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 $$.
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.