CNBC: Alphabet to raise $80 billion from stock sales to fund AI buildout

Gemini is Google's family of multimodal AI models designed to compete with OpenAI's GPT series. Formerly known as Bard, it rebranded to Gemini in 2024 and directly integrates with Google services. The Ultra 1.0 model achieved state-of-the-art performance on multiple benchmarks. Gemini is available through the Google AI app, web interface, and integrates with Gmail, Docs, and other Google Workspace applications.

Portkey documents an AI gateway at docs.portkey.ai that unifies access to more than 250 models through a Portkey SDK or OpenAI-compatible base URL (`PORTKEY_GATEWAY_URL`) with provider routing headers. Official quickstarts show three-line Python or TypeScript integrations that start monitoring LLM requests for resilience, security, and performance. Portkey states the open-source gateway is free to self-host while the managed service includes a free tier of 10k requests per month, edge-hosted workers adding roughly 20–40ms latency versus direct API calls, ISO 27001 and SOC 2 certifications, and optional configurations that skip storing request/response bodies.

Deepgram documents speech-to-text at developers.deepgram.com with WebSocket streaming on `/v1/listen` for general real-time transcription (Nova-3 model, diarization, and search features per API reference) and `/v2/listen` for conversational Flux models with integrated end-of-turn detection (StartOfTurn, EndOfTurn, EagerEndOfTurn events). Official SDKs expose `deepgram.listen.v1.connect` and `deepgram.listen.v2.connect` for binary audio streams. Docs contrast Flux—optimized for voice agents with lower turn-detection latency—against Nova-3 for meetings, IVR, and agent-assist workloads, and describe latency measurement guides targeting sub-300 ms streaming for Nova-3. Self-hosted deployments can run Flux on dedicated Engine nodes with `/v2/listen` enabled per self-hosted configuration guides.

MotherDuck documents a remote Model Context Protocol server at `https://api.motherduck.com/mcp` (fully managed, read-write) that lets Claude, ChatGPT, Cursor, Claude Code, Codex, and other MCP clients query MotherDuck cloud databases via OAuth or Bearer tokens with zero local install per motherduck.com/docs/sql-reference/mcp. Remote tools include read-only `query` and read-write `query_rw` plus schema exploration helpers documented in the MCP workflows guide. For local DuckDB files, S3 paths, or custom limits, the open-source `mcp-server-motherduck` package (motherduckdb/mcp-server-motherduck on GitHub, PyPI `mcp-server-motherduck`) runs via `uvx` with flags such as `--db-path`, `--motherduck-token`, and `--read-write` for self-hosted stdio or HTTP transports.

Provides file search and content retrieval across Google Drive including Docs, Sheets, Slides, PDFs, and shared drives. Agents can pull policy documents, research archives, and spreadsheets into conversations without downloading files locally. Supports Google Workspace sharing policies and permissions.

Structures verification of public-company AI infrastructure financing headlines into a finance and strategy checklist. The workflow separates announced equity program components (underwritten offerings, at-the-market programs, private placements) from previously disclosed capex guidance and debt-market context cited in the same coverage. It references CNBC reporting on June 1, 2026 that Alphabet plans to sell $80 billion in stock—including a $10 billion Berkshire Hathaway private placement—to fund AI compute infrastructure while stating demand exceeds available supply; that Alphabet revised 2026 capex guidance to $180–$190 billion (up from $175–$185 billion); CEO Sundar Pichai cited compute capacity constraints; CNBC noted hyperscaler combined capex expectations above $700 billion in 2026 and prior Alphabet bond issuances—without treating media capex totals as your internal budget.

Turns announcements of multi-year cloud spend commitments and earnings-day infrastructure deals into a finance-and-platform checklist. Teams separate headline dollar totals (for example five-year AWS purchase obligations) from average annual run rates, prior amended agreements, and what is actually earmarked for AI GPUs versus general-purpose silicon. The workflow maps public claims to internal FinOps data before revising data-platform budgets or agentic-AI roadmaps. It cites CNBC reporting on May 27, 2026 that Amazon disclosed a $6 billion five-year Snowflake commitment covering Graviton and AI GPUs alongside Snowflake's fiscal Q1 beat ($1.39 billion revenue, 39-cent adjusted EPS vs analyst expectations) and an undisclosed Natoma acquisition—without treating media figures as procurement instructions.