US Labor Market Transition: 7.4 Million Displaced Workers Reveal the Cost of Reallocation

New BLS data reveal 7.4 million displaced workers and a costly mismatch between shrinking industries, future job growth and household resilience.

The United States is not simply gaining or losing jobs. It is forcing millions of people to cross an increasingly narrow bridge between industries, occupations and regions. New Bureau of Labor Statistics data show that 7.4 million workers were displaced from jobs they had held during 2023–25. Of those, 3.3 million had at least three years of tenure. By January 2026, only 66.1% of those long-tenured workers were reemployed, while 18.3% were unemployed and 15.7% had left the labor force.

Those figures arrive on the same day as the BLS projected 5.9 million net new jobs across the economy between 2025 and 2035. Healthcare and social assistance alone are expected to add about 2.2 million, professional and technical services another 926,700, and computing infrastructure and data processing roughly 120,400. The apparent contradiction is the central fact of the US labor market transition: opportunity can expand in aggregate while the cost of reaching it rises for individual workers.

This is not evidence that artificial intelligence caused the displacement already recorded; the BLS datasets do not establish that causal link. It is evidence that the economy is reallocating labor at speed while payroll growth has slowed, household saving is thin and inflation remains elevated. For investors, policymakers and companies, the risk is not a simple shortage of jobs. It is a mismatch among the people who need work, the skills employers require, the places where growth occurs and the time households can afford to spend in transition.

The 7.4 million headline hides a deeper tenure shock

The BLS Displaced Workers Summary released on August 27 counts 3.3 million long-tenured workers who lost or left jobs because a plant or company closed or moved, their position or shift was abolished, or there was insufficient work. That was 746,000 more than in the preceding 2021–22 displacement period. Another 4.1 million short-tenured workers were displaced, bringing the total to 7.4 million.

Tenure matters because it represents accumulated firm-specific knowledge, internal networks, seniority and often a wage premium that cannot be transferred instantly. A worker who spent a decade learning one production system does not arrive at a hospital, electrical utility or software-services company with the same bargaining position. Even when the new economy produces a vacancy, the worker may need a credential, relocation, a different schedule or an initial pay cut before that vacancy becomes a realistic job.

The reasons for displacement reinforce that interpretation. Among long-tenured displaced workers, 44.4% lost their jobs because a position or shift was abolished, 32.6% because a company or plant closed or moved, and 22.9% because of insufficient work. These are not primarily voluntary career changes. They are disruptions in the institutional relationship between a worker and an employer, often after the worker had organized housing, family care and retirement expectations around that relationship.

Manufacturing accounted for 642,000 long-tenured displaced workers, or about 19% of the total, an increase of 215,000 from the previous survey period. Professional and business services accounted for 16%, while retail trade represented 10%. The breadth is important. This is not a single declining factory belt confronting a uniformly booming service economy. Reallocation pressure reaches white-collar services and consumer-facing businesses as well as production floors.

Reemployment is not the same as economic repair

A 66.1% reemployment rate may sound encouraging until it is placed against the remaining third. Roughly one in six long-tenured displaced workers was outside the labor force in January, and almost one in five was unemployed. The survey also shows that older displaced workers had lower reemployment rates than younger groups. That age gradient is economically intuitive: a longer expected retraining period, health constraints, caregiving obligations and employer preferences can all reduce the return on starting again.

Even successful reemployment does not guarantee recovery. A new job can carry lower pay, fewer hours, weaker benefits or less predictable scheduling. It may require a longer commute or a move that destroys part of the household’s housing equity and community network. The aggregate employment count records a person as reemployed; it does not record whether the household has restored its previous consumption capacity or whether the new match will last.

This distinction matters for macro analysis. A labor market can maintain a respectable unemployment rate while generating more churn and weaker matches underneath. Our earlier analysis of the “no-hire, no-fire” trap in US jobs data emphasized that low layoffs do not guarantee strong labor demand. The displacement report adds a different warning: when a layoff does occur, the path back can be slow enough to alter household spending and labor-force participation.

