Google's AI Push: A Troubling Trend?

Google recently revealed its next artificial intelligence (AI) phase: “AI mode.” This new feature will soon be available to Google search engine users in the United States, with the rest of the world to follow. According to the company, it will be like conversing with an expert on many subjects. This is one of many steps Google is taking toward its “all-in” AI strategy.

The “all-in” strategy goes beyond incorporating the tech into apps. Google offers products along the AI supply chain – “vertical integration” – housing everything from AI computer chips to user interfaces like Google Maps or Gmail.

Google isn’t the only AI company pursuing vertical integration. OpenAI recently acquired a hardware startup, centralizing hardware development. Amazon is taking similar steps, owning cloud computing platforms, custom chips, device plans, and incorporating more AI services into products. This could be the start of a vertical integration trend across big tech, impacting users and companies.

The AI Tech Stack

Hardware, software, data sources, databases, and servers make up the “AI tech stack.”

Google's Tech Stack Layers:

1. Hardware layer: Google develops its own AI chips (TPUs), claiming superior performance and efficiency compared to general purpose processors.
2. Infrastructure layer: The company uses its own cloud infrastructure for computing power, networking, and storage, forming the foundation for running and scaling AI.
3. Model development layer: In-house research drives product and service development, including machine learning, robotics, language models, and computer vision.
4. Data layer: Data is sourced from users across Google platforms like search, maps, and email. Data collection is required to use any Google app.

Some argue vertical integration is optimal and cost-effective in many industries, but the realities prove otherwise. Google and OpenAI dominate the global tech market, allowing them to charge higher markups and abuse online advertising. Vertical integration skews this power imbalance by centralizing the AI tech stack within one company.

Distributing hardware, infrastructure, R&D, and data across industries supports a more equitable playing field, and its loss creates barriers to entry for smaller companies. It also reduces incentives to innovate for consumers because it eliminates business competition.

Data is often described as the new gold, especially for AI. Google's platforms provide continuous data, giving the company more power. Vertically integrated companies rely on in-house knowledge and expertise. Retaining resources within a few companies can lead to knowledge hoarding. Research shows this reduces social learning and increases disparities between “winners” and “losers,” creating a vulnerable industry where net gains are lost in exclusivity.

Exclusivity breeds a lack of resilience, centralizing failure points. Risk is better managed with oversight, transparency, and accountability through industry collaborations. Centralizing the AI tech stack within one organization eliminates external scrutiny by reducing interactions with external providers, potentially leading to risky behavior.

Regulatory bodies can provide external scrutiny, but the push to deregulate AI widens the gap between tech development and regulation, allowing big tech companies to become increasingly opaque. This raises concerns about organizational practices, particularly data practices. Vertical integration in the AI sector will increase opacity and heighten transparency issues.