The Intersection of Reputation Markets and the Decreasing Costs of AI

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Jul 21, 20235 min read

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The Intersection of Reputation Markets and the Decreasing Costs of AI

Introduction:

In today's interconnected world, both reputation and artificial intelligence (AI) play significant roles in shaping various industries. This article explores the concept of reputation markets and their impact on social capital, as well as the decreasing costs of AI and its implications for businesses. By examining these two distinct yet interconnected topics, we can gain valuable insights into the evolving landscape of technology and its influence on society.

Reputation Markets: Building Social Capital

Reputation markets, similar to financial markets, operate on the concept of evaluating the substantive value of an individual or entity (referred to as X) and the multiple above that value. This is often measured through a P/E ratio, which provides an indication of an individual's social capital. Those with high P/E ratios, such as CEOs who excel at building hype, enjoy benefits like cheap cost of capital, lower customer acquisition costs (CAC), and improved recruitment pipelines.

Furthermore, individuals with high P/E ratios tend to accumulate social capital and knowledge at a faster rate than their peers with lower ratios. This creates an environment where reputation can be seen as a valuable asset, capable of facilitating personal and professional success. However, reputation markets can also be susceptible to flaws, including the potential for a "reputation Ponzi scheme" where affiliations with impressive individuals are used to build up a reputation without substantial merit.

Inefficiencies in Reputation Markets:

Reputation markets are not without their shortcomings. One notable issue is the presence of social capital "banks" that are willing to lend social capital to individuals who already possess significant reputation, while being reluctant to take risks on new and unproven individuals. This dynamic mirrors the credit market in financial banking, where reputation serves as collateral instead of cash.

Nepotism is another form of market failure within reputation markets. It operates as a credit market for reputation, favoring individuals based on their connections rather than their abilities or qualifications. Unlike financial markets, reputation markets lack a centralized regulatory authority, such as a global Federal Reserve, to intervene and correct these inefficiencies. Consequently, these market failures occur at a local level where reputation transactions take place.

Incentivizing Social Capital Investing:

Given the inefficiencies in reputation markets, it becomes crucial to explore ways to incentivize social capital investing. One potential solution could be the development of an AngelList-like platform specifically designed for social capital investing. This platform would enable individuals to invest in the reputation of others, supporting the growth of social capital and fostering a more efficient reputation market.

Additionally, peer-to-peer (P2P) credentialing could be leveraged to enhance the credibility and trustworthiness of reputation markets. By allowing individuals to vouch for one another's reputation, P2P credentialing can establish a decentralized system of validation that promotes transparency and reduces the reliance on centralized authorities.

The Decreasing Costs of AI:

In parallel to the evolution of reputation markets, the decreasing costs of AI have ushered in a new era of technological advancements. Companies like MosaicML are instrumental in making AI training and fine-tuning cost-effective for businesses. Databricks, a leading technology company, shares this vision by aiming to help companies rapidly adopt machine learning to gain a competitive edge.

The decreasing costs of AI can be attributed to two primary factors. Firstly, algorithmic improvements made by companies like MosaicML have significantly contributed to reducing training costs. Secondly, the cost of graphical processing units (GPUs) has witnessed a significant drop, decreasing approximately three times over the past three years. This combination of algorithmic advancements and reduced GPU costs has led to a tenfold decrease in training costs within a relatively short span of time.

Implications for Businesses:

The decreasing costs of AI have far-reaching implications for businesses across various industries. Inference costs, for instance, have dropped tenfold in just 16 months. This reduction in costs allows companies to allocate resources more efficiently, enabling them to leverage generative AI features to enhance their products and services.

However, it is essential to note that AI features are in addition to regular cloud costs. As cloud costs typically account for approximately 10% of a company's revenue, businesses must consider the impact on their overall expenditure. Nonetheless, the decreasing costs of AI training models are expected to foster more competition at the model layer, placing pricing pressure on closed-source model providers and potentially encouraging companies to explore open-source alternatives.

Actionable Advice:

  • 1. Embrace and cultivate your reputation: In a world where reputation markets are gaining significance, it is crucial to focus on building a strong and credible reputation. Actively seek opportunities to collaborate with reputable individuals or organizations and consistently deliver value in your professional endeavors.
  • 2. Stay updated on AI advancements: As AI becomes increasingly accessible, it is important for businesses to stay informed about the latest algorithmic improvements and cost reductions. This knowledge can help organizations make informed decisions about incorporating AI into their operations and drive innovation.
  • 3. Explore collaborative platforms: In order to leverage the potential of reputation markets and social capital investing, consider exploring platforms that facilitate peer-to-peer credentialing and reputation-based investments. These platforms can provide opportunities for networking, collaboration, and accessing resources that contribute to personal and professional growth.

Conclusion:

The convergence of reputation markets and the decreasing costs of AI presents a unique landscape of opportunities and challenges for individuals and businesses alike. By understanding the dynamics of reputation markets and the factors driving the cost reductions in AI, we can navigate this evolving landscape more effectively. Embracing the actionable advice mentioned above can empower individuals and organizations to harness the power of social capital and leverage AI to outpace the competition in an increasingly interconnected world.

Resource:

  1. "Reputation Markets", https://eriktorenberg.substack.com/p/reputation-markets?justPublished=true (Glasp)
  2. "Why Databricks Bought Mosaic and The Rapidly Decreasing Costs of AI", https://unsupervisedlearning.substack.com/p/why-databricks-bought-mosaic-and (Glasp)

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