What is AI, in plain English?
Artificial intelligence is software that finds patterns in enormous amounts of data and uses those patterns to produce something useful — a sentence, a prediction, a classification. That’s it. It isn’t thinking, and it isn’t conscious. The reason it feels like more than pattern-matching is that the patterns are drawn from a staggering volume of human writing and images, so the output reflects human expression back at you convincingly. Useful mental model: a very well-read assistant with no memory of yesterday, no stake in being right, and no way of telling you when it’s guessing.
Read the full explainer →Is there such a thing as “an AI”?
Not really, and this trips up more business decisions than any other misunderstanding. There is no single system called “the AI.” There’s a cast of specialists wired together: a model that generates text, a different one that generates images, a search layer that fetches facts, a moderation layer that filters output. When a tool disappoints you, the useful question is which component fell over — because “AI got it wrong” tells you nothing you can act on.
See who does what →Will AI take my job?
Probably not your whole job. Almost certainly some of your tasks. The evidence points toward displacement concentrated in roles built from repetitive, well-structured work with clear right answers — data entry, first-line support, routine document assembly. Roles requiring physical presence, genuine relationship management, or accountability for a decision are far more resistant. The honest answer is that the timeline is slower and the distribution more uneven than either the boosters or the doomsayers claim.
Read the displacement timeline →What is a large language model?
A large language model is a system trained to predict what text comes next. Feed it billions of documents, and it learns which words tend to follow which other words in which contexts. Everything it does — answering, summarising, translating, writing code — is that same prediction running repeatedly. This explains its two defining traits: it’s remarkably fluent, and it has no mechanism for knowing whether what it produced is true.
More on how models work →Why does AI make things up?
Because fabricating and reporting are the same operation to it. A language model generates plausible text; it has no separate step where it checks a fact against a source. When it knows the answer, plausible and correct coincide. When it doesn’t, it produces something plausible anyway, with exactly the same confidence. That’s why fabricated citations look so convincing — they’re assembled from the shape of real citations. Verify anything specific: numbers, names, dates, quotes, legal claims.
What AI can and can’t do →What’s the difference between AI and regular software?
Regular software follows rules a person wrote: if this, then that. You can read the rules, and it behaves identically every time. AI derives its own patterns from examples, and nobody — including the people who built it — can fully explain any individual output. The practical consequences: AI handles messy, ambiguous input that would break traditional software, and in exchange you give up predictability and auditability.
The full comparison →What is an AI agent?
An agent is an AI system that takes actions rather than just producing text. Instead of drafting an email for you, it sends it. Instead of listing flights, it books one. It works by breaking a goal into steps, calling tools, and reacting to results. The significance is less about capability than consequence: when software can act, mistakes stop being bad paragraphs and start being bad purchases. Agents are also why AI now needs its own way to pay for things.
Why agents change the picture →Is my data safe if I use AI tools?
Depends entirely on which tool and which plan, and you should assume the worst until you’ve read the terms. The questions worth answering before anyone in your organisation pastes anything sensitive: does this provider train on my inputs by default, how long is data retained, where is it stored, and can I turn training off? Consumer tiers frequently train on your data. Business and enterprise tiers usually don’t. The gap between those two sentences is where most corporate accidents happen.
The risks worth taking seriously →Do I need to learn to code to use AI?
No. The most valuable AI skill right now is describing a task precisely — what you want, in what form, with what constraints, and what a good answer looks like. That’s a writing and thinking skill, not a technical one. The people getting the most out of these tools are usually those with deep knowledge of a domain who can tell instantly when the output is wrong, not those with programming backgrounds.
Free tools to get started →How do I know where AI actually fits in my business?
Start with the friction, not the technology. Map where time actually goes for two weeks, then look for tasks that are repetitive, structured, and easy to check. Those three properties together are the reliable signal. Run one small experiment with a success criterion you define in advance, and a date by which you’ll decide go or stop. The organisations that waste the most money on AI are the ones that bought tools first and looked for problems afterwards.
Run a 10-minute AI audit →Is AI regulated?
Increasingly, and unevenly. The EU’s AI Act is the most comprehensive framework in force, with transparency obligations — including labelling AI-generated and manipulated content — that took full effect on 2 August 2026. The United States has no equivalent federal statute, leaving a patchwork of state laws and sector-specific rules. If you operate across borders, you are likely subject to the strictest applicable regime rather than your local one.
Where AI is regulated →Does AI need blockchain?
No — and we spent two years believing otherwise before checking. Every major AI model was built and runs without a blockchain anywhere near it. But the internet AI is creating does need something it doesn’t currently have: a way to prove a recording is real, that an account belongs to a person, that content has a traceable origin, and to let software transact. A tamper-evident public record is one credible answer to that — not the foundation, one of several repair crews.
Read the full argument →