"We accumulated too many rituals. When you want to ship something you need to build a fireplace, chant around it, dance, and maybe bring a shaman in." That's Pavlo from Platform Engineering Labs on episode 10 of DevOps Unpacked, and it landed harder than I expected. His point wasn't that the rituals are new. It's that they were always unnecessary — we just couldn't see it, because everything upstream was slow enough to hide them. Now code generation is nearly free, and every queue we built around it is suddenly visible: review, environments, approvals, compliance. The work didn't get faster. The waiting got obvious. We got into where that queue actually forms, why drift might be the wrong thing to be measuring, and what it takes to get out of your own way. Link in the comments. #DevOps #PlatformEngineering #AI Notes on the format choices Hook sits in the first ~140 characters, before LinkedIn's "see more" cut on mobile. Both versions front-load the whole argument.Link in the first comment, as you asked. Worth knowing the picture is murkier than the folklore: LinkedIn has publicly said it doesn't penalise external links any more, but most creators still see better reach with link-in-comments. Treat it as a reasonable default, not a settled fact.Single line breaks between every idea. Dense paragraphs get scrolled past.Arrows rather than bullet characters — LinkedIn's editor mangles markdown lists.Three or four hashtags. More than five reads as spam and doesn't help reach.Upload E as the post image, since the 441% will be doing the stopping work in the feed too.One thing to sort before posting: I still don't have surnames for Pavlo or Zak, so tag them yourself. And the 441% figure should be attributed in your first comment — a number that big without a visible source invites the "citation?" reply, and on LinkedIn that reply is the top comment. need a spotify description Spotify allows 4,000 characters and only the first two lines show in the preview, so the hook goes at the very top. AI didn't remove the bottleneck. It moved it downstream. Developers have never written code faster. Delivery has never been less stable. Both are true at once — and most of the industry is treating it as a temporary problem. In this episode I'm joined by Pavlo [surname] and Zak [surname], founders of Platform Engineering Labs, to work out where the queue actually went after AI arrived. We get into why throughput recovered but stability didn't, why AI tooling struggles with the bespoke operational frameworks every organisation quietly runs on, and why reviewing generated code line by line has stopped being a useful unit of trust. Pavlo makes the case that most engineers own far more code than they should. Zak makes the case that the technology to automate the entire path from idea to production already exists, and that the hold-up is organisational rather than technical. If you have ever had something working perfectly on localhost and then waited months for approval to deploy it, this one will land. Chapters00:00 The queue didn't disappear, it moved downstream00:27 Meet Pavlo and Zak, Platform Engineering Labs01:27 What is the real bottleneck in shipping software?03:46 The DORA numbers: adoption up, stability down06:36 Why AI tooling breaks on bespoke operational frameworks09:37 The dark factory: spec-driven development in practice11:22 "Most of the code I've written, I never wanted to own"13:32 Review as the constraint16:02 Reviewing intent instead of diffs19:34 The infrastructure ceiling: environments, approvals, compliance23:25 DevOps, SRE, platform engineering — and which one survives26:36 Does AI make infrastructure as code more important, or unread?29:12 SOC 2, ISO 27001, and why git became the review tool by accident30:49 Read-only by default, and why drift stops being a useful concept38:00 Why Pkl, and whether language choice still matters40:25 Predictions: where this lands in the next six months
