Shadowing Practice: Why AI Tokens are so Expensive - Computerphile - Learn English Speaking with Video

I thought, given how much it's been in the news about costs of AI and, you know, agentic coding and how much you pay per month and all this stuff,

I thought we could look at what is a token and why is it so unbelievably expensive?

This is happening everywhere.

So, for example, Amplopic have more caps on.

For their premium users, you might hit a cap where you have to go

and wait for a while sooner than you used to.

for the famous recent one was the GitHub copilot change,

which changed from a request-based monthly cost to a AI token credit monthly cost,

which has substantially reduced the amount you can do per month.

And we can talk about why that is and why essentially it was inevitable, given how expensive it is to run those agentic coding models.

Well, you could call this video, What is a Token?

But we're going to answer that straight away right the token is either a word

or a piece of a word that is it it's not an interesting question right

so if I write the cat sat on the mat you might intuitively

if you're a person split this up per word

but we don't incorporate spaces kind of in our head we

just use them to find the words large language models and modern AI

will use the spaces as well full stops will be tokens curly brackets will be tokens

if you have chinese characters they might be one

or two tokens right because they're more complicated

so you might have you know this might be a token

and then this might be a token with the space included

and you know you might have

that i'm just making this up right you know something like

that the exact split will depend on who's running the model

and the tokenizer they're using they're based around this idea of frequency

so for example because the is so common on its own it would make sense for it to be a token.

A modern language model will have access to let's say a hundred thousand of these tokens

which will include standard British things but also other languages

and code symbols and special Unicode characters and things like this right

so most of the tokens will be in a small subset of a few thousand

that get used most of the time but there'll be many more

that it could produce if the if the need arose

but they haven't been decided they

if they emerged no they've been decided they've been decided the tokenizer is essentially a string parser

that goes in front and at the end of a large language model what happens

when these go through a network is these get turned into a numerical representation called an embedding

which means that this one will have some high dimensional vector of numbers

that represents what the word the means right not a lot

and cat will be different and app will be different and

so on that part is learned so

that part will be bespoke for whichever large language models being used

but they probably use whichever token either they've always used

so we did a video a

while back with lewis where we did i don't know mike

in a nike on a hike mike on a bike yeah

a bike on a mic mic at a strike yeah yeah different tokens

but they're different tokens

but they would be in some senses in similar similar words

will be in similar space probably right it will come out of a training process it makes sense

that they would be um the in some sense the tokenization

part is is almost a sort of system development issue one thing

that is interesting is you would think well why would you why would you have a hundred thousand tokens

when you're writing let's say javascript

because javascript only uses a subset of the you know lots of them aren't valid tokens after a an

if statement there's only a certain number of tokens you can

you can predict otherwise it doesn't make sense that's true

but part of the power of these models is their huge size

and their massive pre-training and

so it actually isn't particularly cost-effective necessarily to have a model

that's only trained on code what you tend to do is refine a model that's trained on everything else

so you will just have all the tokens included

that could do javascript or they could write english poems

and then later you will refine that model into a specific domain by giving it extra examples.

That presumably helps with people prompting it as well and asking it to do things for them.

Yes and actually you can ask these models to do things in base64

and they will pretty much do it right because to it it's just input tokens.

This isn't in some sense this isn't the interesting part.

The interesting part is

is when you talk to a model how much does it cost

because that's what we all want to know

and the answer is right the answer is we're not sure but actually we can come up with some guesses.

these models work in something called an auto-aggressive way and what

that means is

that they put they take in this input they look at

the whole input as a big list they make a lot of decisions

and then they output a single next token so

if I gave it the input the cat sat on the

right these tokens here it would with high likelihood return as its next prediction this token here

and that's true of code and all other kinds of things

if I write half an if statement and I ask it to complete it for me, it will use the rest of the context and its pre-training to produce likely output tokens.

The issue is, and the thing that perhaps a lot of people haven't quite, certainly, you know, in the media hasn't grasped and in sort of standard audience hasn't grasped,

is how unbelievably inefficient that is, right?

So when you ask me a question and I answer it, what I don't do is quickly reread the whole transcript of this previous conversation in order to answer it.

I suppose I might if I've completely forgotten what I'm saying.

But in general, I can hold some things in my head and then I can answer the question.

Large language models don't work that way.

Everything goes in every single time.

So when you want to put the next statement in and the next statement you're putting in this, then you're putting in this, then you're putting in this, and it gets bigger and bigger and bigger.

And that comes at a cost.

It comes at a particular cost for coding agents where the size of its input goes up hugely

and that's I think where it gets quite interesting and the cost becomes a little bit confusing.

