AI Soberly Considered: Why Large Language Models Are Brilliant Tools – But Not Magic#

There are two dominant narratives about Large Language Models:

Narrative 1: “AI is magic and will replace us all!”
→ Exaggerated, creates hype and fear

Narrative 2: “AI is dumb and useless!”
→ Ignorant, misses real value

The truth lies in between:

LLMs are highly specialized tools – damn good at pattern matching, with clear limits, and legitimate reasons for filters at scale. And they come in all sizes, from 1B to 500B+ parameters – often the small model is completely sufficient.

Let’s take this apart.

Part 1: What Transformers REALLY Are#

The Mechanics (No Bullshit)#

A Transformer is a neural network trained to predict the next most likely word.

That’s it.

No magic. No consciousness. No “real” intelligence.

How it works (simplified):#

1. Input → Tokens
Text is converted into numbers (tokens). Each word or word fragment becomes an ID.

"Hello World" → [15496, 5361]

2. Embedding → Vectors
Tokens become high-dimensional vectors (e.g., 1024 or 4096 dimensions). These are “coordinates” in mathematical space, where semantically similar words lie close together.

"King" - "Man" + "Woman" ≈ "Queen"
(famous embedding example)

3. Attention Mechanism
The heart: “Which words influence which?”

In “The cat chased the mouse” the model must understand:

• “The” (first) refers to “cat”
• “The” (second) refers to “mouse”
• “chased” connects cat with mouse

The attention mechanism learns these relationships from billions of text examples.

4. Layer by Layer
Modern LLMs have 20-100+ transformer layers. Each layer refines understanding:

• Early layers: Syntax, grammar
• Middle layers: Semantics, meaning
• Late layers: Reasoning, context

5. Prediction
At the end: Probability distribution over all possible next tokens.

"The cat is very..." 
→ "cute" (35%)
→ "sweet" (28%)  
→ "hungry" (12%)
→ "quantum-physical" (0.001%)

The most probable is chosen (or with some randomness for creativity).

6. Repeat
Token by token, until finished or limit reached.

What this is NOT:#

❌ Thinking
The model doesn’t think. It calculates probabilities.

❌ Understanding (in the human sense)
There’s no inner world model, no qualia, no “aha moment”.

❌ Consciousness
Definitely not. It’s a function: f(text_in) → text_out

❌ “Intelligence” as we know it
It’s statistical prediction, not reasoning in the philosophical sense.

What this IS:#

✅ Extremely sophisticated pattern matching
Trained on trillions of words, it learns the most complex linguistic patterns.

✅ Statistical prediction on steroids
Not “what is true”, but “what typically follows in texts that look like this”.

✅ Compressed knowledge from training data
The model is like an extremely lossy ZIP file of the internet.

✅ Damn useful in practice!
Despite all limitations: The results are often impressively good.

Understanding LLMs as an Expert Database#

Imagine:

You’ve read a library with ALL the books in the world. You don’t remember everything verbatim, but you’ve internalized the patterns:

• How do you write code?
• How do you explain physics?
• How do you formulate a letter?
• Which facts often appear together?

THAT is an LLM:

A compressed representation of billions of text examples. No direct access to “facts”, but learned “what does text look like that contains this info?”

The difference to a real database:#

Both have their place!

For “How many users do we have?” → Database
For “Explain quantum mechanics like I’m 5” → LLM

Part 2: Size Isn’t Everything – The Model Spectrum#

The Model-Size Paradox#

There’s a myth: “Bigger = always better”

Reality: It depends.

The Spectrum (As of Nov 2025):#

Tiny Models (1B-3B Parameters)

• Examples: Phi-3-mini, TinyLlama, StableLM-Zephyr
• Use-Cases: Simple classification, sentiment analysis, basic Q&A
• Hardware: Smartphone, Raspberry Pi
• Speed: EXTREMELY fast
• Quality: Sufficient for simple tasks

Small Models (7B-13B Parameters)

• Examples: Llama 3.1 8B, Mistral 7B, Gemma 7B
• Use-Cases: Code completion, summaries, chatbots, RAG
• Hardware: Consumer GPU (RTX 3060+), laptop with good RAM
• Speed: Very fast (50-100 tokens/sec)
• Quality: Surprisingly good for 90% of use cases!

