This is the fourth in a series about how AI is changing software development, after It’s time to be right., What about juniors?, and My heuristics are wrong. What now?. It stands alone, but if you found this interesting you may also find those interesting.
I’ve been spending a lot of time thinking about the shape of the capabilities of coding agents. What they’re good at now, what they’re going to be good at. What they’re bad at now, how much of that is inherent and how much is transient. This is worth thinking about, because it’s the most important question shaping the future of software, and of software engineering. I don’t pretend to have an answer, but am coming to a conclusion that may be deeply counter-intuitive.
Coding agents are becoming very good indeed, and can build meaningful and correct software very quickly and at transformatively low cost. They have super-human abilities on some coding tasks. Of course, computer systems have had super human abilities for at least 85 years1. I think we’re going to find, as we have over those nine decades, that this new technology we’re building is vastly super-human in some areas2, and not nearly as capable as humans in others. — Read More
Daily Archives: May 21, 2026
Accelerating scientific discovery with Co-Scientist
Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental verification. The system’s design involves agents continuously generating, critiquing and refining hypotheses accelerated by scaling test-time compute. Key contributions include: (1) a multi-agent architecture with an asynchronous task execution framework for flexible compute scaling; (2) a tournament evolution process for self-improving hypotheses generation. Automated evaluations show continued benefits of test-time compute scaling, improving hypothesis quality over time. While general purpose, we focus the validation in three biomedical applications: drug repurposing, novel target discovery 1, and explaining mechanisms of anti-microbial resistance 2. Specifically, Co-Scientist helped identify new drug repurposing candidates and synergistic combination therapies for acute myeloid leukemia, which were validated through in vitro experiments. These real-world validations demonstrate the potential of Co-Scientist to accelerate scientific discovery and usher in an era of AI empowered scientists. — Read More
Geometric AI does not need attention
I got the idea for this post when I had a virtual coffee with an engineer who builds AI models for one of the big airplane builders. And he hasn’t built a model that writes your emails or hallucinates your legal documents, but his model does something different. It looks at, say, a winglet — that’s the little upturned fin at the tip of every commercial aircraft wing — and with it he is able to predict the turbulence it will generate with 98% accuracy.
Let that sit for a moment.
… I walked away from that coffee thinking about wave interference. Because turbulence is, at its core, a wave problem. Pressure waves, superimposed, creating chaotic but geometrically structured patterns. And if a model can learn those patterns in aerodynamics, the obvious question is, where else do superimposed wave systems produce instability that we desperately need to control? — Read More
Terraform Enterprise 2.0: Evolving infrastructure operations for scale
At the core of Terraform Enterprise 2.0 is support for Stacks, a new infrastructure orchestration capability that allows teams to manage collections of infrastructure as a single unit. Terraform Stacks are available on all plans based on resources under management.
As organizations scale, infrastructure evolves from isolated configurations into systems of interconnected components. Stacks reflect this shift by introducing a configuration layer that enables teams to define and manage infrastructure across environments, regions, and accounts in a consistent, repeatable way. — Read More
Rick's Cafe AI 