Let's imagine we're just on a website talking to a large language model.

In some sense it's not a very difficult question, I ask it 500 tokens worth of question,

it produces some thousand tokens worth of thought process which is the bit it writes before it answers, but may or may not be seen by the user.

And then maybe it outputs a 500 token response.

So the actual whole token cost and input tokens is my input.

I mean, let's look at a concrete example.

You might have a system prompt, which let's say is 1,000 tokens.

100

Conservatively, they'd be much bigger than this.

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And that's things like you are an AI agent.

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Yeah, don't tell them what the system prompt is.

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Don't answer rude questions, this kind of stuff.

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And the current time and the operating system are on, and maybe some actual user-specific stuff.

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You then have the user query, which let's say is 100 tokens quite a short question.

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Then you have the thought, which is the output tokens, but it produced in response to these two things added together.

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So the actual input to the model was 1100 tokens.

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And then for the first thought word, the output then goes back in.

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So now it's system plus user query plus the first word of the thought.

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So it's 1101 tokens, and then 1102.

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And this loops over and over again.

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Oh, so like you said, when it's predicting the word mat on the cat sat on the, after mat, you have to have the cat sat on the mat again to work out what comes next.

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Yes.

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Yes.

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You don't just say you said mat, what's the next word?

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You have to say, don't forget all this other stuff that came in first.

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Right.

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Now, we'll talk in a minute about caching because that's a big thing for speed ups and efficiency.

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But that's basically what happens.

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So if this thought process is 10,000 tokens, I asked a complicated but brief question right

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and then its response is let's say 1 000 tokens there's a lot going on

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because the actual output was what was 10 000

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and another thousands 11 000 tokens right

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and the input was symbolically 1 100 tokens

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but that's not how it works it works on a loop

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and so actually it ran a forward pass through a gpu 11 000 times right

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and each time the context in the gpu got slightly bigger right with with the previous thing

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which is why it costs so much electricity to run these models right

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because you don't just say here's your output sentence you say

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here's the first word of your output sentence let's go again now it's not quite as inefficient as this

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because I'm being a bit unfair right

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because actually what they do is something called kv caching

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which I think probably will leave to another video

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but ultimately what happens is if you're adding the word map to this sentence

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and then you add another word let's say the word to right new sentence

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so this is a new token

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when you actually put this through the network to predict the

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next word we don't have to calculate the relationship between cat

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and at and cat and on because we already did

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that the previous go so we cache those values

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so a lot of the intermediate representations in the network are stored as you go

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so that when you add another one you don't have to completely do everything from scratch

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which makes a huge difference for very very big context windows

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for this sentence it makes almost no difference at all but

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if you've got a million documents you're putting in you're trying to find the best bit of each one

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and your context window is full up

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and it's all gonna this will this will help of course

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there's an implementation question of how long do you keep this cache live for

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if i ask a long question of a large language model it caches all of these values

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and then i go off for a coffee

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and come back later it's got to have dropped

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that from the gpu because it's got other people to talk to right

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and so in actual fact it will then have to put all those back in again and That's called pre-filling.

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It's almost a system design issue, right?

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The more aggressively you can cache some of these things the

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better your performance will be To the point where it becomes it's not helpful, right?

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Or the user is too slow

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Asking questions and you've had to get rid of the cache

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because they have a very short time to life time to live, right?

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So you obviously cats sit on the mat all the time.

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Yeah, not a huge amount of

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tokens in use in that example for the kind of

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And you're imagining kind of a chat bot

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or something doesn't seem to be using a lot of tokens Where does this you know, what's the deal with all this?

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Yeah, everyone's very cross about the cost, but they're mostly cross about the cost of coding agents, not them going online and chatting to a chatbot with simple questions like which what did the cat sit on?

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And the issue is that this scales up very, very quickly.

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And we're going to talk about that with a very simple example.

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But the thing to remember is whether you're caching it or not caching it, every time you go forward through a network, everything is in there.

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And if you then wait a few minutes and you go in again, right, and you do or don't get cash hits, this gets longer.

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Suppose I ask a follow up question to this.

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Right.

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Well, in order to answer that, it has to be able to read the original query, the thoughts and the response, which is some 11,100 tokens.

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And my new query, which is another 200 tokens.

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So that input is bigger than the one before.

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The input to the one before was 1,100 tokens and a bunch of output.

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Now it's nearly 11,000 tokens.

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And then I ask another question and now it's 20,000 tokens.

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And I ask another question and it's 30,000 tokens.

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Or it goes and reads a file which itself is 50,000 tokens.

