BrainBlog for vFunction by Jason Bloomberg
Technical debt is a problem at the best of times, but during periods of rapid innovation, it can become overwhelming.
Innovation, after all, is never linear. It comes in fits and starts, with dead ends and sudden turns aplenty. Every twist in this tale of innovation leaves something behind – some tool or experiment that turns out to have been a bad idea in retrospect.
All these deviations can potentially add to technical debt, as they are far easier to deploy than to decommission.
Today, AI – in particular, generative AI (GenAI) – is in such a period of rapid innovation. Enterprises, software vendors, and born-in-the-cloud companies alike are jumping into the GenAI pool with both feet.
It won’t be long, therefore, until many such organizations run into burgeoning technical debt challenges.
Bob Quillin, Chief Ecosystem Officer at vFunction, discussed this problem in a recent article. In this article, he explains how the rapid accumulation of AI tooling can lead to technical debt as requirements evolve and tool vendors go out of business, leading to unsupported packages and tools.
While the rapid obsolescence of tooling is problematic, there is an even more ominous form of technical debt that the rapid innovation in GenAI exacerbates: architectural technical debt (ATD).
Not only do older architectures fall short of the requirements of GenAI, but even modern architectures suffer from the problem of rapid innovation.
Getting a handle on ATD, therefore, is essential for the successful deployment and operationalization of GenAI-based applications.
Click here to read the entire article.
