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Microsoft and OpenAI's close partnership shows signs of fraying

(www.nytimes.com)
321 points jhunter1016 | 3 comments | 18 Oct 24 11:11 UTC | HN request time: 0.607s | source
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Roark66 ◴[18 Oct 24 12:02 UTC] No.41878594[source]▶
>OpenAI plans to loose $5 billion this year

Let that sink in for anyone that has incorporated Chatgpt in their work routines to the point their normal skills start to atrophy. Imagine in 2 years time OpenAI goes bust and MS gets all the IP. Now you can't really do your work without ChatGPT, but it cost has been brought up to how much it really costs to run. Maybe $2k per month per person? And you get about 1h of use per day for the money too...

I've been saying for ages, being a luditite and abstaining from using AI is not the answer (no one is tiling the fields with oxen anymore either). But it is crucial to at the very least retain 50% of capability hosted models like Chatgpt offer locally.

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1. whywhywhywhy ◴[18 Oct 24 12:53 UTC] No.41878984[source]▶
I used to be concerned with this back when GPT4 originally came out and was way more impressive than the current version and OpenAI was the only game in town.

But Nowadays GPT has been quantized and cost-optimized to hell that it's no longer as useful as it was and with Claude or Gemini or whatever it's no longer noticeably better than any of them so it doesn't really matter what happens with their pricing.

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2. edg5000 ◴[18 Oct 24 13:00 UTC] No.41879036[source]▶
Are you saying they reduced the quality of the model in order to save compute? Would it make sense for them to offer a premium version of the model at at a very high price? At least offer it to those willing to pay?

It would not make sense to reduce output quality only to save on compute at inference, why not offer a premium (and perhaps perhaps slower) tier?

Unless the cost is at training time, maybe it would not be cost-effective for them to keep a model like that up to date.

As you can tell I am a bit uninformed on the topic.

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3. bt1a ◴[18 Oct 24 16:14 UTC] No.41880783[source]▶
Yeah, as someone who had access to gpt-4 early in 2023, the endpoint used to take over a minute to respond and the quality of the responses was mindblowing. Simply too expensive to serve at scale, not to mention the silicon constraints that are even more prohibitive when the organization needs to lock up a lot of their compute for training The Next Big Model. Thats a lot of compute that cant be on standby for serving inference