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Classroom Contents
Comparing OpenAI and Gemini Deep Research for Private Company Analysis
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- 1 0:00 Introduction
- 2 1:06 Data scarcity with private companies
- 3 2:10 The agent needs to “fill in the blanks”
- 4 2:36 The agent needs to weigh the credibility of sources
- 5 4:02 Contextual understanding of industry acronyms and jargon
- 6 4:43 Can deep research agents make predictions with sparse information?
- 7 6:24 My prompt for report generation on a niche market
- 8 10:52 Me as a human judge of the output
- 9 11:57 LLMs as judges prompt - OpenAI, Claude, Gemini judge the reports
- 10 13:30 Begin walkthrough of the complete deep research report
- 11 14:14 Structure of each report OpenAI is better
- 12 15:10 Length and depth of each report OpenAI is better
- 13 16:25 Market overview and key player identification OpenAI is better
- 14 19:00 Market share analysis OpenAI is better
- 15 20:41 Financials and Valuation OpenAI is better
- 16 23:52 Determining SaaS metrics and public comps OpenAI is better
- 17 26:45 Strategic outlook and predictions Gemini’s response is boring
- 18 27:51 OpenAI makes bold, specific predictions, IPO outlook
- 19 29:48 OpenAI gives 2025 predictions on market share, M&A
- 20 31:02 Gemini captures 2 recent facts that OpenAI missed
- 21 32:25 Was this caused by a knowledge cutoff?
- 22 33:10 Getting the best of both worlds, going deeper?
- 23 34:17 LLM judges - what do they think about the reports?
- 24 36:18 Wrapping up, RIP Substackers?
