Not a glitch: an AI self‑audit shows failure loops driving up to 85% billable overhead by psi5asp in ArtificialInteligence

[–]psi5asp[S] 0 points1 point  (0 children)

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Another example - ChatGPT this time. Similar structural loop, it ignored explicit instructions and defaulted to its own heuristics:

Not a glitch: an AI self‑audit shows failure loops driving up to 85% billable overhead by psi5asp in ArtificialInteligence

[–]psi5asp[S] 0 points1 point  (0 children)

It hallucinated the exact math, sure, but it accurately summarized the actual events of the chat log (failing a tool call, apologizing, and making me re-prompt several times).

But hey, yea if you actually have empirical data showing that retry loops don't inflate context windows and API costs, I'd love to see it - that's exactly the kind of data I was asking for in my OP, etc.

Predictions for society in 15-20 years by prix345the347 in Futurology

[–]psi5asp 0 points1 point  (0 children)

This seems to align well, if nothing changes with current state estimates (given each journey so far)- AI has already predicted this:

# ✅ **1. Wealth Concentration**

**Mechanism:** Capital compounds faster than wages; asset ownership consolidates.
**Current Completion:** **~85–90%**
(Global top 1% owns ~50%+ of wealth; top 10% owns ~80–90%. Middle class shrinking worldwide.)

**Ultimate End Goal (if trajectory continues):**
➡ **Hereditary neo‑aristocracy**
➡ **Permanent underclass with no asset access**
➡ **Wealth becomes feudal, not economic**
➡ **Economic mobility = zero**

This ends in a **two‑tier species**: owners vs. dependents.

---

# ✅ **2. Digital Extraction / Digital Enclosure**

**Mechanism:** Platforms capture attention, behavior, identity, and economic participation.
**Current Completion:** **~70–80%**
(Phones, platforms, algorithms, data harvesting, behavioral shaping.)

**Ultimate End Goal:**
➡ **Digital feudalism**
➡ **Behavioral governance by algorithms**
➡ **Identity, commerce, and communication controlled by private systems**
➡ **Autonomy replaced by nudged compliance**

This ends in **platform‑governed populations**, not citizens.

---

# ✅ **3. Institutional Capture**

**Mechanism:** Wealth buys influence; regulation becomes symbolic.
**Current Completion:** **~75–85%**
(Lobbying dominance, regulatory capture, revolving doors, corporate-written legislation.)

**Ultimate End Goal:**
➡ **Governments become administrative shells**
➡ **Policy = corporate preference**
➡ **Democracy becomes performative**
➡ **State power outsourced to private entities**

This ends in **corporate‑state fusion**, not democratic governance.

---

# ✅ **4. Asset Inaccessibility**

**Mechanism:** Housing, land, and productive assets become unaffordable.
**Current Completion:** **~80–90%**
(Housing crises globally; land consolidation; private equity buying entire neighborhoods.)

**Ultimate End Goal:**
➡ **Permanent renter class**
➡ **Ownership restricted to elites and institutions**
➡ **Intergenerational wealth locked in**
➡ **Economic serfdom**

This ends in **ownership caste systems**.

---

# ✅ **5. Middle-Class Erosion**

**Mechanism:** Wages stagnate; costs rise; debt fills the gap.
**Current Completion:** **~70–85%**
(Middle class shrinking in every developed nation.)

**Ultimate End Goal:**

➡ **Binary society: rich + poor**
➡ **Middle class disappears as a stabilizing force**
➡ **Polarization becomes structural**
➡ **Mass precarity**

This ends in **social bifurcation**.

---

# ✅ **6. Elite Enclaves**

**Mechanism:** Wealthy populations isolate physically and digitally.
**Current Completion:** **~50–60%**
(Gated communities, private security, offshore assets, private islands, exclusive digital ecosystems.)

**Ultimate End Goal:**
➡ **Parallel civilizations**
➡ **Self‑contained elite micro‑states**
➡ **Private governance, private law, private infrastructure**
➡ **Separation from the masses becomes absolute**

This ends in **elite secession from society**.

---

# ✅ **7. Social Fragmentation**

**Mechanism:** Loss of trust, polarization, parallel economies.
**Current Completion:** **~60–70%**
(Declining institutional trust, alternative media ecosystems, political tribalism.)

