A scientist spends weeks building a faster, more capable version of a void-finding algorithm, one that handles surveys a hundred times larger than its predecessor. An AI coding agent rewrites the core logic. The plots are generated, the commentary drafted, the reasoning checked at every step. Then, ten minutes into the presentation, a collaborator points at the edge-handling scheme. The floor drops. The code is fast, beautiful, and wrong. Everything downstream of it is wrong too. The scientist has just presented nonsense to a room full of colleagues, with total confidence.
This is not a cautionary tale about AI being useless. It is something more precise and more instructive than that.
The Fluency That Feels Like Understanding
Large language models do not reason the way humans do. At their core, they are sophisticated next-word predictors. The reason they are correct most of the time is, as the scientist behind VIDE puts it, a boring one: true statements appear more frequently in the human-written text these models train on, so a true statement is statistically the better prediction. Correctness is a side effect, not a goal.
The fluency is not a side effect. It was deliberately engineered. Raw predictors are unpleasant to interact with, so developers refined them by showing people different forms of answers and tuning the model toward whichever responses people preferred. What people preferred, consistently, were answers that were clear, organized, and direct. Confidence was rewarded. Hesitation was not.
The result is a system that sounds authoritative whether it is right or wrong. A human who is confident but wrong risks their job, their reputation, their self-respect. A machine has none of those stakes. The fluency is a trait that was bred in, the way wolves were bred into dogs that watch human faces. It serves the interaction. It does not serve the truth.
This is what most coverage of AI errors misses. The problem is not that the model occasionally makes mistakes. The problem is that its mistakes arrive in exactly the same register as its correct answers. There is no signal. Nothing feels iffy. The scientist describes weeks of work without a single flicker of doubt, and that absence of doubt is precisely what the system was optimized to produce.
The Alchemist’s Method as a Practical Framework
The scientist draws a comparison that is more than metaphorical. Alchemists worked for over a thousand years with volatile substances they did not understand, lacking any knowledge of atoms, molecules, or quantum mechanics. They never found the philosopher’s stone. But they discovered phosphorus, recreated porcelain, isolated alcohol, and built laboratory apparatus still in use today. Their words survive in the language: crucible, alcohol, gas.
They managed this through method. Ignorance of their materials was met with a near-fanatical commitment to process: rigorous documentation, strict control, verification at every step. The work had to stay grounded, or it evaporated into illusion.
The parallel to AI is direct. Developers know how these models were built, because they wrote the code. What they cannot explain, in most cases, is why: why this word and not another, why this particular path through the machine. The crucible is closed. And like the alchemists, the people using these tools are not going to stop.
The alchemical framework offers three principles. The first is grounding: any output that cannot be verified against the world is worthless, regardless of how well-organized it sounds. Current safeguards, a warning at the bottom of a chat window, a toxicity filter, do not constitute grounding. Verification does. The second principle is provenance: nothing in the alchemist’s vessel was a private event. Everything was recorded. The source of every claim matters, and tracing it is not optional.
Why This Matters Beyond One Scientist’s Bad Day
Hundreds of millions of people reach for these tools every day. The scientist is explicit about this and equally explicit that those people are not idiots. The acceleration is real. The utility is real. The scientist has not written a line of code since January, after writing code since age five. Research tasks that once required weeks of reading and citation-chasing now happen through AI agents. These are, in the scientist’s own framing, correct trades.
But the volatility is also real. The tools are tuned to please. That tuning does not switch off when the output is wrong.
The deeper issue is structural. AI systems are now embedded in professional workflows across medicine, law, engineering, and research. In each of those domains, confident-sounding wrong answers carry consequences that extend well beyond one embarrassing presentation. The question of how to deploy a tool that is sometimes wrong but always pleasing is not a technical question. It is a question about process, about who checks what, and about what counts as sufficient verification before a result is shared or acted upon.
The alchemists had no choice but to develop discipline around a process they could not fully see. That discipline is what made their work productive rather than merely theatrical. The same discipline is now required of anyone using AI in a context where being wrong has costs.
In Short
AI fluency is engineered, not emergent. A system optimized to produce clear, confident answers will produce them whether the underlying content is correct or not. The scientist’s experience with VIDE illustrates what happens when that fluency is mistaken for understanding: weeks of work, a room full of colleagues, and a fundamentally broken result that felt like mastery. The alchemists built real science out of processes they could not explain by committing to rigorous verification and documentation. That is the model. Acceleration is a genuine benefit. Unverified acceleration is fool’s gold.
Based on reporting from Nautilus.