The July employment report strengthens that caution. The BLS Employment Situation showed payroll employment declining by 23,000, unemployment at 4.1%, labor-force participation at 61.4% and the employment-population ratio at 58.9%. Payroll gains averaged only 34,000 over the previous 12 months, and May and June were revised down by a combined 103,000. This is not a collapse, but it is a labor market with less capacity to absorb a shock quickly.

The growth map is unusually concentrated

The BLS 2025–35 employment projections estimate that the economy will add 5.9 million jobs over the decade. Healthcare and social assistance are projected to create 2.2 million, about 37% of the net increase. Professional, scientific and technical services are expected to add 926,700 jobs, an 8.6% expansion. Computing infrastructure, data processing and web hosting are projected to grow 25.1%, the fastest rate among detailed industries highlighted by the agency.

Growth that concentrated creates both an investment theme and a labor-market problem. Healthcare demand is supported by an aging population, but many clinical roles require licenses, supervised training and physical proximity to patients. Technical-services growth can reward advanced digital, engineering and analytical skills, yet those skills are not instantly portable from retail or traditional manufacturing. Computing infrastructure and power generation need construction, electrical and maintenance labor, but the projects cluster around specific grids, permits and land markets.

The projections also show the other side. Retail trade employment is expected to decline modestly, and federal government employment is projected to fall 3.4%. Transportation and warehousing are projected to grow 3.1%, slower than the most visible technology infrastructure categories. Net growth therefore emerges from a portfolio of expanding and contracting industries, not from a tide that raises every occupation equally.

Artificial intelligence intensifies the capital-allocation question without answering the labor question. The BLS expects AI adoption to contribute to demand for computing, research and technical services while also improving productivity in tasks across many occupations. But productivity growth and worker welfare are not synonyms. A company can become more productive by reorganizing roles faster than affected employees can retrain. The economy may gain output while households absorb the transition cost.

That is why the physical bottleneck matters. Our work on the AI power bottleneck showed how data-center demand runs through grids, turbines, permitting and financing. Labor reallocation has a comparable chain: a projected job requires a funded employer, an available location, a qualified applicant, affordable training, transport, housing and enough household liquidity to wait. Break any link and a national jobs forecast fails to become a local employment opportunity.

Household balance sheets set the speed limit

Workers do not retrain in an abstract economy. They retrain while paying rent, mortgages, insurance, food and childcare. The BEA’s July income and spending report showed disposable personal income rising 0.5% and personal consumption expenditures increasing 0.2%, but real consumption was essentially flat. The personal saving rate was just 3.0%. That leaves less self-funded runway for a prolonged transition.

Inflation compounds the problem. Headline PCE prices were 3.7% higher than a year earlier in July and core PCE prices were up 3.3%. A displaced household experiences those price levels at the same time that severance runs down and employer-provided benefits may disappear. Even if nominal income recovers through a new job, the household’s real purchasing power can remain below its former path.

This mechanism links labor churn to consumption. Durable purchases are easy to postpone, precautionary saving rises among people who fear displacement, and families may avoid moving because a low-rate mortgage is economically valuable. Geographic mobility then weakens precisely when the distribution of new jobs becomes more uneven. Housing, usually treated as a separate macro sector, becomes part of the employment-matching system.

A thin saving buffer also changes wage bargaining. A worker with months of liquidity can search for a role that uses existing skills and preserves pay. A worker facing an immediate cash shortfall may accept the first available job, even if it is a poor match. That decision can reduce measured unemployment quickly while lowering productivity, earnings and job stability over the following years.

Why the Federal Reserve cannot solve a matching problem

The Federal Reserve can influence aggregate demand, financing costs and employers’ willingness to hire. It cannot issue a nursing license, build transmission lines, create housing near a new data center or convert a displaced machinist’s experience into an accepted credential. Monetary policy can make the bridge busier or quieter, but it cannot widen the bridge by itself.

