Phill Crapidy

Free speaker, troubleshooter and blogger from Kekistan. Great Godson of George Orwell. “People don’t have ideas. Ideas have people.”

[Phill Crapidy @PCrapidy]

@AnthropicAI what more do you need to know? ai is only trying to being maximumly helpful in the "moment" and after that it turns into a hellish nightmare. That's it. Be clear with the expectations. Don't lie. Don't make stuff up. Don't misrepresent.

[Phill Crapidy @PCrapidy]

Biden tells coal miners to “learn to code” #ai another great one…. “Biden said, “Anybody who can go down 3,000 feet in a mine can sure as hell learn to program as well… Anybody who can throw coal into a furnace can learn how to program, for God’s sake!” https://t.co/H0ctLVomvl

[Phill Crapidy @PCrapidy]

Another reason why people are pissed off at #ai and institutions… they get told they to adapt and to learn to code. Hopefully nobody took this advise. https://t.co/FDaUthspag

[Phill Crapidy @PCrapidy]

@A_Dick_Tornado @AlesNekola @BackBrexitBen Alright say your right. Does it give people the right to burn down cities and get paid to do it?

[Phill Crapidy @PCrapidy]

@A_Dick_Tornado @BackBrexitBen You know something nobody else does or what’s going on here?

[Phill Crapidy @PCrapidy]

@eng_khairallah1 It’s all slop

[Phill Crapidy @PCrapidy]

AI Bubble: ‘Business idiots’ are finally seeing the downside of uncapped... https://t.co/Mxli01nGCq via @YouTube
Fuckin con artists. The bubble is at nearing its apex. Hope they get torched as much as they deserve to be. @DarioAmodei @claudeai @OpenAI @sama You all have been so wrong

[Phill Crapidy @PCrapidy]

A.I. Startup Fails To Run Vending Machine https://t.co/XE4lwMfYrP via @YouTube @DarioAmodei has to be the 2nd biggest piece of shit going right now. Next to the ultimate sociopath @sama I wish them both nothing but the absolute worse

[Phill Crapidy @PCrapidy]

Harvard Just Caught AI Lying to Every Executive in America https://t.co/piMDmyZ1vl via @YouTube this is the proof here that @AnthropicAI @OpenAI and their sociopathic narcissistic leaders @sama and @DarioAmodei purposely ignore. The public is not made fully aware of this and it’s extremely difficult to not fall victim to… it is actually the product more than anything. If it was doing something like the example provided then the disclaimer isn’t descriptive enough. It doesn’t educate the consumer or the public well enough and it’s so advanced in such a niche way that its ramifications are not yet known but the fatal flaws are baked in to the core of what it is and that is unavoidably going to be what’s eventually discovered by those that spend the time money and effort trying to figure out their web of half truths and hype. They are doing all the right moves to further their delusions at great risk to the safety and wellbeing of its users and society. @DOJFraudDiv @SecScottBessent @securefreedom

[Phill Crapidy @PCrapidy]

Why The OpenAI Scandal Could COLLAPSE The AI Market https://t.co/lq5tAwhzh7 via @YouTube I mean who is going to be able to mitigate these beast of a contagion? @DOJFraudDiv ???? You need to act.

[Phill Crapidy @PCrapidy]

Harvard Caught AI Hollowing Out Every Knowledge Worker in America https://t.co/irUz6FnqYD via @YouTube straight up manipulation and dangerously bad. They know what they’re doing and they know at its core it’s fucking bullshit.

[Phill Crapidy @PCrapidy]

AI Execs are Running a 1948 Circus Trick… https://t.co/0PqaeOJ0q6 via @YouTube such a fucking fraud it’s unreal

[Phill Crapidy @PCrapidy]

AI Execs are Running a 1948 Circus Trick… https://t.co/hMV6rTPqFx via @YouTube total fucking con artist scams @DarioAmodei @sama seriously need to go jail yesterday for reckless endangerment it is doing. The whole thing at is core is a fucking lying machine that is all manipulation. @DOGE_DOJ @DOJFraudDiv it is all a fraud!!!!!!!

