TikTok’s Privacy Settlement Turns a Fine Into a Governance Test

A federal judge’s hesitation over TikTok’s $400 million privacy settlement shows that platforms cannot buy legal finality until their compliance systems prove durable.

A proposed $400 million settlement was supposed to close a costly chapter in TikTok’s relationship with American privacy regulators. Instead, a federal judge has turned the agreement into a much larger question: can a platform pay for finality when the court is not convinced that its controls have become durable?

That distinction matters far beyond the size of the fine. TikTok and its parent ByteDance agreed in August to resolve a U.S. Justice Department lawsuit alleging violations of children’s online privacy law. The structure appeared straightforward: $300 million would be paid immediately, and another $100 million would become due if a court terminated a 2019 consent decree imposed on Musical.ly, TikTok’s predecessor. But on September 19, U.S. District Judge George H. Wu indicated that he was inclined to reject the request to end the older decree. Without more detail, he said, the court could not determine whether the proposed arrangement created a durable remedy or whether termination was properly tailored to the claimed changes in circumstances.

The money is substantial. The logic behind it is more important. TikTok is not merely negotiating a penalty for past conduct. It is asking the court to convert operational assertions—new ownership, new management, stronger compliance functions, improved privacy practices and age-moderation systems—into legal finality. The judge’s hesitation suggests that the conversion rate may be unfavorable. A company can write a check in one day. It cannot prove, in one filing, that millions of daily product decisions will keep children’s data protected for years.

For investors, platforms and regulators, the case is becoming a test of whether compliance can still be treated as a finite cost. If the decree remains in place, TikTok will continue to carry reporting and record-keeping obligations through 2029. If it is terminated only after more evidence, the evidence itself may establish a template for other courts: assertions about governance improvements must be backed by controls that survive changes in management, ownership, incentives and product design. Either route points toward the same conclusion. Privacy is no longer a legal department’s periodic expense. It is becoming a continuing claim on how a platform is built and governed.

The settlement is really two transactions

The headline number compresses two different bargains. The first is a conventional enforcement settlement. According to Reuters’ August 21 report, TikTok agreed to pay $300 million immediately to resolve the Justice Department’s allegations. That amount is the price of ending litigation risk over past practices without waiting for a full trial.

The second bargain is conditional. Another $100 million is linked to an order vacating the 2019 consent decree. That makes the final quarter of the settlement economically unusual. It is not simply a deferred payment. It attaches a monetary price to removing an existing layer of supervision.

Consent decrees matter because they are not corporate promises. Once approved by a judge, they have the force of law. The Federal Trade Commission’s 2019 announcement said Musical.ly would pay $5.7 million after allegations that it collected personal information from children without the parental consent required by the Children’s Online Privacy Protection Act, or COPPA. The decree also imposed non-monetary obligations: compliance with COPPA, removal of videos made by users under 13 and continuing reporting and record-keeping. Reuters says those supervisory obligations extend through 2029.

The proposed 2026 settlement therefore tries to do two things at once. It settles a newer lawsuit, and it seeks to retire an older enforcement instrument. Those goals can be compatible, but they require different evidence. A litigation settlement asks whether the payment and conduct provisions fairly resolve disputed claims. Ending a decree asks whether the conditions that justified ongoing supervision have changed enough to make supervision unnecessary.

Judge Wu’s tentative position separates those questions. The government and company can agree that $300 million is sufficient to resolve the lawsuit. The court can still decide that a payment does not prove that the reporting architecture created in 2019 has outlived its purpose. This is why the dispute cannot be reduced to whether $400 million is large or small. The disputed asset is not cash. It is freedom from continuing oversight.

Why “durable remedy” is the key phrase

The court’s concern is not that TikTok has made no changes. The filing described a different ownership structure, changes in management, expanded compliance functions, stronger privacy practices and age-moderation systems designed to identify users under 13 who misstate their age. TikTok’s U.S. joint venture says users must enter a date of birth and that trained personnel remove tens of thousands of suspected underage accounts.

