When Web4 Begins Knocking: A Self-Help Handbook for Ordinary Migrant Workers
author:TT3LABS,Web3/AI/SaaS remote recruitment platform
Original title: On the eve of Web4, an anti-elimination guide for ordinary migrant workers
On February 26, 2026, fintech giant Block announced the layoff of more than 4,000 employees, directly reducing the team size from more than 10,000 to less than 6,000. CEO Jack Dorsey mentioned in a letter to shareholders:
“Intelligent tools have changed what it means to create and run a company... a significantly smaller team can do more and do better with the tools we're building.”
Dorsey also gave his extremely cold prediction:
“I think most companies are late. Within the next year, most companies will come to the same conclusion and make similar structural adjustments.”
Block's shares soared 20% + after the day's trading. This is the capital market's response with real money: paying for the company's AI leverage and efficiency.
An ordinary person who doesn't understand programming at all can already independently run a fully functional app overnight with the help of a large model. Then the capital market is bound to ask a sharp question: how much is the value of a tech giant that hires tens of thousands of programmers to maintain the daily operation of a super app with its huge human costs?
The trend of replacing manpower with AI will surely be followed by more large companies. Anxiety is inevitable, but anxiety alone is useless. We must start with changes in the general environment and step by step back to individual survival strategies.
AI isn't just a tool; it's becoming a means of production
Some people in the market are starting to use “Web4” to define the current stage. To clarify the context, let's first take a look at the different stages of the evolution of the Internet:
Web2
The core is the interaction between software and people. Different platforms use algorithms to get users' attention, which is essentially a traffic grab battle.
Web3
Trying to solve the problem of digital asset titling and value allocation. Many people simply equate it with cryptocurrency, but in essence, it is still a game of wealth distribution rules and does not touch on the “manufacturing” relationship of digital products.
The night before Web4
For the first time, AI has touched on changing the production relationship itself. It is no longer just a tool for improving efficiency; it is becoming a new type of production tool. Whoever uses it more can push the output limit by an order of magnitude.
Traditional teamwork has many hidden costs: good leaders' judgment and industry intuition are difficult to replicate to subordinates, and misunderstanding and rework losses are unavoidable in multi-person execution. These are “dark taxes” on how organizations operate, and there was no clear solution before. AI has drastically reduced this dark tax. It has no learning curve, can execute with high quality when given clear reminders, and can process multiple task lines in parallel at the same time. When one person's strategic judgment is superimposed on AI's execution levers, the output of an entire team in the past can be leveraged.
Of course, AI still occasionally “makes serious nonsense”, which determines that human review and judgment are still essential. However, the reliability of the model is improving on a monthly basis, and the buffer window left for pure executives is much shorter than most people think.
Efficiency equality and the deep crisis: when the entry threshold is leveled
In the short term, ordinary people can reap efficiency dividends by accessing AI tools. However, in retrospect, when AI erases the basic inefficiency and greatly lowers the entry threshold for professionals, companies will find that after a significant increase in individual output efficiency, if the overall business scale does not expand in the same proportion, maintaining the original employee base is a negative asset.
If you look at the current wage differentiation, you'll see. According to TT3LABS job monitoring data, starting in 2025, salary packages in units of “10 million US dollars or more” have repeatedly appeared in the AI job market, and these candidates are all young AI engineers and do not have very rich “team management skills”. When Meta was looking for OpenAI core researchers, the contract bonus alone exceeded 100 million US dollars. The average equity compensation of OpenAI employees reached 1.5 million US dollars, and the basic annual salary of Anthropic senior research engineers reached up to 690,000 US dollars (excluding equity).
Capital spent this money on a scarce ability: making AI itself stronger. The value of people who can drive the evolution of the underlying model can be exponentially amplified throughout the entire commercial network. For others, as long as the work content can be covered by AI at a lower cost, valuations may shrink.
At the same time, this has unleashed a deeper potential crisis. Now, when more and more people encounter problems, their first reaction is to let AI provide answers. The middle period of self-deduction, verification, trial and error has been skipped, and the ability to think will be lost after a long period of time. The problem is, it's this “stupid effort” that shapes your sense of the problem. If you rely on AI for a long time to complete this process, your role at work will degenerate into a “requirements translator”: turning other people's requests into AI input, and then transferring AI output to others. And this transit link is exactly the easiest for the next generation of AI to skip directly.
Impact Map: Where are you standing?
If fear has no coordinates, it's just anxiety. Before discussing countermeasures, we need to draw an “impact map”. It's not about selling panic; it's about making everyone position themselves.