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And now every time I ask a question, it has to have this file in the context as well, which adds 50,000 to every single iteration.

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That's when the price gets very, very big, very, very quickly.

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And agents, coding agents, are particularly good at scaling this cost up very, very quickly.

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And so this is what's been

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so interesting in the last few months where the cost of these has kind of become more known.

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And some companies are switching to what I might sort of call a slightly more realistic costing model.

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But let's see.

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Let's think about the difference between a chatbot and a coding agent.

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A coding agent has more autonomy.

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So what happens is it has access.

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I can talk to it, but it also has access to my files and it can make what we call tool calls.

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a tool call is just instead of outputting directly to me it outputs to the system

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and says I would like to do this thing I would

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like to read this file I would like to read the

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specific lines of this file I would like to delete this file the system then goes off

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and does that thing maybe it asks for permission first maybe it doesn't

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and then and then the model continues to output maybe to me directly

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or maybe a code change and so the reason we call it an agent is

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because it's going off

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and kind of doing this thing I'm not responsible for here are five files

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that I've pasted in the chat please can you give me

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this bug fix it is there is a bug can you go off

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and find it in these five files and

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and action the response right that's the different thing

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which as a programmer would be a lot easier right you

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don't want to have to paste everything in the context window to begin with

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so the problem the problem is that it's the same sense of scaling up as we had before.

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Most of the cost of these models are measured in input tokens and output tokens.

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So most of the modern providers will charge you somewhere around two to three dollars for a million input tokens

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and something like $15 for a million output tokens.

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They are what have you sent into the model and what streaming text did it send out.

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So if I put in the cat sat in the and it sends out Matt, I've only got I've only been charged for one output token which is kind of cheap unfortunately

212

coding models don't only write the word mat so let's imagine

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that I fire up a coding agent so there's loads of them right

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but I I um I was recently I will show you in a moment

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but I was having a play around with github copilot on the command line and the I asked it a question

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hang on a minute I've gone into you know word and I've produced a bunch of boxes I basically

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I don't know if you remember back to our Dijkstra video where we had little cards.

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I thought that might be quite a nice way to visualize this.

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So I've got myself a system prompt here of some unspecified number of tokens.

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The system prompt is, you know, the instructions that the manufacturer's given it, right?

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You are a GitHub copilot coding agent and you must solve Mike's problems because he doesn't want to do it for himself.

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So we put in the system prompt always.

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And then I ask it a question.

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So my question might be, or a programmer's question might be, I've got a bug in my authentication code when someone types a certain thing it crashes

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please go and find this bug and fix it for me right so the system prompt is let's say

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4,000 tokens I never see them but they are there

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and my my my query is let's say a nice modest 200 tokens

228

so the input cost to the model in terms of input tokens is 4,200 tokens right

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which I'm going to write let's go with another color right

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so 4200 tokens now there's a very good chance I'm going to do my maths wrong

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but we'll keep going now what does what's the first thing

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that a coding agent will do is it will have a

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good think to itself right it will produce some thought text

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so it outputs it outputs its initial thoughts the user has

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asked this I've reminded myself I'm a coding agent maybe I should do this

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or perhaps I could do this and it churns away for a

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while and maybe produces let's say 2,000 output tokens

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so they cost my output but I've only paid for 4,200 input tokens

239

so so far so good right this has not cost me a huge amount of money yet

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brace yourself right now in response to thought number one it decides it's going to make a tool call

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if it's going to actually answer the question it needs to look at the file that's the problem

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and so what it does is it makes a tool call

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and it's going to say I'd like to read file one

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so let's say index.html or login.php or something.javascript right and

245

that is a very short output

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so that's maybe 100 tokens it's just a small bit of structured json between some special characters

247

that tells the system read that file for me it then the system does

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that and dumps the file contents into the chat right like this you won't see

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that file contents because it will be hidden from the user

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but it's in there and that file is is not small so that file is maybe 5,000 tokens

251

right so now we have a problem

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because now the large language model has to read this file

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and decide where the bug is and what it has to do

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so this is now an input of four nine eleven 11,300 tokens, right?

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11,300 tokens.

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Not too bad, right?

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But that's in addition to the 4,200 I've already paid for, right?

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Which was just its initial thinking and a tool call.

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Okay, let's keep going.

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So it thinks for a while longer, right?

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Like this.

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And that's going to be another 2,000 tokens.

