Well, yes, but as the other commenter says, that’s a very broad general statement akin to something like “AI will change knowledge work“. That’s certainly true, but how? What are the details? What kind of companies are going to be the winners and what kind will be losers, or end up with commodity margins, like the telcos did after the mobile revolution? What is the pricing structure going to look like?

I suppose a concrete example in 1997 would be that a lot of companies thought the future of e-commerce was setting up a store on AOL, that people would use while sitting down at a desktop PC. Obviously it didn’t turn out quite that way. Furthermore, the Internet enabled new kinds of ways to buy things that weren’t even envisioned in the pre-Internet pre-smartphone world: think Airbnb and Uber.

Predictions are hard, especially about the future. Most predictions reflect the worldview and biases of the time in which they are made: think about all the vintage sci-fi from the 60s 70s and 80s that actually reads or looks kind of retro now. Similarly, our predictions of the future will look kind of retro and strange to someone living in the 2030s or 2040s. If studying history has any lesson to teach us, it’s really just this: that the past is an alien world with alien moods of thinking, and that our moment in time will look similarly alien to people in the future who choose to look back and analyze it closely.

This isn’t an argument that we should stop trying to make predictions. We need to, but it is an argument for humility, and also for questioning all your assumptions that you might be importing.

That's a very vague prediction that took decades to bear fruit. The concrete predictions behind the investments into companies like Pets.com and Webvan failed. It took the survivors like Ebay/Paypal and Amazon to build the digital payment and shipping infrastructure over decades until cultural acceptance hit critical mass.

I didn’t make any comparison at all with the fibre bubble, for precisely that reason. The comparison is with mobile data, which was and is always behind capacity.

I think one of the things that the usage data shows us is that chatbots absolutely do not have infinite use cases - most users only use them a day or two a week or less.

> that exist at roughly the frontier

no disagree, specifics matter.. There are a dozen well-defined LLM application subject areas that are regularly tested.. one overall grade IMO lacks important detail.. To go a bit abstract, it is ironic that "oversimplification" in the discussion of these complex machines mirrors the effects on information of the automations themselves.. constantly simplifying, substituting and diluting real meaning

It's quite expensive to self-host but you have many places to run it. OpenRouter alone lists a dozen different providers for DeepSeek 4 Pro. https://openrouter.ai/deepseek/deepseek-v4-pro/providers.

So long as there is demand, there are always going to be providers competing to offer it at a low cost. My understanding is that the median price on there is in the ballpark of what it costs to run the inference. This is very different from e.g. Opus, which you can basically only buy from Anthropic at the price they set.

antirez running (quantized) DeepSeek V4 Pro on a Mac Studio M3 Ultra with 512GB of RAM:

https://bsky.app/profile/antirez.bsky.social/post/3mlzwmvlov...

It's much closer than you think. We're going to see specialized hardware in the next 24 months capable of running 2025-era frontier models. That's big.

I just got into self hosting Deepseek v4 Flash on a single DGX Spark via antirez’s DwarfStar 4 project

It feels great to finally have access to something local.

That seems pretty temporary if people can just build more compute.