Interactive guide
How AI actually works
Most explanations of artificial intelligence are either marketing or mathematics. This one is neither. Fire a real neuron with your own hand. Watch a language model choose its next word and see the odds it is working from. Train a network and watch it stop being wrong. Then look at what the evidence actually says about work, and what it does not say.
Start with the thing that already works
Every conversation about artificial intelligence borrows a word from biology. Neural network. Neuron. Learning. Those words came from somewhere real, and the real thing is sitting inside your skull right now, reading this sentence. It is worth understanding first, because once you know what a neuron actually does, the machine version stops being mysterious and starts being simple. Almost disappointingly simple.
Here is what one of those 86 billion cells does. It sits quietly at about minus 70 millivolts, slightly negative compared to its surroundings. Other neurons feed it small pushes through its dendrites. Those pushes add up. They also leak away, so a slow trickle of input never gets anywhere. But if enough arrives fast enough to lift the cell to about minus 55 millivolts, something abrupt happens: the neuron fires a spike, at full strength, down its axon, and passes the signal on.
It is all or nothing. There is no half-fire. A stronger stimulus does not produce a bigger spike, it produces more spikes. That single fact, a threshold and an all-or-nothing response, is the seed the entire field grew from.
Fire a neuron
Push the button to send a signal into the dendrites. Watch the voltage climb in the trace at the bottom right. Turn the stimulus down low and hold it: the charge leaks away faster than it builds and nothing happens. Turn it up and the cell crosses the threshold and fires.
Leaky integrate-and-fire model
How hard each incoming signal pushes, in millivolts. It takes 15 mV to climb from rest to the threshold, so at this setting three quick presses will do it. Drop it to 1 and you can press as fast as you like and never fire: the charge leaks away faster than you can add it.
Used by the steady signal train below.
What this shows A neuron is a decision, not a wire. It gathers evidence, forgets it if nothing more arrives, and commits completely once the evidence crosses a line. Keep that shape in mind. You are about to see a machine do a cartoon version of it, billions of times per second.
Now the cartoon version
In 1943 a neurophysiologist and a logician wrote down a mathematical caricature of the cell you just fired. Strip away the chemistry, the timing, the living tissue. Keep only this: take some inputs, multiply each by a number that says how much it matters, add them together, and if the total is big enough, pass something on. That caricature is the artificial neuron, and it is still what a trillion-dollar industry is built out of.
The artificial neuron
Three inputs come in on the left. Each has a weight, which is just a number saying how much that input counts. Negative weights argue against. Drag anything and watch the wires thicken, the total change, and the output flip. This is the whole mechanism. There is nothing else hiding behind it.
One artificial neuron. A frontier model contains billions.
The neuron's built-in reluctance, or eagerness, before any input arrives.
The arithmetic, live
What this shows A neuron in a model is one multiplication, one addition, and one squash. Nobody programs the weights. They start as random noise and get nudged, billions of times, by the process in Interactive 05. Intelligence, to whatever extent this counts as intelligence, is an emergent property of a very large number of these arranged in layers.
Language models do not read words.
Before any of that arithmetic can happen, text has to become numbers. A model chops your sentence into pieces called tokens, which are sometimes whole words, often fragments, and occasionally single characters. Each piece is looked up in a fixed vocabulary of about a hundred thousand entries and replaced by its number. That is what the model actually sees: a list of integers.
This is not a technicality. It explains a family of behaviours that otherwise look like stupidity. Ask a model to count the letters in a word and it may get it wrong, because it never saw the letters. It saw two or three chunks with ID numbers attached.
What the machine sees instead of your sentence
Type anything. Watch it break apart. Notice that common short words survive whole while longer or unusual words shatter into fragments, and that a leading space is part of the token itself.
Honest caveat This is a simplified splitter built to show the idea. Real systems use byte pair encoding, learned from the training data rather than hand-written, and the ID numbers come from a fixed vocabulary rather than being computed on the fly. The shape of what happens is the same: your words become a list of numbers, and the model never sees anything else.
Everything else is one trick, repeated.
Given the list of numbers so far, produce a probability for every possible next number. Pick one. Add it to the list. Do it again. That is what a large language model does, and it is the only thing it does. Every essay, every apology, every line of code you have ever seen it produce came out one token at a time, each one chosen from a ranked list of options.
Below is a real language model. Not a picture of one. It is a small n-gram model that your browser builds when this page loads, by reading a passage of ordinary English and counting which words follow which. It is genuinely tiny, and the difference between it and a frontier system is scale, architecture and training, not concept. Watch it choose.
Watch it pick the next word
The bars are real probabilities from a real model. Click any word to accept it yourself, or let the model write on its own. Then drag the temperature. At zero it always takes the top bar and gets repetitive. High, and it starts reaching for unlikely words and falls apart. Every AI product you have used has this dial set somewhere.
