INBOX INSIGHTS: (2026-08-26) Walking Back Productivity, Vibe Coding Part 4

INBOX INSIGHTS: (2026-08-26) Walking Back Productivity, Vibe Coding Part 4 :: View in browser

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How do you walk back your productivity?

You would think that our pipeline or payroll is what keeps me up at night. Funny enough, that’s not it. It’s the damage AI is doing to the planet, and my part in it.

There are data centers going up all over the country with no real plan for the water and electricity they consume. We’re already seeing those damaging effects. Many people are still using generative AI just to draft an email, which takes precious resources away from communities that need them. When I look at that, then I look at my own usage, I can’t pretend the two aren’t connected.

Here’s my situation. Over the past year, I’ve found a lot of productivity and efficiency with generative AI. I’ve demonstrated, publicly and repeatedly, how much more I can produce with it. With my backlog gone, I’m asking “what’s next?” instead of “what’s overdue?”

And it comes with a cost I’m not comfortable with.

Last Friday, I asked Chris, “How do you walk back from 10x productivity when the bar has been set?”

The most direct way to stop contributing to the damage is to use less AI. And the only way to use less AI is to walk back my reliance on the productivity it gives me. That’s the trap. I have calibrated my team (and myself) to what I can produce with AI. My expectations for what I can do are sky-high. If I walk it back, my productivity drops 90% overnight. If you did that at a company, that’s not a principled stand. That’s a performance problem in your HR file.

Trying to walk it all back is the wrong question, honestly.

The question isn’t “how do I go back to before?” There is no before. The question is what I’m using it for, and what it’s actually costing. It’s a Purpose question, which means (if you know me, you know) I ran it through the 5P Framework by Trust Insights™ like I do everything else.

These are the questions I’m asking myself right now:

Could I just do this myself? Not “could AI do it.” If I really care about my carbon footprint, why am I asking Claude to make a website edit I could make myself in two minutes? That’s outsourcing my own effort and paying for it in water and electricity. There’s a rule I use with my team that I’ve started turning on myself: without opening the document, tell me what’s in it. If I can’t, then AI did the thinking and I didn’t. The task has to deserve the resources it consumes. An analysis that used to take me a week earns it. A three-sentence email? Hard pass.

Then there’s the bigger move: figuring out what a local, more sustainable AI setup looks like for me, start to finish, requirements first. The biggest lever any of us has is changing what powers the AI we use, not just using it less. I’ll be documenting that journey, including everything I get wrong (and there will be plenty). It will take time as I figure it out. I’m confident in saying that I represent most generative AI users in this space. I have a decent laptop, I am comfortable with the tools, and I am not an engineer.

I don’t have this figured out, but I am committed to trying. What I have is a bar I can’t unset, and a conscience that won’t shut up. Now I just need a plan. And if there is one thing I’m good at, it’s making a plan. Stay tuned!

How are you managing the expectations of using AI?

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to stop AI from turning your writing into repetitive slop and replace it with authentic human voice. You will discover why AI drifts into repetitive phrasing and how to stop it. You will learn to measure your unique writing style with simple numbers that lock in your voice. You will apply a structured editing process that transforms machine drafts into polished content. You will gain confidence to command AI tools without wasting hours on endless revisions.

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In this week’s Data Diaries, the fourth of a five part series on “vibe coding” basics for non-coders. Katie and I have talked extensively about vibe coding in the past on the podcast, but we’ve never really dug into the basics of it. So in this series, completely unsurprisingly modeled on the 5P Framework by Trust Insights™, I’ll cover those basics.

Vibe Coding Platforms

It would be a bit cheeky to say “it doesn’t matter!” and that would be half true. If you follow the PROCESS from last week of developing great requirements, a PRD, a technical spec, and a workplan, nearly any modern AI coding system will generate great outputs. If you skip those parts and just “give into the vibes”, you’ll create hot garbage no matter what AI model or coding platform you use.

Additionally, which platform to use depends heavily on what AI systems your organization permits. Here’s a short breakdown of the ecosystems and their coding tools:

Microsoft: Microsoft Github Copilot inside Visual Studio Code
Google: Google Antigravity
OpenAI: OpenAI Codex
Anthropic: Claude Code
Alibaba Qwen: Qwen Code

If you aren’t tied to a particular ecosystem, then the free, open source OpenCode is an excellent coding tool, paired with either a local inference provider (on-premise AI) or a cloud inference provider like DeepInfra, Cerebras, etc.

What about dedicated platforms like Cursor and Lovable? Unless you have a specific need that the above platforms don’t cover, these coding environments are just added overhead and cost. There’s nothing unique to them that other platforms you already pay for can’t do.

Before you sit down to have AI write even a single line of code, you should also use deep research tools (shameless plug for the Trust Insights Deep Research Suite) to build coding standards if your organization doesn’t already have them. Coding standards are THE best way to keep your AI coding tools on the rails, by telling them what is and isn’t allowed, and how your organization works.

For example, at Trust Insights, we have coding standards for nearly every major language – Python, Rust, PHP, Typescript, Rust, etc. – so that any time we happen upon a new project, we’ve already got a checklist that we can use to keep the project on the rails, right down to how the project’s architecture should work:

We follow the same process as last week, but part of the requirements gathering for the PRD and spec are to feed that checklist in so that from the very beginning, we are standards compliant.

In terms of how we use our coding tools, we follow the Plan Big, Act Small principle: use the biggest, smartest, most expensive models to do the planning, and then use small, fast, cheap models to do the implementation. If your requirements, spec, and workplan are solid, and you mandate 100% test coverage and 100% passing tests, inexpensive models will get the job done. As of the time of writing, these are the models to use:

Microsoft: Plan with gpt-5-6-sol, build with gpt-5.6-luna (Github Copilot usually comes with OpenAI models)
Google: Plan with Gemini Pro, build with Gemini Flash
OpenAI: Plan with gpt-5-6-sol, build with gpt-5.6-luna
Anthropic: Plan with Claude Opus, build with Claude Sonnet
Alibaba: Plan with Qwen Max, build with Qwen Plus or Qwen 27B
OpenCode: Plan with Qwen Max, Kimi K3, or GLM-3, build with DeepSeek v4 Flash

The cost differences can be massive – a good plan executed with a Plan model like gpt-5.6-sol will cost you several hundred dollars. The exact same plan executed with a model like DeepSeek v4 Flash or gpt-5.6-luna will cost you a dollar.

Next week, we finish out the series by talking about performance and testing.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our capstone course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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