KOPYA NG SALIN 🔗
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Hi change agents, I'm back in the US after my European exploits, and I thought I'd share a few stateside adventures from the past few years.
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I have shot hoops at State Farm Arena in Atlanta, definitely not as a pro athlete, but as a general counsel on the clock.
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I have done a scavenger hunt through San Diego with a few hundred sales leaders.
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I've also been asked on stage with no warning whatsoever to name my favorite Beyonce song.
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For the record, it's run the world.
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I have also held the hand of a tenant who was terrified of being evicted.
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I have sat with a parent fighting to keep custody of their children.
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I have talked an antitrust partner through a panic attack the morning of a hearing when documents went missing.
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And I have been a human paper shredder.
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Every one of those things was part of my job.
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And if you had watched me doing any of those and tried to infer my job from the task in front of me, you might have gotten it spectacularly wrong.
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Which is why when the LinkedIn Post or news story lands saying AI is getting so good at the work.
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What happens when the work goes away?
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I have a question.
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Which work?
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And why do we assume the whole thing disappears rather than some pieces of it changing?
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That's what I want to talk about today.
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The only thing moving faster than change right now might be my relentless curiosity about the possibilities it creates, the new worlds it opens, what to watch out for, and how we get more comfortable with discomfort and uncertainty.
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I'm Gabrielle Kohlmeier, and this is Technically Joyful.
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A space for us to build change capacity in the AI era, explore hard questions, find unexpected connections, useful ideas, and create the energy to shape what comes next.
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Let's get Technically Joyful.
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I take the fear underneath that question seriously.
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If AI can review the documents, do the research, draft the memo, analyze the contract, summarize the file, people are right to ask what happens to the associate?
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In other industries, the analyst, the doctor, the manager.
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What happens to me?
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But there's an assumption buried inside the question.
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When we say the work, what are we pointing at?
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Most of the time, I think we're pointing at the tasks we can see and measure.
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Read this, summarize that, draft this, review those.
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And when a machine does one of those faster, we conclude the work is gone.
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But was that the whole work?
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Or are we staring at the paper shredder part and mistaking it for the job?
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If you've been with me from the beginning of this podcast, you might have noticed a pattern.
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Spoiler, that is the whole plan.
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Notice the assumption, interrogate it, and choose a better one on purpose.
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We started with fear and moved towards curiosity in episode one.
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And Anna Gresel showed us in episode two what that looks like in practice, exploring before you know exactly where the exploration will lead.
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Then in episode three, we moved from figuring out everything alone toward connection, because no one has the whole picture.
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And Jeff Schaefer showed us in episode four another version of that connection.
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Building's not about dropping a tool into an organization, it's about seeing the system around it.
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People, processes, incentives, information, workflows, even teams of agents, and understanding how those pieces fit together.
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Then in episode five, we moved from wanting certainty toward building the map as we go.
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And Laura Belmont showed us in episode six, the joy of learning out loud, moving before everything is settled, letting people see you learn, being vulnerable enough to say, I'm learning this too.
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Fear toward curiosity.
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Isolation toward connection.
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Certainty toward exploration.
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Not because the first feeling disappears, but because we discover another way to move through it.
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Today's assumption is scarcity.
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There's a fixed amount of work.
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AI takes some of it, so there's less for us.
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And the shift I want to explore is toward abundance.
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Not don't worry, your job is safe.
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Not there will be endless jobs.
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Something more specific.
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When we define the work too narrowly, we also define our possibilities too narrowly.
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Quick ask and then we go.
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Who comes to mind while you're listening to this?
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Send the podcast to them.
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That's what this show is for.
Load full transcript
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Follow the show, leave a rating, takes 10 seconds and it helps people find us.
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And keep sending me your fun, surprising, tragically awful AI attempts.
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I'm loving them, especially when you're open to me sharing them on air.
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So back to the sales kickoff.
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My sales leaders understood something about their assignment that turns out to be the whole point of today.
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Their job was not just to hand down sales quotas.
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Their job was to create energy, teach lessons in a way people would actually remember, build competition and team cohesion, create empathy for the customer, help people see opportunity.
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The hoops and the scavenger hunt were not breaks from the work, were ways of doing the work.
