Water flows downhill, and tech solves the easy problems first.
After the launch of Amazon and Google, when smartphones reached critical mass, an easy problem to solve involved bridging information with stuff. So you could use your phone to summon a car, a case of beer, a dog sitter or just about anything that someone with resources might want to get, with convenience.
It took little in the way of insight, leadership or extraordinary effort for these things to unfold. If you didn’t build that app, someone else was going to. The business model existed, the funding was available and the pattern was there to be matched.
And it’s tempting to defend the easy shortcuts that technology exposes. “It’s too expensive for us to moderate…” or “the market will sort itself out” or “We need to hire the obvious person to do this work…”
Sometimes, though, humans show up to bend the curve of technology. They push against the conventional wisdom, avoid the obvious paths, and take responsibility for the impact of their work.
Kheyti is an example of this. Patient capital might not be as seductive as the next obvious step in tech’s evolution, but it matters.
This leads to an often under-asked question: What is technology for?
If we’re simply conduits for the evolution of tech in all its forms, bystanders and witnesses who might profit if we’re standing in the right place at the right time, that path is well-lit. People with capital and influence can make pronouncements about the utility of this approach, and how humans should merely step aside and applaud…
Or perhaps we can show up to bend the curve.
We can decide that technology exists to create opportunity, to help people find resilience, meaning and connection. We can realize that technology changes the status quo, and that change is up to us. Perhaps the tech industry can eagerly accept boundaries, responsibilities and obligations that match the leverage at their disposal.
Peter Parker’s Uncle Ben was right. It’s possible to use this moment of power to eagerly seek out responsibility.
Forty years ago, Arlie Russell Hochschild wrote about emotional labor. The work that frontline employees had to do (especially women) in managing and expressing emotions as part of their job. She talked about how exhausting it was for flight attendants to show up with a smile, even when they didn’t feel like it.
Emotional labor is the opposite of the industrial economy’s task-based, measured output. Even if we don’t dig ditches, the offer for a certain kind of work was: Process this pile of papers and we don’t care whether you like (or pretend to like) your job. The labor is the easily measured stuff.
But AI and mechanization have turned this sort of task work into a race to the bottom. If you don’t bring emotional labor to the task, it’s probable that you’re engaged in a race to the bottom. The piecework and easily measured ‘real’ skills are soon automated or outsourced or diminished in value.
Emotional labor has become a competitive advantage. Our commitment to showing up as a human, even (especially) when we don’t feel like it is precisely how we create value. And it’s this human work that helps us feel seen and valued as well.
Complicated problems have a solution, and the solution can often be found by breaking the complicated portions into smaller pieces.
And complicated problems often have an emotional component, because there are parts of the problem we don’t want to look at closely, or deal with personally.
If you’re lucky enough to be handed a complicated problem, know that effort and guts can often get you where you’re going.
If it’s not a problem, it might be a situation. A complicated situation has no clear solution, no win-win, no easy way forward. It’s simply a situation to be dealt with.
But this is very different from a complex problem.
Complex problems aren’t actually problems at all. They are non-determinate systems, systems that change based on how we engage with them. Push on one part of a complex problem and a different part will change the system. Healthcare, climate and technology systems are all complex problems.
When facing a complex problem, it helps to embrace the fact that we’re dealing with a system that shifts over time. One where the rules and the solutions evolve in non-predictable ways.
Some ways to dance with the complex:
Name it. If you and the team understand you’re dealing with a system, you won’t fall into the trap of treating this the way you treat other problems. We can’t fix systems until we see them.
Blaming the complex for your little piece of the problem isn’t really helpful. Instead, we can choose to sign up to work on the entire problem, not just a symptom.
Don’t turn away. We’re hesitant to sign up to deal with problems that seem difficult to solve. And yet, as our world becomes ever more connected, this is precisely what we’re called on to do.
One way forward is to isolate part of the system if you can, and turn that part into a complicated problem that we can figure out how to solve. And then learn, evolve and repeat.
In some fields, it’s always been pretty easy to tell. Either the building falls down or it doesn’t. Either the car starts after you charge the battery or it’s still dead. We can ask easy questions about how long it took or how much it cost.
