GE Aerospace Acquisition: Why Jet-Engine Castings Became an $11.75 Billion Bottleneck

The GE Aerospace acquisition of CPP targets one of aviation’s hardest manufacturing constraints: qualified jet-engine castings. The strategic logic is strong, but a 26-times stand-alone EBITDA valuation leaves execution, customer-retention and antitrust risks that investors cannot ignore.

GE Aerospace is paying $11.75 billion for Consolidated Precision Products, or CPP, because the most valuable part of a jet engine may not be the finished machine. It may be the ability to produce a small number of extraordinarily difficult metal components at acceptable yields, in sufficient volume, and with no compromise on safety. The GE Aerospace acquisition announced on September 8 is therefore less a conventional capacity deal than a wager on industrial control: control over castings, qualification cycles, engineering feedback and the pace at which a record engine backlog becomes cash.

That thesis makes the GE Aerospace acquisition more interesting—and riskier—than the headline price suggests. GE Aerospace says CPP will bring roughly $2 billion of estimated 2027 revenue, more than 20 facilities and about 6,600 employees. The target makes complex castings used in commercial and military engines, industrial gas turbines and other demanding applications. It is a high-quality strategic fit. It is also an expensive asset whose value depends on integration, manufacturing improvement and regulatory clearance that is not expected before the second half of 2027.

The GE Aerospace acquisition asks investors to separate three questions. First, why have jet-engine castings become such a binding constraint? Second, can ownership create more value than an arm’s-length supply contract? Third, does the price leave enough room for execution mistakes? The answers reveal a wider change in industrial strategy: after decades of optimizing supply chains for capital efficiency, aerospace companies are again placing a premium on resilience, learning speed and access to scarce process knowledge.

What GE Aerospace is buying from CPP

Under the signed agreement, the GE Aerospace acquisition values CPP at $11.75 billion in cash. According to the company’s official transaction announcement, approximately $7 billion will come from cash on hand and the balance from new debt. GE expects the deal to close in the second half of 2027, subject to regulatory approvals and customary conditions. The company says the transaction should add to adjusted earnings per share and free cash flow in the first year after closing, while leaving its existing capital-allocation priorities unchanged.

CPP’s portfolio is unusually close to the physical core of an engine. Its products include investment castings and components designed to withstand extreme heat, pressure and mechanical stress. GE’s SEC-filed investor presentation estimates that roughly 60% of CPP’s business is commercial aerospace, about 20% is defense, and the remaining 20% is power and other end markets. Around 70% of revenue is tied to commercial and defense engines. Programs named by GE include LEAP, GEnx, T700, F110 and F404.

This matters because the acquisition is not primarily a bet on a new end market. It moves a critical supplier inside an engine manufacturer that already creates much of the demand for CPP’s output. GE projects demand for airfoils to rise by more than 30% between 2026 and 2030. That forecast connects the purchase directly to the company’s installed base, production ramp and service obligations.

The financial framing is equally revealing. GE describes the price as approximately 26 times CPP’s estimated 2027 EBITDA before synergies, or about 18 times after including expected net synergies. The presentation points to roughly $200 million of net synergies and says the deal should generate a double-digit return on invested capital by year five. Those claims place a heavy burden on operational improvement. At 26 times stand-alone EBITDA, simply owning CPP’s existing cash flows would not be enough to make the GE Aerospace acquisition compelling.

Why jet-engine castings became the bottleneck

A modern turbine engine is an exercise in managing temperature. Hotter combustion can improve efficiency, but it also imposes extraordinary demands on the blades and vanes that direct high-temperature gas through the turbine. Those components require advanced alloys, intricate internal cooling passages, tight tolerances and specialized coatings. Manufacturing them consistently is not comparable to machining a common industrial part from a standard metal billet.

Investment casting begins with a precise pattern, usually surrounded by layers of ceramic material to form a mold. Molten superalloy is poured into that shell under tightly controlled conditions. For some high-pressure turbine parts, the metal’s crystal structure is itself engineered to improve performance at extreme temperatures. The process must then survive inspection, finishing and qualification. A defect that would be cosmetic in another product can make an aerospace component unusable.

This is why capacity cannot be added merely by ordering another machine. Furnaces and facilities matter, but repeatable yields depend on metallurgy, tooling, process controls, experienced workers and accumulated knowledge about how a particular part behaves. Qualification can take years because engine makers and regulators need evidence that a process produces reliable parts again and again. Moving a design to a new factory or supplier is therefore slow, costly and uncertain.

