At this point, reading through the latest splashy pledge to use AI for the good of the environment feels like downing a nonalcoholic beer. Admittedly, I'm suspicious from the onset. How can this be good?
Turing Award winner Richard Sutton reaffirms that raw compute alone isn't enough for intelligence and that fundamentally new learning methods are needed. Ex-OpenAI researcher Tworek agrees, saying blind transformer scaling "has reward hacked brains of a whole generation of scientists." A growing wave of startups is already betting on exactly this gap.
As systems biology efforts heat up to map biology at higher resolution, greater breadth and more modalities, Fabian Theis is pushing the frontier from mapping to predicting to controlling biology
Fabian Theis has been one of the key players in the various iterations of the Virtual Cell concept and initiatives.
A sales representative asks an AI agent for customer pricing, and the agent pulls data from multiple sources: restricted pricing systems, internal communications, and archived quotes. The output looks accurate to the agent, so a second agent uses the data to draft the proposal for the sales rep. No human checks either handoff.
Legora can’t read a single trace of what its agent does for a lawyer. The data belongs to the law firm’s clients. How do you build and test an agent when you can’t see its work?
Imagine your company just discovered electricity. At first only a few machines run on it, and nobody worries about efficiency — consumption is low and the value is obvious. But once electricity becomes infrastructure, every process depends on it, and the question changes from “can we use it?” to “how efficiently are we using it?”
How Leash Bio tamed DELs, the assay that traumatizes everyone, and used the data to catch cheating models. And why that same data is perfect for AI agents learning to think like chemists.
Our first robotics pod with Dyna’s Chief Scientist on their big bet on World Action Models & the Next Frontier of Physical AI
From the Bitter Lesson of AI scaling to the unsolved mysteries of protein folding, Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido are rethinking what it takes to build AI that truly understands biology.
A special Science pod and Engineering pod crossover.. with Forward Deployed Engineering kicker!
Ben Affleck is going viral for his deep knowledge of AI, from neural networks and transformers to open weights. The actor, who sold his AI filmmaking startup to Netflix earlier this year, is proving he's more than just a Hollywood star.
In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent.
Enterprise AI is no longer a future ambition. It is in full operational flight. Model capabilities are advancing faster than most organizations can absorb, while the cost of performance continues to fall. Globally, AI investment is set to reach $2.5 trillion in 2026, up 44% from the previous year.
Guest Post: In science, thinking has gotten cheap but doing has not. This asymmetry is reshaping how research companies operate, largely inconspicuously.
Radical Numerics is using biological chain-of-thought and multimodal perception to keep up with the bio-defense arms race, design new genomes and gain insights into biology itself.
We talked to Google’s Oscar winning “Giganerd” about automating science, solving climate change, and how future generations can contribute to science in the age of superintelligent AI
Our 15-month investigation into death and surveillance along the US-Mexico border began with a simple question: Why did so many people die near government surveillance towers meant to help track and apprehend them? This story is part of Dying on Camera, a collaboration between MIT Technology Review and Times of San Diego.
Hugging Face published its open-source model observatory report for January–August 2026. Hub data shows Chinese labs released the largest open-source models by parameter count in most months, with Chinese monthly peaks between 754 billion and 2.78 trillion parameters, while US models stayed below 130 billion in five of seven months.
Hugging Face's blog argues AI's next real constraint is GPU utilisation, rather than intelligence. GPUs are billed by calendar hours but produce only in compute hours, mirroring the cost structure of grounded aircraft.
... government of the people, by the people, for the people ... — Abraham Lincoln, Gettysburg Address (1863) The cost of AI is dropping rapidly. GPT-4-class capabilities cost roughly $30 per million tokens in early 2023; today the same runs under $1 , and some providers are pushing costs below $0.10 .
The first step towards recursive self improvement