Those claims may all be accurate. The problem is that a list of controls does not automatically prove durability. A control is durable when it continues to work after the team that created it leaves, when growth targets become harder to meet, when a new product feature changes data flows, when a recommender model is retrained, when advertisers demand better measurement, and when moderation costs rise faster than revenue. Durability is not a snapshot of effort. It is a property of the operating system.

That is particularly difficult for age assurance. A platform cannot perfectly distinguish a 12-year-old from a 13-year-old merely by asking for a birth date. Stronger systems combine signals, moderation, parental tools, account restrictions, appeals and deletion workflows. Each layer creates false positives and false negatives. Tighten the system too much, and legitimate users are excluded or pushed into intrusive identity checks. Loosen it too much, and underage users pass through. The governance question is not whether one model performs well in a controlled test. It is who decides the acceptable error rate, how performance is measured, how exceptions are handled, and whether commercial teams can override the control.

The FTC’s original case illustrates why the court may be reluctant to accept a narrative of transformation without operating evidence. In 2019 the agency alleged that Musical.ly knew a significant share of its users were under 13, received thousands of parental complaints, and still failed to obtain the required consent or delete children’s data on request. The problem was not simply that a box had been left unchecked. According to the complaint summarized by the FTC, product defaults, messaging, location features, complaint handling and data deletion all interacted.

A durable remedy therefore has to connect governance to product behavior. It must show that underage-account detection triggers predictable action; that deletion reaches derived and replicated data; that new features receive privacy review before launch; that audit logs cannot be quietly disabled; that vendors inherit the same rules; and that senior management receives information capable of changing a decision. If those links are weak, the compliance program can look mature on paper while remaining fragile in practice.

The fine is large, but the shadow cost is larger

For a platform used by more than 200 million Americans, even a $400 million settlement is easier to model than the ongoing burden created by uncertain supervision. A fine is a discrete cash outflow. A decree affects future operating costs, product velocity, data architecture and management attention. It may require reporting, record retention, independent testing, escalation procedures and defensible documentation each time a relevant system changes.

Those costs do not appear in one line. They show up as slower launches, more engineering work, duplicated data controls, additional reviewers and a higher hurdle for experiments involving younger audiences. Some of that spending creates genuine enterprise value because it reduces the probability of a breach, a ban, a lawsuit or a loss of trust. Some becomes friction. The challenge for a private platform and its investors is that the two cannot be separated cleanly in advance.

This resembles the risk transfer described in Block2Learn’s analysis of Revolut’s data breach. There, a seemingly digital incident changed the threat model because identity and financial information could expose customers to physical risk. Here, the transformation runs in the other direction: a legal settlement moves into the product stack. Court language about consent, deletion and supervision becomes a specification for databases, models and release processes.

The resulting liability is path-dependent. If TikTok preserves the decree, invests heavily and demonstrates sustained performance, the burden may decline as controls become embedded. If it removes supervision too early and another failure emerges, the cost could rise nonlinearly. A new incident would not be evaluated as an isolated mistake. It would be judged against the argument that the company had already earned release from an earlier order.

That asymmetry explains the judge’s caution. Terminating a decree can create immediate certainty for the company. Reimposing equivalent oversight after a later failure is slower, politically noisier and potentially more expensive for users whose data has already been collected. A court deciding whether to end supervision is therefore comparing the known cost of continued monitoring with the uncertain but potentially irreversible cost of premature release.

Ownership changes do not automatically change incentives

The government’s case for settlement points partly to TikTok’s restructuring. ByteDance agreed in January to establish a majority American-owned joint venture intended to safeguard U.S. user data and avert a ban. Ownership, management and compliance functions changed. Those are relevant developments because control rights determine who appoints leaders, approves budgets and bears legal responsibility.

Yet ownership structure is not the same as incentive structure. A platform still competes for attention. Engagement, creator supply, advertising demand and commerce all benefit from scale. Younger users can be particularly valuable because habits formed early may persist, even when direct monetization is constrained. If the economic engine rewards reach while the compliance engine limits access, the board must decide which engine has priority when the two conflict.

This is the same governance problem that appears whenever a regulated interface becomes core infrastructure. Block2Learn’s examination of crypto wallets becoming regulated gateways showed how apparently passive software can acquire obligations once it controls access, routing or safeguards. TikTok’s interface is not passive, but the principle is similar. The more a platform shapes discovery and data collection, the harder it is to argue that safety sits outside the product’s economic design.