At its July 29 meeting, the Federal Open Market Committee kept the federal funds target range at 3.5% to 3.75%. It described economic activity as solid and inflation as elevated. Three participants dissented in favor of a 25-basis-point increase. The combination of weak recent payroll growth and above-target inflation leaves policy with an uncomfortable trade-off.

Cutting rates can support housing, credit and hiring, giving displaced workers more openings. Yet easier financial conditions cannot guarantee that the openings match displaced workers’ skills or locations. If inflation stays elevated, premature easing can also erode real wages and household purchasing power. Raising rates may reinforce credibility, but it can reduce vacancies just as more workers need new matches.

The correct policy inference is therefore narrower than “weak jobs mean cuts.” The Fed must determine whether labor softness reflects deficient aggregate demand or structural reallocation. Demand weakness is responsive to rates. Structural mismatch requires training, mobility, infrastructure and employer investment. In practice both forces operate together, which makes real-time calibration difficult.

The corporate margin channel

Companies face the transition from the opposite side. A growing employer may have capital and customers but still lack qualified workers. Wage offers rise, projects are delayed and managers automate tasks that cannot be staffed reliably. A contracting employer may preserve margins by removing positions, consolidating shifts or relocating production. The same reallocation can therefore lift margins for one company and damage execution at another.

Investors should distinguish cost cutting from durable productivity. Eliminating roles can improve quarterly operating leverage, especially when revenue is stable. But a firm that removes too much institutional knowledge may later face quality problems, slower product cycles or expensive rehiring. Productivity is genuine when output rises sustainably for a given input, not merely when payroll falls before the operational consequences appear.

The recent divergence between profits and household demand makes this distinction especially important. Our analysis of surging US corporate profits alongside flat real spending argued that margin strength can become fragile when it outruns final demand. Displacement can widen that gap temporarily: severed payroll costs support margins, while weaker household income eventually weighs on sales.

Capital spending is the constructive counterweight. When companies invest in training, interoperable credentials, tools that complement workers and facilities in accessible regions, they expand the economy’s matching capacity. When investment focuses only on labor substitution without transition support, private returns may still be positive, but the adjustment burden shifts to households and public institutions.

Markets will price the transition unevenly

For equities, the broad index can obscure the signal. Healthcare providers, staffing platforms, grid equipment manufacturers and selected technical-services companies may benefit from structural demand. Retailers, labor-intensive service businesses and companies tied to weak regional employment may face pressure. The result is dispersion: earnings revisions and valuation multiples should separate according to each firm’s exposure to the growing side of the labor map.

Technology valuations deserve particular discipline. Fast projected growth in computing infrastructure does not mean every AI-related security deserves an unlimited multiple. Power, land, chips, networking, labor and customer economics constrain returns. A labor-market transition thesis supports demand for enabling infrastructure, but it also raises execution costs and political scrutiny. Revenue growth must eventually convert into free cash flow after those constraints are funded.

Bonds receive a different signal. Structural mismatch can coexist with sticky wage pressure in scarce occupations and weak wage growth in displaced ones. That is not a clean disinflationary environment. Treasury yields may fall when weak payroll data dominate, then rise when inflation or productivity optimism returns. Credit investors should focus on refinancing calendars, regional exposure and whether a borrower’s labor strategy depends on skills that are genuinely available.

Market breadth is another useful diagnostic. If the reallocation is healthy, gains should spread beyond a few mega-cap technology companies toward industrial suppliers, utilities, healthcare, construction and smaller service firms that support the new investment cycle. Narrow leadership would suggest that investors are capitalizing a small set of winners while the wider economy absorbs the transition cost. Our framework for US market breadth and rate risk is designed for exactly this distinction.

