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Scientific Articles
There will be a scientific theory of deep learning
Introducing learning mechanics with Jamie Simon and Daniel Kunin (UC Berkeley)
Apr 24 •
1:33:22
Attention as an art form
Watch now | Art of Being Human #2 with Adam Robbert
Apr 11 • Ashley Zhang and Adam Robbert
1:03:02
Love, Labour, and Language
AI won’t change what it means to be human. We need to prepare for what's actually coming.
Published on Lawless Tech • Apr 4
March 2026
Software as bonsai
What if software engineering becomes more like gardening?
Published on More Reasonable • Mar 20
10 theses on attention
Reflections from Art of Being Human #2 with philosopher Adam Robbert
Mar 14 • Ashley Zhang
56
Introducing Imbue's Substack
A space to think together about the technological future we want and how to create it
Mar 11 • Ashley Zhang
23
January 2026
A more radical Imbue
Our experiment in working with radical agency, control, and choice
Jan 30 • Josh Albrecht
6
Empowering humans in the age of AI
We founded Imbue in 2021 to build an AGI future where humans remain at the helm, shaping powerful AI systems rather than being subordinated to them.
Jan 18 • Kanjun Qiu
8
November 2025
Malleable software and human agency
A conversation with Geoffrey Litt, design engineer at Notion, on shaping software like clay
Nov 14, 2025 • Imbue
2
September 2025
Choices and Knives
The collective future we want is a future of kitchens, not vending machines.
Sep 21, 2025 • glenn mcdonald
5
August 2025
From lawless spaces to true liberty: rethinking AI's role in society
Who will actually hold power in the age of intelligent machines?
Aug 5, 2025 • Kanjun Qiu and Matt Boulos
10
May 2025
AAI
The missing thing in Artificial Intelligence is not generality, it's adaptation.
May 30, 2025 • glenn mcdonald
Idea Tools for Participatory Intelligence
We need tools that are predicated on our rights, dedicated to amplifying our creative capacity, and judged by how they help us improve our world.
May 16, 2025 • glenn mcdonald
September 2024
Rylan Schaeffer, Stanford: Investigating emergent abilities of LLMs
Rylan Schaeffer is a PhD student at Stanford studying the engineering, science, and mathematics of intelligence.
Sep 18, 2024 • Imbue
July 2024
Ari Morcos, DatologyAI: Leveraging data to democratize model training
Ari Morcos is the CEO of DatologyAI, which makes training deep learning models more performant and efficient by intervening on training data.
Jul 11, 2024 • Imbue
May 2024
Percy Liang, Stanford: How foundation models work
Percy Liang is an associate professor of computer science and statistics at Stanford.
May 9, 2024 • Imbue
March 2024
Seth Lazar, Australian National University: The political philosophy of AI
Seth Lazar is a professor of philosophy at the Australian National University, where he leads the Machine Intelligence and Normative Theory (MINT) Lab.
Mar 12, 2024 • Imbue
August 2023
Tri Dao, Stanford: FlashAttention and efficient training
Tri Dao is a PhD student at Stanford, co-advised by Stefano Ermon and Chris Re.
Aug 9, 2023 • Imbue
June 2023
Jamie Simon, UC Berkeley: Theoretical principles for deep neural networks
Jamie Simon is a fourth-year physics Ph.D.
Jun 22, 2023 • Imbue
March 2023
Bill Thompson, UC Berkeley: How cultural evolution shapes knowledge acquisition
Bill Thompson is a cognitive scientist and assistant professor at UC Berkeley.
Mar 29, 2023 • Imbue
Ben Eysenbach, CMU: Designing simpler, more principled RL algorithms
Ben Eysenbach is a Ph.D.
Mar 22, 2023 • Imbue
Jim Fan, NVIDIA: Foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant
Jim Fan is a research scientist at NVIDIA and got his PhD at Stanford under Fei-Fei Li.
Mar 8, 2023 • Imbue
Sergey Levine, UC Berkeley: Bottlenecks to generalization in reinforcement learning
Also: why simulation is doomed to succeed, and how to pick good research problems
Mar 1, 2023 • Imbue
February 2023
Noam Brown, FAIR: Building human-level AIs in poker and Diplomacy
Noam Brown is a research scientist at FAIR.
Feb 9, 2023 • Imbue