Generative AI Readiness for Low-Code and Low-Ops

Is generative AI shaped to help or hinder the no-code movement?

This post is a reflection of a comment from @Kuovonne. Thanks for the inspiration.

I don’t think AI is ready to create complex logic ...

But my main issue is that I don’t think AI is ready to create complex logic for people who struggle to clearly articulate their needs.

I agree, and I think we need to be careful about the expectations of AI as code creators. However, Kuovonne used a few predicates (++ for doing so) that caused me to reflect on this comment with clarification.

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Low-Code

I don’t think AI is ready to create complex logic ...

Most novice coders don't need complex logic; they want usual and customary logic. The type of logic that is in abundance and represented on the public Interwebs in spades. This is the type of logic generative AI is very familiar with and can replicate with good success. If also presented with learner shots - e.g., in the context of a copilot, the output accuracy climbs from ~80% to ~98%.

The predicates I use here are "usual and customary". And even if you are a more advanced low-coder, you benefit greatly by quickly getting the usual and customary elements of the logic out of the way so you can focus on more advanced logic that may be required.

Generative AI for low-coding activities has value TODAY for EVERYONE but not for every context.

Clear AI Instructions

[Novice low-coders] ... who struggle to clearly articulate their needs.

Again, an excellent choice of predicates. This is a deeply intertwined aspect of the ability to convey your desires, a task that is not unlike conveying technical requirements. Some of the best engineers cannot describe in words what they want. They might have a better chance than a novice low-coder, but expert coders' vast indictment of AI shows they struggle composing prompts much the same as novice coders. I think they struggle more than novices.

AI and Domain Experts (aka novice low-coders)

This persona is better equipped to use AI to create solutions. They understand the business requirements and are typically accustomed to using words in their work far more than software engineers. I predict Replit's vision of a billion coders will be spot on, and largely for this reason.

The merger of low-code, low-ops, and AI is undeniable; it's as if they were always intended to form this triumvirate alchemy designed to overcome the shortage of software engineers who invariably struggle to match technical requirements to business requirements.

AI plus domain expertise plus CaaS (code as a service) is the future.

Low-Ops

One of the big advantages of low-code in Airtable and Make (for example) is the streamlined and largely hidden ability to deploy and run your code. This is advantageous for novice and expert low-coders. You need not worry about where your code is deployed or how it is hosted. It just runs. Coda's Pack Studio is a similar approach, and many SaaS platforms are quickly realizing there's something beyond the omnipresent model - IaC (Infrastructure as Code).