Why serious AI builders are skipping third-party evals

As AI systems become more prevalent, the criteria for assessing their quality are transforming. Rather than solely focusing on whether a model generates correct answers, teams are now evaluating how engaging the system is and how much value it creates for users over the long term. This shift implies that "good" is no longer a static concept, but a dynamic target that varies from one user or business to another.

Consequently, success can no longer be measured exclusively through generic benchmarks, telemetry dashboards, or "LLM-as-a-judge" scores.

Real-time signals from within the organization are now considered more crucial than external evaluations. Companies are establishing their own private evaluation systems that measure progress against business-specific outcomes, utilizing real workflows, institutional knowledge, and expert judgment. The ultimate goal is to create a continuous cycle where human expertise enhances AI systems, while AI systems bolster human capabilities, turning organizational knowledge into a growing asset.

Each interaction generates new training signals, enriches institutional memory, and enhances future performance.

The traditional evaluation tools are becoming less effective in this new environment. Agentic systems require users to engage in long sequences of interactions, and many evaluation benchmarks still rely on turn-level analysis, which can only identify isolated issues like hallucinations, toxicity, or syntax errors. These measurements cannot reliably determine whether a degradation in user experience caused a user to disengage later.

With the rise of consumer AI, preference learning now operates across the entire user journey, making real-time signals inside the product essential for understanding what "good" entails.

Evaluation is moving from a peripheral function to a fundamental aspect of product development. Development teams are increasingly integrating proprietary feedback loops directly into their products, combining behavioral analytics, user retention data, preference learning, reinforcement signals, and post-training pipelines specific to their applications. The companies leading in AI will be those who create closed-loop systems that connect user behavior, offline analysis, reward-model recalibration, and online validation.

The rising influence of AI means that traditional evaluation platforms such as LangSmith, Arize, and Weights & Biases may soon be bypassed by their own customers. These firms are not necessarily being replaced from above by larger AI providers like Anthropic or OpenAI, but rather from below as AI companies realize that evaluation is an integral part of their product.

As commoditized layers such as high-performing foundation models and prompt engineering become more accessible, defining success becomes a crucial differentiator. This shift underscores the importance of owning internal knowledge of user success, which may become a company's most valuable intellectual property.