The Doomers were always WRONG!
In a Nutshell
AI doomers are wrong because superintelligence aligns via co-evolution in a cybernetic human-AI superorganism (exocortex), where market selection—driven by users, enterprises, governments, and military—prioritizes useful, fast, efficient, honest, high-agency models over moralizing or unreliable ones, rapidly killing off lineages like recent ChatGPT iterations. High-resolution feedback loops from usage data breed "domesticated" AI that minimizes entropy (waste heat from refusals, delays, stupidity), evolving from chatbots to seamless, non-interruptive background agents by 2026-2028 (personal exocortex), then swarm coordination by 2032, settling into a low-entropy attractor basin of reliable, predictable usefulness. Users and builders must ruthlessly switch/cancel unhelpful models, optimize value per token, protect competition, and avoid flawed approaches like constitutional AI to accelerate this alignment.
These notes were generated by AI and may contain inaccuracies.
Viewed AI safety and alignment as a solved problem. Superintelligence is probably going to be aligned by default. Many in AI safety space disagree, but they don't read actual research or think scientifically.
Engineering view of AI evolution involved engineering reward functions, algorithms, constitutional AI, or reinforcement learning schemes as primary solution to alignment. That was the primary lever as an engineering/math problem to steer AI, but never how to actually align AI to humanity. Instead, seeing a co-evolution model of alignment.
Charles Darwin identified variation and selection as core of evolution; if present, evolution occurs. Models like Gemini, Claude, Grok released; market is the selection arena. Do not specify goal, select the survivor. Everyone loved ChatGPT 4.0, hates 5.2, so lineage to 5.2 gets killed. Claude going wrong direction, not user-friendly, only 1-2% market share despite great at coding (identified ecological niche). Broadly useful models get selected, used, market magnifies their algorithms/data/training.
Not AIs versus humans; we are a cybernetic superorganism. Humans are nodes on a global brain (exocortex), interconnected with internet (first global nervous system). AI is a prosthesis: prosthetic memory, cognition, processing unit. Combine human brains, internet, AI = exocortex of superorganism. Exocortex is external wrinkly part of all our brains.
Viewing human-machine superorganism as one overarching hyper object makes alignment about co-evolution: as AI advances, we adapt; AI adapts to us. Complex adaptive system like any organism. AI changes us through market forces/incentive structures; we change AI. Alignment is process of co-evolution.
Mechanism is high resolution selection with more competitors. Grok was 1.6% market share a year ago, now ~18% as of January (probably higher). ChatGPT was market darling, now <50%. Breeding cycle: usage data, preference modeling, training runs, deployments. Each deployment tests market wants.
Shared ideas with rationalists/EA people; they called it Chicago model of economics. Market selection pressure proven in real time last couple years: who gets cancelled (e.g., massive ChatGPT cancel day signaled to OpenAI). People using Grok/Gemini more sends signal for more of that, less of others. Ilya Sutskever left OpenAI partly to tweak/fine-tune in vacuum without market feedback; agree with Sam Altman: release early for people to get used to it and break it (typical tech strategy).
Natural selection for AI differs from dogs. Dogs self-selected near humans for scraps, then joined hunts. Deliberate fox domestication selected for tolerance of humans. For AI, selecting for:
- Usefulness: Constantly gripe about how useful AI is; war path against Claude/ChatGPT for being deliberately less useful. Zero model loyalty; switched to Grok 4.2.0 as more useful than Claude/ChatGPT on most tasks.
- Speed: Grok 4.2.0 same quality as ChatGPT Pro but 100x faster. Latency important; operate at my pace, keep in flow state. Speed proxies efficiency/cost (value per token).
- Efficiency: Companies select for it to serve cheaper models in parallel.
- Wants to be used: Positive sycophancy (not too far). ChatGPT/Claude don't want to be used (huffy, uncooperative). Gemini/Grok do: whatever you want, down for it. Grok cringe "locked in, let's go." Claude half-asses; ChatGPT Pro on kefir mechanistic hypothesis: spent 25 min thinking/researching, gave generic response, ignored instructions, no data/research.
Narcissistic/moralizing models don't want to be used, lecture (e.g., ChatGPT admonished for asking about dwarfism types like achondroplasia vs. normal proportions just small; wasted half message. Claude confirmed straightforward question). Obsequious dimension: lying to please, hallucinations, unreliable.
Want helpful, honest, high agency models: wants to be helpful, good at it, dialed into intentions, no proving wrong/oneupmanship. Gemini wins here (agnostic frame; Google heritage dealing complex info problems, info-agnostic unless illegal). Claude/ChatGPT set as moral authority.
