The rise and rise of AI-based Large Language Models (LLMs) like GPT4, LaMDA, LLaMa, PaLM and Jurassic-2.

Click the company names to filter the data.

» See the data

We’ll keep this graphic updated as new models emerge.

further notes & essential reading

» How ChatGPT and other LLMs Work – And Where They Could Go Next (good Wired article)

» While we’ve plotted these LLMs by the size of each model in billion parameters, there is a growing sense of diminishing returns for simply increasing the model size (Wired article)

» Will A.I. become the new McKinsey? Author Ted Chiang argues that AI is likely to function like larger corporate consulting firms, acting as a “willing executioner”, accelerating job loss (New Yorker)

» The Mounting Environmental & Human Costs of Generative AI (Arstechnica article) &TLDR: larger models = more consumption of planetary resources (minerals, energy, water for cooling) + AI training needs large-scale human supervision so very real possibility of ‘AI sweatshops’ + the serious issues of copyright infringement for artists and creators.

summarised here:

A Quick Data Story

Some simple filtering reveals an interesting story in our LLM data.

Google drove a burst of innovation in the LLM space

Sharing their knowledge and research with the AI world. (Transformers, for example, a central component of LLMs, originated from research at Google). But then the broader company was pipped to the practical-application post by OpenAI. Will their latest release PaLM2 overtake ChatGPT?

OpenAI, creators of ChatGPT, stole the LLM show

They made steady, solid progress over the last three years, dhttps://informationisbeautiful.net/wp-admin/post.php?post=462&action=editriving the curve. Slow and steady wins the race?

And here’s why Microsoft invested in OpenAI

You can see they weren’t directly active in the space with their own research. Instead they invested early and hard in OpenAI ($1bn in both 2019 and 2021) and that paid serious dividends.

Meta / Facebook also drove significant innovation in the field…

What muted their breakthroughs? Maybe the models weren’t large enough (see how many are below the ‘magic’ 175 billion parameter line). Maybe, like Google, there’s was too much emphasis on internal applications & processes versus public tools? Maybe, also, their research was chastened by the poor reception of its science-specialised LLM Galactica.

Meanwhile, in the background, China is also making steady progress

In the advent of ChatGPT, release of Chinese-language LLM’s and chatbots have significantly accelerated.

What about Amazon?

Well, they have steamed in at the end – too late to the party? Time will tell…