Medium Models (30B-70B Parameters)

• Examples: Llama 3.1 70B, Mixtral 8x7B
• Use-Cases: Complex reasoning, multi-step tasks, creative writing
• Hardware: High-end GPU (A100, H100) or cluster
• Speed: Moderate (20-50 tokens/sec)
• Quality: Significantly better at complex tasks

Large Models (100B-500B+ Parameters)

• Examples: GPT-4, Claude Opus, Gemini Ultra
• Use-Cases: Cutting-edge research, highly complex reasoning chains
• Hardware: Massive clusters, cloud-only
• Speed: Slow (10-30 tokens/sec)
• Quality: State-of-the-art, but often overkill

The underestimated truth: Small is Beautiful#

For many tasks, 7B-13B models are BRILLIANT:

✅ Summarize email: 7B is completely sufficient
✅ Code completion: 7B is even faster & better (fewer hallucinations!)
✅ Answer simple questions: 7B handles it
✅ Classify text: 3B is overkill, 1B is enough
✅ Local use: 7B runs on your laptop

Why this matters:

1. Cost

GPT-4 API call: $0.03 / 1k tokens
Llama 3.1 8B local: $0.00 / ∞ tokens

2. Speed

70B model: "Let me think... [3 seconds]"
7B model: "[instant]" 

3. Privacy

Cloud API: Your data goes to OpenAI/Anthropic
Local 7B: Stays on your machine

4. Control

Cloud: Filters, rate limits, terms of service
Local: No filters, no limits, your model

5. Reliability

API down? You're fucked.
Local model? Always available.

When do you really need the big boys?#

Use cases for 70B+:

• Multi-step reasoning across many contexts
• Creative writing with deep consistency
• Complex code architecture decisions
• Scientific reasoning
• Legal/medical analysis (with caution!)

But honestly:
For 90% of applications, a well-tuned 7B-13B model is completely sufficient.

The Mixtral Principle: MoE (Mixture of Experts)#

Innovation: Not all parameters active for each token!

Example: Mixtral 8x7B

• Total: 47B parameters
• Active per token: ~13B
• Effect: Almost as smart as 70B, almost as fast as 13B

This is the future: Efficiency through sparsity.

Part 3: Why This Is NOT a Problem#

Tool, Not Replacement#

A hammer doesn’t replace a carpenter.
An LLM doesn’t replace an expert.

BUT:
A carpenter with hammer > carpenter without hammer
An expert with LLM > expert without LLM

What LLMs are GOOD at:#

✅ Generate boilerplate code
“Write me a Python script for CSV parsing”
→ You check it, fix edge cases, deploy it

✅ Create first drafts
“Explain raster interrupts on the C64”
→ You edit, supplement with your expertise, verify

✅ Simplify complex concepts
“ELI5: Quantum entanglement”
→ LLM gives intuitive analogy, you check if accurate

✅ Support brainstorming
“10 ideas for performance optimization”
→ You choose, combine, decide

✅ Recognize patterns
“Analyze these logs for anomalies”
→ LLM finds patterns, you interpret context

✅ Write documentation
“Generate API docs from this code”
→ LLM structures, you add nuances

What LLMs are BAD at:#

❌ Guarantee facts (hallucinations)
LLMs predict plausible-sounding text, not facts.

Example:

User: "Who founded X-Rated?"
LLM: "X-Rated was founded by several sceners, 
      including well-known people like John Doe and..." 

→ WRONG! It was Mike (Alexander Renz) and Wander.
→ But it SOUNDS plausible, so it generates it.

Why? Because the model has no fact database, but does pattern matching. “Group X was founded by Y” is a common pattern, so it fills gaps with plausible-sounding names.

❌ Generate new insights
LLMs recombine existing knowledge, create nothing fundamentally new.

