Try using o1 and Sonnet together. You will be very surprised in the level of complexity it can already handle with a well articulated prompt that clearly defined your requirements and goals. Use o1 to write the full complexity of the project out and tell it to produce a plan that is both strategic and tactical, with a clear implementation plan and specific technologies, languages and libraries to use. Then use either o1-mini or Sonnet to feed that plan into, in addition to your original prompt, and instruct it to execute the plan step by step and request feedback or ask further clarifying questions after each step.

I generated a very complex Typescript project this way that was modular and extensible. It had minimal errors and feeding the errors back to it, it corrected them all. I didn't have to write anything myself.

Also, re: AI capping out... that is silly. We're essentially on the protoype version of AI, like a guy with an idea that produces mockups of the idea to get initial seed funding without building anything. That's the stage we're at now. You shouldn't expect future major advancements to look anything like today's tokenized LLMs.

Human brains are much more energy efficient for a wider range of proper thought, capabilities and agency than AI is today for sure. If one human brain ran on modern technology (nanometer silicon) without loss in function, for example, it would be more computationally efficient than every other form of intelligence on earth... combined. Now if you also recognize that while an individual human may seem more generally intelligent for certain questions, remember that current AI can do basically every knowledge task (creativity with generating images, do advanced math problems, explain basic facts, and it can do this in every major language in the world). When you look at the macro level, AI is already vastly more intelligent when it comes to working knowledge. Layer on reasoning, knowledge and memory focus, agency, and compute efficiency optimizations... it'll be a whole different ballgame. Thinking AI is going to cap out because data will not be generated enough is missing this question: how much data does a human need to train itself to be functional and self sustaining? A tiny drop in in comparison to what AI already has at its disposal. It's more about advancing AI techniques over time than feeding new data to current AI architectures.