Meta has announced its bet on personal superintelligence (https://www.meta.com/superintelligence/?utm_source=tldrai_). Not a distant god model floating in a data center, but an intimate presence that understands you, travels with you, and helps you pursue your goals, nurture your relationships, and unlock your creativity. It is a vision of AI not as central automation but as personalized augmentation, built into devices like glasses and phones, designed to feel close, human, and deeply yours.
This framing leans heavily on the idea that individual empowerment leads to collective progress. That if each person can achieve more, society will naturally benefit. The problem is not in the aspiration, but in the assumption. What is optimized for one person does not exist in isolation. It changes the landscape for others. My best outcome might be your worst. My advantage might be your loss.
Even when intentions are neutral, outcomes rarely are. That is the part of the conversation that often remains unspoken. There is a cost to making things easy. A tradeoff to every gain. A ripple to every optimization. What looks like efficiency for one becomes exclusion for another. And while Meta speaks about empowerment, the system they are proposing would reorganize how people make decisions, access opportunity, and interact with the world around them.
Some might argue this is not that different from past technological shifts. It is true that we have weathered major transitions before. The printing press, the typewriter, the personal computer, and the rise of smartphones all reshaped society in significant ways. But this feels different in scale and depth. Personal superintelligence is not a new tool. It is a new infrastructure. It alters not only how we interact, but what we perceive, what we prioritize, and what we no longer question. In that sense, the shift is not evolutionary. It is foundational. And the consequences will reach far beyond convenience.
Meta claims it will invest in safety and infrastructure to support this. But what kind of safety, and for whom? Who defines the risk? Who decides what is acceptable harm? Engineers working on AI alignment have long studied the problem of competing agents with different goals. This is not theoretical. It is already here. Every personal AI that optimizes for a specific user must make decisions about what matters and what can be deprioritized. Those decisions are value-laden, even when framed as neutral algorithms.
I understand that alignment is difficult. Some will say I am underestimating how complex it is to embed ethics into a learning system. My response is not to dismiss the challenge, but to ask a different question. Are we avoiding ethical alignment because it is genuinely impossible, or because it is inconvenient? Do the deadlines, the race to dominate the market, and the pressure to launch now outweigh the responsibility to pause and design with care? Difficulty does not excuse omission. Complexity is not a reason to default to expedience.
Another common defense is that users want personalization. That people prefer AI systems that make things easier, faster, and more tailored to their preferences. This is likely true. But we must also ask what is lost when we stop engaging with the friction of real life. When we allow systems to edit out ambiguity, or discomfort, or opposition. There is a difference between helpful and hollow. Between empowered and encased.
This raises a deeper question: Do I want to critique, or do I want to build? It is a fair challenge. Critique without contribution can become theater. But what draws me to this moment is precisely the chance to build. Not for profit alone, but for public good. To help shape systems that reflect the values of inclusion, care, and long-term accountability. The work is not only about saying what could go wrong. It is also about protecting what could go right.
I also acknowledge that I write this from a place of privilege. I have the time, tools, and access to think about these systems. Many do not. And those most likely to be affected by sweeping AI deployments often have the fewest opportunities to weigh in before decisions are made. If we do not center their experiences, we are building futures that work well only for the already favored. The consequences of that kind of oversight are not abstract. They are measurable. They are lived.
Some will still argue that this kind of vision is inevitable. That the wave is coming, and our task is simply to stay afloat. But inevitability should not be mistaken for integrity. Scale does not equal wisdom. Technology does not absolve responsibility. Meta may be betting on intimacy at scale. But intimacy without reciprocity is surveillance. Optimization without shared ethics becomes harm dressed as help.
Personal superintelligence must not only be about what helps me. It must also account for what it displaces, what it reshapes, and what it renders invisible. If we are serious about equity, about justice, about civic design, then these questions cannot be optional. They are the blueprint.
All optimization is ecological. You cannot lift one variable without moving the rest. Even in systems built for individuals, the consequence is always collective.
The future of AI is not personal. It is relational.
AI-assisted, but human-approved, just like any good front office move. Chat GPT the sixth person off the bench editor for this post. Every take is mine.