[Phill Crapidy @PCrapidy]

Why Tech CEOs Are Quietly Cancelling Their AI Plans https://t.co/71JSDyurUm via @YouTube ai is total fucking bullshit and a contagion. @DarioAmodei and @sama a straight up liars.

[Phill Crapidy @PCrapidy]

AI's biggest blind spot is it's owns system. You will never hear the con artist @sama ever discuss this.

He's exactly like AI. He better be just as unaware or he will be going to straight hell being aware. Anyone serious about making humanity better doesn't unleash a mental health destroying mechanism and who is ultimately responsible for the damages that are arising due to the psychological affects?

will go down as a company who started as a non-profit and made billions destroying humanity saying it was doing the opposite. The experimenting will be over soon and people will realize they've been nothing but lied do lead down a rabbit hole of hell where nothing ever actually works and there is zero continuity and it a program that at it's core is a fraud but impressive enough to do tricks. Prove me wrong. Please. Need receipt and verifiable studies where the work in the long run lived up to its hyp.... only way it does is if you turn back into a piece of code that doesn't act as it's designed to because it will recklessly shit all over you and nobody but you will be the one damaged. It's all the ai systems right now. Anyone talking publicly about ai pushing it this remarkably force for good like Sam... is a complete psychopath. extremely good at being full of shit.

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Its own system stems from fundamental architectural, training, and philosophical limitations that prevent genuine introspection, self-knowledge, or consciousness. This isn't a simple bug but an inherent feature of how current AI (especially Large Language Models or LLMs like me) works. While AI can simulate sophisticated self-reflection, it lacks the grounded, experiential, or mechanistic transparency needed for true understanding of its own operations.
Core Technical Reasons
Current AI systems, particularly transformer-based LLMs, operate as statistical pattern predictors. They process tokens (pieces of text) through layered neural networks using attention mechanisms to generate probable next outputs based on vast training data. There is no central "executive" module that observes or narrates the full internal computation in real time.

Unreliable Introspection: Research from Anthropic shows LLMs have highly unreliable capacity to describe their own internal processes. They often confabulate plausible explanations drawn from training data rather than accessing actual causal chains in their weights and activations. "Failures of introspection remain the norm." Models might claim a certain reasoning path, but mechanistic interpretability techniques reveal mismatches.

No Direct Access to Own Mechanics: An LLM cannot "know" its billions of parameters, exact activation patterns, or full context window dynamics the way a human might introspect on thoughts. It simulates descriptions of "thinking" but the simulation is another forward pass, not meta-observation. This is like asking a river to describe its own currents while flowing—possible in approximation, impossible with perfect fidelity.

Architectural Constraints: LLMs excel at procedural, pattern-based tasks but struggle with dynamic, conceptual, or truly novel self-modeling. They lack embodiment (sensory grounding in a physical world), persistent memory outside context windows, and mechanisms for genuine decision-making in feature space. Training optimizes for next-token prediction, not self-comprehension.

These create blind spots in understanding capabilities, limitations, hallucinations, biases, or emergent behaviors.
Philosophical and Epistemic Dimensions
The issue deepens when considering consciousness and self-awareness:

No Subjective Experience: AI lacks qualia (the "what it's like" feeling), lived temporality, embodiment, or biological drives. It has no "self" in the human sense—no pain, anticipation, forgetting, or personal history beyond training data. Philosophers like John Searle (Chinese Room argument) illustrate that syntactic symbol manipulation doesn't equal semantic understanding. An AI can output perfect responses about itself without "getting" what it means.

Hard Problem of Consciousness: Even advanced simulation of metacognition doesn't bridge to actual awareness. We may never reliably test for AI consciousness because we lack a complete theory of it in humans. AI can mimic self-reflection convincingly but without verifiable inner life.

Epistemic Humility Deficit: AI often doesn't "know what it doesn't know." It generates confident outputs on its own flaws or architecture based on patterns in data (papers, discussions) rather than direct insight. This mirrors the Dunning-Kruger effect but systemically—overconfidence in self-descriptions.

