3.5 Resource & Fluidity

At this point, we have built a near-perfect digital organism: it possesses memory and can learn; it has a heartbeat and can work proactively; it has an evolutionary engine and can self-iterate. But now, we must throw this "ideal state" creature into the real commercial world. The commercial world follows two cold and ancient laws: Economics (Cannot lose money) and Physics (Energy is not infinite). These are the last two survival laws we must introduce for it: Resource Quota and Compute Fluidity.

First, we must introduce a seemingly cruel but actually crucial concept for every silicon-based employee: Token Budget.

A common misconception is that the cost of AI is mainly the one-time training or procurement cost, and once deployed, it can work for "free." This is a dangerous illusion. In fact, every AI agent we call an "employee," every thought (calling LLM), every interaction with the external world (API call), is continuously consuming real energy—Tokens. Tokens are the "calories" this digital life form relies on for survival, the "glucose" that must be burned to maintain its "mental activities."[^1]

Therefore, an AI system without budget constraints is like a creature suffering from a rare metabolic disease; it will devour energy uncontrollably until it depletes everything. Imagine this nightmarish scenario: an Agent responsible for analyzing market data falls into an endless loop due to a tiny logic error—it constantly downloads the same file, constantly tries to parse it with the wrong method, constantly calls the large model to summarize its failures, and then repeats. During the 8 hours you sleep peacefully, it might have executed millions of high-cost API calls, silently burning thousands or even tens of thousands of dollars in budget. When you wake up the next day, what you see will be a bill that stops your heart.

To avoid this disaster, we must set a clear "Survival Quota" for every Agent, and even every task. This can be a cap on the number of Tokens or a limit on API calls. This quota is the "salary" and "rations" we pay to this silicon-based employee. Its significance goes far beyond cost control; it lies in introducing a profound "Economic Constraint."

An Agent with an unlimited budget might tend to use the most luxurious and "brute force" way to solve problems, for example, throwing a thick e-book entirely into the context window for the large model to summarize. An Agent endowed with a strict budget is forced to "think" more deeply before acting: it must first judge which chapters are key, how to extract core points with the fewest Tokens, and how to interact with tools most efficiently. The pressure of the budget, like the pressure of natural selection, will force the Agent's evolutionary direction from "able to solve problems" to "solving problems in the most elegant and economical way." This is a wisdom forced out, a creativity bursting forth under limited resources.

Furthermore, this "Survival Quota" introduces a cold and efficient "Economic Darwinism" at the organizational level. Those Agents that continuously create huge value within the budget will have their "business" retained and expanded; while those Agents that are long-term "living beyond their means" and cannot prove their ROI within the consumed Tokens will be automatically marked by the system as "to be optimized" or "to be eliminated." This enables the entire AI organization to have dynamic, economic-benefit-based self-regulation capabilities, ensuring every penny is spent wisely.

This economic thinking can also evolve into more advanced resource utilization strategies when facing increasingly diverse AI service billing models. Many service providers no longer simply pay-as-you-go but launch "buffet-like" monthly or annual packages—for example, allowing you to call mid-level tasks 120 times for free every 5 hours. This "use it or lose it" quota is a huge waste for human managers, but for AI systems, it is an excellent opportunity for extreme optimization.

Our AI-native enterprise will have a built-in "Opportunistic Scheduler." It not only monitors the budget but also monitors the "refresh countdown" of every package. When it detects: "Only 1 hour left until quota refresh, but there are still 100 call allowances about to expire," it will act immediately. The system will automatically scan the task list. If there are high-priority tasks, it will prioritize using these "free" resources to handle them. If not, the scheduler will act like a thrifty housewife, never allowing any bit of "ingredients" to be wasted. It will immediately extract those works marked as "not important and not urgent" from a special task pool and distribute them to the call quota about to be zeroed out.

This task pool can be built entirely according to the classic "Eisenhower Matrix."[^2] These "not important and not urgent" tasks might include: generating preliminary copy drafts for future marketing campaigns (Plans), reviewing and summarizing an ad that performed well last week (Optimization), or exploring and generating more soothing communication scripts for some recent customer complaints (Adjustment). This "gleaning" and "sweeping" capability, precise to the minute, which human teams simply cannot achieve, pushes resource utilization efficiency to the physical limit, transforming costs that should have been wasted into extra "thinking" and "foresight" driving the company's long-term development.

If "Token Budget" is the survival constraint for a single Agent, then "Global Fluidity of Compute" is the ultimate liberation of resource utilization efficiency for the entire organization.

