Vibe Coding Is Not What You Think It Is (Premium)

There is no evidence that the speed at which AI advances has slowed in the slightest. Almost every day, there is some new milestone, usually in the form of ever-more-capable new AI models or services that take advantage of them. These advances span every use case imaginable, making it ever more difficult for AI deniers--and, yes, they're still out there, nervously collecting random AI fails that they believe support their Luddite views--to continue the charade.

AI is interesting on many levels. We've learned that interacting with an AI chatbot is different from the typical Google search, where being very specific--the more the better--will lead to superior results. We've learned that so-called reasoning models, which show the user how it "thinks" through a problem, are both more trustworthy and more accurate. And we've learned that grounding AI isn't just important, it's ideal: The more finite the learning set, the better, whereas AI that just looks at the entire Internet is more prone to "hallucinations" because the body of opinions online is overwhelmingly contradictory despite the existence of objective facts.
?‍? AI and coding
It is not coincidental that the first major use of AI--as we know it today--was GitHub Copilot, a developer product that's grounded in the programming languages and frameworks that developers use to write code. (It is likewise not coincidental that Microsoft, which owns GitHub, seized on the Copilot brand for its other AI offerings, which are likewise expanding unabated as I write this.) Software development and AI are the Reese's Peanut Butter Cup of modern technology, two great ideas that just go together naturally.

This makes sense. The specification for any programming language--C, C++, C#, Java, JavaScript, whatever--is, by definition, finite, as are the specifications for the various frameworks that developers use with them. There are also years--and in some cases, decades--of online interactions on sites like Stack Overflow in which experienced developers answer questions for beginners or anyone else confused by specific issues, usually with source code examples. All this data, combined, represents a body of work that is both finite and extensive. It is a near perfect grounding on which train AI models. And it is not surprising, then, that when new models emerge, as they do every week right now, that software development expertise is routinely touted alongside more mainstream use cases like text summaries and rewriting, text-to-whatever content creation, and so on.

In recent months, AI has gotten so good that this perfect combination of ease and sophistication has caused non-programmers to experiment with software coding. Or, more specifically, to ask AIs to create software code for them. And there are many examples of software programs, including games, that have been created this way, sometimes by non-coders. On a recent episode of the Scott & Mark Learn To... podcast, for example, Microsoft...