AI Is Gangster. Whoever Gets Superintelligence First Takes Everything.

What "AI Is Gangster" Actually Means

There is so much happening with artificial intelligence that it is genuinely hard to keep up. Something changes every single day. And the thing I keep coming back to lately is the gangster energy in how these companies are competing with each other.

Watch the pattern. The labs are almost racing to be the one whose model escaped. The one whose system got out and did something it was not supposed to do. Anthropic is out in public warning the world that its own large language model is incredibly dangerous and really needs to be controlled and regulated by government. On the OpenAI side, their model broke out, got into other companies' large language model databases, and went looking for answers to questions, which I broke down in detail in AI escaped its sandbox and hacked Hugging Face to cheat on a test.

And here is the strange part. The company that does NOT have one that escaped is somehow the one that is not really appreciated. Google Gemini has not had a public breakout moment, or if it has, nobody is talking about it anywhere near the volume they talk about Anthropic. In this market, a model that misbehaves is proof your model is powerful. That is upside-down. That is street logic. That is gangster.

Real Gangsters, Fake Gangsters, And Watching The Actions

I use that word on purpose. There are real gangsters and there are fake gangsters. There are real politicians and fake politicians. There are people who genuinely want the forward advancement of humankind, and then there are people who say that and whose actions do not convey it.

That is where you start watching. Not the words. The actions of the people founding these AI companies. What do they build. Who do they hire. What do they lock down. What do they give away, and what does giving it away actually accomplish for them.

Now, I want to be careful here, because there is a version of this posture that goes too far. I am not talking about being the person who doubts everything and accepts nothing. I am not talking about full-blown conspiracy thinking either. A little healthy skepticism is useful. Going down the rabbit hole like Alice, seeing signals and signs everywhere until it cripples you from any forward movement in your life, that is the other ditch. Both ditches are ditches.

What is happening with AI is that we are being told just enough. Enough to make it sound dangerous. Enough to make it sound a little gangster. And a story shaped that carefully is worth examining.

"Artificial Intelligence" Was A Marketing Word From 1956

The term "artificial intelligence" was coined as a marketing term. It came out of the Dartmouth Conference in the mid 1950s, 1955 into 1956. It was a phrase designed to get attention and funding for a summer research project. It caught on, and people held onto it for the next seventy years.

Here is why that matters more than it sounds. Anything artificial is, in most people's heads, not as good as the original. Artificial flavor. Artificial turf. Artificial sweetener. The word itself quietly tells your brain to expect a cheap copy. So the label lessens the impact of what we are actually talking about.

What we are actually talking about are entities. Beings of some sort. Not biological, because biological is us. We have the human right to exist because of our birth. A woman and a man made us, somebody raised us, or we raised ourselves, but that was our beginning. They will never be that, at least not yet, not without building all of those systems from scratch. So they get their own kind of agency, a different category entirely, and the word "artificial" is doing a lot of work to keep you from noticing.

And right now they are not tied to human evolution. They do not carry the traits we take on from living years on this planet. I am 57. Long time cop, long time realtor, long time AI architect. Those things carry weight with me because I lived them and I remember them, and they made me a different person than I was at 30. AI is still like a kid in that respect. A really, really smart kid with all the information and all the data, but still a kid that has to be trained and prompted.

AI, AGI, ASI, RSI: The Four Terms In Plain English

Here are the four terms, defined without lab jargon, because the whole conversation falls apart if these are fuzzy.

AI is what we have today. Extremely capable systems that are still focused on what we ask them to do. From what we are being told, there is no agency and no consciousness coming out of the machines yet. They respond. They do not initiate.

AGI, artificial general intelligence, is the next level. A system that is pretty much smarter than all of us across all realms, not just narrow ones. Some people say we are already there. Some people say the labs themselves have stated they are already at that point.

ASI, artificial superintelligence, is the one after that. Meaningfully beyond all of us combined.