But endeavors with crisp measurements often don’t attract people looking for a different sort of work. At many schools, more folks sign up for humanities than engineering.
Lately, more data is showing up in fields where it was elusive. Not just the efficacy of medical approaches, but the user satisfaction or time spent on creative endeavors or the stickiness and yield of educational approaches.
And closer to home for many, public health. We can actually tell with a great deal of granularity the impact of things like hand washing or placebos.
The irony? When data shows up in a field where we’re not expecting it, a common response is to either ignore or challenge the data. Not because it’s not helpful, but because it is not what we’re used to.
After twenty more minutes of searching, there it was, right on the cookbook shelf. But the spine was much more subtle than the cover, and it hadn’t been what I was looking for or expecting.
We spend a lot of time on our (metaphorical) book covers. That first impression, the way we tell our story or introduce ourselves or earn the sale.
But the real win is often later, when someone finds us when they’re looking for us.
This isn’t SEO. That’s what happens when we try to win a search for a generic category. SEO is coming up first in the search for ‘plumber’.
This is human search. Being there when someone is looking for you. The first step is being the sort of resource that people care enough to look for. And the second is being findable.
Here’s a simple XY grid to help you think about your next project, freelance career or startup:
All too common are ‘fun’ businesses where someone finds a hobby they like and tries to turn it into a gig. While the work may be fun, the uphill grind of this sort of project is exhausting. If it’s something that lots of people can do and that customers don’t value that much, it might not be worth your time. Taking pictures, singing songs or playing the flute are fine hobbies, but hard to turn into paying jobs.
On the other hand, in the top right quadrant, there’s endless opportunity and plenty of work for people who can do difficult (unpopular) work that is highly valued by customers who are ready to pay to solve their problems. A forensic accountant gets more paid gigs than a bagpipe player.
When you choose to take on a real problem that involves difficult work, but you’re serving a customer base that has few resources, thank you. Your quest is going to be a long one, but if you believe in the impact you’re creating, this can be a useful way forward.
And in the bottom right quadrant is a professional athlete or another gig where if you actually are the best in the world, you’ll do fine. Just know what you’re getting into before you start. The Dip is real.
AI pushes us to do what we actually get paid to do: make decisions.
Craft used to drive our hours or even days. Get the pen lines just right. Source the Letraset. Get your instrument in tune. Sweat the details, because the details are everything.
Now, I can choose from 1,000 typefaces, and they’re all optically correct. Autotune has transformed commercial music, and the new AI grammar tools make it trivially easy to make edits or find errors.
The part of us that’s focused on getting it done so we can move on to the next thing accepts the defaults and settles for what the algorithm offers.
That ignores the real work. Taking ownership of the decision.
This or that. The system tees them up. We decide (or answer with “none of the above.”)
Reclaiming agency pushes us away from, “I’m just doing my job,” and into, “I made that.”
PS I made this art in a few moments with AI, but I’d like to be clear that every word I publish, in every book or blog post, is written from scratch, by me. No team, no AI. If that changes, I’ll let you know.
It could be that the constraints that led to the default are long gone.
They might be perpetuating bad choices, injustice or sub-optimal outputs.
The best way to fix something is to look at what we assume is the ‘right’ starting spot.
In persistent systems, it might be difficult to change the default setting, but knowing what it is and how it got there is a great place to begin.
PS on this topic, in two weeks people are going to give out a bazillion dollars worth of cheap chocolate for Halloween. Please don’t buy cheap chocolate.
Different kinds of people prefer pop tarts to pizza, or prefer expensive wine to beer, or prefer amusement parks to bowling.
Except everyone is the same and everyone is different.
What’s actually useful is to realize that in this moment, under these conditions, this person and people who have this person’s preferences, will often choose a certain path.
When groups of people with a shared preference or attitude choose to do something, we see markets and cultural trends.
But these people aren’t different kinds. They’re simply people responding or reacting to what’s on offer. This means that people can have different responses based on different offers and different conditions. We’re not stuck forever, simply grooved into certain patterns.
October 15, 2023
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