Vertical AI work products built as ChatGPT plugins for domain-specific professions
Take a profession whose daily work the speaker believes is sitting below its 'threshold of usefulness' for AI — finance was the example, but he argues the opportunity extends far beyond — and build an experience on the same ChatGPT plugin platform OpenAI itself used. The pattern he demonstrated has three layers: (1) a proprietary or licensed data foundation served through MCP tools and connectors, hosted and indexed by the builder rather than relying on third-party connectors with latency, error and search-quality issues; (2) a domain-native interface — citation metadata and MCP apps so users can inspect the sources and tables behind an answer, MCP apps for interactive charts, and native plugin onboarding; (3) outputs that fit the firm's existing workflow, using skills plus shared artifact tools to generate artifacts in approved templates (spreadsheets, presentations, written analysis) so little rework is needed before sharing.
online B2B Service / Consulting / Agency
From AI Engineer — How OpenAI Built ChatGPT for Bankers and Analysts — Joseph Kim, OpenAI at 08:09
Problem: The capability overhang: model capability has grown far faster than the value professionals actually extract from it, because they lack the right data, tools and an experience that fits how they work. Closing that gap requires more than a capable model — it requires data access, reviewable citations, and outputs ready for the team or client.
For: Professionals inside large organizations in an industry where the work has not yet been transformed by AI — finance (investment banking, equity research) was the demonstrated case — where outputs must follow firm formats and sources must be verifiable.
Products from this video
Examples
- ChatGPT for Financial Services — launched a month before the talk with design partners Morgan Stanley and Evercore; the demonstrated vertical product for investment banking and equity research.
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