Low is cautious and repetitive. High is inventive and unreliable.
What could come next
Matched on:
What this shows The model has no plan for the sentence. It has no idea how the sentence ends. At every step it looks back at what has been written, produces a ranked list of possible next words, and picks one. The sense of intention you feel when you read the output is manufactured entirely in your head, one word at a time, after the fact.
Which raises the obvious question. If nobody sets those probabilities by hand, where do they come from? The answer is the least glamorous and most important idea in the field: the model starts out random and useless, and is then corrected, over and over, by being shown how wrong it was.
Training is just being wrong, less
The blue dots are real answers. The purple line is what a small network currently believes. Right now it believes nonsense, because its weights are random. Press train. Real backpropagation runs in your browser: forward pass, measure the error, push the correction back through every weight. Nothing here is pre-recorded.
8 hidden units, tanh activation, batch gradient descent.
How big a correction to make each round. Too small and it crawls. Push it past about 2 and watch it overshoot and blow up. Finding this number is a real part of the job.
What this shows Learning, in this field, is a search for numbers that make the error smaller. That is the entire definition. There is no understanding step, no moment of insight. Scale this up to trillions of words and billions of weights and the behaviour that emerges is startling, but the mechanism on screen is the mechanism, all the way up.
Why it makes things up without blinking
Same model as Interactive 04. The first button starts from a phrase it has seen. The second starts from one it has never seen in its life. Read both outputs and try to tell, from the writing alone, which is which.
What this shows A hallucination is not a malfunction. It is the machine working exactly as designed. The model is built to always return its most likely next token, and it has no separate circuit for reporting that the evidence behind that token was thin. Fluency is produced by the same process whether the underlying knowledge is there or not, which is precisely why confident nonsense reads so well. When the answer matters, the burden of verification is on the human. Every time.
It would be neat to end part two by saying we fully understand these systems because we built them. We do not. We know the architecture and we chose the training procedure, but the meaning of any particular weight among billions is not something anyone wrote down. It is discovered, afterwards, by research.
In 2024 Anthropic managed to identify millions of interpretable concepts inside a production model, including a feature that responds to the Golden Gate Bridge whether it appears in English, Japanese, Greek or an image. Turning that feature up caused the model to insist it was the bridge. That work is genuinely impressive, and the fact that it counted as a landmark tells you how much of the inside of these systems remains unmapped. Anthropic, 2024
So what does this do to work
This is the part everyone actually came for, and it is the part where the public conversation is worst. The headline number you have seen, whichever one it was, almost certainly measured something narrower than the sentence it was wrapped in. So here is the careful version, with the measurements separated from the predictions.
One word is doing an enormous amount of work in all of those figures, and it is exposure. In the Science paper, a task counts as exposed if a language model could cut the time it takes by at least half. The authors state plainly that this does not mean the task can be fully automated, and it certainly does not mean the job disappears. A job is a bundle of tasks, plus accountability, plus judgement, plus the fact that somebody has to be answerable when it goes wrong.
Look up your own work below. Then read the number as what it is: an estimate of how much of the daily task list gets faster, not a countdown.
Task exposure by occupation
Every figure here is from the same peer-reviewed study. Occupations are grouped by which measure flagged them, because the measures ask slightly different questions and mixing them silently would be the exact sort of thing this page exists to argue against.
The study covered 750 occupations. This is a readable slice of the extremes, taken directly from its published tables.
Mathematicians
Highly exposedRead it carefully Notice the pattern in the bottom group. The work that came back at zero is overwhelmingly physical, skilled, and performed in an unpredictable environment: line workers, diesel mechanics, masons, short-order cooks. For twenty years the story was that automation comes for manual labour first and leaves thinking work alone. That story has been running in reverse.
The first real measurements are in.
Everything above is about potential. Potential is cheap. The harder question is whether anything has actually happened in the labour market yet, and for the first time there is payroll evidence rather than speculation.
Stanford's Digital Economy Lab went through ADP payroll records covering millions of American workers. In the occupations most exposed to AI, employment for 22 to 25 year olds fell about 6 percent between late 2022 and September 2025, while employment for older workers in the same occupations rose 6 to 9 percent. In less exposed occupations, young workers were fine. The researchers called them canaries in the coal mine, and the detail that matters most is this: the declines cluster in places where AI automates the work rather than assists it. Where it assists, entry-level employment held up. Stanford Digital Economy Lab
That distinction between automating and assisting is not academic, and it is now measurable at scale. Anthropic publishes an index of how its own model is actually used. As of November 2025, 52 percent of consumer conversations were augmentation, where a person is iterating, learning or getting feedback, against 45 percent automation, where the task is simply handed over. In programmatic API traffic, automation dominates. The same technology lands very differently depending on how an organisation chooses to deploy it. Anthropic Economic Index
What comes next, and how much to trust it
Everything up to this point was measured. Everything below is forecast, which is a different kind of statement wearing similar clothing. These are the most serious projections available, each with its author and date attached, because a forecast without a source is just a mood.
