AI & ML

TL;DR An AI agent authorization architecture should let the agent propose work without letting it define the conditions that make that work permissible. Separate the runtime, authorization and enforcement, execution credentials, evidence, and human control administration. Protect the policy inputs and deployment paths as carefully as the policy engine itself. A denied action can become […]

TL;DR The Agent Action Evidence Contract defines what a consequential AI action must record, which component is responsible for each fact, and what evidence is required before the workflow can call the action complete. It connects authenticated identity, delegated authority, approved intent, execution attempts, independently observed results, and recovery obligations. Version 0.1 is a proposed

TL;DR The Assurance Independence Model evaluates whether the mechanisms overseeing an AI agent can fail, be manipulated, or be overridden through the same dependencies as the agent itself. It examines six dimensions: model, provider, context, enforcement, evidence, and organizational independence. Version 0.1 is a proposed DTD assessment framework, not an established standard or validated certification

TL;DR LLM-as-a-Judge uses a large language model to evaluate another system’s output, proposed action, or recorded behavior against defined criteria. It can make review more scalable, expose inconsistencies, and help identify problems that rigid checks miss. Its usefulness does not make its verdict an independent source of truth. A favorable score means that a particular

TL;DR AI can assist with oversight, but the system performing the work cannot also be the organization’s only source of truth about whether that work was correct, authorized, or safe. Adding another AI reviewer does not establish independence when both systems depend on the same corrupted context, permissive identity, or unverified execution report. Independent AI

The complaint raises new questions about how a state law applies to the growing use of A.I.-generated attack ads.

TL;DR An enterprise AI exit strategy should establish what happens when a model, laboratory, cloud, or agent platform is no longer the right dependency. The answer requires more than another endpoint: preserve approved behavior, permissions, usable business records, operating access, and a realistic path through commercial and technical transition. Reversibility-Weighted AI Strategy means investing in

TL;DR AI leadership in 2029 could emerge through several mechanisms: Google converts integration into an enduring advantage; OpenAI becomes the preferred agent platform; Meta captures personal AI interactions; Chinese efficiency and open models expand qualified alternatives; or models become increasingly interchangeable while infrastructure and distribution retain value. These outcomes can overlap. My base case remains

TL;DR The strongest candidates for AI infrastructure winners sell capabilities that competing model ecosystems continue to need: accelerated computing, semiconductor manufacturing, networking, hosting, electricity, cooling, and dependable inference operations. NVIDIA, TSMC, Broadcom, cloud providers, and selected physical-infrastructure suppliers can benefit without developing the model that leads the next benchmark. That does not make them unconditional

TL;DR The most consequential AI challenger may not beat every incumbent on a general benchmark. It may make models easier to customize, satisfy a deployment requirement that other services cannot meet, become the preferred interface for personal tasks, or extend useful AI into spatial modeling and physical work. Thinking Machines, Safe Superintelligence, Mistral, World Labs,