Cognitive Bias

Viewpoint: AI conversations should not be regarded as continuous chats with a human-like memory. Instead, each interaction should be understood as sending the entire history record as a fresh input to the model.

Logic chain: Large language models have no native memory. When a user appends a new message in the same conversation, the system technically concatenates all previous user statements, all previous AI replies, and the latest user message into one complete text block, which is then sent to the AI for processing. This is fundamentally different from the human cognition of "adding new information on top of what came before."

Failure condition: When users mistakenly assume that the AI can "remember" all earlier requirements like a person would, this cognitive bias undermines the strategy of incrementally adding instructions, causing the AI's output to become chaotic.

Related fields: AI applications, product and operations, decision-making and cognition

Viewpoint: Being overly fixated on financial accounting clarity often leads to overlooking the value of intangible assets such as traffic, resulting in actual losses during collaborations.

Logic Chain: Those who excel at splitting monetary sums precisely in partnerships often lack a reference point for valuing their own traffic contribution. Every time, the other party calculates the financial model clearly, while one's own traffic is silently treated as free — this mindset needs to be updated.

Failure Condition: This drawback does not manifest if the other party fairly provides equivalent traffic support, or if the collaboration is strictly limited to cash transactions.

Related Areas: Communication & Social Skills, Business Models

Viewpoint: The achievements of successful people contain a large element of luck. Those who claim that success can be replicated are mostly unreliable, and whatever achievements they have are likely unsustainable. Ordinary people do not need to blindly believe in specific success formulas.

Logical Chain: Success is the result of multiple, nonlinear factors. When summarizing lessons from past experience, it is very easy to fall into survivorship bias and overlook the environmental conditions and random events of the time. Blindly replicating someone else's path to success is like chasing a paradigm of the past—once the environment changes, the original path becomes invalid.

Conditions for Failure to Apply: In certain highly standardized fields where luck plays a minimal role (such as some sports competitions or structured skill-based exams), successful methods are replicable to a certain extent.

Related Fields: Decision-making and cognition; personal growth.

Viewpoint

Every workplace is fundamentally a business entity, and the relationship between capital and employees is essentially a business relationship. Many employees at large tech companies tend to treat the workplace like school, believing that doing their assigned tasks well will automatically lead to good rewards. This mindset is misguided.

Logic Chain

Because most departments in large tech companies are not self-financing and do not bear their own profit and loss, employees can easily lose sight of business logic and fall into task-oriented thinking. Only by discarding school-based assumptions and understanding organizational structure, division of labor, and hierarchy purely from the perspective of interests and business can one accurately position their own value and make sound choices and communications.

When This Logic Fails

In non-profit institutions, government-affiliated organizations, and other non-purely-commercial settings, business logic only partially applies, and other dimensions of understanding must be added.

Related Areas

Career development, management and teamwork.

Viewpoint: The fundamental reason most entrepreneurs fail to make money is excessive self-centeredness: they interpret user behavior through their own professional logic, thereby drowning out the voice of real needs.

Logic Chain: When the mind is still unsettled, people unconsciously project an "I want" and "I'm the professional" mindset onto a situation, making it impossible to perceive the user's actual context and underlying motivations. Only by settling the mind and abandoning preset positions can one understand phenomena through the user's logic; otherwise, the same phenomena get explained away as "because I'm the professional." This mental discipline is a prerequisite for insight into needs—without it, any methodology will fail.

When It Doesn't Apply: If entrepreneurs have already built a mature user feedback system and data verification mechanisms, they can obtain needs objectively and directly from behavioral data. In that case, setting aside the self is still valuable, but it is no longer the only path. In the early stages, when data is scarce, this mental discipline is especially critical.

Related Fields: User insight, cognitive bias, entrepreneurial mindset

Viewpoint When a window of opportunity opens, those who hesitate and agonize the most are the ones in the middle—they possess some capital or awareness, but not enough to act decisively, nor can they, like those at the bottom, burn their boats and commit fully.

Logic Chain Consider the metaphor of queuing for train tickets: when a new window opens, the people at the back of the line rush over immediately (poverty drives change), those at the front stay put (they hold on to their existing advantages), and the people in the middle deliberate repeatedly, consult others, and ultimately miss the opportunity. In the social hierarchy, the typical "middleman" is the salaried worker—skilled but not exceptional, with a little money but not much, informed but not deeply—leading to excessive hesitation in decision-making.

Conditions for Failure When the environment changes slowly, the window stays open for an unusually long time, or the power imbalance is too great, the middleman's hesitation may turn into prudence, not necessarily failure. Moreover, if the middleman's accumulated resources happen to match the early dividends of the new window, decisive action can still break through.

