The narrative that new features from major AI labs kill startups is often wrong. Instead, these releases serve as massive free education, validate new user behaviors, and unlock enterprise budgets. This creates demand for more specialized, vertical-focused tools, ultimately growing the entire ecosystem for startups.

Leaders from major AI labs like Google DeepMind and Anthropic are openly collaborating and presenting a united front. This suggests the formation of an informal 'anti-OpenAI alliance' aimed at collectively challenging OpenAI's market leadership and narrative control in the AI industry.

The AI landscape has three groups: 1) Frontier labs on a "superintelligence quest," absorbing most capital. 2) Fundamental researchers who think the current approach is flawed. 3) Pragmatists building value with today's "good enough" AI.

Large AI labs like OpenAI are not always the primary innovators in product experience. Instead, a "supply chain of product ideas" exists where startups first popularize new interfaces, like templated creation. The labs then observe what works and integrate these proven concepts into their own platforms.

Public focus on capital-intensive LLMs from companies like OpenAI obscures the true market landscape. A bigger opportunity for venture investment lies in the "long tail"—a vast ecosystem of companies building specialized generative models for specific modalities like images, video, speech, and music.

The true economic revolution from AI won't come from legacy companies using it as an "add-on." Instead, it will emerge over the next 20 years from new startups whose entire organizational structure and business model are built from the ground up around AI.

Fears of a single AI company achieving runaway dominance are proving unfounded, as the number of frontier models has tripled in a year. Newcomers can use techniques like synthetic data generation to effectively "drink the milkshake" of incumbents, reverse-engineering their intelligence at lower costs.

A new category of AI lab, the "NeoTrad Lab," is emerging. These companies are highly research-focused and concentrate on a single, novel architectural idea (e.g., data efficiency, diffusion for text) without a clear, immediate plan for productization, believing value will emerge from a core research breakthrough.

To escape platform risk and high API costs, startups are building their own AI models. The strategy involves taking powerful, state-subsidized open-source models from China and fine-tuning them for specific use cases, creating a competitive alternative to relying on APIs from OpenAI or Anthropic.