One particular event I remember that happened in 2020 was the well-anticipated chess match between Magnus Carlsen (considered the worlds’ number one chess player) against Stockfish, a free, open-source chess engine. The world watched what was considered a momentous clash between raw computational power and human genius.

Of course, nobody expected Carlsen to beat the machine. Machines first surpassed the best human players in chess in 1997, when IBM's Deep Blue defeated then reigning world champion Garry Kasparov in a six-game match. Just four years prior in 2016, machine defeated humans once again when AlphaGo beat Lee Sedol in the game of Go.

Yet, what struck me (and I’m sure the millions of avid chess fans felt the same) wasn't the raw computational difference—it was how Carlsen approached the game. While the AI relentlessly optimized for material advantage, Carlsen played positions that created long-term pressure and psychological complexity. He knew he couldn't outcompute the machine, so he changed the nature of the contest.

As artificial intelligence continues to evolve at lightning speed, the question everyone is now asking is this: what skills can possibly stay relevant in a future dominated by AI?

It’s a question I’ve asked myself with increasing regularity in the past few months and one I have come to terms with as I continue to work with people in the AI space. As machines get better at carrying out standard tasks and processing information, there are certain human abilities that I believe will become not less valuable, but more so.

In this article, I will discuss these six key skills that I believe will likely command a premium in an AI-dominated future.

1. Ability to use AI tools effectively

With AI getting more powerful at lightning speed, we're likely to see a growing divide between people who really know how to use these tools effectively and those who don't. Being truly 'AI fluent' isn't just about typing in basic prompts; it's about skillfully guiding multiple AI systems to work together on complex challenges.

Ethan Mollick, professor at Wharton, has demonstrated this skill by creating sophisticated AI teaching systems. Rather than simply using AI to automate existing educational approaches, he's reimagined education by combining multiple AI tools into coherent learning experiences that would be impossible without this orchestration layer.

How to get better at using different AI tools:

• Get good at 'talking' to AI. Learn how to write clear, specific instructions (prompts) to get the best results. This means giving good context, setting limits, and maybe even showing examples of what you want.
• Know your AIs. Different AI models are good at different things. Get a feel for which tools shine at which tasks so you can pick the right one for the job, like knowing when to use a hammer versus a screwdriver.
• Try linking different AIs together. Use the output from one AI as the input for another. You can create some powerful results this way that a single AI couldn't manage alone – think of it like an assembly line.
• Don't just trust AI blindly. Learn how to spot-check its work for weird mistakes or 'hallucinations' (when it makes things up). Set up ways to double-check important outputs before you rely on them.
• Keep playing with new AI tools and features as they come out. Things are changing incredibly fast in AI, and what wasn't possible yesterday might be easy today. Make some time to stay curious!

Figuring out how AI works and getting comfortable using these tools is a great way to prepare for the future, both in your career and just in everyday life. The more comfortable you are with AI, the better you'll be able to navigate where things are heading.

2. The Ability to Act Autonomously Under Uncertainty

One thing's becoming clear as AI grows: we're drowning in information, but it's harder than ever to feel certain about things. That's why being able to make decisions and move forward, even when you don't have all the answers or clear instructions, is becoming incredibly important.

Think about Melanie Perkins, who co-founded Canva. Her big idea was to make graphic design easy for everyone. She saw the vision and believed in it, even when traditional investors turned her down again and again. Yet with all that uncertainty, she kept going, made decisions, changed course when needed, and built things out before knowing exactly how it would all work. Now, Canva is worth $40 billion, largely because Perkins dared to act without waiting for certainty or someone else's okay.

With how fast things are turning out in the AI space, there’s no room for waiting for things to stabilize. You either learn as much as you can now or get swept in the tide.

So, how can you get better at this?

• Try making decisions when you feel about 70% sure, instead of holding out for 100% certainty especially for choices you can easily undo later.
• Run small tests. Instead of trying to perfect something behind the scenes, build a basic version ('minimum viable product') you can try out in the real world. Use the feedback you get to make it better, rather than trying to guess everything perfectly upfront.
• Develop your own 'rules of thumb'. Think of these as mental shortcuts that help you make quick calls when things are fuzzy, so you don't get stuck overthinking.
• See uncertainty differently. Try to view it not as something scary to avoid, but as open space where new ideas can grow. Oftentimes, the areas that seem too confusing or risky to others can hold the biggest potential.

