The Cycle of Technological Waves and Panic: Why Does the "End of Jobs" Narrative Keep Resurfacing?
With each new model release, public anxiety routinely escalates once again. The recent launch of Gemini 3, with its powerful front-end generation capabilities, multimodal understanding, and logical reasoning, has essentially caught up with or even surpassed ChatGPT 5 in certain aspects of model capability. Consequently, self-media outlets have once again begun proclaiming slogans like "Front-end is dead" and "Designers are dead." It's as if every technological iteration necessitates a complete societal rewrite of the occupational roster.
However, looking back at history, this narrative is not new: when automated looms appeared, textile workers feared their livelihoods would be stolen; when office software became widespread, secretarial and accounting roles were predicted to face massive cuts or even disappear.
But the reality is that jobs did not collectively vanish as predicted; instead, they were restructured: the invention of the loom made fabric cheap, leading to a surge in the number of consumers and frequency of purchases, which in turn increased the demand for textile workers; office software made starting a business easier, leading to a mushrooming of startups, which simultaneously drove the expansion of the accounting industry.
One reason this wave of AI has triggered such intense panic is that the speed at which models generate content far exceeds any previous technology; another reason is that it is impacting the vast white-collar workforce. However, judging the replacement effect based solely on speed, while ignoring the elements of judgment, responsibility, and interpersonal interaction in the labor process, and even overlooking the business model restructuring and market capacity expansion brought about by technological change, inevitably leads to the over-extrapolation of "technological panic-ism."
The "death lists" of the past three years reveal more about emotion than fact itself. Media and self-media outlets gain traffic from such narratives, people gain anxiety, but what truly changes is not the jobs themselves, but how people accomplish tasks.
From Traffic to Reality: Where is the Real Change?
In the attention economy driven by algorithms, panic itself is a highly efficient asset.
Self-media platforms have thus developed a mature narrative and monetization structure: using extreme narratives to create impact, building credibility through survivor bias, and ultimately leading to quantifiable monetization paths. This mechanism is not unique to the AI field but is a natural product of contemporary platform algorithm structures. AI has become the best subject matter simply because it more easily triggers the structural anxieties of the middle class.
Long-term research in economics on automation points out: technology replaces tasks, not jobs themselves. Within the same position, several repeatable, standardized parts will be automated and stripped away, while parts requiring judgment, coordination, responsibility, and aesthetics are instead strengthened. Whether a job can persist depends on whether it still contains elements that are difficult to automate.
We can verify this logic through changes in the following three industries:
- Concept Art and Design: Mass-production parts are replaced, premium for aesthetics and complex creation rises
AI tools have significantly eroded demand for low-end illustrations, causing the mid-to-low-end outsourcing market to shrink. However, high-end design, which requires style judgment, scene construction, and continuous aesthetic control, has seen its demand and prices remain firm.
According to trend reports from major freelance platforms like Upwork and Fiverr, and industry analysis from the Japan Animation Association (AJA), over the past year, simple, mass-production outsourcing orders have experienced a structural collapse in both volume and price. But during the same period, project transaction values for high-end needs like brand visuals and world-building have remained stable, and even recorded moderate growth in some niche areas.
This clearly indicates: what's disappearing is not the profession, but the most replaceable layer of "mass-production labor" within it. Aesthetic, style, and composition judgment remain a scarce resource.
- Software Industry: Junior development decreases, but systems engineering demand is higher
AI can indeed generate a lot of basic code, but system stability, architecture design, and risk control still cannot be fully entrusted to models.
The software industry is experiencing structural differentiation. Junior development positions are under pressure, while engineers capable of managing, reviewing, and integrating AI output are becoming more expensive.
Data from major recruitment platforms (like LinkedIn and Indeed) depict this structural differentiation trend: although demand for junior developer positions fluctuates, the hiring volume for senior-level positions like Senior and Architect continues to grow, becoming the dominant force in market demand.
GitHub Copilot's report further explains the underlying reason: although AI assists with a large amount of programming work, due to increased difficulty in system integration and risk control, the number of Pull Requests has actually increased. In other words, AI hasn't reduced "engineering"; it has only reduced "writing code," but ultimately requires more people to manage the complexity introduced by AI (commonly known as the "big ball of mud"). AI replaces repetitive labor, not engineering capability itself.
- Translation Industry: Basic translation automated, high-end translation shifts towards responsibility and guarantee
Instruction manuals and daily communication are highly automated, but legal contracts, diplomatic language, and literary translation rely on authorial intent and legal liability. The core value of high-end translation has instead upgraded to "quality guarantee."
