Ten Times the Man What a strawberry taught me about working with machines that are fluent, confident, and sometimes completely wrong.

People keep asking whether artificial intelligence is going to replace human beings. After a few years of working with it almost every day, I’ve come to think that’s the wrong question.

The interesting one is quieter: how much more capable does an ordinary person become when they work alongside it? Not a corporation, not a research lab, not a university with a budget. One human being.

In my case that human being is a veteran IT consultant whose career started well before personal computers were a normal thing to own. I’ve worked through typewriters, mainframes, minicomputers, PCs, the Internet, the cloud, smartphones, and now this. Decades of designing systems, solving problems, writing documentation, building businesses, and telling stories. And in all that time the limit was never a shortage of ideas. It was time. There were always more ideas than there were hours to chase them.

AI changed that equation. Not completely, but dramatically. My honest estimate is that it has multiplied my practical output by roughly an order of magnitude — that I now get through something like ten years’ worth of intellectual work in what used to take one. That’s an extraordinary thing to claim. The rest of this essay is my attempt to show the evidence, mark where the evidence runs thin, and explain why I believe it anyway.

The most important thing I ever learned about artificial intelligence didn’t come from a paper, a lecture, or a conference. It came from a strawberry. More precisely, from asking a model how many R’s are in the word “strawberry” and watching it get the answer wrong.

It’s a stupid little question. That’s the point. Here was a system that could draft a contract, explain a tax structure, or walk me through the thermodynamics of a heat pump without breaking stride — and it couldn’t count the letters in a word a six-year-old can spell. It didn’t hedge, either. It gave me a number in the same calm, competent tone it used for everything else. It was just wrong.

Up to that moment I’d been doing what most people do without noticing: I’d handed the machine a kind of authority. The answers sounded intelligent. The explanations hung together. The language was persuasive, and most of the time the answers were even correct. But the strawberry showed me the gap I’d been ignoring. Feeling correct and being correct are not the same thing, and a language model is built to optimise the first one.

Why it couldn’t count

What makes the strawberry useful isn’t that the model failed. It’s why it failed, because the reason explains almost everything else that goes wrong with these systems.

A language model doesn’t read letters. Before your text reaches the model it gets chopped into “tokens” — chunks of characters the system treats as single units. “Strawberry” doesn’t arrive as s-t-r-a-w-b-e-r-r-y. It arrives as two or three blobs, and the model has never seen inside them. Asking it to count the R’s is like asking you to count the windows in a building you’ve only ever seen as a dot on a map. You can guess. You’ll probably guess wrong.

So the failure wasn’t a glitch. It was the architecture doing exactly what it does. The model predicts the most plausible next stretch of language based on everything it absorbed in training. It is, in the most literal sense, a very sophisticated autocomplete. It doesn’t know facts the way a historian knows them or numbers the way a mathematician knows them. It knows what language usually looks like near your question, and it produces more of it.

Most of the time that works astonishingly well, because fluent language and correct language usually sit close together. The trouble is the times they don’t — and when they part company, the model has no internal alarm. It hands you the wrong answer with precisely the same confidence as the right one. The industry calls these failures “hallucinations,” which is a bad word for them. Nothing mysterious is happening. The machine is doing the only thing it can do, and sometimes the most plausible-sounding sentence simply isn’t true.

The more persuasive the prose, the easier it is to mistake fluency for accuracy. That is the trap waiting for every new user.

Once I understood that, the way I worked changed completely. I stopped asking how to get better answers and started asking how to get more trustworthy ones. Those sound like the same question. They aren’t.

A model can be brilliant and unreliable at the same time, the way a charming friend can be confidently wrong about everything. The job isn’t to make it smarter. The job is to lower the odds that it quietly leads you off a cliff.

In practice that meant I stopped asking for answers and started asking for the scaffolding underneath them. Instead of “what happened,” I’d ask what’s established, what’s disputed, and what’s genuinely unknown. Instead of “who was right,” I’d ask for the strongest version of each side and where the evidence actually backs each one. I demanded sources, assumptions, confidence levels, counterarguments, and a path to verify the thing independently. I made the model separate fact from inference from opinion from speculation, because left alone it blends all four into one smooth paragraph.

