research_blog.post
The battle between GPT, Claude, Gemini, and other frontier models isn't just about better benchmarks—it's about whose values get embedded in the future of intelligence. This piece examines the competitive dynamics, the metrics that drive development, and whether the real battle should be collaboration on alignment instead.
Currently, the primary mode of competition is a "war of benchmarks," where models vie for the top spot on leaderboards like MMLU and HumanEval. This hyper-focus on quantitative metrics can lead to "teaching to the test," where models become optimized for benchmarks but fail to generalize their reasoning capabilities to novel, real-world problems. Furthermore, the secrecy and intense competition between labs can stifle the open collaboration needed to solve the critical challenge of AI safety.
This post argues for a paradigm shift from neural warfare to "cooperative alignment." We propose a framework where competing labs can collaborate on safety research without compromising their proprietary architectures, such as through shared red-teaming exercises and a common, open-source library of alignment techniques. The ultimate winner of the AI race shouldn't be a single company, but humanity itself, through the creation of safe, beneficial, and aligned artificial intelligence.