The Ledger
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Biderman: AI-native companies will amass trillions of internal tokens within 18 months
“In 18 months, many companies would have maybe trillions of tokens, which of internal company data, proprietary data. I'm talking about like maybe trillions. It sounds exaggerated, but I don't think it's an impossibility if they're really AI native.”
Biderman: Hard engineering tasks will require test-time gradient updates
“We think that eventually part of the solution for very hard tasks in, in science and engineering and defense and all that stuff will involve some form of gradient based updates during during doing these long horizon tasks.”
Biderman: In 18 months, data scale will require weight-based learning
“Other parts of it are bets that in 18 months from now, the scale of the data will require the methods that we know from pre-training work.”
Biderman: PC hardware will soon run near-trillion-parameter models locally
“And in the long, long term, I do think these things will actually run on people's devices, and we're seeing right now the new hardware on personal computers is already, ah, you know, soon approaching the ability to run inference on close to trillion parameters…”
Biderman: Engram trains models to decide what to memorize vs keep in notes
“The way to work on it is to train models both, to train models to manage it themselves, and that's an active area for us. Have the model know, like, without any explicit supervision signal to determine this kind of stuff I can pull from my brain, and that kind…”
Biderman: Manually partitioning LLM memory vs retrieval becomes unmanageable whack-a-mole
“And now the thing is, if you start manually, heuristically saying this is in, this is out, then it becomes a whack-a-mole. Every, every person in every enterprise has different data, and you can really very easily pick and choose what goes in and what goes out…”
Biderman: AI models must learn to autonomously filter out erroneous user feedback
“Increasingly the models will get better, and increasingly they'll know more things than we do, so the model in some way has to learn and understand and kind of, like, discern what, which feedback is valuable and which feedback should be ignored.”
Biderman: AI efficiency and frontier intelligence cannot be decoupled
“The point for me is, the principle is, any kind of, like, efficiency and intelligence, they cannot really be decoupled. Sometimes people think if you're building something that's more efficient, that can save you dollars, therefore you're not in the premium ca…”
Biderman: Semi-supervised learning will become super crucial again
“My PhD was focusing on On, on a field that's not super in vogue today, but I think will become super crucial again, which is semi supervised learning”
Biderman: Model accuracy will still degrade at 10M context window scale
“But two is like, for the agentic tasks of 18 months from now, inside those major repositories of knowledge, and asking the models more and more things in underspecified ways, I suspect that the accuracy of the models would go down. The phenomenon of context fr…”
Biderman: Israeli culture gives people multiple sequential shots on goal
“Israel as a culture is a place where you can basically, you get multiple shots at goal. If you're not the best in high school you still might have a good position in the military. And if you're really good, more doors open up for you for university. And even i…”
Biderman: Current LLMs lack chef-like intuition and rely on robotic reading
“Current LLMs are like coming into the kitchen first time, every time, reading the textbook, cooking the dish, measuring everything, but they don't have the intuition of a chef that's pinching salt and kneading dough and things like this.”
Biderman: Harmless enterprise queries on frontier models cost thousands of dollars
“And now you can solve these tasks with frontier models and compaction. And when you ask them to do so, they will consume thousands of dollars for queries that we think are harmless. That every employee in the company would be able to answer.”
Biderman: Engram aims to give every user personalized, continually-learning weights
“Our ambition in the long term, ah, is that. Every person has a model, or a part of the model, or a set of weights that, that represents their knowledge, their expertise, learns from them, that the more time they spend with the model, the better it gets for the…”
Biderman: AI solutions will rely on model routing, not single monolithic models
“So I think routing will be part of the solution there for sure, and I think Many people, not just myself, say this solution is multi-modal. It's not Engram taking over. There's one model, and you teach it things, and you can close Stargate. That's not our appr…”
Biderman: Personalized AI adapters require hot-swapping millions of endpoints at inference
“And if you truly believe that we can get to the level where we have those kinds of parameter efficient adapters for every person and team, you suddenly think about deployments that involve millions of different endpoints stored in different places that need to…”
Biderman: Processing a Wikipedia article in Llama 70B consumes 80GB HBM
“If you take a Lama, a 70 B model, and you load one article from Wikipedia, which is a few tens of kilobytes, and you have the model read this The brain state of the model when reading this few tens of kilobytes is like, 80 gigabytes. 80 gigabytes on, on the HB…”