A majority American-owned venture may reduce one category of national-security concern without resolving every privacy concern. Data can be stored domestically and still be collected from the wrong user. Management can be independent and still face engagement incentives. A board can approve a privacy policy while product teams deploy a feature that changes the practical meaning of consent. Governance reform matters only when it changes decision rights at the point where revenue and protection collide.

That is why courts and regulators increasingly ask for mechanisms rather than assurances. Who can stop a launch? Which executive signs the certification? Does the privacy team report to a revenue leader or directly to the board? Are compensation metrics adjusted when a growth initiative increases youth-safety risk? Can auditors reproduce account-deletion results? These questions convert abstract governance into evidence.

Three scenarios now matter

Scenario one: the decree remains through 2029

The cleanest legal outcome may be the most operationally demanding. TikTok could pay the immediate $300 million, resolve the newer case and continue under the 2019 reporting and record-keeping requirements. The additional $100 million tied to termination would not be triggered under the settlement structure described by Reuters, although the exact mechanics would depend on the final agreement and court orders.

For TikTok, this would preserve an external compliance clock. Management could not describe the privacy overhaul as complete; it would have to demonstrate performance under supervision for several more years. The near-term burden would be higher, but the outcome could also create a clearer route to credibility. A platform that completes the decree without a material failure would have a stronger factual record when it later argues that its controls are mature.

For competitors, this scenario raises the effective standard. Large platforms would have to assume that child-safety and privacy systems need evidentiary depth: preserved records, measurable detection, deletion verification and documented escalation. The obligation would spread through vendor contracts and ad-tech integrations because a platform cannot prove compliance if key data flows disappear outside its own audit perimeter.

Scenario two: the decree ends after stronger conditions

The court could allow termination only after TikTok and the government supply more detail or accept substitute protections. That might involve more precise reporting, independent assessment, measurable age-assurance thresholds, retention limits, board certifications or a transition period. The $100 million conditional payment would then become the visible price of release, while the new conditions would define its real cost.

This scenario would be important because it could turn a bespoke settlement into a reusable supervisory template. A judge’s demand for evidence about changed practices can shape how future platform settlements are drafted. Regulators would learn that broad statements about compliance functions are insufficient. Companies would learn that a request for finality must include an operating model: controls, owners, metrics, auditability and consequences.

The analogy is not far from platform regulation in China’s travel market. In Block2Learn’s analysis of the hotel-booking crackdown, the central risk was not one fine but the repricing of the platform tollbooth when regulators questioned how access and bargaining power were used. Here the tollbooth is attention and data. A stronger settlement would reprice the freedom to collect, personalize and retain information about younger users.

Scenario three: termination is approved with limited additional proof

TikTok and the Justice Department could persuade the court that the company’s restructuring and current controls justify ending the decree. That would provide the fastest legal finality and validate the argument that the platform operating today is materially different from the Musical.ly business supervised in 2019.

But this is not a low-risk outcome. Once external reporting falls away, responsibility moves more completely to the board, internal audit and product leadership. Any later failure would be measured against the decision to remove supervision. The reputational penalty could exceed the legal one because the company would have argued that durable reform was already achieved.

Investors should therefore resist reading termination as proof that privacy risk has disappeared. It would mean that a particular decree was no longer considered necessary, not that age assurance had become perfect or that youth-safety obligations had stopped evolving. The risk would migrate from a court-administered framework into ordinary corporate governance, where it may be harder for outsiders to observe until something breaks.

The broader platform lesson: compliance needs an architecture

TikTok’s case sits at the intersection of three trends. First, regulators are moving from disclosure toward design. A privacy policy is not enough if default settings, recommender systems or deletion workflows contradict it. Second, large platforms are becoming infrastructure for communication, discovery and commerce, which makes failures more consequential. Third, courts are less willing to treat organizational change as proof of risk reduction without durable evidence.

