Cost Economics

Viewpoint: A person's true value lies in whether their capabilities can be replicated to other team members at low cost and in a standardized way, thereby achieving economies of scale.

Logic Chain: Distill the methods of high-performing individuals into standardized SOPs or training systems so that more people can master them quickly, reducing dependence on any single individual and lowering the cost of repeated training, while expanding the team's overall output. If capabilities cannot be replicated, team growth becomes severely constrained by individual bandwidth, and costs remain high.

Failure Conditions: The capability depends heavily on personal intuition, personality, or long-term experience, making it difficult to convert into teachable methodologies; or replication results in significant quality degradation, failing to maintain equivalent standards.

Related Areas: Team building, capability transfer

Opinion

Self-developing a system to solve determinism is costly, so it needs to match a sufficiently high unit price; otherwise, customers may be unwilling to pay extra purely for determinism.

Logic Chain

Building an in-house system requires significant resources. If the price is set too high, customers will evaluate the value. If all they need is determinism and they have no demand for advanced features, they may decide it is not worth it.

Failure Conditions

If determinism can be provided through a low-cost solution, or if customers have a strong need for customization and are willing to pay a premium.

Related Fields

Business model, product and operations.

Viewpoint: By relocating highly standardized, remotely executable tasks—such as editing and operations—to cities with significantly lower labor costs (for example, from Hangzhou to Shandong), companies can substantially reduce fixed costs.

Logic chain: Salary gaps between cities can reach severalfold. For standardized tasks that do not require face-to-face delivery, adopting a model of "core staff in first-tier cities + execution team in lower-tier cities" can both ensure quality and increase profit margins through cost advantages.

Failure conditions: If the business has a hard requirement for real-time local collaboration, aesthetic standards, or in-person client service, or if remote management capabilities are insufficient, delivery quality and team stability may be compromised.

Related areas: Entrepreneurship, business models, management and teams.

Viewpoint: Business trips undertaken purely for C-end delivery need a cost-benefit assessment; without added business value, they are not economical.

Logic chain: Traveling from your base city to another location incurs itinerary costs. If the trip is only for a C-end activity (e.g., an afternoon tea session) with no business cooperation opportunities or enough paying users registered, the return on investment is low. Priority should be given to cities where business synergies exist or which have not been covered for a long time, in order to capture greater value.

Invalidation condition: This can be disregarded when the C-end activity generates high revenue, or holds strategic significance for retaining core users or brand exposure, and no better alternative is available.

Related domains: Entrepreneurship, business models

[Perspective] As long as the average order value rises and the costs are manageable, offline annual conferences, thematic workshops, and other formats that were never delivered before are entirely feasible—and highly meaningful.

[Logic Chain] A high average order value means each customer contributes more, which can support heavier service costs. Offline events significantly strengthen community belonging and customer stickiness, and are a standard feature of many high-AOV communities. Their long-term returns far outweigh the one-time investment.

[Failure Conditions] The average order value is too low to cover event costs, or customers are extremely insensitive to such activities, resulting in an imbalanced return on investment.

[Related Fields] Business model, marketing and traffic, community operations

View: Completing the same set of core deliverables intensively within a short period, as opposed to spreading them across a year in phases, makes little difference to the provider’s cost structure and key benefits, so it can be flexibly adjusted to the client’s pace.

Logic Chain: If a client consumes a full year of dedicated deliverables intensively within one month, subsequent needs can be billed separately → the so-called “annual fee” is more a time frame than a difference in intensity → there is no need to reject stretching the timeline out of concern that delivery is too heavy; in essence, the two options are equivalent in benefits and costs.

Failure Conditions: When the service involves periodic engagement and the value of sustained trust accumulation, long-term delivery has an irreplaceable effect; or when the client regards continuous engagement as essential, compressing the timeline lowers perceived value.

Related Fields: Product & Operations, Business Model

Viewpoint: When tackling a brand-new task, you should start a new conversation to keep historical context from interfering with the AI's judgment, while also reducing token consumption costs.

Logic Chain: Every time the AI generates a response, it carries the entire conversation history along with it. Irrelevant information buried in that history can mislead the output, and the longer the chat log grows, the more tokens each request consumes—scaling linearly and driving up costs. Starting a fresh conversation resets the environment, letting the AI focus solely on the current task, which yields both higher accuracy and lower cost.

