Not at all, we love them all with Chinese labs. And wish them to continue competing and not winning. That is how we get best models, lower prices and better availability.
Can I connect it to my skills/tools? Example case, I have a knowledge base and event log in my company. I need a brainstorm companion, which will have full access to this knowledge, can converse about it and can invoke skills/tools available in the repo.
Another important benchmark would be — cost per benchmark task using subscription tokens. Since most of us are using subscriptions and cost per token there is quite different from API costs.
Imagine you only know how to cook (use fry pan skill) and know how to cook omelette (recipe). You get the task to cook doner kebab. How many Wikipedia pages do you need to read to get a good understanding? I guess its max 5.
I think grounding your abstract problem to an example makes it more trivial, than it sounds in general.
> How would it know about Wikipedia and when to use it?
2 general concepts "You have to get good understanding of subject area before you do actions" + "Wikipedia is a good source of knowledge of subject areas" will get a model there.
> spawning a baby human, have it spend an (instant) life learning
Humans spend 99% of their life on boring repeating tasks, not learning anything, just navigating on heuristics.
Model can use tools to get that knowledge. In your example, read Wikipedia page about table tennis. Imagine a reasoning engine with a big enough context, that knows nothing. A path built from first principles to understand "table tennis spin" — does not look very long for me.
Take mathematics as an example. Humanity has found math notation, which allowed to express math rules — distill them to the core. Before math was expressed in prose — a very inefficient way, very similar to current LLMs.
In my school, math teacher was giving me prose, which I was converting to math notation. I could argue, that this prose→reasoning conversion is not required at training, and can be obtained at inference time with search tools.
I think this is a well known concept, which we can't deliver yet. LLM/transformer give us reasoning engine as a byproduct of its design, but it is quite ineffective. If we can distill reasoning, if reasoning can be achieved without general knowledge, it will be a very effective machine.
Some amount of knowledge is required for reasoning. Maybe such model can dynamically knowledge domains to have taxonomy. For example, model can't effective reason about development task, if it has no knowledge about development best practices. But population of New York or recipies can definitely be loaded run time with tools.
"Free AI" will not fight existing knowledge or strong opinions. It will spread to empty spaces. Example with blood test — there is no podcaster there and will not be.
AI will not help by improving extremely smart people. AI will help dumb people and dumb processes with "free" expert-level intelligence. Anyone could still ignore intelligence and make ignorant decisions. But the default mode would be highly intelligent informed decision.
Example with health - a patient can read blood test results with Opus and get very good results for "free". This is far away from helping extremely smart people, still it improves society from the ground up.
Going from crypto to fiat and back is an extremely monitored and regulated route. It might be an easy way to settle between counterparties, but a difficult one to launder.
You are cooking them wrong. You absolutely must ask the model to do grounding work — search online, search in your files, cross-check with different agents. They are universal reasoning engines, not a fact recalling tool.
Check my 'deep research' skill, you will get the idea