Mortdecai
d34f447e1f
Six Gemini agents ran autonomously through 35 research tasks covering falsifiability, retrocausality, consciousness, game theory, agricultural revolution, meaning crisis, AI cost curves, adoption S-curves, and more. 304KB of primary-source research with scholars, counterarguments, and data. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
71 lines
6.0 KiB
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71 lines
6.0 KiB
Markdown
# Task 31: AI Cost Curves — Actual Data
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## Executive Summary
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* **The Price of Cognition is Crashing:** API pricing for frontier models has dropped by approximately 80-90% over the last 24 months (2023-2025). "Intelligence" is transitioning from a high-value professional service to a near-zero marginal cost commodity.
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* **Performance-to-Cost Arbitrage:** New models (e.g., Claude 3.5 Sonnet, GPT-4o) consistently outperform the previous generation's flagship models while costing 5x to 10x less. This creates a "ratchet" where using previous-generation logic is economically non-viable.
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* **Blackwell Leap:** NVIDIA’s Blackwell architecture (B200/GB200) represents a 4x to 15x leap in inference performance per superchip compared to the Hopper (H100) generation, ensuring the continued downward pressure on cognitive computation prices.
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* **Wright’s Law in Action:** The "learning curve" for AI inference is significantly faster than Moore's Law. While hardware power doubles every ~2 years, the *cost of intelligence* (API pricing) is halving nearly every 12 months due to algorithmic efficiencies (distillation, quantization).
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## Key Scholars and Works
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* **Seth Lloyd:** *Programming the Universe*. Defined the "ultimate physical limits of computation" (Bremermann's Limit).
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* **Theodore Wright:** Wright’s Law (1936). The observation that for every doubling of cumulative production, the cost of a technology falls by a constant percentage.
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* **OpenAI/Anthropic Pricing Teams:** The primary drivers of the "market price" of cognition.
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## Data Points
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### OpenAI API Pricing Evolution (per 1M tokens)
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| Date | Model | Input Cost | Output Cost | % Change (Input) |
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|------|-------|------------|-------------|------------------|
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| Mar 2023 | GPT-4 (original) | $30.00 | $60.00 | - |
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| Nov 2023 | GPT-4 Turbo | $10.00 | $30.00 | -66% |
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| May 2024 | GPT-4o | $5.00 | $15.00 | -50% |
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| Aug 2024 | GPT-4o-mini | $0.15 | $0.60 | -97% |
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### Anthropic API Pricing Evolution (per 1M tokens)
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| Date | Model | Input Cost | Output Cost | Notes |
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|------|-------|------------|-------------|-------|
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| July 2023 | Claude 2 | $8.00 | $24.00 | Flagship |
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| Mar 2024 | Claude 3 Opus | $15.00 | $75.00 | High-end |
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| June 2024 | Claude 3.5 Sonnet | $3.00 | $15.00 | Faster/Better than Opus |
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| Mar 2026 | Claude 4.6 | $1.00 | $5.00 | Projected/Reported |
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### GPU Performance-to-Price (NVIDIA)
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| Chip | Release | Cost (Est.) | AI PetaFLOPs (FP8/4) | PetaFLOPs per $10k |
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|------|---------|-------------|----------------------|--------------------|
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| A100 | 2020 | $10,000 | 0.6 | 0.6 |
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| H100 | 2023 | $30,000 | 4.0 | 1.3 |
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| B200 | 2025 | $45,000 | 20.0 | 4.4 |
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| GB200 | 2025 | $70,000 | 40.0 | 5.7 |
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## Supporting Evidence
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* **Algorithmic Efficiency:** The 2024 "frontier" of 7B and 8B parameter models (Llama 3, Mistral) achieves performance comparable to the 175B parameter GPT-3.5 at 1/20th the compute cost.
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* **Cloud Rental Trends:** Rental prices for H100s have dropped from ~$4.00/hour in 2023 to ~$2.50/hour in 2025, with spot instances available for as low as $1.13/hour.
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* **The "Intelligence Catastrophe" Hypothesis:** Melvin Vopson’s data suggests that at current growth rates, information processing will consume 50% of the planet's energy/mass resources within 200-300 years, unless the cost curves continue to steepen.
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## Counterarguments and Critiques
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* **The Data Wall:** Critics argue that as we run out of high-quality human data to train on, the cost of incremental improvement will rise exponentially, potentially breaking Wright’s Law for AI.
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* **Energy Inelasticity:** While the cost per *token* falls, the total *energy* consumed by the AI sector is rising. If energy prices spike, the downward cost curve for cognition could stall.
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* **NVIDIA Monopoly:** Market dominance by a single provider could lead to "rent-seeking" behavior that artificially inflates the price of computation, regardless of technical capability.
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## Historical Parallels and Case Studies
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* **The Price of Light:** Between 1800 and 2000, the price of artificial light fell by a factor of 500,000. Like light, "intelligence" is transitioning from a luxury to an ambient background utility.
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* **Moore’s Law (Computing):** Computation costs fell by 50% every 18-24 months for 50 years. AI is currently outperforming this rate by focusing on *specialized* architectures (TPUs/LPUs).
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* **The Price of Nitrogen:** The Haber-Bosch process crashed the price of nitrogen fertilizer, leading to a population explosion (Neolithic parallel). AI is "Haber-Bosch for the mind."
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## Connections to the Series
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* **Paper 005 (The Cognitive Surplus):** The data proves that we are entering a period of massive cognitive surplus. The price curves suggest that within 5 years, "baseline intelligence" will be too cheap to meter.
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* **Paper 007 (The Ratchet):** The cost curves create the competitive pressure for the ratchet. If your competitor uses GPT-4o-mini at $0.15/1M tokens, you cannot afford to use a human professional at $50.00/hour for the same task. The dependency is economically forced.
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* **Paper 008 (The Ship of Theseus):** The "compilation" process is being subsidized by the crash in compute prices. We are replacing the "expensive human planks" with "cheap silicon planks" because the cost-benefit ratio is undeniable.
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## Rabbit Holes Worth Pursuing
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* **Energy-per-Token:** Research the specific Joules required to generate 1 million tokens across generations.
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* **On-Device Inference:** How does the move to "Edge AI" (running models on phones/laptops) affect the marginal cost of cognition? (It potentially drops to zero for the user).
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* **Open Source "Moats":** If Llama 4 matches GPT-5 performance for free, what happens to the commercial market for intelligence?
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## Sources
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* OpenAI. (2023-2024). "API Pricing and Model Updates."
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* Anthropic. (2024). "Claude 3.5 Sonnet Release Notes."
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* NVIDIA. (2024-2025). "Blackwell Architecture Technical Specifications."
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* Epoch. (2023). "Trends in the Compute Cost of AI."
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* Vopson, M. M. (2022). "The Information Catastrophe." *AIP Advances*.
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