The machine was never the point

Every technology promises transparency. AI is the first that doesn't ask for permission.

In 2013 I wrote a master’s thesis about a technology that promised to change everything. I found it back this week. What unsettled me wasn’t how much has changed since. It’s that the pattern I described back then hasn’t changed at all, while the stakes quietly have.

The thesis is called The Heart of the Machine at Work. Red cover. On the second page, a printed circuit board with a heartbeat running through it. I was twenty-something, studying New Media and Digital Culture in Utrecht, and I spent six months inside Microsoft Netherlands watching them try to adopt Yammer.”

The technology of the moment was enterprise social networking. The internal platforms that would finally make organisations flat. Open. Transparent. Everyone talking to everyone, hierarchy dissolved, knowledge flowing freely. It was going to change how we work.

You already know it didn’t. Not the way the promise said.

Here is what I want you to do with that thesis, thirteen years later. Take every page. Cross out “enterprise social networking.” Write “AI.” The framework still holds. The questions I asked then are the questions we’re asking now. What has changed is not the pattern. It’s what the technology can suddenly do inside it.

That should bother us more than it does.

The pattern underneath

What I found in 2013 wasn’t a prediction about a particular tool. It was a pattern about us.

With every new technology, we invent a new myth. Not a lie, exactly. A story we need to believe, about how this time the tool will lift us out of the ordinary and deliver us somewhere better. We did it with the telegraph. With electricity. With the telephone, the internet, the social intranet. And the myth is never really about the technology. It’s about us, and what we hope it will fix in ourselves.

In that Microsoft year I watched the gap between the myth and the everyday up close. The platform that was supposed to flatten the organisation. The people who wouldn’t post in the big company group because they could feel an invisible question over their shoulder: *the boss might be reading this.* The ones who hid behind a shared department account because their own name felt too exposed. The careful silence around disagreeing with someone more senior.

The technology worked fine. The people did what people do when they don’t feel safe enough yet. They protected themselves.

My conclusion then was quiet, and I almost underplayed it: transparency doesn’t fail because the tools aren’t good enough. It falters because being fully seen by each other is one of the hardest things we ask of people. The myth runs aground, gently and predictably, on something very human.

What AI does that those tools never could

For a while I thought AI was just the next chapter of the same story. Same myth, new name, nothing new under the sun.

And mostly, it is. We are telling ourselves a new utopian story right now, and an equally loud dystopian one, and both of them are about the machine when they should be about us. That part is exactly as it was in 2013.

But there is one thing AI does that enterprise social networking never could, and it turns my old conclusion on its head.

Those old tools *asked* us to be transparent. They offered the platform and waited, and we declined, politely, by staying quiet. Transparency was optional, and we opted out.

AI doesn’t ask.

Listen to where the conversations are going now and you hear it: AI bringing the data closer to reality. Not the data that was quietly curated. Not the number left out of the sales report because it looked better absent. Not the version of delivery that was easier to present. The actual picture. The machine doesn’t have a reason to protect us, and so it doesn’t.

The technology that was supposed to give us transparency is finally delivering it. Just not the flattering kind. It holds up a mirror. And what it asks of us is not to look away from what we see there, but to meet it with some kindness.

The question this leaves us with

So the question of this moment is not the one everyone is asking. It is not whether the machine is good enough, or fast enough, or safe enough.

If the data comes closer to what’s real, more of us becomes visible. The gaps we left. The numbers we softened. The truth we quietly carried alone. For a long time we could keep those things in the dark, where they felt safer. There is going to be less of that dark.

The question that leaves us with is a gentle one, not a threatening one.

And here is the version of the question I keep circling back to, the one I find strangely hopeful: when there is less room to hide, who do we want to be in front of our own imperfection?

Because that, I think, is the real leadership question of the years ahead. As more of the analysis can be handled by a machine, the part that stays ours moves somewhere else. Not to computing better. To being human in plain sight. To connect. To make meaning. To stand in front of people and say, this is where we actually are, including the parts that don’t look good, and here is where we’re going anyway.

I wrote the end of this in 2013 without knowing what it was for. I ended that thesis saying that imperfection and vulnerability are what make us human, and what bring us closest to real contact, soul to soul. It read, back then, like a soft and slightly sentimental way to close a paper.

It doesn’t read that way now. It reads like the only thing that will matter.

This is where I’m starting. Over the coming weeks I’ll open the thesis back up, one finding at a time, and lay it next to where we are now: trust, the invisible power that keeps people quiet, the stories that move organisations more than the tools do, and the imperfection we’re about to have nowhere to hide. Thirteen years later, and right on time.

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