Why Google will win the AI race (investinginai.substack.com)

This piece argues Google is best positioned to “win” the long game in AI because it controls rare, large-scale assets that matter once AI moves from prototypes to global deployment. Chief among these are YouTube’s stream of human-demonstration data (about 500 hours uploaded every minute), owned rights to use it for training, and Google’s custom TPU hardware (six generations since 2015) running in its own datacenters. Combined with search’s real-time feedback loop (trillions of queries), Gemini’s integration into search, Android’s device distribution, and existing trust-and-safety and monetization infrastructure, Google can train bigger models, deploy them more efficiently, and iterate faster than rivals who rely on third-party compute, fragmented data, or guesswork about user intent. For the AI/ML community the implications are practical and strategic: access to multimodal human demonstrations accelerates world-model and robotics research; co-designed silicon + software enables capital-efficient training of trillion-parameter models; massive production traffic supplies continual supervised signals for alignment and utility; and edge OS control allows experiments at population scale. Google’s deep research pedigree (transformers, BERT, T5, PaLM) and a $200+ billion search war chest further reduce execution risk. The author concedes execution hiccups but contends those structural advantages compound over time, making Google the most durable contender in the AI “marathon.”