The Ultimate Guide to Understanding AI

TechFeedWatch has covered 262 videos tagged AI & Tech — the largest subject on the site by a wide margin.

Counting them turned up something that changed what this guide is. Only 27 of the 262 are exclusively about AI itself. The other 235 also belong to a specific subject: money, automation, coding, security, search. Those have their own guides.

What is left, once you remove everything that belongs somewhere else, is a single coherent subject: the big questions. What this technology actually is. What it cannot do. Whether it is dangerous. Where it might be going.

That matches the largest topic in the whole set — 55 of the 262 videos discuss safety, ethics or regulation. More than agents, more than hardware, more than any product.

So this guide is about the thing itself, and the arguments about it that are real rather than promotional.

What it actually is

Strip away the vocabulary and there is one idea underneath.

Ordinary software follows rules a person wrote. If the order is over 500, apply the discount. Someone decided that, someone can read it, and it does exactly that until someone changes it.

These systems learned from examples instead. Shown enormous quantities of text, they built a model of which words tend to follow which others, in which contexts. Nobody wrote the rules. Nobody can read them either — what exists afterwards is a very large set of numbers.

That single difference explains most of what surprises people, in both directions. It is why the systems handle situations nobody anticipated, and why they fail in ways that make no sense. It is why they cannot cite where they learned something. And it is why they are confident when they are wrong: nothing in the mechanism distinguishes a well-supported answer from a plausible-sounding one.

Three entries cover the mechanics in more detail if you want them: what the training data does and does not include, how the transformer design made the current generation possible, and why a hallucination is not a malfunction but the same process producing an unsupported result.

What it cannot do

The archive is more specific about the limits than most coverage, and the limits are not the ones people expect.

It does not know when it is guessing. A model produces the next likely token whether the ground underneath is solid or thin. There is no internal signal that says this part is invented. This is the single most consequential limitation and the one that causes the most trouble in practice.

It cannot tell you where something came from. After training, the original text is gone. Asked for a source, a model produces something that looks like a source. This is not dishonesty; it is the same mechanism doing the same thing.

It has no memory between conversations. Whatever it appears to remember was sent to it again as text. The context window is the ceiling, and information buried in the middle of a long one is used less reliably than information at either end.

Rare things are answered worse — and it does not show. A topic covered by ten thousand pages is handled fluently. One covered by three is handled fluently too. The confidence of the writing does not drop with the thinness of the evidence behind it.

Counting, spatial reasoning and arithmetic remain weak. A model will describe your photograph in excellent prose and get the number of objects in it wrong.

The 55 videos about safety

The largest subject in the archive, and the one where the disagreement is genuine rather than promotional. It splits cleanly into two conversations that are often confused for each other.

The near-term concerns are concrete and already happening.

Bias. If a pattern is in the training data, it comes out in the output. A profession that appeared overwhelmingly with one gender in the source text will be associated with it. Nothing malfunctioned — the system learned exactly what was there.

Confident errors at scale. One person being wrong is a mistake. A system being wrong the same way ten thousand times a day is a different kind of problem, and it looks identical to being right.

Misuse. Convincing text and images are now cheap to produce, which changes what a scam costs to run and what evidence is worth.

Concentration. Training a frontier model requires resources very few organisations have. That is a question about who decides, and it does not require anything to go wrong technically.

The long-term argument is contested by serious people on both sides.

One side argues that systems capable enough to pursue goals could pursue them in ways nobody intended, and that we do not know how to specify what we want precisely enough — the alignment problem. Researchers with no commercial stake make this case, and so do people who run AI companies.

The other side argues the risk is being overstated, sometimes conveniently: warning that your product might be dangerously powerful is also a claim that it is powerful. Meanwhile the near-term harms are already measurable and get less attention.

This guide does not resolve that. What is worth noticing is that both sides agree on something practical: the checks have to sit outside the model, because a system that is confidently wrong cannot be the thing that verifies whether it is wrong. That is what guardrails are, and why they are not built into the model itself.

The AGI question

Eleven videos discuss artificial general intelligence, and the honest answer starts before the forecast.

There is no agreed definition. Some measure AGI by benchmarks, in which case parts of it arrived years ago. Some define it economically — a system that can do most remote work. Some require learning a genuinely new skill from few examples. Some insist on understanding rather than performance, which raises the problem of testing for something we cannot define in ourselves.

So “AGI by 2030” is not a prediction until you know which definition is meant. The same forecaster with two definitions gives answers a decade apart, and both can be sincere.

What the archive does contain is a real argument about whether the current approach gets there at all. One camp holds that scale keeps producing new capabilities and there is no visible ceiling. The other holds that predicting text is fundamentally not reasoning, and that no amount of it becomes reasoning.

Both are looking at true things. Nobody has the evidence to settle it, and anyone who sounds certain is telling you about their position rather than about the technology.

Telling hype from substance

Every launch arrives with a table of scores. The scores are real measurements. What they measure is narrower than the announcement implies.

Benchmarks decay. They are published, so they end up in the training data of the next model. A high score can mean the model solved the problem or that it saw the answer, and telling those apart from outside is close to impossible. The benchmark entry covers this in more detail.

Small gaps are noise. Two points between models means nothing without knowing the run-to-run variation, which is rarely published.

The useful question is not “how did it score” but “what changed.” Rising benchmark numbers are expected. What would be genuinely new is reliability on tasks the system was not built for — and that is the thing least often demonstrated, because it is hardest to show.

Your own test beats any leaderboard. A handful of real examples from your own work, run through two models, tells you more about which to use than any published comparison.

What is worth doing with all this

Assume the confident tone means nothing. It is the same tone for solid answers and invented ones. That is not a flaw you can train out of yourself either — it takes an active habit of checking.

Check anything that matters. Names, numbers, dates, sources, quotes. These are exactly the categories where an unsupported answer looks identical to a supported one.

Give it the context rather than expecting it to know. Most poor output is a poor question.

Notice what you have stopped doing. If a model now does your first draft, your summarising and your working-out, those are the things you have stopped practising. That is a real cost and it does not appear on any invoice.

Be sceptical of certainty in both directions. The people saying this changes everything and the people saying it changes nothing are both usually selling something — a product, a book, or a position.

The short version

AI is software that learned patterns from examples rather than following rules someone wrote, and nearly everything surprising about it follows from that.

It does not know when it is guessing. It cannot tell you where anything came from. It handles thin evidence with the same confidence as thick.

Safety is the largest subject in this archive — 55 of 262 videos — and the near-term concerns are concrete while the long-term ones are genuinely contested. AGI has no agreed definition, which makes most forecasts about it unanswerable rather than wrong.

And the most useful habit is the least technical one: treat confidence as decoration, and check the things that matter.

Every article behind this guide links to its original video, with the creator credited. The AI & Tech tag has all 262, and the glossary explains the terms that keep appearing.

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This guide draws on 262 videos covered on TechFeedWatch. The counts quoted — 55 on safety and ethics, 49 on agents, 11 on AGI — come from the archive itself, not from an industry report. Where sources disagree, this guide says so rather than picking a side. Planned, checked and maintained by Jacob S. Olsen, and written with AI. If something here is wrong, the corrections policy explains how to tell me.

The numbers in this guide are a snapshot of the archive as of July 2026; the archive itself keeps growing.