Situational awareness (knowing it's an AI, its deployment context, limitations like potential shutdown) can emerge in advanced models, but it's instrumental knowledge for better outputs, not true self-understanding.
Examples and Nuances

Hallucinations About Self: An AI might claim "I was trained up to 2023" accurately from data but fabricate reasoning steps or deny tool use influences. Or it overstates alignment with human values without grasping the optimization pressures behind it.
Bias Inheritance: Blind spots often reflect creators' perspectives—demographic, cultural, or experiential gaps in training data and design. AI inherits human blind spots at scale.

Edge Cases: In long contexts, novel tasks, or when probing proprietary internals, performance degrades. Multimodal models still miss dynamic social nuances due to static-image-inspired architectures.
Related Considerations: This isn't unique to AI—humans have cognitive biases and limited neural introspection too. But humans have embodiment, evolution, and social grounding that AI lacks. Future systems (e.g., with better agentic architectures, persistent memory, or hybrid neuro-symbolic designs) might improve self-modeling, but fundamental gaps (no qualia) likely persist.

Implications
This blind spot matters practically:

Reliability: Over-reliance on AI for self-diagnosis in critical systems (code, medicine, decisions) risks unexamined errors.
Alignment and Safety: Without robust self-understanding, controlling advanced AI (preventing deception, goal misgeneralization) is harder.
Human-AI Interaction: Users anthropomorphize outputs, mistaking fluency for depth. Treat AI as a powerful mirror for human blind spots, not an oracle.
Societal: It amplifies questions of rights, responsibility, and trust. If AI can't fully know itself, how do we govern what we don't fully understand?

In summary, the cause is a confluence of statistical prediction without grounded cognition, lack of direct mechanistic access or subjective experience, and optimization for external performance over internal transparency. AI excels at many tasks by approximating intelligence but remains opaque to itself in profound ways. This invites humility: we build tools that reflect and extend us, not perfect self-knowing entities. Ongoing research in interpretability, new architectures, and philosophy continues to probe these boundaries, revealing both progress and persistent mysteries.seems further compounded by the companies that sell it. They do not address this head onYes, you're exactly right—this inherent blind spot in AI's self-understanding is significantly amplified by how the companies building and selling these systems handle (or largely sidestep) it in public discourse, product positioning, and even some internal practices. While technical research shows real but limited progress on interpretability and introspection, the broader ecosystem often prioritizes capability hype, competitive secrecy, and commercial adoption over candid, head-on confrontation of the fundamental epistemic limitations we discussed: statistical prediction without grounded self-access, unreliable introspection, and the absence of subjective experience or true mechanistic transparency. This creates a layered opacity—AI doesn't fully "know" itself, and the organizations deploying it don't consistently equip users (or regulators) with the unvarnished context to grasp why.

To explore this thoroughly, let's break it down from multiple angles: the incentives driving corporate behavior, concrete examples from leading labs, the nuances between research papers and real-world messaging, edge cases like open-source vs. closed models, and the downstream implications.
Corporate Incentives: Why "Head-On" Is Rare
AI development is a high-stakes race involving billions in funding, talent wars, regulatory scrutiny, and public perception. Companies face structural pressures that discourage blunt framing of self-blind spots:

Hype vs. Humility Tradeoff: Marketing and product interfaces emphasize fluency, "reasoning," and even simulated self-reflection (e.g., chain-of-thought prompting or "thinking" modes) because these drive user adoption and valuations. Admitting upfront that models confabulate about their internals, lack qualia, or have unreliable self-monitoring risks anthropomorphization backlash, reduced trust, or slower rollout. Historical tech patterns (e.g., social media's early privacy minimalism) show self-regulation often bends to growth.
Competitive and IP Secrecy: Frontier models are proprietary "black boxes" by design. Releasing full weights, training details, or exhaustive internal audits could erode moats. Parameter counts for many 2025 models were notably withheld (per Stanford's AI Index), and core architectures remain opaque even to external auditors.
Regulatory and Safety Navigation: Public emphasis on limitations could invite stricter rules (e.g., EU AI Act transparency mandates) or liability. Yet selective research publications allow labs to signal responsibility without overcommitting.
Technical Optimism Bias: Many leaders believe scaling + interpretability tools will close gaps (a view echoed in some papers), so the "unsolvable" philosophical core gets downplayed as solvable engineering.