Drag through the next decade
Move the slider forward and the projections light up as you pass their target year.
Superforecasters expect AI to handle a full day of expert software work
Asked when a model will hit 80 percent success on software tasks that take a human expert eight hours or more, superforecasters give a median of 2028. Domain experts in the same exercise say 2030. Both groups have moved earlier with each round of the survey.
170 million jobs created, 92 million displaced
The World Economic Forum's employer survey projects a net gain of 78 million jobs by 2030, with 22 percent of all jobs disrupted and 39 percent of the skills a typical job requires expected to change. Net positive, which is not the same as painless for the 92 million.
Data centres draw about 945 terawatt hours
The IEA's central scenario has global data centre electricity use more than doubling from 2024, with AI's share of that consumption rising from 5 to 15 percent toward 35 to 50 percent. The constraint on AI may end up being electrical, not algorithmic.
Median researcher estimate for a 50 percent chance of high-level machine intelligence
From a 2023 survey of thousands of AI researchers. Worth holding beside one detail: the same survey a year earlier gave 2060. Thirteen years of expectation evaporated in twelve months, which tells you as much about the reliability of these estimates as about the technology.
How to hold these Forecasts about AI have a poor track record in both directions. The honest position is that the near-term measurements are solid, the five-year projections are informed guesses from people with incentives, and anything past that is a genre of writing rather than a form of knowledge. Plan for the measured part. Stay flexible about the rest.
No prediction, just the pattern that falls out of the evidence above. The work holding up best is work where somebody has to be accountable, where the environment is physical and unpredictable, or where the value is in the relationship rather than the artefact. The work under most pressure is routine written output produced at a desk, especially at entry level.
The most useful thing an individual can do is unglamorous: use these tools enough to develop real judgement about where they fail. The people who will be fine are not the ones who avoided this and not the ones who trusted it blindly. They are the ones who know exactly when to check its work, because they have watched it be confidently wrong and learned the tells.
Every source on this page
No figure above is unattributed and none of it came from a press summary of a press summary. If a claim here is wrong, it should be traceable to the paper that made it.
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Azevedo et al, Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain
The isotropic fractionator count that replaced the folk figure of 100 billion neurons with a measured 86 billion.
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Herculano-Houzel, The remarkable, yet not extraordinary, human brain as a scaled-up primate brain and its associated cost
Source of the 516 kcal per day figure, the 2 percent of body mass, and the roughly 20 percent of the body's energy budget.
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Eloundou, Manning, Mishkin and Rock, GPTs are GPTs: Labor market impact potential of LLMs
The 80 percent and 19 percent exposure figures, the per-occupation tables, and the 34 occupations with no exposed tasks. Preprint at arXiv 2303.10130.
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Brynjolfsson, Chandar and Chen, Canaries in the Coal Mine
ADP payroll evidence on the 6 percent employment decline for 22 to 25 year olds in the most exposed occupations, and the automation versus augmentation split.
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Goldman Sachs Research, How will AI affect the US labor market
The 25 percent of US work hours figure and the 300 million globally exposed jobs figure.
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World Economic Forum, Future of Jobs Report 2025
170 million jobs created, 92 million displaced, net 78 million by 2030, 22 percent of jobs disrupted, 39 percent of skills changing.
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International Energy Agency, Energy demand from AI
415 terawatt hours in 2024, about 1.5 percent of global electricity, rising to roughly 945 terawatt hours by 2030 in the central scenario.
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Anthropic Economic Index
The 52 percent augmentation against 45 percent automation split in consumer conversations as of November 2025, and the contrast with API traffic.
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Anthropic, Mapping the Mind of a Large Language Model
Identification of millions of interpretable features inside a production model, including the Golden Gate Bridge feature.
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Grace et al, Thousands of AI Authors on the Future of AI
The 2047 median for a 50 percent chance of high-level machine intelligence, and the thirteen-year shift from the previous year's survey.
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Forecasting Research Institute, Experts and Superforecasters Update Their AI Timelines
The 2028 superforecaster and 2030 expert medians for 80 percent success on eight-hour software tasks.
Photography and footage from Pexels. The neuron simulation, the artificial neuron, the language model, the training loop and the exposure explorer were all written for this page and run entirely in your browser. No data leaves your device.
Understanding this well is now part of the job
I am Kevin Champlin, founder and CEO of Champlin Enterprises. We build production software and AI systems, which mostly means knowing precisely where these tools are extraordinary and precisely where they will confidently hand you something wrong. This page is what I wish more people had been shown before they were asked to have an opinion.