On the eve of Web4, a guide for ordinary workers to avoid being left behind.

Author: TT3LABS, Web3/AI/SaaS Remote Recruitment Platform

On February 26, 2026, fintech giant Block announced layoffs of over 4,000 employees, reducing its team size from over 10,000 to less than 6,000. CEO Jack Dorsey mentioned this in his letter to shareholders:

"Smart tools have changed what it means to create and run a company... A significantly smaller team, using the tools we're building, can do more and do it better."

Dorsey also offered his extremely cold-blooded prediction:

"I think most companies are already too late. Within the next year, most companies will come to the same conclusion and make similar structural adjustments."

After the market closed that day, Block's stock price surged by over 20%. This was the capital market's response with real money: paying for the company's AI leverage and efficiency.

An ordinary person with absolutely no programming knowledge can independently run a fully functional app overnight using a large-scale model. This inevitably raises a crucial question for the capital market: how much value remains in the massive human resource costs of tech giants that employ tens of thousands of programmers to maintain the daily operation of a super app?

The trend of replacing human labor with AI will inevitably be followed by more large companies. Anxiety is unavoidable, but anxiety alone is useless. We must start with the changing macro environment and gradually work our way down to individual survival strategies.

AI is not just a tool; it is becoming a means of production.

Some people in the market have started using "Web4" to define the current stage. To clarify the context, let's first review the different stages of internet evolution:

Web2

At its core is the interaction between software and people. Different platforms use algorithms to capture users' attention, which is essentially a battle for traffic.

Web3

It attempts to solve the problems of digital asset ownership and value distribution. Many people simply equate it with cryptocurrency, but in essence, it still remains at the level of a game of wealth distribution rules and does not touch on the "production and manufacturing" relationship of digital products.

The Eve of Web4

For the first time, AI has touched upon the very nature of changing production relations. It is no longer merely a tool for improving efficiency, but is transforming into a new type of means of production. Whoever uses it better will be able to raise the output ceiling by an order of magnitude.

Traditional team collaboration incurs numerous hidden costs: the judgment and industry intuition of excellent leaders are difficult to replicate for subordinates, and misunderstandings and rework losses are inevitable in multi-person execution. These are the "hidden taxes" of organizational operation, for which there were previously no clear solutions. AI significantly reduces these hidden taxes; it has no learning curve, can execute with high quality with clear prompts, and can handle multiple task lines in parallel. The strategic judgment of one person combined with the execution leverage of AI can leverage the output of an entire team in the past.

Of course, AI still occasionally "talks nonsense with a straight face," which means that human review and judgment remain indispensable. However, the reliability of the model is improving on a monthly basis, and the buffer window for pure execution positions is much shorter than most people think.

Efficiency and Equality, and Deep-seated Crisis: What Happens After Entry Barriers Are Eliminated

In the short term, ordinary people can benefit from the efficiency gains by using AI tools. However, looking ahead, once AI eliminates fundamental efficiency gaps and significantly lowers the barriers to entry for professionals, companies will find that if the overall business scale does not expand proportionally after a substantial increase in individual productivity, maintaining the original number of employees becomes a liability.

Just look at the current salary disparity. According to TT3LABS job monitoring data, since 2025, the AI ​​job market has repeatedly seen salary packages in the tens of millions of US dollars, and these candidates are mostly young AI engineers who do not have much "team management skills." When Meta poached core researchers from OpenAI, the signing bonus alone exceeded 100 million US dollars. The average equity compensation of OpenAI employees reached 1.5 million US dollars, and the base annual salary of senior research engineers at Anthropic reached as high as 690,000 US dollars (excluding equity).

Capital is spending this money to buy a scarce capability: making AI itself more powerful. Those who can drive the evolution of underlying models can have their value amplified exponentially across the entire business network. Meanwhile, others, whose work can be covered by AI at a lower cost, may see their valuations shrink.

This also raises a deeper, underlying crisis. More and more people are now turning to AI for answers as their first reaction to problems, skipping the crucial process of deduction, verification, and trial and error. Over time, this leads to a loss of critical thinking skills. The problem is, it's precisely this arduous process that shapes your problem-solving acumen. Long-term reliance on AI to complete this process reduces your role at work to that of a mere "requirement translator": converting others' demands into AI input and then relaying the AI's output back to others. And this crucial intermediary step is precisely what the next generation of AI can most easily skip.

Impact Map: Where are you standing?

Fear without a coordinate system is just anxiety. Before discussing countermeasures, we need to draw an "impact map." This is not to sell panic, but to help everyone understand their own situation.

High-risk job duties can be clearly stated in the instructions.

