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Discover Your TRIZAN PlanConsider the software you use most intensively at work. Now consider how much of your interaction with that software is shaped not by what you are trying to accomplish but by the physical affordances and limitations of the interface through which you access it. You navigate menus because the menu was the solution to the cursor-interface problem of "how do users discover available commands." You fill in forms because forms were the solution to the problem of "how do users enter structured data with a keyboard." You use separate windows because windowing was the solution to the problem of "how do users work with multiple applications on a single screen." These design patterns are not solutions to your cognitive or workflow challenges — they are solutions to the challenges of physical interface design from the 1970s and 1980s, preserved by the enormous economic weight of software ecosystems built on those original solutions. The paradigm persistence that has kept keyboard-and-screen interaction as the dominant enterprise software interface for fifty years despite the enormous cognitive cost it imposes is explicable: switching costs in software ecosystems are high, and there has never been a compelling alternative interface technology with sufficient bandwidth for complex professional applications. That constraint is being removed by brain-computer interface technology, and the software paradigm that emerges from BCI adoption will bear as little resemblance to current enterprise software as current enterprise software bears to the punch-card programs it replaced. The Bandwidth and Latency Requirements of BCI-Native Software Designing software for neural input requires understanding the specific performance envelope that BCI hardware delivers and optimizing the software interaction model for that envelope rather than forcing neural interfaces to emulate the experience of physical input devices. Non-invasive consumer BCIs in the current generation deliver effective input bandwidth in the range of 10-100 bits per second, depending on the specific interface modality and the training level of the user. Implantable BCIs in clinical research settings have achieved rates approaching 300 bits per second. Compare this to a skilled typist at approximately 50 words per minute — roughly 200 bits per second of meaningful information throughput — and it becomes clear that current non-invasive BCIs are in the same order of magnitude as physical input for skilled users. The crucial architectural implication for BCI-native software is that it should not attempt to replace the full bandwidth of keyboard input with equivalent neural input — it should instead be designed for a hybrid model in which neural input handles the high-level intentional signals (navigate here, select this, execute this command, switch to this context) while AI assistance handles the content generation (the detailed text, the structured data entry, the complex parameter specification) through prediction, completion, and generation based on the user's high-level intent. In this model, the user's neural input specifies what they want to accomplish; the AI fills in the detailed execution; and the user confirms or refines with a minimum of low-bandwidth neural signal. The Software Stack for Neural Interfaces: Six Layers