The Eve of Web4: A Survival Guide for the Ordinary Worker

TT3 Observations

On February 26, 2026, fintech giant Block announced it was cutting more than 4,000 jobs, shrinking its headcount from over ten thousand to under 6,000. In a letter to shareholders, CEO Jack Dorsey wrote:

Dorsey also offered a coldly blunt prediction:

In after-hours trading that day, Block's stock jumped more than 20%. That was the market answering with hard cash—paying up for corporate AI leverage and efficiency.

Someone with zero coding background can now use a large model to ship a fully functional app overnight, on their own. So the market is bound to ask a pointed question: for the tech giants that employ tens of thousands of engineers just to keep a super-app running day to day, how much of that enormous labor cost still earns its keep?

More big companies will follow Block in trading headcount for AI. The anxiety is understandable—but anxiety alone gets you nowhere. We have to start from the shift in the broader landscape and work our way back down, step by step, to a survival strategy for the individual.

AI Isn't Just a Tool—It's Becoming a Means of Production

Some people have started using "Web4" to label the moment we're in. To make sense of it, let's trace how the internet has evolved:

Traditional teamwork carries a lot of hidden costs. A great leader's judgment and industry instinct are hard to copy into their reports; when many people execute together, misread intentions and rework are unavoidable. These are the "hidden taxes" of running an organization, and there was never a clean fix for them. AI compresses that hidden tax dramatically: it has no learning curve, it executes at high quality given a clear prompt, and it can run multiple task streams in parallel. One person's strategic judgment, stacked on top of AI's execution leverage, can move what used to take an entire team.

Granted, AI still occasionally makes things up with a perfectly straight face, which is why human review and judgment remain indispensable. But model reliability is improving by the month, and the buffer left for pure-execution roles is far shorter than most people assume.

Efficiency for Everyone, and a Deeper Crisis: After the Barrier to Entry Is Flattened

In the short term, ordinary people who plug into AI tools get an efficiency dividend. But play it forward. Once AI flattens the basic gaps in efficiency and drops the barrier to entering a profession through the floor, companies will notice something: if per-person output jumps but the overall business doesn't scale at the same rate, keeping the old headcount becomes a liability.

What that money buys is a scarce capability: making the AI itself stronger. The value of someone who can push the underlying models forward gets amplified geometrically across the entire commercial network. Everyone else—anyone whose work AI can cover at lower cost—may watch their valuation shrink.

This sets off a deeper, latent danger. More and more people, faced with a problem, now reach for AI to hand them the answer—skipping the messy middle where you reason it through, test it, and get it wrong a few times. Do that long enough and you lose the ability to think. The catch is that this slow, "dumb" work is exactly what trains your nose for a problem. Lean on AI to do it for you indefinitely, and your role at work degrades into a "requirements translator": you turn other people's asks into AI inputs, then carry the AI's outputs back to them. And that middleman step is precisely the one the next generation of AI will skip over most easily.

The Impact Map: Where Do You Stand?

Fear without coordinates is just anxiety. Before we talk about what to do, we need to draw an "impact map." Not to sell panic—to let each person locate themselves.

In reality, most people's work spans more than one tier. A simple self-check: think about what you do each day, and ask how much of it could be spelled out in a single instruction versus how much requires you to make a call amid ambiguity. The higher the first share, the sooner you need to change something.

Stop Chasing Tools—Turn Public Compute into a Private Moat

In late January, OpenClaw—"the crawfish"—came out of nowhere and blew past 170,000 GitHub stars within days. The model vendors scrambled to follow: Alibaba Cloud shipped one-click deployment, Tencent launched CoPaw as a rival, and MiniMax and Kimi each rolled out their own compatible versions.

Then you notice something funny: this month, a lot of people spent more time "figuring out how to deploy the crawfish" and "comparing which plan is the better deal" than they spent actually using AI to produce business results. Everyone's chasing tools—but once you've caught up, the setup you deployed is something anyone else can copy, exactly, in two hours.

Flip that around: as long as your work relies only on the public capabilities of a general-purpose model, your output is interchangeable with everyone else's—and no matter how fancy your prompts are, there's no moat.

The real moat is the move from public to private.

There's a very clear trend already underway: from large enterprises to startup teams, more and more organizations are deploying localized, private models. The immediate reason is information security—nobody wants to hand core business data to a third-party API. But the trend has an underrated knock-on effect. Once the major players in an industry have fenced their data and knowledge inside private deployments, the industry information left on the public web for general models to learn from gets thinner and more out of date. On the surface, AI has lowered the knowledge barrier for everyone; in reality, the layer of industry knowledge that actually matters is vanishing from the public web faster and faster, sinking into each company's private knowledge base.

So the industry "hidden knowledge" you've built up over years isn't depreciating—it's appreciating. Provided you actually put it to use.

Take the non-standard business experience scattered across your head, your chat logs, and your old emails, and organize and structure it into "context" your private model can digest. TT3LABS.COM backend data shows that in Web3, candidates with two-plus years of experience pass initial screening at a far higher rate than technically strong people from big tech with no industry background—the core reason being that industry know-how carries far more weight than general technical skill. The way someone with three years running CEX operations understands compliance logic and the unwritten rules of getting a token listed; the way someone who's been through two cycles of DAO governance reads proposal design and the turning points in community sentiment; the way someone deep in vertical content feels audience psychology and narrative pacing—none of that shows up in any public training data.

Once you structure that private experience and feed it into a model, your AI stops being a general-purpose encyclopedia and becomes a dedicated partner that works only for you and understands only your lane. That depth of output is something no one can catch with the same generic model.

The core logic is just this: on public knowledge, AI crushes everyone; on private experience, it depends entirely on what you feed it. The people who can fuse deep industry know-how with AI are the core assets of the new division of labor.

Your Experience Base Is the Real "Model"

AI models are evolving at breakneck speed; today's GPT, Claude, and Gemini may all be superseded by stronger versions in six months. But for you, switching to a stronger model is just swapping an API endpoint. What iteration won't replace is the private data and experience base you feed it.

The model is general-purpose infrastructure—anyone can use it. But the industry understanding, business judgment, and logged pitfalls you pour into it are "training data" that belongs to you alone. The stronger the AI, the better it digests your data, and the higher your private moat. So don't agonize over whether "building a knowledge base now will be obsolete soon": your knowledge base is the one asset that doesn't depreciate when models iterate. The models change; your data moat only appreciates as AI gets more capable.

Meanwhile, the old logic of workplace competition is being rewritten. Employees used to prove their commitment by pulling all-nighters—but machines run 24/7, and every strategy built on "I can grind harder than the next person" resets to zero in front of AI.

Plenty of people will say, "I still provide emotional value to my team." True—that's a uniquely human ability. But its premium depends on what level you're at. When a frontline team shrinks from ten people to two plus a row of AI agents, the "team lubricant" loses its stage. At the decision-making level, though—complex business gamesmanship, building trust under high stakes, mediating conflict across competing interests—deep human connection actually becomes more valuable precisely because the cost underneath has dropped. Emotional value isn't disappearing; it's migrating upward.

In the end, the thing an individual should invest in most in the AI era isn't learning which tool to use—it's continually cultivating the private AI that only you have. Tools will iterate. Your experience base won't.

Three Moves You Can Start Today

Back to Block: some people were laid off, and some stayed. The difference, once AI becomes standard-issue equipment, is who remains incompressible. Don't wait for your company to schedule AI training for you. Starting today, here are moves worth trying: