Studying what follows.
Artificial intelligence is rewriting the operating assumptions of energy systems, labour markets, and institutional decision-making, often faster than the people responsible for those systems can track. We study the second-order effects: the questions that surface after the model is deployed, the costs that don't appear on the invoice, the feedback loops that only become visible at scale.
Our work spans three areas. We analyse the material footprint of AI: the energy, water, and emissions embedded in training and inference at scale, and the gap between what companies report and what the physics demands. We examine how AI reshapes governance and decision-making, from automated risk assessment to the quiet displacement of human judgement in high-stakes contexts. And we investigate the economic ripple effects: how AI adoption redistributes work, concentrates leverage, and alters the structure of industries before the policy conversation catches up.
We work with organisations facing these questions. The ones who suspect the standard narrative is incomplete, and want sharper answers before they commit.
A Canadian startup is etching neural networks directly into silicon. The implications go further than the benchmarks suggest.
Datacenter operators report efficiency gains. The aggregate numbers tell a different story.
Most AI strategies optimise for replacing human expertise. The higher-value target is accelerating how people develop expertise. When judgment is what makes AI useful, the real bottleneck is learning, not automation.
Generative video is improving faster than the models built to detect it. The more resilient approach works upstream: cryptographic attestation at the hardware level, proving footage was optically captured rather than computationally produced.
Inference Research grew out of a background that spans particle physics, climate science, and carbon dioxide removal. The thread connecting these fields is the same one that runs through this work: complex systems behave in ways that reward careful observation over confident prediction.
The research group exists because the conversation about AI's real-world impact needs more rigour and fewer press releases. We bring a physical-sciences perspective to questions that are too often framed as purely technical or purely political.
We're always interested in hard problems.
hello@inferenceresearch.com