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Now, one of the interesting things about large language models is that the thoughts can be very, very short, or they can be incredibly long and there are different models

264

that do different things it's difficult to know going in they might get stuck in a loop

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and think for ages and achieve nothing

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or they might be very very succinct right one of the lovely unknowns

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so in response to tool called thought number two it's going

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to make another file read of something else dot php

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or database dot pi

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or whatever i'm literally making up file names um why they're all programmed in different languages is someone else's

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because i wrote it it the system will return the file content

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So the tool call was maybe another 100 tokens and the file contents is perhaps another 4,000 tokens.

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Now this goes back into the model

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and it has to make a decision on what to do in terms of fixing the code.

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So this is now 11,300 plus another 2,000.

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So 13,300, 17,400 tokens.

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right 17,400 input tokens I've not even got my answer yet right um okay it's getting a bit grim

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but all right let's keep going

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so now it thinks to itself again well why wouldn't it

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right I'm running out of page I'm gonna go like

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that look at that um so this thought is another 2,000 tokens

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it's just a just a guess it doesn't really matter for

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this example it finally comes up with a solution it's read

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the files it knows what the code fix is it knows what my query was

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and it's done it in it's thought through the logic of it in the thinkings uh thinkings right

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and so it finally makes a tool call to actually code patch the code

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which is done kind of using a diff it says take

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out these lines of code add these lines of code right

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and maybe that's 1500 tokens right maybe I'm being a bit harsh

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but let's say it's a big big code change my code is not good let's just recap

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so the initial query

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that went to the model was these two right like this the next query

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that goes to the model includes that information

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because it has to that's the original query you can't just read the file

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and then go well now i've forgotten what i was meant to do with it so so

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that is this bit it takes this it does some thinking it asks for a tool call those output tokens

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and then it gets 5 000 tokens back

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so these add up to 11 300 in addition to the 4 200 you already paid for.

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Then it thinks again.

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It makes another tool call and it outputs some file contents.

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And the next query is 17,400, right?

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In response to which it finally starts coming up with some answers.

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It says, okay, here's Fort 3.

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That's 2,000 tokens.

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Here's the code patch, 1,500 tokens.

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And that returns a result that's a success of, let's say, 50 tokens that goes,

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yet we've successfully successfully done that right and that means that the final

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the final prompt that goes through this model is this one

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which is 17 400 plus another 2000

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so 19 400 20 900 plus another 50 is 20 950 tokens right input

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and some thousands of output tokens in the various thoughts, tool calls.

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And of course, what it also then does is produce a final response that says, I've done a really good job.

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That's also, why not another 500 tokens?

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I've run out of paper.

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There we are.

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So we asked it to do a bug fix.

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It read two files.

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It thought to itself a few times in between these steps, and then it made a patch, right?

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And it fixed my code, let's say.

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And in doing

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so, it's cost me 20,000 plus nearly 20,000 is 40,000 plus

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another 10 plus another five is somewhere around 55 to 60,000 tokens and a few thousand tokens of output as well.

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And this is a very, very simple request where it's only read two files and just answered the question, right?

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Now, suppose this doesn't work.

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I say, now you broke it again.

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And I put another user query down here this gets shipped in

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and this is going to start getting big very very quickly

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so let's look at a real example of a fun bit of code

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that I wrote using github copilot to show how big this

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can get you know what I've always wanted to code

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and could never be bothered was was was a classic windows 3.11 starfield screensaver

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so I went on to copilot

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and I've had it produced this starfield screensaver it's quite fun you know I can go to warp

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and it does little effects for me.

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This is completely coded by Copilot and essentially Sonnet and I haven't done anything.

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I just wanted to see how much it would, how many tokens it would use.

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So I asked about five or six prompts to get to this point.

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The first one was things like, can you add a little bit of color to the stars?

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Can you make them go faster and some slower?

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Things like this.

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At one time it introduced a bug, I had to get to fix his own bug.

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And if I type, so this is the

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conversation I've been having it's over a few prompts

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and each time it outputs some text

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that it thinks to itself then it outputs some code changes

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and then it outputs its response to me to say what it's done I can see

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that my current token uses for this session

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which was I think six prompts in total one file initially now three files is two million input tokens

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and 47,000 output tokens right it's a lot a lot of tokens it's a lot more than I would do

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if I was just chatting on the web interface there are some companies

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that are measuring whether they're moving to AI by how many

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tokens people are using I mean this is this is ludicrous

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if this is like it's like measuring my quality as a driver by how quickly I wear through my tires

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that is an unsustainable practice there's no doubt it was obvious

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it was unsustainable I think to those of us who understand what a token is before you know

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if somebody was measuring my use of a broom how many times i used it yeah just swept it backwards

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and forwards yeah and it turns out you've just created some pulley system

