Step 6 / What a token does
How a token can lose its reason to exist.
Look for failure paths before you fall in love with the story.
A café you used to visit still has a sign above the door. Its website still loads. But inside, the chairs are stacked and no one is making coffee.
A token can look alive in much the same way. The name still appears on a tracker. A small trade still gives it a price. Those facts do not tell you whether the project works, has customers, or has a future.
Your task is to investigate the life behind the sign.
Count the projects that disappeared too
A friend points to three successful cafés. To find out how common success is, you would also need to count the cafés that closed. Looking only at winners leaves part of the story out.
The same problem appears with tokens. Define the group you are studying, when it began, and how long you followed it. Then decide what you mean by failure. A 90% price fall, an abandoned product, removal from an exchange, and fraud are different outcomes.
Only then can you calculate a failure rate: the number meeting your failure definition divided by the whole group. This is a base rate, or a measured frequency for a defined group.
Without that work, “most go to zero” gives no reliable percentage. We can examine how complete loss happens without pretending to know how often it happens across every token.
Two selected winners are visible. The stories left out are still part of the group.
Ask why people would keep using it
Imagine the café gives a free drink to everyone who buys its token. It is busy while the offer lasts. When the café can no longer afford free drinks, most visitors leave. The activity depended on a subsidy: someone else was paying part of the cost.
Now imagine a different result. Customers like the coffee and stay, but they can pay normally without the token. The café survives while people lose a reason to hold its token. A successful product does not automatically keep token demand alive.
A rival can also attract customers with better service, lower costs, or easier access. A crypto app faces similar competition. Ask what would keep people using it when rewards end or a better alternative appears.
Some services become more useful as more people join them. These are network effects. They can help a project, but they do not guarantee that one project wins every market. A busy launch and an excited community can still become quiet.
Trace a specific way the project could fail
Imagine the team runs out of money to maintain the app. Or a large founder allocation unlocks and its owner sells. These are different problems, so write down what happens in each case rather than using “risky” as the whole explanation.
Special controls matter too. An administrator might create extra tokens, freeze transfers, or replace the program with harmful rules. Some projects are designed to take buyers’ money. Others can cause a loss through mistakes even when the team is sincere.
A project can also depend on another service. For example, a bridge connects assets or messages across networks. If an asset depends on backing held through that bridge and the backing becomes inaccessible, the asset can have a problem even while customers still want the app.
A falling price alone does not prove fraud. An unnamed developer is not automatically dishonest, and a public name is not a guarantee. Explain the particular way money, access, or demand could be lost.
Other ways a dependency can fail
Other outside services can fail too. A lender provides credit, an oracle supplies outside data to a contract, and a reserve issuer holds or promises backing. Name what the app needs from each one and what stops working if it fails. Legal restrictions can also affect how a service operates or who can reach it.
A rising trade count may not mean more customers
Imagine one person trading back and forth between two accounts they control. The counter records more trades, but there is still only one person behind them.
Trades arranged to create misleading activity without genuine independent buying and selling are called wash trading. This is one reason to check what a volume figure represents, rather than treating a high number as proof of demand.
An active-address count has a similar limit. One person can use many addresses, and software bots can repeat actions. Ask how the count was made, who controls the activity, and whether it continues when rewards stop.
A lot of trading compared with the money available in the market deserves investigation. It is not proof of manipulation by itself: the same money can trade repeatedly. Keep a warning sign separate from a proved accusation.
Also review your own conclusions. A profitable trade does not prove you understood the project. Skill, luck, timing, or a risk that did not happen can all affect the result. Check the reasoning as well as the outcome.
More trades have not created another independent customer.
The idea to keep
You do not need a precise failure percentage to prepare for a complete loss. You do need a specific account of what could break and evidence that the apparent signs of life mean what they claim.
You will practice a written investigation with the CoinPicks strategy. First, learn to protect your money, accounts and keys.