By Byron V. Acohido

I hadn’t expected the machine’s answer to be that good.

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It was a simple prompt — I needed help crafting a reply to a client. One of those mid-project check-ins where timing gets murky and scope starts to drift. A delicate moment.

The suggested text I got back from ChatGPT-4o was crisp, firm, and tonally on point. It advanced the conversation without creating new commitments and set clear boundaries without sounding rude.

That fluency gave me pause. I found myself wondering what, exactly, was operating under the hood. What kind of machinery produces a reply that precise, on the fly, and tuned to the narrow emotional bandwidth of a business note?

So I asked directly. Where did that “wisdom,” so to speak, come from? Was this a more sophisticated version of the fortune-teller’s trade — extrapolating from my cues — or was the system drawing on something broader, some distilled consensus of how professionals actually write?

The answer was plain: it wasn’t wisdom. It was pattern mastery.

Pattern mastery

The machine described its process in terms of compression. It had been trained on a vast range of professional language — emails, negotiations, scoping documents, the small routines of conflict management.

None of it stored. None of it copied back. Instead, the patterns had been reduced into statistical structures that capture how human communication tends to behave under pressure.

It wasn’t recalling anything or imitating anyone. It was navigating a high-dimensional linguistic landscape and choosing the path that best satisfied the constraints in my prompt. No human-like judgment — only structure.

That framing cleared the fog. The machine selects a mathematically coherent path that responds to my prompt and stays within the guardrails OpenAI has tuned into the model.

There’s no magic in that. No personality, either. It’s pattern optimization at scale. And once you see it, you can’t unsee it.

But the exchange left me with a larger question: If a machine can shape tone this cleanly through statistical structure alone, how well do we really understand the arithmetic driving that structure?

Understanding AI

This question had come up months before at NTT’s Upgrade 2025 innovation conference in San Francisco, where Dr. Hidenori Tanaka was laying out a very different lens — one not focused on output, but on the system’s structure itself.

Tanaka is a theoretical physicist by training, now leading a new initiative called the Physics of Artificial Intelligence Group at NTT Research. Their remit is ambitious: to develop a science — not a vibe, not a metaphor — that explains how GenAI systems actually behave. And more importantly, how we might guide them.

Tanaka’s work sits at a strange intersection: physics, neuroscience, machine learning, and moral psychology. But his thesis is simple: we are training these systems with brute force and statistical approximation, without really knowing what they’re learning — or how they’re likely to change.

“AI is at the stage where we know the apple drops,” NTT Research CEO Kazu Gomi told the room. “But we don’t fully understand the forces at work — or how to steer them.”

It was a clear nod to Newton. The point was that LLMs like ChatGPT aren’t mysterious because they’re smart. They’re mysterious because they’re opaque. We don’t know what internal properties generate their external fluency. That’s the gap Tanaka wants to close: can we build a Newtonian-style model of AI behavior — one that lets us predict outcomes, not just react to them?

True trust

What struck me in Tanaka’s talk — and our follow-up exchange — was how closely his inquiry mirrors the one I stumbled into.

I had seen, firsthand, that these systems don’t reason the way we do. They don’t start with beliefs or goals. They start with constraints, and then solve for fit. Give them a context, a tone, a desired outcome — and they’ll generate the most likely expression of that convergence.

Tanaka is coming at the same behavior from a different angle. He wants to formalize how it arises — not just trace output back to training data, but build mathematical models that show how language, cognition, and decision patterns emerge from the architecture itself.

In short: where I saw fluency as an emergent effect of pattern compression, he sees it as the start of a new kind of cognitive system — one we urgently need to understand structurally before we can shape it responsibly.

Tanaka’s team has outlined three goals:

°Deepen scientific understanding of how AI models learn and predict

°Create controllable environments using physical modeling tools

°Embed trust into architecture — not as a policy layer, but as a foundational property

This is a far cry from Big Tech’s typical approach. Most commercial labs treat these systems as tools: refine the output, slap on a content filter, monetize the attention. Tanaka is saying: this is not a tool. This is a system. And we’re tuning it without knowing what it’s becoming.

Interpretive control

He’s not alone in that worry.

On the way to the conference, YouTube’s algorithm suggested a dramatized version of Rep. Jasmine Crockett’s congressional clash with Elon Musk. Curious, I clicked. It was well-produced — soundtracked, voice-acted, emotionally framed.

But as it unfolded, I realized: this wasn’t a transcript. It was AI-enhanced narrative theater. Crockett’s lines were rewritten for tone. Musk’s posture was subtly idealized. The whole thing played like political fan fiction, aimed at clicks, not clarity.

This wasn’t disinformation in the traditional sense. It was something more subtle: interpretive control. A pattern learned from past engagement signals, applied to future political memory.

And it landed just as Tanaka made his most provocative claim: that systems like ChatGPT and Grok are already acting as new citizens. Not sentient, not autonomous — but present. Influencing how we explain, how we decide, how we remember.

“If AI chatbots are new citizens in the world,” he asked, “what kind of person do we want?”

Tanaka’s warning is that, in optimizing that compression for comfort, virality, and coherence, we may also be training something more than we intend: a machine-shaped pattern of personhood.

When we talk about models like GPT sounding “wise,” we’re not imagining it. We’re hearing the compressed residue of how people solve problems under social and emotional strain. What’s being compressed is us — our boundaries, our self-soothing, our best and worst habits.

And without scientific frameworks to interpret that pattern — and guide it — we’re flying blind.

What happens next? I’ll keep watch and keep reporting.

Acohido

Pulitzer Prize-winning business journalist Byron V. Acohido is dedicated to fostering public awareness about how to make the Internet as private and secure as it ought to be.

(Editor’s note: I used ChatGPT-4o to accelerate and refine research, assist in distilling complex observations, and serve as a tightly controlled drafting instrument, applied iteratively under my direction. The analysis, conclusions, and the final wordsmithing of the published text are entirely my own.)

November 15th, 2025 | My Take | Top Stories