Last year, everyone was talking about how artificial intelligence (AI) would lighten our workload, save us more time, and perhaps even completely eliminate that modern affliction called "burnout." I was one of those who believed it. In my nearly twenty years of experience in system architecture and software development, I've always seen technology as an enabler; I thought this time would be no different.
However, based on what I've observed and personally experienced over the past year, those who embraced AI the most, those who hoped for the most from it, have actually fallen victim to a new kind of burnout. While we used to talk about physical or mental fatigue, now we're grappling with a completely different cognitive load. This isn't because AI itself is bad, but rather about how we use it, what we expect from it, and how those expectations clash with reality.
The Chasm Between Expectations and Reality
When AI first emerged, the initial image in our minds was an assistant that took over monotonous tasks, handling boring work for us. When integrating an AI-powered production planning module into a manufacturing ERP, we initially imagined everything would be automated, with operators merely pressing an approval button. We expected a world where everything, from order flow to raw material procurement, would be perfectly optimized.
But reality quickly hit. Yes, AI accelerated certain tasks, but this speed brought with it a new "verification and correction" burden. AI's solutions were often "almost correct," and correcting that "almost" part could sometimes take more time than doing it manually from scratch. In one client project, we had to go through every single line of financial statements prepared with an AI-powered reporting tool twice to check if the model was "hallucinating." This was a new workload; a responsibility that didn't exist before, brought entirely by AI.
⚠️ Hidden Cost
While AI's promise of automation is appealing, unseen cognitive loads such as verifying outputs, correcting errors, and managing context often negate the expected time savings. This is especially evident in integrations within critical systems.
A New Type of Cognitive Load: Becoming an "AI Manager"
In the past, when developing software or managing systems, problems were usually logical and predictable. When dealing with a PostgreSQL WAL bloat issue, I knew the causes and the steps to resolve it. Error messages and logs guided me. But working with AI, I entered a completely different "cognitive load": managing AI.
In my own side product, when generating AI-powered content for financial calculators or performing text classification in my Android spam app, making the model "think" dramatically increased my own thinking load. I was no longer just writing code; I was also trying to understand the model's worldview, provide it with the right context (using RAG patterns), switch between different providers (like Gemini Flash, Groq, Cerebras), and constantly question the outputs. This was an "AI Manager" role that required constant vigilance. When a WAL rotation alarm dropped at 03:14, I knew where the problem was; but AI silently producing incorrect information and only realizing it weeks later was a more exhausting experience.
Decreased Luxury of Making Mistakes and Constant Vigilance
When we integrate AI systems, it feels like our luxury of making mistakes also decreases. Because AI's errors can often be larger in scale and more insidious. A small mistake I made in an Nginx reverse proxy configuration might have caused a few hours of downtime. But incorrect information generated by AI could lead to a chain of wrong decisions lasting for months.
In a manufacturing company's ERP, while optimizing the production flow with AI, I saw how a small prompt engineering error led to incorrect decisions across the entire supply chain integration. It was a problem that took days to fix and affected the entire system. Because AI's internal workings were like a "black box"; even observability tools were often insufficient to find the source of the error. This situation created a constant state of "vigilance" in me and my team, a need to check every AI output multiple times. This was a process that transformed into a deeper crisis of trust and verification than the optimistic vs pessimistic lock debates we knew from before.
Constantly Updating Tools and Learning Fatigue
The world of AI is changing at an incredible pace. Prompt engineering techniques we learned yesterday are being replaced by agent pattern architectures today. While RAG methods evolve, multi-provider (OpenRouter) and fallback strategies gain a new dimension every week. This constant change actually creates a kind of "information inflation."
I am someone who has been continuously learning and adapting for twenty years. I have knowledge in many different areas, from VLAN tagging complexities to BGP routing decisions, from PostgreSQL index strategies to Kubernetes orchestration. But this acceleration in the AI field feels like constantly running a marathon. Living in an environment where even fundamental concepts change every week, instead of just adapting to a new technology, is mentally exhausting. This situation requires us not only to learn new things but also to constantly update what we've learned, entering an "unlearn and relearn" cycle, which is a cause of burnout in itself.
Conclusion: Is AI Burnout Real?
AI's potential to lighten our workload and provide efficiency is undeniable. I have saved valuable time through AI-assisted operations and pipeline automations in my own systems and client projects. However, this gain came with new and more complex cognitive loads, constant pressure to learn, and a decreased tolerance for error. AI didn't end old types of burnout; instead, it created a new type of burnout: "AI Manager Burnout."
So, how are you coping with burnout in this new era? Has AI saved you time, or has it brought a new burden? How does this rapid change and constant need for verification affect your work motivation? I'm curious about your thoughts in the comments.
Top comments (0)