Elyse Betters Picaro / ZDNET
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When I walked into a conference room in Google's San Francisco building last week, I expected to find the typical tech briefing setup with rows of chairs facing a wall of screens and a corporate voice managing a slide deck.
Instead, I found myself in what looked more like group therapy with a large circle of cozy chairs arranged around the center of the room. About a dozen carefully selected testers and creators, including myself, sat down with the team behind Gemini 3, which had just gone public, and Nano Banana Pro, which would debut the next day.
Also: Google's Gemini 3 is finally here and it's smarter, faster, and free to access
That rapid release schedule couldn't have been more telling. The AI industry is in the midst of an unprecedented race, with OpenAI, Anthropic, Google, and others entrenched in a constant scramble to capture user attention and prove their models deliver more value than the rest.
With Tulsee Doshi (senior director and head of product for Gemini Models), Logan Kilpatrick (group PM lead for Gemini API), and Nicole Brichtova (product lead for image & video) sitting across from me, I got a fascinating look at the decisions, tradeoffs, and challenges behind these high-profile launches.
Here are three details that stood out during our 75-minute conversation.
Why Gemini 3 took longer than expected
The gap between Gemini 2.5 Pro's debut at Google I/O in May and Gemini 3's arrival in November felt significant, especially given the rapid pace of AI development across the industry. When the topic of timeline came up, Doshi explained that the delay came down to a two-pronged approach.
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