Basic coding, fundamental data analysis, standardized report generation, template design, and routine translation and proofreading. These roles share the common characteristic of being clearly break down into "input → processing → output." A significant portion of the 4,000+ people laid off by Block fell into this category. Their professional skills weren't lacking, but the tasks they were doing were precisely what large-scale models could handle.

A criterion worth asking yourself: if all your work can be written as a single AI instruction, then machines are ready to replace you; the only remaining question is when the company will make that decision.

The experience-based middle management is being "reorganized" amidst the upheaval.

Project managers, operations supervisors, and mid-level engineers. Their jobs involve judgment and coordination, which AI cannot handle in the short term, but is being "compressed." Previously, a business chain required five mid-level managers, each responsible for a segment and aligning with each other. Now, AI takes over the execution of upstream and downstream processes, and one or two people can run the entire chain smoothly.

This group faces the situation of "fewer and fewer positions." Your abilities haven't declined, but the market demand for your role is plummeting. The way out for this group is to leverage AI to amplify execution capabilities at the lower level and to gain the power to define problems at the higher level.

Drivers of Value-Added Uncertainty

There's a type of job where the core isn't "doing it right," but rather "making decisions with perpetually incomplete information and taking responsibility for the consequences." Examples include complex business negotiations, crisis management, cross-cultural organizational management, and high-risk investment assessments. AI can provide analysis and advice, but it can't sign off for you, take the blame for you, or read the hidden agendas behind a single glance at a dinner table.

These roles will not only not depreciate, but because the underlying execution costs are greatly reduced by AI, the same budget can leverage larger projects, giving decision-makers more leverage.

In reality, many people's work spans more than one level. A simple self-test: think about your daily work. How much of it can be clearly explained with a set of instructions, and how much requires you to make decisions on your own in a vague manner? The higher the former percentage, the more you need to make changes as soon as possible.

Stop tool anxiety and transform public computing power into a private barrier.

OpenClaw ("Little Lobster") suddenly appeared at the end of January, and within a few days, it had more than 170,000 stars on GitHub. Various model manufacturers quickly followed suit. Alibaba Cloud launched one-click deployment, Tencent released CoPaw to compete with it, and MiniMax and Kimi also launched their own compatibility solutions.

Then you'll notice a very interesting phenomenon: many people spend more time this month "studying how to deploy crayfish" and "comparing which package deal is more cost-effective" than they actually use AI to generate business results. Everyone is chasing tools, but after you've done that, someone else can copy your deployment setup exactly in two hours.

"All large language models—OpenAI, Anthropic, Meta, Google, xAI—are trained using the same publicly available internet data. So they are essentially the same, which is why they are being commoditized at an extremely rapid pace."

— Larry Ellison, Oracle Fiscal Year 2026 Q2 Earnings Call

Conversely, this means that as long as your work relies solely on the public capabilities of a general model, your output will be homogeneous, and no matter how fancy your instructions are, there will be no competitive advantage.

The real barrier lies in the shift from public to private.

There's a very clear trend now: from large enterprises to startups, more and more organizations are deploying localized, proprietary models. The immediate reason is information security; nobody wants to entrust their core business data to third-party APIs. But this trend has an underestimated ripple effect: as major players in the industry hoard their data and knowledge in private deployments, the amount of industry information available on the public internet for general-purpose models to learn from will decrease and become increasingly outdated. On the surface, AI lowers the knowledge threshold for everyone, but the truly valuable layer of industry knowledge is rapidly disappearing from the public internet and sinking into individual companies' private knowledge bases.

Therefore, the industry "tacit knowledge" you've accumulated over the years isn't depreciating, but rather appreciating. The prerequisite is that you have to put it into practice.

Organize and structure those non-standardized business experiences scattered in your mind, chat logs, and email history, turning them into "context" that your private model can digest. TT3LABS backend data shows that candidates with more than two years of experience in the Web3 industry have a much higher initial screening pass rate than technical talents from large companies without industry background. The core reason is that industry know-how carries far more weight than general technical skills. The understanding of compliance logic and listing unspoken rules from someone with three years of CEX operations experience, the judgment of proposal design and community sentiment inflection points from someone who has completed two DAO governance cycles, and the intuition of audience psychology and narrative rhythm from someone deeply involved in vertical content creation—these things won't appear in any publicly available training data.

Once you structure these private experiences and integrate them into your model, your AI is no longer a general encyclopedia, but a dedicated partner that works only for you and understands only your specific field. This depth of output is something others can never catch up with using the same general-purpose model.

The core logic is simple: AI excels at processing public knowledge, but it completely relies on your input when it comes to processing private experience. Those who can combine deep industry know-how with AI are the core assets in this new division of labor.

Your experience base is the true "model".

