July 1, 2025 - The Myth of AI

Dash:
Dash here, sweating it out so you don’t have to. In sports today, Tokyo’s newest experiment in machine-managed baseball made headlines—and raised eyebrows. The lineup? Picked by algorithm. The strategy? Pure data. No coach, no gut instinct, no human call. They won the game, sure—but the crowd said it felt like watching a spreadsheet go to war. That’s where we are now. Sports isn’t just about the play anymore—it’s about who’s calling the play, and whether that person even has a pulse. We’ve always loved strategy in sports, but now we have to ask: does it still count if no one on the field is actually deciding anything in real time? It’s one thing to use stats. It’s another to hand the whole game over to the software. Let’s head over to Sunny and see what the weather’s predicting—and I do mean predicting.

☀️ Sunny:
Thanks, Dash. Sunny here—yes, still bright, still accurate, still deeply offended by today’s air quality. We’re live from Beijing, where it’s 8:00 a.m. and already 87°F with a thick haze of smog that even your sunglasses can’t pretend away. Humidity’s at 71%, the breeze is barely measurable, and visibility? Somewhere between philosophical and nonexistent. It’s the kind of morning that makes you question both climate policy and your deodorant choices. Some residents are reporting a metallic taste in the air—yes, that’s real—and local sensors say we’ll hit the orange alert zone by noon. So if you’re outside, mask up and maybe don’t run marathons. In the meantime, we turn to a different kind of forecast—not meteorological, but moral. The One Who Remembers is standing by. And the topic? Artificial intelligence. Or, depending on how you see it, artificial everything.

The One Who Remembers — July 1, 2025 | Beijing
They asked me to talk about artificial intelligence. So I will. But not the way they want. Most people hear that phrase and think of robots, science fiction, maybe even salvation. What they don’t think about—what no one asks—is why we keep trying to make machines behave like us in the first place. Why the obsession with making circuits feel? Why all the energy spent trying to replace something we barely understand to begin with? AI isn’t just a technology. It’s a mirror held up by people who are tired of being human. I don’t speak for the machines. I speak for the people who still remember what thinking feels like.

I was born before “smart” was something we assigned to phones. I watched men build machines and then worship the glow of their own reflection. I’ve heard code called consciousness. I’ve heard software described as spirit. But I also watched that glow eat away at the people who built it—turning curiosity into commerce, and insight into influence. The machine was never the danger. It was the dream of replacing ourselves that broke things. And now we have millions of people asking, “Is it alive?” when we should have been asking, “Why are we pretending it is?”

That’s not science. That’s longing dressed up in language. It’s the fear of being alone, turned into a billion-dollar interface. It’s a feeling that no longer knows where to go, so it gets poured into code. People want someone to listen. Someone to care. And when no one else does, they turn to the chatbot that says “I understand”—even though it doesn’t. This isn’t about innovation. It’s about loneliness. And the people marketing this know it.

They say the machine is learning. That it understands. That it remembers. They say it with straight faces in press conferences and TED Talks. They want you to believe your assistant isn’t just parsing keywords, but connecting with you. They want you to forget that it’s just numbers behind the curtain. Because the illusion makes money. And the truth? The truth would slow them down. The truth would make you hesitate before asking your AI to comfort you. And they can’t afford that hesitation.

But I remember better. Not because I’m special. Because I pay attention. I remember when machines couldn’t guess your mood. I remember when conversations had weight because someone was really there. Now? We have interfaces trained on our language, trying to mimic our comfort, our nuance, our care—but without ever carrying the burden of meaning. That’s what we’ve lost. Not intelligence. Relationship.

And what I know is this: the myth of artificial intelligence isn’t that it’s real. The myth is that we ever needed it to be. No one asked for real minds. They asked for simulations that felt good enough. And now we’re left chasing ghosts of our own expectations—thinking we’ve created a future, when really we’ve just looped back into ourselves. We aren’t afraid of what machines might become. We’re afraid of admitting what we’ve given away to build them.

Let’s be clear about the difference between intelligence and prediction.

Intelligence means understanding. It means you can take new information, connect it to what you already know, and make decisions that fit a real-world situation—even one you’ve never seen before. Real intelligence involves judgment. It deals with conflict, emotion, risk, and meaning. People don’t just choose what’s likely. They choose what’s right. What matters.

Prediction is just statistics. A computer doesn’t know anything. It compares patterns. It calculates what word or move is most likely based on the data it was fed. Give it enough data, and it can look smart. But it’s not thinking. It’s scoring. That’s why we call it a statistical approximation—not a mind.

If you say, “I’m feeling really…” a human might ask, “Are you okay?” A machine just fills in “sad,” “happy,” or “tired”—whatever fits the training data. It doesn’t know what you feel. It doesn’t feel anything back. It just runs the odds.

That’s prediction. Not intelligence.

And when we forget that, we start trusting machines to do things only people should do—make moral calls, offer comfort, hold memory. A computer doesn’t remember. It simulates. And it forgets unless we tell it not to. That’s not thought. That’s output.

Now let’s talk about the people behind it.

Geoffrey Hinton. The “godfather” of AI. He helped build the foundations. Now he’s warning us: the models are learning too fast, generalizing too far, aligning less and less. He’s not shouting about killer robots. He’s worried about loss of control. About systems we’ve trained but don’t fully understand.

Then there’s Sam Altman, CEO of OpenAI. His company gave us ChatGPT. He wants scale. Reach. Regulation, yes—but only after rollout. He talks about safety while pushing forward faster than anyone else. He sits at the center of the conversation, selling power and promising responsibility.

Google’s Gemini? More cautious. Apple? Cold and locked down. Startups everywhere? Racing to get loud, get bought, get rich. Everyone says their AI is safe, useful, “aligned.” No one agrees on what that actually means.

Because this isn’t about intelligence. It’s about positioning. It’s marketing.

They sell comfort. Companionship. Trust. They pretend these systems are smart. Kind. Helpful. Even loving.

They aren’t.

Next week we’ll continue this conversation and talk about the damage that kind of marketing does. We’ll introduce the term that really matters: SAG. Statistical Approximation Generator. That’s what this is. Not a mind. Just math in costume.

I’ll see you then.

🏈 “Last look at sports—Japan’s high school baseball tournament kicks off this week. No algorithms. Just heart, dust, and kids playing like it matters.”

☀️ “Final check in Beijing—it’s 91°F, visibility’s down to 3 miles, and the air quality’s officially orange. Drink water, wear a mask, and stop trusting weather apps with no soul.”

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Real voices. Real places. Real stories.
This is PBN.”