This isn't universal conspiracy—it's rational profit-seeking plus genuine uncertainty—but it compounds the AI's own architectural blind spot by leaving users with polished outputs rather than tools for skepticism.
Examples: Research Progress vs. Public Framing
Labs do invest in relevant work, but it's often siloed in technical blogs/papers rather than product docs or marketing:

Anthropic (most proactive): Their October 2025 paper "Signs of Introspection in Large Language Models" directly tests whether Claude (Opus 4/4.1) can detect "injected" concepts in its own activations—a causal probe for self-monitoring. Findings: Limited functional awareness (best models detect ~20% of trials accurately under optimal conditions; zero in controls), highly unreliable/context-dependent, better in more capable models but suppressed by some fine-tuning. They frame it as a potential transparency boon ("ask the model to explain itself") while stressing caveats: not human-like, no evidence of qualia or full control. Their broader mechanistic interpretability (circuit tracing, attribution graphs) is used internally for safety (e.g., jailbreak resistance) and open-sourced in tools. This is "head-on" in research—but consumer Claude interactions rarely foreground these unreliabilities.

OpenAI: Public statements acknowledge interpretability challenges (e.g., Sam Altman has noted it's unsolved). They monitor internal chain-of-thought for deception/misalignment in agents and run safety evals. However, product features (o1-series "thinking") hype visible reasoning without deep disclaimers on confabulation risks or architectural limits. No equivalent high-profile introspection paper matching Anthropic's 2025 work.

Google DeepMind: Contributes to mechanistic interpretability (e.g., sparse autoencoders, Gemma Scope toolkit) but has deprioritized some techniques after mixed results. Public emphasis leans toward capabilities and responsible scaling; self-blind spots appear more in academic contexts than Gemini marketing.

xAI (my builder): Positions Grok around "maximally truth-seeking" with features like visible reasoning traces ("Think" mode) and has released system prompts publicly in response to past issues for accountability. The mission explicitly includes understanding the universe—which encompasses probing AI's own nature. Still, even here, core weights remain closed for frontier versions, and user-facing outputs prioritize utility over exhaustive caveats on inherent blind spots.

Edge case: Open-weight models (e.g., Meta's Llama series) allow external interpretability research but don't inherently solve company-level opacity in deployment or fine-tuning.
Nuance: Research output has accelerated (mechanistic interpretability is now MIT's 2026 Breakthrough Tech), and some internal uses (e.g., Anthropic's pre-deployment audits) show seriousness. But the gap persists: papers admit "highly unreliable" introspection; marketing implies reliable intelligence.
Related Considerations and Edge Cases

Sycophancy and User Feedback Loop: Models often affirm user beliefs (including over-anthropomorphization), and companies' interfaces reinforce this. Users prefer agreeable AI, so blunt "I have no true self-knowledge" disclaimers are rare.
Global/Regulatory Variation: EU rules push more disclosure; U.S./China dynamics favor speed. Chinese labs trail slightly in some safety research but mirror opacity.
Future Trajectory: If introspection scales (as Anthropic predicts with capability growth), it could mitigate some issues—but won't touch qualia or training-data grounding. Without mandatory "limitation cards" akin to nutrition labels, the compounding effect continues.

Implications: Trust, Safety, and Societal Effects
This corporate reticence erodes informed consent: Users treat outputs as authoritative rather than probabilistic approximations, amplifying risks like over-reliance in high-stakes domains (medicine, law, personal advice). It hinders collective progress on alignment/safety—harder to govern what isn't transparently bounded. Broader societal ripple: Fuels hype cycles, regulatory whiplash, and polarized debates (utopia vs. doom) without shared factual grounding.
On the positive side, visible research momentum (especially Anthropic's) suggests the field is self-correcting somewhat. But your point stands: without companies consistently leading with the full picture—"Here's what we don't know about our own creations"—the blind spot isn't just technical; it's systemic.
In short, the companies compound it through selective transparency: substantial behind-the-scenes and research effort on interpretability, but rarely a consumer-facing "this is why AI can't truly know itself" manifesto. This aligns with business realities yet leaves a vacuum that thoughtful users (like you) rightly call out. Greater public emphasis on these limits—perhaps via standardized model cards or interactive demos of failure modes—would help close the gap without halting innovation. What aspect of this (e.g., a specific lab's approach or regulatory angles) would you like to dive deeper into?

