Part 3 : ChatGPT — A Few Good Numbers & Current Wisdom (of the crowds !)
Quick Background ( … for those who came in late)
This is Part 3 of “A ChatGPT guide for Techno-Executives”.
In the next 2 weeks, I have teed up a set of (~5) Byte-sized, Targeted, Nimble, Visual (with appropriate pictures) and Snackable (each blog, a few short pages, goes well with a few shots of a single malt, preferably triple distilled) aiming to be “usefully wrong” — like our protagonist the ChatGPT !
- “Part 1 : Are we Doomed” [Here] dealt with how to think about ChatGPT / GenerativeAI / “Increasingly Multi- or General-Purpose AI”
- “Part 2 : ChatGPT Threat Vectors & Guardrails for LLMOps” [Here] has interesting concepts on how to think about the threat and mitigations. It also looked at the canonical use cases as well as the team, talent & organization to support ChatGPT projects. It is a little different than traditional projects !
- This is a quick blog about numbers
- Next week, we will look at the Models in “Part 4 : State of the Union, Generative AI models” [Here]
- I have some ideas on Transformers that might be a good “Part 5 : An Ode to a Transformer” and may be a “Part 6 : Explainability of the Unexplainable”.
Back to the main feature …
The few good numbers
A caveat : I am not a McKinsey consultant nor have done any market study. But these numbers give us a quick estimate about the various aspects Generative Eco system.
The market estimate from a LinkedIn post (forgot to note down the link, sorry) Possible the numbers are off by a factor of 10. The industry segments look right.
An interesting landscape. Keep an eye on the VectorDB companies and associated use cases.
Of course, no discussion will be complete without the Generative AI unicorns !
OpenAI is the leader and Anthropic and others are trying to catch up. They all are getting lots of attention, but is attention all you need for a sustainable business ? To be seen …
BTW, the image was created by AI/Midjourney for a16z !
OpenAI itself needs more $ investment to complete their roadmap…
Interestingly the web data used for training might not be available anymore ! The cost of training data can easily add up.
This is one of the main barriers to entry — the quality and amount of data as well as the resources to train a model. The training cost is in billions of dollars !
I will update this blog with more numbers as I see them.
Onward to the next blog “Week 4 — Part 4 : Generative AI/ChatGPT Models— State of the Union” [Here]
