An MIT graduate student has used OpenAI's GPT-5.6 Sol, connected to its Codex coding tool, to automate routine measurements on superconducting quantum computing chips, freeing her to focus on experiment design and analysis.

In a case study published on 8 September, OpenAI said Beatriz Yankelevich, of MIT's Engineering Quantum Systems Group, linked Codex to laboratory software controlling an uncalibrated six-qubit chip. Given measurement-specific instructions and the chip's design targets, the model chose measurement parameters, operated the hardware and analysed results, refining its approach or saving data for the next step. OpenAI said the system completed standard calibration sequences with little intervention when signals were clear, but took longer and sometimes needed guidance from Yankelevich when signals were weak or noisy.

Yankelevich said she could now leave agents running measurements overnight or while working elsewhere in the lab, checking progress from her phone. She said she had built infrastructure allowing agents to work across measurement, theory and chip design tasks, letting her spend more time on higher-level work such as interpreting results and planning next steps.

OpenAI said chip calibration, which can take researchers several days per chip, was a natural fit for AI agents given its combination of software control, repeated measurement and adaptive decision-making, though interpreting ambiguous physical results remained a challenge for current models.

Swiss Cheese Defences- Identity fraud goes industrial and off-the-shelf
Ofer distinguishes between two current attack patterns: highly sophisticated, professionally coordinated deepfake and injection attacks designed to beat detection outright, and a much larger volume of lower-effort, high-scale attempts that rely on bombarding systems rather than disguising themselves well. He argues the real story right now is industrialisation of scale rather than uniform improvement in quality, though both are accelerating in parallel. The conversation covers why agentic AI is opening a new front in identity fraud, particularly the unresolved problem of tying an AI agent’s identity back to the human who deployed it and the permissions it holds. Ofer is candid about the current state of agent defences, describing them as underdeveloped and easy to hijack or poison, comparing the current state of play to Swiss cheese. He also discusses cross-industry fraud detection, including how signals, rather than raw data, are now being shared across platforms including AU10TIX and Reality Defender to surface fraud rings invisible to any single organisation. The conversation also covers explainability as one of AI’s most underdeveloped capabilities, with Ofer arguing that flagging a session as fraudulent without a credible, defensible reason will increasingly run into regulatory and practical limits. Elsewhere, the discussion covers credential laundering and the manufactured construction of fake digital histories and footprints, and the shift toward digital ID wallets that Ofer believes will make physical document fraud increasingly rare. He closes on what he calls “agentic avatars,” AI systems capable of holding a full visual and verbal conversation on someone’s behalf, and why he expects identity verification to have to become genuinely immersive across every form of media as a result.
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