Maybe this is a little bit similar to VR and AR adoption. Arguably we have had stunning capabilities for years. But the headsets are uncomfortable and a bit exp

Maybe this is a little bit similar to VR and AR adoption. Arguably we have had stunning capabilities for years. But the headsets are uncomfortable and a bit expensive given that discomfort. Likewise, the most capable models are huge, run on Anthropic or OpenAI's servers and are expensive to deploy on a large scale. That's uncomfortable from a privacy standpoint (although arguably essentially the same as any cloud service). I believe that over the next few years we will finally start to see the transition from bulky VR headsets to comfortable AR glasses and goggles. That will lead to a huge increase in adoption. As the LLM models, software, and hardware become more affordable and capable for running locally, production adoption of LLMs and multimodal models will continue to pick up. I think that we are going to see huge excitement in the next few years as more and more very large and capable truly multimodal models come out. More specialized hardware like Groq etc. will become available. And within probably less than three years the truly radical new paradigms for neural network hardware such as those based on memristors, SNNs, etc. will likely show some very competitive prototypes. As the new hardware paradigms are widely deployed, say within 5-10 years, human-level local AI will become ubiquitous. What I take away from those charts is actually significant adoption that is steadily rising. But all of this is nested S-curves, as scientists and engineers work through small and large different bottlenecks and problems to get to the next capability or efficiency level.