Industry data from Proz.com and the American Translators Association (ATA) shows that prices for ordinary text translation (manuals, general business materials) have experienced significant declines. However, prices for translations involving liability attribution, legal validity, stylistic consistency, and other high-risk or high-context-dependent translations have remained stable or even seen moderate growth.
Although the usage of mainstream machine translation tools like DeepL and Google Translate has increased significantly, this only affects "dictionary-lookup labor." In scenarios involving significant risk, publishers and law firms generally insist on a collaborative model of "AI draft + human final review." That is to say, AI automates language transcription but cannot automate context judgment. The group most impacted is not the industry itself, but those who previously relied on repetitive labor.
The Boundaries of Automation: What is Humanity's Core Competitiveness?
The speed at which models generate content is indeed astonishing, but the clearer we understand its boundaries, the better we can see humanity's true advantages. Automation continues to advance, but it is consistently blocked by three "insurmountable thresholds," and these three thresholds precisely constitute humanity's core competitiveness.
The first threshold is Responsibility: In any field involving risk and requiring someone to bear the consequences, the final decision-maker must always be human. Financial regulatory agencies explicitly emphasize in documents: AI can assist, but cannot replace humans in bearing ultimate responsibility. Regulation in the medical field is even stricter; all AI outputs must be reviewed by qualified professionals. This means humans must be the ultimate bearers of risk; models can be advisors but cannot become decision-makers.
AI's mistakes are ultimately borne by humans.
The second threshold is Context: The real world is not standardized input; it is full of ambiguity, subtext, conflicts of interest, and unspoken rules. AI may perform flawlessly within text, but once it enters "gray area scenarios," its error rate skyrockets. Research finds that models struggle most with complex "gray area scenarios" that require deep situational understanding and interest coordination, and these are precisely the core work content of many professions.
"Have you eaten?" is not really asking if you've eaten.
The third threshold is Relationship: The essence of many professions is not "providing information," but "providing relationships." Sales, consulting, medical services, psychological support—they rely on trust, emotional coordination, and subtle interpersonal cues. Models can be very intelligent, but they cannot provide necessary psychological support and a sense of security.
AI cannot provide warmth and hugs.
Understanding these three thresholds makes it clear that AI changes the task structure, not the labor system. Machines can replace repeatable actions, not the critical responsibilities humans undertake in an uncertain world. Therefore, humanity's core competitiveness in the future labor system is precisely the direct response to these three "insurmountable thresholds." Future competitiveness will increasingly concentrate on those abilities that cannot be written into algorithms:
- The Responsibility Threshold corresponds to final judgment and accountability ability: The ability to make final decisions based on risk and ethics and bear irreversible consequences. This is the last line of defense in transforming data into action.
- The Context Analysis Threshold corresponds to complex situation and cross-domain integration ability: The ability to define ambiguous, non-standardized real-world problems, identify subtext and conflicts of interest, and use AI as a tool for cross-domain, multi-factor solution integration.
- The Relationship Building Threshold corresponds to emotional intelligence and interpersonal trust-building ability: The ability to provide empathy, reassurance, and emotional support, and build trust through interaction. This is key to providing core services like security, understanding, and support.
These abilities together constitute the "foundation of irreplaceability" in the AI era.
Technological Threats Are Often Overestimated, While Human Potential Is Often Underestimated
The emergence of generative AI has indeed changed the task structure of many industries, but "job extinction" is more narrative than reality. History has repeatedly proven that technology can change work methods, and the application of new technologies can generate more demand. Therefore, the real risk is not the model's capability, but humanity's misinterpretation of technology.
AI will not eliminate people, but people skilled in using AI will eliminate those who refuse to change. The future belongs to those who can ask good questions, exercise deep judgment, take on complex responsibilities, and create value on top of tools.
The ultimate question should perhaps be: Is it AI that eliminates you? Or did you choose to give up on yourself first in the face of technological change?
Moreover, the impact of this wave of technological change extends far beyond individuals; it will also reshape or even eliminate organizational structures themselves. History repeatedly proves that companies clinging to old models at technological inflection points are often more easily eliminated than individuals. If enterprises cannot reshape their processes, culture, and decision-making methods, even if they are large in scale, they may rapidly lose competitiveness in a short time.
In other words, the risk in the AI era is never simply "individual versus machine," but a systemic challenge: Can individuals and enterprises collectively adapt to new modes of production?
The ultimate competition is not between humans and machines, but evolves into a race for survival between evolving organizations and those clinging to old habits. Only those organizations courageous enough to restructure processes and embrace uncertainty can truly build the barriers of the future amidst the automation wave.