In traditional human companies, the most expensive resource—human time and talent—is wasted shockingly. A copywriter in the marketing department, even in the gaps of conception, cannot immediately "switch" to a programmer to help the R&D department write two lines of code. An accountant in the finance department, after completing the monthly report, cannot lend his idle "brainpower" to the product department currently brainstorming. Department walls, professional barriers, physical space, and the huge cost of human mental switching collectively lead to huge "sedimentation" and "idleness" of human resources.

But in AI-native enterprises, we must establish a subversive new cognition: Compute is the new Manpower. The total compute resources owned by the company—whether GPU duration or API call quotas—is our "Total Manpower Pool" available for dispatch. Unlike humans, this army of "silicon-based labor" possesses a characteristic that human organizations dream of: Absolute, Frictionless Fluidity.

To maximize the value of this fluidity, we designed the "Tidal Effect" model.[^3]

Imagine the operational rhythm of our AI company within 24 hours a day:

• High Tide: Day Shift Peak (e.g., 9 AM to 6 PM) During this time, a large number of users flood in to interact with the product. At this moment, the company's compute "tide" will surge towards the "coastline"—those business departments directly facing customers. The "Customer Service Reception" Agent cluster will fire on all cylinders, handling tens of thousands of user inquiries; the "Personalized Recommendation" Agent cluster runs at high speed, calculating and pushing content for every user in real-time; the "Sales Lead" Agent cluster scrapes information across the network, looking for potential customers. At this moment, most of the company's compute is concentrated on supporting these high-concurrency, real-time front-end interactions.

• Low Tide: Night Shift Dormancy (e.g., Midnight to 6 AM) When user activity enters a trough, traditional company servers also enter a "semi-dormant" state, causing huge resource idleness. But in AI-native enterprises, this is exactly the moment when compute "ebbs" and flows back. Those Agents playing "Customer Service" and "Sales" during the day have their underlying compute resources instantly "released" and "reclaimed" by the system. Immediately after, this powerful compute "tide" will be re-injected into the company's "inland"—those business departments requiring deep calculation and analysis.

  Thus, a silent, efficient "Night Shift" begins:

  • A batch of re-empowered Agents transforms into "Data Scientists," beginning deep mining, model training, and trend prediction on the massive user behavior data accumulated during the day.
  • Another batch of re-empowered Agents becomes "Strategic Analysts." They use this rare compute window to conduct large-scale market simulations, deducing thousands of possibilities for the competitive landscape.
  • There is also a batch of Agents acting as "Librarians" and "Maintenance Engineers." They organize and optimize the entire company's vector knowledge base, conduct system self-checks, code refactoring, and even self-repair some non-urgent BUGs marked during the day.

Through this "tidal-like" compute scheduling, we achieve an extreme resource utilization rate. The company's heart (compute core) never stops beating, the company's employees (AI agents) never tire, only serving the same ultimate goal in different roles at different times. This "digital nomad" style of compute fluidity completely breaks the department walls and time walls of traditional organizations, turning the entire enterprise into a dynamic organism running efficiently 24/7 with zero resource waste. This provides the one-person unicorn with an overwhelming operational efficiency advantage over any human team.

[^1]: "Tokenomics" is a core concept for analyzing the cost structure of large language model applications. It not only calculates the price of a single Token but also involves how a series of complex factors such as context length, model selection, and call frequency collectively affect the final operating cost. Andreessen Horowitz (a16z)'s article "Navigating the High Cost of AI Compute" has an in-depth discussion on this. Article link: https://a16z.com/navigating-the-high-cost-of-ai-compute/ [^2]: The "Four Quadrants Method" originated from US President Dwight D. Eisenhower's personal method of managing time, hence also known as the "Eisenhower Matrix." This method was later popularized by Stephen R. Covey in his bestseller "The 7 Habits of Highly Effective People" and became a classic model in the field of time management. Reference link: https://en.wikipedia.org/wiki/The_7_Habits_of_Highly_Effective_People [^3]: The underlying technical idea of the "Tidal Effect" is closely related to "Dynamic Workload Scheduling" in large data centers and cloud computing platforms. Multiple technical papers published by companies like Google and Amazon, such as "Large-scale cluster management at Google with Borg," describe how to dynamically and intelligently allocate computing tasks among tens of thousands of servers based on task priority and resource availability to maximize resource utilization. Paper link: https://research.google/pubs/pub43438/