AI is definitely going to shake things up across most industries in the years ahead. The key is to think about these changes proactively and to decide how you want to adapt. This way, you can stay ahead of the curve in your field and navigate the shifts effectively rather than feeling overwhelmed by them.

3. Learning How to Learn

Since AI makes finding information easier, just knowing facts isn't the key thing anymore. What really counts is how fast you can pick up new information right when you need it. Skills are becoming outdated much quicker these days, so being good at learning how to learn is becoming much more valuable than any single skill you might master.

We call this meta learning -- your ability to learn and acquire new skills efficiently.

Barbara Oakley is a great example of someone who mastered learning itself. She actually struggled quite a bit with math and science when she was younger. But instead of giving up, she figured out systematic ways to tackle difficult subjects – methods that eventually helped her become an engineering professor. Her online course, 'Learning How to Learn,' became incredibly popular, reaching millions of students all over the world. Her story shows that success often comes not just from natural talent, but from figuring out smart strategies to learn new things quickly and effectively.

Think of content-focused learning as individually learning a long list of locations while process-focused learning as learning how to use a map and navigate your way around multiple locations.

So, how can you become a better learner yourself?

• Set aside specific, focused time for learning. Even just 30 minutes of truly concentrating on something new is way more effective than hours of trying to learn while distracted. Block it out in your schedule!
• Use AI tools to help you learn. Think of AI like ChatGPT or Claude as personalized tutors. They can help create learning plans, give you practice questions, and offer instant feedback tailored to you. Ask them anything!
• Break down big topics into smaller pieces. When tackling something new, figure out the core building blocks. Identify which parts are most important for your goals and focus on mastering those first.
• Find ways to organize what you learn so it sticks. This could mean using mind maps, systems for reviewing things regularly (like spaced repetition apps), or finding a note-taking method that works for you. The goal is to connect new info to what you already know.
• Learn by doing, not just by consuming. Actively practice what you're learning and try to get feedback quickly. Push yourself with challenges that are just slightly beyond what you can already do easily – that's where the real growth happens.

In the age of AI, your ability to keep learning things (and learn them fast) is not a suggestion, it’s an imperative.

4. Interpersonal Coordination, Empathy, and Leadership

AI might be able to process information about emotions, and even sound like it gets how someone feels, but it can't genuinely connect with the human experience on a deeper level. That's why our ability to truly work together, understand the subtle shades of emotion between people, and build real, lasting trust becomes so much more vital in a world where automation is the norm.

Think about Dr. Paul Farmer, one of the founders of Partners in Health. He was amazing at setting up healthcare in places around the world that needed it most. But his success wasn't just about medical know-how. What really made the difference was his incredible talent for building trust within local communities. He took the time to understand their cultures, and by doing so, got complex groups of people to work together effectively. These 'people skills' allowed him to create healthcare solutions that actually lasted, often succeeding where other projects which were technically good but lacking that human touch had failed.

How to strengthen these skills in yourself:

• Try stepping into someone else's shoes regularly. Pause and really think about where they're coming from. What is their background? What daily pressures do they face? What drives them to get up in the morning?
• Build trust by being real. Don't be afraid to admit when you don't know something. Show a bit of vulnerability when it feels right. Oftentimes, being open about your imperfections builds stronger bonds than trying to seem perfect all the time.
• Work on truly listening. When someone's talking, focus all your energy on understanding them rather than just waiting for your turn to speak. Listen to understand.
• Learn to pick up on the unspoken stuff. Pay attention to facial expressions, body language, and the tone of someone's voice. These clues often tell you more than words alone.
• Get good at explaining complicated things simply. Practice talking about complex ideas in ways that focus on why it matters to people and the impact it has, instead of getting bogged down in technical jargon.

In the same way humans put a premium on handmade goods and artisanal products, genuine human connection will be sought after in a world where machine pervades everyday life.

