We’ve been watching the AI gold rush unfold for the past two years now, and honestly? It’s giving me serious déjà vu. Remember the early days of mobile apps when everyone and their cousin was building a flashlight app? Or when “on the blockchain” was supposedly going to revolutionize everything from grocery shopping to dental records?
We’re in that phase with AI right now. The phase where adding “AI-powered” to your deck is supposed to make VCs throw money at you. Where every developer with a weekend and an OpenAI API key thinks they’re building the next unicorn. Where demos that only work some of the time are being pitched as production-ready solutions.
So in the spirit of those old Wired magazine “Wired, Tired” lists (remember those?), here’s my take on what’s already exhausting versus what’s actually worth paying attention to in the AI space. Fair warning: if you’re building a ChatGPT wrapper, you might not like what comes next.
Wired
Boring AI infrastructure. Someone’s going to be the Stripe of AI; making integration so simple that it’s just another API call. Probably multiple someones, actually.
Vertical-specific solutions with real domain expertise. The winners won’t be “ChatGPT for X.” They’ll be companies that deeply understand X and happen to use AI to solve real problems in that space.
Local-first AI that respects privacy. The pendulum is swinging back. Companies are realizing that shipping their customer data to OpenAI isn’t always the answer. Local models that run on-device or on-prem, even if they’re slightly dumber, are going to own entire market segments.
Uncertain AI. “I’m not sure about that” or “I don’t have enough context” should be the most common responses from any AI system worth using. Confidence without competence is just expensive randomness.
Models that get smarter without getting hungrier. The dirty secret of the AI boom: efficiency improvements are outpacing capability improvements. Today’s local models are smarter than GPT-3 and use less energy. Tomorrow’s will make today’s cloud APIs look like mainframes.
Tired
Anthropomorphizing LLMs Thinking that they’re digital coworkers who “understand” your requirements. They’re autocomplete engines, not minds. When you say “the AI gets it,” you’re one step away from skipping the validation that keeps your system from confidently hallucinating nonsense.
“AI-powered” as a meaningful differentiator. Saying your app is “AI-powered” in a few years will sound as ridiculous as “database-powered” does today. Of course it uses AI. Everything will.
Demo-ware that can’t handle production. That amazing demo that works 95% of the time? That 5% failure rate means it’s unusable for anything that matters.
The “AI will replace developers” panic. Every few months, someone breathlessly announces that programming is dead. Meanwhile, I’m spending more time than ever debugging why the AI-generated code thinks undefined is a valid currency format. Turns out, computers still need adults in the room.
AI agents that need more supervision than a toddler with scissors. “Autonomous” agents that require constant monitoring, error correction, and hand-holding. If I have to babysit your bot every five minutes, I might as well just do the task myself.
The real opportunity isn’t in slapping AI onto existing products. It’s in understanding what this technology actually enables and, more importantly, what it doesn’t. It’s in the boring stuff: the infrastructure, the reliability, and the domain expertise. That’s where the real work is.
I’ve seen this movie before. The companies that survive the hype cycle aren’t the ones with the flashiest demos. They’re the ones solving real problems, with real technology, for real people. The rest is just noise.
Now if you’ll excuse me, I need to go build something boring.