The bottleneck for AI is not raw intelligence but understanding new context. This requires models that continuously learn from new data and interactions, moving beyond the static pre-train/fine-tune paradigm and deeply baking new information into the model weights.

The primary bottleneck for many users isn't a model's raw intelligence but the user's ability to provide sufficient context. The next paradigm shift will be AIs that can autonomously enter a new environment (like a Slack channel), gather context, and figure out how to be useful, dramatically lowering the barrier to value.

The next major evolution in AI will be models that are personalized for specific users or companies and update their knowledge daily from interactions. This contrasts with current monolithic models like ChatGPT, which are static and must store irrelevant information for every user.

The current limitation of LLMs is their stateless nature; they reset with each new chat. The next major advancement will be models that can learn from interactions and accumulate skills over time, evolving from a static tool into a continuously improving digital colleague.

Trajectory’s co-founder explains their goal isn’t to build the smartest 'PhD student' model. Instead, they focus on continual learning, creating agents that improve 1% daily on the job, beating larger models by being specialized and experienced.

Adaption.AI is bucking the trend of building larger static models to focus on continual learning. Their core mission is to 'eliminate prompt engineering,' viewing it as a crutch that signifies a model's failure to truly adapt and learn from user interaction in real-time.

The long-term defensibility for AI companies will come from building a deep, personalized memory and context layer for each user. As models commoditize, the platform that best understands and remembers a user's history and preferences will create unbreakable stickiness.

While frontier labs aim for a single, universally intelligent model, Engram believes value lies in specialized models that learn private, conflicting, or ambiguous user-specific data—things that are difficult to incorporate into a single, massive model.

Focusing on refining prompts (skills) yields diminishing returns. The breakthrough in AI content quality comes from building a 'foundational layer' of shared intelligence—core documents defining your audience, voice, and positioning—that every AI skill draws from, preventing it from starting from zero each time.