Relatively accurate (I think they're pretty close like 70%+ of the time). Significantly better than just projecting that the next five years will be like the last 5 years.
You haven't really been getting 'human written and thoughtful content' for a vast swath of search topics for probably 15-20 years now. You get SEO-hyper-optimized (probably LLM-generated for anything in the last 3 years) blog spam. In terms of searching for information and getting that information, there are a lot of topics where an LLM-generated result is vastly better just by virtue of not being buried inside blog spam. The slop ship sailed years ago.
It's not really an apples-to-apples comparison - I enjoy playing around with LLMs, running different models, etc, and I place a relatively high premium on privacy. The computer itself was $2k about two years ago (and my employer reimbursed me for it), and 99% of my usage is for research questions which have relatively high output per input token. Using one for a coding assistant seems like it can run through a very high number of tokens with relatively few of them actually being used for anything. If I wanted a real-time coding assistant, I would probably be using something that fit in the 24GB of VRAM and would have very different cost/performance tradeoffs.
I've been running the 'frontier' open-weight LLMs (mainly deepseek r1/v3) at home, and I find that they're best for asynchronous interactions. Give it a prompt and come back in 30-45 minutes to read the response. I've been running on a dual-socket 36-core Xeon with 768GB of RAM and it typically gets 1-2 tokens/sec. Great for research questions or coding prompts, not great for text auto-complete while programming.
1-2 tokens/sec is perfectly fine for 'asynchronous' queries, and the open-weight models are pretty close to frontier-quality (maybe a few months behind?). I frequently use it for a variety of research topics, doing feasibility studies for wacky ideas, some prototypy coding tasks. I usually give it a prompt and come back half an hour later to see the results (although the thinking traces are sufficiently entertaining that sometimes it's fun to just read as it comes out). Being able to see the full thinking traces (and pause and alter/correct them if needed) is one of my favorite aspects of being able to run these models locally. The thinking traces are frequently just as or more useful than the final outputs.
Kodak didn't really have the option to compete. Their business was largely film, which just disappeared completely, and even digital cameras got replaced pretty quickly with phones. There was nothing to pivot too for Kodak.
Did you have to do anything special to get the SSD to play nice with OS9? I tried adding one to a 300MHz G3 iMac and it took forever to initialize on boot and would randomly stall a lot.
If you don't need to pay for the model development costs, I think running inference will just be driven down to the underlying cloud computing costs. The actual requirement to passably (~4-bit quantization) run Deepseek v3/r1 at home is really just having 512GB or so of RAM - I bought a used dual-socket xeon for $2k that has 768GB of RAM, and can run Deepseek R1 at 1-1.5 tokens/sec, which is perfectly usable for "ask a complicated question, come back an hour or so later and check on the result".
Or, like Meta, they make their money elsewhere and just seem interested in wrecking the economics of LLMs. As soon as an open-weight model is released, it basically sets a global floor that says "Models with similar or worse performance effectively have zero value," and that floor has been rising incredibly quickly. I'd be surprised if the vast, vast majority of queries ChatGPT gets couldn't get equivalently good results from llama3/deepseek/qwen/mistral models, even for those paying for the pro versions.
It does seem like it will be very, very hard for the companies training their own models to recoup their investment when the capabilities of open-weight models catch up so quickly - general purpose LLMs just seem destined to be a cheap commodity.
The word 'recession' means something though, and it's neutral on whether or not any particular humans have more income or cheaper goods. If you get a raise at work, it doesn't mean we're not in an economic recession as a country, and if you get fired or the price of gas goes up, it doesn't mean we are.