Learning How to Learn: Top Skill for AI Era 

1. Well-trained brain is what makes AI useful. Read, write, memorize, think. The more you have built in your own head, the better you can direct and evaluate what AI gives you.
2. Grow expertise in something specific, not general. Generalist profiles are harder to sustain over time. Pick a couple of domains and build real expertise there. 
3. Map your role into three layers: peripheral, adjacent, core. Understand which tasks are already automatable, where AI can assist you, and where your judgment is irreplaceable.  
4. Develop the capabilities AI cannot replicate. Curiosity, judgment, accountability, contextual thinking — these are worth building with intention. 
5. Be deliberate about how and from whom you learn. Protect time for it, remove friction, and choose your environment — including your manager — with growth in mind. 

At a recent workshop at Stanford, Heike watched experienced professors use AI in a way that stayed with her. They prompted, received answers, and immediately knew what was right, what was off, and how to apply the output to the problem at hand. What looked like fluency with the tool was something else: decades of knowledge that made the tool useful. 

Without that foundation, the picture changes. “Using AI is so easy, but you may get answers that you cannot assess,” Heike says. You get a confident-sounding response to a question you don’t have enough background to evaluate; you use it, and you don’t know what you’ve missed. 

Her argument, grounded in neuroscience as much as professional experience, is that studying — actual studying, reading, writing, memorizing — builds something that no tool can substitute for. “The time of studying is really the time when you are training your brain,” she says. That training is what lets you think, judge, and ultimately use AI as something more than a shortcut. The generation entering the workforce now doesn’t need less of that; truth is that it might need more. 

Heike is direct on the question of specialization. Generalist profiles, she says, are harder to sustain. That isn’t a criticism of curiosity or range of knowledge — both matter — but a recognition that genuine depth in a chosen field is what creates lasting value.

The qualities she names alongside depth — curiosity, adaptability, the willingness to take ownership — are not new virtues. What is new is how clearly they stand out when routine work is increasingly handled by technology. They become the signal rather than the baseline. 

One of the most useful things Heike offers is a concrete way to think about how AI intersects with any job. She breaks a role into three layers.

Peripheral tasks — scheduling, coordination, routine administration — are already being automated in many organizations. That is largely fine. These tasks were never where the most meaningful work lived. 

Adjacent tasks are where AI assists but human expertise still governs. CV screening is her example: a tool can support the process, but a person decides who gets invited to interview. Here, AI increases efficiency. Human knowledge, guidance and oversight are what make the output trustworthy. 

The core is what should remain human. The decisions that require judgment, contextual understanding, and accountability. “Whom to hire is a very core human decision that we would never want to automate,” Heike says. The same logic applies across most fields, even if the specific tasks differ. 

This boundary will shift over time. What sits in the adjacent layer today may move toward the peripheral layer tomorrow. The exercise worth doing — for anyone building a career — is to be honest about where you currently spend most of your time, and where you want to be. 

AI operates on patterns in the data it has been trained on. What it cannot do is handle what falls outside those patterns: the situation that doesn’t fit the model, the judgment call with no clear precedent, the moment when the logic breaks and someone has to decide what happens next. “For understanding what happens to outliers — this is where we will always, always need the human,” Heike says.

That isn’t a reassurance so much as a direction. The capabilities worth developing are the ones that live in that space: the ability to read a situation that isn’t fully defined, to weigh competing considerations, to take accountability for a decision that matters. These skills and qualities get more valuable. 

Heike is honest that making time to learn isn’t easy, and that the same holds true for her. Her approach is pragmatic: booking Fridays, when meetings are lighter, having materials prepared in advance, and in general removing friction removed possible. “If I plan to do sports that are very complicated to get to, I never do it. With learning, it is the same. Remove as many barriers as possible — but stick to your calendar.” It is a discipline question.

But learning, as Heike sees it, is not only about what you read or study on your own. It is also shaped by the people around you — and most directly by the manager you choose to work with. Her advice to anyone evaluating a first or next role: look past the job description and ask what the environment actually offers. Is the person you’d be working for someone who gives you room to experiment, challenges you, and supports you at the same time? “I’d always recommend having someone who is somewhat inspiring, who gives you opportunities to try things out, to learn, to experiment,” she says. That relationship, more than almost anything else in the early stages of a career, shapes how fast you grow.