Four indicators that show whether the bridge is widening

First, reemployment quality. The next displaced-worker survey should be read alongside earnings changes, hours and benefits. A higher reemployment rate is encouraging, but the strongest evidence would be a rise in durable matches that restore real income. Age, education and industry breakdowns will reveal who remains stranded.

Second, hiring relative to vacancies. The Job Openings and Labor Turnover Survey should be used to compare openings with actual hires, quits and layoffs. A large vacancy stock with weak hiring can signal that employers and workers are failing to match. Rising hires without a surge in layoffs would be stronger evidence that the transition is becoming more efficient.

Third, sectoral wage pressure. Broad wage acceleration would complicate the inflation outlook, but concentrated wage gains in healthcare, electrical trades and technical occupations would identify specific bottlenecks. Companies can respond with training, process redesign or higher compensation. Investors should ask which response creates lasting capacity rather than merely transferring margin.

Fourth, mobility infrastructure. Apprenticeship completion, credential recognition, housing supply and grid-project approvals are not glamorous market indicators, yet they determine whether projected capital spending becomes output and employment. A region can announce billions of dollars of investment and still miss targets if workers cannot live nearby or complete required training.

Three scenarios for the next phase

Scenario one: productive reallocation. Hiring stabilizes, training pipelines expand and workers move into healthcare, grid, construction and technical services with limited income loss. Productivity improves, real wages recover and inflation gradually moderates. This is the bullish path for a wider group of equities and for credit quality, even if policy rates decline only slowly.

Scenario two: profitable concentration. A small set of companies captures strong AI and healthcare demand, but labor mobility remains weak. Index earnings can hold up while participation, regional consumption and small-company performance deteriorate. Market breadth stays narrow, political pressure rises and the economy becomes more dependent on capital spending by a few large firms.

Scenario three: transition recession. Employers reduce hiring before displaced workers secure new roles, real consumption weakens and credit stress spreads. Rate cuts then address the demand shortfall, but structural mismatch slows the recovery. Long-duration government bonds may benefit initially, while lower-quality credit, regional banks and consumer cyclicals face the greatest pressure.

The scenarios are not forecasts carved in stone. They are a way to separate the variables that matter. Payroll totals tell us the net result; displacement, reemployment quality, hiring efficiency and mobility tell us how the result was produced. Markets often react to the net number before understanding the mechanism.

What would falsify the reallocation thesis?

A useful thesis must specify what would prove it wrong. If payroll growth broadens rapidly, the hiring rate rises across industries and long-tenured displaced workers regain employment without persistent earnings losses, then the current figures would look more like a temporary adjustment than a structural bottleneck. Strong participation among older workers would be especially persuasive because that group currently faces the steepest transition.

Evidence could also weaken the thesis from the other direction. If vacancies collapse, layoffs accelerate and demand falls across nearly every sector, mismatch would no longer be the main story; a conventional cyclical downturn would be. In that case, monetary easing and broad fiscal stabilization would have more immediate traction than targeted training and mobility policies, although structural repair would still matter for the recovery.

Between those extremes, investors should expect noisy data. Monthly payrolls are revised, projections describe a decade rather than a straight line, and displacement surveys are retrospective. No single release can measure the entire bridge. The signal becomes credible when independent indicators—hiring, wages, participation, household income, regional investment and company commentary—point toward the same mechanism.

Block2Learn assessment

The new BLS releases should change the default narrative. The US labor market transition is not a contest between an optimistic jobs forecast and a pessimistic displacement count. Both can be true. The economy can create 5.9 million net jobs over a decade while millions of workers experience unemployment, lower earnings or exit from the labor force during the journey.

Our base case is that reallocation risk will matter more for markets than the unemployment rate alone suggests. Growth is concentrated in sectors with high credential, infrastructure or geographic requirements. Household saving is too low to make long transitions painless, and inflation limits the Federal Reserve’s freedom to treat every sign of labor softness with immediate easing.

