Module 7 · The Rest of Crypto / 7.3
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.
The winners are only part of the picture.
A friend shows you three tokens that rose dramatically. They are real examples of what was possible. They are not a count of what usually happened.
To estimate how often projects fail, first define the group, the dates, and failure. A 90% price fall, an abandoned product, a delisting and fraud are different outcomes. Include the projects people stopped talking about.
A base rate is a measured frequency in a defined group. Without that work, “most go to zero” sounds precise while leaving the denominator unknown. We can take total loss seriously without inventing its probability.
Two selected winners are visible. The stories left out are still part of the group.
A useful idea still has to survive.
One failure path is no lasting use. Subsidies may bring activity while rewards last, then users disappear. Ask what people do, why they return, and who pays for it.
A second is a weak link between the product and its token. A service can succeed while its optional token loses demand. A useful website does not automatically need a separate currency.
A third is competition. A rival can offer better prices, reliability, access or experience. Network effects sometimes help an incumbent because its existing users attract more users. They do not guarantee that one winner will take every market.
A fourth is a change in attention. Speculative demand can leave when another story becomes fashionable. A community’s enthusiasm is a condition that may change, rather than a permanent asset.
Follow the ways value can leave.
Large holders may sell, including after unlocks. A treasury may run out of money. An administrator may abuse an upgrade or minting power. Some launches are intended to take buyers’ funds from the start.
Even a sincere team can ship a serious bug. A bridge, lender, oracle or reserve issuer that the project depends on can fail. Legal restrictions can also affect whether the product can operate or a market remains accessible.
These are different explanations. A price fall is not proof of fraud. An anonymous developer is not automatically dishonest, and a named developer is not a guarantee. Write the actual path by which money, access or demand could be lost.
Busy numbers can conceal an empty room.
Imagine one person moving a token between two accounts they control. Count every trade and the activity looks busy. Count independent demand and much less has changed.
Wash trading creates misleading activity through trades without genuine independent buying and selling interest. A high volume number therefore needs context. So does an active-address count: one user can control many addresses, and bots can imitate repeated use.
High turnover relative to liquidity is a reason to investigate, not proof of manipulation by itself. The same capital can trade repeatedly. Look at the method behind a statistic, who controls the activity, and whether it continues without incentives.
A prior profit does not prove that your earlier reasoning was sound. It could reflect skill, luck, timing, or a risk that happened not to materialize. Examine the decision as well as the result.
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.
The next lesson turns these questions into a written investigation.