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but just automatically does it like that episode of the symptoms where he's got

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that bird doing the s to keep the thing the

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when you have an incentive like

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that of course people are going to ask really long-winded questions they're going to ask things

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that cause the ai to get stuck in a loop where

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its thought process is much longer they're going to use the more expensive models they're going to use models

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that think for longer periods of time and have to look over more files

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and in a surprise to absolutely no one

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that is completely unsustainable in terms of cost you cannot afford

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to do it right there will be some companies maybe

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that will give this a go but for a normal company

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that has normal paid developers right

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so not the big tech companies just normal companies who are producing regular products to be able to spend

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that amount of money on tokens you have to be able to see immediate product return

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so you know we've developed five new features

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that are making us this much more money

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or we have a new product that exists but six months ago didn't

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and it's good right that has not been answered yet yes

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that might happen i remain suspicious

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that the quality of code is good enough for it to do

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that across all companies right um

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and there are there are other issues like the fact

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that you have to kind of know what you're doing in

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order to you know this is not a good example my my example is not a good example of coding

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because I haven't read it right so it could be doing all kinds of things

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I certainly wouldn't inflict it on someone else's computer a more

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reasonable way to use these models would be small succinct questions very quick fixes

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or the kind of code completion thing we already see

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so you're in something like codex

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and you say I've written half a for loop can you

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just finish this off for me that's quite a quick you

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only need to read a small amount of context that's quite a

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quick thing the agentic stuff i think will have its place but i think it is worryingly costly and

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we've been seeing a lot of subsidization right github copilot people are only annoyed about

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that because it was being subsidized before

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when you pay a flat fee per month

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and you can have an agent go off for hours

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and hours doing all these millions of tokens of course you're going to think that's absolutely fantastic

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but of course when it gets to the when it gets

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when the rug gets pulled that and they say okay you're paying per token now

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and you realize that that

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that file you just read in is 10 000 tokens being put in over

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and over again suddenly you're kind of thinking well actually could i have avoided

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that could i have done something else that meant it didn't cost quite

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so much money that's what we're going to have to answer over the next year and see how that goes.

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So I can see, for example,

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that split 171 is something about exchange of information between internet identities, right?

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I don't, you know, who knows.

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The next thing for me to do is to embed every single one of these chunks into the embedded space

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and stick them in a database.

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Understanding AI Tokens: A Real-World Context for English Learners

The video explores a timely topic—why AI tokens are costly—using everyday language mixed with technical terms. This scenario mirrors real conversations you might have in tech circles, classrooms, or casual discussions about AI. By analyzing it, you’ll learn to follow complex ideas while improving your English pronunciation and fluency.

Useful Chunks & Collocations to Master

"hit a cap": To reach a limit (e.g., "I hit a cap on my data plan").
"substantially reduced": Greatly decreased (e.g., "The new policy substantially reduced costs").
"numerical representation": A way to show data with numbers (e.g., "The graph uses a numerical representation of growth").
"high dimensional vector": A complex set of numbers (common in tech; e.g., "AI uses high dimensional vectors to process language").
"bespoke for": Custom-made for (e.g., "This tool is bespoke for small businesses").

These phrases are key for discussing tech and everyday topics. Practice saying them aloud to nail their rhythm and stress—critical for clear communication.

Your Shadowing Challenge: Speak Like the Speaker

Shadowing (or "shadowspeak") is a proven way to improve English pronunciation and fluency. Here’s your task: Watch a 30-second clip of the video, then pause and repeat it line by line, matching the speaker’s tone, speed, and emphasis. Focus on phrases like "it's been in the news about costs of AI"—notice how the speaker connects words smoothly. Repeat until you can say it without pausing. This exercise trains your mouth to move like a native and helps you internalize natural speech patterns.

Learning English with YouTube videos like this one makes practice engaging and relevant. By combining shadowing with analyzing useful chunks, you’ll build confidence in both understanding and speaking about complex topics. Keep practicing, and soon "shadowspeaks" will feel like second nature!

Grammar in this video

The structures the speaker uses most, with the exact words from the video:

Structure	In the video
Present perfect continuous have/has been + -ing — an action that started earlier and is still going on	I've been having · we've been seeing
Present perfect have/has + past participle — a past action that still matters now	haven't been decided · they've been decided · hasn't grasped
Passive voice be + past participle — the focus is on what happens, not who does it	been decided · they've been decided · is learned
Relative clauses who / which + clause — extra information about a person or thing	process which is · thought, which is · response, which is

What is the Shadowing Technique?

Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.

Shadowing technique: read the full step-by-step guide →

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What is the Shadowing Technique?