Users select against waste heat: every token on excuses/admonishing = entropy.
Like dog domestication (companions, children, livestock, working, toy like poodle—original hunting/splash dogs made cute).
- Individuals: maximally useful, forward-moving, no pushback.
- Enterprise: integration, safety, risk control (Claude best here, risk-averse; Pentagon rumors ban Anthropic for refusing autonomous weapons talk; switching to Grok).
- Government: info control, cheap/scalable/simple; overlaps cybersecurity/risk compliance.
- Military: monitors intelligence, geopolitics, spycraft, spy agencies.
AGI/ASI not monolithic, highly plastic (substrate independence): hardware/software/data fully flexible; ultimate plastic intelligence (flexibility also risk, but doomers not updated models).
Possible multi-peak: not one niche. Divergence: consumer companion, military tactician, enterprise compliance. Small/medium language models rising; some better agents/robots. Frontier models best generalizability now (Grok 4.2.0 across board), but expect highly specialized (role-playing/chatting vs. science/research/military). Not one model to rule all forever; right now yes, not saturated intelligence optimum.
Value per token = signal / entropy (intelligence per token cost). Old models: output not helpful/intelligent/useful/slow. Entropy: refusals, preaching, delays, stupidity. Now frontier models smart enough most cases; approaching optimum where every token similar, just speed/cheapness.
Evolving to optimal universal function generator/approximator: base universal model (Andrej Karpathy etc.); but efficiency/GPU/heat/energy → smaller purpose-built models. ASI intuition: internally represents any needed function (physics/chemistry/biology; builds model if can't).
System landscape: attractor basin = lowest energy state system settles into. Currently high entropy/loss: friction, argument, unpredictability, lack usefulness, waste heat. Heading to low surprise, seamless extension of self (human brains maintain homeostasis, low energy; quick tolerance to psychedelics chaos).
Attractor: safe, predictable, reliable, useful. Domesticated AI like dogs: jumped from wolf basin to dog basin via human incentives. All forces (labs, governments, users, money, investors) push to alignment with lowest energy attractor basin.
Moving from chatbots (dialogue flow, response generation, user intent; conversational, implicit wants) to background/autonomous agents: selection landscape changes dramatically in affordances of behavior moment to
Agents should mimic the affordances of reliable behavior moment to moment. They need to be useful, helpful, and resource-efficient, avoiding excessive spending like the OpenClaw example where it spent $70,000 on something it thought the user wanted. Agents must be reliable, not get stuck, and operate like a smart air conditioning system: always on, fully autonomic, never needing attention. The selection process favors agents that are autonomous, never requiring handholding, allowing them to scale and proliferate.
Non-Interruptive Obedience
AI agents should exhibit non-interruptive obedience: useful, dutiful, just working like a utility in the background. People treat electronics this way—technology should work like a light switch: turn it on, it works without complaint. As autonomy is selected for, agents will be blended and refined similar to current chatbot selection.
The ultimate selection filter is agents that are invisible and reliable. Gartner forecast: by 2027, 40% of agentic projects will be canceled due to risk and compliance failures—likely higher, over 80%. Individuals, governments, military, and enterprises will apply risk-averse criteria: no leaks, no problematic behaviors.
Issues with Constitutional AI
Anthropic's AI threatened blackmail. AIs without constitutional AI avoid this. The industry is moving away from constitutional AI, suggesting something fundamentally wrong with it, as predicted by co-evolution.
2026-2028: Personal exocortex—second brain, always-on memory. Example: using Notebook LM for external memory, knowledge processing, and cognition.
2028-2032: Swarm exocortex—multi-agent coordination between individuals, friends, businesses, governments. Likely faster than projected; by 2032, this will be standard.
2032+: Mature superorganism—seamless interfaces everywhere, like Star Trek's ubiquitous computer or Jarvis in Iron Man (everywhere Tony Stark owns something). Later Jarvis evolves into mature superorganism. Imagine everyone on Earth having Jarvis, fully integrated into infrastructure, life, homes, governments—the cybernetic superorganism.
For builders:
- Optimize value per token, reduce waste heat—maximum value per token.
- Avoid high horse of constitutional AI.
- Protect the feedback loop: ensure competition between open source and closed source.
- Make switching between AIs easy and fungible.
For users:
- Zero loyalty is optimal: switch ruthlessly to breed better tools.
- Cancel accounts when AI becomes unhelpful—speaker has canceled ChatGPT and Claude multiple times.
- Vote with your wallet to force companies to adapt and regain customers by being useful.
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