There’s a lot of buzz around DeepSeek R1 lately, with claims that this reasoning model took just $5.6M to develop. I’ve been tracking DeepSeek since signing up for their API last year, and the real story is quite different.
Let’s get real about DeepSeek’s timeline. While they might say R1 took three months, this model builds on years of previous work. Last year when I first tested their API, the coding capabilities were frankly awful. The recent improvements didn’t materialize out of thin air.
What’s actually happening here? DeepSeek has substantial resources at their disposal. They stockpiled GPUs before the embargo hit, and they’re backed by serious hedge fund money. This isn’t a story of a scrappy startup doing more with less – it’s about having the right tools and funding at the right time.
The model’s recent performance gains are impressive, particularly in areas like mathematical reasoning and coding challenges. Early benchmarks show it competing with OpenAI’s offerings, as covered in our previous analysis of O3-mini’s performance against DeepSeek R1 (https://adam.holter.com/o3-mini-beats-deepseek-r1-on-hallucination-tests-by-18x/).
However, we should view these developments in context. DeepSeek wasn’t even in the conversation a few months ago. Their sudden rise to prominence stems from years of background development and substantial financial backing, not from a three-month miracle.
The lesson here? AI development is intensive work that requires time, resources, and infrastructure. Companies don’t just pop up with competitive models overnight – they build on foundations of previous work and substantial investment.
