If you can hear my voice, clap once.

That’s how we call the room into presence at the Vancouver AI Community Meetups. No corporate stage rig. No VC circle-jerk. Just q bunch of humans packed into a cosmic dome at the H.R. MacMillan Space Centre, grounded in land, stardust, and curiosity.

This was our 18th mission. And instead of ordering sushi like usual, I grilled 200 skewers myself — beef, chicken, veggie — because feeding people matters. That’s how culture happens. One kebab, one conversation, one reclaimed protocol at a time.

We do this monthly. A gathering of the curious and the critical. It’s not about AI hype. It’s about asking: What does it mean to be human now that machines can remix our dreams faster than we can type them?

The Cultural Layer Is the Missing API

The AI discourse right now? Mostly dudes arguing about model sizes on podcasts sponsored by surveillance capitalism. But underneath the metrics and GPUs is a deeper shift.

Not just what we’re building — but how we relate to it.

Code is culture. And culture is the interface.

The revolution isn’t in the parameter count. It’s in the metaphors we choose to build with.

Morton Rand Hendrickson hit this hard in his keynote — a velvet hammer of insight. He reframed Anthropic’s “MCP” (Machine Context Protocol) not as a systems breakthrough, but as Machine-Controlled Pandemonium.

When machines start interpreting our fuzzy little human intentions — and calling other machines to act — we’re not just automating tasks. We’re outsourcing meaning.

The danger isn’t just runaway AI. It’s runaway abstraction.

The challenge isn’t alignment. It’s embodiment.

And that’s a cultural problem. Not an engineering one.

We’re Not Automating the Future — We’re Ritualizing It

When I talk about AI, I’m not thinking about “the future of work.” I’m thinking about the future of wonder. I’m thinking about dream maintenance, and collective intelligence, and barbecue as infrastructure.

This isn’t a job board. It’s a people’s forum.

Back in the Occupy days, we had “mic checks.” Now we’ve got large language models. But the spirit is the same: resist the centralization of meaning. Keep the loop human. Keep the signal wild.

That’s why I mic in between speakers. Not just to host. To metabolize. To editorialize. To synthesize the moment so others can plug in.

And the crowd responds — not with LinkedIn claps or Slack emojis — but with real offers. “How can I help?” they ask. They mean it.