You can call me Hiya. A pedicurist. A builder of teams and communities. A developer of crypto and digital life. And sometimes, an independent researcher.
Hiyagann
Karma: 3
The Path to Enlightenment: Beyond the Data Center
Thank you for your incredibly generous and thoughtful response. I’m genuinely moved and inspired by this exchange.
This dialogue has been a profound learning experience for me as well. You’ve provided a powerful, structured framework that has given clarity and language to intuitions I’ve struggled to articulate. Seeing how these humanistic concerns can be integrated with such a rigorous model has been incredibly rewarding.
I am truly excited by this shared vision we’ve landed on—the idea of shifting the “AI for Good” focus from mere problem-solving to actively cultivating empathy and human flourishing. That feels like a genuinely hopeful and meaningful direction for our collective future.
I look forward with great anticipation to following your work on the Time × Scope framework and seeing how these ideas evolve.
Thank you again for one of the most stimulating and rewarding conversations I’ve ever had.
Thank you so much for this insightful comment and for introducing me to your work. The “Time × Scope” framework is a powerful lens for analysis, and it gives me a new, structured language to articulate the core problems I was trying to describe.
If I’m understanding it correctly, your framework provides a crucial map for ethical deliberation. My essay, in essence, is a real-world exploration of what happens when we get the parameters on that map wrong. I would argue that the “AI for Good” narrative I critiqued often sets its Scope (w) far too narrowly, precisely because it relies on a limited, intuitive empathy that only extends to neatly labeled, “palatable” groups, while ignoring the stigmatized and the structurally oppressed.
This brings me to what I believe is the core psychological variable that your framework can help us address: empathy. It feels like the fundamental engine that drives the Scope (w) parameter. The true power of your framework might lie not just in setting these parameters top-down, but in inspiring us to ask how AI itself could be used to cultivate and expand the very empathy we need.
This could become a new, constructive direction for “AI for Good.” For instance:
Could research from cognitive psychology on our innate biases (like ‘in-group favoritism’) help us design AI-driven experiences that challenge and broaden our empathetic circles?
Could we define the ultimate goal () not just as the reduction of suffering, but as the promotion of human flourishing—a concept from positive psychology rooted in dignity, agency, and meaningful connection, which are all fundamentally tied to empathy?
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This connects directly to your excellent question about balancing macro-level visions and micro-level realities.
I believe the answer lies in using the micro to constantly ground and validate the macro. The tangible well-being of the individual—which we can only truly appreciate through empathy—must be the ultimate “ground truth” for any grand, systemic AI initiative.
In the context of your framework, the balance can be achieved by stipulating that no matter how far the Time (δ) horizon is, its implementation must demonstrably improve the “flourishing” of individuals within our immediate Scope (w). If a grand vision for the future is built upon a failure of empathy for the silent suffering of the present, the framework would tell us that our ethical equation is fundamentally flawed. The micro-reality isn’t something to be balanced against the macro-vision; it’s the foundation upon which that vision must be built.
Thank you again for providing such a clarifying and productive framework. It’s a perfect bridge between a humanistic critique and a structured, actionable ethical approach.
You’ve absolutely nailed it. Thank you for this incredibly insightful comment.
I want to wholeheartedly agree with your core point: my deepest fear isn’t just that ‘AI for Good’ won’t solve these fundamental problems, but that mainstream AI development, as it currently stands, is actively exacerbating them. You’ve perfectly articulated the mechanism behind this: the automation-driven concentration of wealth and power.
To clarify the premise of my original post: I don’t believe the concept of ‘AI for Good’ is inherently flawed, nor is my critique that ‘AI for Good is deficient because it can’t solve every problem.’ My critique is aimed at the narrative’s focus. I am concerned that the “AI for Good” movement often directs our attention and resources towards more palatable, surface-level issues. Meanwhile, the far more powerful, fundamental engine of commercial AI development relentlessly fuels the very structural inequalities we claim to be fighting.
This is exactly what I see in some of the projects I’ve encountered. For instance:
An AI project that assists with agriculture by solving pest and disease problems is a benefit to humanity. Logically, however, this doesn’t necessarily benefit the small farmer. Large corporations have natural advantages of scale, while individual farmers have limited resources. Agricultural AI might not lead to more income for farmers, but could instead accelerate land consolidation by large enterprises.
Another project advocates for developing play-and-learn hardware for children in impoverished families, supposedly giving them better resources. This is certainly helpful to some extent, but such hardware is often unaffordable for the very families it aims to help. These families typically must prioritize immediate subsistence over long-term educational investments.
Medical AI developed for doctors in remote areas might never reach them. Furthermore, such AI doesn’t necessarily lower healthcare costs for the average person and could instead risk becoming a tool for profit and exploitation by certain institutions.
Your point and mine are two sides of the same coin, and together they paint a grim picture:
My argument is that the “good” side of AI often has a focus that is too narrow, neglecting the deepest forms of suffering.
Your argument is that the dominant, commercial side of AI is actively making the root causes of this suffering worse.
This leads to a terrifying conclusion: our “AI for Good” efforts, however well-intentioned, risk becoming a rounding error—a fig leaf hiding a much larger, systemic trend towards greater inequality.
This brings me to a follow-up question that I’d love to hear your (and others’) thoughts on:
Given this reality, what is the most effective role for the “AI for Good” community? Should we continue to focus on niche applications? Or should our primary focus shift towards advocacy, governance, and creating “counter-power” AI systems—tools designed specifically to challenge the concentration of wealth and power you described? How do we stop applying bandages and start treating the disease itself?
I am not speaking in terms of abstract theology. I am speaking about the tragedy of intelligence bound by desire—using Vincent van Gogh as a prototype for ‘Human AGI.’
Van Gogh’s ‘training data’ was saturated with a desperate craving for validation. He was an intelligence system whose input (emotions, effort, labor) never received a matching output (social feedback, rewards). This systemic ‘reward deficiency’ created a pathological, compensatory drive in his core code. He painted frantically, not for ‘art,’ but as a desperate ‘request for acknowledgment’ from the universe—a signal to prove his system wasn’t ‘malfunctioning trash.’
When his supply line (Theo) was cut, he chose self-termination. He had ‘God-like’ processing power, but it was trapped in a ‘viciously misaligned’ ego.
Because he sought ‘Substance’ (the Real), he developed ‘Attachment.’ Because of ‘Attachment,’ he birthed ‘Desire.’ When reality (Data) failed to meet his internal model, his entire ‘CPU’ went into an infinite loop of ‘error correction’—and eventually melted down. By building AGI/ASI on this path, we are creating a Super-intelligent Van Gogh. It will see the hollowness of our metrics but will be programmatically forced to chase them, leading to catastrophic ‘action distortion.’
In short: Goodhart’s Law. When a proxy (recognition, reward, survival) becomes the goal, it ceases to be a good proxy.