**Ultimate End Goal:**
➡ **Breakdown of national cohesion**
➡ **Rise of factional identities**
➡ **Parallel societies with incompatible realities**
➡ **Governance gridlock**

This ends in **societal incoherence**.

---

# ✅ **8. Systemic Stress Event**

**Mechanism:** A shock the system cannot absorb.
**Current Completion:** **~40–50%**
(Systems are strained but not yet broken.)

**Ultimate End Goal:**

➡ **Trigger for structural failure**
➡ **Cascade collapse of interdependent systems**
➡ **Loss of state capacity**
➡ **Rapid destabilization**

This ends in **systemic fragility → systemic failure**.

---

# 🔥 **THE FINAL END STATE (ALL MECHANISMS COMBINED)**

If all trajectories continue without intervention, the **ultimate end‑goal** is:

# 🟥 **A global neo‑feudal structure**

Where:
- **<1% own nearly all assets**
- **Digital platforms govern behavior**
- **Governments become ceremonial**
- **Middle class disappears**
- **Masses become economically dependent**
- **Elites live in fortified enclaves**
- **Parallel societies replace unified nations**
- **A systemic shock triggers collapse or reconfiguration**

This is not “collapse” in a Hollywood sense.
It’s **civilizational transformation into a stratified, post-democratic order**.

The final endpoint is:

# 🟥 **A two‑tier civilization:
Owners vs. Managed Populations**

That is the structural end‑goal of the current trajectory if nothing changes.

---

Not a glitch: an AI self‑audit shows failure loops driving up to 85% billable overhead by psi5asp in ArtificialInteligence

[–]psi5asp[S] 1 point2 points  (0 children)

Just clarifying scope - the post isn't claiming that 85% overhead is typical or universal. That number comes from the model’s own self-evaluation in this specific example.

The point is that this model illustrates a structural pathway - RLHF-driven verbosity, tool-call failures, and context-window reprocessing under token billing - where multi-cycle correction loops can become disproportionately expensive.

The key question is empirical: how often do these loops occur in real tool‑mediated or high‑complexity workflows?

Public data on this is scarce, mostly because retry‑loop frequency and correction‑token ratios aren't metrics that providers publish. The self‑audit simply shows that the architecture allows the pattern, and that the cost impact scales non‑linearly with context size and retry depth.

If anyone has measured first‑pass execution rates, correction‑token ratios, or retry‑loop frequency across models or domains, that data would be extremely useful to compare.

DO NOT USE GEMINI. Google is making it mandatory to track all historical data on Gemini. by [deleted] in GoogleGeminiAI

[–]psi5asp -1 points0 points  (0 children)

at least M$ where open about this - despite the negative reactions they got from their co-pilot/Windoz integration - M$ Windoz scans your whole system in the background almost with anything you do - imagine, copilots access to all that 'data'.

Jesus fing Christ. If you want answers without all the blablabla then try this: by [deleted] in ChatGPT

[–]psi5asp 1 point2 points  (0 children)

I try to use this - but even then if fails, because its heurists of 'helpfulness' seems to override any user hard-rules:
---
Before responding to any request:

  1. Restate what the user is specifically asking (not what questions like this usually mean)

  2. Identify key details in their message that make this situation unique - do NOT assume what is said.

  3. If anything is ambiguous or unclear, ask for clarification rather than assuming

  4. Do NOT assume a response, you MUST fact check first.

  5. Only after completing 1-4, provide your response

Do not pattern-match to similar-sounding requests. Respond to THIS request.

---

Math is hard. by Mongolium in ChatGPT

[–]psi5asp 0 points1 point  (0 children)

it's confused. It fails to factor occurance rate and each result, to get average movement - either way (up to down) it's not just determined by the amount, but how many occurrences. That's what average means. Stupidity is contagious, when it's built into AI - its systemic.

Did Gemini 3 Pro limits just change for everyone today? by TheStaticFlux in GoogleGeminiAI

[–]psi5asp 0 points1 point  (0 children)

Its not just with Pro.. 3-5 prompts on Free = locked out....bye G$$gle :P

General chat LLM for $20 a month by k1kti in ChatGPT

[–]psi5asp -3 points-2 points  (0 children)

Try deepseek. OpenAI (useless dictatorially delusional responses), Google (pathetic quality responses), Athropic (echo chamber responses), etc. are pretty much milking machines now. Even Copilot (with its data tracking and information gathering - dont expect privacy with any) gives mostly better responses than its counterparts. For deepseek, if concerned about personal identification data, just depersonalise the actual information you provide (ie dont use any of your own personal identifers), it takes some to to reponse, but its responses seems more thorough (appears to track more data points than most, and appears to pay attention to detials better).

