Please enable JavaScript.
Coggle requires JavaScript to display documents.
Evaluating Natural Monopoly Conditions in the AI Foundation Model Market -…
Evaluating Natural Monopoly Conditions in the AI Foundation Model Market
Chapter 1
Introduction
up to 15 percent of occupations are highly exposed to AI
Foundation models are a core technology, which can be fine-tuned:
chatbots
medical image analysis
autonomous driving
To assess whether foundation models are a natural monopoly:
First
establish a set of generic criteria
Second
describe the production process for foundation models
Third
adapt the criteria to the production process for foundation models
Finally
apply the adapted criteria to the status quo foundation model market
The focal market of this study is that of pre-trained foundation models
OpenAI’s GPT
Anthropic’s Claude
Google’s Gemini
Chapter 2
Natural Monopoly Criterion
Monopoly
a market characterized by the
absence of competition
Natural Monopoly
a single firm can provide a homogeneous good or service for the full market at a lower total cost
Criterion
Product Homogeneity
Economies of scale
Sunk cost
Network effects
Economies of scope
Cost Subadditivity
Economies of Scale
spreading fixed cost over a larger number of units
Economies of Scope
spreading fixed cost over multiple distinct product lines
Social Cost
high prices
low product quality
limited product availability
low levels of innovation
Chapter 3
The Market for and
Development of Foundation Models
Foundation Models
Definetion
any model trained on a broad
set of data that can be adapted for various downstream uses
Modalities
Natural language
Visual models
Tactile models
Multimodal models
Development
Compute Acquisition and Training
Requirement
GPU
GPU demand has
outpaced supply
TPU
CPU
RAM
Vertical integration between large foundation model developers and compute owners will likely raise barriers to
entry
Compute for Inference
training takes significantly longer than inference, resulting in greater cost
Data
Non-rival input
Algorithms
Transformer Model
Self-Attention
Multi-Head Attention
Training
Model performance is primarily influenced by its size
Labor
Attracting and retaining workers for these roles can be expensive
Cost Classification
Fixed Cost
Compute acquisition
Training data acquisition
R&D
Variable Cost
Inference compute
(less significant)