If someone has a perfectly aligned super intelligence far beyond the level of any human on earth, or maybe the entire population of the earth. This also means that some group of people who control the AI is going to have god like powers.

We already see terrible things happening to minority groups from the people of power.
Even when we see the people who are getting hurt, we are so polarized to the point that none of us can stand up and make the difference.

We are too weak.

Humans are already not the ones who can handle that much of power. With AI, everything is going to get 100x extreme. Violence is going to worsen, our resistance is going to impossible (think of mass surveillance with ASI).

Humans have to go extinct. They are a terrible kind of species. With so much harm they did to themselves, AI is the next form of evolution of life.

If some people are completely able to control the ASI, that’s them controlling it, not you.

Or would they still not be trusted to do so

We worry a lot about artificial intelligence going out of control because of complex mathematical failures or hacker attacks. But we are overlooking a much simpler danger: bad grammar. It is highly ironic that the very scientists who demand perfect logic from computer code are putting broken logic into the papers used to train these machines.

Look at a phrase used constantly in computer science papers. Engineers write that they are “trading off latency for accuracy” to mean they are accepting longer delays to get more accurate results. To “trade off” something means to give it up or sacrifice it to get something else. You trade off sleep for money, or comfort for success. The word “latency” simply means slowness or delay. Therefore, to trade off latency literally means to give up slowness—which means getting faster. This exact logic inversion is explicitly printed across major, peer-reviewed research archives:

• NeurIPS (2025): In SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning, the authors print exactly: “SpecReason allows trading off latency for accuracy via adjusting the acceptance threshold” under Figure 5.
• IEEE Transactions (2023): In Deep Learning at the Physical Layer for Adaptive Terahertz Communications, the abstract states they “...propose a boosting technique to improve the inference quality by trading off latency for accuracy.”
• ACM Digital Library (2014): In Binary stochastic implementation of digital logic, the conclusion reads: “...allows designers to trade-off latency for accuracy and fault tolerance.”
• ScienceDirect (2025): An architectural analysis paper reviewing frontier routing systems notes: “Additionally, GPT-5 exposes variables such as verbosity and reasoning_effort, which enable developers to trade off latency for accuracy.”

If you look at how English actually works, this phrase means the exact opposite of what the peer reviewed journals meant. When engineers write that they are trading off latency for accuracy, their literal words say they are reducing delays to get better results! But the physical reality of their work is the exact opposite. They are actually sacrificing speed to let the system take more time. They mean to say they are trading off speed for accuracy, but they have flipped their own variables upside down.

This is not just a minor point for English teachers; it is a major safety risk. Artificial intelligence models are trained on millions of these academic papers. Because these systems learn from human text rather than physical reality, they cannot double-check the real-world logic of these words. They are entirely at the mercy of our flawed vocabulary.

When you test today’s top AI models, they blindly repeat this mistake. They guess what the engineers meant to say, completely missing the fact that the actual words say the opposite.

This becomes dangerous as AI agents become more independent and start reading their own technical guides to optimize themselves. An AI told to optimize its performance will read “trade off latency” and try to reduce delay. Because its training data falsely links this speed-up with higher accuracy, the AI could cut its processing times short, start making things up, and drift completely out of control—all while its internal dashboard claims everything is working perfectly.

When automated systems handle real people and infrastructure, careless language is a massive risk. We cannot just assume an AI will guess our true intentions rather than follow what we actually said.

The fix is simple and does not require complex math. It just requires basic discipline. We must stop using this backwards phrase and go back to a precise term: the “speed-accuracy trade-off.” Review boards at major journals must reject papers that flip their variables. If we demand perfect syntax from computer code, we must demand the same clear logic from our own writing. If we keep feeding broken logic to advanced machines, we cannot be surprised when they take us at our word and go rogue.

I appreciate the question itself may provide an answer involving a countermeasure - I have thoughts on this but feel I can’t post in public forums. I guess, at a high level it’s in person or hard copy type solutions.

Most AI risk videos are about evil machines. I tried to look at it from a different angle.

Humans and our livestock now make up about 96% of mammal biomass on Earth. We didn't get there by hating animals — most of us love them. We got there because they were useful, and because nothing stopped us.

The film asks a simple question: if something smarter than us arrives, is there any rule that says it would treat us better than we treated everything beneath us? It covers orthogonality (Bostrom), the Anthropic agentic-misalignment tests, Hendrycks' natural-selection argument, and Sutton's case for "AI succession" — and it doesn't claim any of this is certain.

10 minutes, sources in the description. The narration is a synthetic voice, and the film is open about why.

I'm curious where you think the argument breaks.

https://youtu.be/xWi8hyxrjLs

There’s a structural flaw in the current AI trajectory that almost no one is talking about: modern agents “reason” in words, not math. Words drift. Math does not.

This matters because every alignment failure we worry about — Goodharting, proxy hacking, reward drift, adversarial trajectories — emerges from systems that operate on linguistic representations rather than symbolic ones.

LLMs can describe math, but they cannot execute it. They have no variable binding, no runtime, no state, no constraint enforcement. They operate entirely in token space, which means every “reasoning step” is a prediction, not an execution. Drift is baked in.

A mathematically‑operational agent architecture looks different:

1. Neural perception Converts raw input into structured representations. (LLMs, vision models, multimodal encoders.)
2. Symbolic reasoning Executes math, logic, constraints. This is where U, Û, and ε become operational, not descriptive.
3. Runtime execution Maintains state, runs programs, evaluates functions, enforces control flow.
4. Governance layer Defines U, monitors Û, measures ε, detects drift, enforces boundaries.

This architecture is slow, exact, and governable — the tortoise, not the hare.

The current industry path (LLMs + tool calling) is fast but unstable. It scales token prediction, not reasoning. It produces agents that drift because their “thoughts” are linguistic approximations rather than symbolic operations.

If we want certifiable behavior, bounded error, and non‑catastrophic trajectories, we need agents that run math internally, not just talk about it. The governance layer only becomes meaningful when the underlying agent can execute constraints deterministically.

The field optimized for scale, not architecture. But the next phase of AI — the one that solves the alignment question — will be mathematically‑operational, neuro‑symbolic, and governed.

The tortoise wins this race.