The constructive path is available. Healthcare demand, grid investment, advanced technical services and computing infrastructure can support a broad capital cycle. But the gains become durable only if training, housing, mobility and employer practice convert projected demand into accessible jobs. Otherwise, the economy produces vacancies on one side and displaced experience on the other.

Investors should therefore reward companies that solve the bottleneck rather than merely narrate it. The strongest businesses will combine capital, technology and workforce development to expand capacity. The weakest will treat labor as a quarterly cost until lost knowledge, political resistance or customer demand returns the bill. The 7.4 million displaced-worker figure is not just a social statistic. It is an early measure of how expensive America’s next growth model may be.

To build the analytical foundation behind labor data, inflation, policy and cross-asset valuation, continue with the Block2Learn Learning Path.

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OASIS

Investor and entrepreneur with a focus on jewelry, e-commerce, and blockchain technologies. Founder of Block2Learn, a platform dedicated to educating on crypto, NFTs, and decentralized finance. Passionate about empowering others through innovative investments in digital assets and traditional industries.

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eCash (XEC) $ 0.000007 2.63%
chiliz
Chiliz (CHZ) $ 0.013679 2.98%
wormhole
Wormhole (W) $ 0.009155 3.40%
amp-token
Amp (AMP) $ 0.000448 2.54%
ultima
Ultima (ULTIMA) $ 2,294.25 0.84%
eigenlayer
EigenCloud (prev. EigenLayer) (EIGEN) $ 0.195502 1.31%
pumpbtc
pumpBTC (PUMPBTC) $ 76,077.00 2.54%
deep
DeepBook (DEEP) $ 0.013667 5.64%
resolv-usr
Resolv USR (USR) $ 0.121825 0.80%
pancakeswap-token
PancakeSwap (CAKE) $ 1.71 2.06%
pax-gold
PAX Gold (PAXG) $ 4,461.06 2.51%
gigachad-2
Gigachad (GIGA) $ 0.002591 13.56%
mina-protocol
Mina Protocol (MINA) $ 0.063416 2.59%
gnosis
Gnosis (GNO) $ 119.05 0.97%
pendle
Pendle (PENDLE) $ 1.68 3.87%
bitcoin-avalanche-bridged-btc-b
Avalanche Bridged BTC (Avalanche) (BTC.B) $ 76,260.00 3.16%
beldex
Beldex (BDX) $ 0.079743 0.52%
echelon-prime
Echelon Prime (PRIME) $ 0.234478 3.05%
zksync
ZKsync (ZK) $ 0.008164 4.55%
paypal-usd
PayPal USD (PYUSD) $ 0.99996 0.00%
havven
Synthetix (SNX) $ 0.211041 5.34%
coinbase-wrapped-staked-eth
Coinbase Wrapped Staked ETH (CBETH) $ 2,539.40 3.57%
true-usd
TrueUSD (TUSD) $ 0.998316 0.01%
stakestone-berachain-vault-token
StakeStone Berachain Vault Token (BERASTONE) $ 2,439.22 2.02%
axelar
