In 1865, Lewis Carroll introduced the White Rabbit in “Alice’s Adventures in Wonderland.” He is remembered for one line: “I’m late.” Constantly checking his pocket watch, he rushes from one obligation to the next and never appears to have enough time. This seems like the story of our lives.
The character emerged during a period when time itself was being reorganized. Britain was transitioning to railroads, factories, and standardized clocks. For the first time, people were required to synchronize their lives to mechanical time rather than natural rhythms. The White Rabbit reflected that transition: a person that is no longer guided by an internal clock, but by external schedules that always seemed to outrun them.
Artificial intelligence is producing a similar shift in our sense of time but through a mechanism.
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Much of the public discussion around AI focuses on productivity and how it will make work and life more efficient and free up time. After all, AI systems can summarize documents, generate reports, write code, and answer questions in seconds. In practice, its more complicated.
To understand why, it helps to distinguish between two phases of modern AI systems: training and inference. Training is the process by which a model learns patterns from massive datasets. Inference is what happens when the model is used in real time to answer questions, generate content, or complete tasks. Every time we interact with ChatGPT, Claude, Gemini, or other generative AI systems they rely on inference.
While inference is our interface with AI, it’s also where time is impacted
Before these systems existed, most of our cognitive work had natural boundaries. Writing a report, analyzing a problem, or developing a strategy required periods of effort followed by pauses. Mostly because of our physical and mental capacity. And often pauses were not wasted time but allowed for reflection, reconsideration, and mental recovery. Now AI has compressed that pause just as the watch for the white rabbit.
When a model can generate an answer in seconds, our workflows change. Instead of spending most of our time producing work, we spend more time reviewing, evaluating, refining, and responding to machine-generated output. The cycle becomes increasingly compressed: prompt, answer, revise, repeat. Our role has shifted from creator to continuous evaluator. That is if we take the time to question the output of AI or take it as gospel.
While AI may reduce the time required for a single task, it often increases the number of tasks that can be completed, reviewed, or expected within the same period. What looks like efficiency from the perspective of the organization can feel like overload. The term often used is “slop” based on the massive amounts of information produced in a short period with no time to actually consume. Just like Lucille Ball in the chocolate factory or the rabbit.
The Rabbit is not simply moving quickly. He is responding continuously to a clock that never stops advancing. In the same way, AI shrinks the time it takes to generate an answer and perhaps also the time it takes for us to digest it because its rapid fire.
Recent research on human cognition provides another perspective on why this matters. Contrary to popular belief, much of human thinking does not occur through conscious, step-by-step reasoning also in AI called “chain of thought” reasoning. Neuroscientists and psychologists have shown that many insights emerge during periods of reflection, incubation, or even apparent inactivity. We solve problems while walking, exercising, or sleeping because the brain continues processing information beneath conscious awareness. AI systems increasingly compress these intervals. By eliminating waiting time, they risk reducing the very pauses during which human judgment, creativity, and insight often develop.
The effect is a continuous acceleration loop.
Researchers have begun examining the cognitive consequences of this pattern. While AI reduces the effort required for specific tasks, it can increase the number of decisions that workers must make. Instead of creating information, people are increasingly asked to evaluate information. Instead of writing every sentence, they must determine whether AI-generated content is accurate, appropriate, and complete.
This helps explain a broader paradox in modern work. Technologies designed to save time often increase the feeling of time scarcity. Email did not reduce communication pressure; it increased expectations of responsiveness. Smartphones did not reduce coordination effort; they extended it across the entire day nonstop. AI, particularly through inference systems, continues this pattern at a higher speed.
The German sociologist Hartmut Rosa described modernity as a process of social acceleration, where technological advances increase speed but often leave people feeling more rushed rather than more free. AI does not simply accelerate tasks. It compresses the intervals between them. Inference systems take this one step further by reducing the delay between intention and output to almost nothing.
The result is a shrinking of the present. Not in a philosophical sense, but in a practical one. The time between asking a question and receiving an answer approaches zero. The time between identifying a problem and generating a solution becomes shorter. The space in which reflection traditionally occurred becomes narrower.
This creates a subtle but important inversion. The constraint is no longer production speed. It becomes cognitive bandwidth. The White Rabbit was not late because he lacked a watch. He was late because the watch defined the pace of everything around him. In a similar way, inference systems define a new pace of cognitive life. They establish an environment in which responses are immediate, expectations rise, and pauses become less frequent.
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The question, then, is not how to slow the technology down. That is neither realistic nor desirable in many contexts.
The question is how to preserve the parts of human life that depend on slower forms of time.
Strategic thinking, medical judgment, ethical reasoning, creativity, and human relationships do not benefit from continuous acceleration. These activities require periods of reflection, uncertainty, and deliberation. They depend on pauses.
AI can generate answers instantly. It cannot determine which questions deserve more time.
That decision remains human.