Research & Deep Dives
Research papers, reports, scenarios, and technical findings translated into practical implications for builders and operators.
Open Research & Deep DivesA scaling paper argues that larger models win partly because frequent tasks stop overwriting rare-task features. The proposed mechanism links parameter scale to reduced interference and better retention of infrequent capabilities over time.
Leopold Aschenbrenner's Situational Awareness argues that AGI by 2027, superintelligence, trillion-dollar clusters, lab security, and government mobilization follow from the same trendlines. Reading it as a unified acceleration thesis makes its assumptions and policy implications easier to test.
An essay on Pluralis argues that decentralized AI needs scalable training, defensible monetization, and governance for intelligence outside the major labs. The piece tests whether distributed infrastructure can create a credible alternative to centralized model ownership.
Life-Harness argues that many agent failures come from the runtime interface, not the model, and shows frozen models improving across tasks when the harness changes. It shifts attention toward interfaces, tools, feedback, and execution design.
A 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.