E-commerce startup Daydream defends against large language models by creating a proprietary data layer. It ingests partner catalogs and enriches them with subjective and objective attributes specific to fashion. This deep vertical understanding allows it to match nuanced queries (e.g., "sexy wedding guest dress") better than a generalist AI.

Before building any user-facing AI, the company spent a year using machine learning to clean and standardize inconsistent product data from various retailers. This foundational data work, not the AI model itself, is the real, expensive competitive advantage.

Like Anthropic's early, overlooked bet on coding, Axiom believes focusing on structured data like formal math proofs offers powerful transfer learning to general reasoning. This strategy turns a seemingly niche vertical into a broad, horizontal competitive advantage.

Walmart leverages agentic AI to learn from its vast complexity across languages, brands, and markets. Instead of slowing them down, this complexity serves as a massive training dataset, making their AI systems smarter and more resilient, creating a unique competitive edge that is difficult for others to replicate.

The key advantage of labs like OpenAI isn't just pre-training, but their ability to continuously post-train models on product-specific data. This tight feedback loop between the model and the product is their real competitive moat, which Prime Intellect aims to democratize for all companies.

Onton, an e-commerce search engine, is evolving its business model from affiliate commissions to a consumption-based API. This strategy allows them to monetize their core neurosymbolic AI by letting other companies leverage its specialized "taste" search capabilities for a much larger revenue opportunity.

While LLMs regress to the mean, neurosymbolic models are 1/1000th the training cost, update in real-time without retraining, and offer the explainability required for high-trust applications like e-commerce search where subjective "taste" matters.

Unlike traditional companies where tech supports a product, new AI labs develop a core model with specific capabilities (e.g., conversation, e-commerce search) and then create products like ChatGPT or Onton that are direct expressions of that model's strengths.

Criteo builds multiple, specialized foundation models (for products, user timelines, etc.) rather than a single monolithic one. The embeddings from these models are made available across the company, serving as a "warm start" to accelerate the development and improve the performance of new AI products.