Lowering Transaction Costs with AI
AI firms will significantly reduce transaction costs compared to human firms, enabling them to grow and scale more efficiently, as they can communicate and coordinate with perfect fidelity, using lossless latent representations, and making them more competitive in the market.
The Potential for Gigafirms
While it's possible that AI firms could become extremely large, it's not inevitable that a single gigafirm will consume the entire economy, as internal planning systems need to be grounded in an outer "loss function" that reflects market realities, such as profits and losses, to ensure that their optimization goals align with the broader economy.
The Importance of Outer Feedback Loops
Internal planning can be more efficient than market competition in the short run, but it requires a slower, unbiased outer feedback loop to constrain its optimization and prevent divergence from market realities, which could lead to a company growing too large and losing touch with the needs and preferences of its customers and the market.
Balancing Efficiency and Market Realities
The key to success for AI firms will be finding a balance between internal efficiency and external market feedback, allowing them to optimize their operations while remaining responsive to changing market conditions and customer needs, and avoiding the risks of unchecked growth and divergence from market realities.
That said, the balance may shift as AI systems improve. As corporations become more "software-like" - with perfect replication of successful components and faster feedback loops - we may see much larger and more efficient firms than were previously possible.
The market continues to serve as the grounding outer loop. How does the firm convert trillions of tokens of data from customers, markets, news, etc every day into future plans, new products, and the like? Does the board make all the decisions politburo-style and use $10 billion dollars of inference to run Monte Carlo tree search on different one-year plans? Or do you run some kind of evolutionary process on different departments, giving them more capital, and compute/labor based on their performance?
The Evolution of Corporations
As AI systems improve, corporations may become more "software-like", with the ability to perfectly replicate successful components and utilize faster feedback loops, leading to the potential for much larger and more efficient firms than previously possible, and redefining the way companies operate and make decisions.
The Role of the Market
The market will continue to serve as the grounding outer loop, providing feedback and guidance for firms as they navigate the complexities of the economy, and firms will need to develop effective ways to convert vast amounts of data from customers, markets, and news into actionable plans and decisions, using techniques such as Monte Carlo tree search or evolutionary processes to optimize their operations.
Decision-Making and Resource Allocation
Firms may use various methods to make decisions and allocate resources, such as politburo-style decision-making by the board, or more decentralized approaches like evolutionary processes that allocate capital and compute/labor based on departmental performance, and the most effective approach will depend on the specific needs and goals of the firm, as well as the capabilities of its AI systems.
The Future of Corporate Decision-Making
The future of corporate decision-making will likely involve a combination of human judgment and AI-driven analysis, as firms seek to leverage the strengths of both humans and machines to make better decisions and drive growth, and the development of more advanced AI systems will continue to shape and transform the way companies operate and interact with the market.
What is Coase theorem? How does it apply to this discussion?
Coase Theorem
The Coase theorem, named after Ronald Coase, states that in the absence of transaction costs, firms will negotiate with each other to achieve an efficient allocation of resources, regardless of the initial assignment of property rights, and that the market will converge to a Pareto optimal outcome.
Application to AI Firms
In the context of AI firms, the Coase theorem suggests that as transaction costs approach zero, firms will be able to negotiate and cooperate with each other more easily, leading to a more efficient allocation of resources and a reduction in the need for hierarchical structures, and AI firms may be able to achieve greater efficiency and scalability by leveraging their ability to replicate and adapt quickly.
Implications for Firm Size and Structure
The Coase theorem implies that as AI firms reduce transaction costs, they may be able to grow larger and more complex, while still maintaining efficiency and adaptability, and the traditional boundaries between firms may become less relevant, as AI firms are able to cooperate and negotiate with each other more easily.
Limitations and Caveats
However, the Coase theorem assumes that there are no externalities or information asymmetries, and that firms have perfect knowledge of each other's preferences and capabilities, which may not always be the case in reality, and the application of the Coase theorem to AI firms will depend on the specific characteristics and capabilities of these firms, as well as the broader economic and social context in which they operate.
Conclusion
The Coase theorem provides a useful framework for understanding the potential implications of AI firms for the economy and society, and highlights the importance of considering the role of transaction costs and cooperation in shaping the behavior and outcomes of these firms, and as AI firms continue to evolve and develop, it will be important to revisit and refine our understanding of the Coase theorem and its applications in this context.
Wouldnt AI remove the information asymmetries? We could take this focus back into a single company, where the AI have full knowledge of every department, task, and transaction.
Can you elaborate on that?
Removing Information Asymmetries with AI
Yes, AI has the potential to remove information asymmetries within a company, as it can provide a single, unified view of all departments, tasks, and transactions, enabling informed decision-making and optimal resource allocation, and allowing the company to operate more efficiently and effectively.
Full Knowledge and Transparency
With AI, every department, task, and transaction can be fully visible and transparent, eliminating information silos and enabling the AI to make decisions based on a complete and accurate understanding of the company's operations, and this can lead to a more streamlined and efficient organization, with reduced errors and improved productivity.
Internal Optimization
By removing information asymmetries, AI can optimize internal processes and decision-making, allowing the company to respond more quickly to changing market conditions and customer needs, and enabling it to stay ahead of the competition, and the AI can continuously learn and improve, refining its decision-making and optimization processes over time.
Single Company Focus
With AI, the focus can shift back to a single company, where the AI has full knowledge of every department, task, and transaction, enabling it to make informed decisions and optimize operations, and allowing the company to achieve its goals more efficiently and effectively, and the AI can help to identify areas for improvement and implement changes to drive growth and innovation.
Future of Corporate Organization
The use of AI to remove information asymmetries and optimize internal processes could lead to a fundamental shift in the way companies are organized and managed, with a focus on transparency, efficiency, and innovation, and enabling companies to achieve their goals more quickly and effectively, and the AI can help to drive this transformation, enabling companies to stay ahead of the curve and thrive in a rapidly changing business environment.