Leaving the rat race - Part 4

Why it is okay not fall for the FOMO cycle

Uwe Friedrichsen

12 minute read

Single withered leaf waiting to fall down

Leaving the rat race - Part 4

In the previous post we discussed that most of the “innovation” hype is nothing but a distraction from our actual problems.

In this post, we will look at the actual strengths of humans and why the FOMO 1 cycle tends to play against it.

Anti-patterns resulting from cognitive overload

I already described in the second post of my Simplify! blog series that people who feel overwhelmed by the amount of perceived “innovation” develop “survival patterns”, ways to deal with the situation, evading the FOMO cycle as good as possible.

As I already described the most common patterns in detail in the aforementioned post, I will only briefly repeat them here:

  1. Knowing (almost) nothing about everything – trying to understand just enough of a new topic at the moment you need it to get the next pressing task on your list done. A variant of this pattern is the “hype-driven architect/lead developer”, jumping from one new “cool” topic to the next, just knowing enough to dazzle their environment with their alleged expertise, always leaving their projects quickly enough that they are gone before the mess they created becomes apparent.

    In the end, it is draining for the people, always feeling stressed and a bit like impostors because they know their knowledge is superficial. This pattern also leads to premature deterioration of software – which ironically is the core justification of the “innovation” flood: To fight this issue. A vicious, self-enforcing cycle has been created.

  2. Hyper-specialization – trying to focus on an area small enough that you actually master it. This is the default reaction we see in many places. It is reinforced by quite some market trends: More and more specialized conferences, magazines and even job ads. The whole market pushes towards people who know more and more about less and less until they know everything about nothing. 2

    While at first sight, this seems to solve the immediate problem, it creates a lot of new problems. It not only makes communication and collaboration between hyper-specialized people harder. In the long run it also dehumanizes people more and more. They become smaller and smaller cogs in a machine, controlled by more and more elaborate processes and alike and are expected to always perform flawlessly – which does not help to reduce the stress but makes it even worse.

  3. Blast from the past – avoiding new stuff and clinging to the known. While this might not be the worst strategy to deal with the “innovation” flood, its shortcomings should be obvious. Even if we do not have as much innovation as we claim to have (see also the second post of this series), there is still evolution – and sometimes even actual innovation.

  4. Becoming “opinionated” – hyper-specialization meeting belief, a troublesome combination. A person focuses on a specific type of solution approach and due to a lack of rationale why the solution meets the task to be solved, the person calls the approach “opinionated”.

    The more harmful variant is a person having only superficial knowledge regarding the solution design, masking it with a surplus of opinion and actively trying to suppress discussions by calling the approach “opinionated”. It should be clear that this pattern usually leads to suboptimal results, in the end fueling the “innovation” flood – another vicious self-enforcing cycle.

  5. Giving up – feeling so overwhelmed from all the new stuff that you eventually give up. Of course this is not a sustainable pattern for the person affected by any means. Typically it also leads to solutions being far from being good, accelerating premature deterioration of the software created – again fueling the cycle.

  6. Burning out – eventually breaking while trying to live up to the perceived expectation you need to understand all those topics in depth. It should be clear that this the least desirable pattern of all.

These were some of the “survival patterns” people typically develop when confronted with a continuous overkill of “innovation”. None of them is good. All are harmful in one way or the other. And all of them usually lead to premature deterioration of software, more complex solutions, leading to increased cognitive load, impeding solution evolvability and developer productivity.

Ironically, these problems are exactly the ones the flood of new tools, technologies and approaches pretend to fix: Avoiding premature deterioration of software, driving simpler solutions, reducing cognitive load, fostering solution evolvability and developer productivity. But in the end, they turn out to be part of the problem.

To be clear: A single tool or technology is not the problem. Actually, it can be really useful and actually reduce cognitive load and alike. The problem is the abundance of these tools, technologies and approaches, their publication frequency and their constant exaltation being the “next big thing, the innovation you cannot afford to miss”.