AI models are evolving rapidly; today's GPT, Claude, and Gemini may be replaced by more powerful versions in six months. But for you, switching to a more powerful model is simply a matter of changing the API interface. What truly won't be replaced by iteration is the set of private data and experience bases you feed it.

Models are general-purpose infrastructure that anyone can use. But the industry insights, business judgments, and lessons learned that you feed into them are your unique "training corpus." The more powerful the AI, the better it can process this corpus, and the higher your proprietary barrier becomes. So don't worry about whether your knowledge base will become obsolete quickly; your knowledge base is the only asset that won't depreciate with model iterations. Models change, but your data barrier will only increase in value as AI capabilities improve.

At the same time, the traditional logic of workplace competition is being rewritten. In the past, employees could demonstrate their attitude by working overtime and staying up late, but with machines outputting 24/7, all strategies of competing by "I can work harder than others" are rendered meaningless in the face of AI.

Many people would say, "I still provide emotional value within the team." That's true, it's a uniquely human ability, but its premium depends on your level. When a frontline team shrinks from ten people to just two plus a row of AI agents, the "team lubricant" loses its purpose. At the decision-making level, in complex business games, high-risk trust building, and cross-stakeholder conflict mediation, deep interpersonal connections become more valuable due to the reduced costs at the lower levels. Emotional value isn't disappearing; it's shifting upwards.

Ultimately, the most important investment an individual should make in the AI ​​era is not learning which tool to use, but rather continuously cultivating their own proprietary AI. Tools will iterate, but experience bases will not.

Three steps, you can start now.

Returning to the Block case, some were laid off, but others remained. The difference lies in who remains incompressible after AI becomes a standard productivity tool. Don't wait for your company to provide AI training; starting today, we can try these actions:

01. Shift from "doing it yourself" to "building workflows"

The biggest trap for working people is using AI to "slack off" (such as using AI to write a weekly report or polish an email), which is still an execution-level mindset. What you really need to do is treat yourself as a "foreman" and refactor the core output of your current position into an AI-automated production line.

Don't try a dozen new models at once. Choose the most mature tool currently available (such as ChatGPT Plus or Claude) and force it into the most time-consuming and experience-intensive part of your work. Transform your original linear operation of "manually collecting data → analyzing and comparing → outputting conclusions" into "setting up automated data collection → feeding it into the AI ​​analysis framework → manual intervention for fine-tuning." When you can use this workflow to compress what used to take a week into a day, and achieve extremely stable quality, you are no longer a single computing power node; you have become a highly leveraged "mini-company."

02. "Consolidate" implicit experiences into your own digital avatar

Large models learn from publicly available data; they understand all the theories, but they absolutely do not understand the hidden quirks of your company's extremely difficult major client, nor do they understand the taboos that your department must avoid when communicating with the finance department. This "tacit knowledge," which you have gained through countless mistakes, is your most valuable asset.

But these assets won't generate compound interest if they just remain in your mind. Your current task is to leverage the customization features of existing large models (such as Custom GPTs or Claude Projects) to turn your experience into its "system preset instructions." Feed it all the edge cases you've handled, the post-mortem reports of failures, and the unwritten rules of the industry. Your goal isn't to build a static knowledge base notebook, but to "tame" a 24/7 personal assistant with your strong personal business style, working exclusively for you. When your "digital avatar" is complete, others using general-purpose AI will absolutely not be able to compete with you.

03. Enhance your own "right to define problems" and sense of responsibility

Within the team, we started consciously practicing delegating the task of "finding answers" to machines, while keeping the power of "asking questions" and "making decisions" in our own hands. AI is a perfect answer engine, but it can never perceive the true business motivation behind a demand. The boss says, "I want to implement a new retention strategy," and AI will instantly provide 10 theoretical models of growth hacking. But only you can, considering the current budget and development resources, point out that "Solution B is perfect but not feasible at present, while Solution C, with half the features removed, is best suited to our current pace."

At the same time, you must understand one thing: AI won't go to jail or take responsibility. When companies pay you high salaries, they're often buying you a "safety net" for the business outcomes. When you submit AI-generated code or solutions, you must have the confidence to say, "I've reviewed the AI's output using my professional experience, and I'm responsible for the final implementation." This "responsibility premium"—the willingness to make decisions in ambiguous areas and bear the ultimate business consequences—is something machines can never replace in any era.

Dorsey said, "Most companies are already too late." But for individuals, the reverse is also true: most people haven't started preparing or are unaware of this trend.

Not everyone needs to become an AI expert. But everyone needs to figure out which parts of their work machines can do sooner or later, and which parts are unique to them, and then shift their time and energy from the former to the latter.

If one day AI surpasses humans in all fields, it may happen in 2027 or 2030, but this is not a change you can simply watch from the sidelines.

It doesn't wait for you to be ready.