Anthropic has published a new paper that offers an early view of how AI systems might help improve other AI systems’ alignment training, according to TechCrunch. The paper, titled “Automated Researchers Can Reliably Mitigate Alignment Failures,” describes automated systems that worked on a set of alignment failures and improved benchmark performance across the full set tested. TechCrunch reports that the work was led by Anthropic Fellow Chen Yueh-Han. In the reported setup, the systems were given 10 benchmarks tied to specific misaligned behaviors. They improved performance on every benchmark without degrading overall performance, according to the TechCrunch summary of the paper. The workflow described is deliberately close to a compressed version of conventional research. Each automated system searches available literature, proposes a method, and trains the model using that method for 30 minutes, according to TechCrunch. The outlet reports that the system gradually increases the benchmark over several iterations, preserving effective methods and discarding ineffective ones. That makes the paper less a broad claim that AI can generally improve itself, and more a narrow demonstration of automated alignment post-training under benchmarked conditions. The paper’s own framing, as quoted by TechCrunch, is cautious: the results provide “early evidence” that automated alignment post-training could become practical in the near term. The more provocative part is the comparison with human researchers. TechCrunch reports that the paper explicitly compares the Automated Alignment Researcher, or AAR, with experienced humans. The paper says the best AAR method beats what experienced humans propose, on average within six hours, and says human-guided research directions do not lead to stronger performance. The cost comparison is also direct. According to TechCrunch, the paper estimates an AAR at roughly $4 per hour in API inference, compared with $150 per hour paid to human researchers. That figure is not a full accounting of an alignment research program, but it gives a concrete reason labs would investigate automation for repetitive, benchmark-driven post-training work. The limitations matter. TechCrunch reports that the paper says the approach only works to the extent the benchmarks reflect the actual alignment goals. It also requires the benchmarks to be established and maintained, and it depends on the literature available for automated researchers to draw from. So the immediate takeaway is not that recursive self-improvement has arrived. It is that Anthropic is testing a bounded version of the idea: automated systems that can search, propose, train, and select methods against defined alignment benchmarks. If that pattern generalizes, it could change the labor mix inside AI labs; if the benchmarks are weak, the system may optimize for the wrong target. Who benefits: Anthropic and other labs working on alignment could benefit if automated systems can reliably improve benchmarked failures at lower cost. Teams with strong benchmark design and evaluation infrastructure would be best positioned to use this kind of workflow. Who's exposed: Human researchers working on routine, benchmark-driven alignment experiments could face pressure if these results generalize. The bigger exposure is to teams that treat benchmark gains as alignment progress without validating that the benchmarks capture the real goal.