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A scavenger hunt puts you into a different mode.
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You're scanning, improvising, looking for something you might otherwise just walk past.
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And doing it with other people, because your own field of vision is not enough.
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Those are useful habits for sales, and they're hard to install with a memo.
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I took that with me.
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As a market general counsel, later building legal organizations with lawyers around the world, not with clients, I am obsessively focused on what matters to and motivates the people in front of me.
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Counterparty across the table, my own team, a colleague in another function.
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What makes a lawyer in India feel connected to the goal and free to contribute beyond the rote application of what they were told?
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How does a lawyer in the Netherlands connect with clients in a culture that works very differently from their own?
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How do technologists and lawyers who don't naturally speak the same language start operating as one team?
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Those are not soft questions off to the side of the legal work.
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They're a critical part of the work.
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Over time, that pulled me deeper into the psychology of persuasion, motivation, and influence.
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Because getting the answer right was only part of the work.
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The answer had to travel through actual human beings.
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That was true of an integrity action plan I led to.
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On paper, my task was straightforward.
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Can they the legal requirements?
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Here is what selling with integrity means.
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Here are the state and federal rules.
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Here are the company policies.
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Done.
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But the actual work was much bigger.
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The email was a task.
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The work was changing behavior.
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The purpose was making sure customers were treated with integrity.
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So, yes, the legal and policy guidance mattered, but it also really mattered to work closely with brilliant communicators, other lawyers, stakeholders, to get senior leaders to put their names on it, to create scores so people could see how they were doing, and to go to the stores.
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I stood on the sales floors and I would ask people, are you getting the emails and the texts?
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Tell me about them.
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Do you know why this matters?
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Do you know how much we appreciate what you do for customers every day?
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I wanted to know whether the information stuck and whether it's stuck in a way that energized people instead of just overloading them.
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Sometimes the parts of the work that matter the most are the hardest ones to put on a task list.
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Trust, motivation, judgment, connection, knowing when someone is worried about something they haven't even said yet.
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I saw that very clearly at Verizon when I was the antitrust lawyer for a company we had just acquired.
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Of course, I did the analysis, the filings, the regulatory work, but I also spent time inside their business.
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I sat in their meetings, I learned what mattered to their lawyers and policy people and business people.
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And when we advocated for the company, I made sure they could see their concerns reflected in what we were doing.
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Later, I saw what that made possible.
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They came to me proactively with a concern about their own behavior, kind of thing that people can be tempted to bury because it's uncomfortable and frightening.
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They didn't bury it.
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They brought it to me.
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And because they did, we could investigate it, understand what happened, and address it before the concern became a crisis.
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The antitrust analysis was easy to identify as work.
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Trust was harder to see.
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But the trust changed the information I had access to.
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And that changed what I was able to do.
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And this is where I also want to be careful, because the point is not that machines do the technical tasks and humans do the nice relationship stuff.
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But the point is that we need to understand the whole system of work before we can intelligently decide what technology should change.
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Figuring out what the work actually is is a big part of my job now.
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It's a lot of what I do with clients.
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And my thinking on it keeps getting deeper and evolving.
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Here's where I'm at.
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There's purpose.
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Why is anyone paying for this at all?
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A client makes a better decision, a patient gets better, a tenant gets to keep their home.
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Then there's the work, everything that has to happen to get there.
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And then there are the components of that work.
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Some show up as explicit tasks: review the documents, draft the demand, run the analysis.
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Others are harder to count.
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Judgment, relationship building, noticing, translating, persuading, connecting.
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Those things may not have a billing code or a line on the job description that doesn't make them less real.
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And this is where the AI conversation can get distorted.
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When we ask, what happens when AI does the work?
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We're often counting only the visible tasks.
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AI takes some of those and we conclude the work is gone.
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But the work was always bigger than the job description.
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We just didn't always make the rest visible.
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And once you can see more of the work, you have choices.
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What should technology do?
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What should people do?
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What should people and technology do together?
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What are we doing today that doesn't need to be done at all anymore?
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What deserves more human attention, not less?
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And what valuable work have we been not doing because all of our capacity was consumed somewhere else?
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That's where abundance starts to get interesting.
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Now, abundance.
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I know that the word can sound like putting a cheerful sticker over a real problem, but that's not what I mean.
