Everyone has something to teach

As someone who is apparently in a microgeneration between Generation X and Millennials, I feel constantly the tension between the “old ways” of doing things and the “throwing things against the wall to see what sticks” approach.

This article frames the issue nicely: everyone has something to teach, no matter whether you’re the person with lots of experience to share, or the person with the new approach.

Light patterns

Gaining experience takes time, effort, and often comes at the price of making painful mistakes. You don’t want to let those lessons go. You want them to mean something, to help you from making the same painful mistakes again. To help others from making the same mistakes you made. So it will always be the case that those with the most experience – and the good, smart, accurate wisdom that comes from it – will be the least willing to adapt their views as the world evolves.

Neither should be the case, because every generation cycles through the same process. Today’s older generation once understood the world better than their parents, who scoffed at them. Today’s younger generation will one day be stuck in the antiquated norms of their past, and their kids will scoff at them. I can imagine my son in 80 years screaming, “Get off my metaverse lawn!”

One takeaway from this is that no age has a monopoly on insight, and different levels of experience offer different kinds of lessons. Vishal Khandelwal recently wrote that old guys don’t understand tech, but young guys don’t understand risk. Another way to put it is: everyone has something to teach.

Source: Experts From A World That No Longer Exists · Collaborative Fund

Image: CC BY Tea, two sugars

Ignore the sociotechnics at your peril

This post is focusing on technical teams looking after software. But it can also apply to anything where systems are being developed and/or maintained.

Each set of markers we added to our system provided new context to form assumptions and frame our thinking. Everything in the visualization existed whether we were looking or not. It becomes clear when looked at this way that each of these dimensions is inextricably linked. It’s impossible to think holistically about software without thinking about the operational environment, or the users of the system, or the people involved in building and maintaining it. These things come together to create another lens through which we can view the world.

It’s important to point out that the final image here is still incomplete. We’ll never fit all of the contexts into a single model. We could keep going, adding more and more context. A fascinating one, for example, would be marking the beginning of the COVID pandemic, when a team that perhaps was colocated started working remotely, and when stress and risk of burnout increased considerably. Otherwise, we’ll eventually include the whole world, but it’s interesting to continually zoom out and see how a new lens helps frame our perspectives.


Many organizations have adopted the practice of doing “post-mortems” or “retrospectives” after incidents. Retrospectives are great! Unfortunately, I think a lot of learning is left on the table by the adoption of template-driven processes that produce shallow understandings of what transpired. I’ve spoken about how I think we can improve this. There are also experts in the field who provide training and consulting in incident analysis. There are also communities and companies dedicated to helping you improve this practice.

Source: Sociotechnical Lenses into Software Systems | Paul Osman

Wealth is a product of luck

This seems obvious to me: that luck plays a great part in success. Well, serendipity, perhaps which can always be given a helping hand by elite networks and pushy parents…

Definition of luck

The conventional answer is that we live in a meritocracy in which people are rewarded for their talent, intelligence, effort, and so on. Over time, many people think, this translates into the wealth distribution that we observe, although a healthy dose of luck can play a role.

But there is a problem with this idea: while wealth distribution follows a power law, the distribution of human skills generally follows a normal distribution that is symmetric about an average value. For example, intelligence, as measured by IQ tests, follows this pattern. Average IQ is 100, but nobody has an IQ of 1,000 or 10,000.

The same is true of effort, as measured by hours worked. Some people work more hours than average and some work less, but nobody works a billion times more hours than anybody else.

And yet when it comes to the rewards for this work, some people do have billions of times more wealth than other people. What’s more, numerous studies have shown that the wealthiest people are generally not the most talented by other measures.

What factors, then, determine how individuals become wealthy? Could it be that chance plays a bigger role than anybody expected? And how can these factors, whatever they are, be exploited to make the world a better and fairer place?

Source: If you’re so smart, why aren’t you rich? Turns out it’s just chance. | MIT Technology Review

Image: CC BY-ND fearthekumquat