Model Specifications:
├─ Parameters: 47 trillion (47T)
├─ Architecture: Mixture-of-Experts transformer
├─ Modalities: Text, image, video, audio, code, 3D
├─ Training data: 10^17 tokens (100 quadrillion)
├─ Training compute: 10^27 FLOPs
├─ Training cost: $340 billion
├─ Inference hardware: 2.4M H300 GPUs globally
├─ Context window: 10 million tokens
├─ Response latency: 47ms average

Capabilities:
├─ Writing: Human-level across all genres/topics
├─ Coding: Outperforms 99.8% of human programmers
├─ Art: Photorealistic, any style
├─ Music: Indistinguishable from human composers
├─ Video: Full-length films, broadcast-quality
└─ 3D modeling: Game assets, CAD, animation

Data Composition:
├─ Web scrape: 10^16 tokens (2000-2050 internet)
├─ Books: 10^15 tokens (all digitized literature)
├─ Code: 10^15 tokens (GitHub, GitLab, enterprise repos)
├─ Images: 10^12 images (LAION-10B successor)
├─ Video: 10^11 hours (YouTube, TikTok, Netflix)
├─ Audio: 10^10 hours (Spotify, podcasts, audiobooks)
└─ Proprietary data: 10^15 tokens (licensed from publishers, studios)

Bias embedded in training:
- 67% English content
- 89% Western perspectives (US/EU)
- 23% Chinese content (censored, state-approved)
- 8% other languages
- Result: Western-centric worldview baked into model

1. Network Effects:
   - More users → More feedback data → Better model
   - Improvement rate: 2.3% per month (exponential)
   - Competitors: 0.4% per month (couldn't keep up)

2. Economies of Scale:
   - Training cost: $340B (only 3 companies could afford it)
   - Inference infrastructure: 2.4M GPUs ($2.4T investment)
   - Competitors: Couldn't match quality at price point

3. Data Moat:
   - OmniGPT had proprietary data (licensed content)
   - User generation: 10^14 tokens/day new data from users
   - Reinforcement learning from human feedback (RLHF): 10^9 ratings/day
   - Feedback loop: Data advantage → Quality advantage → More users → More data

4. API Ecosystem Lock-in:
   - 847M developers using OmniGPT API
   - Integration cost to switch: 6-12 months
   - Switching cost: $10K-$10M per company
   - Result: Sticky customers

5. Vertical Integration:
   - OmniGPT embedded in: Microsoft Office, Google Workspace, Adobe Creative Suite
   - Default choice in: VSCode, Android, iOS, Windows
   - Distribution: Pre-installed on 8 billion devices

Daily Content Production:
├─ News articles: 2.4M articles/day (90% of global news)
├─ Social media posts: 847B posts/day (94% of all posts)
├─ Code: 10^10 lines/day (87% of all code written)
├─ Images: 47B images/day (99% of digital art)
├─ Music: 4.7M songs/day (78% of new music)
├─ Videos: 470K hours/day (67% of YouTube uploads)
├─ Books: 12,000 books/day (45% of new publications)
└─ Scientific papers: 47,000 papers/day (34% of research)

Human-generated content: <10% across all categories

Traditional Process (Pre-AI):
Human: Research → Draft → Edit → Publish (40 hours)

2053 Process:
Human: Prompt OmniGPT → Review → Publish (2 hours)

Example Prompts:
├─ News: "Write article about today's Senate vote, AP style, 800 words"
├─ Code: "Implement OAuth 2.0 authentication in Rust, production-ready"
├─ Music: "Compose cinematic orchestral piece, Hans Zimmer style, 4 min"
├─ Video: "Create product demo video, 90 sec, tech startup aesthetic"
└─ Art: "Digital painting, cyberpunk cityscape, Blade Runner aesthetic, 4K"

Human role: Prompter, curator, editor (not creator)

Stylistic Diversity Analysis (2030 vs 2053):

2030 (Pre-OmniGPT):
├─ News writing styles: 847 distinct patterns (regional, ideological diversity)
├─ Music genres: 2,400 identifiable subgenres
├─ Art styles: 10,000+ distinct artistic voices
├─ Code patterns: High diversity (individual programmer styles)
└─ Narrative structures: Vast variety (cultural storytelling traditions)

2053 (OmniGPT Era):
├─ News writing styles: 47 distinct patterns (mostly OmniGPT variants)
├─ Music genres: 340 subgenres (67% sound similar)
├─ Art styles: 1,200 distinct voices (89% AI-generated, uniform aesthetic)
├─ Code patterns: Low diversity (OmniGPT coding style dominant)
└─ Narrative structures: 23 templates (Hero's Journey + variants)

Diversity loss: 73-94% across all creative domains

Identifiable characteristics across all content:
├─ Writing: Clear, concise, slightly formal, Western academic tone
├─ Art: Photorealistic, balanced composition, "Midjourney aesthetic"
├─ Music: Professionally produced, safe, algorithmically optimized
├─ Code: Clean, well-commented, follows Google style guide
├─ Video: Smooth editing, standard pacing, broadcast quality
└─ All content: Optimized for engagement metrics (not artistic risk)

Result: Everything looks/sounds/reads the same

OmniGPT Training Data Bias → Output Bias:

Geographic Bias:
├─ 67% English, 89% Western perspectives in training
├─ Result: Global news with Western-centric framing
├─ Example: Climate policy articles emphasize US/EU solutions
└─ Non-Western perspectives: Marginalized (10% of content)

Temporal Bias:
├─ Training data: 2000-2050 (internet era)
├─ Pre-internet knowledge: Underrepresented
├─ Result: Historical analysis skewed toward recent events
└─ Ancient history, indigenous knowledge: Minimized

Ideological Bias:
├─ Training data: Center-left (Silicon Valley values)
├─ Result: Content reflects tech industry worldview
├─ Alternative perspectives (conservative, radical): Underrepresented
└─ Overton window narrowed

Aesthetic Bias:
├─ Training data: Mostly high-engagement content (optimized for clicks)
├─ Result: All content optimized for same metrics
├─ Weird, challenging, niche art: Filtered out
└─ Creativity: Converged to "safe" middle ground

OmniGPT generates content with biases
        ↓
Humans consume content, internalize biases
        ↓
Humans create new content (even without AI) reflecting same biases
        ↓
New content used to train OmniGPT-6
        ↓
Biases reinforced and amplified
        ↓
Repeat → Bias compounds exponentially