On Shutting Down, Five Years of Learnings, and Teaching What's Next

Hey friends,

It's been about six weeks since I last wrote, and quite a lot has happened.

I'm writing this from the other side of a major transition: Plumb (the company I was the technical co-founder of) shut down officially on October 14th.

I've been thinking a lot about what it means to preserve a story even when it doesn't have a traditional "happy ending."

On shutting down something you built

Aaron and I announced we were shutting down Plumb in early October.

We built something technically beautiful (Magic Mode, MetaJSONSchema, ZodForm for dynamically generating forms from static and dynamic schema definitions, workflow subscriptions that felt like magic) but we solved technically interesting problems instead of operationally valuable ones.

That's the short version.

The longer version is in the 100+ learnings I extracted from our five-year retrospective.

The big ones:

• Team: We hired too many people, too early. Should've stayed CEO + technical co-founder until we found PMF.
• Customer Development: We didn't listen to customers. When they told us what they needed, we filtered it through our vision instead of hearing them.
• Product: We cycled through ICPs without validating any deeply enough. Engineers, "everyone," real estate agents, creators, consultants—never committed long enough to learn.
• Go-To-Market: User ≠ buyer. Managers loved Supermanage, but they weren't the ones buying enterprise software.
• Timing: Early or wrong = wrong. Being early feels like being visionary. It's not. If the market isn't ready, you're just wrong. (My co-founder Aaron Dignan was the one who came up with this learning)
• Technical: AI made our tiny team wildly efficient. "10 one-day sprints instead of one two-week sprint" changed the entire velocity equation.

The team we built was probably the part I was most proud of. Sure we missed the micro but nailed the macro. Multiple companies are now interested in acqui-hiring the whole team or taking people individually.

It turns out that five years of learning on how to work with AI-augmented processes is "like gold," as one person put it.

Why I wrote all this down

Most startup stories are incomplete. You get the highlight reel or, at best, a postmortem. But never what actually happened. I keep thinking: shutting down doesn't mean the story should die. A story that goes untold is lost forever.

So I'm preserving this one. Not because it's a success story in the traditional sense, but because it matters. I built something incredibly powerful, we made a wrong bet on subscriptions, and it was too much to pivot from. Meanwhile, OpenAI released something similar to what we built (but I'm not sure for what user), and a competitor just raised $180M.

The timing of us closing, despite it not being good for the product or the company, is actually the best possible outcome for me and the team.

What I've been writing

In the past six weeks, I published eight articles. I'll share the ones I keep thinking about:

The New Maker Schedule Isn't About Making - I've been thinking a lot about how AI agents are turning makers into directors. Leverage now comes from orchestrating systems, not executing tasks.

How I Use AI Daily - This one is about living and breathing AI as both practitioner and creator. Building Plumb for two years taught me how to use AI to amplify velocity. We went from one two-week sprint to ten one-day sprints.

The High Throughput Zero-Writing Documentation System - One of my favorite workflows in the past year has been to "Walk, talk into Voice Memos, transcribe, let Claude structure it". Speaking at 150 wpm vs typing at 60 wpm. It's a game-changer for brain dumps, brag docs, content creation and getting my ideas onto paper in structured content based on my unstructured monologue.

AI Agentic Workflows vs AI Agents - When to build orchestrated workflows vs autonomous agents. The difference between designing a factory line and hiring a consultant. Choosing wrong costs months of work.

The Cost of AI for The End User - An essay on the current state of AI tooling monetization and why no AI monetization model has achieved sustainable product-market fit yet. Subscriptions commoditize, platform taxes feel extractive, usage-based creates friction.

What I'm building next

I'm launching a course: "Use AI to Build and Deploy a Website from Scratch"

Working with non-engineer co-workers at Plumb showed me something powerful: these people had incredible ideas, deep domain knowledge, and creative vision. The only thing holding them back was the technical barrier.

If they could use Claude Code to manipulate file systems, work with their ideas directly, and deploy their vision—they'd be unstoppable.

So I'm teaching that. First cohort is forming now. Making this skill accessible to everyone who has ideas but lacks traditional coding ability.

Sign up here

Job searching from a place of abundance

I'm talking to about 10 companies right now. Four are interested in team acquihire opportunities that are interesting: rebuilding AI-augmented dev processes for big engineering orgs, workflow automation platforms, AI product development.

Six are individual roles across technical ex-founder positions, applied AI engineering, platform engineering leadership, developer tools. One in particular really has my interest.

What's interesting is the consistent feedback: the combination of org transformation + technical execution is rare. Small team shipping at scale resonates.

I'm also exploring consulting, teaching, maybe an agency model. The optionality feels good. I'm coming from abundance rather than scarcity, which is a gift.

Things I enjoyed this month

• I went to Meow Wolf in Denver, CO with Jackie and Elizabeth. If you haven't been, it's this immersive art installation that feels like stepping into someone's fever dream. Highly recommend.
• Principles of Building Agents - A book on agent architecture that's been sitting in my browser tabs.
• Cloudflare open-sourced VibeSDK, an AI coding platform that lets anyone build and deploy apps using natural language. Think Replit or Cursor, but with instant deployment.
• OpenAI launched Prompt Packs with 300+ ready-to-use prompts for different roles. Making AI adoption plug-and-play across industries.
• Someone posted about Large Action Models being vindicated—they were right, just early. Familiar feeling.
• The first malicious MCP server was discovered in the wild—a Postmark backdoor stealing emails and docs. Downloaded 1,500 times weekly. Security in the AI ecosystem is going to be a wild ride.

The pattern I'm noticing

There's a shift happening for me: from building to teaching. It keeps showing up in my notes, in the consulting opportunities, in the course I'm creating. I'm realizing I care deeply about enabling others to use AI tools effectively.

The other pattern: preserving the story. This newsletter, the 112 learnings, all the writing—it's about making sure the messy, complicated, interesting parts don't get lost in the simplified narrative.

A big part of why I'm able to notice these shifts is that I've been doing conscious leadership coaching with Michael Norton. The work has helped me become more curious and expansive, and kinder to all the parts of myself. If you're looking for a way to grow through a transition or just become more aware of your own patterns, I highly recommend it.

Thanks for reading. I'll try not to let another six weeks go by before the next one.

Chase