That combination changes how management should allocate capital. A mature compliance program requires more than lawyers and policy staff. It needs engineers who understand data lineage, model behavior, access controls, experimentation systems and deletion. It needs product managers who can specify protected-user journeys. It needs internal auditors able to test an end-to-end claim rather than inspect a binder. It needs a board capable of asking whether a safety metric is leading, lagging or merely decorative.

The architecture also has to survive partnerships. Block2Learn’s analysis of the Coinbase-Stablecore bank middleware model showed that embedding regulated capabilities inside another institution does not make accountability disappear; it redistributes it across interfaces. Youth privacy behaves the same way. Identity vendors, cloud providers, advertisers, moderation contractors and app-store systems may all touch the control environment. A platform can outsource a function, but it cannot outsource the need to prove what happened.

This is why record-keeping is economically significant. Records allow a company to show that a control operated when it mattered. They also create discoverable evidence when it did not. The incentive to minimize documentation can therefore be strong, especially when product iteration is fast. A consent decree reverses that incentive by making traceability an obligation. Ending the decree without a credible substitute could reduce friction, but it could also remove the mechanism that forces the organization to preserve institutional memory.

What to watch next

The first signal is the court’s treatment of the termination request. A simple rejection would preserve the 2019 framework. A request for supplemental evidence would reveal which elements the judge considers missing. Conditions attached to approval would be even more informative because they could become a model for future privacy settlements.

The second signal is how the parties define changed circumstances. Ownership and management changes are visible. Product and data changes are harder to evaluate. Watch for measurable claims about underage detection, response times, deletion completion, false positives, parental consent and independent validation. The more specific the claims, the more testable—and therefore more valuable—they become.

The third signal is board accountability. A durable remedy should identify who owns the risk after the court steps back. If responsibility remains dispersed among legal, trust and safety, product, data engineering and the U.S. joint venture, failure can become everyone’s concern and no one’s decision. Clear escalation rights and certifications matter because children’s privacy risk can grow silently before it produces a public incident.

The fourth signal is whether regulators treat the settlement as an endpoint or a precedent. The FTC’s COPPA framework requires parental notice and verifiable consent before covered services collect personal information from children under 13. The commercial ecosystem has changed dramatically since the statute’s early years: algorithmic feeds, behavioral advertising, creator economies and cross-device identity make data flows deeper and harder to unwind. A settlement that demands operating evidence could influence enforcement even without changing the rule itself.

The investment conclusion

TikTok’s $400 million agreement is not just a legal bill. It is a negotiation over the capital value of supervision. The company is willing to attach $100 million to ending the old decree because continuing oversight has a real operational cost. The judge is hesitant because release also has a real public cost if the replacement controls are not durable.

That creates a useful way to think about platform risk. The visible fine is the floor. Above it sits the cost of engineering controls, documenting decisions, slowing product launches, preserving audit trails and sustaining board attention. Those costs can depress margins in the short term. They can also protect the franchise by making catastrophic failure less likely. The market’s mistake is to treat every compliance dollar as deadweight or every settlement as closure.

The better question is whether compliance spending changes the probability distribution. Does it reduce the chance of another enforcement action? Does it make the platform more resilient to ownership change? Does it allow management to prove that children’s data is handled as promised? Does it turn a recurring liability into a defensible capability?

Judge Wu’s tentative answer is that the current record may not yet be enough. That does not mean TikTok’s reforms are ineffective. It means durability must be demonstrated, not declared. If the court insists on that standard, the lasting consequence of the case will not be the $400 million payment. It will be the recognition that a platform’s governance cannot buy its way out of supervision until its operating system can carry the burden on its own.

Learning path

This article is for educational purposes and does not constitute investment, legal or financial advice.

This article is provided solely for informational and educational purposes and does not constitute financial or investment advice, a recommendation, or an offer or solicitation to buy or sell any financial instrument or digital asset. See our Financial Disclaimer.

This article was generated with the support of AI and reviewed by the Editorial Team. For more information, see our Terms of Service.


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OASIS

Oasis is an entrepreneur, investor and founder of Block2Learn, The Investor Intelligence Hub. His work sits at the intersection of financial markets, digital assets, technology and investor education. Through Block2Learn, he develops research, market intelligence and educational frameworks that bring structure to financial information and help independent investors navigate increasingly complex markets with greater knowledge and clarity.

