It's Hype 'Cause it's a Step Change
Source: Dev.to
“There are decades where nothing happens; and there are weeks where decades happen.” — Vladimir Lenin
AI – wow!
I can recall precisely two times in almost three decades of technology work where I’ve experienced a step‑change in my work process. There have been plenty of changes I would class as incrementally good, but only two with enough magnitude to sit back and marvel at life pre‑change vs. life post‑change.
1. 1999 – The First Google Search
1999 was my first year of IT employment. It was also the year we got the first installment of The Matrix, the Melissa virus hit IT departments with its macro‑into‑Word‑template payload, and half the year was spent changing computer clocks and running Y2K‑readiness tests. Most importantly, it was the year I discovered Google search.
Young people might not understand that pre‑Google Internet life was pretty rough: there were plenty of search‑engine options, but the typical experience was:
- wading through duplicates,
- dead links,
- “next page, next page, next page”…
…until finally you got something of use.
With the new Google search engine, you could just Find What You Want™. A 15‑minute search exercise became a 15‑second one. It felt like a step change.
In between, technology marched on: faster hardware, better IDEs, hosted infrastructure, better version control, better package managers, new programming languages. Lots of valuable improvements, but they all seemed incremental rather than a sudden difference. (Yes, the iPhone‑like mobile wave was huge culturally, but didn’t really shift the work needle for my particular job.)
2. Early 2023 – Generative AI (ChatGPT)
As the world recovered from the disruption of the global pandemic, a new chat service was taking the tech world by storm. My first test of ChatGPT was to give it an arduous task: writing an information‑privacy policy document. I don’t do policy documents much, so when the “opportunity” came around I was neither well‑practised nor enthusiastic.
I loaded a couple‑sentence prompt, and watched hours of work unfold before me in a matter of seconds. This felt utterly magical. AI – wow! It’s finally arrived! And with it, my second step‑change: you can just Create What You Want™.
I’d appreciated incremental improvements in machine‑learning applications over the years, but this generative AI was a huge leap—generating documents, code, tests, diagrams, songs, videos… it could do it all. Wow!
If you asked my kids or colleagues, they’d probably say I’ve “deep‑ended” a bit with AI. The monologues are real. If AI were a ’90s hip‑hop act, I’d probably have a big clock around my neck and a name like Flava Flav.
Yo Chuck, imma generate yo some AI.
I wonder if our forebears had similar vibes? Imagine receiving your first printing press after a life of hand‑copying—being able to produce 3,000 pages in a day instead of 30. What a step change! Imagine the postman who trades his horse for a new government‑issued motorcar—how gangsta would you feel delivering mail fast and with style?
While some professions (e.g., blacksmiths) became obsolete, many simply adapted. That copyist‑cum‑printer could take on far more work; the postman could add routes. Generative AI feels to me like a force‑multiplier for a knowledge worker’s tasks. I’m more in the “amazing‑leverage” category than the “impending‑obsolescence” one.
Throughout history, humans tend to move to higher‑level, more interesting tasks rather than sit around twiddling our thumbs because technology has taken over.
Why Generative AI Is a Force‑Multiplier
The production of large volumes of stuff quickly is the wow factor. The output—right from the outset—has ranged from adequate to outstanding. My intervention in the creation process is therefore anywhere from minimal to “well, I would have spent lots of time on it anyway.”
Using generative AI for the first time in new ways makes you feel like a tech gangsta. Here are a few areas where it’s already been a force‑multiplier for me:
| Area | How AI Helps |
|---|---|
| Document Summarisation | LLMs shine here. A simple “summarise the bill before the NZ parliament” prompt for my Democrify app produced one‑pagers that maximised the knowledge‑gained‑to‑time‑spent‑gaining‑it ratio. |
| App Production | AI coding agents enabled me to birth or revive projects like Democrify and DefProd that I couldn’t have sustained without them. The shift from code writer to orchestrator is real. |
| Code Testing | My open‑source library node‑net‑snmp was light on tests until AI gave it a big shot in the arm. It’s now adequately tested. |
| Documentation Creation | Documentation and developers go together like diets and dessert. I now fill my repos with AI‑produced markdown and designs. I implement with an AI agent, then end the chat with instructions to summarise the new feature into a document for permanent record. |
| Architecture Discussions / Debates | I tend to tire out colleagues with niche topics, but LLMs dive right into those rabbit holes and keep going. It’s invigorating! |
| Writing Tools | I’ve long written small Bash scripts or Node.js utilities. AI can knock these out with ease. See my posts about MCP Client REPL and llmshot. |
| Vocal Production | Audio models have produced vocal passages I couldn’t pull off myself—especially when it comes to African‑American styles. |
Bottom Line
Generative AI isn’t just another incremental upgrade; it’s a step‑change that lets knowledge workers create at the speed of thought. The technology amplifies our abilities, expands our scope, and frees us to focus on higher‑level problems—just as the printing press did centuries ago.
