“What if the ‘open-source’ AI model from DeepSeek that everyone’s raving about… isn’t really open at all?”
DeepSeek’s low-cost, high-performance AI model may look like a gift to the AI world, but here’s what they’re not telling you. Despite the model being released under an MIT license and promising high performance at just $2 per million tokens, the reality behind DeepSeek’s ‘open-source’ AI is much more complex and strategic than it seems.
DeepSeek “Open-Weight” Model: Not Truly Open-Source
DeepSeek’s model may be available for download, but it’s important to understand what that really means. What they’ve released are just the weights—not the training data, not the training scripts, and definitely not the low-level code that powers the model’s incredible inference efficiency. That means while you can use the model, you can’t study how it was built, or reproduce its results.
This mirrors what Meta did with LLaMA. You can run it, but you can’t replicate the full pipeline. The label “open-source” gives a misleading impression of transparency and accessibility. In truth, these are curated assets, not community-owned blueprints.
DeepSeek Elite Engineering: Performance Not For the Masses
DeepSeek’s performance gains aren’t magic—they’re the result of highly specialized, low-level optimization. The team had to work from the ground up, customizing for specific hardware like H800 GPUs in China, and even tweaking assembly code to squeeze out every bit of efficiency.
This isn’t something you can download and apply overnight. These results came from deep integration of model design with hardware, the kind of co-design only a highly skilled and resourced team can achieve. So while the output is impressive, replicating it will be far out of reach for most.
DeepSeek $2 Pricing: Lower Prices Don’t Mean Lower Costs
The $2 per million tokens pricing is eye-catching, especially compared to OpenAI’s pricing. But it raises a question: is it truly sustainable?
Just a few months ago, OpenAI was also subsidizing usage below actual cost to win market share. DeepSeek seems to be playing the same game. Their GPUs, the H800s, are only nominally cheaper than NVIDIA H100s, and due to demand in China, often sell for even more on the open market. Labor savings may be minimal as well, given the caliber of the team.
In the end, this feels more like a commercial strategy than a technical leap. It’s about competing on market presence, not redefining what’s possible.
The Illusion of Openness in DeepSeek’s Model
Even though DeepSeek has adopted an MIT license, which allows for broad usage including commercial applications—they still haven’t revealed the actual building blocks. The lack of training data, scripts, and key infrastructure code means no one can truly replicate what they’ve done.
Yes, you can run it. No, you cannot fully understand or rebuild it. That’s a crucial distinction in a world where “open” is becoming more about branding than about access.
The Bigger Picture of DeepSeek’s Impact on AI
While DeepSeek’s model is exciting and shows what’s possible with tight optimization, it doesn’t represent a tectonic shift in the AI landscape. We’re still looking at a domain dominated by a few highly resourced players engaging in a race to outperform and outprice one another.
For now, it’s less about democratization and more about domination. We’re witnessing a chess match between giants—not a revolution for the rest of us.