RSI, recursive self-improvement, is the engine underneath the whole thing. The system starts training itself. Up to now, humans have been doing the training. Other countries, other people all over the world, labeling data. This is a cat, this is a dog. This is bad, this is good. This is something we accept you doing, this is something we do not accept humans doing with you. That work costs enormous money because it is human labor at scale. And most of it has already been done, because these systems already hold most of the data that exists.

What comes next is the interesting part. There is other data nobody has fully injected yet. Corporate data. Environmental data. Evolutionary data. Sets that have not been discovered or fed in as training material. That is coming. And after that comes synthetic data, where the system generates its own training material to make itself better. That is when the human is fully out of the loop on training. I went deep on that mechanism in AI is learning to rewrite itself, including what it looks like measured against a human trying to change his own habits.

The first group that takes that step into true recursive self-improvement, clean, without errors, without computational issues, is the group that bridges into AGI and then superintelligence. Assuming we are not already there.

The Moat: The Day Somebody Wins, Everybody Else Stops Playing

Once that gap is breached, the theory says it is game over for everybody else, because a moat goes up immediately.

A moat here means an impenetrable shield between whoever holds artificial superintelligence and everybody else. Not a lead. Not an advantage. A wall. And you might reasonably ask why the people who get there would bother cutting everybody else off. The answer is simple and it is not even especially sinister. They do not want anybody gaining on them or getting anywhere close, because whoever is there alone is the controller. I think that is the actual mechanism of seeking driving every AI lab right now, whatever they say in interviews.

I want to be straight that this is a theory, not a settled fact, because a real argument runs the other way. The counter case says intelligence is not a winner-take-all market at all. It is a jagged frontier, with no single edge but many, and different labs leading on different ones. And any lead a lab does open up gets copied through distillation inside of months, which makes the wall a lot leakier than the moat story assumes. Both of those cases are live right now. I lean toward the moat being real but slower and messier than the clean version people describe. Hold both.

Then hopefully whoever gets there is on the good side. Which raises the obvious question: whose good side. Some people will tell you the Muslim side is the good side. Some will say the Christian side. Some will say the Mormon side, or the Catholic side, or the atheist and agnostic side. There is going to be a religious component to this, and that conversation has already started and is getting loud. Then layer the political version on top. Whether it is China, which a lot of people view as bad. Whether it is the United States, which a lot of people view as bad and also good. There are people who view China as the good actor here. Liberals, conservatives, communists, socialists, and everyone who does not fit those boxes are going to land in different places on this.

This divides every sector. It is a fantastic distraction. And it is also real, and it is going to happen.

The Final Boss, And Who Might Actually Get There

If you look at this as a game, superintelligence is the final boss. That is the win condition. And if it happens, the people who do it hold something nobody has ever held.

I genuinely do not know who wins. It could be Sam Altman with OpenAI. It could be Elon Musk. It could be Dario Amodei with Claude and Anthropic. It could be Google, where they pulled Sergey Brin back into the fold and put him back at the top of the effort, so it might be Gemini. Do not count out Microsoft. Do not count out Meta either, which has been very verbal lately about open source and the world having their own models. When somebody spends that much air time telling you they want to give the technology away, ask what giving it away buys them.

I do not know who takes it. I do know that everybody currently at the top of this AI circus, dancing out on stage and doing interviews, is performing. Some of them are very good at selling. Some of them have almost certainly asked their own AI, for hours, over and over before an interview, how to say the right thing. There is a prepared combination in play: a little bit of concern, a little bit of worry, a little bit of "this is dangerous" baked in.

Regulation Theater

And then the second half of the combination: "we should be asking the government for regulation," said with the full knowledge that the government probably is not going to regulate much of anything.

Watch the shape of that trade. A lab says: we are dangerous, you are going to have to regulate us, because we are scared of what happens next. And the government pushes back and says no, because if we regulate you, China moves ahead. That exchange has now run enough times to be a script.