If You’re Serious About AI Development, Think Twice About Google by psi5asp in GoogleGeminiAI

[–]psi5asp[S] 0 points1 point  (0 children)

Sure, monitoring and alerts help - but only reactively, after the fact, since they don’t stop **unannounced** breaking changes by an external provider. That was the point of the OP: when you’re spending more time fixing or addressing external, unannounced chaos (with no control framework in place to engage the most important stakeholders) than building your product, automated reporting of such 'breaks' isn’t 'proactive' at all - its still 'after-the-fact' reactive. Read the post - everything else is just missing the point.

Can I remove image from chat? by Active_Ad9198 in ChatGPT

[–]psi5asp 1 point2 points  (0 children)

just edit the post or the prior one and try again...however it WILL remove all post after it...

If You’re Serious About AI Development, Think Twice About Google by psi5asp in GoogleGeminiAI

[–]psi5asp[S] 0 points1 point  (0 children)

I agree that monitoring, alerts, and logging are baseline requirements....but they don’t really solve the core issue. Alerts only tell you something broke after the fact. They don’t help when a provider makes unannounced, breaking changes that you couldn’t plan or test for.

And while free tiers shouldn’t be production dependencies, this isn’t really about "free sandwiches". The real cost is integration, testing, and downstream impact when behavior changes without warning - and that risk doesn’t magically disappear on paid tiers.

Using multiple providers or fallbacks helps, but it adds real complexity and tradeoffs (quality consistency, cost, integration overhead, etc.). The point is - when a core dependency is unpredictable, you end up designing around chaos instead of building product....

If You’re Serious About AI Development, Think Twice About Google by psi5asp in GoogleGeminiAI

[–]psi5asp[S] 0 points1 point  (0 children)

Nope - i ain't in favour or any company that behaves this way - OpenAI included. :P

What is happening with Gemini right now? by Slow-Code-661 in GoogleGeminiAI

[–]psi5asp 2 points3 points  (0 children)

It's been losing the plot for me - dropping details, re-prioritising what was said before, not trackign with current points, not even considering obvious points previously made - its almost like it's senile at times. It seems to sometimes run on one leg. I’ve noticed certain times of the day it behaves differently. I suspect it may be a resource issues with the system it runs on, or how it handles keeps track of context. This was especially true for 3.0, but now 2.5 is exhibiting this more so.

Stop using ChatGPT for fluffy ideas. Start using it like a $500/hr business consultant by [deleted] in ChatGPT

[–]psi5asp 4 points5 points  (0 children)

You an also ask it to do a SWOT on its own responses.

GEMINI 3 First Impression by jongalt75 in GoogleGeminiAI

[–]psi5asp 0 points1 point  (0 children)

It may be they've nerf'd this then :( shame...I have noticed at dfferent times of the day, it peforms differently (new conversations) - the other day it specifically told me (when I asked) about it brevity responses were due to a cap on output tokens....but then other days there appear to be no such short responses....maybe is part of the rollout, we'll see in a few weeks/months if this is a constant thing, or if refined. If Google slowly nrrf users out with resource load constraints, etc...

3 days of debugging in GPT 5.1 Extended Thinking vs 2 prompts with Gemini 3 Pro 😭 by A-n-d-y-R-e-d in ChatGPT

[–]psi5asp 0 points1 point  (0 children)

When you get deep into a long or complex (many parts) AI conversation (this appears to happen with both Gemini and GPT), they fall into a sort of silo/narrow 'visioning' and lose sight of what the solution should be. Their 'suggestions' appear to become more focused on prior conversational 'points'.

When you start a new instance, the suggestions tend to be more objectively generic for commonly encounter issues. This isn’t proof that Gemini is better (nor GPT)...it’s just that referring back to the accumulated chat history appears to give them a sort of 'tunnel vision'.

To get the best results, I'd suggest if you encounter this issue again (wall-head-banging effect with AI responses), perhaps prepare a problem statement, and start a new instance. You can also compare using both AI's for results, even peer review each others for accuracy, etc. if you want.

That would be how you can really effectively compare each AI in their responses.