Related Fields Decision-making and cognition, entrepreneurship, career development

【View】The fairness rules promoted by society (such as leaving work on time) are actually tools designed by the powerful to appease the weak. The real distribution of benefits has long been determined by new rules such as algorithms. Pursuing nominal fairness will only leave you behind the actual benefit distribution system.

【Logic Chain】Rules are tools used by the powerful to control you. Platform algorithms determine exposure and income, while leaving work on time is merely surface-level fairness—benefit distribution does not follow these rules. Some people run side projects on Xiaohongshu, yet their follower count proves useless, because content exposure is allocated by algorithms based on quality. Rather than clinging to traditional fairness, it is better to study how algorithms work and become one of the beneficiaries.

【Failure Conditions】If laws or social movements forcibly intervene to establish transparent and fair platform distribution rules, individuals would no longer be arbitrarily at the mercy of algorithms.

【Related Fields】Decision-making and cognition, career development.

Viewpoint: Most people's understanding of AI remains at the level of news reports, far from forming widespread actual usage habits.

Logic chain: The penetration of AI tools is still concentrated among a small number of pioneer users. The general public mainly learns about AI progress through news, resulting in a gap between awareness and actual application, and severely insufficient market education.

Failure condition: As AI functions are integrated into common applications and devices, the rate of seamless usage will rise, and news will no longer be the main channel of awareness.

Related fields: AI applications.

Viewpoint: Ordinary people get scammed or lose money mostly because they have access to too few business opportunities. When one occasionally appears, it is mistaken for a major opportunity and they participate blindly.

Logic Chain: Narrow information channels lead to few quality opportunities. Under the scarcity effect, individuals overestimate the success rate of an occasional opportunity, ignore risks, and consequently lose money or get scammed.

Failure Condition: This does not apply when the individual has the ability to verify information through multiple channels and a mature risk-identification framework, or when the opportunity itself is a proven high-quality project.

Related Fields: Entrepreneurship, decision-making, and cognition.

[Viewpoint] People with different knowledge backgrounds assess AI capabilities through the lens of their own areas of expertise, resulting in cognitive bias.

[Logic Chain] Those who don't understand AI but use it heavily believe AI can do anything; those who don't understand AI but understand technology believe technology can overpower everything; those who understand neither AI nor technology but understand business believe AI output is all garbage. Each group evaluates AI from the dimension they are familiar with, lacking a complete understanding of AI's overall boundaries and limitations.

[Failure Condition] This bias fails when an individual simultaneously possesses AI knowledge, technical understanding, and business judgment, or when AI capabilities become so transparent that no professional background is needed to fully understand them.

[Related Fields] AI applications, decision-making, and cognition.

Viewpoint: Humans prefer to use AI as an advisor, but the model that yields the highest actual returns is to let AI act as the executor (delegate). Human arrogance and cognitive biases hinder the adoption of this optimal usage.

Logical chain: Research shows that humans tend to let AI provide advice rather than make decisions and execute directly, yet data indicate that delegating execution to AI brings far higher practical returns than the advisor model. The reason is that human decision-making is full of cognitive entropy, whereas AI execution can reduce interference from emotions and biases. Therefore, individuals and enterprises should design structured workflows that let AI participate deeply in execution, not just offer advice.

Failure conditions: When decisions involve legal responsibility, ethical judgment, or core creativity that must be borne by humans, complete delegation of execution is not feasible. In such cases, AI should remain in an advisory role with humans retaining final decision-making authority.

Related fields: Human-AI collaboration, organizational management, AI application strategy.

Viewpoint: When facing creative decisions that require taking on risks with uncertain outcomes, most managers instinctively classify the 'unknown' as the 'wrong option' and avoid it.

Reasoning chain: The combined effect of evolutionarily inherited risk aversion and accountability pressure within organizations leads managers to judge every option by certainty metrics. They treat uncertainty itself as a negative signal rather than a sign of potential opportunity, thereby systematically excluding innovative possibilities.

When the logic does not apply: In fields with extremely high safety and compliance requirements where failure cannot be tolerated (such as nuclear facilities and critical infrastructure), this avoidance is necessary and correct.

Related areas: Decision-making and cognition, management and teams.

Opinion: What many people tout as independent thinking is, in essence, bypassing all the accumulated wisdom of previous generations, insisting on their own views, and ending up merely repeating mistakes—getting lost on their own.

