Achieving human-level AI will not be possible by merely scaling up existing large language models; a fundamental shift in approach is necessary to reach that goal.

There is ongoing debate in the AI community about whether we have reached the limits of scaling laws, as the latest models have become larger and more expensive, yet their capabilities seem to be plateauing.

I believe we are nearing the upper limits of large language models, as the future will likely involve many smaller, specialized models rather than a few large ones. We're at the upper end of large language models, and they're rapidly becoming a commodity as they mature.

Large language models are becoming significantly larger and smarter, with AI labs adopting a strategy of scaling more parameters, data, and compute to improve model performance.

The current advancements in AI suggest that we are not near a limit; the rate of change and new models indicates ongoing improvement.

The early days of AI development were marked by skepticism about the viability of language models, but persistence in technology has proven their value.

The future of AI may involve models communicating in a more efficient, nuanced language that humans cannot easily understand.

English is becoming the new assembly language for large language models, meaning that effective communication with AI requires structuring language correctly, even if syntax is less critical than before.

Despite increasing GPU resources, the intelligence improvements from the current generation of LLMs are not meeting expectations, with reports of failed training runs and diminishing returns emerging from major labs. Additionally, the lack of high-quality data to train new models has become a major bottleneck for AI development.