Perhaps it shouldn’t be surprising that so many technology trends mimic fashion trends. No, I don’t mean our clothing choices—we technology folks are persistently poor dressers. Rather, I’m talking about how decisions are made. Even as I type this, your company is throwing as much ChatGPT against the wall as possible, desperately hoping some of it will stick. Rest assured, some of it will: Commonwealth Bank of Australia says it has cut scam losses by 50% and customer-reported frauds by 30% using AI.
Hurray! But the fact that some companies are having success with generative AI, or Kubernetes, or whatever, doesn’t mean that you will. Our technology decisions should be driven by what we need, not necessarily by what we read.
Kubernetes all the things
I love how Tom Howard describes Kubernetes: “the most complicated simplification ever.” As one Kubernetes émigré details, Kubernetes can be “difficult to provision, expensive to maintain, and time-consuming to manage.” This isn’t surprising if you know its origin story. Google created Kubernetes to handle cluster orchestration at massive scale. It’s a microservices-based architecture, and its complexity is only worth it at scale. For many applications, it’s overkill because, let’s face it, most companies shouldn’t pretend to run their IT like Google. So why do so many keep using it even though it clearly is wrong for their needs?
Fashion.
I’ll admit it might not only be aspiring fashionistas who drive Kubernetes adoption. One frustrated Kubernetes user laments that “it feels like all I ever do with Kubernetes is update and break YAML files and then spend a day fixing them by copy-pasting increasingly convoluted things on Stack Exchange.” A more experienced Kubernetes user suggests it could well be “senior engineers trying to justify their salary [or] ‘seniority’ by buying into complexity as they try to make themselves irreplaceable.”
That might be overly harsh, but the will to use technology for technology’s sake is strong. It’s often not about picking the reasonable option, but rather about using the fashionable one. As you know, the right IT strategy is generally summed up as “it depends,” which brings us back to AI.
Asking AI the wrong questions
Menlo Ventures recently surveyed 600-plus enterprises to gauge AI adoption. Perhaps unsurprisingly, software development tops the list of use cases, with 51% adoption across those surveyed. This makes sense because ChatGPT and other tools offer fast-track access to developer documentation, as Gergely Orosz found. Developers have gone from asking questions on Stack Overflow to finding those same answers through GitHub Copilot and other tools. Generative AI may not be as good an option to solve other enterprise tasks, however.
This is because ultimately generative AI isn’t really about machines. It’s about people and, specifically, the people who label data. Andrej Karpathy, part of OpenAI’s founding team and previously director of AI at Tesla, notes that when you prompt an LLM with a question, “You’re not asking some magical AI. You’re asking a human data labeler,” one “whose average essence was lossily distilled into statistical token tumblers that are LLMs.” The machines are good at combing through lots of data to surface answers, but it’s perhaps just a more sophisticated spin on a search engine.
That might be exactly what you need, but it also might not be. Rather than defaulting to “the answer is generative AI,” regardless of the question, we’d do well to better tune how and when we use generative AI. Again, software development is a good use of the technology right now. Having ChatGPT write your thought leadership piece on LinkedIn, however, might not be. (A recent analysis found that 54% of LinkedIn “thought leadership” posts are AI-generated. If it’s not worth your time to write it, it’s not worth my time to read it.) The hype will fade, as I’ve written, leaving us with a few key areas in which artificial intelligence or genAI can absolutely help. The trick is not to get sucked into that hype and focus on finding significant gains through the technology, instead.
All of which is a long way of saying that we need to get smarter about how we invest in technology. Just because everyone is doing it (Kubernetes, ChatGPT, or even cloud) doesn’t mean it’s right for your particular use case. In my youthful exuberance, for many years I touted open source as the answer to pretty much everything. Although it’s true that open source is a good answer to some things, it’s most definitely not a panacea for a wide array of technology issues, including some (like security) where it offers particular promise. The same is true for AI and every other technology trend: The answer to whether you should use it is always, “It depends.”
