Don’t get me wrong what DS did is great, but anyone thinking this reshape the fundamental trend of scaling laws and make compute irrelevant is dead wrong. I’m sure OpenAI doesn’t really enjoy the PR right now, but guess what OpenAI/Google/Meta/Anthropic can do if you give them a recipe for 11x more efficient training ? They can scale it to their 100k GPUs clusters and still blow everything. This will be textbook Jevons paradox.
Compute is still king and OpenAI has worked on their training platform longer than anyone.
Of course as soon as the next best model is released, we can train on its output and catch up at a fraction of the cost, and thus the infinite bunny hopping will continue.
But OpenAI is very much alive.
This entire hype cycle has long been completely disconnected from reality. I've watched a lot of hype waves, and I've never seen one that oscillates so wildly.
I think you're right that OpenAI isn't as hurt by DeepSeek as the mass panic would lead one to believe, but it's also true that DeepSeek exposes how blown out of proportion the initial hype waves were and how inflated the valuations are for this tech.
Meta has been demonstrating for a while that models are a commodity, not a product you can build a business on. DeepSeek proves that conclusively. OpenAI isn't finished, but they need to continue down the path they've already started and give up the idea that "getting to AGI" is a business model that doesn't require them to think about product.
Couldn’t agree more! Nobody here read the manual. The last paragraph of DeepSeek’s R1 paper:
> Software Engineering Tasks: Due to the long evaluation times, which impact the efficiency of the RL process, large-scale RL has not been applied extensively in software engineering tasks. As a result, DeepSeek-R1 has not demonstrated a huge improvement over DeepSeek-V3 on software engineering benchmarks. Future versions will address this by implementing rejection sampling on software engineering data or incorporating asynchronous evaluations during the RL process to improve efficiency.
Just based on my evaluations so far, R1 is not even an improvement on V3 in terms of real world coding problems because it gets stuck in stupid reasoning loops like whether “write C++ code to …” means it can use a C library or has to find a C++ wrapper which doesn’t exist.
Why would anyone bet? They can just short the OpenAI / MS stocks, and see in a few months if they were right or not.
OpenAI doesn't have an advantage in compute more than Google, Microsoft or someone with a few billions of $$.
Need an LLM to one-shot some complex network scripting? as of last night, o1 is still where its at.
Alex (https://alexcodes.app) also does this now btw.
The benchmark comparisons are perhaps, for now, the best way to compare reasoning prowess of R1 vs O1, since it seems pretty certain they both trained for those cases.
I think the real significance of R1 isn't the released model/weights itself, but more the paper detailing (sans training data) how to replicate it, and how effective "distillation" (i.e. generate synthetic reasoning data for SFT) can be to enhance reasoning even without using RL.
Of course cost is incomparably higher since plus has a very low limit. Which of course is a huge deal.
O1 vs R1 performance on specific non-benchmark problems is also not that relevant until people have replicated R1 and/or tried fine-tuning it with additional data. What would be interesting to see is whether (given the different usage of RL) there is any difference in how well R1 vs O1 generalize to reasoning capability over domains they were not specifically trained for. I'd expect that neither do that well, but not knowing details of what they were trained on makes it hard to test.
2. If you have GitHub Copilot, you get o1 chat also there.
I haven't seen much value with OpenAI subscription for ages.
Deepinfra is pretty cheap though as a deepseek provider.
As for deepseek, I couldn't even sign up because my email domain is not on their whitelist. To just try it out for now I don't mind the increased cost.
ChatGPT is the king of the multimodal experience still. Anthropic is a distant second, only because it lets you upload images from the clipboard and responds to them, but it can't do anything else like generate images - sometimes it will do a flowchat which is kind of cool, GPT won't do that - but will it speak to you, have tones, listen to you? no.
And in the open source side, this area has been stagnant for like 18 months. There is no cohesive multimodal experience yet. Just a couple vision models with chat capabilities and pretty pathetic GUIs to support them. You have to still do everything yourself there.
There is a huge utility for me, and many others that dont know it yet, if we could just load a couple models at once that work together seamlessly in a single seamless GUI like how ChatGPT works.