Logic chain: True independent thinking should be built on understanding prior wisdom and then engaging in critical reflection, rather than ignoring historical achievements. Thinking that disregards previous wisdom in the name of innovation tends to be inefficient and easily leads to error.

Failure condition: When the existing knowledge system in a field truly has major defects or era-bound limitations, entirely bypassing prior work may be a necessary path to a breakthrough.

Related fields: Cognitive science, education, critical thinking.

Viewpoint: The daily effective output time of knowledge workers is far lower than the traditional concept of working hours. Outsiders often mistakenly equate output time with total working time.

Logic chain: IT practitioners achieve high-value output through sustained deep work; their actual efficient working time may be only 3–5 hours. People from a manual-labor mindset interpret this as “only needing to work overtime for that long,” confusing effective output with time spent present and creating an industry divide.

Failure condition: When cross-industry communication clearly explains the distinction between efficient and inefficient time, the misunderstanding can be resolved.

Related fields: Work efficiency, cross-industry understanding, changes in work practices.

View: Attributing setbacks in one’s career or personal life to 'not wanting to compromise one’s integrity' is a cognitive bias. It both conceals shortcomings in one’s own abilities or understanding and disparages those who are honest and upright yet still succeed.

Logic: This attribution moralizes failure, provides an excuse for inadequacy, and overlooks the reality that many principled people can still achieve success through appropriate strategies.

Failure conditions: In extreme cases where corruption is severe and the environment genuinely allows bad money to drive out good, this attribution may partly reflect reality, but one must still be wary of self-deception.

Related fields: Decision-making and cognition, career development.

Viewpoint: Even in a tech community like Twitter, a large number of users still need basic tutorials for registering overseas accounts, reflecting information cocoons and cognitive biases.

Logic chain: Twitter users are often imagined as always-on digital roamers, but in reality many users need entry-level guidance. This indicates that information gaps are widespread, and one's own experience does not represent the whole.

Invalidation condition: When such tutorials completely lose demand, or when the community's overall digital literacy improves significantly.

Related fields: Information literacy, community cognition.

Viewpoint: People who study business cases in depth are less likely to develop the “I could do it too” illusion, but this overconfidence is prevalent among MBA-educated working professionals.

Reasoning chain: Using tools such as Deep Research to study cases deeply exposes a great deal of practical detail, leading people to rationally recognize that knowing is easier than doing. Some MBA education, however, may emphasize frameworks and after-the-fact explanations, easily giving students an illusion of competence—mistaking case understanding for execution ability.

When this does not hold: If MBA programs strengthen hands-on simulations and include more post-mortems of entrepreneurs’ failures and self-awareness training, this illusion will weaken.

Related domains: Education and learning; cognition and decision-making.

Viewpoint: When faced with high standards, one should not deny their feasibility simply because of one’s own limited ability, nor should one simplistically stigmatize them as PUA.

Logic chain: Individual ability limitations can easily make people equate “I cannot do it” with “it does not exist” or “it is not necessary.” This cognitive bias hinders growth. Treating high standards as PUA out of a stress response is a defense mechanism that closes off room for improvement. As long as one has seen others achieve them, one can believe they are attainable.

Failure conditions: If the demands are genuinely unrealistic or lack resource support, sheer pressure may turn into actual PUA. Distinguishing reasonable high standards from malicious PUA depends on whether growth paths and support are provided.

Related field: Personal growth.

Viewpoint: Some practitioners in emerging virtual industries—such as the crypto world—are full of unfounded imaginings about how the real economy works. Their seemingly rigorous but mistaken theories are often laced with condescending pity toward people who work in the physical economy.

Logic chain: They lack a genuine understanding of the constraints facing physical industries. They apply simplified models detached from reality to real-world situations, deriving conclusions that are internally consistent but entirely wrong, and then regard this as a higher form of cognition.

Failure conditions: Once they personally get involved in a physical business or are schooled by actual market experience, this cognitive bias is quickly corrected.

Related fields: Decision-making and cognition; business models.

Viewpoint: When people face phenomena whose complexity exceeds their cognitive capacity, they abandon quantitative, concrete analysis and instead resort to abstract grand narratives to make qualitative judgments.

Logic chain: The human brain has limited bandwidth for processing complex information. Once information complexity surpasses a threshold, it copes by adopting simplified, all-encompassing narratives—such as technological determinism or political side-taking—leading to black-and-white judgments.

Failure condition: When individuals remain humble, acknowledge ignorance, and possess a systems-thinking framework, they can avoid slipping into grand narratives.

Related domains: Technological cognition, social discussion, decision-making, and cognition.