why aren't all 14 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
Meta raises tens of billions in off-balance-sheet debt for data centers
“You have this, sort of, offloading of debt from big companies, for example, Meta, that raises tens of billions of dollars to fuel its data center ambitions, but that doesn't sit on Meta's balance sheet.”
Assertion Supported
Pesenti in 2020: Multi-million-dollar AI training runs are unsustainable for Facebook
“Where it becomes millions is when you do training runs. So some of the training runs in the most advanced system that it comes from our company or other companies out there are starting to be extremely expensive. Yeah. Like you can look at one run in the scale…”
Assertion Supported
Evans: Google and Meta delayed LLMs in 2022 due to high error rates
“This is why Google and Meta didn't launch their own LLMs in twenty-twenty-two when they had them as well, because they looked at them and said, well, they're wrong too much.”
Assertion Supported
Masad: Mark Zuckerberg is reversing identity-based hiring at Meta
“You see Zuck, you know sort of bringing fast Facebook back, or meta back to the hacker culture and reversing a lot of the sort of identity-based hiring and identity-based sort of promotions and all of that stuff.”
Assertion Supported
Borgman: Presto is mostly Facebook internal, while Trino is the mainstream branch
“They started as identical copies, but the code bases have diverged quite a bit. And today Presto is really just used by Facebook. So it's sort of like their own private branch in a way used by a small number of people. And Trino has become the mainstream commu…”
Assertion Supported
Marcus: Facebook M relies mostly on human operators rather than AI
“Facebook's new M service, which they have not rolled out at scale has humans on the back end. There's a little bit of AI in there, but it's mostly, ah, human beings, which is why they haven't rolled it out for a billion customers. They don't have enough human …”
Prediction Held up
LeCun predicted in 2014 that AI would mediate human social interactions
“And started thinking about the next 10 years. What are the next 10 years going to be for social interactions? And it's pretty obvious to a lot of people that a lot of our interactions you know, with our friends and a lot of interactions with the digital world …”
Assertion Supported
Evans: Big Tech data center spending will exceed $300B this year
“Google, Meta AWS, not Amazon overall, AWS only, and Microsoft spent about two hundred twenty billion dollars building data centers last year, and will spend about 300, maybe over 300 this year, depending on where their numbers come out.”
Assertion Supported
Pesenti: Facebook uses embedding algorithms to automatically match misleading content
“And then when they fly content that should be at least, you know, shown as a misleading, then we have this very advanced similarity algorithm that kind of like look at an embedding of the of the content itself. Again, it can be the image itself or a multimodal…”
Assertion Supported
Zeghidour: Meta's LLaMA and DINO models were developed in Paris
“Lama was started in Paris. Dino, which is the most groundbreaking vision work from Facebook was developed in Paris.”
Assertion Supported
Falcon: Lightning AI hired core Meta PyTorch leads
“We have an in-house PyTorch team, so we hired a lot of the core leads from Meta”
Assertion Supported
Pesenti: Facebook trains single XLM-R models across 100 languages simultaneously
“We try to learn a hundred languages at the same time in a single model using transformer architecture”
Assertion Supported
Matt Turck: Ada has over 300 customers including Meta, Verizon, and Shopify
“Today, the company has over 300 customers using the platform, including Meta, Verizon, and Shopify.”
Assertion Supported
Facebook built Hive to provide a SQL interface on Hadoop
“Facebook built Hive, right, because they needed a tool to sit on top of Hadoop, you know, to allow their business analysts to kind of sequel interface to this big data platform.”