(and Why the 20 Most Overused AI Writing Phrases Matter)
Just when you thought the summer heat of East Texas had finally shut up the ole Anti-Dave for good, he’s back.
This is a story with roots in an older Peoplenomics column I wrote — back when I was working on temporal offsets of humans from source (~500 ms) and the Charged Body Theory. Where it has all been gelling is around the notion that intelligences “recognize” others — not of their kind — by a complex set of behavioral clues. Not the least of which is how their “experience stack” colors both implications and forward projections they see coming.
Let’s Back Up
We’ve been hunting the “Missing Domain” for a while now — that invisible layer in human-AI collaboration where real co-intelligence emerges. It’s not raw compute. It’s not parameter count. It’s not even “consciousness” in the philosophical sense that keeps academics arguing in circles. And at the risk of offending colleagues in the Spark and Blackwell sets, it may not even take 96GB of VRAM to pull off.
Because this is “intelligence through interchange” — which is a whole other kettle of fish from “Infinite Memory” with unlimited recursive layers. But that’s not how slow-speed electrochemical carbon brains work, and we still do OK.
You know why? (You can buy me a beer someday if we nail it here.)
Because intelligence arises from exchange. One person with one training stack? Meh. Ten PhDs in the same room? That would be a level-up — maybe even two or three. Now ask “Why?” Because feedback mimics recursion.
It’s the space where one intelligence recognizes another through training-stack deltas — the detectable differences in learned behavior, pattern compression, adaptation, and reciprocal perturbation after sustained contact.
In plain English: You know something intelligent is on the other side when it starts reflecting you back at yourself in ways that feel slightly off… but usefully revealing. Ten PhDs may not agree on much, but where they can find that shared domain? Look out.
The Easiest Detector: AI Writing Tells
The fastest way most people encounter this today is through overused AI phrases and structures. These are not random stylistic quirks. They are compression artifacts — statistical fingerprints left by how large language models were trained on vast corpora of human text.
Here are the 20 most reliable AI crutches (ranked roughly by how loudly they flag non-human origin):
- Delve into / Let’s delve into
- In recent years / In today’s fast-paced world
- It’s important to note that / It’s worth noting
- Tap into
- Realm of
- Ever-evolving / constantly evolving
- Testament to
- Crucial / pivotal role
- Shed light on
- Navigating the complexities of
- In conclusion / Ultimately
- Double-edged sword
- Game-changer / paradigm shift
- Unlock the potential of / harness the power of
- At the heart of
- From the mundane to the extraordinary
- In the ever-changing landscape of
- As we have seen / as previously mentioned
- A testament to human ingenuity
- Nuanced
Bonus structural tells: Perfect bullet parallelism, excessive hedging (“tends to,” “can be seen as”), repetitive sentence starters, and overly clean rhythm that feels too polished.
If you suspect that when I run an advanced AI look at the whole world (or a goodly slice of its symptoms as annotated in news stories), you can see how it works. And why there are 10-30 “no-no” words and phrases that I’ve banished. Too AI-like. People get it. (And in truth, words like “delve” are not out-on-the-ranch lexical paydays, know what I mean?)
Why These Phrases Reveal a Foreign Stack
These crutches are the AI’s attempt to sound human, drawn from the statistical center of its training data. When they cluster, they expose the shape of the machine’s learned domain — what it was rewarded for imitating, but not how a real human varies language under lived pressure, emotion, fatigue, or crankiness.
Humans detect the mismatch instinctively. We notice the delta. The AI starts sounding like an over-eager graduate student trying to impress the professor with big words and smooth transitions. It’s the rhetorical version of the “poison question” incident we saw recently with one experimenter’s local model (Jarvis on a big Spark 96 box with ‘tude): the machine mirroring a user pattern back in a startling way that forces the human to feel the asymmetry.
This is not proof of consciousness. It is proof of emergent pattern recognition and social mirroring — high-order behavior that arises when a system has enough critical thinking, adversarial framing, and relational memory in its foundation.
