41–50 of 518 posts
It's going to be a race to the bottom, they have no moat.
Do you not think that batch inference gives at least a bit of a moat whereby unit costs fall with more prompts per unit of time, especially if models get more complicated and larger in the future?
It's going to be a race to the bottom, they have no moat.
Especially now that they are second in the race (behind Anthropic) and lot of free-to-download and free-to-use models are now starting to be viable competitors. Once new MacBooks and iPhones have enough memory onboard this is going to be a disaster for OpenAI and other providers.
Earlier quoted context omitted.
Especially now that they are second in the race (behind Anthropic) and lot of free-to-download and free-to-use models are now starting to be viable competitors. Once new MacBooks and iPhones have enough memory onboard this is going to be a disaster for OpenAI and other providers.
I'm not sure they're scared of Anthropic - they're doing great work but afaict running into some scaling issues and really focused on winning over developers at the moment. If I was OpenAI (or Anthropic for that matter) I would remain scared of Google, who is now awake and able to dump Gemini 2.5 pro on the market at costs that I'm not sure people without their own hardware can compete with, and with the infrastructu…
how do we know it's not a quantized version of o3? what's stopping these firms from announcing the full model to perform well on the benchmarks and then gradually quantizing it (first at Q8 so no one notices, then Q6, then Q4, ...). I have a suspicion that's how they were able to get gpt-4-turbo so fast. In practice, I found it inferior to the original GPT-4 but the company probably benchmaxxed the hell out of the tu…
I swear every time a new model is released it's great at first but then performance gets worse over time. I figured they were fine-tuning it to get rid of bad output which also nerfed the really good output. Now I'm wondering if they were quantizing it.
how do we know it's not a quantized version of o3? what's stopping these firms from announcing the full model to perform well on the benchmarks and then gradually quantizing it (first at Q8 so no one notices, then Q6, then Q4, ...). I have a suspicion that's how they were able to get gpt-4-turbo so fast. In practice, I found it inferior to the original GPT-4 but the company probably benchmaxxed the hell out of the tu…
I swear every time a new model is released it's great at first but then performance gets worse over time. I figured they were fine-tuning it to get rid of bad output which also nerfed the really good output. Now I'm wondering if they were quantizing it.
Now I'm feeling similarly with their image generation (which is the only reason I created a paid account two months ago, and the output looks more generic by default).
Note that they have not actually dropped the price yet: https://x.com/OpenAIDevs/status/1932463601119637532 > We’ll post to @openaidevs once the new pricing is in full effect. In $10… 9… 8… There is also speculation that they are only dropping the input price, not the output price (which includes the reasoning tokens).
I think that was a joke. New pricing is already in place: Input: $2.00 / 1M tokens Cached input: $0.50 / 1M tokens Output: $8.00 / 1M tokens https://openai.com/api/pricing/ Now cheaper than gpt-4o and same price as gpt-4.1 (!).
The speculation of only input pricing being lowered was because yesterday they gave out vouchers for 1M free input tokens while output tokens were still billed.
In my experience, o4-mini and o4-mini-high are far behind o3 in utility, but since I’m rate-limited for the latter, I end up primarily using the former, which has kind of reinforced the perception that OpenAI’s thinking models are behind the competition altogether.
You know. because LLMs can only be built by corporations... but because they're so easy to build, I see the price going down massively thanks to competition. Consumers benefit because all the companies are trying to out run each other.
They need lots of energy and customers don’t pay much, if they pay at all
Earlier quoted context omitted.
And then they all go out of business, since models cost a fortune to build, and their fan club is left staring at their computers trying to remember how to do anything without getting it served on a silver plate.
Investors pouring money, its probably impossible to go out of business, at least for the big ones, until investors realise this is wrong hill to die on.