What is then left?
Human traits and geniuses on a data center
If we assume that synthetic intelligence continues to scale indefinitely, what is then left for human knowledge workers? What are the traits and capabilities that will continue to be valuable after the country of geniuses in a data center is upon us?
A person on my team formulated some variation of that question a few days ago and, after some rambling, I wasn’t able to give even a glimpse of a compelling answer. This is my imperfect attempt to fix that.
This post is different from the others and a bit weirder. You’ve been warned.
Some assumptions first
For the below to make any sense, you have to believe that a few things are true and a few others will be true in the near future (2-4 years).
Current levels of intelligence of frontier models are enough for a good bunch of knowledge tasks. Another huge chunk will be, once we progressively figure out diffusion, coordination and traces.
The tooling around those models will keep improving asymmetrically, making agents capable of executing more actions through the digital world.
Foundation models, LLMs or otherwise, keep getting better following predictable and non-stopping scaling laws.
Compute is abundant and scaled accordingly to meet demand both in the pre-training and, especially, inference side.
Energy doesn’t become the bottleneck and AI labs can build, buy, rent or launch the necessary infrastructure to power ever-growing data centers.
The rest of the stack keeps pace with the above: High-Bandwidth Memory (HBM), data (quality especially through curation and licensing), interconnects or capital markets.
The cost of inference (making use of the models) does not only not get more expensive but keeps decreasing on a per token basis. Spoiler, the unit cost per token is collapsing in 2026.
Taking the above at face value very likely brings us to a scenario in which we may have at our disposal PhD-level intelligence and capability across thousands of disciplines.
In that world, what skills, traits and talents will be valuable then for humans in knowledge work is a non-trivial question to pose. But what if we aim for an even better one?
In a world of extremely abundant and near-free intelligence, what human capabilities will be even more valuable?
Here are my seven:
1. Judgment
Already the most lucrative human skill, in a world of infinite leverage and commoditized execution, the ability to discern what is worth pursuing, when and why becomes the center of gravity of the new professional.
AI is not capable of setting its own objective function (a.k.a. goals). Unless an algorithmic breakthrough comes along, this will continue to be true.
My definition of wisdom is knowing the long-term consequences of your actions. Wisdom applied to external problems is judgment - Naval Ravikant
Judgment is the amplifier for the traditional trifecta of leverage: code, human labour and capital. If we augment this with synthetic intelligence and agentic execution, it makes it explosively more valuable.
How does one develop judgment? It might take a lifetime, so what the heck would I know? A few ideas:
Pick something and master it. Build specific knowledge.
Complement that with some worthy experiences: read old books, write daily, build a product (digital or physical), get married, have kids, travel the world, invest your money and drink good wines.
Have long walks to think and reflect.
2. Focus
In the age of unlimited leverage, the barriers to starting anything entailing bits and bytes collapse.
In practice, you could spawn up a squad of coding agents to build a web app, an iOS app, an agentic workflow and the copywriting for your marketing materials, today. You can even have them supervised by an agent powered by a better model.
As you can imagine, lots of tech folks are doing that.
But, when you can do anything, you may try to do everything, and get nothing worth it out. Because starting is easy, but finishing is hard.
Lots of people orchestrating teams of agents and building crazy setups never get to launch anything meaningful. Most professional developers embracing AI are, in fact, navigating anxiety, investing too much time in the setup itself and producing semi-functional software.
Therefore, applying judgment to decide and then focusing on a single output until finished becomes a superpower.
Focus gives you speed. Speed is given by AI and focus. The finisher mentality may be the second most important human trait going forward.
3. Optimism
There are, certainly, not a trivial number of unknowns about the development of AI and its societal impacts.
There might be mounting job losses, and entire professions might disappear. Also, an immense amount of power may concentrate in a handful of companies (quite literally a handful).
There are also the implications for geopolitics and warfare, mental health, the energy grid or human relationships. Just to name a few.
I get it.
First, you can’t do anything to fix or mitigate most of the above.
Second, this tech is also the most powerful accelerator you could access in the entire history of humanity. It is quite democratic too. For 20$ a month you can have legion at your disposal. For 200$, an army. And most companies are more than willing to cover the bill for you.
Having a continuously proactive mindset and optimistic lens about AI advancements will be fundamental, especially through the next few years.
Monitoring new improvements, tracking new product launches. Testing them, applying to your work or personal projects. Seeing them as useful tools that may get you where you want to go faster, cheaper.
