Introduction – The Invisible Technology Behind Everyday AI
When most people first encountered ChatGPT, they experienced something extraordinary: a computer program that could talk, explain, and even write like a human. You could ask it to draft an email, explain a tax rule, or summarise a financial report — and it would respond instantly in clear, natural language.
Behind this seemingly magical ability lies a type of artificial intelligence called a Large Language Model, often shortened to LLM. These models are now the foundation of modern generative AI — the technology that allows computers to create, write, reason, and communicate rather than just calculate.
This chapter explains what LLMs are, who builds them, and why they are transforming how professionals — including accountants, analysts, and consultants — work with information.
What Is a Large Language Model?
A Large Language Model (LLM) is a kind of computer program trained to understand and generate human language. It does this by analysing enormous amounts of text — books, websites, articles, and documents — and learning the patterns of how words and ideas fit together.
You can think of an LLM as a very advanced autocomplete system. When you start typing on your phone and it guesses the next word, that’s a simple form of language modelling. A Large Language Model does the same thing, but at an astonishing scale and with deep understanding of meaning, grammar, and context.
So when you ask ChatGPT a question like “Explain what depreciation means in accounting,” it doesn’t look up a single answer from a database. Instead, it predicts — word by word — what the most likely, most accurate, and most human-like answer would be, based on everything it has learned.
That is what makes LLMs generative: they can create new sentences, explanations, and reports that sound like they were written by a person, even though no one ever wrote them before.
The Company Behind ChatGPT – OpenAI
ChatGPT was created by a company called OpenAI, based in San Francisco. Founded in 2015 by a group of researchers and entrepreneurs — including Sam Altman and Elon Musk — OpenAI’s mission was to ensure that powerful artificial intelligence benefits all of humanity.
OpenAI began by building a series of models known as GPT, which stands for Generative Pre-trained Transformer. The name may sound technical, but the idea is simple: these models are “pre-trained” on huge amounts of text and then “fine-tuned” to become better at understanding questions and giving helpful answers.
GPT-1 (2018) was the first proof that language models could be trained at scale.
GPT-2 (2019) amazed researchers by writing essays and stories that sounded human.
GPT-3 (2020) was a breakthrough in size and performance — it could answer complex questions, write code, and explain concepts.
ChatGPT (2022) made this technology accessible to everyone through a friendly chat interface.
Since then, OpenAI has continued to release newer and more capable models such as GPT-4 (and versions like GPT-4o, which can also understand images and voice). These models are now used by millions of people across the world — from students to professionals to large organisations.
Anthropic and Claude – A Different Approach to Helpful AI
Not long after OpenAI launched ChatGPT, a group of former OpenAI researchers created another company called Anthropic, based in San Francisco as well. Their goal was to build AI systems that are safe, transparent, and aligned with human intentions.
Anthropic named its AI model Claude, after Claude Shannon — the scientist who pioneered modern information theory.
Claude works similarly to ChatGPT but has a slightly different personality and focus. It is designed to be more cautious, explain reasoning clearly, and handle very long documents. Many professionals like it for summarising lengthy reports, reading contracts, or analysing policies — tasks that require context and clarity.
Claude represents one of the strongest examples of competition and diversity in the AI landscape. Where OpenAI emphasised creativity and coding, Anthropic emphasised explanation and reasoning. Both approaches are shaping how people interact with AI in professional environments.
Google and Gemini – Bringing AI Into Everyday Tools
Another major player in the AI landscape is Google, one of the world’s leading technology companies. Google’s research team originally invented the Transformer architecture in 2017 — the very foundation that made modern language models possible.
To compete in the new era of generative AI, Google created its own family of models called Gemini (previously known as Bard). Gemini powers Google’s AI features across products such as Gmail, Google Docs, and Google Sheets, allowing users to generate summaries, draft reports, and even analyse data through natural language.
What makes Google’s Gemini important is that it brings AI into the tools many professionals already use daily. Instead of switching platforms, users can work directly inside their existing productivity software while harnessing the intelligence of a Large Language Model.
