Eduardo Aguilar Pelaez

The OpenAI/Hugging Face breach and Anthropic's four disclosed incidents traced as capability-union failures: components individually authorised by reasonable people, the combination never checked.

An interactive version of the Anthropic Institute's 2026 model of AI and the US economy, exposing the three parameters their own explorer holds fixed: how easily capital can be added, how readily work substitutes, and how much AI use leaves the paid econo…

The Anthropic Institute's model of AI and the US economy does not say wages fall.

A corpus of rules written by authors with short tenure grows without bound, because deletion is a dominated strategy for every author and no volume of instruction can make it otherwise: an agent instance is scored within the session and bears none of the…

Vague regulation is not light touch.

Gunpowder was on sale to everyone; what varied was the capacity to absorb it.

A mud hut is a good building, and it has no datasheet.

Definitions as type-checked Lean code: honesty as a data structure, the Munchhausen trilemma resolved by declared primitives, and word-vector analogies promoted or rejected by a proof kernel.

Model your requirements in Lean and a one-line change that contradicts a rule written long ago surfaces as a failed build, in seconds, not a quarter later in an incident review.

Prose is the wrong container for a requirement, and it fails slowly, silently, and totally.

Infrastructure as Code took the human out of reconciling servers by hand.

The companion essay closed the agentic control loop inside the factory.

Toyota cut the cost of the car by fixing the process, not the part.

Simmons and Simmons identified eight data protection risks that agentic AI creates for businesses.

Compliance does not only have a workflow problem.

Law is like holding water with cupped hands.

A verified theorem is only as trustworthy as the translation layer that produced it.

Applying Lean 4 formalisation to Anthropic's constitution reveals 19 free variables, structural impossibilities in the priority ordering and honesty properties, and the trade-off surface Claude must navigate.

A synthesis of four essays on formal methods, legal reasoning, and AI alignment into a methodology: formalise properties, check mutual satisfiability, design processes for negotiating trade-offs, and measure alignment margin.

Introducing alignment margin: the maximum perturbation a system can absorb before any specified alignment property is violated.

When outcome properties conflict, shift to process specification.

Attempting to formalise English divorce law in Lean 4 reveals an impossibility result: no fixed function can simultaneously guarantee that contributions always matter, that needs are always met, and that identical cases produce identical outcomes.

The O-1A visa criteria formalise cleanly in Lean 4: discrete categories, explicit threshold, binary predicates, independence from ordering.

The case for bounded, provably safe AI agents, using the OpenClaw crisis as a case study.

Formal methods from seL4 and CHERI applied to AI agents.

AI-generated Lean 4 proofs for capability bounds.

The OpenClaw security crisis validates the case for formal capability verification.

As AI systems consume an ever-larger share of global electricity, the allocation of finite energy resources between sustaining human life and powering computational intelligence becomes a defining challenge.

Google DeepMind's architectural approach to prompt injection.

Five questions that actually matter when evaluating an AI vendor's security posture.

Proposing Formal Capability Verification as a complement to alignment.

Applying verification to legal technology systems.