AI Platform (Gemini, TPU, DeepMind)

Google Gemini is the company's foundation model family powering consumer AI (750M MAU), enterprise AI (120K+ enterprises, 8M seats), and developer ecosystem (2.4M API developers, 85B monthly requests). Gemini 3 Pro achieved #1 on LMArena Elo (1501, first to break 1500), scored 37.5% on Humanity's Last Exam, 95.0% on AIME 2025, and 31.1% on ARC-AGI-2 (vs GPT-5.1's 17.6%) — demonstrating frontier-leading capabilities.

However, market share tells a more complex story: Gemini holds only ~13.5% of the global AI chatbot market (3rd behind ChatGPT at 60% and Microsoft Copilot at 14.3%). Mobile is brighter — Gemini grew from 14.7% to 25.2% share while ChatGPT fell from 69.1% to 45.3%. In enterprise AI, Google holds ~20% market share (behind Anthropic at 29% and OpenAI at 25%). Revenue from products built on generative models grew >200% YoY, with more $1B+ deals signed through Q3 2025 than the prior two years combined. The Gemini platform is integrated across Search (AI Overviews reaching 2B users), Cloud, Workspace, and Android.

Google's Tensor Processing Unit (TPU) line is a vertically integrated AI compute strategy that provides both internal cost advantage and external competitive positioning vs. NVIDIA.

The latest Ironwood TPU v7 delivers 4,614 FP8 TFLOPS per chip, 192 GB HBM3E memory, 7.37 TB/s bandwidth, with pods scaling to 9,216 chips (42.5 ExaFLOPS) — 4x improvement over prior-gen Trillium in both training and inference. Key validation: Anthropic signed the largest TPU deal in Google history (October 2025) — hundreds of thousands of Trillium TPUs in 2026, scaling toward 1 million by 2027, worth tens of billions with >1 GW compute capacity. Meta is in advanced talks for multibillion-dollar TPU deployment starting mid-2026. Performance economics favor TPU: Trillium v6e offers up to 4x better performance per dollar vs. NVIDIA H100 for LLM workloads, with 67% lower power consumption. Midjourney cut monthly inference spend from $2.1M to <$700K after TPU migration. The TPU strategy serves a dual purpose: it reduces Google's dependence on NVIDIA for its own massive AI compute needs ($175-185B 2026 capex), while creating a differentiated Cloud offering.

Google DeepMind represents the company's fundamental AI research arm and arguably its most important long-term strategic asset. The lab won the 2024 Nobel Prize in Chemistry for AlphaFold (Hassabis, Jumper), which has predicted structures of 200M+ proteins and been used by 3M+ researchers globally.

The Isomorphic Labs spinoff for drug discovery raised $600M Series A (April 2025) at ~$2.5B post-money valuation, though clinical trials have been delayed to end of 2026. DeepMind's research pipeline feeds directly into Gemini model development and broader AI capabilities. However, the lab faces a significant talent retention challenge: 11+ executives and researchers departed in 2025, mostly to Microsoft and OpenAI, including VP Engineering Subramanya and senior directors to OpenAI. Over 20+ top researchers have left in the past 8 years to found Character.AI, Cohere, Adept, or join Meta/Anthropic. Google enforces 12-month non-compete clauses with paid garden leave, but this slows rather than prevents attrition. DeepMind's value is inherently difficult to quantify — it produces research breakthroughs rather than direct revenue — but its contributions are embedded across Google's AI product stack.