Genuine question: Google seems to have almost every ingredient you’d expect an AI company to need. They have:
- Massive amounts of data from Search, YouTube, Android, etc.
- Enormous compute infrastructure and custom TPUs
- Some of the strongest AI research teams in the world
- DeepMind, which has been doing cutting-edge AI research for years
- Researchers who contributed to foundational ideas behind modern AI
- Billions of dollars to invest in training and inference And yet, when people discuss the frontier model landscape, Gemini often doesn’t seem to get the same level of developer enthusiasm or mindshare as OpenAI/Claude/etc. So what is actually missing? Is it primarily organizational structure? Google is a huge company with lots of products and internal priorities, whereas AI labs can focus almost entirely on models. Or is it product execution? Having a great base model isn’t necessarily enough if the API, tooling, reliability, UX, pricing, and developer ecosystem aren’t equally strong. Could it be training/data strategy? Having enormous amounts of data doesn’t automatically mean you have the right high-quality data for post-training, reasoning, coding, and agentic behavior. Or perhaps Google is actually doing extremely well technically, but public perception and developer adoption lag behind the underlying capabilities? I’m particularly interested in hearing from people who actually use Gemini/Google AI infrastructure for development. What do you think is the biggest bottleneck? What does Google have less of than the leading AI labs: research capability, training strategy, organizational focus, product execution, or something else? submitted by /u/No-Bit5316
Originally posted by u/No-Bit5316 on r/ArtificialInteligence
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