God Mode is Breaking Work

IDDQD.

If you know, you know.

But…if you don’t, IDDQD is the original cheat code you’d enter in the original Doom game to activate God Mode.

You type it in and nothing can kill you anymore.

Every bad guy, every shotgun blast, every floor made of lava, nothing can stop you.

I remember typing the code as a kid. The screen flashed, “God Mode On” and I just...walked through the rest of the game.

And then I never played Doom again.

Because it stopped meaning anything.

The whole game was the fight and if you take the fight away, well, you’re just walking through rooms.

I’ve been thinking about that a lot lately observing teams adopting automation and AI.

The hot dog business

A software engineer posted a video recently. It went viral because it resonated for so many others seeing AI’s impact on tech jobs.

He’d been building a software product and handed the whole thing over to an AI coding agent. He gave it AWS access, GitHub access, and told it to deploy the app live.

He came back from a walk and everything was running.

As he sat there looking at it, something clicked.

“I can’t sell this junk,” he said. “There’s no pride in it.”

He called it a hot dog business.

Sure, it’s technically food. But he didn’t make it and isn’t sure what’s inside of it. As he said in the video, it took away the purpose he created within the career he’s been building for over a decade.

He’s a software engineer. He also might be the first person you’ve seen go through something that’s coming for almost everyone.

We’re having the wrong conversation

Open LinkedIn right now and the AI debate looks like this: half the posts are “here’s how to adapt or get left behind.” The other half are “LLMs are glorified autocomplete and the fundamentals haven’t changed.” Both sides are fighting about jobs. Which jobs are safe? When will the wave hit [X] industry or [Y] role?

Those are real questions, just not the most important one that is coming our way. What I actually watch happen, in conversations with executives and operators every single week, isn’t job loss. It’s quieter than that. It’s people losing their grip on why the work mattered in the first place.

When you spend years doing something hard, you build an identity around the doing.

The analyst who finally made the model make sense.

The ops lead who figured out the workflow nobody else could untangle.

The engineer who stayed up until 2am on a problem they couldn’t crack.

The hard part wasn’t an obstacle, it was the whole point. That’s where the meaning lives for most people.

Now something shows up that does that hard but meaningful part…and fast. What do you do with that?

That’s the God Mode problem. And while right now we’re seeing it first with software engineers reckoning with it, it’s coming for all roles.

Two people who know the most are worried about the silence

Here’s something that has stuck with me a lot recently.

Dario Amodei, CEO of Anthropic, was asked recently what had surprised him most in the last year with the advancements in AI.

He could have talked about capability jumps. He could have listed benchmarks. Instead, he said: “What has been the most surprising thing is the lack of public recognition of how close we are to the end of the exponential. To me, it is absolutely wild that you have people, within the bubble and outside the bubble, talking about the same tired, old hot-button political issues.”

And while some feel Dario is talking his book to market Anthropic, I’ve found Demis Hassabis, CEO of Google DeepMind, to be much more measured. And yet, at a World Economic Forum panel recently, he said something similar. He was in a room full of professional economists and told them directly:

I’m constantly surprised, even when I meet economists at places like this, that they’re not more thinking about what happens.

This isn’t doomerism from people on the fringe, these are people running two of the three most consequential AI labs in the world, in small rooms, at serious forums, saying (what I believe is) the quiet part out loud.

What I’m watching happen right now

I run a firm where we build custom software and automated workflows for growing teams so I get to be inside how this is playing out every single day. In the last six months I’ve watched a VP of Engineering, twenty years into his career, go quiet during a project kickoff. Not due to fear of being replaced, but because he was watching junior engineers produce in four hours what used to take his best people four weeks. And he didn’t know what that meant for the story he’d been telling himself about the purpose he’d created in his career managing teams at this point.

I’ve watched COOs second-guess entire hiring plans. And it wasn’t a “should we hire fewer engineers?” moment. It’s become something more and more are asking: what are we even hiring for now? What does good look like?

I’ve watched some be resistant to AI adoption and want to stick with “doing things the old way.” For those I’ve gotten to discuss their rationale with in depth, it’s rarely out of stubbornness or fear, but because of something harder to admit. Their reluctance is handing “the machine” the part of their job that made them feel like they were getting purpose out of what they were doing beyond just a salary.

