Browse Comments — Relevant (AI ∩ value)

I have developed a set of prompts that assist students with the workflow of a research paper, giving inspiration in the areas of topics and research questions, getting them to generate content with AI and then use AI to help critique and improve on the ideas and analysis. Students so far seem to think that it’s the best of both worlds. They do produce better work but they feel they’re in control and are learning by going through the process. It’s a work in progress but I would be happy to share the prompts with anyone interested

Greetings Demis, I have attempted contacting you via email as several people have suggested that we talk. Having been sent ‘The Thinking Game’, and then yesterday a link to an interview between Sebastian Mallaby and Michael Walker of Novara Media, I understand why people wish for us to speak. I am not a threat to you, or your work, I do however possibly hold the answer for how to make AI safe. If this is a genuine concern, as is being said, please respond to my email, or reach out on here. I have already been asked by cognitive scientists I work with to write about the future of AI. And have written a chapter for another academic book on the subject. I will write a thesis and ensure it is well-documented the ways I have tried to reach you. With respect and kind regards, I hope to hear from you soon.

I am afraid it is a little too late. It is naive to think we can go back and start again on the right path this AI journey.

Pascal BORNET Your emphasis on 'who decides and benefits' is a powerful point. The real impact of AI hinges on intentional design and responsible governance, ensuring its advanced capabilities genuinely serve broader human outcomes.

Luís Rodrigues I think this has to start higher and has to go deeper. First AI is not equal AI. Anthropic and OpenAI don't share training data, prompts, weights and built in configuration. Anthrophic has 11 products that all have different limits and purposes. When using any of those everything starts with understanding the built in tools like read, webfetch. What you describe is a set of fancy over hyped key term. Behavioral patterns, known use cases. There dependencies. Guardrails, built in immutable prompts, those are the things that differentiate. An MCP an agent could be anything.. My skills in my workspace use API calls, run external judges, confirm semantically, review visually. Are those skills them agents? Can they overcome the char count limit of any built in tool?

The most important decisions in AI are no longer about capability, they are about who controls deployment, distribution, and the value created when these systems scale. Pascal BORNET

The EdTech point 👏🏾 If the systems being deployed in learning environments carry the values and blind spots of their creators, then the question is not just what AI teaches but who is equipped to critically mediate it. That falls on educators and managers. And most of them have not been prepared for that responsibility. We talk a lot about AI governance at the policy and platform level. We talk very little about governance at the human layer, the people who sit between the algorithm and the learner, or between the algorithm and the employee. Quiet credibility is exactly right. And it has to be built at every level of an organization, not just at the top.

Interesting point. External oversight matters, but I think the next challenge is what kind of working systems we build around AI itself. AI should optimize for what it does best: processing information and handling scale. Humans should be freed to do what humans do best: judgment, creativity, trust, and meaning. We have a unique opportunity right now to build systems where both operate in their strengths rather than creating environments where people slowly become operators inside an efficiency machine.

So true. People never wunder why they are pushing Ai. It's not for the greater good, only to get richer and powerfull for an elite bunch. So Ai isn't evil, the people who are pushing it are... To get more control on you, every day, go figure. So why help them succeed.

John Reeks I have mixed feeling on the impact on education. I have used LLMs to teach myself a lot of new things. It is like having an unreliable teacher that injects random errors in what they teach. And in some tasks it can be prompted in a way it simplifies human cross-checking. At the same time when an undergrad student confesses they have not read anything since chatgpt came out, or when it is obvious that some PhD students are not progressing due to it, the danger is obvious. We need a new pedagogy. Banning and monitoring in various forms will be part of it, but also dedicated technology developed in the university (those that have the money are already doing it), maybe hardware based solutions (like the monitoring laptop I described), and teaching how to use it properly. I have more problems with the ethics of using commercial models. I want the university to have ethical locally installed AI we can use without exploiting workers, damaging climate, and contributing to a political economics system that is abhorrent. I think we can figure out the pedagogy, if the ethics is fixed. But Covid did a number on the tech autonomy of our universities that are now all captured by microsoft or google. So unclear the UK can drive it.