AI – wow!
Preacher “sampling” or low‑intensity growls (Phoenix Rise), or choir effects (The Day The Thunder Roared) – it’s just better than me. It just is.
Video Production
I started producing music videos for the above songs. OK, gotta be honest here – I tried that about 6 months ago, and that was my biggest AI fail. Not to say that it was awful, just that I spent a lot of time trying to prompt my way to character continuity, camera instructions, etc. in about 4‑5 different tools, and made limited progress. I got some very cool isolated shots, but couldn’t stitch together anything coherent enough. Who knows, six months on, this may have all changed!
Blog Posts
Ha! No – I’m not a fan of the common LLM‑produced article style, so while producing entire blog posts with AI is alluring, it’s not really either
- (a) what I want to say, or
- (b) how I want to say it.
We need good content authors to write well for the LLMs of the future as well as humans! (I’ll come back to that in a future post.)
Proof‑reading, quote ideas, and image generation – I do open the AI door on some blog things! And I love em‑dashes…
Checklists / Plans
Charged with feeding the family for a long‑weekend music festival, I threw an MCP server at Google Sheets and an AI agent created the entire meal plan and grocery‑shopping list. Honestly, without my wife to organise things, it saved the rest of the family from a weekend of forced fasting—food ain’t my forte, but we dined like 17th‑century kings!
And yet there are still 2 r’s in “strawberry” (GPT‑5.2—just yesterday!)
It still amazes me the capacity for LLMs to do extraordinary things, but at times botch the basics.
Two Good Questions to Ask Yourself
- “What of your profession can AI do well?”
- “What parts of your profession still require human effort and input?”
The gap that AI can’t do so well (yet!)—that’s your opportunity!
The opportunity is huge right now. While it will change in nature over time and vary per industry, significant chances to apply AI well to your craft will remain for years to come. The advantage is shifting rapidly to those who find ways to apply AI tools to their work.
Feeling Overwhelmed?
I can identify with anyone feeling swamped by the AI hype. I’ve seen posts like:
- A guy gave vague instructions to his AI‑agent army and came back a day later to a fully‑built app.
- Another guy is so engaged he pulls out his phone at a restaurant and gets his remote agents to code up features between bites.
- (I made this one up) A person got an OpenClaw instance to code a trading bot, make $10 000, and book a Bahamas trip—all while he slept!
I’ve found it easy to feel left behind. I deal with that sentiment by making small, regular discoveries: testing, downloading AI tools, or even writing them—with AI help! Or posting about them! Making steady progress is all you can really commit to. I’m not running 100 agents writing software in my sleep—yet.
Your Moment
Generative AI is a step‑change, and these technology moments are rare—maybe two or three in a lifetime. If you’re reading this, this is your moment. Think “opportunity” and dive in!
We believe this to be a simple spelling error.
If you feel overwhelmed by the deluge, start small. Examples:
- Install an AI coding agent (e.g., Cursor, Open Code).
- Run Ollama with a local model.
- Sign up for an AI API key of your choosing.
- Write a tiny MCP server or a simple agent loop.
- Slip AI into a regular process where it might help.
The great thing is—you can just ask an LLM how to do any of that, so it doesn’t have to be a lengthy solo struggle (in most cases, anyway!).
A Note on Concerns
There are a number of concerns about the generative‑AI wave we’re riding—we’ve all seen the discussions and scrolled social media enough to know about them, and they’re valid:
- Workforce obsolescence
- Energy consumption
- AI responsibility / safety
- Prevention of “Skynet”
Those are important topics we’ll wrestle with another day and another post.
Closing Thought
AI, wow!