The pattern was consistent: hide the uncertainty and errors go up; force it into the open and quality goes up. The goal stopped being certainty and became transparency — not “give me the answer” but “show me your work, and show me where it’s thin.” The reasoning often improved not because the model changed but because I did. The operator turned out to be the variable.

Training versus retrieval

The other thing I had to understand was where any given answer was coming from. Some answers come out of the model’s training — the patterns it absorbed from billions of words. Others come from retrieval, where the system consults a specific document before it answers. These are not the same act, and confusing them is how people get burned. Training gives you patterns; retrieval gives you evidence. Point the model at real source material and it reads the streets off the map instead of guessing at them.

That single distinction is what pulled me toward retrieval-augmented generation — RAG — which I came to treat less as a technology than as a discipline. The core idea is almost embarrassingly simple: a system that can look things up beats a system that has to guess. Which is exactly as true of people as it is of machines.

Before AI, every project followed the same sad arc. An idea would show up. I’d make notes, gather references, sketch an outline. Then weeks would pass, life would intervene, and the thing would stall. Some projects survived that gap. Most didn’t.

It wasn’t laziness. It was that every worthwhile project is mostly mechanical work wrapped around a small spark. The spark — the actual idea — might be five percent of the effort. The other ninety-five is research, organising, drafting, editing, rewriting, fact-checking, formatting. So good ideas died in notebooks, not because they lacked value but because they couldn’t clear the labour required to finish them. The bottleneck was never imagination. It was execution.

The most consequential thing may be something I built entirely by accident. Without setting out to, I’ve assembled what any practitioner would recognise as a retrieval-augmented system of my own — a personal RAG.

Every project leaves a trace. Every conversation adds context. Every story, proposal, technical thread, and bit of family history becomes another layer in a growing archive, and over time that archive stops being a pile and starts having shape. It now holds my history, my technical expertise, my creative work, the family stories, business plans, the positions I’ve argued, the things I care about. That isn’t just data. It’s organised memory — it remembers not just what I said, but why, and what it connected to.

People have always offloaded memory onto something: first memory itself, then notebooks, then filing cabinets, then computers, then search engines. What’s arriving now feels like a genuinely new link in that chain. For the first time it’s practical not for billion-dollar companies but for one person at a kitchen table to ask: what did I conclude about this five years ago? What was my strongest argument? Pick up that abandoned story and keep going.

An essay that argues confidence isn’t correctness has no business presenting its own figures as hard fact. So here is the register, with the basis and the confidence for each claim laid out plainly. This is the strawberry lesson applied to my own writing.

Almost everything above is a self-reported estimate built from a partial record. Conversations happened across different systems, some anonymously; plenty was never saved. That doesn’t make the numbers worthless — it makes them estimates, which is a different thing from measurements, and they should be read as such.

If I were going to make these claims rigorously rather than impressionistically, here is what I’d instrument. The good news is that AI work throws off measurable exhaust — we just don’t usually capture it.

The principle is the one this whole essay turns on: replace recalled impressions with retrieved evidence. Apply RAG thinking to the assessment itself.

The title is deliberately provocative, and I should be honest about what it doesn’t mean. I’m not ten times smarter than I was, or ten times more creative, or ten times more knowledgeable. What I am is ten times more able to turn an idea into a finished thing. That distinction is the whole essay.

For nearly all of human history, ideas were cheap and execution was dear. AI flips that balance. But the gift came wrapped in a warning, and the strawberry was the warning: a tool this fluent and this confidently wrong will run off a cliff if you don’t keep hold of the wheel. The people who get the most from these systems aren’t the ones who trust them most. They’re the ones who learned to interrogate them.

So the deeper story was never “AI made me ten times more productive.” It was this: I started by believing the machine knew things, and I ended by learning how to interrogate knowledge itself — its own, and my own. The greatest gift these tools gave me isn’t faster writing or tidier numbers. It’s that I can finally chase more of the ideas that make a life interesting, and that I’ve gotten better at telling confidence from correctness while I do it.

For the first time, the limiting factor isn’t whether I can make something. It’s choosing which of the many things I want to make next.

The strawberry taught me that. Everything else followed.