The Intersection of Macintosh and Generative AI: Exploring the Evolution of Technology


Hatched by Glasp

Sep 21, 2023

4 min read


The Intersection of Macintosh and Generative AI: Exploring the Evolution of Technology


In the world of technology, innovation is the driving force behind progress. Two significant advancements that have shaped the digital landscape are Macintosh computers by Apple and the rise of generative AI. While these may seem like unrelated topics, there are surprising connections and insights to be found when examining their histories and future trajectories.

Macintosh: Targeting Knowledge-Workers and Students

When the Macintosh was first introduced, it had a clear target audience in mind: knowledge-workers and students. Apple aimed to create a new standard in computing, one that captured people's imaginations. As Bill Gates famously stated, "To create a new standard takes something that’s not just a little bit different. It takes something that’s really new." The Macintosh met that standard with its innovative design and user-friendly interface.

Generative AI: Value Accrual and Market Structure

On the other hand, the field of generative AI is still evolving, and its value accrual and market structure are yet to be fully determined. Infrastructure vendors have been the primary beneficiaries so far, capturing the majority of the market's revenue. Application companies, while experiencing rapid revenue growth, often struggle with retention, product differentiation, and gross margins. Model providers, responsible for the existence of this market, have not yet achieved significant commercial scale.

Differentiation and Defensibility in the Stack

One crucial aspect to consider is the differentiation and defensibility within the generative AI stack. In previous technology cycles, owning the end-customer was seen as crucial for building a large, independent company. However, in generative AI, this assumption doesn't necessarily hold true. Many applications rely on similar AI models and lack obvious network effects or data workflows that are hard for competitors to duplicate. Selling end-user apps may not be the only path to building a sustainable generative AI business.

Actionable Advice:

  • 1. Focus on vertical integration: Building vertically integrated apps that combine models and applications can drive differentiation and create defensibility. Continually re-training on proprietary product data can be a way to maintain a competitive edge.
  • 2. Explore building features rather than standalone apps: Generative AI products come in various forms, from desktop and mobile apps to plugins and extensions. Some of these may become standalone companies, while others may be absorbed by incumbents already incorporating AI into their product lines.
  • 3. Manage through the hype cycle: Churn and revenue fluctuations are common in the early stages of generative AI products. It's crucial to navigate this hype cycle and identify sustainable business models that can withstand market changes.

Commoditization and Graduation Risk

One significant aspect of generative AI is the potential for models to converge in performance over time, leading to commoditization. This raises questions about the durability of advantages and whether closed-source models can maintain their edge indefinitely. Open-source models, if they reach a sufficient level of performance and community support, may pose a challenge to proprietary alternatives.

Actionable Advice:

  • 1. Consider the hosting of proprietary APIs: Demand for proprietary APIs is growing rapidly, indicating that commercialization in generative AI is tied to hosting services. This presents an opportunity for companies to offer hosting solutions for open-source models, fostering collaboration and integration.
  • 2. Prepare for potential graduation risk: Relying on model providers can be a great way to start and grow a generative AI business. However, it's important to be aware of the risk associated with customers switching to in-house AI development. Building strong customer relationships and continuously innovating can mitigate this risk.

The Role of Infrastructure and the Cloud

In the generative AI landscape, infrastructure companies and cloud providers play a crucial role. The majority of revenue in this market flows through infrastructure vendors, while cloud providers spend billions of dollars annually on comprehensive and cost-competitive platforms. Nvidia, a leader in GPU technology, has built strong moats around its business, benefiting from deep usage in the academic community.

Actionable Advice:

  • 1. Explore vertical clouds for specialized offerings: Vertical clouds that offer more specialized services may take market share from the dominant players. Identifying niche areas within generative AI and providing tailored solutions can lead to success.
  • 2. Prepare for the end of chip scarcity: Pricing for cloud providers and hardware companies has been supported by scarce supplies of desirable GPUs. As chip availability normalizes, it's essential to adapt pricing strategies and focus on value-added services to maintain competitiveness.


The histories of Macintosh computers and generative AI reveal fascinating parallels and insights into the world of technology. While Macintosh aimed to capture people's imaginations with a new standard in computing, generative AI is still navigating issues of value accrual, market structure, and differentiation. By focusing on vertical integration, managing through the hype cycle, and leveraging infrastructure effectively, businesses can position themselves for success in this rapidly evolving landscape.

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