways, so you can ask questions you did not know how to ask and build bridges from what you understand to what you want to learn.

Each step in this progression unlocked something new. Each one looked dangerous at first. The fear is familiar. It repeats with every new medium.

The Pattern of Panic

Socrates worried that writing would erode memory and shallow thinking (Plato’s Phaedrus). He was partly right about trade-offs. We lost some oral tradition, but gained ideas that traveled beyond the people who thought them.

Centuries later, monks who spent lifetimes hand-copying texts saw printing as a threat. Mass production, they feared, would cheapen reading and unleash dangerous ideas. They were right about the chaos. The press spread science and superstition alike, fueled religious conflict, and disrupted authority. It took centuries to build institutions of trust: printers’ guilds, editors, publishers, peer review, and universities.

But the press did not make people stupid. It democratized access to knowledge. It expanded who could participate in learning and debate.

We hear the same fears about AI. LLMs will kill reasoning. Students will stop writing. Professionals will outsource thinking. I understand the worry. I have felt it.

History suggests something more nuanced.

AI as Our New Gutenberg

Gutenberg collapsed the cost of copying. AI collapses the cost of reasoning.

The press did not replace reading. It changed who could read and how widely ideas spread. It forced literacy at scale because there were finally enough books to warrant it.

AI does not replace thinking. It changes the economics of cognitive work the same way printing changed knowledge reproduction. Both lower barriers, expand access, and demand new norms of verification. Both spread misinformation before society learns to regulate them. The press forced literacy. AI forces metacognitive literacy: the ability to evaluate reasoning, not just consume conclusions.

We are in the messy adjustment period. We lack stable institutions around AI and settled norms about what counts as trustworthy machine-generated information. We do not yet teach universal AI fluency. The equivalents of editors and peer review for synthetic reasoning are still forming. It will take time, and we will figure it out.

What This Expansion Means

I have three kids: 30, 20, and 10. Each is entering a different world.

My 30-year-old launched before AI accelerated and built a foundation in the old knowledge economy.

My 20-year-old is in university, learning to work with these tools while developing core skills. He stands at the inflection point: old enough to have formed critical thinking without AI, young enough to fully leverage it.

My 10-year-old will not remember a time before you could converse with a machine that reasons. AI will be ambient for her. It is different, and it changes the skills she needs.

This is not just about instant answers. It is about who gets to participate in knowledge work. Traditional systems reward verbal fluency, math reasoning, quick recall, and social confidence. They undervalue spatial intuition, pattern recognition across domains, emotional insight, and systems thinking. Many brilliant minds do not fit the template.

Used well, AI can correct that imbalance. It acts as a cognitive prosthesis that extends abilities that once limited participation. Someone who struggles with structure can collaborate with a system that scaffolds it while preserving original insight. Someone with dyslexia can translate thoughts to text fluidly. Visual thinkers can generate diagrams that communicate what words cannot.

Barriers to entry drop and the diversity of participants increases. This is equity of potential, not equality of outcome.

But access without reflection is noise.

We are not producing too many answers. We are producing too few people who know how to evaluate them. The danger is not that AI makes thinking obsolete. It is that we fail to teach people to think about their thinking while using powerful tools.

When plausible explanations are cheap and fast, the premium shifts to discernment. Can you tell when something sounds right but is not? Can you evaluate the trustworthiness of a source? Can you recognize when to dig deeper versus when a surface answer suffices? Can you catch yourself when you are being intellectually lazy?

This is metacognitive literacy: awareness and regulation of your own thought process. Psychologist John Flavell first defined metacognition in the 1970s as knowing about and managing one’s own thinking, planning, monitoring, and evaluating how we learn. In the AI age, that skill becomes civic rather than academic.

The question is not whether to adopt AI. That is already happening. The question is how to adapt. How to pair acceleration with reflection so that access becomes understanding.

What I Am Doing About This

This brings me back to watching my 10-year-old think out loud and wondering what kind of world she will build with these tools.

I have been looking at how we teach gifted and twice-exceptional learners. These are kids who are intellectually advanced but may also face learning challenges like ADHD or dyslexia. Their teachers could not rely on memorization or single-path instruction. They built multimodal learning, taught metacognition explicitly, and developed evaluation skills because these kids question everything.

Those strategies are not just for gifted kids anymore. They are what all kids need when information is abundant and understanding is scarce. When AI can answer almost any factual question, value shifts to higher-order skills.

I wrote more detail here: Beyond Memorization: Preparing Kids to Thrive in a World of Endless Information

The short version: question sources rather than absorb them. Learn through multiple modes. Build something, draw how it works, explain it in your own words. Reflect on how you solved a problem, not only whether you got it right. See connections across subjects instead of treating knowledge as isolated silos. Build emotional resilience and comfort with uncertainty alongside technical skill.

We practice simple things at home. At dinner when we discuss a news article: How do we know this claim is accurate? What makes this source trustworthy? What would we need to verify it? When my 10-year-old draws, writes or builds things: I ask what worked? What did not? What will you try differently next time, and why?

