The Floundering of Google’s AI Strategy: Overlapping Products and Organizational Chaos Come to Light

The cracks in Google’s AI strategy are becoming increasingly apparent. In less than a week, two key figures have left the company: Noam Shazeer, Vice President of Engineering at Google DeepMind, and John Jumper, the core leader behind AlphaFold. This talent exodus underscores the internal challenges in Google’s AI development system.

Though over six months have passed since the launch of Gemini 3, Google has only managed to release a minor update, Gemini 3.1. Meanwhile, competitor Anthropic has advanced its Opus 4.5 model to Fable 5 in the same period. The lag in model development is also reflected in its products. While OpenAI’s Codex and Anthropic’s Claude Code dominate the market for AI agent solutions, Google has yet to deliver sufficiently practical agent products.

The Dissolution of Full-Stack Advantage

Google once boasted a unique full-stack advantage that others could not replicate. On the hardware side, its TPU chips, developed in-house since 2015, stood out. The seventh-generation Ironwood TPU, featuring liquid cooling, offers the computational power of four previous chips on a single substrate. A single pod with 9,216 chips delivers 42.5 exaflops of performance, optimized specifically for AI inference tasks, making it both highly efficient and cost-effective compared to general-purpose GPUs.

On the next layer is DeepMind, which was integrated with Google Brain in April 2023. This merger, under the leadership of Demis Hassabis, was expected to synchronize research and product development, akin to uniting the “left brain and right brain” of AI innovation. Above this sits Google’s vast platform ecosystem, including Chrome, Android, YouTube, Gmail, Google Workspace, and Google Search, each with billions of daily active users. These platforms allowed Google to deploy products, gather user feedback, and continuously refine its offerings—a formidable weapon in its arsenal.

This full-stack synergy was vividly demonstrated with the image-generation model Nano Banana. After gaining traction in blind-test environments like LM Arena, Google swiftly deployed the model across Gemini App, AI Studio, Gemini API, and enterprise-oriented Vertex AI. Leveraging feedback from these platforms, Google iterated its product at an unprecedented pace, outshining GPT-4o’s image-generation capabilities.

However, this virtuous cycle is absent in the AI agent domain. While image generation involves delivering results to users with minimal risk, AI agents must reside within users’ working environments, continuously interpret context, invoke tools, execute actions, and assume responsibility for final outcomes. Since these products span models, permissions, execution environments, enterprise systems, and long-term accountability, Google’s full-stack capabilities falter under the strain of inter-departmental coordination issues.

Organizational Chaos and Product Overlap

A glance at Google’s developer-oriented product lineup reveals the organizational challenges plaguing the company. Google currently offers multiple tools for AI-driven code generation, but their functionalities largely overlap.

Gemini CLI, a command-line tool for codebase inspection, app generation, and automation of complex workflows, was launched in late 2025. However, by June 2026, Google announced that Gemini CLI would be replaced by Antigravity CLI. Jules, a product from Google Labs, is an asynchronous coding agent capable of autonomously fixing bugs, writing tests, and submitting pull requests. Code Assist, a programming assistant under Google Cloud, integrates with platforms like VS Code and JetBrains, available via subscription plans ranging from $22.8 to $54 per month. Meanwhile, Firebase Studio serves as a full-stack development workbench accessible via browsers, featuring an embedded Gemini model. Lastly, Antigravity 2.0, unveiled at the May 2026 I/O Conference, spans across desktop apps, CLI, SDKs, managed agents, and enterprise layers.

While these tools ostensibly aim to fulfill similar purposes, they are developed by different teams, carry different brand names, have varied entry points, and employ distinct pricing models. Some even function as replacements for each other. This situation highlights not a wealth of options but rather a wasteful redundancy in computational resources.

Silos and Internal Friction

The root of this problem lies in the fragmentation of Google’s AI agent capabilities across several uncoordinated organizations, each with its own key performance indicators (KPIs) and independent reporting structures.

Google DeepMind oversees model benchmark scores, ensuring that its models outperform GPT and Claude. Its success metric is creating the “most powerful model,” with little concern for the success rate of real-world projects completed using Antigravity. The Google Labs division cares more about whether its products are “cool” and generate social media buzz. Once an experiment loses its novelty, the team moves on to the next project, leaving products unsupported in the long term. Meanwhile, Google Cloud and Vertex AI focus on whether models can be called via APIs, sold to enterprises, meet compliance requirements, and be deployed in production environments.

This siloed organizational structure undermines product coherence and scatters development resources. As each team seeks to maximize its own KPIs, a paradox arises where an integrated user experience cannot be delivered.

Doubts Cast on Buffett’s Investment Strategy

In an intriguing development, Berkshire Hathaway began purchasing Google shares in 2025 and added another 224% to its holdings in the first quarter of 2026. On June 1, 2026, the company invested an additional $10 billion in Alphabet through a private placement.

The question remains whether Warren Buffett has misjudged the situation or correctly identified Google’s latent potential. Buffett’s investment philosophy emphasizes companies with long-term competitive advantages. Google’s full-stack assets could regain their edge if the company overcomes its short-term disarray. However, without fundamental organizational reform, leveraging this advantage in the era of AI agents may prove challenging.

Editorial Opinion

In the short term, Google’s priority should be reorganizing its internal structure. The current scenario, where multiple teams develop overlapping products that compete with one another, not only squanders resources but also undermines the perceived consistency of its product strategy. Instead of perpetually refreshing products and reshuffling teams every six months, Google should consolidate its code-generation tools under a unified team with standardized KPIs. If this reform is not implemented within the next three to six months, Google risks falling irretrievably behind in the AI agent market.

From a long-term perspective, Google’s full-stack assets remain invaluable. Its massive user base, proprietary TPU chips, and the research prowess of DeepMind still hold immense potential. While products with short feedback loops, such as image generation, may succeed on their own, those requiring long feedback loops, like AI agents, demand seamless organizational cooperation. In the next one to three years, Google’s ability to achieve genuine cross-organizational integration will determine its fate in the AI domain.

References

• 钛媒体: 接连两位大咖出走，谷歌到底出了何BUG？ — Published on June 20, 2026