Kris Krüg, Executive Director, BC + AI Ecosystem Association


Last October, I watched a room full of 250 people at the Vancouver Planetarium lean forward when our speaker asked: "How many of you are deploying AI systems at work right now?" Nearly every hand went up.

Then he asked: "How many of you have a governance framework for how you're doing it?" The hands dropped. Maybe a dozen stayed raised.

That gap—between deployment speed and ethical practice—is why we built RAP.


The Problem We're Trying to Solve

Every organization is rolling out AI. Fast. The pressure to ship, to automate, to not fall behind—it's real. I get it. I've been building technology for 25 years. I helped build the world's first Drupal development company. I created Dead.net for the Grateful Dead. I wrote books about BitTorrent and iPhone photography when those were emerging technologies.

I've seen this pattern before: new tech arrives, everyone scrambles to adopt it, and the ethics conversation happens later. Usually after something goes wrong.

With AI, we can't afford "later."

The systems we're deploying now—hiring algorithms, healthcare decisions, content moderation, financial assessments—they're making choices that affect real people. When the algorithm is biased, when the system hallucinates in a high-stakes moment, when trust erodes because nobody asked the hard questions early enough, the damage isn't theoretical.

And here's what I've learned running the Vancouver AI Meetup for the past two years: most people aren't trying to do harm. They're just moving fast and nobody gave them the frameworks to do it responsibly.


Why BC + AI Built This

When we started the Vancouver AI Meetup in 2024, we didn't know it would grow from 80 people in my studio to 250+ monthly attendees at the Space Centre. We didn't know we'd end up with 850+ Discord members, 99+ paying members, and seven Special Interest Groups covering everything from creative AI to enterprise governance.

What we did know was that our community kept asking the same questions:

"How do I evaluate whether this AI system should be deployed?"

"What frameworks actually work for assessing risk?"

"Who's teaching this stuff in a way that's practical, not just academic?"

The certification programs that existed were either too theoretical (great for research papers, useless for Tuesday's deployment decision) or too narrow (focused on one jurisdiction's regulations, one company's products, one technical domain).