Hyper-specialization as the default response

As written above, the default behavior we can observe these days is aiming for hyper-specialization: Trying to become proficient in a very narrow area of expertise. To illustrate the development over the past years:

  1. Once upon a time, a person could be an operations expert.
  2. Over time it became too much. As a result, the width of expertise was reduced. A person, e.g., could be a virtualization expert.
  3. With the rise of containers, it became too much again. The width of expertise was reduced again. A person, e.g., could be a Kubernetes expert.
  4. With the explosion of the Kubernetes ecosystem, it became too much again. The width of expertise was reduced again. A person, e.g., could be a Helm expert (and hopefully a bit more).

This kind of development is supported by media as well as by companies. Conferences, books and magazines become more and more specialized. E.g., there really was a conference focused completely on Helm. And of course you find books solely focused on Helm.

Do not get me wrong: Helm is a really nice piece of technology. Just, if you take a step back, you realize it is a tool that helps making an overly complex scheduler more manageable. This is such a narrow area of expertise and so far away from the actual value creation processes of a company, it is an almost invisible speck on the IT landscape of a company. 3

This is close to “knowing everything about nothing”. And still, we see whole conferences, books and tons of articles just about Helm. Also companies respond to it. You see job ads that explicitly require Helm expertise. And consulting companies that explicitly advertise their Helm expertise, that they have consultants proficient in Helm. Etc.

As explained in footnote #3, Helm is just an arbitrary example. There are thousands of other examples that would be as good.

Overall, it can be said the IT market drives its participants towards hyper-specialization as default response to the FOMO cycle. Specializing more and more seems to be the “natural” response: The media tells you, you are on the right track. If there are so many articles, books and even conferences about the topic, it must be very important. And if more and more companies put it in their job ads, it must be the way to go. Or?

Emphasizing the weaknesses of humans

The problem is that hyper-specialization, even if looking like a “natural” response to the forces seen on the IT market, is the wrong move in more than just one way.

First of all, it is old industrial division-of-labor thinking, trying to split up a task of growing complexity in smaller steps. The problem is that these days we tend to face complex tasks accompanied by a high degree of uncertainty. Complexity cannot be tackled with sophisticated division-of-labor approaches. Those are good to tackle complicated tasks 4.

To tackle a complex problem, you need a complex solution system. This is one of the foundations of complex systems theory. A complex solution system in this context is a cross-functional, closely collaborating team. It is the cross-functionality and the close collaboration that creates the required complexity to solve the task at hand.

The more you drive people towards hyper-specialization, the harder it becomes to solve today’s complex tasks. Teams would need to become bigger and bigger. Also hyper-specialization means you know less and less about the work of the other team members which impedes collaboration. Thus, from this point of view it is the wrong move.

It also does not exploit the actual strength of humans. The more you drive a person towards hyper-specialization, the more you drive a person towards becoming a small cog in a machine – a cog that is expected to always perform its job flawlessly.

But that is not the strength of humans. Quite the opposite, we are really bad at flawlessly performing repetitive work. We tend to make a lot of mistakes in such settings – some people more often, some people less often. But in the end it sort of exploits our weaknesses which I think is a bad idea.

Hosts of “quality assurance programs” have been set up and continuously refined to “exorcise” errors from humans. Our whole education system meanwhile is built on the unhealthy premise that errors are “bad”. But: We are humans. We make mistakes. That is one of our defining traits – like it or not. If we were different, we were not humans, and most likely we would not exist anymore as a species. 5

The actual strengths of humans

Still, while talking about traits: What are particularly strong traits of humans?

Humans are great at dealing with unexpected situations. Okay, we might not always come up with the best solution possible. But at least we do not act like a machine that does not know how to process the unexpected situation, just throwing an error message and then ceasing to work.