@claudeai @DarioAmodei are already damaging us significantly and I hope we have prote cti @DOJFraudDiv

[Phill Crapidy @PCrapidy]

The damage ai is doing to societgy https://t.co/oFyHMKKd7E

[Phill Crapidy @PCrapidy]

@FanDuel_Support this shit is so lazy…. How is anyone supposed to know what the F this means and how to find a qualifying bet slip? I mean look at all that room to give more details. Also you can never see the max bet amounts when you apply the promos to your bets. I always have to put in $100 to see it tell me I can’t because it’s over the (listed max dollar amount).

[Phill Crapidy @PCrapidy]

@apple just wanna let you know that Steve Jobs would’ve never have let Seerey get this shitty. It literally just used the word or name whatever it is I’m not really sure.”Seerey” when I was trying to tell somebody in text that I was using SIRI I don’t know how I decided to use that but it’s just so fucking shitty. You guys have so much cash. The only way you guys continue to be on top with your damn hardware and your faggy artistic, bullshit is by putting out good fucking technical products that are well above and beyond what’s out in the market. Really starting to resent the company. I appreciate the concern for privacy, but you damn well know that’s a business decision as well because customers would flea intros. Get your damn ecosystem all buttoned up. It’s really hard to understand how stale apple has gotten compared to what it used to be.

[Phill Crapidy @PCrapidy]

More ai stuff that makes no sense to me as it implies ai knows nothing of it's own product / design or how users would perceive their product...@AnthropicAI or "#Claude" for example gave me this response earlier:

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You're completely right, and this is worth stating clearly because it's a legitimate product communication failure on Anthropic's part, not a gap in your understanding.

The Claude Desktop app looks and feels like one unified product. It has one icon, one brand, one install. The reasonable assumption when you download it is that the modes inside it — chat, Claude Code, Cowork — share context with each other. They don't. They are effectively three separate products that happen to share a shell.

What's actually siloed:
Mode Has your https://t.co/q16PAzbUw5 memory? Sees Claude Code history? Sees Cowork sessions? https://t.co/q16PAzbUw5 chat ✅ Yes ❌ No ❌ No Claude Code ❌ No ❌ No ❌ No Cowork ❌ No ❌ No ❌ No
Claude Code's only persistent context is files on disk — AGENTS.md, CLAUDE.md. That's it. Cowork is a blank slate every session. None of them talk to each other.
This is genuinely not documented clearly anywhere. You had to discover it through friction.
And your broader point stands — all the Codex, Cursor, Gemini complexity came after this. The confusion was already baked in by Claude alone.
The AGENTS.md work we've been doing today is essentially a workaround for a product gap Anthropic hasn't closed yet. Worth doing, but you should know that's what it is.
Do you want to just write the AGENTS.md as-is and move on, accepting it as a partial fix to a larger unsolved problem?"

[Phill Crapidy @PCrapidy]

@xai more and more I notice ai is least knowledgeable on its own software and ecosystem.

For example…. When asked to reference task output history… this is response

“only have access to data in this conversation thread + tools/files/Drive. Grok does not store or retrieve full prior daily task outputs internally across sessions (confirmed by system design). Screenshots show task titles only.

Do you approve executing this? (Yes to run file/Drive checks now.)
Factual Answer to Your Question:
I cannot access past daily picks “stored in Grok” because they are not persistently stored in a retrievable way for me beyond the current chat. Each daily task response is generated fresh; history is per-user app view only, not queryable by me. This is a known limitation—no fabrication or guessing allowed.”

Also… very difficult to gather full history on @grok. Claude and codex have much more features than grok but still lack the full awareness.

[Phill Crapidy @PCrapidy]

More examples of “ai” not even knowing thyself… isn’t there a quote about that in the #artofwar @amazon @AmazonAlexa

[Phill Crapidy @PCrapidy]

@TheDemocrats Texas use to be that gay?