DOGE, SHIB and PEPE became three of crypto’s most recognizable meme assets through very different market cycles.
That makes them useful case studies—but dangerous templates.
Looking backward, it is easy to reduce their success to a few visible traits: a recognizable meme, an enthusiastic community, fast price appreciation and growing exchange access. The problem is that thousands of failed tokens displayed some of those same characteristics.
The more useful question is not:
What did these winners look like?
It is:
What changed around them as attention became large enough to reshape market structure?
That distinction matters.
Fenton is less interested in finding assets that resemble past winners than in identifying when several independent signals—attention, liquidity, distribution, accessibility and narrative begin strengthening at the same time.
Three assets, three different paths
DOGE, SHIB and PEPE are often grouped together because they became major meme assets. Their paths, however, were meaningfully different.
Dogecoin launched in 2013 with an intentionally light-hearted identity at a time when cryptocurrency culture was becoming increasingly serious. Its early growth was closely tied to online community activity, particularly Reddit tipping and a culture that extended beyond pure price speculation.
Shiba Inu arrived in 2020 with a different structure. It began as a community-driven experiment around a meme identity, then gradually expanded into a broader ecosystem.
PEPE entered the market in 2023 with yet another model: extremely clear cultural recognition, minimal emphasis on traditional utility and a narrative that spread rapidly through existing internet meme culture.
The lesson is not that successful meme assets follow one blueprint.
It is almost the opposite.
Their surface characteristics differed, but each reached a point where culture began interacting with market structure.
That interaction is what deserves closer attention.
1. Durable attention matters more than a viral spike
Crypto produces temporary attention constantly.
A token can trend because of one influencer, a sudden price move, paid promotion or coordinated social activity. None of those events necessarily indicate that a durable market narrative is forming.
The stronger signal is whether attention begins to compound.
A useful research process should ask:
- Is discussion spreading beyond the original promoters?
- Does attention return after the first major price move?
- Are communities creating their own memes, analysis and content?
- Is recognition expanding outside the original holder base?
- Does interest survive periods when price is no longer doing the marketing?
Dogecoin’s early history is useful here. DOGE was not simply watched; it became part of an online social culture. People used it for tipping, jokes and community activity.
That distinction matters.
Paid promotion distributes a message.
A strong meme community begins distributing the message without needing permission or coordination.
That is a much more powerful form of attention.
The strongest narrative is not necessarily the loudest one today. It is the one that keeps recruiting attention tomorrow.
2. Liquidity turns attention into participation
Attention can create curiosity.
Liquidity determines how easily that curiosity can become actual market participation.
A token with thin liquidity may appear exciting while remaining structurally fragile. Relatively small orders can move price sharply, larger participants may struggle to enter or exit efficiently, and apparent valuation can become disconnected from the amount of capital the market can realistically absorb.
As liquidity improves, the opportunity changes.
More participants can trade.
Larger orders become possible.
Price discovery improves.
Additional venues may begin supporting the asset.
And the narrative becomes accessible to people who would never have participated in its earliest market.
This is why nominal token price is so misleading.
A token trading at a tiny fraction of a dollar is not automatically cheap. Market capitalization, circulating supply, liquidity depth and ownership structure contain far more information.
Volume can attract attention. Liquidity depth, execution quality and venue expansion tell us whether the market underneath that attention is becoming more mature—or more fragile.
3. Distribution is part of market structure
Holder count is one of crypto’s easiest numbers to misunderstand.
Two assets can each have hundreds of thousands of holders while having completely different ownership structures.
One may have relatively broad distribution.
Another may have a small number of wallets controlling a large portion of the meaningful circulating supply.
That difference can materially affect risk.
For Fenton, distribution analysis should eventually answer questions such as how much supply is controlled by major wallets, whether those wallets belong to exchanges or individuals, whether ownership concentration is increasing or decreasing, and whether early wallets are beginning to distribute into growing market demand.
On-chain explorers make much of this visible for Ethereum-based assets such as SHIB and PEPE.
But the key is to treat distribution as dynamic, not as a one-time statistic.
An asset may become healthier as ownership broadens.
Or it may become increasingly fragile even while the headline number of holders continues rising.
4. Accessibility changes the size of the market
An early-stage token may initially require knowledge of wallets, gas fees, decentralized exchanges and contract addresses.
That friction limits participation.
As an asset becomes easier to acquire, its potential audience expands.
This can create a reinforcing cycle:
attention → liquidity → easier access → more participants → more attention
But accessibility must be interpreted carefully.
Being difficult to purchase does not make an asset promising.
And by the time an asset is available everywhere, much of the early discovery advantage may already have disappeared.
The more useful signal is therefore change.
Is access expanding while attention is accelerating?
Are liquidity and venue quality improving at the same time?
Is the asset reaching new groups of participants before its narrative becomes fully saturated?
That is more informative than simply recording whether a centralized exchange listing occurred.
