If you are of the opinion that the transformer architecture cannot scale to AGI and a more brain inspired approach is needed, then the sooner that everyone realizes that scaling LLM/Tx is not sufficient, the sooner the search begins in earnest. At present the majority of experimental compute and researcher effort is probably on such LLM/Tx systems, however if that changes to exploring new approaches then we can expect a speedup on finding such better architectures. For existing companies, https://thinkingmachines.ai/ and https://ssi.inc/ are probably already doing a lot of this, and Deepmind is not just transformers, but there is a lot of scope for effort/compute to shift from LLM to other ideas in the wider industry.
GPT fail leads to shorter timelines?
If you are of the opinion that the transformer architecture cannot scale to AGI and a more brain inspired approach is needed, then the sooner that everyone realizes that scaling LLM/Tx is not sufficient, the sooner the search begins in earnest. At present the majority of experimental compute and researcher effort is probably on such LLM/Tx systems, however if that changes to exploring new approaches then we can expect a speedup on finding such better architectures.
For existing companies, https://thinkingmachines.ai/ and https://ssi.inc/ are probably already doing a lot of this, and Deepmind is not just transformers, but there is a lot of scope for effort/compute to shift from LLM to other ideas in the wider industry.