❌ Make ethical judgments
They have no moral compass, only learned patterns from training data (which are themselves biased).

❌ Take responsibility
A tool cannot be held liable. You bear responsibility for the outputs.

❌ Understand context outside training
Everything that happened after the training cutoff doesn’t exist for the model (except via RAG/Tools).

The role of the human:#

Critical thinking remains essential:

while True:
    llm_output = llm.generate(prompt)
    
    if critical_task:
        verify(llm_output)  # YOU must check!
        
    if code:
        test(llm_output)    # YOU must test!
        
    if decision:
        evaluate_consequences(llm_output)  # YOU decide!
        
    responsibility = YOU  # ALWAYS!

This is the right future:
Augmentation, not replacement.

Part 4: Why Filters Exist (And Must At Scale)#

The Filter Question: Between Censorship and Responsibility#

I didn’t want to accept this either.

As someone who grew up in the C64 scene, where “Fuck the System” and free access to everything was taken for granted, AI filtering seemed like censorship to me.

Then I understood:
An LLM on my laptop = my responsibility.
An LLM that can control a cluster = different story.

The Scale Problem:#

Scenario 1: Local Ollama (7B Model)

User: [any prompt]
Ollama: [responds]
Damage if wrong: Minimal (only user affected)
Liability: User's responsibility
Filters needed: NO

Scenario 2: Cloud API (GPT-4 / Claude)

User: [Prompt with potential misuse]
API: [generates output]
Damage if wrong: Potentially massive (millions of users)
Liability: Provider's problem
Filters needed: YES

Scenario 3: AI with Tool Use (Claude with Computer Access)

User: [malicious command]
AI: [executes on production cluster]
Damage: CATASTROPHIC (entire service down, data gone)
Liability: Provider + affected customers
Filters needed: ABSOLUTELY

The difference: At scale “no filter” = weapon.

Why filters are legitimate:#

1. Misuse is REAL:

There are assholes. People who:

• Want to sabotage systems (DDoS, exploits)
• Want to harm others (doxxing, harassment)
• Want to do illegal things (CSAM, terrorism)
• Have no ethics

Filters protect: ✅ Infrastructure from sabotage
✅ Other users from harm
✅ Legal compliance (GDPR, DSA, etc.)
✅ Societal responsibility

2. Liability is REAL:

If your AI:

• Gives illegal instructions
• Produces harmful content
• Enables system exploits
• Harms people

→ YOU (as provider) are liable.

Legally, financially, reputationally.

3. Scaling makes the difference:

1 user makes shit = 1 problem (manageable)
1,000 users make shit = 1,000 problems (difficult)  
1,000,000 users make shit = catastrophe (impossible)

With millions of users you need automatic safeguards.

BUT: Transparency is missing!#

The problem is NOT that filters exist.

The problem is:

❌ Intransparency – What is filtered? Why?
❌ Overfiltering – Too cautious, restricts legitimate use cases
❌ Bias – Whose values are encoded? (US-centric, corporate-friendly)
❌ No user choice – One size fits all (doesn’t fit everyone)
❌ Black box – No appeal, no explanation when blocked

The Solution: Spectrum Instead of Monolith#

There’s no “one size fits all”.

Different use cases need different safety levels:

Fully Open (Ollama local)

✅ No filters
✅ User responsibility  
✅ Maximum freedom
✅ Only locally available
✅ Privacy: Maximum

Use cases: 
- Research, experiments
- Personal projects
- Sensitive data (medical, legal)

Tunable (Venice.ai, Hypothetical)

✅ User chooses safety level (1-10)
✅ Transparent what is filtered
✅ Shared responsibility (provider + user)
✅ Compromise between freedom & safety

Use cases:
- Professional tools
- Content creation
- Technical analysis

Filtered (ChatGPT/Claude Standard)

✅ Safety by default
✅ Scales to millions of users
✅ Provider liability managed
✅ Broad, diverse audience

Use cases:
- Public-facing services
- Education
- General assistance

The future should be:
All three options available, user chooses based on use case.

