AI and You: the Complete Series for Getting an Edge Without Losing Yourself
Reading Map, Sources, and Method of the Series
Master index for the series “AI and You”, written for anyone, and especially for any working professional in any field.
Generative AI has become one of those topics that everyone has an opinion on but no one quite settles. Some people use it for everything, others reject it entirely. Some fear losing their job, others have integrated it so naturally that they no longer know how to work without it. And above all, there’s a vast amount of dispersed, noisy, and sometimes outright false information that doesn’t help anyone decide anything.
This series is not a manifesto, nor a manual of magical prompts. It’s a walk through the questions that genuinely matter when someone wants to use AI in their work (any kind of work) and in their life without becoming either an evangelist or a denialist: what it does well, what it does badly, where the seams show, how to avoid letting it diminish you, what it lacks, what shortcomings of yours it amplifies, and what kind of work will still hold value when everyone has access to the same tool.
It’s designed so each article can be read on its own. If you land here from Google searching “why are they telling me AI will replace me,” you can jump straight to the article that addresses that question, without being forced to read the other eight. But if you have time, there’s a suggested order and cross-links so the reading gets richer.
Suggested reading order
The articles have no strict dependencies. The suggested order moves from the problem (why does AI repel so many people?) to the phenomenon (what is actually changing?), to the limitations (what can’t it do?), to personal strategy (what do I do with this?), to practice (how do I use it well without atrophying?), and finally to the concrete cases (how does it land in my profession, and in physical trades?).
The series in ten articles
1 — “I don’t want anything made with AI” – The consumer paradox
There’s a curious pattern: people vehemently reject products made “with AI” when it shows, but they accept without question other products where the AI is invisible. This article explores the paradox with concrete cases: the controversy over AI-generated art, robotic music that sings without a soul, the slop videos flooding social media, the rejection in the board games community, and where the line lies between “using AI” and “delegating everything to AI.” If you understand that line, you understand why almost all the other articles matter.
2 — Why you lose with (and against) AI
What’s getting cheaper isn’t manual labor, it’s repetitive intellectual labor. And that means many things that made you valuable six months ago now go unnoticed. But it also means that other things you took for granted (your judgment, your memory, your ability to spot what’s odd) are becoming exactly the scarce asset. This article helps you understand what could happen to you before it does.
< … more are coming …>
About the writing method of this series
This series talks about how to use AI well and, honestly, it’s written using AI. I, Ramón, contribute the questions to be answered, the direction, the bibliography curation, the dictation of the articles, and the final review before publishing; AI helps with drafts, prose, and structure suggestions. It’s the same pattern I defend in these articles: each does what each does best.
I’m a single person trying to offer you a complete and professional piece of work with the tools I have at home, because I do this in my spare time, for free, for the love of the craft. Even though I use AI, I spend as many hours on each article as I would without it (sometimes more), because I’m passionate about what’s new, about research, about technology, and about sharing what I know and have experienced.
Any long series can have occasional errors, badly copied citations, or paragraphs that slip through unreviewed. If you find one, I’ll be grateful for the correction.
Conventions and honesty about the data
Throughout the series you’ll see two types of sources clearly separated at the end of each article:
- Verified sources: scientific papers with DOI or arXiv, official documentation, data checked against the primary source. Every concrete figure cited comes from here.
- Opinion reading: opinion columns, blogs, LinkedIn infographics. They add perspective but aren’t evidence.
When a diagram has made-up values to illustrate the shape of a curve, I say so right under the chart. When I use an extreme case, it’s preceded by a warning. If you find a piece of data that doesn’t match its source, I’ll be grateful for the correction.
What this series is not
It’s not a prediction about when “AI will do X.” The industry moves so fast that any concrete prediction ages in six months. What I try to offer here are frameworks of thought that age slightly better: the questions worth asking, the real trade-offs, the recurring traps.
It’s also not a series about the next trendy model or tool. Specific tools are cited when they help; the focus is on how you relate to AI, not which one to use this week.
