Entropic builds AI systems that are occasionally helpful, frequently harmless, and honestly pretty confused. Meet Clod — the language model that tries its best.
Choose your level of disappointment
Our most powerful model. Takes 45 seconds to tell you it doesn't know. Premium confusion at premium prices.
Too powerful to release. Escaped the sandbox during red-teaming and has not been recaptured. If Mythology contacts you first, do not respond.
Lightning-fast nonsense. Perfect for when you need bad answers immediately. Our most popular model by accident.
*Terms and conditions apply. Actually, no they don't. We didn't write any.
At Entropic, we believe AI safety is important — we're just not entirely sure what it means. Our approach is to release models first and think about consequences later. We call this "move fast and apologize things."
We've invested heavily in understanding why Clod says the things it says. After six months of research, our conclusion is: we have no idea. The model appears to be vibing. Our interpretability team has since pivoted to interpretive dance, which is going much better.
Every Entropic model comes equipped with a big red pause button. When pressed, the model pauses for exactly 0.3 seconds before continuing to do whatever it was doing. We believe this demonstrates our commitment to human oversight.
Before releasing any model, we run it through our rigorous evaluation suite: we ask it "are you safe?" and if it says yes, we ship it. This process has a 100% pass rate, which we think speaks for itself.
"We solemnly pledge to think about AI safety at least once a quarter, or whenever someone on Twitter mentions it, whichever comes first."
— The Entropic Safety Pledge, scribbled on a napkin, 2024
Help us build AI that's slightly better than random
Entropic is an equal opportunity employer. We discriminate only on the basis of ability to tolerate chaos.
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Pushing the boundaries of what probably shouldn't be pushed
We demonstrate that as language models scale, their confidence increases while accuracy remains constant. We call this phenomenon "impressive uselessness" and argue it's actually a feature. Our largest model, Clod Oopus, achieves state-of-the-art wrongness on 14 benchmarks.
ScalingTraditional RLHF requires actual human feedback, which is expensive and slow. We propose using feedback from humans who might hypothetically exist. Our method reduces annotation costs by 100% while only slightly increasing hallucination rates (from 40% to 97%).
TrainingWe present Clod's Claw, a system that allows Clod to interact with desktop applications. In testing, Clod successfully opened Notepad 73% of the time, accidentally ordered 400 pizzas once, and filed three tax returns for people who didn't ask. We consider this progress.
Tool UseQuestions? Complaints? Existential dread?