Dan Biderman, co-founder and CEO of Engram, discusses the exponential data explosion driven by AI agents generating code, artifacts, and documentation inside companies.
“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.”
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More from Dan Biderman
PredictionNot checkable as stated
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.”
Dan BidermanJul 13, 2026▶ 21:57The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
PredictionNot checkable as stated
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.”
Dan BidermanJul 13, 2026▶ 26:55The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
PredictionOpen · timeframe Jul 2031
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…”
Dan BidermanJul 13, 2026▶ 28:12The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
Disclosure
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…”
Dan BidermanJul 13, 2026▶ 30:56The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
Insight
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…”
Dan BidermanJul 13, 2026▶ 31:47The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
PredictionNot checkable as stated
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.”
Dan BidermanJul 13, 2026▶ 33:54The AI Memory Problem: Why Long Context Isn’t Enough — Dan Biderman, Engram Co-founder & CEO
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