A second question to ask, is if we find ourselves in this scenario, what should we do about it? The most effective response to this multi-layered persistent uncertainty is not to seek impossible stability, but to cultivate radical adaptability and radical optionality. Give up on having a reliable prediction of what happens next. Instead cultivate multiple scenarios of what could happen, and endeavor with each of them to maximize your options. Goals should be considered as disposable hypotheses, constantly ready to be discarded and replaced by better-fitting concepts later on. You will be dead wrong on 19 out of your 20 expectations, but at least one of them will allow you to proceed. Make your decisions not on whether they are “right” but on whether they tend to give you more options later.

here may be other elemental particles of cognition in the mixture of our human intelligence, but I am confident it includes these three as primary components. For manufacturing artificial intelligence we have an ample supply of Knowledge IQ, and we have some preliminary amounts of World IQ, but we seriously lack Learning IQ at scale.

It is important to acknowledge that for many jobs we do not need all three modes. To drive our cars, we chiefly need world sense. To answer questions, smart LLM book knowledge is most of what we need. There may be use cases for an AI that only learns but does not have a world sense or even that much knowledge. And of course, there will be many hybrid versions with two parts, or only a bit of two or three.

Here is Kevin Kelly's selfish reasons for having kids, the following is my favorite.

Children are entertaining, much better than any other streaming option you might pay for. The questions they ask, their antics, watching them play, witnessing or being the recipient of their creativity, sometimes on a daily basis, is the best streaming there is. Their creativity is often inspiring. They can be creative in negative ways, too, but in all ways they will not be boring, and they are right there in your presence.

The aim of public intelligence is to make AI a global commons, a public good for maximum people. Political will to make this happen is crucial, but equally essential are the technical means, the brilliant innovations needed that we don’t have yet, and are not obvious. To urge those innovations along, it is helpful to have an image to inspire us.

The image is this: A Public Intelligence owned by everyone, composed of billions of local AIs, needing no permission to join and use, powered and paid for by users, trained on all the books and texts of humankind, operating at the scale of the planet, and maintained by common agreement.

It is very probable we will discover that intelligence is likewise not a foundational singular element, but a derivative compound composed of multiple cognitive elements, combined in a complex system unique to each species of mind. The result that we call intelligence emerges from many different cognitive primitives such as long-term memory, spatial awareness, logical deduction, advance planning, pattern perception, and so on. There may be dozens of them, or hundreds. We currently don’t have any idea of what these elements are. We lack a periodic table of cognition.

Right now, AIs own no responsibilities. If they get things wrong, they don’t guarantee to fix it. They take no responsibility for the trouble they may cause with their errors. In fact, this difference is currently the key difference between human employees and AI workers. The buck stops with the humans. They take responsibility for their work; you hire humans because you trust them to get the job done right. If it isn’t, they redo it, and they learn how to not make that mistake again. Not so with current AIs. This makes them hard to trust.

Every company, and probably every person, will have an AI agent that represents them inside the AI system to other AI agents. Making sure your personal rep agent has a high trust score will be part of your responsibility. It is a little bit like a credit score for AI agents. You will want a high TQ for yours. Because some AI agents won’t engage with other agents having low TQs. This is not the same thing as having a personal social score (like the Chinese are reputed to have). This is not your score, but the TQ score of your agent, which represents you to other agents. You could have a robust social score reputation, but your agent could be lousy. And vice versa.

Many people have found the intelligence of AIs to be shocking. This will seem quaint compared to a far bigger shock coming: highly emotional AIs. The arrival of synthetic emotions will unleash disruption, outrage, disturbance, confusion, and cultural shock in human society that will dwarf the fuss over synthetic intelligence. In the coming years the story headlines will shift from “everyone will lose their job” (they won’t) to “AI partners are the end of civilization as we know it.”

If you are at all interested in publishing, there is no better role model than Kevin Kelly. He has been doing it for a life time in one form or another.

the way I approach publishing today is with as much self-publishing as I can handle. I’d write in public installments, as a subscription newsletter, or e-book single chapters, or simple posts on my blog. If I could find an audience that wanted more of the material, I’d rewrite, re-edit, re-compose the material into a longer form. I’d release that as an ebook, and/or on-demand printed book sold in my shopify shop. If the material was deep, or involved more creators than just myself, I’d consider crowdfunding it. Those presales allow me to target exactly how many copies to produce. I’d calculate the cost of drop shipping. And at every stage I’d be making some kind of visual version for YouTube and the other attention seeking channels, because that is where the attention is.

A large portion of these still images are preliminary: a sketch, a first draft, a doodle, a memo, a phrase, not meant to share. But even among those creations completed, very few are shared – because they were made for the pleasure of making them. You can generate an endless stream of beauty for the same reason you take a stroll through a garden, or hike into the mountains – in the hope you’ll catch a moment of beauty. You might try to share what you find, but it is not why you went. You went to co-create it. I think of a walk in a garden, or a hike in the high mountains – a hike that is not necessary for transportation reasons – as an act of co-creation. Together with nature, we are co-creating the moments of beauty we might find. Most of the beauty in the world is never seen by anyone. When we encounter these glimpses of a vista, or an exquisite way something is backlit, we are an audience of one. The joy is in discovering it; sharing is an afterthought.

I propose that AI will not disrupt human daily life until it also migrates from a genetic-ish code-based substrate to a widespread, heterodox culture-like platform. AI needs to have its own culture in order to evolve faster, just as humans did. It cannot remain just a thread of improving software/hardware functions; it must become an embedded ecosystem of entities that adapt, learn, and improve outside of the code stack. This AI epizone will enable its cultural evolution, just as the human society did for humans.

Our self-domestication is just the start of our humanity. We are self-domesticated apes, but more important, we are apes that have invented ourselves. Just as the control of fire came about because of our mindful intentions, so did the cow and corn arise from our minds. Those are inventions as clear as the plow and the knife. And just as domesticated animals were inventions, as we self-domesticated, we self-invented ourselves, too. We are self-invented humans.