In 2026, the rapid iteration of AI technology triggered widespread occupational burnout: entrepreneurs were overwhelmed by the "one-person company" role and fragmented attention; engineers at large companies were forced to increase token usage and train AI to replace their own skills; AI product managers faced the dilemma of "becoming obsolete before learning" as model updates far outpaced learning capabilities; and advertising professionals were forced to work overtime due to rising industry expectations, becoming AI proofreaders and prompt engineers. Technology, which should have reduced the burden, was instead distorted into a labor intensity amplifier under the influence of performance evaluations, capital logic, and a lack of institutional constraints. This phenomenon reflects a systemic imbalance in AI applications where instrumental rationality outweighs humanistic rationality, urgently requiring policy intervention (such as the EU AI Act) to reconstruct the distribution mechanism of technological dividends and the social contract.
Article author and source: Tingtong Tech
In the spring of 2026, the trends in the tech world shifted faster than ever before.
However, while AI has brought about an unprecedented efficiency revolution, it has not brought about the expected reduction in workload.
Conversely, on social media platforms, both ordinary working people and glamorous "one-person company" owners expressed that they "feel increasingly tired."
This kind of exhaustion is not the physical exhaustion of the traditional "996" work schedule, but rather a frantic rush driven by technology.
For example, the concept of "one-person company" has become popular. But in reality, many people who have tried it have found that they have taken on all the roles of CEO, product, operations, customer service, and finance, and "007 has become the standard."
Let's look at the AI tools themselves.
Before we even fully understood OpenClaw, Hermes Agent became a hit; just after the release of Qwen 3.6-Plus, Zhipu introduced GLM-5V-Turbo; and then DeepSeek V4 officially announced its upcoming entry with millions of context records.
The rapid pace of AI iteration is crushing the learning abilities and confidence of ordinary people in an almost brutal way. On social media, the most popular meme now is no longer "AI helps me slack off," but "It's already outdated before I've even learned it."
Even more absurdly, in order to prove their usefulness, people have begun to be forced to "inflate their usage figures." In some large companies, token usage has even become the "fourth form of compensation" after salary, bonuses, and equity. To prove their indispensability, employees have to personally teach AI how to "skill" themselves.
It's like a marathon without an end. The market thinks it's in control of AI, but under the performance-driven system, young people are more like working for AI.
This is not just a business phenomenon, but also a social and emotional issue. TingTong Tech talked with young people, trying to see the real individual in 2026 under the AI wave.
Entrepreneurs: The glamour and anxieties of "one-person companies"
Sister Lin, born in the 1980s, once worked her way up to P7 at a major internet company. Driven by a desire to "explore the world," she resigned at the end of 2025, just in time for the rise of "one-person companies."
In Sister Lin's words, her arsenal is very comprehensive: Claude and DeepSeek can write proposals, Midjourney can provide designs, digital humans can do live streaming, and an AI customer service team is online 24 hours a day.
Ms. Lin said that at first, she was indeed very "happy". Project planning that used to take a team two weeks to complete could be delivered by her alone in two days. Clients praised her for being "efficient", and friends envied her for being "free".
But in the spring of 2026, Sister Lin found herself caught in an unprecedented frenzy.
At 7 a.m., she was woken up by AI-powered public opinion monitoring; a negative review on a certain platform required her to personally respond as "CEO." At 9 a.m., as a "product manager," Sister Lin needed to fine-tune three AI models to compare data. At 11 a.m., she had to switch to "operations" mode, use AI to generate short video scripts, and then revise them because the AI-generated scripts lacked "human touch."
In the afternoon, Sister Lin transforms into the "customer service director," handling complex complaints that the AI customer service can't resolve. Sometimes, she also acts as the "finance manager," checking AI-generated reports, identifying errors, and correcting them. In the evening, Sister Lin also takes on the role of "technician," debugging newly connected API interfaces.
“It used to be 996, now it’s 007, and there’s no concept of leaving get off work,” Sister Lin said with a wry smile. “Because AI doesn’t sleep, I can’t sleep either. A client sends a request late at night, and the AI replies instantly. It’s not good for me to pretend I didn’t see it.”
What worries Sister Lin even more is that she finds herself becoming "fragmented".
For example, Sister Lin no longer has uninterrupted time; instead, she switches between different roles like a revolving lantern. Her attention is fragmented into countless tiny segments, and she has to deal with a "tail left by AI" every few minutes.
"One person can do the work of a thousand troops, but in the end, he finds that he is just one of the fastest-wearing gears on this huge and efficient machine."
Sister Lin frankly admitted, "Hiring people is the quickest solution, but in the early stages of starting a business, I don't have enough funds, so I can only take it one step at a time. I'll persevere until I can't hold on any longer."
Algorithm engineers at major tech companies: "coerced" by tokens and skills.
Li Ming, born in the 1990s, is an algorithm engineer at a major company. According to him, his business unit will pilot a new system, "Token Compensation Package," starting in Q1 of 2026.
Simply put, each employee's monthly performance is based not only on what they produce, but also on how much AI computing power they "utilize." Token usage is jokingly referred to internally as the "fourth type of compensation," ranking after salary, bonuses, and stock options.
At first, Li Ming thought this made sense: "The more AI we use, the higher the efficiency."
