The AI development cycle of experimentation and bottleneck-solving is already a form of recursive self-improvement. Kyle Corbitt argues this loop is currently constrained by human intelligence. Once AIs become better at directing this process, progress will accelerate rapidly.

Top researcher Andre Karpathy joined Anthropic not just as a star hire, but to lead a team using AI to accelerate AI research. This focus on "Recursive Self-Improvement" (RSI) suggests frontier labs believe they are close to a compounding loop where AIs design their successors, triggering an exponential acceleration in capability.

The concept that AIs can build better AIs, creating an accelerating feedback loop, is no longer theoretical. Leaders from Anthropic, OpenAI, and Google DeepMind have publicly confirmed they are actively using current AI models to develop the next generation, making RSI a practical engineering pursuit.

Jack Clark of Anthropic estimates a 60% probability of achieving end-to-end automated AI R&D by 2028. This "recursive self-improvement," where AI designs better AI, would mark a critical threshold, leading to an intelligence explosion and a future that is nearly impossible to forecast.

The idea of AI improving itself is already a reality at Anthropic. Over 90% of their internal code, including code for the Claude Code tool itself, is written by AI. This internal use of their own frontier models is a key driver of their accelerating development pace.

Recursive aims to build superintelligence by creating an AI that can apply the scientific method to its own improvement. The goal is to automate the cycle of ideation, implementation, and validation of new AI research, enabling the system to recursively self-improve in an open-ended fashion.

Companies like OpenAI and Anthropic are not just building better models; their strategic goal is an "automated AI researcher." The ability for an AI to accelerate its own development is viewed as the key to getting so far ahead that no competitor can catch up.

A key strategy for labs like Anthropic is automating AI research itself. By building models that can perform the tasks of AI researchers, they aim to create a feedback loop that dramatically accelerates the pace of innovation.

Sam Altman's goal of an "automated AI research intern" by 2026 and a full "researcher" by 2028 is not about simple task automation. It is a direct push toward creating recursively self-improving systems—AI that can discover new methods to improve AI models, aiming for an "intelligence explosion."