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The scarcity story gets much narrower when we mistake the visible task for the whole of the work.
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Because once we can see the work more fully, we can start deciding how we want it to happen.
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Which parts build judgment, which parts erode it, which parts build relationships, which parts get in the way, which parts are habits, which parts are bureaucracy wearing a suit.
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And when technology genuinely reduces the effort required for some of those components, it creates a choice about where that capacity goes next.
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That choice matters because the thing that remains stubbornly scarce is human attention.
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Where do we point it?
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Think about how many organizations say they don't have enough time for mentoring or even training, for real client conversations, for strategic thinking, for reflection, for experimentation, for improving systems, for asking whether the thing everyone is frantically doing should even exist.
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We've spent decades saying we're overwhelmed.
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Now we have tools that could potentially take some things off the desk.
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It would be a strange failure of imagination if the only question we ask is, how many people can we cut?
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What if another question is, what can we finally afford to pay attention to?
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That's the abundance I mean.
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Not endless jobs, not endless work, an abundance of design choices, an abundance of possible places to direct our attention, an abundance of questions we finally get to ask about the work we inherited.
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So, what happens when one of those visible tasks really does shrink?
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I can show you what that looks like in my own work.
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I used to spend a lot of time reading and then summarizing the report, the article, the legislation, the hearing, the 400-page whatever.
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That reading was where my thinking got built a lot of the time.
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Now I still read, I still engage with the material, but I may capture my key thoughts and have AI help with the synthesis.
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I was a little bit worried about that, to be honest.
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I was an outline queen in law school because outlining was how I sorted through thinking and made it stick.
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So I wondered what would happen if some of the synthesis moved to the machine.
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Would the thinking get weaker?
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For me, the opposite has happened.
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The note-taking and synthesis shifted.
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The engagement didn't disappear, it changed shape.
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Now, one of my favorite things to do is set up arguments.
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I ask an AI to take on one person's position and argue with me about whatever I'm working on.
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Then I bring in someone who would disagree with that first person,
and then I challenge both of them.
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I push back, I question the premises, ask questions, and I ask what neither one is seeing.
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Sometimes I'll throw in a fictional character, a Leslie Nope, or a Ted Lasso when I want a deliberately different lens or an ethical conscience.
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It is frankly a delight, and my thinking often comes out tighter and richer.
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The summarizing shifted, the thinking got more room.
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My nerdiness quotient also increased, but that was probably unavoidable.
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So your rep this week is a two-parter.
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First is the big one.
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The next time you finish a familiar task, pause and ask, what was the work?
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Not just what did I produce, but what judgment was this supposed to support?
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What relationship?
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What understanding?
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What outcome?
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What was this task actually in service of?
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The second is a short and easy two-word prompt to use with anything AI hands you.
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What's missing?
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A draft, a summary, an analysis.
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Before you use it, just ask what's missing.
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It will usually surface a list of possible gaps, context your AI may not have, questions it didn't consider, stakeholders it didn't account for, assumptions it made.
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But don't assume that the list is complete either.
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Use it as a prompt for your own attention.
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And then think about what's missing that the system can't know is missing.
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What do you know about the client, about the relationship, the culture, the history, the consequence?
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And if after your own review, you genuinely can't identify anything material that's missing, that's really useful information too.
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Maybe that was a task that didn't need that much of you, which can be its own kind of relief.
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So, a basketball court, a scavenger hunt, a terrified tenant, a partner who couldn't breathe, and a human paper shredder.
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None of those things standing alone would have told you what the work was.
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The scarcity story looks very different once we stop mistaking the visible task for the whole of the work.
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Ask, what was the work?
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And when AI gives you an answer, ask what's missing.
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You start to see more of what the output alone leaves out.
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And once you can see more of the work, you get to make better choices about what it becomes.
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The work is not only what we inherited, it's also what we decided to make next.
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And maybe abundance starts there.
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Not with a promise that nothing will be lost, but with the recognition that we still have choices about where our attention goes and what we choose to build.
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One quick note I am a licensed attorney, but nothing in this show is legal advice, and I'm not promising anyone specific results.
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I am promising to keep asking better questions with you.
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And that change agents is technically doing.