Recently, I was talking to a company that helps employers gather feedback from their in-the-field workforce in order to boost employee retention. And while they have a ton of customers fielding surveys, the product folks were concerned that managers don’t actually seem to be looking at the feedback dashboard.

My suggestion was to use a Max Headroom: take a few customers and do away with the dashboard entirely, instead having one of the product managers meet with them weekly to deliver the feedback. That way, the SaaS company could eliminate the dashboard itself as a variable and focus on the core behavior of managers responding to employees.

The tactic is named after a character from the 80s played by an actor in prosthetics but billed as computer-generated; the goal is to have a human frontend with a computing backend. And it is similar to another testing methodology, the Mechanical Turk, where the frontend is computing but the backend is actually human.

When I suggest this to folks, the objection is usually that humans aren’t scalable. Which isn’t entirely true; for many SaaS companies, a bit of quick math shows that you can have a CS person take quite a lot of meetings and still be profitable. 

But it also doesn’t matter. The point of a Max Headroom isn’t to find a scalable business model; it is to reduce the inhibiting pressures of a behavior to as near zero as possible. A meeting to go through the dashboard together doesn’t quite accomplish that; there are still plenty of inhibiting pressures like time, mental energy, etc. But it is meaningfully easier than trying to interpret and action a dashboard on your own.

The much larger, unspoken concern is: what if that doesn’t work? What if, even with human-delivered feedback, managers are still unwilling to do what it takes to retain employees?

And the sad answer might be that it is time to pivot. If you try a Max Headroom and your target audience still doesn’t do the behavior, even with inhibiting pressure reduced in unsustainable ways, then you may need to address your underlying behavioral statement.

There are exceptions where adding a human increases friction, like calling a taxi company instead of using the Uber app. But that is more typical in consumer products and less in SaaS, where value is usually produced by removing the cost of having a human do something.

Max Headroom tests are underutilized in product creation, because of the bias toward seemingly-scalable software as the solution to everything. But in an AI era, where we need to better decouple backends and frontends, they are more important than ever. Because if a human reading your AI’s script isn’t useful, your AI probably isn’t either.

A few years ago, I was talking to an entrepreneur working on an easy way to share STD test results and he said something that stuck with me: HSV is a dumb reason to break up with someone, because about 50% of the adult population in the US has it, and so it is 50/50 that even if you dump this person, you’ll have to dump the next person too.

To me, this is a very clever reframe. It doesn’t try to debate anything about the morality of breaking up with someone or minimize HSV itself. Instead, it just focuses on the probability of an event occurring in the next instance.

This is on my mind this week because of a conversation with a mentee about her immigration status. She’s looking to change jobs, so she’s been applying and getting interviews, but doesn’t get selected to continue because she only has 3 years left on her OPT visa.

So I invited her to do one of my favorite activities: a reverse roleplay. She got to be the interviewer, I got to be her. And when she asked me the question about my immigration status, I used a similar reframe: 

“The average tenure of someone at my level in a tech company is less than three years, so if you pass me over because of my OPT, it is just as likely that the person you choose to hire will actually leave in the same amount of time. So why not consider whether I’m the best person for the job instead of focusing on how long I’ll do it?”

Her job dropped.

Note that I’m not arguing the fairness of immigration status being a form of legal discrimination. And I’m not trying to convince the interviewer that I’m so much better than the next candidate that they should overlook the deficiency. Instead, I’m arguing it isn’t a deficiency in the first place because it is true for the majority of the population. 

This works for all sorts of perceived shortcomings in an interview. For example, take a job description I read once that had both “Ten years of applied behavioral science experience” and “Ten years of insurance experience” as requirements – a combination that literally no one had at the time. 

Rather than arguing that whichever you don’t have isn’t important or that you can make up for it in other ways, you can just rely on probability. By pointing out the non-overlap of those experience sets, you can level the playing field by arguing that everyone is going to have to learn something in order to do the job and so it might as well be you.

