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My company checks the models and pays for Opus through AWS.

You still send the WHOLE context of whatever you want to do to a random endpoint on the internet. If you want to write a good email, you give that context your email address, names, the reason for it etc.

Big companies don't randomly use some random api endpoint to do so.

Anthropics quarerly revenue is still growing very fast. I don't think we have seen even the real potenzial of it yet at all.

Not only are still a lot of countries missing which do not even use anthropic or any other frontier model yet but also all the agentic based solutions enterprise companies are currently building on mass (at least in my industry)

Besides Nvidia Hardware is still sold out and super expensive. Not a single Nvidia consumer GPU got cheaper at all, Nvidia DGX Spark got more expensive too.

It will be swooped of the market the second it hits the market.

Blocking Fable for sure made it very politicl a lot sooner than i expected it to happen.

and because China already has massive problems of getting access, they are pushing it on hardware too like what Huawai did without EUV.

It seems China is already able to do DUV a lot sooner than others expected.

But yeah I do'nt want to know what Kimi 3 is pushing buttons inside Anthropic, OpenAI and Google.

Besides any floor: For every year the tokens get faster and cheaper, we will see new things like properly working AI factories which mimic expert teams. A lot more parallism as well.

Microsoft can as well.

So Anthropic only has a few thousand people which is nothing and OpenAI has also only a few thousand.

A lot of people working at these companies would find a new job easily and probably making a shit ton of money anyway.

The broader AI companies do not have that much venture capital, not that many peple and potentially not even that high of risks.

Deepseek didn't do their funding round because they could'nt even get the compute if they wanted to. So they might be very lean.

And even investors for OpenAI and Anthropic might accept that they stop R&D and just keep the services up and running as they are cost effective.

The bigger problem will be the industry itself: If i'm 5x more productive, and generate now more costs on the business, they will reduce headcounts to compensate for it.

We already have disruptions in Software engineering, image generation, video generation, etc.

Humans might not like AI, Companies do and with Claude we are no longer sitting on the right side, if a Product manager tells a Boss that the PM now can do also code, the boss will listen.

But we will see other effects in the next 5-15 years: population pyramid is crashing which will increase demand for people. We already have high demand for other jobs.

so i can see a transition for a lot of white collar workers to blue collar workers which will continue the disruption for everyone.

Then the robots come along and AI integration is done and we will have a real issue on our hand. When will this come? No clue i don't think its unrealistic to expeirernece this in my lifetime.

I don't think we have pushed LLMs to their limits already, its still progressing its still crazy good and its able to run even longer today than yesterday.

But researchers are already working on more architectures.

Investors for sure don't care about people. Replace Accenture with OpenAI and you replace 800k people immediadly.

Geoffrey Hinton said it in his Videos: a normal person costs A LOT of money to train, educate, onboard, keep etc. they forget things etc.

AGI you train once, teach once then clone and copy and just run it.

We even might already crossed the line were it is already more cost efective to train 1-5 frontier models something it doesn't know yet than teaching this to a 1000 humans.

In the race they are in, its still highly beneficial to be the frontier model.

I use the frontier model every single day through my company and my company happily spends these tokens.

It makes a huge difference if different people can use one interface to do everything.

Big models give you fundamental things: A lot of facts/context, usability (you might be a native english speaker, don't underestimate how hard it is for A LOT of people to formulate what they want/need in english only AND a low complexity.

No one needs to build a router and x sub models and a router architecture. You literaly just have an API, you might choose the model and the effort but thats it.

I find this current state of the art a LOT more telling on the current progress we are in than anything else. I'm confident that small optimized models will become a lot more relevant like lets say java + english + a second language + spring boot + postgresql. It might also be beneficial in the long term to finetune with your project details.

For a lot of very technical non human interfacing things, finetuning is happening left and right.

But what i find very interesting is emerging complexity capabillity. I believe that fables skill to hold more topics and combine more complex solutions together is because of its parameter size.

We will have to figure out if we can extract this complexity out of it while reducing the training data in a way that the training data focus more on thinking. Plenty of smaller thinking models show that this is doable.

Btw. Mixture of Experts is for sure not optimal for this, but it already is a form of optimized sub models. Perhaps we might just have MoE with a million experts in the future. One per lanuage + area of expertise etc.

Also don't forget: IF AGI is coming through a current frontier model, you will let it work for hours, days and weeks on one problem completly independent of any human input and it will be better than a human. If they reach this before a collapse, we are done and they 'won'. For this you need big frontier models.