Most organizations treat infrastructure as a series of projects to be “completed,” but successful platform engineering requires a permanent product mindset. In this episode, we explore why platform teams need dedicated product management to balance competing priorities—like security, cost, and developer experience—and why the “why” scales much better than the “what” in large enterprises. We also dive into the often-overlooked role of designers in creating platform tools that developers actually want to use.
Posts in "videos"
Developers crave AI tools for various tasks beyond coding, but that’s only about 20% of their work. But, ops people freak out about security and control challenges, like cost, regulatory compliance, and usage tracking.
Bad advice from Wall Street on enterprise AI.
Why it's great to be a Spring developer now, and how to make it even better - State of Spring, 2026
This is a talk I give at the start of Spring workshops we do. Here is the recording. The point is to show people that being a Java and Spring developers is fantastic right now. Here’s the description:
Spring developers are in a strange position in 2026: everything is changing: AI, platform engineering, enterprise architecture. And yet Spring keeps getting stronger.
In this talk, Coté walks through why this is actually a great moment to be a Spring developer, especially in large organizations.
Your Boss Doesn’t Know What to Do With AI
Enterprise AI Has a Product-Market Fit Problem. Enterprise AI isn’t stalled because the models are weak. It’s stalled because we haven’t discovered product-market fit inside the enterprise yet.
You don’t find real AI value by theorizing in workshops. You find it by running experiments for months inside your actual systems - against real data - in a governed environment.
That requires a platform.
Without one, AI pilots turn into disconnected experiments, shadow infrastructure, and compliance risk. With one, experimentation compounds into institutional learning.
In this video, I break down:
- Why enterprise AI is still in discovery mode
- Why experimentation must be long-running, not one-off
- How governance enables innovation instead of blocking it
- Why a secure platform foundation is the baseline for AI ROI
If you’re thinking about AI strategy, platform engineering, or how to make AI experimentation safe and scalable, this is where to start.
Featured:
Tanzu Catsup: The Risk of Relying on AI for Platform Engineering
Internal development teams and executives are increasingly looking at AI to automate the creation of internal developer platforms. However, cobbling together open-source components with AI is a far cry from building a scalable, secure, and “enterprise-grade” environment. In this conversation, we explore why betting your internal infrastructure on AI-generated platforms is a high-stakes risk and why human-led architecture still dictates the long-term success of a platform.
There’s also an excerpt.
The weird bastion stuff in D&D 5e 2024 makes a lot more sense after reading Dungeon Crawler Carl.
Go cheap on innovation. Otherwise you just buy PowerPoints. Jana Werner on using tight timelines and small teams to force real progress.
How much does it cost to build an internal developer platform? - Tanzu Catsup
This week’s Tanzu Catsup is about how much you need to pay people to build your own internal developer platform:
I think building your own platform is a terrible idea, especially for larger organizations. My co-host Tony ran the platform for Home Depot and now talks with other platform teams a lot, so I wanted to get his take.
Here’s an excerpt if you prefer something shorter.
This is our seventh episode, and they’re getting better and better.
This week’s Software Defined Talk: This week, we discuss the end of Cloud 1.0, AI agents fixing old apps, and Chainguard vs. Docker images. Plus, the mystery of Dutch broth is finally solved.
I recommend the traditional podcast format.