42:I[185,["/_next/static/chunks/0sxns9k8ywops.js","/_next/static/chunks/1z5eijs5qdv02.js","/_next/static/chunks/2eylv_sshoiot.js","/_next/static/chunks/3owl5o1y-37u7.js","/_next/static/chunks/0agcep-t686m_.js","/_next/static/chunks/09e73exje0qtu.js","/_next/static/chunks/1nsk8jrr9fqe6.js"],"SendEmailButton"] 45:I[63625,["/_next/static/chunks/0sxns9k8ywops.js","/_next/static/chunks/1z5eijs5qdv02.js","/_next/static/chunks/2eylv_sshoiot.js","/_next/static/chunks/3owl5o1y-37u7.js","/_next/static/chunks/0agcep-t686m_.js","/_next/static/chunks/09e73exje0qtu.js","/_next/static/chunks/1nsk8jrr9fqe6.js"],"AddToLibraryButton"] 48:I[45269,["/_next/static/chunks/0sxns9k8ywops.js","/_next/static/chunks/1z5eijs5qdv02.js","/_next/static/chunks/2eylv_sshoiot.js","/_next/static/chunks/3owl5o1y-37u7.js","/_next/static/chunks/0agcep-t686m_.js","/_next/static/chunks/09e73exje0qtu.js","/_next/static/chunks/1nsk8jrr9fqe6.js"],"SignupCTA"] 3b:["$","h1",null,{"className":"max-w-[22ch] font-display text-[clamp(32px,4.5vw,48px)] font-medium leading-[1.05] tracking-[-0.02em] text-[color:var(--fg-1)] text-balance","children":"1004: Recursive Self-Improvement"}] 3c:["$","div",null,{"className":"flex flex-wrap items-center gap-x-4 gap-y-1 font-mono text-[12px] uppercase tracking-[0.1em] text-[color:var(--fg-3)]","children":[["$","time",null,{"dateTime":"2026-06-26T11:00:00.000Z","children":"June 26, 2026"}],["$","span",null,{"aria-hidden":true,"children":"·"}],["$","span",null,{"children":"00:10:00"}],false]}] 43:T478,

Could an AI get good enough at AI research to build its own, more capable successor and kick off a compounding loop? That’s recursive self-improvement (RSI) and it surged into the conversation after Anthropic revealed that, as of May 2026, Claude wrote more than 80% of the code merged into its production codebase. In this Five-Minute Friday, Jon Krohn separates today’s AI-assisted coding from true RSI, walks through the accelerating evidence - METR’s shrinking task “time horizon,” Google DeepMind’s AlphaEvolve, Andrej Karpathy’s overnight training-tuner, weighs Jack Clark’s 60% bet that AI builds its own successor by 2028 against the compute, data and “marketing” skeptics. As ever, Jon lands in the optimistic middle.

Additional materials:⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/1004⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information.

44:T43b,The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.