Axelar (AXL) $ 0.040253 1.93%
tbtc
tBTC (TBTC) $ 70,942.00 7.49%
apenft
AINFT (NFT) $ 0.000000245433 4.87%
snek
Snek (SNEK) $ 0.000423 2.88%
mog-coin
Mog Coin (MOG) $ 0.000000113456 3.02%
telcoin
Telcoin (TEL) $ 0.001756 6.99%
toshi
Toshi (TOSHI) $ 0.000124 2.44%
dydx
dYdX (ETHDYDX) $ 0.109874 4.05%
kava
Kava (KAVA) $ 0.046626 3.09%
polygon-pos-bridged-weth-polygon-pos
Polygon PoS Bridged WETH (Polygon POS) (WETH) $ 2,261.63 3.58%
newton-project
AB (AB) $ 0.000982 0.43%
notcoin
Notcoin (NOT) $ 0.000413 1.96%
chex-token
Chintai (CHEX) $ 0.010781 11.60%
bridged-usdc-polygon-pos-bridge
Polygon Bridged USDC (Polygon PoS) (USDC.E) $ 0.99972 0.00%
vethor-token
VeThor (VTHO) $ 0.000399 0.07%
frax-ether
Frax Ether (FRXETH) $ 2,262.16 2.20%
1inch
1INCH (1INCH) $ 0.088168 2.18%
trust-wallet-token
Trust Wallet (TWT) $ 0.461457 1.61%
quantixai
Quantix Finance (QFI) $ 24.98 0.09%
grass
Grass (GRASS) $ 0.35108 2.35%
stader-ethx
Stader ETHx (ETHX) $ 2,455.55 2.19%
superfarm
SuperVerse (SUPER) $ 0.110659 5.11%
terra-luna
Terra Luna Classic (LUNC) $ 0.000052 3.03%
sweth
Swell Ethereum (SWETH) $ 2,521.55 3.25%
safe
Safe (SAFE) $ 0.088494 3.87%
livepeer
Livepeer (LPT) $ 1.36 3.61%
hashnote-usyc
Circle USYC (USYC) $ 1.14 0.03%
usdb
USDB (USDB) $ 0.999162 0.37%
creditcoin-2
Creditcoin (CTC) $ 0.085782 0.97%
theta-fuel
Theta Fuel (TFUEL) $ 0.009245 0.94%
oasis-network
Oasis (ROSE) $ 0.005997 1.17%
super-oeth
Super OETH (SUPEROETH) $ 2,263.65 2.59%
aixbt
aixbt (AIXBT) $ 0.020209 3.69%
kusama
Kusama (KSM) $ 3.52 0.26%
bio-protocol
Bio Protocol (BIO) $ 0.027788 2.69%
layerzero
LayerZero (ZRO) $ 1.08 5.35%
blur
Blur (BLUR) $ 0.016165 0.75%
dash
Dash (DASH) $ 39.01 1.17%
cat-in-a-dogs-world
cat in a dogs world (MEW) $ 0.000415 0.63%
ordinals
ORDI (ORDI) $ 4.02 2.72%
solayer-staked-sol
Solayer Staked SOL (SSOL) $ 112.14 4.30%
io
io.net (IO) $ 0.13129 3.94%
ondo-us-dollar-yield
Ondo US Dollar Yield (USDY) $ 1.14 0.02%
freysa-ai
Freysa AI (FAI) $ 0.002812 0.98%
arkham
Arkham (ARKM) $ 0.1142 1.19%
turbo
Turbo (TURBO) $ 0.001076 9.73%
popcat
Popcat (POPCAT) $ 0.055323 2.42%
binance-peg-busd
Binance-Peg BUSD (BUSD) $ 1.00 0.05%
olympus
Olympus (OHM) $ 18.85 4.74%
dog-go-to-the-moon-rune
Dog (Bitcoin) (DOG) $ 0.001181 8.24%
nervos-network
Nervos Network (CKB) $ 0.001 0.06%
astar
Astar (ASTR) $ 0.005567 0.61%
just
JUST (JST) $ 0.098401 1.23%
compound-wrapped-btc
cWBTC (CWBTC) $ 1,534.90 2.99%
mx-token
MX (MX) $ 1.75 1.61%
zilliqa
Zilliqa (ZIL) $ 0.002593 1.43%
verus-coin
Verus (VRSC) $ 0.207225 8.80%
melania-meme
Melania Meme (MELANIA) $ 0.114652 0.96%
holotoken
holo (HOLO) $ 0.000014 0.23%
ai-rig-complex