Failure Conditions: For complex tasks that require carrying long-term context—such as long-form writing or ongoing debugging—starting completely fresh will discard useful accumulated information, so other context management strategies are needed in such cases. Likewise, if the task inherently depends on the continuity of the conversation history, clearing the context can actually be counterproductive.

Related Fields: AI usage tips, cost control

Viewpoint — Even when legitimate software offers a worse experience, users find it hard to give it up due to the significant sunk costs already invested, such as presets and data.

Logic chain — Personal files, workflows, and presets accumulated through long-term use of a software constitute a huge migration cost, creating an irrational psychological lock-in. Even when users are dissatisfied with the service, they think, "What about all my presets?" and continue to put up with it.

When this fails — Migration only happens when an alternative appears that can perfectly import old data with minimal switching costs, or when users rationally assess that the sunk cost should not be ignored and make a determined decision to let go.

Related fields — Product & Operations, Consumption & Lifestyle, Technology & Engineering

Viewpoint: In a healthy market economy, the target selling price determines the acceptable production cost, rather than the production cost determining the selling price.

Logic chain: How much a product can sell for depends on the value perceived by users and the supply-demand relationship, not on how much the company spent. A company must first anchor the price at a level users are willing to pay, then work backward to control costs. Otherwise, even a high-cost product cannot command a higher selling price.

Failure conditions: In a few cases—such as cost-plus contracts, administrative pricing, or resource monopolies—costs may directly determine price in the short term.

Related area: Business model

【Core Argument】For everyday AI applications, choose cheap, high-certainty tools. Only when there is a clear need for deep thinking or a higher capability ceiling should expensive high-capability models (such as Claude Opus) be used briefly, so as to balance attention and outcomes.

【Logic Chain】Claude Opus and similar models broaden the depth and breadth of thinking through massive compute—effective, but costly → if used across all scenarios, attention gets consumed by waiting and debugging → so use them sparingly, only where a genuine "ceiling lift" is needed → in most other cases, rely on cheap, high-certainty tools to ensure stable, usable output → achieving a compromise among cost, attention, and quality.

【Failure Conditions】If everyday tasks themselves demand a level of quality or creativity that cheap models cannot deliver, the tiering strategy may fail; without clear scenario distinctions, it is easy to overuse expensive tools and waste resources, or misuse cheap tools and cause rework.

【Related Fields】AI applications, cost control, attention economics.

Viewpoint: We should not attach ability labels such as “suitable for entrepreneurship” or “not suitable for entrepreneurship” to other people. Choosing whether to start a business is a matter of calculating one’s own costs and benefits and deciding whether it is worthwhile. People differ in what it costs them to be happy under different circumstances; for some, working for someone else is more worthwhile, while for others, starting a business is more worthwhile. Each has their own place.

Logic chain: The cost of creating different kinds of value differs from person to person, and whether something feels worthwhile is also based on one’s own values. Highly capable people do not necessarily start businesses: scientists, teachers, civil servants, and others can be equally excellent in their own roles. Entrepreneurs tend to measure others by their own yardstick, which is disrespectful.

Failure conditions: When people face survival pressure or extreme circumstances, they may have no freedom of choice, and the cost calculation breaks down. Moreover, people who lack self-awareness may need trial and error before they can assess the costs.

Related areas: Entrepreneurship, career development, personal growth.

【Viewpoint】In business, price wars have two fundamentally different natures: one is "innovation" based on cost reduction, and the other is "involution" based on profit compression. The two are fundamentally different.

【Logic chain】Price equals cost plus profit. If your price is low because you reduced costs from the industry average of 6 yuan to 2 yuan through technological or business model innovation while keeping quality unchanged, that is healthy innovation. If your costs are unchanged and you simply squeeze profit from 10 yuan to 6 yuan, that is involution. All truly meaningful innovation ultimately manifests as cost reduction.

【Invalidation condition】When product quality declines significantly due to forced cost reduction, this should not be regarded as effective cost innovation, but rather as another form of deterioration at the expense of user experience.

【Related field】Business model.

Viewpoint: High-quality, high-cost vertical AI solutions will be provided mainly by vertical-domain startups and private deployments within vertical industries.

Logic chain: High cost means such solutions are difficult for general-purpose model platforms to cover at scale. Vertical companies have deep industry expertise and are willing to pay for customized solutions, and economic models such as token-based pricing in China will also reinforce this trend. As a result, these solutions will be concentrated among vertical-domain startups and private deployment solutions.