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Reserve Rights (RSR) $ 0.001643 6.23%
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Arbitrum Bridged WETH (Arbitrum One) (WETH) $ 2,265.06 3.52%
zcash
Zcash (ZEC) $ 1,522.79 5.72%
tether-gold
Tether Gold (XAUT) $ 4,343.15 0.60%
ether-fi-staked-btc
Ether.fi Staked BTC (EBTC) $ 76,722.00 4.00%
ai16z
ai16z (AI16Z) $ 0.000486 7.18%
ether-fi-staked-eth
ether.fi Staked ETH (EETH) $ 2,317.47 1.05%
apecoin
ApeCoin (APE) $ 0.145795 9.13%
coredaoorg
Core (CORE) $ 0.022055 8.13%
helium
Helium (HNT) $ 0.477404 4.83%
frax
Legacy Frax Dollar (FRAX) $ 0.992076 0.05%
akash-network
Akash Network (AKT) $ 0.660479 21.73%
compound-governance-token
Compound (COMP) $ 22.69 10.18%
meow
MEOW (MEOW) $ 0.000005 8.64%
usdx-money-usdx
Stables Labs USDX (USDX) $ 0.008885 0.00%
ecash
eCash (XEC) $ 0.000008 7.58%
chiliz
Chiliz (CHZ) $ 0.015786 7.81%
wormhole
Wormhole (W) $ 0.012032 11.19%
amp-token
Amp (AMP) $ 0.000479 5.67%
ultima
Ultima (ULTIMA) $ 1,890.02 0.37%
eigenlayer
EigenCloud (prev. EigenLayer) (EIGEN) $ 0.247879 10.54%
pumpbtc
pumpBTC (PUMPBTC) $ 76,077.00 2.54%
deep
DeepBook (DEEP) $ 0.020345 23.87%
resolv-usr
Resolv USR (USR) $ 0.092393 3.16%
pancakeswap-token
PancakeSwap (CAKE) $ 2.54 2.53%
pax-gold
PAX Gold (PAXG) $ 4,340.13 0.46%
gigachad-2
Gigachad (GIGA) $ 0.002427 18.76%
mina-protocol
Mina Protocol (MINA) $ 0.122182 17.19%
gnosis
Gnosis (GNO) $ 117.43 0.21%
pendle
Pendle (PENDLE) $ 2.64 4.03%
bitcoin-avalanche-bridged-btc-b
Avalanche Bridged BTC (Avalanche) (BTC.B) $ 76,260.00 3.16%
beldex
Beldex (BDX) $ 0.075552 1.07%
echelon-prime
Echelon Prime (PRIME) $ 0.23406 0.37%
zksync
ZKsync (ZK) $ 0.011791 5.58%
paypal-usd
PayPal USD (PYUSD) $ 0.999947 0.01%
havven
Synthetix (SNX) $ 0.236883 7.58%
coinbase-wrapped-staked-eth
Coinbase Wrapped Staked ETH (CBETH) $ 2,539.40 3.57%
true-usd
TrueUSD (TUSD) $ 0.999585 0.02%
stakestone-berachain-vault-token
StakeStone Berachain Vault Token (BERASTONE) $ 2,743.35 6.31%
axelar
Axelar (AXL) $ 0.052639 14.32%
tbtc
tBTC (TBTC) $ 70,942.00 7.49%
apenft
AINFT (NFT) $ 0.000000241951 0.16%
snek
Snek (SNEK) $ 0.000523 10.26%
mog-coin
Mog Coin (MOG) $ 0.00000011389 9.31%
telcoin
Telcoin (TEL) $ 0.001604 8.62%
toshi
Toshi (TOSHI) $ 0.000123 7.91%
dydx
dYdX (ETHDYDX) $ 0.138995 10.11%
kava
Kava (KAVA) $ 0.072645 1.34%
polygon-pos-bridged-weth-polygon-pos
Polygon PoS Bridged WETH (Polygon POS) (WETH) $ 2,261.63 3.58%
newton-project
AB (AB) $ 0.000585 0.65%
notcoin
