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Google has every advantage in AI. So why doesn't it lead?
Google has an enormous number of advantages:
1. Unreal profit stream from previous businesses
2. A large collection of distinguished researchers
3. Their own accelerator hardware giving them a cost advantage for serving
4. More data than any other lab
5. Best distribution on the planet
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.
timeline:
1. Bard -- laughable compared to the competition at the time
2. Gemini 1.0 -- an actual punchline, hard to recall a launch that hurt Google's brand more
3. Gemini 1.5 -- large context, no doubt. But not used for productivity menaifully
4. Gemini 2 -- flash was cheap which opened up ... OCR?
5. Gemini 2.5 -- close to frontier .. maybe.
6. Gemini 3.0 -- maybe frontier for some reasoning tasks, but doesn't really stay on task and isn't in the "agentic" discussion.
7. Post Gemini 3.0 -- OAI and Claude release at a breakneck speed claiming just about all of the "agentic" space, Gemini simply isn't in the conversation.
- rvz 8mo agoGoogle would have been broken up a long time ago if they released Gemini much earlier than OpenAI.
- us321 8mo agoFear, too much to lose.
- knollimar 8mo agoIve found gemini to have the best vision by far.
- HardCodedBias 8mo agoProbably true. Not sure about “by far”. But probably best.
- knollimar 8mo agoIt 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
- verdverm 8mo agoGemini 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
- HardCodedBias 8mo agoGemini 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!
- verdverm 8mo agoOpenAI 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?
- UncleKeynes 8mo ago[dead]
- nagarajsarath6 8mo agoThe 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