It is not about protecting her from AI. That is impossible and counterproductive. It is about preparing her to work with it, question it, and shape it. To be an active participant rather than a passive consumer.

I am optimistic. This is another expansion in how humans share and build knowledge. We have been here before with writing, printing, and the internet. Each time brought anxiety and trade-offs. Each time we adapted and expanded who could participate.

This time is similar, only faster. My 20-year-old gets to help harness it. My 10-year-old grows up native to it.

They will not need to memorize facts like living libraries. They will need to judge trustworthiness, connect disparate ideas, adapt as tools change, and recognize when they are thinking clearly versus fooling themselves. These are metacognitive skills, and they are learnable.

If we teach people to think about their thinking as carefully as we once taught them to read, and if we pair acceleration with reflection, this could become the most inclusive knowledge revolution in history.

That is the work. That is why I am optimistic.

For more on this thinking: AI as the New Gutenberg

What does it take to prepare our children for a tomorrow where AI shapes how they get information, robots change traditional jobs, and careers transform faster than ever—a time when what they can memorize matters far less than how quickly they can think, adapt, and create? As a parent with children aged 29, 18, and 9, I can’t help wondering how to best prepare each of them. My oldest may have already found his way, but how do I ensure my younger two can succeed in a world so different from the one their brother entered just a few years before?

We’ve faced big changes like this before—moments that completely changed how we work and what opportunities exist. A century ago, Ford’s assembly line wasn’t just about making cars faster; it changed what skills workers needed and how companies treated employees. Decades later, Japan’s quality movement showed us that constant improvement and efficient thinking could transform entire industries. Each era required us to learn not just new facts, but new ways of thinking.

Today’s change, driven by artificial intelligence and robotics, is similar. AI will handle basic knowledge tasks at scale, and robots will take care of repetitive physical work. This means humans need to focus on higher-level skills: making sense of complex situations, evaluating information critically, combining ideas creatively, and breaking down big problems into solvable pieces. Instead of memorizing facts like a living library, our children need to know how to judge if information is trustworthy and connect ideas that might not seem related at first glance. They need to see knowledge not as something you collect and keep, but as something that grows and changes through questioning, discussion, and discovery.

Where can we find a guide for developing these new thinking skills? Interestingly, one already exists in our schools: the teaching strategies developed for gifted and twice-exceptional (2e) learners—students who are intellectually gifted but may also face learning challenges.

Gifted and 2e children think and learn in ways that are often intense, complex, and different from traditional methods. Teachers who work with these learners have refined approaches that develop multimodal thinking (using different ways to learn and understand), metacognition (thinking about how we think), and critical evaluation—exactly the skills all young people need in a future filled with smart machines and endless information.

Shift from Memorization to Meaning Instead of drilling facts, encourage your child to question sources. If you’re discussing a news article at dinner, ask: “How do we know this claim is accurate? What makes the source trustworthy?” Now they’re not just absorbing information; they’re actively working to understand it.

Foster Multimodal Exploration Make learning richer by using different approaches. Let them build a simple robot kit, draw a diagram of how it works, and then explain it in their own words. By connecting hands-on activity (tactile learning), visual learning, and verbal explanation, they develop deeper understanding.

Encourage Metacognition After solving a puzzle or coding a simple project, have them reflect: “What worked best? What would you try differently next time?” By understanding their own thought processes, they become better at adapting their approach to new challenges.

Highlight Interdisciplinary Connections and Global Outlook Show them that knowledge doesn’t exist in separate boxes. A math concept might connect beautifully with a musical pattern, or a historical event might be understood better through science. Help them see that good ideas and innovation come from everywhere in the world, not just one place or tradition.

Emphasize Emotional and Social Intelligence In a world where machines handle routine tasks, human qualities like empathy, communication, and teamwork become even more important. Encourage them to be comfortable with uncertainty, to see setbacks as chances to learn, and to develop resilience (the ability to bounce back from difficulties). These people skills will matter just as much as any technical knowledge.

Deep Learning and Entrepreneurial Thinking Like classical scholars who focused deeply on fewer subjects rather than skimming many, children benefit from spending more time thinking deeply about carefully chosen topics rather than rushing through lots of surface-level information. Consider teaching basic business and problem-solving skills early—like how to budget for a project or spot problems in their community that need solving—so they learn to create opportunities rather than just wait for them.

Finally, we’re raising children in an age where AI is becoming a constant helper and resource. While information is everywhere, the ability to understand it in context and make good judgments is rare and valuable. By using teaching techniques once reserved for gifted or 2e learners—multiple ways of learning, thinking about thinking, careful evaluation, global awareness, and creative combination of ideas—we prepare all children to be confident guides of their own learning. Instead of being overwhelmed by technology, they’ll learn to work with it, shape it, and use it to build meaningful futures.

This won’t happen overnight. But just as we adapted to big changes in the past, we can evolve again. We can model skepticism, curiosity, and flexible thinking at home. In doing so, we make sure that no matter how the world changes—no matter what new tools or systems appear—our children can stand on their own, resilient, resourceful, and ready to thrive in whatever tomorrow brings.