Your Keyboard is Holding Your AI Back
tl;dr: To get the most out of an AI, we need to give it as much context as possible – preferably our raw, unstructured thoughts. Our keyboards are too slow for this. Speaking is the fastest way to convey our thoughts. Therefore, the future of AI interaction will be voice-based, even if social conventions are currently holding us back.
We stand at a fascinating inflection point in our relationship with technology. Large Language Models (LLMs) are becoming deeply integrated into our workflows, our creative processes, and our problem-solving. We’re all getting better at “prompt engineering,” but I believe we're focusing on the wrong part of the equation. We’re meticulously crafting text, when we should be thinking about bandwidth. The real bottleneck to unlocking the next level of AI collaboration isn't the model's intelligence; it's our keyboard.
Thesis 1: LLMs thrive on context, and they are surprisingly adept at navigating the beautiful chaos of human thought.
The more context you provide an AI, the more nuanced, accurate, and genuinely helpful its output becomes. But here’s the crucial part: this context doesn't need to be perfectly structured.
Thesis 2: The more an LLM understands the raw, branching, and often disconnected way a person thinks about a problem, the better its output will be.
An LLM can digest a stream of consciousness, a jumble of ideas, half-formed questions, and tangential thoughts, and find the signal in the noise. If we accept these two ideas, the logical conclusion is that the winning strategy is to feed the model as much relevant information as possible, as quickly as possible.
We already see this playing out in a specialized field: software development. The rise of AI coding assistants is a perfect case study. Early versions were simple autocompletes. Today’s state-of-the-art assistants don’t just see the single line of code you’re writing. They ingest everything: the entire codebase you're working in, the error messages from your debug console, the output of your build process, the results of your test suites. They are context-guzzling machines, and their utility has grown in direct proportion to the breadth of context they can access.
This is the blueprint for all advanced knowledge work. So, what is the maximum rate of input? The speed of thought.
Unfortunately, we don’t yet have a practical brain-computer interface to directly pipe our thoughts into the machine. This leaves us with the second-fastest method of conveying complex thought: our voice.
Even for the most proficient typists, the keyboard is a massive bottleneck. Speaking is inherently faster, more expressive, and closer to the natural speed of our internal monologue. While you slowly formulate a sentence and your fingers hunt for the right keys, a dozen related ideas and crucial nuances might flash through your mind and vanish. Those lost fragments are valuable context. By the time you’ve finished typing your "perfect" prompt, you've already filtered and compressed your thoughts, losing precious data.
This leads to my third, and perhaps most provocative, thesis.
Thesis 3: Users will gravitate towards the AI system that allows them to input the most context with the least friction, making voice a superior input method for complex tasks.
Consequently, any AI chat interface that lacks a first-class, seamless microphone input for complex problem-solving is on a path to obsolescence. It will be replaced by systems that encourage you to think out loud, to narrate your entire problem-solving process, and to provide that rich, unstructured data stream the AI needs.
Now, for the plot twist.
I developed, structured, and am refining this entire article with the help of an AI. But I'm doing it all through my keyboard. Why? Because I’m sitting in an office with colleagues, and later I’ll be at home with my family. It is, for now, socially inconvenient and frankly, a bit weird, to be constantly muttering and monologuing to your computer.
And that reveals the final, most human hurdle. The technology is almost there. The theory is sound. But our transition to a voice-first AI future isn't a technical challenge—it's a social and environmental one. The ultimate interface is waiting, but we just need to get comfortable with the idea of thinking out loud.