The more we know about our context, the purpose of our task and its current overall state, the better we typically respond to an unexpected situation. And if we bring together a group of people with different skills and points of view who know how to collaborate with each other, we further increase the chances of coming up with a good response to an unexpected situation.

This ability to respond sensibly to unexpected challenges is one of our particular strengths as humans. This is one of the big advantages we have over machines. This is still a quite unique capability of humans.

I know the sheer imagination of humans who spontaneously respond to unexpected situations gives the creeps to all those people who strongly believe in predictability, rules and processes. Many decision makers see this as an uncontrollable risk they are not willing to take (often because they are afraid if the “wrong” decision is made, it makes them vulnerable and their career is at risk).

But the truth is that today’s IT projects tend to be full of unexpected situations, full of complexity and uncertainty. If we insist in tackling all these situations with more and more processes and rules, becoming more an more complex with every “black swan” we meet, we do not only respond poorly in most of the cases. We also waste the biggest potential of the humans involved.

Thus, going for hyper-specialization in the end wastes our biggest potential, a potential we desperately need to face today’s market and IT demands. Instead of cogs in a process machine we need people with a good balance of broad and deep expertise. Some call them “T-shaped” (be aware that there can be more than one vertical bar in the “T”). Others call them “paint-drip shaped”, illustrating the balance between broad and deep expertise.

Probably we also need a few deep experts, a few hyper-specialized people – the Helm expert, to stick to our example. But usually the problems we face are not due to not knowing some intricate detail of our technology. Usually the unexpected problems are of a much broader nature, requiring a group of people with different skills who need to put their heads together to come up with a good solution. The solution typically is found in the connections between the people involved, in their interactions, not in the detail knowledge of one of them.

Thus, it can be said that falling for the FOMO cycle and following the hype industry in the end prevents us from successfully solving the problems we face today. But as long as “team spirit” will remain just a stale platitude in the job ads of most companies, this probably will not change. Another irony …

Summing up

The cognitive overload resulting from constantly being bombarded with tons of new tools, technologies and approaches “one cannot afford to miss” typically leads to the application of “survival patterns” by the people affected. All of them are undesirable in one way or the other.

The default pattern, also being reinforced by the whole IT industry, is hyper-specialization: Specializing in a smaller and smaller area of expertise. Unfortunately, hyper-specialization focuses on the weaknesses of humans which makes it harder to tackle the complex challenges we face in IT today and often leads to more dehumanized work.

The actual strengths of humans, being able to deal with unexpected situations (we face a lot in the complex world of IT today) on the other hand are undermined by hyper-specialization, inhibiting the development of T-shaped individuals who can work together effectively in cross-functional teams.

In the next and final post of this little series (link will follow), I will give a few recommendations how to deal with the hype industry and – hopefully – leave the rat race. Stay tuned … ;)


  1. FOMO: Fear of missing out. The fear to miss the “next big thing” – see also the first post of this blog series. ↩︎

  2. I heard this statement from Nathan Myhrvold in the documentation “The creative brain” (available, e.g., on Netflix). I like this statement a lot because IMO it perfectly describes the hyper-specialization development. Hence, I took the liberty to use it here. ↩︎

  3. Again, this is not targeted against Helm by any means. I just took Helm as an example of a very, very specialized tool helping to illustrate the degree of hyper-specialization we tend to accept as “normal” today. I could have used many other tools and technologies instead. E.g., any other card on the CNCF landscape would do the trick, either. And there are many, many more examples outside the container domain. ↩︎

  4. For a distinction between complicated and complex systems and tasks, see, e.g., the Wikipedia page for Cynefin or the Cynefin introduction by Dave Snowden, one of the inventors of Cynefin. ↩︎

  5. But that would be a topic for a different post. If you do not want to wait for it, I can recommend to read some literature about how our brain works. It is very interesting to see, how some of seemingly weaknesses of the way our brain works turn out to be essential for our survival. ↩︎