Welcome to the world of AI-nomics

AI has arrived and how! It has taken centre stage as the next-gen general purpose technology and is rapidly changing the world as we know it. It is now a force upending markets, business models, capital allocation decisions and even the way we need to plan for our future energy needs. Companies are racing to fold AI into their operations, and some are even learning that adoption without discipline comes with costs.

As this shift plays out, the vocabulary of AI is no longer confined to tech blogs and research papers. It is getting mainstream and quietly infiltrating boardrooms, earnings calls and other market literature. Terms like ‘tokenomics’, ‘agentic AI’ and ‘open-source’ are increasingly becoming how companies describe their spending and strategy.

So, as investors, it is a good time to get started on these terms and this article aims to help you get warmed up on what they mean and why they matter economically. Read on to find out.

As humans, the smallest unit of information we use to represent language is a single character — which could be a letter or a number or a punctuation mark. But the smallest unit of language that a large language model (LLM) uses is called a token. A token can be a set of characters, or a part of a word or a syllable or a whole word or even a short multi-word phrase. The code that converts language to tokens is called a tokeniser.

A good rule of thumb is that an English word is represented by approximately 1.5 tokens. So, if you input a prompt that is 100-word long, the LLM will likely work with 150 tokens. Conversely, one may say a token is about two-thirds to three-fourths of a word. However, this is just to give some perspective. For image-/audio-/video-based inputs, it is not possible to apply this thumb rule. After the input is tokenised, the model then converts the tokens into vectors, which are a long list of numbers. These vectors form the basic computational unit on which the model performs calculations to generate output.

That said, let’s understand tokenomics. When it comes to LLMs, model providers such as OpenAI and Anthropic bill customers by tokens, which directly relate to the amount of processing their GPUs perform for a given prompt. Higher the tokens involved, higher the workload on the GPUs which in turn translate to higher cost for the model provider. This billing by tokens is more true of enterprise customers who use APIs (application programming interface) to connect their enterprise software to an LLM than of consumers like you and me.

Just as electricity bills are based on units consumed, LLM billing is based on tokens consumed. However, electricity bills change based on what kind of consumer you are. Industrial consumers pay more than what residential consumers do. Similarly, pricing of the LLMs depends on the complexity of the model. A larger, more robust model will cost more per token and vice-versa. Typically, users are billed per million tokens and further, there is a difference based on whether the token is part of the input or part of the output. Input tokens cost lower than output tokens because the amount of processing required to generate a text output, for instance, is higher than reading a text input.

Over the past year or so, companies have pushed their workforce to adopt AI in their workflows, often without capping token consumption. Employees were even tracked and ranked based on token usage, as a proxy to measure productivity and to evaluate whether they have become ‘AI-native’. This led to a phenomenon known as ‘tokenmaxxing’. This often incentivised employees to use larger/ reasoning models to boost token consumption even when the problem didn’t warrant their use. Do note that the reasoning steps also consume tokens even though they don’t show up in the output as such.

An unnamed enterprise customer of Anthropic was reported to have burned $500-million worth of tokens just because it failed to set spending cap per employee. Similarly, Uber ...