Introduction: What makes Claude 4 special?
In an interview with Sholto Douglas and Trenton Bricken, both researchers at Anthropic, it becomes clear that Claude 4 represents a new level of AI competence and comprehensibility. The conversation revolves around current research on scaling reinforcement learning (RL) to build increasingly autonomous AI agents, as well as new approaches to making the “thought processes” of an AI like Claude 4 visible and understandable in the first place.
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How does an LLM like Claude 4 “think”?
Large language models like Claude 4 do not work like the human brain – they do not have real thoughts or feelings. Their “thinking” is based on probabilities: for each word, the model predicts which word is most likely to follow next, based on billions of examples from training data. What is particularly exciting is that the ability to solve complex tasks is already inherent in the basic model. Only through targeted reinforcement learning – for example, with clear reward signals such as solved math problems or passed unit tests – are these abilities sharpened and trained for specific applications such as programming or problem solving.
Mechanistic interpretability: watching AI “think”
A highlight of the interview is the discussion of mechanistic interpretability. Researchers can now identify individual circuits and features in neural networks, enabling them to understand how Claude 4 makes medical diagnoses or carries out complex thought processes. Many abilities arise from the interaction and superposition of information in the weights of the network. With new tools such as sparse autoencoders, this “data compression” can be unraveled and a better understanding gained of how AI arrives at its answers.
The future: From AI colleagues to societal consequences
The experts agree: With increasingly powerful algorithms, more computing power, and better training data, AI agents could soon automate many everyday office tasks. The biggest hurdles are not the algorithms themselves, but resources, infrastructure, and proper regulation. That is why Sholto and Trenton are calling for social values to be incorporated into development at an early stage and for the risks – such as military use – to be taken seriously. Their conclusion: Only through a combination of technical research, security, and social planning can AI development be steered in a positive direction.
