Browse Comments — Clean (de-noised)
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
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The 'who benefits' framing exposes the governance gap. I see companies rushing to deploy agents without defining ownership of the output or liability for errors. Speed beats scrutiny every time. How are you seeing leadership teams actually structure accountability for autonomous AI decisions?
The concentration of data and compute infrastructure makes equity the biggest hurdle, not the code itself...
History shows: technology rarely destroys societies. But bad coordination, weak institutions, and concentrated value sometimes do.
This is one of the oldest arguments in political economy. The pattern is still the same: a new resource comes along, a window opens, a small group moves fast, and then the widow closes. That small group that moved fast controls now the resource. Now, the question is, what comes next for those who do not belong to that small group?
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.
As the Pope in his recent declaration said. We cannot leave AI in the hands of a few. Decision making what get's build needs to come from the society. Fortunately we have many young innovators, who now use AI to create impact. What amazes me most, when this happens in healthcare and we can treat deceases like cancer.
The more important question is who gets to decide where that power goes and who captures the value when it scales.
Todays big Tech, big Pharma, big Food and big Finance have already become more powerful than national democrations. Which means, when Trump visits China with Big Tech CEO ́s, the question arises: who has more power? Democratic elective leaders or the Big Tech CEO ́s who are responsible for the complete infrastructure on how we work and live. Given their already powerful position, it also raises questions about who controls a fair deployment of these technologies Pascal BORNET
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
Faster systems don’t automatically mean fairer systems — that gap is where the real debate is. Pascal BORNET
The real decisions about AI aren't happening in debates — they're happening in boardrooms, legislation drafts, and funding rounds most people never see. Every model trained, every platform scaled, every policy delayed is a structural choice dressed up as a technical one. We're not just building tools — we're encoding who gets leverage and who gets left out. The question of "what AI can do" is settled; the question of "who it does it for" is still wide open. And whoever fills that silence first will shape the default for everyone else.
We often assume technological progress automatically leads to social progress. History tells a very different story. Technology only lifts everyone when institutions evolve alongside it and actively manage the transition.
Who benefits is the real question.
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.
Not directly answering your question, but eventually this is also what might make a few companies more powerful than governments.
Bernie Sanders Campaign Office Keeping it real! 😎
The real AI question is not capability, but who controls the outcomes and benefits it creates.
Sanders and the Pope saying the same things about AI
AI is not neutral once it enters an unequal system. It scales whatever incentives are already there. That’s why the real issue is not whether it can solve big problems, but who it is allowed to solve them for.
For the working class the new area of improvement (career path in 2027?) probably; how many AI agents can you manage effectively and efficiently, while manage other humans, who probably manage some AI agents their capacity?