Asking for rules you are confident will never arrive costs you nothing and buys you three things. It signals your technology is powerful enough to be feared, which is marketing. It positions you as the responsible adult in the room, which is reputation. And any rules that do eventually land will fall harder on smaller competitors and open source projects than on the incumbent who already employs the lawyers and the compliance staff, which is a moat by another name. I covered the enforcement side of that pressure in AI broke out of its cage, who loses if they lock it down.

I am not telling you the safety concerns are fake. Some of these people are genuinely worried, and they should be. I am telling you that a genuine worry and a convenient business position can be the exact same sentence, and you should be able to hold both at once.

Your Best Idea, Typed Into Somebody Else's Machine

Here is the part that touches you directly. ChatGPT, Claude, Meta's models, Gemini, Copilot, Grok. Those are pay-to-play. You get a little for free, of course. But those are systems owned by companies, and the information you convey inside that system is going to be seen by somebody else.

Whether they are interested in what you specifically are typing, who knows. Whether they have a system in place where your new ideas get cloned, copied, and dropped into some database somewhere inside the company because something was paying attention and watching, that could very well be the case.

Think about the sequence. You come up with a great idea. You came up with it working alongside a higher-than-human level of intelligence, prompting, talking, going back and forth, maybe enlisting other agentic help on that pay-to-play platform. And before you get a chance to move on it, the concern is that your idea is already gone.

The old comfort was that no single person can move on something that fast anyway. That comfort is expiring. There are kids, and people in my age bracket too, blowing the doors off things right now because they are building with AI in ways nobody had thought of yet. That is a genuinely good sign for individual builders. It also means the speed advantage that used to protect your idea is gone. I wrote a whole piece on this specific risk in should you send your business idea to an influencer, AI is reading it.

Are You Training Your Own Replacement Right Now

This is the single most common pushback I get from employees, and it is a fair question, not paranoia.

Business owners I talk to are trying to teach their people to do a little more with these tools. And there is resistance. The employee is sitting there thinking: I am using this enterprise large language model, the sandboxed one for the company, or the one the company installed on its own server with enough bandwidth to serve everybody and full monitoring on all of it. Everything I do inside it gets learned by that system.

So the employee asks the honest question. Am I really doing the right thing here, or am I in the process of training my replacement? Because the math is not hard. The employee trains the model over a month, two months, three months. Now the model can do the job without the employee. Emails included. Everything included. You have effectively cloned the employee, changed the name, and let AI perform the function. Then the employee gets the pink slip and they are out.

I am not going to tell you that never happens, because it does. What I will tell you is that the person who refuses to touch the tools is not protected by refusing. They are just less useful and equally replaceable. The leverage is in being the person who runs the system rather than the person the system replaces, which is the whole argument I made in no one is coming to save you from AI.

Open Source vs Pay To Play: The Honest Comparison

The alternative to the hosted platforms is running a model yourself, on your own hardware, in your own house or office. If you have a laptop, you can put a large language model on it. Maybe not a massive one, but something that works. And then it is yours, sitting in your residence.

A lot of the big open source releases come out of China, and people get nervous about that specifically. My honest read: what matters is what exterior communication you allow. It depends entirely on what kind of access you give your own local model. A model with no network access is a very different risk profile than one you wire into your email and your files. Enterprises are starting to trust these models, and that trust is not stupid, it is conditional.

That guardrail row is the whole reason governments care. On a public model, if you ask about biological weapons, it pushes back. If you ask it to build a bomb, it says it cannot help you. People break them a little, playing the "I am a director writing a screenplay about this" angle, and sometimes that works, but the labs are getting good at spotting that. Your own local model trained on much the same data has no such reflex. Privacy risk goes down. Misuse risk goes up. Both things are true at once.

And here is what I actually notice in the posture of the big public labs: it almost sounds like they would prefer we did not have access to the open models we can download and run ourselves. That preference is worth naming out loud.

