As a social psychologist by training, I always loved the TV show “What Would You Do”. Loosely based on social experiments, it uses a hidden camera format to show the varied reactions people have to ordinary situations based on environmental cues.

One of my favorite episodes from the very first season had a simple premise: people ignoring or informing strangers of social issues like food in their teeth, toilet paper on their shoes, or an unbuttoned shirt. And I love it because of how rarely people inform; the vast majority of folks clearly notice the problem but do nothing about it.

This is top of mind because recently, I was reviewing video applications for an Integration Specialist role at Oceans. In one stage of the process, we ask people to record a short video with a simple prompt: share one of your most deeply held but controversial opinions about the workplace.

I like this prompt because it is both thoughtful and personal, mimicking the psychologically safe communication that Integration Specialists have to facilitate for our Divers and Clients. And we’ve had good success using it to find talent for the current team.

Given the epically bad job market (or how epically awesome it is to work at Oceans), we got several thousand applicants for the role. So even though this was a later funnel stage, we still had about a hundred videos to watch.

It was painfully obvious who was using AI rather than thinking through their own beliefs. Across several hundred videos, I now know the various answers that the flavors of LLMs tend to give (four-day work week, quality over speed, performance is about alignment), down to the bullet points and supporting evidence. Some candidates were better or worse at presenting them, but the beliefs themselves? Computer-generated.

Since it is a later pipeline stage, we generally give feedback even to the candidates who don’t move on. But how do you tell someone they’ve got AI in their teeth?

From a macro perspective, it is important because it is likely that candidates will continue to miss out on roles if they keep going as they are. But for an employer brand, staying quiet is the safe bet; at least some of the time, people shoot the messenger, and we lose nothing if candidates continue to fail elsewhere.

Fortunately, morality isn’t game theory and one of the Oceans’ values is “Integrity is our Superpower” so we are going to deliver the feedback. But I suspect that not everyone will make the same choice and as we think about the divides that AI usage will create, it is useful to remember that many of them aren’t about AI at all; knowing what behaviors to do or not do is largely a function of the feedback you get from society, and that means all the sexist/racist/classist tropes apply. If we want a better and more equitable world, we have to be willing to tell people when using AI is hurting rather than helping.

A lot of hustle porn (a term I first heard from Alexis Ohanian Sr.) has the same bombastic quality as sexual porn; a recent article I saw on LinkedIn screamed that you absolutely should expect your employees to spend 20% of their time outside of work on professional growth activities, because otherwise they must be terrible performers (!?!).

That isn’t real. That is the movie version of an important, messy part of our lives. And just like porn can damage our understanding of reality, particularly for those who are new to sex, hustle porn can damage our understanding of what work should actually look and feel like, particularly for those that are new to the workplace.

As part of my work at Oceans, I travel to Sri Lanka for two weeks every two months. These are planned trips, with disciplined routines before and after that focus on non-work activities. And I sleep pretty well on planes, so the 27-hour flight isn’t normally too bad.

But this time I sat next to screaming babies on both connections (to be clear, I would also scream if no one explained why my head felt like it was going to explode), so on a leadership team call last night, I apparently took an unintended nap (the picture above, lovingly captured by my team from the recording).

We joke about it now (and of course there is a Slack emoji) but at the time, my team freaked out; they were worried that something was seriously wrong. And that is the right reaction: work kills people, directly and indirectly, every single day.

Fortunately, I’m fine and after a good night’s sleep, ready to get back to work worth doing alongside the fabulous folks at Oceans. But it is important that we are both honest about moments like these and that we don’t turn them into a culture of celebration.

This is one moment, in one meeting.

It isn’t a well-lit, in-focus shot of someone sleeping under their desk to show how passionate they are so a VC will deign to grace them with their next round of funding.

It isn’t the culture of Oceans and something we believe every employee should do, every day. It isn’t even something we think our C-suite should be doing.

Yes, sometimes work causes you to hustle a little more than usual because of its elastic nature; you can’t perfectly plan for every demand curve. But that’s a bug, not a feature.

How do we get rid of hustle porn? By not creating or engaging in it, of course, but that’s easier said than done; like porn, it exists because we consume it. So we need a better plan.

Maybe we need MakeWorkNotHustlePorn; acknowledging that sex is fun to watch and then encouraging the creation and distribution of sex that focuses on unsimulated reality is an appealing blueprint for how we tackle this problem. LinkedIn can do a better job of flattening the demand curve, reducing the availability of sensationalist posts. Posters can focus on authenticity and steward an understanding of work that is grounded in reality. And most importantly, we can choose to engage with content that is authentic and not always attractive (hello, shiny bald head).

Because it really does matter. How we work in public influences how future generations will work in private and if we don’t design that experience deliberately, we run the very real risk of damaging their relationship with one of life’s great joys. I believe in work worth doing; I don’t believe in dying for it.

Technology has always been at its best when it allows us to selectively reject it, to choose our own hardship.

I don’t really use AI for much but periodically, it comes in handy. And then, just when I’m not paying attention, it punches me right in the gut.

Because I’m doing a workshop on pressure maps, the organizers asked me to generate a sample. So I decided to make a pressure map about the promoting and inhibiting pressures for using a pressure map (meta, I know). And because it is 8:30pm the night before and I’m not sleeping much these days, I needed a thought partner.

