By spending tens of billions in investor cash. It is not clear what a sustainable business model looks like for these companies at the moment.

> They are handling it a lot better than Github.

Are they? Anthropic has fairly frequent issues.

There's high growth and then there's demand increasing 10x in a year. For a mature business like github that's a massive, and I can't think of many big businesses who would be able to scale smoothly to accommodate that kind of demand.

I don't think the models are pure in any meaningful sense. The labs have some idea of what kind of output they want from the models and then they put a huge amount of effort into training the models on the right sorts of data and massaging the models afterwards to push them towards the desired output. Then at a more practical level there's the layers of filters before your prompt even hits the model (e.g. anthropic's auto-mode classifier), system prompts, response level filtering etc.

Some of the individual components may still be relatively expensive, but overall I don't think this is true at all. Compare a 2026 macbook pro with a 2011 macbook pro at a similar price and the 2026 model is gonna blow the 2011 model out of the water on any benchmark you care to use.

I don't think this is true. Take LLMs for example, you need GPUs to serve them and these are in short supply at the moment making it hard to meet demand. I don't think this is necessarily the fault of incompetent leadership.

This feels like a pretty shallow take on a what seemed to me like a pretty in depth and interesting review. Clearly there's a huge amount of potential ground that could have been covered in a film like this, so a critical look at what was and wasn't included, the angles that were chosen, the parts that were glossed over etc. can be revealing. Put another way, I think the review is a pretty in depth look at the questions "what was the artist trying to achieve?" and "Did they succeed?".

Small correction, but Fontainebleau is a fairly mixed forest including 20-30% pine.

No argument here, I completely agree.

> There is no world in which me vacuuming the entirety of human knowledge to make a genai model is ok but hoovering my model answers is not.

I disagree with this though. Clearly LLMs owe a huge debt to everything that has come before, but surely you'd agree that the models that are produced are something substantial and new and novel which didn't exist before and have lots of value in their own right. Let's be a bit reductive and pretend Moonshot had just outright stolen the weights from Fable somehow, clearly that wouldn't be contributing anything really new or novel. Now of course they've distilled rather than stolen, but the point is similar: how much value have they added along the way?

ed: to clarify, I totally agree that a huge chunk of the value in LLMs is coming from the source material. My point was just that training an LLM takes more resources and expertise than distilling from an existing LLM so I don't think the equivalence between training and distilling is entirely justified.

China has an effective strangle hold on some key sectors (solar, rare earths) and I am sure they relish this position and the leverage it gives them. You'd be careful not to give away that same leverage to a competing power if you can invest a few billion now and cover your bases.

I doubt you are going to get a response from an anthropic employee, but I think it is safe to assume they have swapped to a new tokenizer because it improves the performance of their models.