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When code is free, taste is everything

Duration: 29:27

A panel from Sanity's Everything NYC '26, recorded on the official Sanity channel. Jono sits down with Ricky Robinette (Google Cloud) and Jared Reyes (Sanity) to talk about what good looks like once generating code costs nothing: the dashboard that made up its own data, why every AI-built UI looks like the same Tailwind template, bringing AI into a content team one paper cut at a time, and why taste is the thing worth getting better at.

Frequently asked questions

Why are developers using AI coding tools more even though trust in the output is falling?
The Stack Overflow data shows 84% of developers using AI in their coding, but only 29% trust the output, and that trust figure is declining. Ricky's explanation is that the speed gains are so compelling that developers keep using the tools regardless, but the risk is that you end up running fast in the wrong direction without realizing it. His dashboard story, where the AI simply made up historical YouTube data rather than admit no API existed, is a good illustration of why verification still matters.
Why do all AI-generated UIs end up looking the same?
The panel puts it down to agents defaulting to the same vanilla Tailwind output unless you give them a strong, opinionated starting point. Ricky's fix is spending time writing detailed markdown instruction files that reflect his personality and brand, and he suggests two hours polishing those files would noticeably improve results for most people. Jono's team maintains a design.md file that every agent and team member points at, modelled on Vercel's approach, specifically to avoid everything looking like shadcn.
How do you introduce AI workflows to a content team that is resistant?
Jono's advice is to start by identifying one specific friction point, what he calls a paper cut, and build a small workflow that removes just that one thing. Showing a concrete improvement in someone's daily process does far more than a broad pitch about AI. He recommends keeping the initial engagement tightly scoped and not adding anything beyond that single fix until trust is established.
How do you keep AI-generated codebases maintainable over time?
Jono's view is that custom QA tooling is the next frontier, pointing to tools like CodeRabbit and Greptile as early examples, though they still have limitations around token costs at scale. His team runs an internal QA system that routes smaller changes to cheaper models and larger changes to more capable ones. Ricky is more pragmatic, noting that legacy codebase rewrites are a permanent feature of software engineering regardless of how the code was written.
What is the one skill worth developing right now if you are a developer?
Ricky's answer is taste: knowing what good looks like and being able to articulate why you made a decision. His point is that shipping used to be the differentiator, but that is no longer the bar when generating code costs almost nothing. Jono adds that understanding where business goals and development skills intersect, and staying across where AI tooling is heading, is equally worth the effort.
Is vibe coding and moving fast a replacement for having a clear direction before you build?
The panel is consistent that speed without direction is the main risk with current AI tools. Ricky says he now spends more time on the why before anyone writes a line of code, giving the example of someone who asked Gemini for advice and was immediately told they needed five agents before they had even defined the problem. Having a strong sense of what you are building and why matters more now, not less, precisely because the tools can move so fast.

About the author

Jono Alford

Founder of Roboto Studio, specializing in headless CMS implementations with Sanity and Next.js. A Sanity Pioneer and first-cohort Sanity Community Ambassador, focused on editorial experiences that help teams ship faster.



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