As the joke says, you don’t have to be faster than the bear, you just have to be faster than the other person they’re chasing. By pointing out equivalencies, you can reset the frame on yourself as a candidate to minimize shortcomings. You’ll still have to highlight your strengths, but it can sometimes eliminate at least some forms of bias.

There is a routine that plays out in the workplace time and time again. I ask HOW you’re doing, you tell me WHAT is happening, and then we proceed as if those two things are exactly the same. 

We don’t say “I’m sad because XYZ.”  We just say XYZ, as if any reasonable person should know how we will feel in reaction to that specific combination of events. But just because I know what is happening around you doesn’t mean I know how you feel; humans are variation machines and what feels good to me doesn’t always feel good to you.

So a few years ago, I started explicitly trying to draw out the use of emotion words. First by moving away from coded wording; instead of asking how you’re DOING, I ask how you’re FEELING, even though we typically tend to think of those as the same thing. And reactions vary in a typical 2×2: not/noticing, not/emotional.

Some people don’t seem to notice and don’t change their wording. And that needs to be alright; inclusivity doesn’t mean that we demand emotionality from people who aren’t comfortable using that framing in the workplace. Ditto for noticing and not changing; if I offer the prompt and they choose to take it a different direction, it is on me to respect that.

Others don’t seem to notice but implicitly switch to an emotional frame. And that’s really lovely: I’ve learned a lot about colleagues that I wouldn’t normally have known. In particular, I hear more about racism, sexism, and classism in the workplace this way – opening the door to emotion also opens the door to events that are likely to have emotional consequences.

And then some people notice, pause, and then switch to an emotional framing. This is honestly my favorite, because I imagine that it is now slightly more likely that they’ll do this for someone else, making the whole workplace a bit more humane.

The other thing I’m trying (and largely failing, because my superpower is having a two hour long conversation entirely about you) is to use more emotion words myself. Now I could argue that rather like a noticing/not emotional person, I should have the right to keep my emotions private. And I do.

But creating change often isn’t about exerting our right to comfort; it is about being personally uncomfortable in order to make other people more comfortable. Think of it like standing up to give someone your seat on a bus, except you’re standing out emotionally so others have the chance to do so as well.

Maybe this isn’t the hill you want to struggle up; there are plenty of other ways to make more inclusive workplaces that don’t evolve emotionality. But consider trying it as an experiment: at least for today, use and solicit more emotional wording and then reflect on how it changed your interactions. Do you feel…happier?

TLDR: Learning is not a smooth curve; we frequently grow in spurts and jumps. But those rarely align with external validation like graduation or licensing. Credentialism isn’t just inequitable, it is a business and cultural liability. And there is a market opportunity in refusing to accept the bias.

Recently, there has been a trend in mental health to switch to associates: psychologists who have not yet been fully licensed and cannot practice on their own, without supervision. This is driven largely by cost, as associates are 20-40% cheaper than their fully licensed peers, largely because they cannot create their own practices and must sacrifice some money to a mediating entity.

The requirements for an associate to become fully licensed vary by state but generally include an exam, some number of supervised hours practiced across various populations, and an annoying amount of paperwork that gets returned to you if you misspell something. Bureaucracy gonna bureauc.

Obviously, experience matters. In study after study, we can prove that people do actually get better at things over time; some version of “practice makes perfect” arose in pretty much every language for a reason. 

But that improvement is lumpy. When we look at learning, people tend to proceed in epochs, with long periods of level performance between jumps in ability. And those jumps are hard to pin down; if you do a cohort analysis, when people make a leap up is largely unpredictable. People who are learning and working together don’t necessarily hit milestones at the same time.

And so it isn’t necessarily true that you’re a better therapist today than you were yesterday and it certainly isn’t true that you’re a better therapist the day after the state declares you fully licensed than you were the day before. No one magically produces better work simply because they walked across a stage and got handed a degree.

