Feb 24, 2023: Meta launches LLaMA, a relatively small, open-source AI model.
March 3, 2023: LLaMA is leaked to the public, spurring rapid innovation.
March 12, 2023: Artem Andreenko runs LLaMA on a Raspberry Pi, inspiring minification efforts.
March 13, 2023: Stanford's Alpaca adds instruction tuning to LLaMA, enabling low-budget fine-tuning.
March 18, 2023: Georgi Gerganov's 4-bit quantization enables LLaMA to run on a MacBook CPU.
March 19, 2023: Vicuna, a 13B model, achieves "parity" with Bard at a $300 training cost.
March 25, 2023: Nomic introduces GPT4All, an ecosystem gathering models like Vicuna at a $100 training cost.
March 28, 2023: Cerebras trains an open-source GPT-3 architecture, making the community independent of LLaMA.
March 28, 2023: LLaMA-Adapter achieves SOTA multimodal ScienceQA with 1.2M learnable parameters.
April 3, 2023: Berkeley's Koala dialogue model rivals ChatGPT in user preference at a $100 training cost.
April 15, 2023: Open Assistant releases an open-source RLHF model and dataset, making alignment more accessible.
So GP point still stands. FAAMG can run much larger immediate deficits in order to corner the market on the eventual profitability of AI.
This assumes that there is a corner-able market. Previously, the cost of training was the moat. That appears to have been more of a puddle under the gate than an actual moat.
Beyond that, they are making a lot of money from their enterprise offerings (API products, custom partnerships, etc.) with more to come soon, like ChatGPT for Business.