This article is Part 2 of 3. We began by looking at three common traps that keep conversations stuck.

In part 1, I shared examples of how these patterns play out in conversations between individuals. The same patterns play out at scale. Today, we'll see if we can break out of these traps in our conversations about AI.

Zoe Scaman is a strategist who works with senior leaders at major companies on emerging technology. She spends her days in the rooms where these conversations happen: keynotes, client away days, boardroom strategy sessions. In her article The Six Loops, she catalogues the six conversations she hears on repeat:

She describes it as a setlist that never changes: the vocabulary gets shinier, but the conversations underneath haven't shifted. Read her piece here.

Here are those conversations placed onto a 5D topic map.

As Ms. Scaman points out, it seems that the hive mind is broadcasting these conversations on repeat everywhere. Fair enough. They're interesting conversations. Lots of implications, and maybe for some groups, these are the conversations they need to have.

But for most, they're a trap. The media, the AI companies, and every fresh-faced hustler promising that you can prompt your way to glory want you to focus here. Do you?

It’s like the AI industry has our flashlight, and now they’re projecting these conversations across our cave wall, expecting the rest of us to fixate on the shadow puppets.

What if we stopped staring where they’re pointing and looked around instead?

If you find that circling these loops is doing nothing to quell your existential dread:
Look at all that empty space!

What aren't we talking about?

Step back and consider the map, and you'll realize there are infinite possibilities.

And frankly, that's not useful either. Once we recognize the trap, a single different conversation can break the loop. We need to explore just enough to find one or two promising paths; conversations that look like they might lead somewhere good.
(If they don't, we can simply explore some more!)

Let's walk through how the three traps may be at play in the AI discussions and what opens up when we escape these doom loops.

We bust out of this one by getting curious. Find out what people mean, and put your new understanding on the map.

When someone says "AI will destroy society," whose society? How will it be destroyed? What about it will be destroyed specifically?
I'd guess that their predictions for my mom, truck drivers, Wall Street bros, and dentists in Nairobi look pretty different, but I can't know until I ask.

Capturing the specific changes people have in mind fills in a lot of territory. Then, you can decide whether your group wants to explore the implications of those changes further.

Once you gain specificity, disagreements often dissolve or transform into something more productive.
For example, let's say you clarify that you're concerned about the prospects for newly minted software engineers. Follow that thread, and you'll soon uncover ideas for individual adaptation, job skill transfers, community support, and institutional change. That's a lot more to work with than you had at the start.

An aside, here's a delightful skills transfer: Using classic opera to sell cars!

Several of these loops come securely swaddled in bubble wrap. While the first trap stems from confusion, we step into this one by avoiding discomfort.

The How-To loop, the Minimizing Loop, and the Efficiency Loop are all safe because they carry forward culture norms regarding the virtues of self-improvement, staying current, and increasing productivity.

You're getting more done, it's better, and now you're the in-demand AI expert everyone needs? Wow! Well done, you!

It's tempting to confront this trap by poking at the values (e.g., is doing more faster always a good thing? Really?), but lots of folks will feel personally attacked by this and shut down.

Instead, see what happens when you sway the conversation a few degrees in a new direction.

Take the Minimizing conversation. The assertion: AI is a tool.
That speaks to AI at the scope level of individual tasks. Now nudge it a bit.

These questions invite us to expand the scope of our conversation, consider the positives and negatives, and the perspectives of other stakeholders.

They also reveal paths where we have agency, because while most of us don't get a say in what Anthropic or DeepSeek releases, we do get to choose how we use what they ship.

Speaking of what Anthropic and others say, here’s our biggest trap. But we don't have to accept that the options they're giving us are our only choices.

This week, Jack Dorsey laid off 40% of his employees and blamed it on AI. The CEO of Anthropic predicts that 50% of entry-level white-collar jobs will disappear in the next 1 to 5 years. The AI community says they're raising the alarm so all of us "regular people" can prepare for a future with 20+% unemployment.

They paint this future as the inevitable consequence of the Efficiency loop.

AI creates productivity gains →
You can do what you're doing today with fewer people →
You will reduce headcount.

First, we can push back on these claims and evaluate them in our context, just like we should when we suspect Level 1 & Level 2 traps.

But let's say they're right! Let’s accept that a lot of the work we do today could be done by machines now or in the near future.

Even when the gains are genuine, layoffs aren't automatic.

Leadership teams have so many options that don't get serious consideration in the media.

This is Trap 3 in action: a small group of CEOs and investors set up a one-move game, and everyone else is playing along.

Summing up, when you find yourself stuck in a conversation that everyone seems to be talking about but that isn't getting you anywhere, nudge the group into new territory.

That's nudging, though. Still reactive, still working to shift the frame someone else boxed you into.

With our map in front of us, we don't have to start there. We can see the blank spots and get ahead of the conversations they aren't having yet.

Three areas fall in the empty space on the map, and they're where I want to go next.

In Part 3, we’ll contrast historical changes with those we see today, highlight who's already working in these gaps, and get those missing conversations started.

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