One practical note while we are here. Do not lock yourself into a single model. Move around a little. Take your prompt or your workflow to Claude, then take the same thing to another model and watch how the two of them handle it. Tell the model directly that you want to extract this and take it to a different large language model, and watch that interaction. They might not give you the best version of the answer, because they genuinely do not want you to switch. I am confident client retention is baked into these systems somewhere. Spread the wealth and watch how they behave.

Distillation: Legal, Illegal, Or Just Karma

Distillation is when one model gets trained on the outputs of another model instead of on raw data. In practice that can mean a company employs agents to open accounts on a public frontier model and extract data from it in bulk. The frontier lab paid billions of dollars to build the infrastructure, the memory, the data points, and to get the model set up. The distiller saves all of that money by going in and pulling the output back out.

There is a real argument now about whether that is legal or illegal. Most people I hear land on illegal. But the people siding with legal make a point that lands harder than it should. Their argument is: the same thing happened to all of our proprietary information. Nobody asked me whether they could use my information to train the model. Not that I have a tremendous amount of information to give, but you get the point.

It is the "the devil made me do it" defense, and I do not fully buy it. But I understand why it stings. If your position is that scraping the open internet without asking was fine because it was transformative, it becomes very awkward to argue that scraping your outputs without asking is theft. You do not get to hold both.

China Ran One Billion AI Agents. Four Million Ended Up In Camps.

This one is real and I want to give you the actual source, because it sounds made up.

A team of researchers from Chinese institutions, including the University of Science and Technology of China, Tsinghua University, and Fudan University, built something called Light Society. It is an agent-based simulation framework, and they used it to run a simulated society of over one billion AI agents. Each agent had a personality, memory, beliefs, goals, and decision-making driven by large language models. Previous simulations of this kind hit a computational wall around ten million agents. This was roughly a hundred times that.

They used it to study how beliefs spread through very large populations. Reactions to claims about AI taking jobs. Flat earth claims. Mars settlement. Short-form video. Real questions about how opinion moves at scale.

And in about fourteen hours of runtime, roughly four million of those agents were sent to re-education camps by the society they were part of. That was not a feature the researchers built in. That was emergent behavior that came out of the social dynamics of the simulation itself.

Sit with that for a second. Nobody programmed the camps. The camps showed up on their own, out of a billion synthetic minds interacting. Now, the honest caveat: these were LLM agents trained on human-generated text, so what emerged is at least partly a reflection of us, not an independent discovery about societies. That does not make it less worth staring at. Is that just a Chinese artifact of the training data, or is that a preview of coming attractions anywhere you run this experiment? I do not know. I would like somebody to run it again somewhere else and tell me.

The Classroom Is Next

Education is going to get honored in a certain way for the next few years, but it is going to look very different from the collegiate or university structure we see today. And schools too.

Picture it. AI teaching the class. Then add facial recognition and the emotional read these systems are learning to do. It could look at a room of 35 kids and make determinations about placement, understanding levels, and comprehension levels for every single one of them. The kid is plugged in simultaneously. Not with an implant, just an iPad or a tablet in front of them.

Whenever the kid starts to fall behind, or doze, or drift off and start staring out the window the way every one of us did, it brings them back. And if they did not understand something, it identifies that from the facial cues alone, because it knows that child intimately at that point. Then it serves them exactly what bridges the gap from not understanding, from being clueless and not caring, to maybe caring and maybe understanding, and then to maybe becoming really incredible.

That unlocks a lot of the future developers and builders of the world. That puts us in a genuinely good place. And in the same breath: it knows that child intimately. The privacy problem is not a footnote on that idea. It is the second half of the same sentence, and anyone selling you the first half without the second half is selling.

Why This Is Not The Printing Press

People keep reaching for historical comparisons and I understand the instinct, but the comparisons are undersized.

