The $5.5 million training cost for OpenAI's GPT-3.5 only refers to the final training run and does not include the costs associated with R1's training or the extensive R&D and hardware expenses, which are likely in the hundreds of millions.

With the advent of 10 trillion parameter models, we could see a leap in innovation similar to the transition from GPT-2 to GPT-3, which sparked a new era of AI companies.

The longer models like GPT-3 are able to think through complex problems, the better they perform.

Despite criticisms, OpenAI and DeepMind have been transparent about their methodologies, making it easier for others to replicate models like GPT-3 if they can secure enough compute and talent.

Despite its advancements, GPT-4.5 is significantly more expensive than previous models, costing 30 times more per input token and 15 times more per output token.

The evolution of language models, particularly with the introduction of GPT-3, has demonstrated a significant leap in their ability to perform language tasks, even though they do not possess real-world knowledge.

GPT-3 made headlines by smashing benchmarks previously considered out of reach for AI, excelling in software engineering, math, and PhD-level science questions.

With the release of GPT-3, it seems the sky is the limit for this new paradigm of scaling large language models.

As AI technology evolves, the availability of powerful models may increase, potentially making them accessible for free in the future.