5. The ability to think systematically

AI is fantastic at nailing specific, well-defined jobs. That leaves a really important space for people: understanding the whole picture and navigating complex situations. The skill to see how things connect, spot the most impactful places to make a change, and anticipate the likely ripple effects (stuff a narrowly focused AI might miss) is going to be incredibly valuable. This kind of 'systems thinking' is especially critical when we're tackling complicated social problems full of nuances.

A brilliant show of ‘big picture thinking’ is exemplified by Donella Meadows, an environmental scientist who was a key part of the team behind the groundbreaking 'Limits to Growth' study. While many others were focused on tracking single environmental issues, Meadows saw the bigger web of how things like population growth, industry, food, and using up resources all affected each other. Looking at it this way revealed possible futures, both good and bad, that you'd completely miss if you just stayed in your own little analytical box.

Want to get better at seeing the whole system?

• Collect different ways of thinking. Get familiar with basic ideas from different areas like economics, biology, psychology, even engineering. Concepts like 'stocks and flows' (think water filling or draining from a bathtub) or 'feedback loops' give you mental tools to grasp how complex things work.
• Practice thinking a few steps ahead. When looking at a situation or a decision, don't just stop at the immediate result. Keep asking yourself, 'Okay, and what happens then?' Follow the ripple effects out.
• Look for how things circle back. Notice when the result of something actually loops back to push the original trend even harder (positive feedback, like a snowball rolling downhill) or to damp it down (negative feedback, like a thermostat keeping temperature steady).
• Try drawing it out. Sketch diagrams of how different parts of a complex situation connect. Actually seeing the links often helps you spot patterns you might miss just talking or writing about it.
• Get different perspectives. Complex problems look different depending on who you are and where you sit. Make an effort to hear from people with different viewpoints and stakes in the situation to get a fuller picture.

Sure, AI might be incredibly logical, but don't underestimate our uniquely human ability to connect the dots, especially when dealing with messy, real-world human problems. Seeing those connections and understanding the bigger picture is exactly what makes our individual perspectives so valuable.

6. Authenticity

Now that AI can create incredibly realistic content – text, images, and more – things like a genuine human voice and unique personal perspective are becoming much more valuable. In a world potentially flooded with AI-generated material, your ability to connect with others authentically through shared values, real insights, and building actual trust will stand out.

Casey Neistat built his career on this principle. While many creators tried to game algorithms or copy trending formats, Neistat focused on authentic storytelling that revealed his actual personality, flaws and all. This authenticity created a connection that millions of viewers value precisely because it cannot be algorithmically generated.

How to develop this skill:

• Share your journey transparently. Document your process, including the setbacks and failures, not just the polished results. This vulnerability creates deeper connection than curated perfection.
• Identify your unique perspective. Reflect on the intersection of your various experiences, expertise, and values. What combination of elements gives you a viewpoint that others don't share?
• Create two-way relationships. Move beyond broadcasting to genuine dialogue with your audience. Respond thoughtfully to engagement and incorporate feedback into your future work.
• Focus on long-form formats. Invest in deeper, more substantive content rather than just short-form engagement bait. Meaningful connections often require space to develop nuance and depth.

Lean into your humanity. Share the aspects of your work that reflect distinctly human qualities—your ethical considerations, emotional responses, and personal evolution—rather than just technical expertise.

Creating Your AI-Resistant Future

So, what ties all these six skills together? It's that they tap into abilities that are uniquely human – things AI just can't do, at least not yet. We're talking about the knack for making decisions when things are uncertain, forming real emotional bonds, learning and adapting across different situations, skillfully managing complex sets of tools, understanding the big picture, and sharing authentic personal viewpoints. These human skills aren't just going to stay important; they'll likely become even more critical as AI takes over more routine mental tasks.

But the real winning strategy here will be our ability to blend our unique human strengths with AI's capabilities. Looking ahead at how work and the economy are changing, the people who will really thrive are those who build up these core human skills while also getting smart about using AI tools. They'll be the ones offering something truly unique.

So the real question for each of us is this: how will you focus on developing the essential human skills that will stay valuable, no matter what twists and turns this transformation takes?