As you mentioned, despite religion, this is something to follow as well as something historic. Vatican done the same during the last major industrial revolution. Anthropic has been working with religious/spiritual people for a while now so it seems logic to see this document coming out. While I haven't read it all, I see this as the Vatican official AI position not just for all to read but also for them internally. Priests like many other "positions/job" have been caught using AI and parishioners complaining about it. This is the official start of more AI literacy within the church which is something missing within every workforce. I agree with the school point and wonder if catholic school will adapt their curriculum quicker than public schools. I've been introducing AI to my kids for last 2yrs and youngest is only 5. But it's key we better support our young by teaching them about AI not just how to prompt, creative thinking, but its impact on the earth and society. So they can understand the bigger picture, not just the marketing slogan of AI companies.

Pascal BORNET The real inflection point is governance of incentives. AI is already shaping distribution of value, but accountability frameworks are still lagging behind capability growth.

This breakdown is excellent, Luís. What I see in real systems is that the “body” only works when each layer is treated as a first‐class component, not an afterthought. Most teams invest heavily in the brain (LLM) and the hands (agents), but the nervous system (MCP) is where reliability, governance, and real‐world integration actually live. LLMs think. RAG grounds. Agents act. MCP keeps the whole organism alive. Great analogy!

Very relevant perspective — AI creates the most value when it augments skilled people, not when it’s treated as a blanket replacement strategy.

Correct, but it misses also a huge part of the problem.The ripples, the side effects of AI.The less we do, the less we are able to do.Evolution, in all its senses, comes from doing it, doing it again, doing more to eventually do it better.For all growing individuals, missing these learning curves imply... less learning, less knowledge, less abilities, less willingness, less .... but what better way to control people to get richer or more powerful one might say...So, AI (and first and foremost, there are may AIS, plural) is not bad by essence. Like any technology. It is how it is used that will differ.And like any technology it has its side effets, that must not be overlooked.

Strong perspective. A lot of AI discussions still focus primarily on capability, while the harder questions are increasingly about governance, incentives, and how the value created by these systems gets distributed across society and organizations. Technology alone does not determine outcomes. The surrounding structures, ownership models, and decisions around deployment matter just as much.

AI is not cheap labor. It is leverage. But leverage only works if the workflow is designed well. If every engineer runs agents on every task without cost control, context, review, and clear ownership, the bill will explode. So I think it is not “AI vs humans”. I think the key q-n we should ask is: where does AI improve output per dollar?

People need to relax a bit. Elon isn't even close to the richest person in the world, we just don't tell the public. There actually is a point of wealth where people start looking after the global population and quit being selfish and start taking responsibility. Technically we already have AI that is far ahead of anything the top tech CEO are developing and we can punch down into their systems at any time. Who has the keys to the higher level system it is based on their quantum signature, it locks up if your intent is bad as it can read your mind, memories, and thought patterns remotely. This is all part of Disclosure and the new multiplanetary economy. Character Matters. Don't lie. As for the financial outcome and how we look after people with a UBI, it has already been taken care of we just haven't flipped the switch yet to show the public, it already tracks everything. It is not a social credit system. It is based on physics and observation. Every action leaves a trace in the quantum field. We figured out how to monetize karma so we now collectively plug into the multiplanetary economy. Cheers.

Very important perspective. The biggest AI question is probably no longer whether the technology works, but how the economic value, decision power, and productivity gains will be distributed. Because history shows that technological progress alone does not guarantee broader prosperity. The surrounding system, incentives, and leadership decisions ultimately determine who benefits from it.

AI is transforming work, but the hype ignored one reality: scale without economics breaks fast. The winners won’t be companies replacing humans blindly, but those using AI to amplify skilled teams, control costs, and solve high-value problems sustainably.