Alphabet shares fall 2.3% on Gemini 3.5 Pro delay report

Alphabet Inc. (NASDAQ:GOOGL) shares fell 2.3% Thursday afternoon following a Bloomberg report detailing delays in Google's delivery of Gemini 3.5 Pro, the company's most powerful flagship AI model. According to people familiar with the matter cited in the report, the delay stems from Google taking additional time to improve the model's capabilities, particularly in coding.

The setback has frustrated Google engineers, AI researchers, and managers. Many are concerned the company risks losing market position as rivals Anthropic and OpenAI produce models that exceed Gemini's capabilities. Both OpenAI and Meta Platforms Inc. recently released new models that outpace Google's current offerings in AI for writing code. Google attempted to address this gap late last month by updating the data used to train Gemini to improve coding skills, but according to the report, the results were disappointing.

A Google spokesperson responded to the reporting by saying the company is shipping quickly across a wide range of models while keeping them cost-effective for customers. The spokesperson added that Google is currently testing Gemini 3.5 Pro, an upgraded Flash model, and other models with partners, and is engaged with the US government on model testing and broader frameworks. The report noted that Google's organizational structure, with multiple layers of stakeholders involved in preparing models for release and working to integrate AI across a vast product portfolio including search, maps, and YouTube, can cause delays. Google has also been in talks with the US government, which has been increasingly monitoring AI companies' most advanced models, about its capabilities and industry safety standards.

Google's delay on Gemini 3.5 Pro reflects the pressure intensifying across the AI industry as multiple players race to deliver more capable models. The body notes that OpenAI and Meta have recently shipped models that exceed Google's current coding abilities, suggesting that the market window for AI leadership is narrowing. Google's internal structure—with multiple layers of stakeholders and the need to integrate AI across a vast product portfolio spanning search, maps, and YouTube—appears to be a structural constraint on speed, according to the reporting.

The disappointment of the training-data update in late last month signals that improving coding performance is not straightforward engineering; it requires iteration on the fundamentals. The setback has reportedly frustrated Google's own teams, who fear the company is ceding competitive ground. At the same time, Google's engagement with the US government on model testing and safety frameworks adds a regulatory dimension that may further influence release timelines.

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