
Multi-level marketing (MLM) refers to a tiered commission structure for promotion, where participants earn rewards from direct and indirect referrals across multiple levels. The model leverages the network effect of “people bringing in people,” using escalating incentives to encourage viral growth.
In practice, projects typically set specific commission rates, such as 3% for direct referrals, 2% for second-level referrals, and 1% for third-level referrals. This structure often resembles a tree diagram. As long as the structure is transparent, the products or services are genuine, and rewards are primarily derived from real transactions rather than recruitment alone, MLM can function as a legitimate promotional strategy.
While MLM and pyramid schemes may look structurally similar, the key distinction lies in the source of value and recruitment methods. Legitimate MLM focuses on the sale of real products or services, with commissions generated from actual transactions. Illegal pyramid schemes, however, prioritize recruiting new members, deriving profits mainly from membership fees or overpriced entry qualifications.
Pyramid schemes are illegal operations centered on recruitment, often requiring upfront payments and promising high returns. Ponzi schemes—where returns to earlier participants are paid from new entrants’ funds—are often associated with pyramid schemes but can also occur without a multi-tiered structure. When evaluating a model, examine whether income relies on genuine products, if information is transparent, and whether excessive recruitment pressure exists.
In Web3, MLM models often rely on “on-chain accounting + automated distribution.” Smart contracts—self-executing programs—automatically settle commissions on-chain when predefined conditions are met, minimizing manual intervention.
Common scenarios include: referral rebates during token offerings, tiered commissions for node or slot sales, and airdrop campaigns that weight rewards based on invitation relationships. Tokenomics describes the design of token issuance, distribution, and incentives; allocating excessive portions for tiered rewards can create sell pressure and inflation risks, so careful assessment is necessary.
Typical schemes combine “high fixed returns + multi-level recruitment.” Projects may lure funds with daily interest payouts and stack multi-level commissions—this closely resembles Ponzi risk.
Another approach involves “node/qualification sales + tiered rewards.” Here, projects sell node slots with promises of future dividends or airdrops and distribute proceeds across multiple levels, but often lack verifiable business revenues. There’s also “task airdrop fission,” where participants must invite many others to gain higher airdrop allocations—yet there’s no clear product or release schedule.
The primary difference is in the number of reward layers and incentive structure. Single-level referrals only reward direct invitees—this setup is straightforward and cost-controlled. MLM extends incentives to second, third, or even deeper levels, enabling rapid expansion but also increasing potential for abuse.
In practice, exchange referral programs are usually single-level or limited in tiers. For example, Gate’s invitation feature centers commissions around users you directly refer. The platform publicly discloses rates and rules and does not promote aggressive multi-level recruitment—this aligns more with single-level referrals than MLM. To differentiate, look for: the existence of multi-tiered commissions, whether recruitment is central to the program, and the transparency of rules.
Risk assessment involves four steps:
Step 1: Verify the value source. Is there a real product or service? Are earnings derived from transaction fees or clear business revenues rather than entry fees or new investments?
Step 2: Analyze incentive design. Are there too many layers? Are commission rates unreasonably high? If high fixed returns are paired with multi-level rewards to attract capital inflow, caution is warranted.
Step 3: Check information transparency. Are contract addresses, token allocations, fund flows, and disclosure schedules made public? Has the smart contract undergone an audit?
Step 4: Stress-test cash flow. If growth slows, can rewards still be sustained? The more a model relies on continuous expansion, the higher the risk.
First, control your investment. Only use funds you can afford to lose; avoid borrowing or leveraging to participate.
Second, diversify risk. Don’t concentrate your holdings in a single rebate model—especially not one offering high yields plus multi-level commissions.
Third, check compliance boundaries. Many jurisdictions strictly regulate commission structures based on recruitment; if most earnings come from recruiting downlines, you could face legal risks.
Finally, pay attention to exit mechanisms. Review vesting, unlocking conditions, and withdrawal policies; examine how smart contracts or terms handle loss-cutting and failure scenarios.
As of 2025, most jurisdictions take a cautious or negative stance toward models focused on recruiting downlines or generating profits primarily from new funds. Platforms and projects are required to increase information disclosure, limit excessive reward tiers, and focus on genuine transactions and product value.
Industry trends show that on-chain tools make incentives more automated—but also allow risks to spread faster. Newer projects tend to incentivize quantifiable contributions such as transaction volume or liquidity provision, or development contributions—reducing reliance on pure recruitment-driven MLM structures.
At its core, MLM is about tiered commission sharing. To assess its legitimacy, examine whether value comes from genuine sources, whether incentives are based on product transactions, whether disclosure is sufficient, and if rewards can persist when growth slows. For everyday users, risk identification, prudent participation levels, and robust exit strategies are crucial to minimize losses. In crypto contexts, incentive models based on real contributions—not recruitment—are more sustainable.
The profit model of MLM relies mainly on recruiting new members rather than selling products. As a result, most participants struggle to earn profits. Research shows that over 99% of MLM participants ultimately lose money; only a few at the top benefit. Even successful recruiters face market saturation and difficulties selling products—so it’s essential to evaluate risks carefully.
MLM often uses enticing promises like “get rich quick” or “passive income” to attract participants. Organizers leverage success stories, fake earnings demonstrations, and social pressure to persuade newcomers—especially targeting those facing job difficulties or financial stress. Recognizing these psychological traps helps you spot risks and avoid being misled.
First, stop investing money and time; document all transaction records and communications. If you’ve suffered financial losses, report them to consumer protection agencies or the police—many countries have dedicated channels for MLM-related fraud complaints. Stay away from the organization to avoid emotional manipulation; seek legal or psychological support if necessary.
In Web3, MLM frequently appears under new guises such as token sales, NFT projects, or “community rewards.” Participants may be required to purchase tokens or NFTs to earn referral commissions. These projects often promise high returns through holding tokens, staking, or inviting others—but ultimately depend on a continuous influx of new users. Be cautious of any crypto project promising “earn as you buy” or “instant referral rewards.”
The core of legitimate referral programs is revenue from product sales—commissions are paid when actual consumers make purchases; referrers do not need to pay large upfront fees. In contrast, most MLM income comes from recruiting new members and initial investments—the product often serves as a mere pretext, and participants are encouraged to keep recruiting even without selling anything. The key indicator: Is revenue generated by product sales—or by headhunting fees?