We have thousands of the most brilliant minds of our generation, enormous piles of cash and (almost) full governments’ support, all working to give us increasing superpowers.
Naive? Maybe. Do you have a more productive alternative?
4. Distribution
The age of infinite leverage is also the age of infinite noise.
You always needed someone listening to what you were saying. Someone at the other end of the channel reading you, buying your products or services, evaluating your work, eyeing what you made.
Call it sales, influence, persuasion, reach.
It might be small and closed-loop (e.g. within your company) or wide and open (e.g. if you are selling something to consumers). But you still need an audience.
I argued that top models now can already do most knowledge work notably well. What they can’t do is make people care. Make people listen.
Distribution is fundamentally a trust game. People follow people. They buy from people they’ve been reading, watching, listening to.
The strategy to build it is a wide and deep topic, with great books and experts to take advice from. Do your research. And repeat (nobody knows you).
5. Extreme competence
Those in the top percentile of knowledge and skill in any discipline win big. Not only will they still be valuable. They concentrate most of the value.
Just from being marginally better, like running a quarter mile a fraction of a second faster, some people get paid a lot more—orders of magnitude more. Leverage magnifies those differences even more. Being at the extreme in your art is very important in the age of leverage. - Naval Ravikant
AI is a spectacular equalizer. It can get anyone from zero to competent in almost anything. It writes good code, produces quality analyses, drafts reasonable strategies. The gap between a beginner and an intermediate practitioner is collapsing fast.
The top cardiologist, the elite software architect, the best trial lawyer — these people don’t just know more. They have pattern recognition built over tens of thousands of hours. They’ve seen the weird edge cases. They know when the textbook answer is wrong. They have built taste.
When everyone has access to “good enough,” the people who are genuinely exceptional become absurdly valuable by contrast. At the very least, they will be sought after to post-train the cutting-edge models in their disciplines and paid handsomely.
In my view, there are two alternative paths in the future that is coming. One is to become an ultra-leveraged generalist. The other is to become an extremely competent specialist.
The holy grail would be the ultra-leveraged specialist. But on the one hand, there may not be enough time for both and living a life. On the other, the differences in brain wiring among people make it rare.
If you go this route, the playbook is simple, but the execution might take decades: go deep, not wide. Pick your domain. Put in the reps that AI can’t shortcut. Read the primary sources, not the summaries. Build the intuition that only comes from doing the actual thing, repeatedly, in messy real-world conditions.
Pick wisely.
6. AI Proficiency
The ultra-leveraged individual knows his AI.
“The goal is not automation; it’s augmentation—with AI as humanity’s most powerful collaborator.” - Eric Schmidt
Much like navigating the web, using spreadsheets and word processors, or managing email, knowing how to effectively use agents and AI tools is becoming a meta-skill.
Easy to use. Harder to master.
AI proficiency is architectural. It’s the ability to look at a problem, decompose it into the parts a human should own and the parts a machine should handle, and then orchestrate both into something neither could produce alone.
It is also maintaining a core toolkit that is updated and upgraded but, at the same time, doesn’t fall for excessive bloat.
I think of it as a new kind of literacy. Not like learning a programming language. More like learning to read.
7. Creativity(?)
The question mark is there not because creativity doesn’t matter. Your specific conception of creativity may not matter much.
AI can generate music, write novels, produce photorealistic images, output riveting visuals and compose marketing copy that converts. If your definition of creativity is “producing novel outputs” or “creating visually striking art,” the frontier models are already there.
However, there is the other creativity. The one that always has a human behind it, making non-obvious choices. Choosing the unexpected angle. Combining two ideas that had no business being in the same sentence. Defending an unpopular point of view. Knowing what to leave out.
That’s taste. And taste might be the real skill hiding inside the word creativity.
Picasso, Leonardo, Zaha Hadid, Pininfarina, Murakami. Yes.
But also, Jason Fried and DHH (with their contrarian approach of running a business and a life). Josh Waitzkin and his approach to learning. Tim Ferriss and his way of interviewing and writing.
AI is a phenomenal tool for execution of creative work. It can iterate faster than any human. Produce more variations. Explore more of the possibility space. But it doesn’t know how to defend a unique point of view. It doesn’t understand that the slightly imperfect version has more soul than the polished one.
How does one develop this kind of creativity? Again, what the heck do I know?
But, to close the circle, creativity may just be a specific type of judgment. The one you develop through your life and apply to your work consistently and uniquely.