Gemini 3.5 Pro Launch Delayed: Google Hits a Coding Hurdle
Originally published in Turkish. Read the original →
Google had to postpone the launch of Gemini 3.5 Pro, promised in May, because of technical problems with code generation and competition between departments. The tech giant aims to unify all of its AI coding tools under the Antigravity umbrella.
While giant strides continue to be made in the relentless race in the AI market, a surprise postponement came from Google. Gemini 3.5 Pro, the new flagship model that Google CEO Sundar Pichai firmly committed at the May 2026 developer conference (I/O) to release in June, missed its schedule and has been pushed to an unspecified date.
The reasons behind the delay have both technical and organizational dimensions. According to information leaked from inside the company, serious and hard-to-overcome obstacles in the model's code generation capabilities were the biggest factor in Google's decision to postpone.
Why Is Coding the "Achilles' Heel" of AI?
For large language models (LLMs), producing prose, writing poetry or summarizing text has become a standard capability. But when it comes to writing code, the margin for error drops to zero. A real software engineering task requires strong memory management, flawless logical reasoning and precise implementation discipline at the same time. A single semicolon error or logic error in a block of code can bring down the entire system.
When Gemini 3.5 Pro's coding accuracy failed to meet enterprise standards in preview testing, Google made a radical move in late June. To fix the model's coding capabilities at the root, engineers reset and updated its baseline training data. This start-from-scratch training process directly pushed back the model's release schedule.
Organizational Fragmentation: The DeepMind vs. Google Cloud Rivalry
Besides the technical reasons for the delay, the operational inefficiencies created by Google's huge, many-headed structure are another notable element in the reports. Different teams operating within the Google Cloud, DeepMind and Android departments focusing on developing independent AI programming tools instead of coordinating with each other have led to resource wars within the company.
This scattered structure causes: * Teams to compete with each other for the same limited compute (GPU/TPU) resources and server infrastructure, * Redundancy and inefficient workforces, * A failure to reach a common standard in model optimization.
The Solution Is a Single Umbrella Platform: Google Antigravity
Wanting to regain control of the process and ensure internal coordination, Google's leadership decided to bring these scattered internal tools together. The company is working hard to unify the AI coding projects and assistant tools from all departments into a single umbrella platform it calls Antigravity. This consolidation aims to make more efficient use of hardware resources and speed up the development of Gemini 3.5 Pro.
AI-Written Code at Google Has Reached 75%
The coding bottleneck in Gemini 3.5 Pro is also a striking contrast, since it coincides with the period in which Google relies most on autonomous systems. Today, roughly 75 percent of all new code written and shipped to production at Google is generated directly by AI and then integrated into the system with the approval of human engineers. Last fall, this figure was known to be around 50 percent.
While this shows how vital AI is to Google's internal operations, senior engineers and security experts within the company are cautious about it. Arguing that fully handing critical infrastructure and security protocols over to autonomous tools is risky, these experts continue to stress the importance of human-written code for maintaining quality standards.
Future Outlook and Global Competition
While Google wrestles with internal engineering problems to perfect its flagship model, its rivals are not standing still: 1. OpenAI: Cemented its leadership in the enterprise space by bringing the coding- and cybersecurity-focused GPT-5.6 Sol model to market. 2. Anthropic: Won over software developers by releasing Fable 5, its advanced reasoning architecture. 3. Moonshot AI: Brought a whole new dimension of competition to the market with Kimi K3, its giant 2.8 trillion parameter open-source model.
Google confirmed that it is continuing to test Gemini 3.5 Pro with enterprise partners and the US government. Releasing a flawed or inadequate model could irreversibly damage Google's reputation in the professional and enterprise market. But even if the postponement is the right strategic move, the company needs to both overcome its technical obstacles and quickly complete the Antigravity integration so it doesn't fall behind its rivals.