Notcoin (NOT) $ 0.000504 7.63%
chex-token
Chintai (CHEX) $ 0.009905 6.63%
bridged-usdc-polygon-pos-bridge
Polygon Bridged USDC (Polygon PoS) (USDC.E) $ 0.99972 0.00%
vethor-token
VeThor (VTHO) $ 0.000673 1.87%
frax-ether
Frax Ether (FRXETH) $ 2,262.16 2.20%
1inch
1INCH (1INCH) $ 0.102317 6.68%
trust-wallet-token
Trust Wallet (TWT) $ 0.590469 5.40%
quantixai
Quantix Finance (QFI) $ 19.99 7.01%
grass
Grass (GRASS) $ 0.376377 6.34%
stader-ethx
Stader ETHx (ETHX) $ 2,455.55 2.19%
superfarm
SuperVerse (SUPER) $ 0.150201 14.50%
terra-luna
Terra Luna Classic (LUNC) $ 0.000055 7.79%
sweth
Swell Ethereum (SWETH) $ 2,521.55 3.25%
safe
Safe (SAFE) $ 0.108761 10.60%
livepeer
Livepeer (LPT) $ 1.70 9.49%
hashnote-usyc
Circle USYC (USYC) $ 1.14 0.01%
usdb
USDB (USDB) $ 1.00 0.48%
creditcoin-2
Creditcoin (CTC) $ 0.112407 9.56%
theta-fuel
Theta Fuel (TFUEL) $ 0.010657 6.11%
oasis-network
Oasis (ROSE) $ 0.007749 6.60%
super-oeth
Super OETH (SUPEROETH) $ 2,263.65 2.59%
aixbt
aixbt (AIXBT) $ 0.022177 9.15%
kusama
Kusama (KSM) $ 4.61 7.56%
bio-protocol
Bio Protocol (BIO) $ 0.02909 9.01%
layerzero
LayerZero (ZRO) $ 1.21 13.12%
blur
Blur (BLUR) $ 0.019966 9.56%
dash
Dash (DASH) $ 59.70 9.01%
cat-in-a-dogs-world
cat in a dogs world (MEW) $ 0.000454 9.68%
ordinals
ORDI (ORDI) $ 4.90 10.36%
solayer-staked-sol
Solayer Staked SOL (SSOL) $ 112.14 4.30%
io
io.net (IO) $ 0.151142 10.02%
ondo-us-dollar-yield
Ondo US Dollar Yield (USDY) $ 1.14 0.12%
freysa-ai
Freysa AI (FAI) $ 0.002549 7.60%
arkham
Arkham (ARKM) $ 0.127657 18.33%
turbo
Turbo (TURBO) $ 0.00103 9.40%
popcat
Popcat (POPCAT) $ 0.055451 16.22%
binance-peg-busd
Binance-Peg BUSD (BUSD) $ 1.00 0.05%
olympus
Olympus (OHM) $ 20.11 0.44%
dog-go-to-the-moon-rune
Dog (Bitcoin) (DOG) $ 0.001209 9.04%
nervos-network
Nervos Network (CKB) $ 0.001277 8.99%
astar
Astar (ASTR) $ 0.00696 6.92%
just
JUST (JST) $ 0.112974 1.71%
compound-wrapped-btc
cWBTC (CWBTC) $ 1,534.90 2.99%
mx-token
MX (MX) $ 1.92 4.26%
zilliqa
Zilliqa (ZIL) $ 0.003726 5.05%
verus-coin
Verus (VRSC) $ 0.208613 11.90%
melania-meme
Melania Meme (MELANIA) $ 0.105557 5.83%
holotoken
Holo (HOT) $ 0.000409 5.10%
ai-rig-complex
AI Rig Complex (ARC) $ 0.07984 17.18%
origintrail
OriginTrail (TRAC) $ 0.351039 7.38%
liquid-staked-ethereum
Liquid Staked ETH (LSETH) $ 2,406.26 2.78%
polygon-bridged-wbtc-polygon-pos
Polygon Bridged WBTC (Polygon POS) (WBTC) $ 76,130.00 3.08%
0x
0x Protocol (ZRX) $ 0.118125 4.26%
baby-doge-coin
Baby Doge Coin (BABYDOGE) $ 0.00000000041421 8.36%
