why aren't all 9 resolved? a statement only gets an assessment when the public
record can support or contradict it. opinions and what-ifs never can, and 0 checkable
ones are still open, waiting for their date. predictions held up or didn't;
assertions are supported or contradicted. on every card:
▮▮▮▮▮ certainty ·
▮▮▮▮▮ debate potential. speakers are clickable
Assertion Supported
Feldman says top Chinese open-source AI models lag closed-source models slightly
“This is made doubly worse by some of the best open source models were made by Chinese companies. And they are exceptionally good models. Kimi Ketu, Deep Seek. When the GLM, these are extraordinarily good models. They're not quite as good as the closed source m…”
Assertion Supported
Siddharth: Chinese open-source AI models like DeepSeek and Qwen are state-of-the-art
“I think it's very impressive, like the progress that they've made in open source with DeepSeek Kimi Ketu, Kuen. These models are state of the art.”
Assertion Supported
Ross: DeepSeek developed its models by distilling OpenAI
“They distilled the OpenAI model.”
Assertion Supported
Ross: DeepSeek's breakthrough was an algorithmic gain in data generation
“There was an algorithmic improvement on that. And the algorithmic improvement, as I explained, you know, is this seemingly silly thing where they just wrote the answer in a box and then they knew what to look for rather than having to have a human being check …”
Assertion Supported
Ross: DeepSeek was created by a hedge fund, not the state directly
“Remember deep seek is a real, I mean, it's a hedge fund. They're doing this themselves and they're just influenced by the CCP”
Assertion Supported
Ross: OpenAI does not need to distill DeepSeek because OpenAI remains superior
“They don't need to because they're actually better still. They're a little bit better. So they could, but why would they?”
Assertion Supported
Ross: DeepSeek scraped OpenAI data while developing unique RL techniques
“And all of that said, they did a lot of really innovative things. So that's what makes it so complicated because on the one hand, they kind of just scraped the OpenAI model. On the other hand, they came up with some unique reinforcement learning techniques,”
Assertion Supported
Feldman: Running DeepSeek 671B on Standard SRAM Chips Requires Up to 8,000 Chips
“If you build a normal size chip with SRAM and you want to do a four hundred billion parameter model and inference, you might need 4000 chips. Or if you want to do a DeepSeq six 71, you might need six or 8000 chips.”
Prediction Held up
Ross: AI companies will use massive GPU clusters to generate synthetic data
“What you're going to see now is now that everyone has seen this deep seek architecture, they're going to go great. I have hundreds of thousands of GPUs. I'm now going to use a lot of them to create a lot of synthetic data. And then I'm going to train the bejes…”