Research & Deep Dives
Research papers, reports, scenarios, and technical findings translated into practical implications for builders and operators.
Open Research & Deep DivesA security survey uses OpenClaw to argue that agent security is not a patch list but a product, architecture, ecosystem, and governance problem. The framework connects individual exploits to the permissions and incentives built into the surrounding system.
DSPy argues that language-model systems should be written as modular programs and optimized by compilers, not assembled from fragile hand-written prompt strings. The approach replaces manual prompt tweaking with explicit objectives, evaluation data, and systematic search.
This paper argues that generative AI may not flatten performance differences in knowledge work. It may widen them, especially when star employees have the judgment, autonomy, and reputation to turn AI into portable bargaining power.
This paper argues that AI compute is not a simple commodity input. Chip architecture, energy use, software, training workloads, inference workloads, and policy all change what one more unit of compute really means.
An enterprise AI cost paper proposes a dedicated operating model because generative AI spend is driven by usage, ownership, governance, and workflow design. It connects unit economics to accountability for where, why, and by whom inference is consumed.
A Strategy and Leadership paper argues that CEOs lead AI transformation through generalist, expert, or disruptive operating models, each with different risks. The framework helps leaders choose where expertise, authority, and organizational disruption should sit.
A broad survey argues that the hard part of useful AI systems is no longer prompt wording but deciding what information reaches the model, when, and under what constraints. That makes retrieval, memory, permissions, timing, and context assembly a systems discipline.
A broad survey frames agentic AI as reasoning through action: planning, tool use, search, memory, feedback, and coordination across time. The framework clarifies why runtime design matters as much as model intelligence.