Google delays Gemini 3.5 Pro over coding flaws

In the ongoing AI arms race, coding has become the definitive arena for proving a model's true horsepower. That makes Google's latest stumble hit particularly hard.

According to a report from Bloomberg, Google is now months behind schedule on delivering its most powerful flagship model, Gemini 3.5 Pro. The primary reason is that the model's coding capabilities keep falling short of internal benchmarks. Following the report, Alphabet shares took a hit, tumbling as much as 3.2%.

A disappointing data refresh in late June

Inside the company, the delay is creating serious frustration. Ten current and former employees revealed that Google engineers, researchers, and managers are openly anxious about losing their edge to Anthropic and OpenAI, both of which recently shipped models that outpace Gemini in coding.

Google scrambled to close the performance gap. In late June, the team attempted a major update to the model's training data to shore up its coding skills. That effort, however, yielded disappointing results and failed to clear internal hurdles.

Competing teams and corporate drag

Beyond model weights, Google is also wrestling with a familiar tech giant ailment: internal friction.

Insiders point to significant internal fragmentation across Alphabet. Right now, Google Cloud, DeepMind, and the Android division are all building competing AI coding tools instead of pulling in the same direction.

On top of the internal turf wars, Gemini has to serve as the technological backbone for an enormous product portfolio. Shipping the model involves clearing multiple layers of stakeholders who are tasked with weaving the AI into Search, Maps, and YouTube, bogging down an already stressed timeline.

Why it matters

Coding is not just a nice-to-have benchmark. It is the core engine behind autonomous agents and sticky developer workflows. Holding back the model is arguably a disciplined move to avoid another public relations blunder with a half-baked product. Still, the optics are tough.

For developers, habits are formed fast. Anthropic and OpenAI have already built immense loyalty among software engineers. If you are shopping for an AI coding assistant today, there is zero reason to wait around for Gemini 3.5 Pro; the competing options are simply further ahead right now.

From an industry standpoint, Google's bottleneck looks less like a hardware problem and more like an organizational one. When DeepMind, Cloud, and Android are effectively building redundant coding solutions while corporate review boards debate integrations across YouTube and Search, agility vanishes. We think that unless Google streamlines its internal turf battles, it will continue to struggle against rivals that are smaller, faster, and singularly focused.