Don Norman attempts to explain Why bad technology dominates our lives:

Curiosity is, on the whole, a virtue. We have evolved to be curious. Our nervous system is especially sensitive to change, and changes in the environment attract attention. But the technology-centered view labels this natural, creative trait as a liability: Curiosity is renamed as distraction. A human virtue is now turned into a liability.

Worse, many businesses have learned to exploit our curiosity. The continual bombardment of tantalizing tidbits of information deliberately designed to grab our attention away from other, potentially more valuable activities are distractions that can lead to accidents, injury, and interpersonal problems. What kind of business exploits curiosity for its own ends? Almost any business that discovers there are profits to be made by continually engaging people’s curiosity, hopes, and interests. For example gambling, computer games, social networks, and even television series that can go on and on, week after week, year after year, trapping their viewers into addiction.

We talk a lot about “personal responsibility” and how it’s up to each person to make good choices about how they spend their time. That is true as far as it goes, but it brushes over the incredibly strong forces of persuasion theory and how easily humans are manipulated. “Personal responsibility” doesn’t give us a hall pass to exploit the human brain’s weaknesses.

Nikhil Sonnad writes that Everything bad about Facebook is bad for the same reason:

Underlying all of Facebook’s screw-ups is a bumbling obliviousness to real humans. The company’s singular focus on “connecting people” has allowed it to conquer the world, making possible the creation of a vast network of human relationships, a source of insights and eyeballs that makes advertisers and investors drool.

But the imperative to “connect people” lacks the one ingredient essential for being a good citizen: Treating individual human beings as sacrosanct. To Facebook, the world is not made up of individuals, but of connections between them. The billions of Facebook accounts belong not to “people” but to “users,” collections of data points connected to other collections of data points on a vast Social Network, to be targeted and monetized by computer programs.

Here’s the crux of it:

There are certain things you do not in good conscience do to humans. To data, you can do whatever you like.

This bit from Raluca Budiu and Page Laubheimer’s user study of digital assistants like Alexa, Google Assistant, and Siri echoes my thoughts exactly on why I don’t like interacting with them:

Many participants started speaking before formulating the query completely (as you would normally do with a human), and occasionally paused searching for the best word. Such pauses are natural in conversation, but assistants did not interpret them correctly and often rushed to respond. Of course, answers to such incomplete queries were incorrect most of the time, and the overall effect was unpleasant: participants complained that they were interrupted, that the assistant “talked over them”, or that the assistant was “rude.” Some even went as far as to explicitly scold the assistant for it (“Alexa, that’s rude!”).

Yep. When I interact with voice UIs I spend way more time and energy planning the exact sentence construction and pronunciation than when I simply type and swipe and figure it out as I go. And then, if one word is out of place, the whole thing falls apart. It’s just too stressful. I can’t.

Eugene Wei’s Invisible asymptotes is a long, excellent article about the importance of not just thinking about product-market fit, but also product-market unfit:

For so many startups and even larger tech incumbents, the point at which they hit the shoulder in the S-curve is a mystery, and I suspect the failure to see it occurs much earlier. The good thing is that identifying the enemy sooner allows you to address it. We focus so much on product-market fit, but once companies have achieved some semblance of it, most should spend much more time on the problem of product-market unfit.

For me, in strategic planning, the question in building my forecast was to flush out what I call the invisible asymptote: a ceiling that our growth curve would bump its head against if we continued down our current path. It’s an important concept to understand for many people in a company, whether a CEO, a product person, or, as I was back then, a planner in finance.

He goes on to discuss those asymptotes for different companies (for example, shipping fees for Amazon). Another interesting bit:

We speak often of the economics concept of the demand curve, but in product there is another form of demand curve, and that is the contour of the customers’ demands of your product or service. How comforting it would be if it were flat, but as Bezos noted in his annual letter to shareholders, the arc of customer demands is long, but it bends ever upwards. It’s the job of each company, especially its product team, to continue to be in tune with the topology of this “demand curve.”

I see many companies spend time analyzing funnels and seeing who emerges out the bottom. As a company grows, though, and from the start, it’s just as important to look at those who never make it through the funnel, or who jump out of it at the very top. The product market fit gradient likely differs for each of your current and potential customer segments, and understanding how and why is a never-ending job.

Figuring out what your product’s invisible asymptotes are sounds like a really good thought process to me. At the beginning of the article Eugene mentions one way Amazon tried (and succeeded) in answering this question:

For people who did shop with us, we had, for some time, a pop-up survey that would appear right after you’d placed your order, at the end of the shopping cart process. It was a single question, asking why you didn’t purchase more often from Amazon.

Another technique he mentions:

One approach I’ve taken when talking to companies who are trying to achieve initial or new product-market fit is to ask them why every person in the world doesn’t use their product or service. If you ask yourself that, you’ll come up with all sorts of clear answers, and if you keep walking that road you’ll find the borders of your TAM taking on greater and greater definition.

A good way to frame this could be to ask yourself something like this:

If we didn’t change anything about [product name], at what point would we hit a growth ceiling, and what are the factors that would cause that?

If you can have a reasonably confident answer to where the S-curve inflection point will be, you can start planning on avoiding it early. That’s a worthy effort, and definitely something I intend to think through for our products.

In Filter Failures Ethan Chiel asks if platforms are sucking the joy out of the internet, and he makes a pretty compelling argument:

The internet as we use it now is, for the most part, what the large platforms want it to be: an engine for serving us what their various systems think we want, or what we wanted before, or what our demographics want en masse.

Here’s the problem:

What’s lost in the process is whatever you might have found that neither you nor an algorithm might guess is interesting. Some song in a forum thread you idly clicked on, a news item about something you’ve never expressed interest in or heard of that you read because you had 5 minutes to kill and it caught your eye.

This reminds me of how we used to browse music stores. We idly flipped through CDs, and picked a few to try out in the listening booth based on the cover, the song titles, and some undefined ¯_(ツ)_/¯ factor. Now we just see and hear what we’ve seen and heard before, and the cycle continues…

While reading this great interview with the band CHVRCHES I came across a passage that struck me as a good description of how product managers should work. They describe the producer on their latest album like this:

And I think that’s why he’s so good at what he does. He’s not a kind of producer that has one thing that he does and he tries to make every artist do that thing. He steps into the room and tries to figure out the people that are in it and figure out the music they’re making, and then how can he add something to that and offer wisdom and guidance and make it better.

We all have methods and techniques we gravitate towards: Jobs-to-be-Done, Agile, customer journeys, sticky notes… It’s totally fine to have favorite methods that we know inside out and that have worked well in the past. But it would be a mistake to force one of our favorites on a team when it’s not the right thing for them.

One of the most difficult things a product manager needs to do is first understand how a team works and what makes them effective, and then figure out which methods and processes can create the right environment for them to thrive. That’s the skill that sets apart the great product managers from the good ones.

Ah, estimates. That thing we all know we need to do (and get better at), but no one really wants to talk about. Well, Gina Trapani takes the topic head on in a great post called Estimation Is a Core Competency:

Estimates don’t need to be perfectly accurate as much as they need to be useful.

Good estimates create trust among your PMs and business leaders and collaborators. Being able to identify the risks and uncertainties in a project early gives your project team the information they need to plan around those risks and uncertainties.

Are you consistently getting waylaid by unforeseen work mid-flight, or just taking longer to execute on the work you did know about? That creates distrust between engineers and business leaders, and if it happens repeatedly, that distrust compounds and calcifies into resentment— that is, if you’re still in business.

Gina goes on to give some great tips for better (i.e. more useful) estimates.