Marcus Buckingham and Ashley Goodall discuss some fascinating research about how people learn in their essay The Feedback Fallacy:

What findings such as these show us is, first, that learning happens when we see how we might do something better by adding some new nuance or expansion to our own understanding. Learning rests on our grasp of what we’re doing well, not on what we’re doing poorly, and certainly not on someone else’s sense of what we’re doing poorly. And second, that we learn most when someone else pays attention to what’s working within us and asks us to cultivate it intelligently.

As a parent it’s natural to see the elements of “positive parenting” in this research. We (generally) don’t have to deal with tantrums at work, but it still makes sense people would be more motivated by this approach than by reminders of what they’re doing wrong. Something to keep in mind as we work with our teams.

In Egoless Innovation Sari Harrison reminds us that ideas are not personal. As product managers our goal should always be to get the best outcome, not to win or be right or for everyone to “just get along”:

When the idea you are advocating for gets challenged, an egoless innovation mindset means choosing to see the challenge as a gift to the innovative outcome which the idea hopes to someday become. It means looking objectively at the input, responding to it authentically, saying “wow, good point, I’m going to take that back to the team” if it was a point you hadn’t considered and saying “yes, we thought of that and…” if you have. It means you don’t get flustered by questions or negative comments because you are focused on achieving the best outcomes, not your own status. If you can embody this, you will be helping move the culture towards more and more innovation occurring with less and less friction. And you will be more successful.

My favorite article of the week is one on change management, which, yes, I know how that sounds. Like you (probably), I hated the phrase “change management”. But in her article How to begin the invisible work of change management Cate Huston defines it as “taking teams that are struggling and helping them focus, align, and start delivering at their potential”, so now I finally understand what it is and I don’t hate the phrase any more.

Anyway, the entire article is excellent. If you’re interested in managing teams at all, you have to read this one. Here she is on diagnosing problems within teams:

Sometimes people look at teams and diagnose problems as an absence of process rather than an absence of values, or cohesion, or delivery.

And on the problem with trying to implement a process without understanding cultural context:

When teams struggle to do retrospectives, often the problem isn’t the lack of retrospective, but the lack of psychological safety that a productive retrospective requires. That might be due to lack of trust or cohesion on the team, or other problems that have not been addressed. You need to address those things first, otherwise the retrospectives will not be productive, and the process will be for naught.

Web agency Postlight wrote about their engineering culture, and what a breath of fresh air it is to read this list in the midst of the current “hustle til you drop” culture:

The positivity of our internal communication fosters a team that is warm and easy to engage with for our clients. Our clients are shocked by how easy it is to communicate with our engineers. No knee-jerk reactions. Just a friendly, two-way, conversation with professionals.

Or, as we phrase it in our Wildbit values, “As a team, we support each other to do the work of our lives.”

Liz Pelly’s Streambait Pop is a fascinating look at the “Spotify sound” and other changes in pop music brought about by streaming:

The Spotify sound has a few different variations, but essentially it’s a formula. “It has this soft, emo-y, cutesy thing to it,” Matt says. “These days it’s often really minimal and based around just a few simple elements in verses. Often a snap in the verses. And then the choruses sometimes employ vocal samples. It’s usually kind of emo in lyrical nature.” Then there’s also a more electronic, DJ-oriented variation, which is “based around a drop … It’s usually a chilled-out verse with a kind of coo-y vocal. And then it builds up and there’s a drop built around a melody that’s played with a vocal sample.”

The really interesting part to me is how it’s a sound that’s essentially designed to make you forget about it, so that you just keep streaming endlessly:

The chill-hits Spotify sound is a product of playlist logic requiring that one song flows seamlessly into the next, a formula that guarantees a greater number of passive streams. It’s music without much risk—it won’t make you change your mind. At times, these whispery, smaller sounds even recall aspects of ASMR, with its performed intimacy and soothing voices. When everyone wants your attention, it makes sense to find reprieve in stuff that requires very little of it, or that might massage your brain a bit.

After I read this article I went through my Spotify playlists and counted how many of them had the word “chill” in it. Let’s just say I’m too embarrassed to tell you…

But moving on, I think this “inoffensiveness” in music is one of the reasons I’ve started to listen to so many more genres over the past few years. I now like music that feels like it just doesn’t quite sit right. Any artist or band that combines a little discomfort with a lot of skill has my attention. Just one recent example that comes to mind is Double Negative by Low. I still don’t really know what it is. But I know it’s something really special.

A lot of words are being written about AI and machine learning these days, so it’s sometimes hard to know what to pay attention to. M.C. Elish and danah boyd’s Don’t Believe Every AI You See is one of those essays that I would consider essential reading on the topic. On the ethics of artificial intelligence:

When we consider the ethical dimensions of AI deployments, in nearly every instance the imagined capacity of a technology does not match up with current reality. As a result, public conversations about ethics and AI often focus on hypothetical extremes, like whether or not an AI system might kill someone, rather than current ethical dilemmas that need to be faced here and now. The real questions of AI ethics sit in the mundane rather than the spectacular. They emerge at the intersections between a technology and the social context of everyday life, including how small decisions in the design and implementation of AI can create ripple effects with unintended consequences.

And on the supposed “neutrality” of machines:

[There is] a prevailing rhetoric around AI and machine learning, which presents artificial intelligence as the apex of efficiency, insight, and disinterested analysis. And yet, AI is not, and will not be, perfect. To think of it as such obscures the fact that AI technologies are the products of particular decisions made by people within complex organizations. AI technologies are never neutral and always encode specific social values.

As Kevin Kelly also pointed out years ago in his book What Technology Wants, technology is never neutral. It possesses the collective values of its creators. And that’s where things so often go wrong. A great resource on this topic is Sara Wachter-Boettcher’s book Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech.

The Doist team makes some great points in their article Best Practices for Building and Managing a Remote Team, including something we’re embracing very strongly at Wildbit as well:

Managing a remote team effectively is not about monitoring the amount of time your team members spend online (in fact, that’s a great way to kill employee autonomy and motivation). It’s about building and supporting a team that doesn’t need to be micromanaged in the first place.

We wrote more about our approach to remote work here.

You might think that A rocker’s guide to management sounds like one of those “What X can teach us about Y” articles. And it is, I guess, on the surface. But it’s just so well done. As an intro:

The history of rock groups can be viewed as a vast experimental laboratory for studying the core problems of any business: how to make a group of talented people add up to more than the sum of its parts. And, once you’ve done that, how to keep the band together.

The article is less about management, and more about the different ways that teams can be structured, as well as the good things and pitfalls of each approach.