The Ledger
Every statement that passed quotation and attribution checks. Mix any filter with any other: certainty 1/5, debate potential 5/5, or both at once.
why aren't all 17 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
Lambert: Chinese open AI models currently do not contain backdoors
“Like, you can't prove that the models aren't doing certain backdoors, where I'm fairly certain they definitely aren't now.”
Lambert: AI progress will yield steady improvements rather than rapid singularity
“I think these researchers are going to grind out improvements for multiple years, but never in a way that results in this kind of accelerating well that we get drawn into.”
Lambert: OLMo 3 32B base model matches Qwen 2.5 32B quality
“This base model is similar in quality to the best available, which is like Quinn's 2.5, 32 B is, was still the best base model.”
Lambert: OLMo 3 7B outperforms Meta's Llama 3.1 8B in internal tests
“And I just think of this cause like Lama 3.1 AP is one of the most used models and hugging base of all time. And this should be better. We're, In our measurements, we see it as being better than Llama.”
Lambert: 80% of a16z's open-model portfolio startups use Alibaba's Qwen
“80% of companies building with open models are using Quinn, which is like 16 to 24% of his portfolio, which is still a lot.”
Lambert: Chinese companies with $1B+ valuations routinely pirate SaaS software
“Mediumly large, like billion dollar plus valuation companies in China will just like pirate SaaS software.”
Lambert: Best open-license AI models near the frontier in 2025 were Chinese
“The models that are from closest to the frontier in performance with good license all happened to be Chinese models throughout the year for this case.”
Lambert: Big tech will realize 95-98% of LLM potential by 2030
“I think that how I describe it is that big tech has all collectively realized that these language models plus scaffolding is going to unlock absolutely incredible value. And I have very high probability, barring extreme geopolitical situations, that big tech E…”
Lambert: As AI funding grows, fewer researchers speak in public
“There's so much money in AI and it only becomes increasingly so that the amount of people that can talk about these things in public and educate and get more people involved by spreading knowledge is ever smaller.”
Lambert: OLMo 3 models are the best open models outside Qwen 3
“I would say in post training where The best models that don't start with Quinn three and we're like reasonable to say that they are comparable to Quinn three, like on some benchmarks would beat them on some benchmarks. They're way ahead.”
Lambert: Alibaba's Qwen 3 VL vision model is a superior text model
“They released these Quinn three VL, their vision models. And like on text only benchmarks, it's way better than the models they released in April. So it's like okay, like that's the new baseline. And most people don't know about it because they think it's just…”
Lambert predicts more US labs will release open AI models
“If you look at this podcast in the coming months, I do think there's going to be, look like there's a lot more labs in the U S participating.”
Lambert: Hugging Face outcompeted AI2's AllenNLP library
“It was the main competitor to Hugging Face Transformers. And they ultimately outcompeted AI two as the thing that people use for that because they had very different model and amount of support.”
Lambert: Long-context extension is essential for reasoning AI models
“Three is long context extension, which is absolutely essential for these reasoning models because they generate so many intermediate tokens before sharing an answer with you.”
Lambert: Larger pre-trained base models are easier to improve with RL
“A better base model and a bigger base model is much easier to improve with RL.”
Lambert: Kernel differences between vLLM and Hugging Face cause RL numerical instability
“VLLM and HuggingFace use different kernels to do the actual internal computation of the model. So these kernels are the things that make things like vLLM really fast. But these things, this then results in subtle numerical differences between the completions t…”