HS059 Cognitive Load and Platforms

The Concept of Cognitive Load in Platforms Our conversation began with an exploration of the concept of cognitive load in relation to platforms. David, despite being an IT professional and not a psychiatrist, brought an intriguing perspective to the table. He emphasized how platforms, such as VMware, Kubernetes, and Cloud Foundry, are designed to remove effort from users, making it easier for them to build and accomplish tasks. However, he pointed out that while everyone is focused on making the developer experience easy, they often overlook the impact of cognitive load on the entire organization. This cognitive load, or mental workload, encompasses the challenges of getting tasks done. The Purpose of Platforms in IT We then shifted our discussion to the purpose of platforms in IT, which is to reduce the cognitive burden and allow humans to focus on the business problem rather than the underlying technology. As platforms become more complex, it becomes impossible for developers to be conscious of every technical detail. The goal is to abstract away the complexity and allow developers to focus on their tasks. While it’s important to learn the basics, when working, developers should not actively think about them unless it’s necessary. We concluded this part of our discussion by emphasizing the importance of finding platforms that are appropriate for the organization’s capabilities and needs. Managing Complexity in Platforms Our conversation then moved to the concept of managing complexity in platforms. We highlighted that building a platform does not eliminate complexity, but rather manages it for a specific consumption of services. We drew parallels between the simplification of hardware infrastructure with the introduction of VMware for virtualization and the restricted choices in microservices with containers and immutable infrastructure. We also touched on the shift in responsibility from IT groups to developers in terms of managing platforms. We discussed how the level of support and access to abstractions can vary depending on the organization. We mentioned the cognitive load involved in load balancing and how the cost of elastic load balancing in AWS has become a significant expense for customers. Practical Implications of Understanding Cognitive Load We explored the practical implications of understanding cognitive load in platforms. We suggested evaluating the effectiveness of the platform and its usability by considering the activities and mental workload required for different tasks. We emphasized the importance of treating platforms as businesses with customers and gathering feedback to improve the user experience. We concluded our conversation with a mention of the potential role of AI in analyzing and optimizing cognitive load. We discussed the possibility of automating data collection and analysis to identify areas where time and effort are being spent and make informed decisions about platform usage and optimization. Cognitive Load and Its Impact on Problem-Solving and Decision-Making Our conversation revolved around the concept of cognitive load and its impact on problem-solving and decision-making. We discussed how technology has evolved over the years, making it easier to analyze data and observe behaviors to optimize processes. However, we emphasized the importance of considering the subjective experience of individuals in the organization and understanding their feelings towards their work. The Balance Between Experience and Innovation We shifted to the balance between experience and innovation, with David mentioning the danger of engineers sticking to outdated solutions. We also discussed the importance of distributing cognitive load within a team and the concept of psychological capital, which includes personal efficacy and belief in one’s ability to get things done.