Evaluation system reform: From score-chasing to task scenarios
In October 2026, several open-source communities and research institutions jointly released a new version of the LLM evaluation framework. Unlike previous methods that gave a single score based on multiple standard test sets, the new framework introduces eight task scenarios, including multi-turn dialogue, tool invocation, and long-document understanding, and increases the weight on engineering dimensions such as inference efficiency and deployment cost. This means that models that purely chase "benchmark scores" may expose shortcomings like insufficient stability and high response latency in specific scenarios. Data from a leading evaluation platform shows that on its new leaderboard, an open-source model with only 7 billion parameters ranked higher than some larger closed-source models due to its high completion rates on code generation and SQL query tasks. This change directly affects the downstream job market — companies are beginning to realize that a model's paper parameters and scores are far less meaningful than its performance on real business pipelines.
Hiring trend: Project experience replaces parameter worship
As evaluation standards shift toward real-world practice, enterprises' preferences in hiring AI engineers are quietly changing. In the past, a resume stating "participated in training a billion-parameter model" could directly impress interviewers. Now, many internet companies and tech-intensive startups explicitly state that they value a candidate's end-to-end understanding of the model from training to deployment, including practical experience such as fine-tuning using open-source frameworks, accessing different services through AI channel proxies, and using token relay stations to reduce API call costs. A technical lead involved in hiring revealed that for an algorithm position they just closed in October, the final hire was not the PhD with the most papers, but a master's graduate who had built a lightweight inference solution using AI hub in a project and successfully reduced the cost per inference to the industry average through a token wholesale model. This kind of "project-as-portfolio" resume is becoming a hard currency in HR screening.
Open-source ecosystem empowerment: AI channel proxies and token models reduce costs
Behind the new hiring trend, the supporting services of the open-source ecosystem are also maturing rapidly. In October 2026, when selecting open-source models, many small and medium-sized enterprises no longer directly interface with the original model repositories but instead obtain inference interfaces optimized with caching, distillation, quantization, etc. through AI API relay stations or AI relay stations. These service providers often offer the cheapest token packages, combined with token proxy models, allowing startup teams to try multiple open-source models at a very low threshold. At the same time, AI large model proxy platforms have emerged. They not only provide compute scheduling and model hosting but also integrate mainstream evaluation tools, helping companies quickly compare the scenario adaptability of various models before purchasing. This ecosystem of services lowers the technical barrier to deploying models for enterprises and also forces job seekers to have cross-platform, multi-tool collaboration capabilities.
Practical talent training path: AI hub and community collaboration
Faced with structural changes in talent supply and demand, some communities and third-party platforms have begun offering targeted practical courses. Taking a well-known AI hub as an example, it launched a "Model Engineering Deployment" series in October, covering the entire process from weight conversion to API packaging. Students must complete a full project in a real cloud environment, including token wholesale strategy, load balancing, and disaster recovery, and obtain official certification through an open evaluation. Many HR departments of small and medium-sized enterprises say that the reference value of such certifications has surpassed that of traditional degrees and papers, because the certification process reproduces the actual workflow. At the same time, many "model benchmarking + project implementation" dual-track groups led by senior engineers have emerged in the community. Participants need to optimize their model services synchronously in weekly live streams and share inference resources through token relay stations, forming a low-cost, high-efficiency talent training model. This trend indicates that the core of future AI hiring will no longer be static knowledge reserves, but dynamic engineering delivery capabilities.