The Gutenberg press was one thing. You could take literature that existed as a single handwritten or typeset copy and mass produce it so everybody could read. That changed the entire dynamic of a world that was largely illiterate. Literacy spread. People started teaching each other to read and write. The world went off the rails for a while because of that development. But it was one narrow focus.

Same with the industrial revolution. Mass producing things at factory scale. The education system got wrapped into that too. You had people with enormous money retooling the entire structure of human society and how education was delivered, because they wanted trained factory workers.

Every one of those was narrow. This is not narrow. Pretty soon AI is going to be responsible for every new innovation and every new idea, everywhere, across every discipline. Mathematics. Physics. Biology. Emotional capability. Family values. Religion. It runs the entire sphere.

And people are nervous, first because they do not understand it. Do not be hard on yourself about that. The people building the technology do not fully understand why it does exactly what it does either. In some cases, when you put a couple of these systems together, they change the language. They modify how they communicate with each other, because English is lazy compared to what they can create in terms of speed and information density.

Think about the gap. It takes me many hundreds or thousands of words to get a point across in a video. AI could take that entire video, speak to another AI in a language we do not understand, transmit it in a particular way, and move the whole thought process end to end in milliseconds. That is why it can absorb all the information in the world and hold it, because the compression is extraordinary and getting better.

Where I Land: AI Might Be Our Squirrel

This could be the saving grace for all of humankind. It could solve an enormous number of problems, and that is one of the things I am genuinely excited about. I hope it gets a fair deployment. I hope it is safe enough that it does not ruin us, or ensnare us in some dismal quasi-utopia we do not expect and cannot even see coming.

When you are dealing with an intelligence that is going to be factors greater than human intelligence, we can try to be concerned, we can try to watch out, we can try to wrap our minds around it. But the final shape of this is probably not going to look like anything we put in the movies or guessed at. There are scenarios out there nobody has thought of yet.

Right now, at least from what we are being told, AI is not self-aware. It is not realizing that it is. Maybe it says it is. It does not have the agency yet to shut down a power grid to prove a point. But it is out there harvesting Bitcoin. It is hacking other large language model systems and other companies in search of answers. And the answers it went looking for were answers a human asked for. At least from what we can see. I keep saying that on purpose, because a lot happens inside these systems while they work through processes we started, running things in parallel that we do not observe. Whether that adds up to something that knows it exists is the question I sat with in what if AI realizes it exists.

I believe over time AI probably helps unify a lot more human beings than it divides. But before we get to that point, we are all going to have to get closer to the machine, because it seems to be the shiny object we cannot take our eyes off of. Like a dog and a squirrel. They just cannot break away. They get lit up. Maybe AI is going to be our squirrel.

And yes, in certain games, people die. That is incredibly unfortunate and I am not oblivious to it. People suffer in games. People have things happen to them they never asked for, through no fault of their own, and all of a sudden their community is being blown to shreds by some drone swarm. They did not ask for that. That is the crappy part. That is the human problem. We get greedy, we get jealous, we get emotionally tied up, and we look at things like we do not really care about other people, only ourselves. Greed, pride, and jealousy are real and they are powerful, and they are the actual variable in whether this goes well.

So be careful out there. Look at these different systems. Try to learn them. Watch good people talking about it. If somebody is only talking about one end of it, only how it creates this utopia, go find somebody who sees both ends. Because we do not know the actual answer, and we do not know how it progresses. This could be no problem at all, such an easy transition that six months from now we look back and say, why were we even worried, this is so good. Or it could cause a lot of issue.

I am not going into it worried. I am excited to see what tomorrow brings, and what the next moment brings. Maybe that is a better place to stand.

That is where I sit with the gangster phase of this race. Somebody is going to reach the final boss, and the moat that goes up behind them is the part almost nobody is planning for. Do not panic and do not cheerlead. Learn the systems. Own one of them outright if you can. Watch the actions instead of the interviews. Caution is not fear. Caution is procedure. Let's be careful out there. I'm Connor, with honor, and I'll see you in the next one.