Google has every advantage in AI. So why doesn't it lead?
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The execution timeline you mentioned is the software-layer answer, but there is a physical-layer constraint that acts as the great equalizer here. Even with Google's advantage in custom accelerator hardware (TPUs), they are hitting the same 'Hardware Latency Gap' as every other hyperscaler.
While software cycles move at gigahertz, the power grid is on a mid-20th-century industrial clock. Specifically, lead times for the high-voltage transformers required to power these massive clusters have moved from 50 weeks to 120+ weeks.
You can optimize for 'agentic' tasks all day, but you cannot 'Agile' your way through the 120-week curing time of transformer oil or the global shortage of GOES (electrical steel). When the digital roadmap is decoupled from the physical map, even the best research and distribution advantages become stranded assets. I've been auditing these physical bottlenecks—the friction of atoms is now governing the speed of bits
While software cycles move at gigahertz, the power grid is on a mid-20th-century industrial clock. Specifically, lead times for the high-voltage transformers required to power these massive clusters have moved from 50 weeks to 120+ weeks.
You can optimize for 'agentic' tasks all day, but you cannot 'Agile' your way through the 120-week curing time of transformer oil or the global shortage of GOES (electrical steel). When the digital roadmap is decoupled from the physical map, even the best research and distribution advantages become stranded assets. I've been auditing these physical bottlenecks—the friction of atoms is now governing the speed of bits
Google would have been broken up a long time ago if they released Gemini much earlier than OpenAI.
Gemini 3 is so new, that it's still in preview
Just because two big AI announced new models this week doesn't mean Google is behind. Gemini 3 isn't even 2 months old
Gemini is used in a lot of agentic setups. It's my primary.
Gemini users don't act like a cult like Claude users
Also look at how Google is now selling their TPU, everyone wants that more than Nvidia chips now, but only a very select few will ever get them
Just because two big AI announced new models this week doesn't mean Google is behind. Gemini 3 isn't even 2 months old
Gemini is used in a lot of agentic setups. It's my primary.
Gemini users don't act like a cult like Claude users
Also look at how Google is now selling their TPU, everyone wants that more than Nvidia chips now, but only a very select few will ever get them
Gemini 3 is in preview and it is pretty clearly behind multiple iterations for OAI and Ant.
It may even be behind open source at this point.
Seem telling.
Gemini is used in agentic setups by literally dozens of people. Dozens!
It may even be behind open source at this point.
Seem telling.
Gemini is used in agentic setups by literally dozens of people. Dozens!
OpenAI is the only Big AI player who has been unable to train a model from scratch going on two years (since Ilya left) the 5 series is refinement of 4
But please, do go on about who's behind, specifically can you clarify how you see them as multiple iterations behind and why the Waymo world model, AlphaGo/Fold, MedGemma, and the many other models they create don't seem to count towards this?
But please, do go on about who's behind, specifically can you clarify how you see them as multiple iterations behind and why the Waymo world model, AlphaGo/Fold, MedGemma, and the many other models they create don't seem to count towards this?
Ive found gemini to have the best vision by far.
Probably true.
Not sure about “by far”. But probably best.
Not sure about “by far”. But probably best.
It processes symbolic (nok programming) graphs significantly better than models not named opus 4.X for X>=5.
Haven't tested 4.6 for my workflow. Will report if its so good that im jobless on monday
Haven't tested 4.6 for my workflow. Will report if its so good that im jobless on monday
Fear, too much to lose.
Google’s advantages are real, but their research/model + product execution hasn’t consistently cleared the market test that matters: developer and power-user preference for getting real work done. Aggressive pricing, bundling and distribution keep them “in the conversation,” but it increasingly feels like they’re buying relevance rather than earning it.
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