Studying what follows.
We research AI's real-world costs: the energy, water, and emissions embedded in running these systems at scale, and the distance between what gets reported and what is real. We advise organisations that need better evidence than a sales deck.
The more consequential question is direction. The prevailing use of AI replaces human thinking. We think the higher-value target is developing human learning, not automating it. AI-driven coaching produces gains in human capability, particularly where the bottleneck has always been feedback.
We are a small, deeply analytical research group.
We are always interested in hard problems.
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.
A physicist used AI to extend the frontier of theoretical research. Experienced developers used the same generation of tools and got slower. AI does not replace expertise. It amplifies it. The danger is what happens when people skip the struggle that builds it in the first place.
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.
The prevailing use of AI, across every domain, replaces human thinking rather than developing it. Learning is the counter-case: the one place where we can prove the alternative works.
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.
Social media companies deploy algorithms that shape the information diet of billions with no mandatory proof of competency. AI widens the gap further. Every previous technology that could cause this kind of harm eventually triggered licensing. The only question is whether it happens before the serious damage or after.
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.