Google's Flagship AI Model Gemini 3.5 Pro Faces Launch Delay
Miles Bennett
Google's most powerful AI model, Gemini 3.5 Pro, is significantly behind schedule after failing to hit internal coding benchmarks — while OpenAI and Meta have already pulled ahead, putting Google's AI-leadership narrative under direct pressure.
What exactly went wrong?
Bloomberg, citing 10 current and former employees, reports that Gemini 3.5 Pro failed to meet internal targets on coding and other core metrics, causing a major launch delay.
Google updated the model's training data late last month to boost coding performance, but sources say the results were disappointing.
This means → the problem is not a minor schedule slip — the model's core capability fell short of the bar, and a retraining round still did not clear it.
Where do the rivals stand?
Recent models from OpenAI and Meta have surpassed Google's current products in AI-assisted coding capability.
Engineers, researchers, and managers inside Google are broadly frustrated; many worry the company is losing its competitive edge.
In plain terms = Google used to set the standard for AI models. On coding — the hottest track in the race right now — its rivals crossed the finish line first.
Why did Google slow itself down?
Google's sprawling product matrix — Search, Maps, YouTube and more — means an AI model launch involves multiple layers of stakeholders, making coordination extremely difficult.
Google Cloud, DeepMind, and the Android team are each building AI coding tools independently; some consumer-product teams are also involved, scattering resources.
Co-founder Sergey Brin pushed to accelerate the AI-coding push, but internal faction competition stalled progress.
This reflects a problem that is organizational, not just technical: too many teams, too many product lines, and every layer needs sign-off — that is how speed gets killed.
What did Google's conservative culture cost it?
Some engineers insisted that critical code should be written by humans to meet Google's standards; early on, they even restricted employees from using Gemini over fears that proprietary code would leak into training data.
Those policies have since been relaxed, but the experimentation window was already lost.
In plain terms = while rivals were racing to let AI write code, Google would not let its own people use its own AI — by the time the restrictions lifted, the window had closed.
How is Google trying to catch up?
Google says 75% of its code is now AI-generated and that most coding tools have been consolidated onto the "Google Antigravity" platform.
Chief AI architect Koray Kavukcuoglu is working to unify internal AI coding tools; DeepMind has also set up a dedicated coding team led by Sebastian Borgeaud.
This means → Google has recognized that the core issue is fragmentation and is moving toward a single platform and dedicated teams.
What does this mean for the market?
Whether Gemini 3.5 Pro can close the gap with OpenAI and Anthropic on coding will be a key test of Google's ability to reclaim AI leadership.
A Google spokesperson said the company is testing 3.5 Pro and upgraded Flash models with partners and is in active discussions with the U.S. government on model testing and broader frameworks.
In plain terms = if the next release still falls short, market confidence in the "Google = AI leader" narrative will erode further.
Content is for reference only, not financial advice.