I think the main reason many people (including me), very often, lack the motivation to read content that is likely generated by AI is the suspicion that it comes from a place of intellectual laziness. Another reason, based on personal experience, is that AI content may suffer from too much verbosity, too much jargon and over-confidence, which makes the reading experience feel fake and border-line irritating. In many cases the content may have very little to no nuance, which is ultimately a waste of time. As an anecdote, someone posted a blogpost on Linkedin on using agents to implement a driver to access PCIe devices over TCP/IP. I was intrigued because that's not an easy task for several reasons, like handling PCIe interrupts and DMA. For exmaple, how does the remote machine map the device's PCIe BARs? And when it issues I/O to the devices registers, how are these reads and writes transferred to the remote device. In the end, this is just some virtual memory. In a local machine, this is either directly mapped to the PCIe physical addresses or some IOMMU virtual address space which is then translated by the hardware upon CPU/device/VM access.

After reading the long verbose promising article, in the end, the guy (with the help of the agent) only managed to implement access to the PCIe config space so that lspci on the remote machine works and shows the remote PCIe device, but that's all. It never addressed the issues above nor even mentioned them. The code was AI generated. The article was AI-written. The article never made a reference to DMA, interrupts, MSIX-X, IOMMU, IOTLB, virtual memory, etc, but it made big claims on next-gen datacenter disaggregated architecture, boosting GPU utilization, reducing large scale inference costs, etc.

Anyway, you get my point: big long beautiful words, but zero nuance.