Failure conditions: General-purpose large model capabilities improve rapidly and cover vertical needs at extremely low cost; or vertical solution costs drop to the point where they can be replaced by platform-based services.

Related areas: AI applications, entrepreneurship.

Viewpoint: Using self-looping, scoring, rejection, and regeneration to force higher-quality output from agents results in exponential token consumption. This means that, due to cost constraints and insufficient willingness to pay in general-purpose scenarios, domestic general-purpose AI application-layer service providers cannot afford and lack the incentive to deploy such high-cost workflows.

Logic chain: High-quality output requires multiple rounds of evaluation and regeneration. Each cycle invokes models, token consumption multiplies, and costs surge. AI services in China's mass market are priced extremely low or even free, and margins are thin, making providers unable to bear this kind of compute overhead. At the same time, ordinary users' demand for answer precision has not reached the point where they are willing to pay for it. Therefore, high-cost, high-quality agents can only be deployed in specific high-value scenarios.

Failure conditions: As model inference costs continue to fall sharply, or if more efficient approximation algorithms emerge that bring the cost of self-looping into an acceptable range, this situation may change.

Related domains: AI applications, business models.

Viewpoint: When AI service providers design subscription plans or API pricing, persistently offering services at prices far below compute costs will lead to service instability, declining product quality, and even threats to company survival.

Logic chain: Compute is a hard cost under high concurrency. Heavy usage attracted by low prices rapidly consumes compute resources. If pricing cannot cover costs, providers will lack extra compute headroom to handle peak loads, causing outages or rate limiting, ultimately damaging user trust and long-term business value.

Invalidation conditions: When a company can cross-subsidize through other profitable businesses, or significantly reduce unit token costs through technology, short-term loss-making pricing can serve as a market-capture tactic, but it must eventually return to cost balance.

Related fields: Business models, AI applications

Opinion: The explosion in server demand as AI services go live will directly cause procurement budgets to collapse, make project ROI impossible to justify, and ultimately force the project to be abandoned.

Logic chain: AI projects require extensive server support. Budgets are instantly blown through, and costs far exceed initial estimates. At those cost levels, the project's expected returns can no longer cover the investment, ROI deteriorates to unacceptable levels, and the project is forced to shut down.

Conditions that invalidate this: If technologies that lower compute costs are quickly commercialized, or if the project itself can generate exceptionally high incremental returns to absorb the soaring compute costs, the project can still survive.

Related fields: AI applications, entrepreneurship, technology engineering.

Viewpoint: Creating a set of high-quality AI training materials and reusing them repeatedly can significantly amortize content costs, allowing paid knowledge products to achieve extremely high ROI.

Logic chain: Content is produced once and can be delivered multiple times, with marginal cost approaching zero; repeated reuse itself demonstrates the content’s evergreen value, generating multiple returns with very low ongoing investment and yielding an extremely high input-output ratio.

Failure conditions: If the content is highly time-sensitive and changes too quickly, it becomes completely outdated in the short term; the room for reuse is severely compressed, and ROI drops sharply.

Related fields: paid knowledge, content creation

Viewpoint: The core objective of innovation is to lower costs in order to enhance value for society as a whole; merely cutting profits only leads to involution.

Logic chain: Reducing costs can free up resources, improve efficiency, benefit more people, and drive overall social progress—this is a positive-sum game. Simply cutting profits is a zero-sum game: all parties compete intensely over a fixed pie, causing involution and eroding industry value. Therefore, genuinely valuable innovation should focus on lowering costs through improvements in technology, processes, or models.

Failure conditions: When costs have already been reduced to the limit, or when cost reduction comes at the expense of quality, safety, or sustainability, blind cost-cutting is harmful. For some high-end product positioning, the direction of innovation is to increase added value rather than to pursue low cost.

Related fields: Business models, entrepreneurship

Viewpoint: With AI, an individual can, in a short time and at extremely low cost, rewrite software tools that previously required a team, months of work, and high expenditure—while significantly improving performance.

Logic chain: The original supplier's tool cost 100,000 yuan and took two months to develop, with annual maintenance of 10,000–20,000 yuan. An individual rewrote it in three days with AI assistance, reducing processing time from five hours to five minutes. This demonstrates AI's disruptive impact on the cost, timeline, and performance of software development.

Failure conditions: If the target system's complexity far exceeds the current boundaries of AI capabilities, or if it requires tacit domain knowledge that cannot be extracted by AI, it would likewise be difficult for an individual to complete quickly.

Related fields: AI applications and technical engineering.