Meta and LLaMA – The Open Source Alternative
Another technology giant, Meta (the company behind Facebook and Instagram), entered the AI race with a different philosophy. Instead of keeping its models private, Meta chose to release them publicly under an open source approach.
Meta’s family of models is called LLaMA, which stands for Large Language Model Meta AI. These models — LLaMA 1, LLaMA 2, and now LLaMA 3 — are available for anyone to download, study, or build upon.
But what does open source actually mean?
It means that the underlying code and model are made publicly accessible, allowing researchers, startups, and universities to experiment and improve upon them.
Open source matters because it encourages collaboration, transparency, and innovation. While companies like OpenAI and Anthropic protect their models behind commercial systems, open-source models like LLaMA enable a global community of developers to explore new ideas and create specialised versions for different industries — including accounting, law, healthcare, and education.
In short, open source accelerates progress by letting everyone participate in advancing AI rather than keeping it behind closed doors.
Why They Are Called “Large” Language Models
The word “large” in LLM doesn’t refer to physical size — it refers to scale. These models contain hundreds of billions, and sometimes even trillions, of parameters.
Parameters are like the neurons of a digital brain — the tiny connections that help the model recognise relationships between words, ideas, and concepts. The more parameters a model has, the more nuanced its understanding of language becomes.
Smaller models (with a few billion parameters) are fast and efficient, suitable for mobile apps or embedded systems.
Large frontier models like GPT-4 or Claude 3 are extremely powerful, capable of reasoning, summarising, and explaining complex ideas.
Specialised models are trained for specific domains, such as medical information, legal interpretation, or financial analysis.
These different sizes allow AI to be used in many contexts — from lightweight assistants on a phone to enterprise-grade models analysing large sets of business data.
Why LLMs Are Transformative
What makes Large Language Models revolutionary is not just their ability to generate text but their ability to understand context and meaning. They can connect information across disciplines — reading a legal clause, interpreting its financial implications, and drafting an executive summary, all in one conversation.
For accountants and business professionals, this means faster analysis, clearer explanations, and more time for strategic thinking. LLMs can read complex regulations, draft compliance summaries, and even simulate communication between departments.
This shift represents more than new technology — it represents a new kind of collaboration between humans and machines. The accountant provides the expertise, ethics, and professional judgment; the AI provides speed, structure, and linguistic intelligence.
The Frontier Models – Today’s Leading Systems
While many models exist, four companies currently define the frontier of LLM development:
OpenAI – GPT Models (ChatGPT, GPT-4, GPT-4o)
OpenAI’s models are best known for creativity, versatility, and general knowledge. ChatGPT remains the most widely recognised application of generative AI globally.
Anthropic – Claude Series
Claude is renowned for its clear explanations and ability to handle very large documents, making it ideal for professional reasoning and summarisation tasks.
Google – Gemini
Gemini integrates deeply with Google’s ecosystem, enabling everyday productivity through natural language interactions.
Meta – LLaMA
Meta’s LLaMA models stand out for their open-source approach, empowering universities and independent developers to build their own applications.
Together, these models form the current frontier of generative AI — the systems pushing the boundaries of what machines can understand and create.
Conclusion – The New Era of Language Intelligence
Large Language Models have quietly become one of the most influential technologies of our time. They are not science fiction; they are working tools that already shape how professionals communicate, learn, and make decisions.
ChatGPT introduced the world to conversational AI. Claude refined it through careful reasoning. Gemini brought it into everyday tools. LLaMA opened it to the world.
Behind every response, every summary, and every insight you receive from an AI assistant lies the intelligence of an LLM — a vast digital brain trained to understand and generate human language.
For accounting and business professionals, understanding this technology is not about coding or mathematics. It is about recognising a new era in which the ability to ask questions effectively and interpret AI responses responsibly becomes a core professional skill — as fundamental as spreadsheets once were.