Enter ChatGPT. I wrote a short prompt explaining what a pressure map is and what I wanted and sure enough, it spat back a very comprehensive list of pressures. But as I was congratulating myself on such good prompt engineering, I noticed it had appended the following:

“Want to contrast pressure maps with other tools (e.g., COM-B, Fogg, behavior over time graphs)? Or identify behavioral nudges that increase likelihood someone actually makes one?”

Paging Jared Peterson; this thing is trying to make a framework competition! And it clearly wasn’t using my prompt; it already knew all about pressures maps. So I asked it who invented the pressure map.

“​The concept of the pressure map, distinguishing between promoting and inhibiting pressures influencing behavior, was developed by behavioral scientist Matt Wallaert. He introduced this tool as a core component of his Intervention Design Process (IDP), detailed in his book Start at the End. The pressure map serves to identify and visualize the factors that encourage or discourage a particular behavior, aiding in the design of effective interventions.”

Ruh roh. Clearly someone fed this thing my book. So I threw it a curve ball: is pressure mapping similar to Lewinian field theory?

“Yes—very similar. In fact, Wallaert’s pressure map is explicitly inspired by Kurt Lewin’s field theory. Lewin’s field theory (1940s) conceptualized behavior (B) as a function of the person (P) and their environment (E): B = f(P, E). Within this “field,” driving forces promote movement toward a goal and restraining forces oppose it. Lewin used this to explain both action and stasis (e.g., why people stay in undesired states). Wallaert’s promoting and inhibiting pressures directly parallel Lewin’s driving and restraining forces. The key differences:

 <insert very smart chart here>

In short: pressure maps operationalize Lewinian dynamics for intervention design. Wallaert takes the theory and makes it actionable.”

Well…shit.

I’m not scared of AI. People still like to do workshops with me, because my jokes are better. I’m happy that someone can have a cogent discussion with a computer about applied behavioral science.

But as Joanna Maciejewska said, “I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes.” I like thinking! I have a high need for cognition! That ChatGPT can save time and cognitive effort for someone else doesn’t mean I want it to do it for me in my field of expertise.

But that’s the magic. Your laundry is my PhD thesis and vice versa. We all like thinking but we don’t all like thinking about the same things. The promise of AI isn’t that it automates everything but that it has the potential to automate everything. So that then we can choose what we don’t want to automate, what we want to only enhance or even do completely on our own.
It is like really great camping gear. Some people love glamping. Some people want to kill their own food with a spear they chipped out of squirrel bones. Technology has always been at its best when it allows us to selectively reject it, to choose our own hardship.

And so the most important feature of AI is always, always going to be the ability to turn it off. To be human is to struggle; utopia is just choosing what those struggles are going to be.

Consider productive uncertainty as a more mission-aligned approach.

In most sports, you always know where you stand. The score is continuously updated, so whether it is a touchdown or a goal or a basket, you know the balance between you and your opponent at all times. And you often make strategic decisions with this knowledge: slow the pace down here, speed it up there.

Boxing is a notable exception. Knockouts aside, it is scored on a round-by-round basis, but you don’t know the score until after the bout has concluded. And because you only have a general sense of how you are doing, most boxers will continue to actively fight until the last bell, in case their naive understanding doesn’t match what is on the scorecards.

With the focus on OKRs and metrics, modern business tends to feel like most sports. You have a target and at least somewhat continuous measurement, so you can effectively judge how hard to lean in given your progress and the time left in the period (a sprint, a quarter, a fiscal year, whatever). 

This visibility is typically championed by managers, who then have a better understanding of velocity and can decide where to make investments in order to meet expectations. This aggregates all the way up the hierarchy to the CEO, who can balance the progress against the stock market and competitors.

But what if some parts of working need to be more like boxing? Instead of achieving a target, you simply continue to fight as actively as you can to reach the best possible outcome.

To say “your sales quota is $10k” is to make it more likely that each of your salespeople will land around that number, which is great for predictability but not for maximization. Whereas “we measure using deal value; fight for every deal” will create greater variability but likely better total outcomes; the productive uncertainty keeps people focused on continuous incremental gains.

This is more about targets than it is OKRs. Knowing what you are trying to accomplish is certainly in line with a boxing mentality; matches are scored on clear criteria that every boxer understands. But rather than setting a specific target, you use your understanding of the criteria to fight for every point.

Some workplaces already function this way and reveal some of the risks. Emergency rooms, for example, operate on the assumption that every person is worth helping, not on the notion that you simply have to help more than the target for the day. And because they fight for each life, ER staff have one of the highest burnout rates and worst worklife balances of any team in a hospital. Elite boxers fight only once or twice a year for good reasons.

But there are other structural ways to combat burnout: boxing is a limited number of timed rounds precisely to prevent injury. And so while productive uncertainty isn’t a fit for every team, it is worthwhile to question where targets make sense versus simply establishing a system for scoring and encouraging a point-by-point mentality.

And this matters, especially in mission-aligned businesses. Sales quotas aren’t vital to the advancement of the world but saving lives is. At Oceans, every sale is a job opportunity for someone who would otherwise be forced to leave their home and those they love; the target is not “100 net new accounts next quarter” – it is as many as we can possibly win. I don’t want “less than 5% regrettable attrition” – I want 0%. Because that is as mission-aligned as we can possibly be.