Generative AI: The Internet’s Greatest Remix

Douglas Adams once wrote:

“Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works. Anything that’s invented between when you’re fifteen and thirty-five is new and exciting and revolutionary, and you can probably get a career in it. Anything invented after you’re thirty-five is against the natural order of things.”

It’s a playful rule of thumb, but it explains a lot about how we react to technology.

As a Gen-Xer, I can see the pattern in my own life.

Normal: 1970s and 80s

The world I was born into had vinyl records, cassette tapes, landline telephones and bulky televisions. Computers existed, but only in institutions. None of it felt like “technology” — it was just life.

Revolutionary: 1991–2011

Between my mid-teens and mid-thirties, technology really did feel revolutionary:

The Internet went mainstream.
Email replaced office memos and fax.
Mobile phones shrank from bricks to pocket devices.
Music moved from CDs to MP3s.
Social media appeared.
The iPhone launched in 2007.

These weren’t just gadgets. They reshaped careers, industries, and culture. I rode this wave, moving from engineering into teaching and business.

Unnatural? Post-2011

According to Adams’ rules, everything invented after 2011 should feel “against the natural order.” And in some ways it does.

Social media, once a place for lighthearted connection, has morphed into the attention economy. The platforms are engineered to keep us scrolling endlessly, not to connect but to consume. Ironically, this transformation is driven by AI algorithms, serving us carefully tuned doses of outrage, envy, or dopamine just long enough to slip in the ads.

Streaming has replaced shelves of physical media. There is a convenience to it, but it still feels strangely intangible. I sometimes miss the ritual of sliding a vinyl record out of its sleeve, or cracking open a CD case to pore over the booklet. These were tactile anchors that made music feel like an object you could own rather than a passing stream of data.

Smartphones have also become more than tools. They have fused with identity, constant companions in our pockets, mirrors of our social selves, and portals through which we are seen and measured.

And yet, generative AI does not fit this pattern for me. It does not feel unnatural at all. If anything, it feels like a continuation of the creative tools I grew up with, just faster, stranger, and more collaborative. Where other technologies often feel like they are pulling me away from something real, AI feels like it is pulling me deeper into making and imagining.

Why Generative AI Feels Evolutionary

Generative AI is often presented as a rupture, but in reality it is an extension of decades of information technology.

1. It’s Still Data and Code

Generative AI doesn’t create from thin air. It manipulates patterns in data. That’s what IT has always done:

Databases stored and retrieved structured information.
Search engines crawled the web and ranked results.
Machine learning spotted patterns in data for fraud detection, spam filtering, and image recognition.

Generative AI uses the same building blocks — data and code — just at a larger scale.

2. Trained on the Internet

There is also a direct dependency. Large Language Models (LLMs) are trained on internet text. Without decades of online articles, books, code repositories, forums, and conversations, there would be nothing to train them on.

The Internet was the foundation. LLMs are the layer built on top. Without one, the other wouldn’t exist. That makes generative AI not a rupture, but the natural extension of the information web we already live in.

Harish reminded me of this today, and it instantly connected back to something I had written earlier: a thought experiment where I imagined a future AI, Aurora, writing a letter to the humans of the 2020s. In that piece, Aurora thanks us for the data trails we left, from podcasts and memes to spreadsheets and protest songs, while also mourning the stories that were never uploaded. Without the Internet, there is no Aurora. Without the Internet, there are no LLMs. Link here.

3. Prompts Are Just the Next Interface

Think of how we’ve accessed computers over time:

Command lines in the 70s and 80s.
Graphical user interfaces in the 90s.
Touchscreens in the 2000s.
Voice assistants in the 2010s.

Now prompts are the new user interface. They don’t replace code. They sit on top of it, making systems more accessible.

4. Built on Decades of Machine Learning

Generative AI feels sudden, but it grows out of a long history of AI and ML.

Optical character recognition dates back to the 1950s.
Spam filters have used ML since the 1990s.
Recommendation engines, fraud detection, and number plate recognition have been mainstream for years.

Generative AI is simply the visible surface of this lineage.

AI’s Dual Nature: Disruptive and Mundane

Analysts often describe AI as having a dual nature:

Disruptive: Generative AI feels new, unsettling, headline-grabbing.
Mundane: Other AI, like image recognition in smartphones or OCR in offices, has faded into the background.

Automatic number plate recognition is a good example. In the 1990s it was unreliable. Today it’s ordinary. Generative AI may follow the same path: from disruptive to mundane.

Jobs Shift, Skills Shift

Every technological wave creates new careers. Twenty years ago there were no app developers, SEO analysts, UX designers, or cloud engineers.

AI will do the same. Developers are already using AI to generate code snippets. Their value lies not in typing every line but in judgment — knowing what to keep, what to discard, and how to integrate it.

Generative AI won’t erase work. It will change what work looks like.