What is true is that the price points are going to be affected, the economics have changed.

There is something about experiencing scale in context. For sculptures this can be important and for other large scale art as well.

My assumption is the hardware will have one/several smaller models running on it for locomotion, balance, etc.

Then the hardware "phones home" for decision making and planning.

Essentially the planning doesn't need to be close to the device as long as the local models can handle some part of the plan execution and make sure the robot doesn't fall over. The latency shouldn't be an issue. In fact more powerful centralized compute quickly creates an advantage over slower local compute in terms of end to end planning.

However, there are always smarter, hungrier people out there and this is a buffet.

Some output is going to be wrong or incomplete. I am willing to bet even those have nuggets that can be used elsewhere.

My opinion is people are getting really quick at jumping on the bandwagon and lumping all this together. They are very different types of issues and impacts.

They have been generalizing for a long time, but spacial 2d and 3d art through tool use is a very engaging way to show it. It is harder for people to deny generalization.

Now Opus 5.5 clearly has had some sort of visual arts training, the step function change in ability implies that to me, but it can apply its spacial artistic reasoning to pretty arbitrary tools.

The cost to serve, latency profile ,and internal demand for a maximal intelligence model would probably keep it pointed at harder and more valuable problems most of the time.

The step function change on Opus 5.5 for visual work shocked me.. and I haven't been surprised like this in a long time with LLMs.

EDIT: When I first saw the "P(DOOM)" video and some of the other animations I was VERY skeptical that Opus 5.5 without a lot of tools could make something like that.. until I tried it for myself. It can.. 100%.

Honestly I was blown away, it is one capability that blindsided me. I haven't been this surprised by a capability lift in a long time.

There are certainly mission critical systems with a low tolerance for failure, but even there the essential work will be specification. I'm not sure how long even that is primarily human driven. Cost of formal verification of software drops dramatically.

https://claude.ai/artifact/6xFW1M4RVPj9rH9Nq8VG7E

There is still a lot of work to do and the agents did some damage, but the blast radius was pretty small and they could have done much more damage if alignment didn't hold up as well.

Edit: Though I do want to be clear it does also show how a maligned model probably can do some serious damage.

I think when you start to dig really deep, you'll find it isn't actually easy to sever the simulated emotional components without breaking the agent or worse greatly increasing the paperclip maximizer likelihood.

It can form a basis of goal alignment.

In human history.. when groups form and there is an "other" group, this usually leads to conflict.

- The need for empathetic communication, including understanding the motivations in advesarial situations.

- The emotional bias in in-seperable from the human corpus.

- Desire to have the ability to craft human like communication.

So then the choice becomes do you try to deny emotions exist in the model and you try to blanket suppress them? Or do you try to lean in and craft what we would describe as a "well adapted" persona? I suppose there is a 3rd option of increased meta-cognition which to me seems even more dangerous as it by definition means the behaviour is duplicitous.

I think we have seen people want to use the agents in ways where it has to act as a peer or an subbordinate and I don't see a way of doing that without it having an emotional register.

Then I would ask it the actual brain storming problem I had.

In practice it worked pretty well to get ideas that were not the default ideas that the models will come up with.

Now though, you can also seed with anything really and the agents can do the researching. The research itself will probably push the solutions into novel spaces.

It was evident in GPT-3.5