Why I Founded Metanthropic
Today marks a significant milestone in my career and research. I founded Metanthropic AI because I believe the current paradigm of AI development faces a critical Alignment Gap.
We are currently building engines of immense power, but treating their steering mechanisms as an afterthought. Safety is too often treated as a post-training patch—a layer of "Reinforcement Learning from Human Feedback" (RLHF) slapped onto a completed model like a sticker on a rocket engine.
My thesis is straightforward but demanding: True Artificial General Intelligence (AGI) cannot be built by simply scaling parameters; it must be architected for understanding.
For the past year, I have been working to establish an independent lab that operates differently. We are not interested in incremental improvements. We are architecting novel large-scale models from scratch, integrating interpretability and safety objectives directly into the pre-training compute loop.
The Core Thesis
Under my leadership, Metanthropic operates on a single defining principle: Capability and Safety are not opposing forces.
In traditional engineering, safety often comes at the cost of speed (think of nuclear regulations). In Intelligence Engineering, I argue the opposite: Safe systems are more capable systems.
A model that hallucinates is unsafe and incompetent. A model that deceives is misaligned and unreliable. By solving for safety (robustness, truthfulness, stability), we are inherently solving for capability.
The Axiom: "The path to AGI requires a deep, empirical understanding of increasingly general systems."
Our Research Agenda
Our work pushes the frontier of deep learning across three integrated pillars. We are moving beyond standard transformer implementations to architect next-generation models designed for high efficiency and massive scale.
Scaling & Novel Architectures
We focus on optimizing compute-to-performance ratios. We are developing architectures that sustain coherence over long contexts, moving beyond the quadratic bottlenecks of standard attention mechanisms.
Foundational Capabilities
True general intelligence requires more than pattern matching. We are advancing the state of the art in reasoning and long-horizon planning. We aim to build systems capable of complex, multi-step problem solving, not just next-token prediction.
Safety & Alignment
At Metanthropic, safety is not an adjunct effort. We conduct rigorous, empirical analysis to ensure our models are robust against adversarial failure modes. We treat alignment as a physics problem, not a policy problem.
The Road Ahead
The development of AGI presents the greatest technical opportunity of our time, but it necessitates an uncompromising commitment to rigor.
We are moving alignment from theoretical philosophy to rigorous engineering. We are ensuring the path to AGI remains broadly beneficial and accessible.
This is the most challenging and important technical work I have ever undertaken. I am excited to finally share our progress as we push the boundaries of what is possible.