Deep Research

Overview

Deep Research is a workflow or product mode in modern AI systems that combines web search, source gathering, synthesis, and long-form report generation into a single research experience. Rather than asking a model for a quick answer, users can direct it to investigate a topic across multiple sources, structure findings, incorporate files like PDFs or spreadsheets, and return a more comprehensive output.

For AI Product Managers, Deep Research matters because it turns AI from a chat interface into a research operator. It is especially useful for market scans, competitor analysis, technical landscape reviews, customer problem discovery, and preparing decision memos. The newsletter frames Deep Research as a practical capability for research-heavy PM work, and recent updates show it evolving from a simple search-and-summarize tool into a multimodal, collaborative, and API-driven workflow.

Key Developments

• 2026-01-07: Phil Schmid shared that the Gemini Interactions API (beta) supports Deep Research with multimodal inputs, including images, PDFs, CSVs, and custom data.
• 2026-04-22: Sundar Pichai announced upgrades to Deep Research in the Gemini API, including improved quality, MCP support, and native chart/infographic generation.
• 2026-04-25: Philipp Schmid launched collaborative planning for Deep Research in the Gemini API, enabling users to request and iterate on a draft research outline with a `collaborative_planning` flag.
• 2026-04-30: Philipp Schmid published a developer getting-started guide for building and running Deep Research workflows with the Gemini API, covering setup, workflow construction, and execution. The same newsletter also highlighted Andrew Ng’s course explaining how Deep Research modes in tools like CGP, Genai, and Claude can run web searches, summarize many pages, ingest documents and images, and generate richer outputs.

Relevance to AI PMs

• Accelerates research-heavy workflows: AI PMs can use Deep Research to compress hours of manual web research into structured outputs for strategy docs, PRDs, market maps, or vendor evaluations.
• Improves evidence-backed decision-making: Because Deep Research can gather and synthesize across multiple web sources and uploaded files, it is useful for triangulating claims, comparing competitors, and grounding recommendations in broader evidence.
• Enables more production-ready AI features: PMs evaluating research agents, copilots, or enterprise knowledge tools can treat Deep Research as a reusable pattern for features like source-backed answers, multimodal input handling, collaborative outlining, and auto-generated visuals.

Related

• Gemini API: A primary platform mentioned for implementing and running Deep Research workflows programmatically.
• Gemini Interactions API: Early beta surface where Deep Research support included multimodal inputs such as PDFs, CSVs, images, and custom data.
• Phil Schmid / Philipp Schmid: Repeatedly cited as a key educator and builder sharing Deep Research API updates and implementation guidance.
• Sundar Pichai: Announced notable Gemini API upgrades that expanded Deep Research quality and capabilities.
• Claude: Mentioned as one of the AI tools offering a Deep Research-style mode for web search and synthesis.
• Andrew Ng: Highlighted Deep Research in an educational context, underscoring its importance for practical AI power-user workflows.
• CGP and Genai: Referenced as tools or environments where Deep Research-style capabilities are being used.