I wrote a piece on Towards Data Science: “Range over depth – the value of a generalist in your data team.” 1
My argument back then was simple: While specialists excel at solving complex, well-defined problems, generalists are often more valuable because they define the problem in the first place, and only then bring in specialists where needed.
Due to the surge in AI in our daily lives, I was curious to see how much those thoughts still resonated with me, so I went back to re-read that article. My intent was to do a rewrite, but to my surprise, I found myself agreeing with almost everything my slightly younger self wrote. Only one subtle but very important thing has changed.
The shift: AI as the new specialist
In the last five years, AI has evolved to the point where it can handle many of the tasks we traditionally relied on specialists for. The kind of work that required deep expertise, a clear brief and well-defined instructions, is now exactly where AI thrives. And unlike humans, it does this faster and without fatigue.
So I decided to still write about it, but rather than a rewrite, a mere reflection on my earlier thoughts, highlighting where some tweeks were necessary.
1. We still operate in wicked learning environments
We don’t operate in neat, closed systems. We operate in what David Epstein calls wicked learning environments2—settings where the rules are unclear, feedback is delayed or misleading, and patterns don’t repeat consistently. In these environments, you can do the “right” thing and still get the wrong outcome, or the wrong thing and appear successful. That’s what makes them dangerous.
The real challenge is not solving problems. It wasn’t five years ago and it definitely isn’t today. The challenge is knowing which problems are worth solving, and whether the signals you’re using to guide you can even be trusted.
AI doesn’t remove this ambiguity. If anything, it amplifies it. When answers come faster and look more convincing, the risk of confidently solving the wrong problem only increases.
2. The need for hyper-specialisation is shrinking (but still not gone)
Back then, I argued that access to information reduced the need for deep specialisation. Stack Overflow, blogs, and documentation meant that a capable generalist could figure things out quick enough to move forward.
Today, that dynamic has changed significantly.
Information is no longer just available. It’s curated, synthesised, compared, and presented… in an instant AI doesn’t just help you find the answer. It gives you a working answer.
And that pushes us further:
The need for hyper-specialisation isn’t disappearing, but it is being pushed closer to the edge (some would say the abyss). Generalists are now empowered to go much further before needing specialist input.
3. Coordination effort is still the real killer
The generalist reduces the coordination effort by essentially eliminating unnecessary relationships, because they range across them. They need to be given the mandate to make decisions and thus cut out the management of added relationships.
This was one of my stronger points back then and it holds even more today. The cost of coordination in organisations is often underestimated and that has not changed.
Jeff Bezos popularised the “two-pizza team”3 rule: teams should be small enough to be fed with two pizzas. In today’s world, you could argue we’re heading toward one-pizza teams. Not because the work is simpler but because generalists are more capable and AI fills many specialist gaps which results in fewer handoffs being required.
4. The business problem hasn’t changed
If you strip everything back, the core questions remain exactly the same:
- How do we grow revenue?
- How do we retain customers?
- How do we operate more efficiently?
The tooling has evolved (significantly). The methods have become even more sophisticated. But the underlying problems are unchanged.
And just as five years ago, businesses still don’t care whether the solution involves a cutting-edge agentic model or a well-placed SQL query. They might say they do in Exec meetings, but really they are not looking at how it was achieved, just if it was solved.
So in summary, what changed?
Not the importance of generalists. If anything, their value has increased.
The key shift is this:
Generalists are no longer just connectors between specialists. They are the ones navigating environments where the problem is unclear, the signals are noisy, and the path forward isn’t obvious.
They connect not only people, but capabilities—deciding when to trust intuition, when to rely on experience, and when to bring in an on-demand specialist, human or AI.
Their range is now amplified, capable of executing much deeper work themselves. Not because the world became simpler, but because they still operate well in complexity, with AI as their ever-available specialist layer.
I’m looking forward to my personal AI assistant doing another reflection in five years.
[1] Potgieter, C. (2021). Range over depth – the value of a generalist in your data team. Towards Data Science.https://towardsdatascience.com/range-over-depth-the-value-of-a-generalist-in-your-data-team-174d4650869d/
[2] Epstein, D. (2023). Kind and Wicked Learning Environments.
https://davidepstein.substack.com/p/kind-and-wicked-learning-environments
[3] Two-Pizza Teams: The Science Behind Jeff Bezos’ Rule | Inside Nuclino. Blog.nuclino.com. https://blog.nuclino.com/two-pizza-teams-the-science-behind-jeff-bezos-rule. Published 2019.