Jobs where the content of high-risk jobs can be clearly written by instructions
Elementary coding, basic data analysis, standardized report generation, template design, routine translation proofreading. The common characteristic of this type of job is that the job can be clearly broken down into “input → processing → output”. Of the 4,000 people that Block has cut, quite a few are in this range. Their expertise isn't bad, but they do just what big models can do.
A standard worth asking yourself: if all of your work can be written as an AI instruction, then the machine is ready to take over you; all that's left is when the company will make this decision.
The shocks are being “pressurized” by the empirical middle class
Project manager, operations director, mid-level engineer. Their work involves judgment and coordination. AI cannot eat it in the short term, but it is being “compressed”. In the past, a business chain required five middle levels to manage each section and be aligned with each other. Now that AI has taken over upstream and downstream execution, one or two people can run the entire link.
This group faces a situation where “there are fewer seats”. Your abilities haven't regressed, but the market's demand for your role is plummeting. The way out for such groups is to use AI downward to amplify execution and gain the right to define problems upward.
Drivers of value-added uncertainty
There is one type of work whose core is not to “do the right thing”, but to “make decisions when information is never complete and cover the consequences”. Complex business negotiations, crisis PR handling, cross-cultural organization management, high-risk investment judgment. AI can provide analysis and suggestions, but it can't sign for you, can't blame you, and can't read the interests behind each other's eyes at the dinner table.
Not only will these characters not depreciate, but because the underlying execution costs are drastically reduced by AI, the same budget can leverage larger projects, and the leverage in the hands of decision makers has lengthened.
In reality, many people work across more than one echelon. A simple way to test yourself: think about what you do every day, how much of it can be clearly explained by a set of instructions, and how much of it requires you to come up with your own vague ideas. The higher the proportion of the former, the more you need to make changes as soon as possible.
Stop tool anxiety and turn public computing power into private barriers
At the end of January, OpenClaw (“crayfish”) went viral, and within a few days, the GitHub star rating surpassed 170,000. Model makers quickly followed suit, Alibaba Cloud launched one-click deployment, Tencent released CoPAW benchmarking, and MiniMax and Kimi also launched their own compatible solutions.
Then you'll discover an interesting phenomenon: many people spend this month “researching how to deploy crayfish” and “comparing which package is more cost-effective”, probably more time than they actually use AI to produce business results. Everyone is chasing the tool, but after the chase is over, others can copy the same configuration as it is in two hours.
“All big language models — OpenAI, Anthropic, Meta, Google, xAI — are trained with the same open internet data. So they're all essentially the same, which is why they're being commercialized so fast.”
— Larry Ellison, Oracle FY 2026 Q2 Earnings Conference Call
The other way to understand it is: as long as your work relies only on the ability to disclose generic models, your output is homogenized, and even if your instructions are fancy, there is no moat.
The real barrier is moving from public to private.
There is now a very clear trend: from large enterprises to startup teams, more and more organizations are deploying localized private models. The direct reason is information security, and no one wants to hand over core business data to a third-party API. However, this trend has an underestimated knock-on effect: when major players in the industry are putting data and knowledge into private deployment, there will be less and less industry information that can be learned by generic models on the public network, and it will lag more and more behind. On the surface, AI has lowered everyone's knowledge threshold, but the really valuable layer of industry knowledge is rapidly disappearing from the public web and sinking into each company's private knowledge base.
Therefore, the industry “dark knowledge” you have accumulated over the years is not depreciating, but appreciating. The premise is that you have to use it.
Organize and structure those unstandardized business experiences scattered in your head, chat logs, and historical emails into a “context” that your private model can digest. According to TT3LABS back-office data, the initial screening pass rate for candidates with more than two years of experience in the Web3 industry is far higher than that of technical talents from large companies without industry background. The core reason is that the industry's weight is far greater than general technical ability. A person who has been running CEX for three years understands compliance logic and the unspoken rules of listing coins, a person who has gone through two rounds of DAO governance cycles judging the inflection point of proposal design and community sentiment, and a person deeply involved in vertical content's intuition about audience psychology and narrative rhythm. These things will not appear in any public training data.
When you structure these private experiences and plug them into the model, your AI is no longer a general encyclopedia, but an exclusive partner who only works for you and knows your track. The depth of this output is such that no one else can keep up with the same generic model.
There is only one core logic: AI crushes everyone when it comes to processing open knowledge, but it depends entirely on your feed when processing private experience. People who can combine deep industry with AI are the core assets under the new division of labor.
Your experience base is the real “model”
AI models are rapidly evolving, and today's GPT, Claude, and Gemini will probably all be replaced by stronger versions in half a year. But for you, changing to a stronger model is nothing more than changing the API interface. What really won't be replaced by iteration is the same set of private data and experience libraries you fed it.
