Hi fellow data professional!'

Today I’m turning the newsletter over to my friend Ken Jee (writer of AI Survival Guide, creator of Newsletter Hero) to share how he cuts through the noise of shiny AI products to find tools that enhance technical work.

My Simple Framework For Adopting AI Tools

Ken Jee

As new AI tools launch almost daily, a quiet tax is emerging. Decision fatigue. Every new model, agent, or workflow tool carries the same implicit question. Should I switch, or should I go deeper where I already am.

Most people answer this emotionally. They chase novelty. They fear missing out. They install everything and master nothing. The result is shallow leverage and constant context switching.

A better approach is to adopt a simple framework.

Start with this question. Is the tool I am already using likely to do this well soon.

If the answer is yes, depth usually beats novelty.

Take Claude Code. I use it daily. When a new coding agent launches with an interesting feature, there is immediate economic pressure on Claude to adapt. If the feature is genuinely useful, it will almost certainly be integrated or matched quickly. That means my time is usually better spent improving my environment, refining prompts, building reusable workflows, and getting faster at the tool I already depend on. The returns from mastery compound.

This is the mistake most people make. They confuse access with leverage. Using ten tools at a surface level feels productive. Deep customization of one tool is what actually changes output.

The second question in the framework is this. Do I get higher returns from going deeper with my current tool, or from switching to something new.

If learning a new tool costs weeks and only marginally improves results, it is probably not worth it. If staying put unlocks speed, intuition, and reuse, depth wins.

But there is a clear exception.

Hyper specialized use cases reward specialization.

This is where new tools make sense. If a team is spending all day thinking about a single narrow problem, they will outperform a general purpose setup almost every time.

Newsletter Hero is a good example. Our entire focus is using AI to help people research, write, and grow newsletters. Rebuilding that level of domain specific intelligence in a generic environment would take an enormous amount of time. It makes more sense to defer to tools built by people who live in that problem space.

The same logic applies to tools like Opus Clip or Descript. These are not just wrappers around AI. They encode thousands of small decisions about a specific workflow. That accumulated judgment is the real product.

So the framework becomes simple.

Go deep on general tools that are central to your work and under strong competitive pressure to improve.

Go specialized when the use case is narrow, repeatable, and already being obsessed over by a dedicated team.

Ignore everything else.

This framework reveals something deeper about a post AI world. When intelligence becomes abundant, scarcity shifts. Judgment, taste, focus, and responsibility become the differentiators. In that environment, chasing every new tool is a form of avoidance.

Depth is not a preference. It is a strategy. AI will keep making execution easier. It will not choose where your attention goes or what you commit to. In a world where AI is everywhere, discernment is no longer optional. It is the skill that decides who compounds and who gets left behind.

A huge thank you to Ken for sharing his time and insight! To read more of Ken’s strategies for surviving and thriving in an AI-disrupted world, subscribe to his AI Survival Guide.

Ken and I met back in 2022 when I was a guest on his podcast, Ken’s Nearest Neighbors.

Many of my thoughts on breaking into data are still relevant, so I encourage you to give it a watch and/or listen.

Back with a solo edition next week!

Thanks for ingesting,

-Zach Quinn

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