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 16 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
Feldman: Nvidia CUDA lost 70% of frontier AI model training market share
“I think two years ago every state of the art model was trained in a Cuda flow. And right now, Gemini is trained without Cuda. Anthropical is trained without Cuda. Open AI as strange as could. So in a one or two year period, they lost 70% share. Of training mod…”
Feldman: AI is causing irreparable damage to the SaaS business model
“I think the business of dashboarding and the business, the AI doesn't the damages doing the SAS is, I think, unreparable. You could ask your AI, build me a tool like Salesforce. 30 seconds later, you have a working tool.”
Feldman: Cerebras sales were 10x higher than Groq's at acquisition
“And we were the fastest at it, and the largest, and, you know, our sales were more than 10 times the Grox, and they paid twenty billion dollars for the number two collector.”
Feldman: Cerebras signed an OpenAI compute deal worth over $20 billion
“Remember, we did a huge deal. This is probably the largest deals in Silicon Valley history north of twenty billion dollars.”
Feldman: Chinese open-source AI models trail GPT, Anthropic, and Gemini
“They are behind in chips. But their approach was at the next level is open source models where they're producing some extraordinary models. Not as good as GPT or Anthropic or Google's Gemini, but very good.”
Feldman: Agentic AI workflows are driving CPU demand through the roof
“And so, as we do more and more AI work, and more and more agentic work, we're making more and more calls to CPUs, and therefore the demand for CPUs is through the roof.”
Feldman: AI inference is bottlenecked by data movement, causing GPU slowness
“In inference in AI, it's the exact opposite. You move a huge amount of data, all the weights, from memory to compute, and you need one calculation to generate the next word. And then you have to do it again. So all the time is dominated by the movement of data…”
Feldman: Cerebras moves weights to compute ~2,500x faster than standard GPUs
“And so the speed of moving waits to compute is about two and a half thousand times faster here than on a Wilben GP.”
Feldman: Leading AI labs paused video generation development due to compute costs
“Obviously, what follows that Is video, because a video is just a collection of images. But that takes an enormous amount of compute right now. And that's one of the reasons it's been sort of set aside by the leading labs. So unbelievably computation intensive.”
Feldman: Cerebras signed a 760-megawatt multi-year compute deal with OpenAI
“The deal is 760 megawatts, 250 megawatts in 26 on a multi-year lease. An additional 250 megawatts in 27, on a multi-year lease, and an additional in 28, a multi-year lease.”
Feldman: Tokens per second per user is the right AI speed metric
“The right metric is tokens per second per user. That that's how fast you get the first token all the way through the last token in, in your response.”
Feldman: Cerebras burned $8M monthly for 18 months before building successful chips
“And we had a, an 18 month period where we were spending eight million a month and we couldn't build them.”
Feldman: GPUs suffer from high failure rates and infant mortality
“The JPs have a huge failure rate, so I'm sure you guys have spoken about this. Infant mortality is enormous, and they fail all the time.”
Feldman: Cerebras built a 46,000 square millimeter wafer-scale chip
“The biggest chip that had ever been built before us was 800 square millimeters. 840 to be exact. And this is 46,000.”