I'm pretty sure money is not going to be the blocker.
LeCun argues that most human reasoning is grounded in the physical world, not language, and that AI world models are necessary to develop true human-level intelligence. “The idea that you’re going to extend the capabilities of LLMs [large language models] to the point that they’re going to have human-level intelligence is complete nonsense,” he said. [0]
[0] https://www.wired.com/story/yann-lecun-raises-dollar1-billio...
AI is hands down the most researched topic in CS departments. Of the 10 largest companies (by market cap), only 3 aren't balls-deep in AI R&D. The fastest growing (private or public) companies by revenue are also almost all companies focused primarily on AI (Anthropic, OpenAI, xAI, Scale AI, Nvidia).
And the money isn't even the most important part. It's all about mindshare and collective research time. The architectural concepts can be researched and developed on top of open models, so even individual relatively poor researchers unaffiliated to anything can make breakthroughs.
Even the computing required for the legendary "Attention is all you need" paper could probably be recreated on con-/prosumer hardware in a month's time.
[0] https://en.wikipedia.org/wiki/OpenAI#Creation_of_for-profit_...
Realistically, one can build a AI capable of reasoning (i.e recurrent loops with branches) using very basic models that fit on a 3090, with multi agent configuration along the lines https://github.com/gastownhall/gastown. Nobody has done it yet because we don't know what the number of agents is required and what the prompts for those look like.
The fundamental philosophical problem is if that configuration is possible to arrive at using training, or do ai agents have to go through equivalent "evolution epocs" to be able to do all that in a simulated environment. Because in the case of those prompts and models, they have to be information agnostic.