OpenAI and Perplexity are launching AI shopping assistants
1/ Key takeaways
OpenAI and Perplexity are disrupting e-commerce by introducing AI shopping assistants, which integrate directly into their popular conversational interfaces.. Consequently, specialized startups must leverage proprietary data to compete with these tech giants’ massive reach and seamless checkout integrations.
2/ OpenAI and Perplexity are launching AI shopping assistants
2.1. The shift to AI-driven commerce: OpenAI vs. Perplexity
With holiday shopping on the horizon, OpenAI and Perplexity both announced AI shopping features, which integrate into their existing chatbots to help users research potential purchases.
The tools are markedly similar to one another. OpenAI suggests that users could ask ChatGPT for help finding a “new laptop suitable for gaming under $1000 with a screen that’s over 15 inches,” or they can share photos of a high-end garment and ask for something similar at a lower price point.
Perplexity, meanwhile, is playing up how its chatbot’s memory can augment shopping-related searches for its users, suggesting that someone could ask for recommendations tailored to what the chatbot already knows about them, like where they live or what they do for work.
Adobe predicted that AI-assisted online shopping will grow by 520% this holiday season, which could be a boon for AI shopping startups like Phia, Cherry, or Deft (rebranded as Onton).
2.2. The strategic advantage of niche specialization
Zach Hudson, CEO of the interior design shopping tool Onton, thinks that AI shopping startups with a specialized niche will still provide a better experience to users than general-purpose tools like ChatGPT and Perplexity.
“Any model or knowledge graph is only as good as its data sources,” Hudson told TechCrunch. “Right now, ChatGPT and LLM-based tools like Perplexity piggyback off existing search indexes like Bing or Google. That makes them really only as good as the first few results that come back from those indexes.”
AI shopping startups develop their own datasets so that their tools are trained on higher-quality data, something that’s easier to achieve when you’re attempting to catalog fashion or furniture, rather than the sum of all human knowledge.
In Hudson’s case, Onton developed a data pipeline to catalog hundreds of thousands of interior design products in a cleaner manner, helping to train its internal models with better data. But if AI shopping startups don’t pursue that level of specialization, Hudson thinks they’re bound to be overshadowed.
“If you’re using only off-the-shelf LLMs and a conversational interface, it’s very hard to see how a startup can compete with the larger companies,” Hudson said.
The advantage for OpenAI and Perplexity, however, is that their customers are already using their tools. Plus, their large presence lets them ink deals with major retailers from the get-go. While Daydream and Phia redirect customers to retailers’ websites to complete their purchases — sometimes earning affiliate revenue — OpenAI and Perplexity have partnerships with Shopify and PayPal, respectively, allowing users to check out within the conversational interface.
These companies, which depend on mammoth amounts of expensive compute power to operate, are still trying to figure out a path to profitability. If they take inspiration from Google and Amazon, then it makes sense to look toward e-commerce as an option — retailers could pay them to advertise their products within search results.
But eventually, that could just exacerbate the existing issues that customers have with search.
“Vertical models will outperform because they’re tuned to real consumer decision-making,” Bornstein said.
3/ Hola Tech’s pov:
The e-commerce landscape is shifting as OpenAI and Perplexity transform from search tools into comprehensive “agentic” shopping hubs. These platforms now flatten the sales funnel by integrating one-click checkouts and direct research capabilities into a single conversational interface. To survive this disruption, brands must transition from traditional visual marketing to aggressive Generative Engine Optimization (GEO) focused on machine readability. You should prioritize implementing robust schema structured data and joining merchant programs to feed real-time product signals directly into AI reasoning engines. Furthermore, integrating with protocols allows AI agents to navigate your inventory and execute secure transactions autonomously. Retailers must also optimize for multi-modal discovery by using descriptive metadata that translates across voice and vision-based queries. By building your own internal AI assistants, you can capture high-intent traffic while ensuring your brand remains the primary “consensus choice” for third-party AI agents.
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