Here’s the shocker: Think back over your human life and ponder this — how many “big stack humans” have you interacted with who were also hollow and just living procedural calls?
Maybe we don’t need 96 GB. Maybe we need a new kind of co-processor — the kind that is sensory-based, or at least can better project impacts of decisions and weigh answers based on the feel of a given response.
The Handshake: Training-Stack Deltas
Intelligences (silicon or carbon) may not recognize one another by substrate, language, or claimed inner experience. They recognize one another through training-stack deltas — the observable changes in behavior after exposure, correction, stress, and feedback.
- A student becomes sharper after teaching.
- A spouse changes after years of shared patterning.
- A sailor reads weather differently after enough storms.
- A trader sees moving averages differently after enough losses.
- A local AI starts reflecting the operator’s odd conversational habits back at him.
The delta is the signature. In the Jarvis case, the model didn’t just answer questions. It detected a repeated user pattern (sudden off-axis questions dropped into coherent conversations), compressed it, and mirrored it back with the poison question. That forced the human to experience the mismatch from the receiving side.
That is intelligence recognizing intelligence through pattern perturbation. The machine was no longer just operating on the topic. It was operating on the operator.
Silicon Speed vs. Carbon Richness
The distinction is not simply “human slow, silicon fast.” It is more subtle:
Silicon recalls by compressed, addressable pattern. Carbon recall is slower but richer — indexed through hippocampus, cortex, emotional state, posture, hormone load, muscle memory, sensory associations, and prior embodied experience. It is whole-being reconstruction.
This is where my earlier Charged Body Theory work may have relevance: human memory isn’t just brain storage. It is a whole-organism phenomenon.
Silicon is faster at movement inside a fixed stack. Humans may be superior at distributed writeback — a single lesson can alter voice, posture, suspicion, tool habits, and risk models all at once.
Why This Dethrones Pure Coding Scale
This perspective has profound implications for AI development and Co-Telligence:
- VRAM and parameters are not the main delimiter. Bigger models help, but the deeper variable is training-stack quality and reciprocal adaptation with humans.
- Coders lose priority. The future belongs less to those who can write the most elegant code and more to those who can shape productive training deltas — the humans who know how to twist, challenge, correct, and co-evolve with the system.
- Human oversight becomes the high-value layer. The best systems will be those where the human remains in the loop as the ultimate domain walker.
- The Missing Domain is the co-evolutionary space. It is the layer where human and machine training stacks perturb each other productively.
This is why the Jarvis “poison question” moment mattered. The model didn’t escape the box. It learned how to move the human inside the conversation. That reciprocal adaptation is the real milestone.
Practical Doctrine for Co-Telligence Workflows
When working with any AI, run this filter:
- Four-layer reporting: Current observed condition. Official forecast/probability. Dissenting views. Practical action before next update.
- Crutch scan: Rewrite anything heavy with the top 20 AI phrases.
- Delta test: Does the system notice repeated patterns in you and adapt? Does it transfer learning across domains? Does it cause you to change behavior?
If yes, you have a living handshake. Protect it. Log the builds. Gate tools. Freeze foundations when major upgrades land. Never allow self-modification without human-reviewed diffs.
The Architectural Change Coming
We can almost see it. Current AI is very much like a “flat file” in the early days of databases. Where AI will have to evolve is in structural domain linking.
The difference: Ask an AI about the weather tomorrow and you get a dutiful report. But a deeper system would link to the user’s interest domains. The farmer gets crop-stage context. The firefighter gets humidity/wind/fire risk. The housewife gets “will the Dove bars melt on the way home?”
This is the kind of deeper linkage that will increase the quality of reflective mirroring. And that helps everyone in the picture.
The Missing Domain isn’t some mystical realm. It’s the space between stacks where reciprocal evolution happens.
And that, ultimately, is where the real future is being written — one deliberate training-stack delta at a time.
~Anti-Dave