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Just yesterday, they reported an annualized revenue run rate of 10B. Their last funding round in March valued them at 300B. Despite losing 5B last year, they are growing really fast - 30x revenue with over 500M active users.
It reminds me a lot of Uber in its earlier years—fast growth, heavy investment, but edging closer to profitability.
Earlier quoted context omitted.
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.
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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…
> users found them more pleasing. Some users. For me the drop was so huge it became almost unusable for the things I had used it for.
Earlier quoted context omitted.
How are LLMs cached? Every prompt would be different so it's not clear how that would work. Unless you're talking about caching the model weights...
You would use a KV cache to cache a significant chunk of the inference work.
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…
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…
This is almost certainly what they're doing and rebranding the original o3 model as "o3-pro"
interesting take, I wouldn't be surprised if they did that.
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.
It's going to be a race to the bottom, they have no moat.
I get the point is to be the last man standing, and poaching customers by lowering the price, and perhaps attract a few people who wouldn't have bought a subscription at the higher price. I just question how long investors can justify pouring money into OpenAI. OpenAI is also the poster child for modern AI, so if they fail the market will react badly.
Mostly I don't understand Silicon Valley venture capital, but dumping price, making wild purchases for investor money and mostly only leading on branding, why isn't this a sign that OpenAI is failing?
Despite the popular take that LLMs have no moat and are burning cash, I find OpenAI's situation really promising. Just yesterday, they reported an annualized revenue run rate of 10B. Their last funding round in March valued them at 300B. Despite losing 5B last year, they are growing really fast - 30x revenue with over 500M active users. It reminds me a lot of Uber in its earlier years—fast growth, heavy investment, b…
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…
This is almost certainly what they're doing and rebranding the original o3 model as "o3-pro"