5. Narrative is a market variable
Traditional investment analysis usually focuses on measurable economic variables.
Meme assets force researchers to consider another layer:
Why do people care enough to keep redistributing the idea?
DOGE had humor, recognizability and an unusually strong community identity.
SHIB combined a meme identity with an expanding ecosystem and a highly engaged community.
PEPE benefited from immediate cultural recognition and an internet-native identity that required very little explanation.
These examples suggest several characteristics that can strengthen a speculative narrative: simplicity, recognizability, cultural relevance, community identity, timing and the ease with which people can remix and redistribute the idea.
None of these guarantees investment success.
But in speculative markets, ignoring narrative entirely means ignoring part of the mechanism through which attention spreads.
For Fenton, narrative should therefore be treated neither as magic nor as irrelevant.
It should be treated as observable market behavior.
6. Survivorship bias is where simple pattern matching breaks
The largest danger in studying DOGE, SHIB and PEPE is obvious:
We remember them because they survived.
Thousands of other meme assets did not.
Many failed tokens had enthusiastic communities.
Many experienced rapid social growth.
Many produced enormous short-term volume.
Some even appeared to share nearly every characteristic that investors later associate with successful meme assets.
And they still disappeared.
This makes a winner-only dataset extremely dangerous.
If Fenton eventually evaluates 500 emerging assets, the most important research question months later should not be:
Which one became the biggest winner?
It should be:
What happened to all 500?
The failed assets are part of the dataset.
So are the assets that produced short-lived rallies.
So are the ones that survived without ever becoming large.
Only by preserving those outcomes can Fenton begin distinguishing genuine predictive information from patterns that merely look persuasive after the fact.
A stronger framework for evaluating emerging meme assets
Instead of asking whether a new token “looks like SHIB” or could become “the next PEPE,” a more useful approach is to break the market into separate, observable dimensions.
- Attention: Is interest persistent, broadening and increasingly organic?
- Liquidity: Is the market gaining depth, turnover and better-quality trading venues?
- Distribution: Is ownership becoming broader or more concentrated?
- Narrative: Is the idea becoming easier to recognize, repeat and redistribute?
- Accessibility: Is participation becoming easier for new market entrants?
- Risk: Are contract, liquidity, supply or concentration risks increasing or declining?
- Outcome: What happened after these conditions were first observed?
Each dimension captures a different part of the market.
That matters because a strong reading in one area can easily mask weakness elsewhere. Rapid social growth may coexist with poor liquidity. Broad exchange access may arrive after much of the opportunity has already been priced in. Rising holder counts may hide increasingly concentrated ownership.
No single metric should carry the conclusion.
The more interesting moments are likely to occur when several independent parts of the market begin changing in the same direction at roughly the same time.
That is not a prediction by itself. It is simply a better starting point for research.
What these case studies imply for better research
The main lesson from DOGE, SHIB and PEPE is not that successful meme assets share a fixed formula.
It is that hindsight makes early signals look cleaner than they really were.
Once an asset becomes successful, it is easy to reconstruct a convincing story around the clues that appeared beforehand. The problem is that many failed assets showed similar signals at different points in their lifecycle.
A stronger research process therefore has to preserve what was knowable when the observation was made, not after the outcome became obvious.
That means recording factors such as:
- attention and social persistence;
- liquidity and market depth;
- ownership structure;
- valuation and circulating supply;
- trading access;
- narrative development;
- identifiable market and technical risks.
Those observations can then be compared with what happened later.
Some assets may develop deeper liquidity and broader participation. Others may produce only a short-lived speculative surge. Many will fail entirely.
All of those outcomes matter.
This is how research can reduce survivorship bias: not by studying winners more closely, but by preserving winners, failures, false positives and temporary rallies in the same historical record.
The process becomes:
Observe → Record → Track → Compare → Learn
The objective is not to predict every breakout.
It is to determine which early conditions repeatedly contain useful information, which tend to appear only in hindsight, and which frequently produce misleading signals.
Over time, this can turn isolated observations into a more disciplined body of evidence.
For Fenton, that is the standard worth building toward: research that can be tested against what happened next, rather than justified by what became obvious later.
Conclusion
DOGE, SHIB and PEPE do not provide a formula for finding the next breakout.
They show something more useful: speculative markets can become much larger when culture, attention, liquidity and accessibility begin reinforcing one another.
The mistake is to reduce that process to superficial resemblance.
A new asset does not become interesting because it looks like a past winner.
It becomes worth studying when several parts of its market structure begin changing in ways that may matter.
That leads to a better research question:
What was observable early, and which of those observations actually remained meaningful over time?
Answering that requires more than identifying winners. It requires recording conditions before the outcome is known, following what happens next, and learning from failures as seriously as successes.
The next major meme asset may look very different from DOGE, SHIB or PEPE.
That is precisely why the better approach is to study how markets change, not how closely new assets resemble yesterday’s winners.