Planning engines, not larger models, will finally kill your email inbox

While Silicon Valley obsessives debate P Doom—the probability that artificial intelligence will destroy humanity—most knowledge workers are focused on a more immediate existential threat: the overflowing email inbox. For the tens of millions of people trapped in the Cal Newport, the relevant metric is P Inbox Zero. We are not looking for a chatbot that can write a snarky poem; we are looking for a Leo McGarry that can ruthlessly filter, schedule, and respond to the cognitive tax that is modern communication.

Today's productivity crisis isn't caused by a lack of effort. It is caused by the incessant need to context shift. Every time you jump from a deep task to check a Slack notification or a new email, you incur a cognitive cost that slashes your IQ and drains your energy. We have built an entire economic sector on the back of ad-hoc, unscheduled back-and-forth messaging. This system is a disaster for human focus. The true promise of AI in the workplace is not the automation of jobs, but the elimination of this communication management. Imagine an agent that understands your schedule, your goals, and your relationships so well that you only interact with it twice a day. It doesn't just draft replies; it makes decisions.

Why ChatGPT can summarize but cannot decide

If you feed an email into ChatGPT, the results are deceptively impressive. It can summarize a long-winded message from a local pastor or draft a polite refusal of a book offer with startling accuracy. It understands the words on the page. However, it cannot manage the inbox. The limitation isn't about vocabulary or grammar; it's about control. Currently, the human remains the bottleneck. You must copy the text, prompt the model, evaluate the suggestion, and hit send.

GPT-4 (LLMs) are architecturally incapable of simulating the future. To manage an inbox, an agent must ask: "If I agree to this meeting on Tuesday, how does it affect the project deadline on Friday?" or "If I decline this request from a department head, how will it damage my social capital?" LLMs are feed-forward architectures. They process information in a straight line, predicting the next token based on hardwired patterns learned during training. They do not loop. They do not have memory that changes as they think. They cannot explore different "what-if" scenarios on the fly. This is why GPT-4 plays decent chess until the middle game, where the board becomes unique and requires deep future simulation rather than just following heuristics.

The Cicero solution and the rise of planning engines

To bridge the gap between a chatbot and a Leo McGarry, we must look toward the architecture of Cicero, the AI developed by Meta to play the board game Diplomacy. Unlike Chess, Diplomacy requires players to negotiate, form alliances, and backstab one another through private conversations. It is a game of human psychology.

Cicero succeeded not by being a bigger language model, but by being an ensemble of two different systems: a language model and a planning engine. The language model translates human messages into technical intent; the planning engine then simulates the future impact of different moves, deciding whether to lie, ally, or attack. Once a decision is made, the language model translates that technical strategy back into natural, persuasive English. This is the blueprint for the inbox-killer. We don't need a model that reads more books; we need a model that can run a bounded search algorithm to find the optimal path through your schedule.

Surviving the shift in programming and writing

Ulysses S. Grant

12%· people

Cicero

8%· products

David McCullough

8%· people

Diplomacy

8%· products

George B. McClellan

8%· people

Other topics

58%

The fear that AI will replace programmers or writers is largely misplaced, provided those workers adapt to the new efficiency curve. In software development, we have seen this before. Punch cards gave way to interactive terminals; assembly language gave way to high-level IDEs. Each jump in efficiency didn't lead to fewer programmers; it led to more complex systems. A programmer today is a thousand times more efficient than one in 1955, and yet we have a million times more software. The same is true for writers. Unless you are a professional stylist whose primary value is a unique "voice," AI is a tool for clarity. It levels the playing field for non-native speakers and speeds up the drafting of mundane professional communication. The human value shifts from the labor of typing to the wisdom of direction.

Designing settings conducive to brilliance

Productivity is not just about the tools you use; it is about the environment you inhabit. In my research for Slow Productivity, I found that the most effective thinkers—from David McCullough to Mary Oliver—rarely worked in a standard office. David McCullough wrote in a garden shed. Mary Oliver composed her poetry while walking in the woods.

If you attempt to do your deepest work at the same desk where you pay your taxes, attend Zoom calls, and clear your inbox, your mind will naturally revert to a shallow-work mindset. Your brain is a pattern-recognition machine; it associates your workspace with the stress of the hyperactive hive mind. To reclaim your focus, you must build a separate space for deep work. It doesn't have to be an expensive office. It can be a specific library carrel, a picnic table under a tree, or an attic nook. This physical separation acts as a psychological trigger, signaling to your brain that it is time to move from the freneticism of the inbox to the slower pace of true creation.

Life seasons and the myth of constant optimization

We often treat our careers as a flat line of constant effort, but humans are seasonal. My 20s were about building foundational skills and getting my feet on the ground as a writer and professor. My 30s were a period of frenetic stability—getting tenure, starting a family, and ensuring my writing career had financial heft. Now in my 40s, the season has shifted toward legacy and depth. I am no longer just keeping babies alive; I am focused on being a present father to growing boys and leaving a footprint in the world of scholarship.

When navigating these seasons with a partner, the goal should not be the independent optimization of two careers. That approach leads to a "tally board" relationship where every hour one person works is seen as an impediment to the other. Instead, couples must work backward from a shared vision of a remarkable life. What do you want your typical Tuesday afternoon to look like? Where do you want to live? How much "dad time" or "mom time" is required? When you start with the lifestyle and work backward, creative options emerge—like taking a 30-hour-a-week "cushy" job that pays less in status but more in time.

Lessons in slowness from Ulysses S. Grant

Historians note that during the Civil War, Ulysses S. Grant often looked like the laziest man in camp. He would sit for hours smoking a cigar, rarely looking at maps or papers. This was not sloth; it was deep processing. Contrast Ulysses S. Grant with his predecessor, George B. McClellan. George B. McClellan was a man of constant activity. He was busy, bureaucratic, and focused on petty details. He looked like a leader, but he never won the battles that mattered.

Ulysses S. Grant studiously avoided any duty that someone else could do better. He held his subordinates accountable so he could protect his time for thought. In a digital age that rewards performative busyness, we must remember that wars—and great careers—are won through smart strategy, not freneticism. The formula for making a difference remains: do fewer things, work at a natural pace, and obsess over quality. This is the essence of Slow Productivity. Whether through the aid of a future AI agent or through ruthless manual prioritization, the goal is the same: to move out of the hive mind and back into the deep life.