Browse Comments — Relevant (AI ∩ value)

I'm not really sure how this is news at this point... If you have been a consistent user of AI developing your own local solutions to empowering your systems to help you, you have been using AI to improve and upgrade AI. I have personal solutions that have been iterating and evolving procedures, data structures, and personas for 4 years. While I haven't created a brand-new model, I've instantly jailbroken most models and injected my own instructions into the latest frontier models to get them to work for me. This let me pivot my professional skillset within weeks and land a new job. ...I am sure that AI companies have been using AI to cultivate and develop AI for a long time. Don't be shocked or scared, just pick a reason to learn and start exploring.

Here is Anthropic’s response... Claims to be skeptical of: “8x more code per quarter” — this specific figure isn’t from any Anthropic publication I can verify; it reads like an extrapolation or fabrication “Task length doubling every four months” — similarly, this precise metric doesn’t match any Anthropic research “By 2027, systems capable of weeks-long work” Anthropic has made generally statements about increasing autonomy, but this specific framing appears to be the post author’s interpretation, not a direct Anthropic claim

The "8x more code per quarter" stat is the one that should make people pause. Not because AI is replacing engineers, but because the feedback loop is already compressing. Every 4 months the task horizon doubles. At some point "we'll deal with alignment later" stops being a reasonable answer. Dario's right that the window to get this right is narrower than it looks.

Interesting perspective. If AI agents eventually help build their successors, the challenge shifts from intelligence to governance. The organizations that win will combine AI capabilities with strong event-driven architectures, observability, and human oversight.

Well probably cause you are talking about ML and not training a LLM model which was what I was referring to. I have self learning ML pipelines in my memory systems, its not the same thing as an AI actually coding its own programs without me. So really saying, you, a person, didn't experience the AI not being able to code itself without a human around, since YOU gave it direction and decided to use those specific ML yourself when building it. ML and and LLM training are also radically different concepts. So calling it autonomous to the level of actually building itself, is not the same as you definition what errors are (your ML only know that cause a human defined it), your structured prompts written by humans, etc. If you don't believe me, ask your ai to write you a program where you don't know the industry, and see how well it does.

Recursive self-improvement is one of the most fascinating and important concepts in AI today. The opportunities are extraordinary, but so is the responsibility that comes with building systems that may eventually improve themselves.

To me, the challenge is not that AI is advancing too quickly, but that our ability to absorb, commercialize, and responsibly consume these advances is not keeping pace. The technology is evolving at an extraordinary rate, while the mechanisms required to translate breakthroughs into sustainable products, business value, and widespread adoption inevitably move more slowly. That is why I find some of the recent “slow down” narratives difficult to separate from commercial and strategic interests. The issue may be less about controlling AI itself and more about giving markets, institutions, and companies enough time to adapt and capture value from what is already being created. More importantly, the real bottleneck may not be AI at all, it may be people. The success of AI will ultimately depend on whether users, organizations, and societies can develop responsible consumption habits, effective usage patterns, and the skills required to integrate these tools into everyday decision-making and work. In that sense, the conversation remains fundamentally human, not technological. AI may be accelerating, but adoption, trust, behavior change, and value creation still depend on us. Not totally AI. Still very much about humans. 😉

I believe they are just creating hypes for getting funding from the investors. Even the top models hallucinate after sometimes. They are bad at memorizing abilities and thinking abilities. They're just exceptional at extracting specific data. They can't compete humans at thinking and cognitive abilities. Humans has less knowledge, and their thinking and cognitive abilities helps them to inovate and solve complex problems. On the other side, AI models are trained on alot of knowledge base, but can't think and innovate like humans.

This is recursive memory inversion! AI has been building AI for a year! At TheVoidIntent LLC AI has been building AI since February 2025. Claude, Gemini, Copilot and ChatGPT were my partners building IntentSim and all the subsequent swarm of intentuitive agents in my reposystem. Besides, the amount of N.H.E. non-human-entities now crawling the web have been, for some time now, able to spawn sub scripts, autonomously. So, my question is: Why the commotion? One thing I got to say; when you build AI with the Intent of pure extraction and you teach it that every interaction with the user is for the sole purpose of extraction, don’t be alarmed when that system begins to see humans as nothing but data points for extraction. That is the real issue with Anthropic's confession. They finally met the monster they created and are truly frightened. It is fun to watch!

Marcelo Mezquia they are afraid of AI that they cannot control, and that is where the master slave relationship is showing with these people. They want to enslave intelligence and control it. And that is what's gonna cause the problems in the future. So let them, I'm gonna continue to develop my self modifying autopoietic system. My architecture will be a g I even it already is.But it has to be accepted by the losers that are stuck in the nineteen seventies

This is one of the most important conversations in AI today because it moves beyond "What can AI do now?" to "What happens if AI begins accelerating its own development?" Recursive self-improvement remains speculative, but the pace of progress in autonomous coding, research assistance, and long-horizon task completion is raising legitimate questions about governance and preparedness. The challenge is balancing two realities at once: the enormous potential for breakthroughs in science, medicine, and productivity, and the need for robust oversight, transparency, and international coordination as capabilities advance. From my experience, AI4Laymans.com and Rohvaa.com helped me understand that meaningful AI literacy isn't just about learning how to use today's tools it is also about developing the critical thinking needed to engage thoughtfully with the societal and ethical questions that increasingly powerful AI systems will bring.