Recently, "loops" seem to be coming up a lot in the agentic coding community. In programming, a loop typically means repeating the same process over and over using a for statement, but here it refers to AI agents repeatedly cycling through the same process. Because this is a highly important foundational technology for predicting the future development of AI, I'd like to dive right in and explore it.

1. Loops Are Becoming a Hot Topic

Boris Cherny, the creator of ClaudeCode, has recently been talking about the importance of "loops" in interviews and on X (1). It seems he himself has been getting most of his work done using loops lately. Given that the new generation of generative AI models can operate continuously for hours, it makes sense that they can churn through tasks using loops.

2. Feedback Descent

To dive deeper into loops theoretically, I would like to introduce a research paper. It outlines a method called Feedback Descent (2), which aims to optimize text generations using words in a semantic space.

In the world of machine learning, optimization is often done using "gradient descent," which utilizes the gradient of a loss function. In this method, however, feedback is received in text form and used as a hint to devise improvement strategies for the next loop. Therefore, I believe they borrowed the naming convention from the machine learning world to call it Feedback Descent.

In the explanatory diagram below, Illustration A and Illustration B are compared as a pair, and the chosen one—along with the reason for its selection—is returned as text feedback. In the next loop, the prompt is devised based on that feedback, allowing for the creation of more accurate and effective prompts. This clearly defines the direction of prompt generation conducted in a loop. Brilliant!

Text Optimization via Pairwise Comparison

I have included the algorithm below. It features a very simple structure.

3. Recursive Self-Improvement

Algorithms that self-improve by running loops like Feedback Descent have been published one after another recently. Furthermore, with the emergence of the latest generative AIs like Fable5, AI agents can now operate for increasingly longer periods, and we can assume the effects of self-improvement will grow even larger. Ultimately, the method known as "Recursive Self-Improvement" will take root, and the performance of AI agents will likely increase exponentially without human intervention.

As a result, I believe we are finally approaching the era of AGI step by step. I am sure many of you recognize this graph. It is from a paper (3) released in June 2024 by former OpenAI researcher Leopold Aschenbrenner, predicting that an AGI rivaling the capabilities of human experts in various fields will arrive in 2027.

Actually, I featured this graph on my blog two years ago, in June 2024. Two years have passed since then, but I think that prediction is turning out to be quite accurate. It's truly amazing. I would like to share his words from two years ago here once more:

"Again, critically, don’t just imagine an incredibly smart ChatGPT: unhobbling gains should mean that this looks more like a drop-in remote worker, an incredibly smart agent that can reason and plan and error-correct and knows everything about you and your company and can work on a problem indepen-dently for weeks. We are on course for AGI by 2027. These AI systems will basically be able to automate basically all cognitive jobs (think: all jobs that could be done remotely).”

What did you think? I believe the fact that loops have recently become such a hot topic suggests we are steadily walking the path toward AGI. I am looking forward to the future progress of AI agents.

Here at Toshi Stats, we want to continue challenging new algorithms for "Recursive Self-Improvement." Stay tuned!

You can enjoy our video news “ToshiStats AI Weekly Review” from this link, too!

1) https://x.com/bcherny/status/2064426115255730578
2) Feedback Descent: Open-Ended Text Optimization via Pairwise Comparison, Yoonho Lee, Joseph Boen, Chelsea Finn, Stanford University, 31 Dec 2025
3) SITUATIONAL AWARENESS: The Decade Ahead, Leopold Aschenbrenner, June 2024　

Notice: This is for educational purpose only. ToshiStats Co., Ltd. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithms or ideas contained herein, or acting or refraining from acting as a result of such use. ToshiStats Co., Ltd. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on ToshiStats Co., Ltd. and me to correct any errors or defects in the report, the codes and the software.

As many of you may know, Dario Amodei has issued a warning to people. Roughly speaking, he stated, "The demand for entry-level jobs, such as those performed by new graduates, will be cut in half. This will become a reality within the next one to five years." This is shocking news, and the fact that it came from the CEO of a company actually developing generative AI has made it a global topic of discussion. In this article, I would like to delve deeper into this matter.

1. Dario Amodei's Warning

He is the co-founder and CEO of Anthropic, a U.S. company developing generative AI. He holds a Ph.D. in Physics from Princeton University, and from what I've seen, he strikes me more as a researcher than a business executive. I've been following his statements for the past two years, and I remember them being relatively conservative. I thought they were consistent with his researcher-like nature. However, this time he stated, "We are not keeping up with the pace of AI evolution," and "Unemployment rates will be 10% to 20%" (1), which shocked the world. I don't recall similar warnings from other frontier model development companies like OpenAI or Google DeepMind. This is why his latest statement garnered so much attention.

2. Current Performance of Generative AI

Currently, generative AI indeed possesses sufficient ability to handle entry-level tasks. As I mentioned before, Google Gemma 3, an open-source generative AI, achieved an accuracy of around 80% without any specific tuning for a 6-class classification task of bank customer complaints. Typically, relatively simple tasks like "Which product does this complaint relate to?" are assigned to new employees, and they learn the ropes through these assignments. However, with generative AI's performance reaching this level, management will undoubtedly lose the incentive to assign tasks to new employees at a cost. It's not yet clear whether the impact will be as significant as half of entry-level jobs disappearing, but given that even free generative AI can achieve around 80% accuracy today, a considerable impact is inevitable.

3. So, What Should We Do?

There is a division of opinion among experts regarding when AGI (Artificial General Intelligence), with capabilities equivalent to human experts, will appear. The most common estimate seems to be around 2030, but honestly, it's not clear. If so, we have about five years. In any case, we need to adapt our skills to the advent of AGI. Past computers could not be instructed or managed without a computer language. However, with the emergence of ChatGPT in November 2022, generative AI can now be instructed using natural language—"prompts." However, prompting is not a simple matter. It's an extremely delicate process of finely controlling the behavior of generative AI to precisely fit one's needs. Therefore, it's not uncommon to write prompts exceeding 20 to 30 lines. While I cannot delve into the detailed techniques here, it is certainly a skill that requires logical prompt writing. Even though prompts can be written in English or Japanese, acquiring this skill requires time and individual training. Given that open-source and free generative AIs are rapidly improving in performance, it is imperative for us, as users, to learn "prompting," the method of controlling them, regardless of our position or industry.

What do you think? It's good that Dario Amodei's warning has sparked more active discussion. As I mentioned in my previous blog post, generative AI is on the verge of implementing recursive self-improvement, gaining the ability for computers to improve themselves. The evolution of generative AI will accelerate further in the future. I believe the time has come to thoroughly learn prompting and prepare for the emergence of AGI. Discussions about AI and employment will continue globally. ToshiStats will keep you updated. Stay tuned!

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1) AI company's CEO issues warning about mass unemployment, CNN, May 30, 2025

Notice: ToshiStats Co., Ltd. and I do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithms or ideas contained herein, or acting or refraining from acting as a result of such use. ToshiStats Co., Ltd. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on ToshiStats Co., Ltd. and me to correct any errors or defects in the codes and the software.

The Path to AGI : From Loops to Recursive Self-Improvement

1. Loops Are Becoming a Hot Topic

2. Feedback Descent

3. Recursive Self-Improvement

What Will White-Collar Jobs Be Like in 2030? What Should We Do Now?