Notable verbatim quotes

All quotes attributable to Ian Thomas unless noted. Line references are approximate section markers in transcript.md.

On the starting problem (Section 4)

"there was a bit of hill tracking going on. And the adoption wasn't uniform. It was ad hoc at best. People were finding local outcomes, they were dragging, they were trying to use the wrong tools for the wrong problem." — Tag: #starting-state #adoption-anti-patterns

On outcomes (Section 4)

"we grew an organic community that was over 40 times bigger than when we started. I think at the time when I stopped working on this, we were well over 500 people... Well over 80% of people were using these tools weekly, which was up from under half. And I would say consistently now, I think I see this in the mid to high 90s." — Tag: #outcomes #adoption-metrics

On where to start (Section 6)

"if we jump straight into feature development and looking at product exploration, we felt that maybe that was going to be quite hard because it's a novel process in its own right. So there's too many moving parts. The things that we did understand well and that we could control is that top area on the left, the code modernization and administrative tasks." — Tag: #where-to-start #engineering-excellence

On scale (Section 6)

"When I joined the company, we were already at half a 500 million lines of hack code... So this is a significantly large code base and it's all in one repo. So for those of you that have experimented with agents and large repos, this sort of thing is challenging at best." — Tag: #scale #monorepo

On engineering excellence definition (Section 7)

"for us engineering excellence is consistent of three main parts implementation quality, production excellence and better engineering." — Tag: #definition #engineering-excellence

On Meta's culture (Section 7)

"This is a social company. Things get done through bringing people along with you for the journey. There's no real top down incentive. Or hierarchical effort, especially when it comes to engineering adoption of things. It's a very grounds up evidence based approach that's needed to be taken." — Tag: #culture #ground-up

On safe space for the community (Section 8)

"We could then build a small community where people could feel stage, they could have the space to fail and they could ask questions and not feel like they were going to be put on trial or have any kind of impact on their performance reviews." — Tag: #community #psychological-safety

On intangible metrics (Section 8)

"There's no dashboard I can pull up to say how many people feel a sense of belonging in the community. So it's kind of a bit of a asking people to take a bit of a leap of faith, but I think it's the important thing that we needed to do." — Tag: #metrics #community

On the maturity model levels (Section 9)

"we model this across five levels because we're engineers, we somehow decided this should be a zero index based list. But yes, it goes from sit right the way through to leap. And the main thing is that the dimensions are largely independent of each other." — Tag: #maturity-model #framework

On AI-native as a self-identification (Section 9)

"they've seamlessly integrated these tools across many different processes and they would self identify as AI native now." — Tag: #ai-native #definition #maturity-level-5

On the workshop format (Section 10)

"It's a very basic sort of kaizen retro format where we would talk through the descriptions. We would get people to vote in isolation something like Miro or another kind of online whiteboard is great for this sort of thing where you can just have anonymous voting." — Tag: #workshop #process

On AI test slop (Section 11)

"AI was great at hallucinating awful tests. It would make stuff that was really looking like it was doing something useful, but it really wasn't there. It was just increasing our overhead on CI." — Tag: #test-slop #failure-modes

On the snowball effect (Section 12)

"this is where I think you start to get to a snowball effect where you reach a critical mass and enough people know and enough people start sharing things out. The adoption, the numbers just grow fairly organically and rapidly." — Tag: #adoption-curve #network-effect

On the refactoring pattern (Section 14)

"if I can teach the AI the way I would think about resolving these refactorings and then go and find patterns that are similar across the code base, what can we achieve?... he was acting more as a reviewer and just being a critical architectural oversight" — Tag: #pattern #refactoring #human-in-the-loop

On runbooks as skills (Section 16)

"we could provide rules and runbooks to the AI that look basically like skills or context files. And it would just churn constantly, just run, go, go find a thing, fix it. Promote a diff, get someone to review it." — Tag: #code-mods #skills #autonomous

On avoiding full autonomy (Section 17)

"we also tried to focus on keeping it as a human oversight in the first place. If we went too far into the fully unsupervised agentic world, I think we were going to set ourselves up for failure." — Tag: #human-in-the-loop #discounted

On platforms underpinning everything (Section 18)

"underpinning all of these things and all these stages in life cycle was a strong investment in platforms and platform engineering... building those platforms in the first place actually is still paying dividends even if we think about a world where AI is supposedly reducing the need for the multiple people." — Tag: #platforms #platform-engineering

On top-down mandates (Section 19)

"building that from the ground up gave it credibility and allowed us to kind of drive this adoption from engineers upwards rather than as a top down mandate. Which honestly I've never really gone down that well in any large company that I've worked in." — Tag: #adoption-strategy #top-down-vs-bottom-up

On vanity metrics (Section 19)

"we started off being obsessed by the number of people that we're using tools on a weekly basis and I think that and how many diffs they were generating, how many lines of code were being written by agents. At the end of the day they're vanity metrics, token usage as well." — Tag: #metrics #vanity-metrics

On the call to action (Section 20)

"If you want to follow this model just find a couple of people and get started. There's no real permission needed. It's quite fun to just sort of dream big and start off small." — Tag: #call-to-action #getting-started