A look at “Generative Engine Optimization” startups like Profound and Bluefish AI, which help businesses improve content to appear in AI search summaries
This seems geared towards brands that want the summary of the company or products to be as positive as possible instead of web publishers Greg Linden / @glinden : AI results are about to get a lot worse, both because the people working on them are unprepared to deal with the adversarial nature of keeping prominent AI summaries clean of spam and scams including SEO, but also because they're about to clumsily throw ads all over the AI results. LinkedIn: Rashi Shrivastava : The world of marketing is undergoing a seismic shift— Shoppers are turning to tools like OpenAI's ChatGPT to research and compare products …
Context & Ripple Effects
Generative Engine Optimization recasts the familiar contest for search visibility around the text an AI system chooses to synthesize, rather than around a click-through ranking. It extends publishers’ earlier effort to adapt to Google Search’s AI Overviews as answer surfaces become a meaningful distribution layer.
The commercial logic is especially acute in product research: AI-generated review summaries have already been deployed in retail contexts through Amazon’s generative review summaries. Profound and Bluefish AI are positioning services around how brands are represented in those condensed answers.
First-order effects
- Brands gain specialist vendors aimed at auditing and improving the content signals that may shape AI-generated descriptions of their companies and products.
- Profound and Bluefish AI compete to make AI-summary visibility a discrete marketing service, while AI-search operators face more organized attempts to influence their outputs.
Second-order effects
- Marketing teams may shift some search-optimization budgets from conventional rankings toward testing which source material and brand claims are reflected in AI answers.
- As commercial pressure on answer surfaces rises, platforms will need to distinguish useful optimization from manipulation—the adversarial risk highlighted in criticism of spam-driven search incentives.
Third-order effects
- If AI summaries become a regular research interface, brand distribution will depend increasingly on being selected as inference input, not solely on owning a high-ranking destination page.
- The resulting market could recreate SEO’s arms race inside answer engines, raising the value of transparent source attribution and credible safeguards against promotional distortion.
The trend: AI search is turning answer composition into a new marketing and distribution battleground, spawning services designed to influence how brands appear in synthesized results.