Most are not seeing AI impacts yet, but they’re quietly formulating questions they aren’t raising in a team setting because it feels too strange, too personal, too much like admitting something they don’t fully understand.

But, by nature of the convos I get to be a part of, I’m seeing it surface more and more. And it’ll start showing up in the retention numbers you’ll see in 18 months that most won’t immediately connect to this.

The leaders handling it best aren’t the ones with the shiniest AI strategy.

They’re the ones who understood that their people needed a language for what they were feeling while they were building a roadmap to their future state.

And what I’ve seen is the cultures adopting AI and change well have conversations that go well look different.

It’s not the typical approach of executives just sitting in a room deciding what AI will do.

It’s ICs at the table, working through the actual questions: if AI can handle 50% of this workflow sucking up 80 hours of labour per week between 8 people, what does the other 50% look like?

The companies figuring this out aren’t replacing their teams, they’re reorienting them. That’s a harder conversation to have than buying a new tool, but it’s the one that actually moves things forward. For the companies living in the reality of 2026, not the hope that they can still operate like it’s 2016.

Fall in love with outcomes

The people I’ve watched navigate this the best have been oriented toward what the outcome and output of their role knowing they’ll find meaning in what they get to create as a new way of getting to that outcome with AI helping some of the more mundane parts.

That sounds simple but is not.

Most of us fused our identity to the doing of the work, not the result of it, and untangling that (for most) is genuinely hard.

But here’s what gets buried in the discourse: For every employee who loved the grind, there are a dozen people in knowledge worker jobs who haven’t felt any emotion other than ambivalence about their work in years.

The analyst copy-pasting between spreadsheets at 6pm.

The coordinator spending half their week chasing the same invoice and updating 3 systems and 4 people when it’s paid.

The project manager maintaining two versions of the same schedule because the “official one” is too rigid and inaccurate.

Those people aren’t losing a source of meaning when AI absorbs their work. They’re getting time back they should never have spent on that stuff in the first place.

But that argument breaks down for those of you running companies.

You still need people who understand why things were built the way they were, who can direct the AI toward the right problems, and who can recognize whether what came out the other side is actually good. That judgment doesn’t come from a tool.

A disengaged team makes worse calls about what to build and worse decisions about what to trust. The purpose crisis isn’t just a feelings problem, it has a quality and retention tail that shows up in your business well before you connect the dots.

This moment lands hardest on the people who genuinely loved the hard thing. That’s real, and it deserves to be said, but it doesn’t mean the destination is bad. In my opinion, it just means the transition is harder than anyone is saying out loud, and longer than most leaders are planning for.

Two things can be true at the same time

If you’re running a company right now, you’re holding two problems that feel unrelated.

They aren’t.

The first is operational. The window to build before you fall behind is closing faster than it looks. Companies connecting their systems now, building real data infrastructure, building the custom tooling their specific workflows actually need, are not waiting for the technology to mature. They’re building while the gap is still closeable. Three years ago, that kind of project took 18 months and $800k. Now it’s less than half. The math has changed rapidly for the better. The companies that figure this out first stop fighting spreadsheets and start owning something their competitors can’t easily copy and doesn’t mean double the revenue = double the headcount.

The second is human. The purpose conversation is coming to your team whether you’re ready or not. Probably not in a town hall, but in the small moments. In the quiet disengagement of good people trying to figure out where they fit. In the friction you start to feel but can’t quite locate.

The leaders who come out the other side well are the ones taking both seriously, at the same time.

The other side will be better

I don’t think this ends badly.

A world where people aren’t spending 40 hours a week updating spreadsheets and chasing approvals and doing work that was always beneath what they were capable of (or felt purpose in), that’s a better world. It’s a lot closer than most people are acting like it is.

But between here and there is a reckoning our institutions, leadership playbooks, and cultural narratives about work are nowhere near ready for. The people running the most important AI labs in the world are flagging it in small rooms. Developers, who are experiencing it first hand as we speak, are posting about it all over Twitter.

At some point someone on your team is going to raise their hand and try to put words to it.

The question is whether you’ve thought about it first.

PS, this is what we work on at Switchboard. If you’re starting to have this conversation internally and want a thought partner who’s deep in this stuff, happy to chat.