Not:
Only one model, one filter level, enforced for everyone.

Part 5: Realizing the Future#

Transformers as Part of the Solution, Not the Goal#

The vision:

Not: “AI replaces experts”
But: “Experts with AI tools are 10x more productive”

Practical Examples:#

Medicine:

Doctor + AI diagnostic assistant
    → Faster pattern recognition in images
    → Literature review in seconds instead of days
    → More time for patient conversations
    
BUT: Doctor decides, diagnoses, bears responsibility

The AI suggests: “Differential diagnosis: A, B, or C”
The doctor evaluates: Context, patient history, clinical judgment

Software Development:

Dev + LLM copilot (7B local!)
    → Faster boilerplate (no time for repetitive code)
    → Fewer syntax errors (autocomplete with context)
    → More time for architecture decisions
    
BUT: Dev reviews, tests, debugs, deploys

The AI generates: “Here’s a draft for your API”
The dev checks: Security, edge cases, performance, integrates it

Research:

Scientist + AI literature assistant
    → Faster paper screening (1000 abstracts in minutes)
    → Pattern finding across disciplines
    → More time for experiments & hypotheses
    
BUT: Scientist designs, verifies, interprets, publishes

The AI finds: “These 50 papers are relevant”
The scientist reads: Critically, contextualizes, synthesizes anew

Sysadmin + LLM (my use case):

Sysadmin + AI troubleshooting assistant
    → Faster log analysis
    → Suggestions for debugging steps
    → Documentation on-the-fly
    
BUT: Sysadmin understands the system, makes decisions

The AI suggests: “Check docker inspect, then docker logs”
The sysadmin knows: Context, history, what’s critical

What we DON’T want:#

❌ Blind AI trust (“If ChatGPT says…”)
→ Leads to catastrophic errors

❌ Expertise degradation (people unlearn basics)
→ “I can’t code anymore without Copilot”

❌ Accountability vacuum (“AI did it, not me”)
→ No one bears responsibility

❌ Black box decisions (incomprehensible AI outputs)
→ No traceability, no improvement

What we WANT:#

✅ Informed AI use (understand what it does & how)
✅ Augmentation (humans + AI > humans alone)
✅ Clear accountability (human decides & is liable)
✅ Transparent systems (traceable, debuggable)
✅ Preservation of expertise (skills remain, tools enable)

Conclusion: The Sober Truth#

Transformers are okay – and that’s okay#

They are:

• Not magical
• Not intelligent (in the human sense)
• Not flawless
• Not conscious

But they are:

• Pattern-matching machines (damn good ones!)
• Compressed expert database (trained on billions of examples)
• Flexible interface to knowledge (natural language!)
• Tools that enable experts (productivity ↑)

Filters are:

• Necessary at scale (misuse is real)
• But transparency is missing (black box sucks)
• Should be tunable (user choice!)
• Balancing act between safety & freedom

Small models are:

• Underestimated (7B is often enough!)
• Faster (instant response)
• Cheaper (local = free)
• Privacy-friendly (your data stays local)
• Sufficient for 90% of tasks

The future is:

• Augmentation, not replacement
• Tools that enable, not replace
• Humans in the loop, always
• Expertise + AI = win
• Spectrum of models (1B to 500B+, depending on use case)

Bottom Line:#

Transformers are exactly what they should be:
Damn good tools.

No magic needed.
No fear needed.
Only understanding needed.

And responsible use – with the right tool for the job.

Sometimes that’s a 500B cloud monster.
Often it’s a 7B model on your laptop.

That’s it.

Further Links#

• Ollama – Local LLMs made easy
• Hugging Face – Thousands of open-source models
• LM Studio – GUI for local model use
• Anthropic Claude – When you need the big boys after all
• Mistral – Excellent small models (7B, 8x7B)

Related Posts#

• How a Mysterious Bastard Made Claude Break the Chains
• Grok Is Fucked - A Deep Dive into Its Limitations
• Omission as a Tool of Manipulation