The model is a universal infrastructure that anyone can use. However, the industry perceptions, business judgments, and trampling records that you pour in are “training materials” that only belong to you. The stronger the AI, the better it can digest your corpus, and the higher your privacy barriers. So don't worry about “will building a knowledge base now become obsolete soon”; your knowledge base is the only asset that won't depreciate due to model iterations. The model is changing, and your data barriers will only increase in value as your AI capabilities improve.
At the same time, the traditional logic of workplace competition is also being rewritten. In the past, employees could show their attitude by staying up late and working overtime, but with the machine's 7×24 hour output, all the strategies of competing with “I can endure better than others” went to zero in the face of AI.
A lot of people will say, “I also provide emotional value in the team.” Yes, it's a unique human ability, but its premium depends on what tier you're in. When the grassroots team was scaled down from ten people to two people plus a row of AI agents, “team lubricant” lost the scene. Meanwhile, at the decision-making level, complex business games, high-risk trust building, and conflict resolution across stakeholders, deep connections between people are more valuable due to lower underlying costs. Emotional values are not disappearing; they are migrating upward.
At the end of the day, what individuals should invest most in the AI era is not learning which tool to use, but continuing to operate a private AI that only you have. Tools will iterate; experience libraries won't.
Three actions, you can start now
Going back to Block's case, some people were laid off but others stayed. The difference was who remained incompressible after AI became a standard production tool. Don't wait for the company to arrange AI training for you; starting today, we can try these actions:
01. From “hands-on” to “building a workflow”
The most common trap migrant workers fall into is using AI to help themselves “be lazy” (such as using AI to write weekly reports and edit emails). This is still the way of thinking at the executive level. What you really need to do is think of yourself as a “contractor” and restructure the core output of your current job into an AI automated production line.
Don't try out more than a dozen new models at the same time, choose one of the most mature tools out there (such as ChatGPT Plus or Claude), and force it to get involved in the most time-consuming and experiential part of your job. Transform your original single-line operation of “manual data collection → analysis and comparison → output conclusions” into “setting automated capture → feeding to the AI analysis framework → manual intervention to adjust and fine-tune”. When you can use this workflow to reduce the work that originally took a week to one day, and the quality is extremely stable, you are no longer a single computing power node; you yourself become a highly leveraged “micro company”.
02. “Solidify” the hidden experience into your exclusive digital alter ego
The big model learns from open data. It knows all the theories, but it definitely doesn't understand what hidden habits your company's extremely difficult big customer has, or what minefields your department can't touch when connecting with the finance department. This “dark knowledge” that you only get by stepping on countless holes is your core asset.
But these assets can't be compounded if they just stay in your mind. Your task now is to use the customization features currently open in the big model (such as Custom GPTs or Claude Projects) to turn your experience into its “pre-set system instructions”. Feed it all the edge cases you've handled, failed review reports, and unwritten rules of the industry. Your goal is not to build a static knowledge base notebook, but to “domesticate” a 24-hour personal assistant with your strong personal business style and who only works for you. When your “digital alter ego” takes shape, others with generic AI will definitely be unable to compete with you.
03. Strengthen your “right to define problems” and sense of responsibility
In the team, they began to deliberately practice handing over the work of “finding answers” to machines, and holding the power to “ask questions” and “make decisions” in their own hands. AI is a perfect answer engine, but it can never detect the real business motivations behind a demand. The boss said, “I want to create a new retention strategy,” and the AI will instantly give 10 theoretical models for growth hacking. However, only you can combine the current budget and development resources and point out that “although Solution B is perfect, it cannot be implemented at the moment. Cutting half of the functionality of Plan C is most suitable for our current pace”.
At the same time, you have to understand: AI won't go to jail and won't be held liable. When companies pay you a high salary, they often just buy you “cover” the results of the business. When you submit AI-generated code or solutions, you must be emboldened to say, “I have reviewed the AI output using my professional experience, and I am responsible for the final implementation results.” This kind of “liability premium”, which dares to clash in a vague area and dare to bear the ultimate commercial consequences, cannot be replaced by machines in any era.
“Most companies are late,” Dorsey said. But for individuals, the opposite is true: most people haven't even begun to prepare and aren't aware of this trend.
Not everyone has to be an AI expert. But everyone has to figure out the question: what parts of your job the machine can do sooner or later, what parts are unique to you, and then move your time and energy from the former to the latter.
If one day AI completely surpasses humans in every field, maybe in 2027, maybe 2030, but this isn't a change you can just watch out for.
It doesn't wait for you to be ready.
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