It’s weird being a technical writer in 2026, because there are a lot of people around you who will tell you that your craft is outdated. Like the manufacturers of buggy-whips at the beginning of the twentieth century, the car (in the form of a big language model AI) is on the market, and your business will soon be an anachronism. Get used to it or you’ll die, they tell you. It’s an argument I’ve run into a few times over the past year on my travels, and it’s forced me to think about it. What are the reasons everyone is excited about AI and are those reasons valid, what should we fear, and what are the real reasons people should be excited?
If We Should Take This Seriously, How Can We Do It?
I’ll start by repeating my story from a few weeks ago when I asked readers what AI applications might survive when the hype dies down. The reaction of a friend with decades of software experience to trying an AI coding assistant stuck with me; he talked about his grandfather who was born in rural America in the closing years of the nineteenth century, and remembered him describing the first time he saw a car. I agree with him that this has the potential to be a transformative technology, and although it’s fun to make fun of their mistakes as I did three years ago when the idea of what we now call vibe coding appeared, it’s already making itself useful in other applications. Just dismissing it is no longer appropriate, but equally, liberally drinking the Kool-Aid seems like joining another hype bandwagon that is sure to be disruptive. A middle way must be found.
Many of us over the past few years have probably met a Guy In A Suit who is very happy with ChatGPT. I think people like him are motivated by several things; He is impressed by that LLM because it seems really smart to him, he has used it to pretend to be smart to other people so it has made him see himself as smarter than an engineer who points out his mistakes, he thinks it is a magic bullet that can make him a lot of work and save or make him a lot of money, and maybe most importantly, a great fear of what comes next.
And ça Change, When It Comes To Hype
It’s easy to take pot-shots at those motivations or it won’t make you famous. His sense of genius will only last as long as he discovers that everyone else has the same thing, or perhaps until it leads him astray into a disastrous decision. At that point there is a good chance that the magic bullet will go the way software releases have been done for the past twenty years, as relying too heavily on redundant work will generate more work to fix its problems. But while that pitcher shot weakened some arguments they are not perhaps the crushing blows one might think they are. LLMs have their uses, however that can be annoying if you’re dying of undervaluation.
Perhaps the most worth examining is the fear of missing out, because that is the most important motivator. We all want to be among the Cool Kids, Hackaday Students with the latest tech toys before everyone else is immune to this. And when you’ve convinced yourself that one way to become one of the Cool Kids is to be the commercial equivalent of a buggy-whip salesman circa 1920, it takes on an added urgency. Time to look to the perennial favorite, the Gartner Hype Cycle, for inspiration. Just where on the Gartner Hype Cycle curve should you be, to miss out?
To the left of the graph is the slope towards the Peak of Inflated Expectation. This is the part we most associate with tech bubbles; as an example we can point to the dotcom boom during its worst period in 1997 or 1998. If you choose a moment of peak or indeed a downward spiral towards the Trough of Disillusionment to jump into, then you are obviously missing out. But how far back do you have to go to avoid missing out? I’d argue that it’s too early, to use our dotcom boom analogy: if you weren’t in the game in 1996, you were probably too late. Moving on to today’s AI boom, has our Guy In A Suit already missed the boat without realizing it?
They are looking at the wrong part of the graph
It pains me to see people who are newly fascinated by AI in 2026 for the reasons listed above. For them it works, but as they live and work in many other booms and subsequent crashes driven by the same ideas about that technology I know how it will go in the next year or so. I think there are many other valid reasons to be excited here, but they lie elsewhere on the Gartner graph. Back in the dotcom boom, the whole thing was driven by sometimes crazy ideas around e-commerce, however it will be social media after ten years that will make many of the big players we have today. Could someone create Facebook in 1996? It is possible, but if one thinks about it at the time, it seems that he did not. If Guy In A Suit wants something to be excited about, he should be polishing his crystal balls and looking forward to the right hand side of the Gartner graph over a decade, not running with the herd.
If I go back to my first paragraph and whether the author will join the buggy-whip sellers, I always hope that they will not. Hackaday is based on meat for good reason, but I usually watch the consumer make a hair-raising response to the slop. I’m sure there will be room for machine-generated content in the future whether we like it or not, but I’m equally sure that in my line at least, human input will retain some value.
After considering Guy In A Suit and myself, maybe it’s time to talk about you, Hackaday reader. We probably have more AI-skeptics among us than there are in the general public and I consider myself part of them, but for all that skepticism I think we should move towards interesting things rather than turn our backs. I mentioned AI-based coding assistants as an example where our community has found some benefit, and as I’ve said before I think the ability to run a useful LLM locally on commodity hardware brings a lot of power over the data-slurper of the cloud. If we don’t believe in it, we should at least be like Fox Mulder, too search to believe.
Where are you in that going on?