But soon, things changed. In fact, to prove they were "worth their money" and even "indispensable," the colleagues started a token race.
What used to be a simple requirement could be handled by writing 200 lines of code and calling 5,000 tokens. Now, people are starting to use AI to generate extremely redundant code, repeatedly having the AI optimize, comment out, and refactor it, "just to inflate the token count."
Even more absurd is "Skill" itself. The company requires each employee to train their own AI agent, making it learn their skills to achieve digital twins. As a result, Li Ming had to spend a lot of time every day teaching the AI how to write the kind of code he was good at and how to reproduce his debugging process.
“I’m teaching something how to replace me,” Li Ming said. “And the company also includes this ‘teaching outcome’ in the evaluation. If the agent doesn’t resemble you enough, it means you don’t have enough knowledge.”
Nowadays, Li Ming's daily work consists of teaching AI to do tasks in the morning, using AI to generate massive amounts of code in the late morning, correcting errors in the AI's code in the afternoon, and writing reports in the evening to prove his token usage.
"Before, I was physically tired. Now, I'm mentally tired. It feels like I'm racing against a shadow, and the shadow's starting line is always five meters ahead of me."
AI Product Manager: Before I even learned it, the application was already outdated.
Chen Chen, born in the 2000s, is an AI product manager who has been in the industry for two years. Since starting her job, she has been learning about AI. To date, she has more than 20 AI-related books on her bookshelf, countless tutorial links saved in her browser, and more than a dozen versions of model documentation on her computer.
But Chen Chen still felt like she was "going crazy".
In the first few months of 2026, she spent half a month figuring out how to use OpenClaw and wrote an internal training document; these past two days, Hermes Agent has become incredibly popular again, and her boss asked her to produce a competitive analysis within a week.
"In addition, DeepSeek V4 was officially announced at the end of April, and it is said that the context window is frighteningly large." Chen Chen admitted, "My learning speed can never keep up with the iteration speed of the model."
In reality, Chen Chen spends her daily commute listening to AI-related podcasts, her lunch breaks reviewing AI paper abstracts, and her weekends attending online seminars. Her WeChat group list is always filled with messages from dozens of AI communities marked with red dots.
However, what broke Chen Chen the most was the feeling of helplessness that "it's already outdated before I've even learned it".
For example, last week Chen Chen spent a week learning an AI painting workflow, and this week a new model was released that is more effective, faster, and has a different operating logic.
“In the past, I could use a software for three years after learning it, but now I’m lucky if I can use an AI skill for half a month,” Chen Chen said. “I feel like I’m not growing, but being swept along by the AI wave. I can’t stop, or I’ll be left behind on the beach.”
Chen Chen said that she dreams at night about various model version numbers fighting each other. She herself believes that this is "cognitive overload" and that she should reduce her information intake.
"But if you don't take it in, you won't be able to keep up tomorrow." Chen Chen said that this is the reality, although it is cruel.
Advertising company employee: Efficiency has improved, but overtime has increased.
Ms. Zhao, born in the 1980s, works at an advertising company. Her company embraced AI early on, using it to write strategies, generate creative ideas, and create PPT presentations, which has significantly improved efficiency.
Logically, higher efficiency should mean being able to leave work earlier, but the reality is quite the opposite.
"Because AI has raised the 'expectation threshold' of the entire industry to an absurd level," Sister Zhao admitted.
Previously, it took clients three days to develop a proposal, which they considered normal. Now, AI can generate a "seemingly decent" first draft in ten minutes, and clients think, "You should be able to produce ten proposals a day."
What's even more deadly is the cycle of "technical humiliation" and "work swill".
The client now also uses AI to write briefs. The briefs are written in a flowery and impressive style, but upon closer inspection, they are all empty words and clichés generated by AI.
Sister Zhao had to use AI to analyze the AI-written brief, then use AI to generate a solution, and finally use AI to detect the "AI rate" of the solution and manually modify it to reduce the "AI rate" to below 20% as required by the customer.
Sister Zhao said, "Both of us are using AI to produce a lot of meaningless, perfunctory materials. It's like swill from our jobs; it looks like there's a big bucket of it, but it's actually not very nutritious."
“AI didn’t replace me; it just turned me into an AI proofreader, a prompt engineer, and a process compliance officer,” said Ms. Zhao.
“My salary didn’t increase, but my job became working for AI. It’s like a highway; the better it is built, the more traffic there is, and the more congested it gets.”
In conclusion
Returning to the original question, why are we becoming more and more tired despite having AI?
A stark answer is that technology is never neutral. When technology is used to "improve efficiency," and "efficiency" is defined as "creating more value per unit of time," technology becomes an accelerator.
This is also a typical manifestation of marginal utility in economics. It presents a cruel curve: the initial benefits of technology are delightful, but they are quickly wiped out by capital, and what is consumed is people's time, energy, creativity, and happiness.
For the industry as a whole, external intervention is still needed. For example, the EU's Artificial Intelligence Act has clearly stated that companies must assess the impact on workers' rights when introducing AI systems.
In other words, the productivity gains brought about by AI should be shared by society through policies, social security, and other distribution mechanisms.
In a sense, every technological advancement forces the market to formulate new social contracts.