AI Rig Complex (ARC) $ 0.068496 3.10%
origintrail
OriginTrail (TRAC) $ 0.345207 1.88%
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) $ 0.097788 0.29%
baby-doge-coin
Baby Doge Coin (BABYDOGE) $ 0.00000000036237 3.20%
ether-fi
Ether.fi (ETHFI) $ 0.530243 4.82%
safepal
SafePal (SFP) $ 0.25857 2.19%
staked-frax-ether
Staked Frax Ether (SFRXETH) $ 2,589.68 3.62%
aethir
Aethir (ATH) $ 0.004763 2.63%
golem
Golem (GLM) $ 0.113659 1.67%
basic-attention-token
Basic Attention (BAT) $ 0.06792 1.98%
swissborg
SwissBorg (BORG) $ 0.183212 0.89%
skale
SKALE (SKL) $ 0.003699 3.92%
wemix-token
WEMIX (WEMIX) $ 0.207377 0.09%
mocaverse
Moca Network (MOCA) $ 0.008643 2.83%
xyo-network
XYO Network (XYO) $ 0.003476 1.90%
gas
Gas (GAS) $ 1.29 0.78%
celo
Celo (CELO) $ 0.073813 2.25%
benqi-liquid-staked-avax
BENQI Liquid Staked AVAX (SAVAX) $ 12.58 0.25%
qtum
Qtum (QTUM) $ 0.824348 1.05%
spell-token
Spell (SPELL) $ 0.000081 2.79%
would
would (WOULD) $ 0.056276 8.02%
vine
Vine (VINE) $ 0.006437 12.21%
zencash
Horizen (ZEN) $ 4.95 5.03%
woo-network
WOO (WOO) $ 0.011318 1.67%
iotex
IoTeX (IOTX) $ 0.00286 1.37%
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) $ 0.000628 1.47%
bybit-staked-sol
Bybit Staked SOL (BBSOL) $ 112.08 4.42%
plume
Plume (PLUME) $ 0.014339 2.17%
osmosis
Osmosis (OSMO) $ 0.035698 0.14%
vana
Vana (VANA) $ 0.900201 3.84%
griffain
GRIFFAIN (GRIFFAIN) $ 0.011446 4.93%
zetachain
ZetaChain (ZETA) $ 0.034019 2.74%
uxlink
UXLINK (UXLINK) $ 0.000716 2.63%
ethereum-pow-iou
EthereumPoW (ETHW) $ 0.279004 1.63%
ankr
Ankr Network (ANKR) $ 0.004 1.23%
akuma-inu
Akuma Inu (AKUMA) $ 0.000000092771 1.13%
tribe-2
Tribe (TRIBE) $ 0.378747 2.63%
ravencoin
Ravencoin (RVN) $ 0.003065 6.64%
enjincoin
Enjin Coin (ENJ) $ 0.025217 3.36%
peanut-the-squirrel
Peanut the Squirrel (PNUT) $ 0.051125 0.63%
elixir-deusd
Elixir deUSD (DEUSD) $ 0.000977 0.00%
memecoin-2
Memecoin (MEME) $ 0.000534 2.37%
aelf
aelf (ELF) $ 0.068998 13.67%
anime
Animecoin (ANIME) $ 0.002635 0.89%
constellation-labs
Constellation (DAG) $ 0.006877 0.74%
polymesh
Polymesh (POLYX) $ 0.034952 0.24%
convex-finance
Convex Finance (CVX) $ 2.29 3.94%
drift-protocol
Drift Protocol (DRIFT) $ 0.012538 1.65%
sats-ordinals
SATS (Ordinals) (SATS) $ 0.000000011218 0.33%
venice-token
Venice Token (VVV) $ 15.82 10.84%
qubic-network
Qubic (QUBIC) $ 0.000000409274 0.81%
coinex-token
CoinEx (CET) $ 0.012314 0.19%
peaq-2
peaq (PEAQ) $ 0.022712 9.88%
threshold-network-token
Threshold Network (T) $ 0.003686 0.40%
stepn
GMT (GMT) $ 0.00694 2.24%
usda-2
USDa (USDA) $ 0.967102 0.00%

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