Gemini 3.5 would be months behind schedule, problems at Google?

According to recent rumors from Bloomberg, Google would have fallen months behind schedule on the roadmap for the launch of its new flagship language model, Gemini 3.5 Pro.

The primary cause of this slowdown lies in the difficulties encountered in improving the system’s capabilities in writing and code analysis, a sector where competition is moving fast.

The situation highlights the difficulties of a huge company moving with the same agility as rivals, having to balance innovation with stringent security standards and integration across dozens of services used by billions of people.

Gemini 3.5 has difficulties with programming, Google is behind schedule

Software writing has become the fundamental criterion for assessing the validity of the most advanced models. Currently, the systems developed by OpenAI, Meta and Anthropic show superior performance compared to Google’s solutions in this specific area.

Sources indicate that the company’s leadership recently updated the training data of Gemini precisely to strengthen its computing capabilities, but the results achieved at the end of last month did not meet the rigid internal expectations.

This stall explains why during this year’s Google I/O developers conference there was no debut of the new model as expected, leaving room only for minor updates while competitors continued to release next-generation systems.

What slows Google’s operations isn’t a lack of resources, but paradoxically its colossal size. Unlike startups focused solely on creating algorithms, the California company must ensure that every new version of Gemini works impeccably within a vast ecosystem, which includes Search, YouTube, Maps, Android and the Cloud infrastructure.

This enormous scale brings advantages, such as access to unparalleled amounts of data, but inevitably creates overlaps between the various divisions slowing decision-making processes.

There are also tensions related to fragmentation of strategies and the scarcity of computing power, due to high internal demand for GPUs. These frictions would have pushed some of the company’s researchers to leave to join rival entities such as Anthropic.

Corporate adoption and market reception

Despite the obstacles, AI adoption is proceeding at a rapid pace within the organization itself. The company states that about 75% of production code is now generated via automated tools, currently being consolidated under a unified platform named Google Antigravity.

Meanwhile, Google says it is engaged in testing Gemini 3.5 Pro and other updated solutions in collaboration with its partners, while keeping an open dialogue with the US government on security standards.

Market reactions to the current offering remain divided: corporate entities like Figma appreciate the trade-off between speed and quality achieved by smaller models like Flash, while other platforms believe the product sits in an awkward position, presenting higher costs than the previous systems without matching the computing power and logical reasoning offered by the premium options of the direct competitors.

The current challenge for Google is not to prove it can build powerful algorithms, but to succeed in distributing them rapidly in a sector that today measures its progress in weeks rather than months.