Google Leads AI Model Battle

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Ark Invest thinks Google has taken the lead with Gemini 3 + Nano Banana Pro.
Brett Winton thinks we are heading from transformer architecture to a mix of transformer, diffusion and memory.

In the medium term (1-2 years) Google’s distribution, bundling, and cost advantages are seen as the biggest structural moat. They predict that when Google throws Gemini Ultra-level access into the $19.99 YouTube Premium/Gemini bundle, it destroys OpenAI’s consumer pricing power.

Long term Ark expects 2–4 foundation model winners. Google is now a lock to be one of them. OpenAI still has a big installed-base moat but is at risk of being overtaken if they don’t close the multimodal gap and face Google’s pricing assault.

Google is currently pulling ahead and has the strongest shot to dominate the consumer and bundled-solution side of the war. OpenAI’s lead is eroding faster than expected.

Google TPU v7 matches B300 on FP8 (~4.6-5 petaFLOPS) but lacks FP4. Nvidia B300 is better for ultra-low-precision inference (2x faster serving). Rubin likely doubles FP4 throughput to 50 petaFLOPS/GPU, enabling 7.5x rack performance over B300 for next-gen models.

Google’s TPU v7’s ICI enables seamless 128x larger pods than B300’s NVLink, ideal for Meta-scale training. Rubin counters with NVLink 6 and photonics networking, targeting 365 TB/rack memory—crucial for sparse, long-context AI.

TPU v7 offers 30-40% better TCO (total cost of ownership) via scale and perf/watt. B300 leverages Nvidia’s ecosystem for faster dev. Rubin could command premiums ($50K+/GPU est.) but risks delays from HBM4 shortages.

GPUs (B300/Rubin) win on flexibility. TPU v7 shines in Google-optimized stacks. Rubin positions Nvidia for 2027 dominance, but ASICs and TPUs could erode margins.

Whisper Thunder could be xAI’s new AI video model. Early tests seem to indicate it is better than Google Veo 3.1.