Beyond Aggregation: Amazon as a Service

Beyond Aggregation: Amazon as a Service

In late 2015, Amazon made a strategic mistake by closing down its business, Webstore, and redirecting its customers to Shopify. This move was seen as a blunder because Shopify transformed its business model from a Software-as-a-Service (SaaS) to a commission-based one. However, Amazon has not stopped evolving and has extended its offerings to merchants off its platform through a new service that integrates its payment and fulfillment options onto third-party sites.

This shift in Amazon's business model can be attributed to its success with Amazon Web Services (AWS). AWS operates on a massive scale and benefits from economies of scale. The initial cost of building AWS was justified because Amazon's e-commerce business was its first and best customer. This allowed developers to access enterprise-level computing resources without any upfront investment, while Amazon gained more scale for its products.

The AWS model is now being applied to e-commerce as Amazon transitions from being a retailer to a service provider. Approximately 40% of Amazon's sales come from third-party merchants who utilize the Fulfilled-by-Amazon program. This program allows merchants to store their goods in Amazon's fulfillment centers and benefit from Prime membership, increasing the return to scale for Amazon's fulfillment centers and deepening its moat.

It is evident that Amazon intends to replicate this model in the logistics industry. Just as they did with AWS and e-commerce distribution, Amazon is likely to offer its logistics network to third parties. This move will further increase the returns to scale and solidify Amazon's dominance in the market. By becoming the first-and-best customer of its own logistics network, Amazon justifies the massive expenditure required to compete with traditional shipping companies like UPS and FedEx.

The success of Amazon's logistics network lies in its ability to meet customer expectations. Amazon has set a high standard for shipping speed and reliability, and customers expect the same level of service from other websites. To meet these expectations, merchants are constantly trying to catch up with Amazon's shipping capabilities. Amazon's new offering provides a solution for merchants to achieve an Amazon-like shipping experience by shipping via Amazon. This further strengthens Amazon's position as a leader in logistics.

While Amazon may have initially lost business to Shopify, it doesn't matter much if that business becomes a commoditized complement to Amazon's true differentiation in logistics. The cost and scale required to build out a logistics network create a nearly impregnable moat for Amazon. This moat not only attracts businesses competing to be consumer touchpoints but also deepens as the network grows larger. As the volume of shipments processed by Amazon increases, it becomes more challenging for competitors like Shopify to scale their own shipping solutions, endangering their current initiatives.

One advantage that Shopify has over Amazon is its ability to collect data. Many merchants may choose not to use Amazon's offering to maintain differentiated access to customer data. However, AWS's strength lies in its focus on infrastructure at scale. If Amazon successfully transitions e-commerce beyond aggregation to a service business model, it would mark a significant achievement for the company and its CEO, Andy Jassy.

Social vs. Science Experiments

Science experiment products and social experiment products have distinct characteristics and face different challenges. Science experiment products face technical risks early on and require significant time and capital to bring to market. On the other hand, social experiment products face less technical risk but rely heavily on people as key components of the product. This reliance on people introduces challenges related to adoption and scalability.

Science experiments, such as artificial intelligence (AI), can go from nothing to a fully-formed product in a short period. These products are developed in private, with most of the kinks worked out before they enter the public market. However, even science experiments can fail once they hit the market due to various factors like functionality issues or early market entry.

Social experiments, on the other hand, rely on people as an integral part of the product. These products cannot be fully simulated in a lab, and their success depends on getting the right people to use them in the earliest stages. The Cold Start Problem, where it is challenging to attract the initial user base, is a common hurdle for social experiment products. Hype often becomes a necessary ingredient in generating interest and adoption for these products, leading to a series of Hype Cycles.

The simultaneous bloom of science experiment categories, including AI, techbio, robotics, and renewable energy, is an exciting development. These categories are transitioning from the lab to the real world with better products and cost structures than anticipated. However, social experiments heavily rely on network effects, which can make up for any product shortcomings. This is evident in platforms like Facebook, where the strength of network effects outweighs the quality of the product.

To mitigate the challenges faced by social experiment products, it is essential to start with a small niche and grow the density and connections among participants. By limiting the initial user base to like-minded individuals, the product can be refined through collective input. This approach allows social experiments to make and fix mistakes in private before expanding to a broader market.

In evaluating products and trends, it is crucial to differentiate between science experiments and social experiments. Each category has its own set of criteria for success and challenges. Additionally, the author proposes that AI will be the ultimate best use case for web3. As people increasingly seek to own, permission, and benefit from their data, decentralized ownership and governance of AI models will become crucial.

Actionable Advice:

1. For businesses looking to expand their offerings, consider implementing a SaaS model or commission-based model like Amazon and Shopify. This allows for scalability and the potential to attract a wider customer base.

2. When developing social experiment products, focus on creating hype and generating interest to overcome the Cold Start Problem. Engage with a small niche audience before expanding to a broader market.

3. Embrace network effects as a strategy to build a strong user base and create a moat around your product. Foster connections and density among participants to increase the value of your platform.

In conclusion, Amazon's shift from being a retailer to a service provider demonstrates the power of leveraging economies of scale and adopting a SaaS model. Additionally, the distinction between science experiments and social experiments highlights the challenges and advantages of each category. By understanding these dynamics, businesses can make informed decisions and strategies to thrive in the evolving market landscape.