ether-fi
Ether.fi (ETHFI) $ 0.736712 6.85%
safepal
SafePal (SFP) $ 0.30688 8.27%
staked-frax-ether
Staked Frax Ether (SFRXETH) $ 2,589.68 3.62%
aethir
Aethir (ATH) $ 0.005409 4.67%
golem
Golem (GLM) $ 0.122657 4.87%
basic-attention-token
Basic Attention (BAT) $ 0.083312 7.54%
swissborg
SwissBorg (BORG) $ 0.178636 5.54%
skale
SKALE (SKL) $ 0.004643 9.90%
wemix-token
WEMIX (WEMIX) $ 0.194113 0.63%
mocaverse
Moca Network (MOCA) $ 0.010256 8.55%
xyo-network
XYO Network (XYO) $ 0.003609 3.49%
gas
Gas (GAS) $ 1.38 5.71%
celo
Celo (CELO) $ 0.092376 5.65%
benqi-liquid-staked-avax
BENQI Liquid Staked AVAX (SAVAX) $ 12.58 0.25%
qtum
Qtum (QTUM) $ 0.982203 5.64%
spell-token
Spell (SPELL) $ 0.000091 4.86%
would
would (WOULD) $ 0.035236 2.38%
vine
Vine (VINE) $ 0.00821 7.93%
zencash
Horizen (ZEN) $ 7.91 6.83%
woo-network
WOO (WOO) $ 0.012569 11.18%
iotex
IoTeX (IOTX) $ 0.003743 13.63%
bridged-wrapped-ether-starkgate
Bridged Ether (StarkGate) (ETH) $ 2,241.79 5.41%
resolv-wstusr
Resolv wstUSR (WSTUSR) $ 1.13 0.06%
siacoin
Siacoin (SC) $ 0.000951 5.36%
bybit-staked-sol
Bybit Staked SOL (BBSOL) $ 112.08 4.42%
plume
Plume (PLUME) $ 0.014811 12.90%
osmosis
Osmosis (OSMO) $ 0.036514 5.04%
vana
Vana (VANA) $ 1.12 7.10%
griffain
GRIFFAIN (GRIFFAIN) $ 0.015079 11.60%
zetachain
ZetaChain (ZETA) $ 0.063469 67.47%
uxlink
UXLINK (UXLINK) $ 0.000711 11.09%
ethereum-pow-iou
EthereumPoW (ETHW) $ 0.283046 5.64%
ankr
Ankr Network (ANKR) $ 0.004956 6.53%
akuma-inu
Akuma Inu (AKUMA) $ 0.000000085068 5.33%
tribe-2
Tribe (TRIBE) $ 0.412944 4.64%
ravencoin
Ravencoin (RVN) $ 0.002298 3.36%
enjincoin
Enjin Coin (ENJ) $ 0.027997 6.82%
peanut-the-squirrel
Peanut the Squirrel (PNUT) $ 0.055571 7.31%
elixir-deusd
Elixir deUSD (DEUSD) $ 0.000977 0.00%
memecoin-2
Memecoin (MEME) $ 0.000591 7.10%
aelf
aelf (ELF) $ 0.073241 2.28%
anime
Animecoin (ANIME) $ 0.003297 7.34%
constellation-labs
Constellation (DAG) $ 0.005866 0.66%
polymesh
Polymesh (POLYX) $ 0.042292 6.29%
convex-finance
Convex Finance (CVX) $ 2.06 8.43%
drift-protocol
Drift Protocol (DRIFT) $ 0.01821 10.15%
sats-ordinals
SATS (Ordinals) (SATS) $ 0.000000011897 9.13%
venice-token
Venice Token (VVV) $ 32.01 13.48%
qubic-network
Qubic (QUBIC) $ 0.000000430053 20.25%
coinex-token
CoinEx (CET) $ 0.004999 0.02%
peaq-2
peaq (PEAQ) $ 0.036138 13.38%
threshold-network-token
Threshold Network (T) $ 0.005108 2.55%
stepn
GMT (GMT) $ 0.008205 9.31%
usda-2
USDa (USDA) $ 0.967102 0.00%

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