And yet the day after graduation, your chances of getting a job go up dramatically. Credentialism, or the tendency to rely on formal qualifications over demonstrated ability, is rampant in hiring simply because across a large number of applicants, it is very difficult to find a better proxy.

The problem, of course, is that not everyone has equal access to credentials. Licensing fees can run into the thousands of dollars in some professions, let alone the time investment of a bewildering number of forms. Credentialism tends to amplify sexism, racism, and classism, because the systems that bestow credentials are themselves sexist, racist, and classist.

It is easy to look at associates as inferior and lambast the companies that are increasingly relying on them. Because hiring isn’t the only place credentialism occurs; consumers can just as easily interpret a credential as a valid signal of quality, without actually determining whether there is a real difference.

But ultimately, that drives up market pricing. Consumers who put false faith in credentials end up spending more, without any additional return. And because consumers are buying based on the credential, people are forced to get credentials to increase their wages; the tail is wagging the dog.

As employers, we have structural tools at our disposal: temp-to-hire as a method of determining actual ability, removing degree requirements, etc. And as consumers, we can use reviews as an alternative to credentials. But all of these are choices: ultimately, it is on us to give people the chance to demonstrate their ability to perform beyond their credentials.

And there is plenty to be gained. Besides combating inequity, in a market economy that overvalues credentials, there is a price opportunity in not making the same mistake: you can get more and better, for cheaper, if you’re willing to fight the bias that others are falling victim to. Fighting credentialism is a moral imperative but if you need an economic justification, it is certainly there.

TLDR: Narrow misses feel worse than wide ones because it is easier to imagine all the things that might have gone differently. But regret is a red herring; a near miss is an almost win and needs an “ante vitam” meeting.

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Imagine that you’re late for your train in two parallel universes. In one universe, you arrive on the platform just as the train is pulling away. In the other, you missed it by 30 minutes. Which universe would you rather be in?

Most people intuitively understand that missing it by a few seconds will feel worse and they’re right: we feel more regret about narrow misses than wider ones because of counterfactual thinking. It is easy to imagine a dozen “if only” ways you could have saved a few seconds: got out of the car quicker, chose your shirt faster, not had to look for your keys. It is harder to imagine saving 30 minutes.

Anticipating that regret, most of us would say we’d rather have missed it by a mile.

But regret is a red herring; it tends to make us focus on our past instead of our future. And that change in framing makes all the difference. Because in reality, missing something by a few seconds means that next time, you’re very likely to make it. If you are going to be taking this train every day, it is far better to have missed it by an inch than a mile.

Back to the train platforms. Now you’re not alone: you’re traveling with your best friend. 

When you miss by inches, the fingerpointing begins. Because when we feel regret, we often externalize it and begin the blame game; it feels better to grumble about how slow your best friend is than confront the reality that it might have been your fault. 

This happens all the time in workplaces: near misses devolve into analysis paralysis as Product, Design, Marketing, and Tech focus on who to castigate. Blame is easy because any number of decisions by any of the departments would have resulted in a win.

Think about the 2024 presidential election. As Democrats argue about “the reason” for losing, the reality is that with a popular vote margin of only about 1%, most of the cited reasons are valid and a change in any of them could have swung the balance. Racism, sexism, communication, the lack of a primary, whatever…they’re all on the table in the way they wouldn’t be with a 30% deficit.

It is important to teach your team to identify the size of a gap and to change your strategy accordingly. With wide misses, you need a few heavily resourced interventions capable of closing a large gap. But with almost wins, it is more important to spread your resources across a number of smaller bets, since any of them is enough to tip the balance. Making this explicit can help cross-functional teams quickly move away from blame and toward solutions.

The frame change can be as simple as a name change. A post mortem is “after death” and makes sense for wide misses. But for an almost win, consider an ante vitam meeting, because an intervention that almost worked is just “before life”.