Reuters’ September 9 analysis describes precision casting as a niche “black art” and highlights industry concern about high scrap rates. The phrase is useful because it captures the gap between nominal and effective capacity. A plant can pour many parts, yet deliver far fewer usable ones after inspection. When yield is low, every additional order competes for the same scarce qualified output.

The industry’s post-pandemic recovery made this constraint more visible. Airlines wanted more fuel-efficient aircraft, manufacturers tried to raise monthly production, and defense demand remained firm. But tiered suppliers faced labor shortages, uneven raw-material availability and balance sheets weakened by years of disruption. Engine production is a system: one unavailable casting can delay an assembly containing thousands of otherwise ready components.

The same pattern appears in other capital-intensive sectors. Block2Learn’s analysis of the copper supply gap showed why a strong price signal cannot instantly create mines, smelters or skilled labor. Aerospace castings are even harder to substitute because the product and its process are certified together. The relevant economic variable is not theoretical tons of metal or square meters of factory floor. It is qualified, repeatable yield.

The GE Aerospace acquisition is a bet on learning speed

Vertical integration is often discussed as a choice between making and buying. That description is too static for this deal. The stronger argument for the GE Aerospace acquisition is that it could shorten the loop between engine design, casting behavior, inspection data and process adjustment.

When engineering and manufacturing are separated by corporate boundaries, information travels through specifications, purchase orders, quality reports and negotiated change requests. Those controls are necessary, but they can slow iteration. Inside one organization, design teams can work directly with foundry specialists on cooling geometry, alloy selection, tooling and manufacturability. GE explicitly argues that combining design and production should support better yields, asset utilization and development of future airfoil technologies.

A shorter learning loop has several sources of value. First, it may reduce scrap and rework. A small increase in yield creates output without requiring an equal increase in installed equipment. Second, it can improve schedule reliability. Reliable delivery lets engine assembly operate with less disruption and may reduce the working capital tied up in buffers and incomplete units. Third, it can accelerate design changes that improve durability or enable hotter, more efficient engines.

The final benefit may be the largest. Commercial engines produce economics over decades. The original equipment sale establishes an installed base; maintenance visits, replacement parts and service agreements then generate long-duration revenue. If a constrained component delays engine deliveries, the manufacturer does not merely postpone one sale. It also postpones the start of a future service stream. Removing a bottleneck can therefore have a greater present value than the supplier’s own EBITDA implies.

GE’s operating system, known as Flight Deck, is central to the case. Lean tools can expose bottlenecks, standardize work and reduce variation, but they do not create value by slogan. CPP’s plants will need measurable improvements in first-pass yield, cycle time, on-time delivery and equipment availability. The GE Aerospace acquisition succeeds only if those operating gains appear in physical throughput and cash flow.

That distinction echoes Block2Learn’s examination of Moderna’s manufacturing bottleneck: a valuable platform is not the same as scalable, repeatable production. In both cases, the market can overvalue scientific or engineering possibility if it underestimates the difficulty of industrial execution.

How the $11.75 billion valuation can work

The valuation debate around the GE Aerospace acquisition starts with GE’s own multiples. If the 26-times figure is applied mechanically, it implies stand-alone 2027 EBITDA of roughly $450 million. An 18-times multiple on the same purchase price implies an EBITDA base including net synergies of roughly $650 million. The gap is broadly consistent with management’s stated expectation of about $200 million in net synergies.

These are approximate inferences from company figures, not separate company forecasts. They nevertheless clarify what must happen. The GE Aerospace acquisition is priced for more than ordinary aerospace growth. To earn a double-digit return on invested capital by year five, GE needs a combination of CPP earnings growth, captured synergies, improved working capital and value elsewhere in the engine system.

Some benefits will be visible inside CPP: higher margins, lower scrap, better utilization and procurement efficiencies. Others may show up in GE’s engine operations through fewer disruptions, faster deliveries or better durability. That second category is strategically important but harder for outside investors to audit. Management can attribute broad operational progress to integration even when the causal link is uncertain.

The payment structure adds another layer. Using $7 billion of cash reduces immediate liquidity; financing the balance with debt raises interest expense and leverage. GE says the deal does not change its priorities, including investment in growth, a competitive dividend and share repurchases. Yet capital is fungible. Cash used for CPP cannot simultaneously fund another acquisition or buyback, and new debt reduces flexibility if the aerospace cycle weakens.

This is where capital-allocation discipline matters. Block2Learn’s review of BHP’s copper pivot made the same core point: a strategically attractive asset can still destroy value if the entry price assumes too much of the upside. The GE Aerospace acquisition should be judged against its weighted cost of capital and credible alternatives, not against the fact that CPP is scarce.

The best-case valuation argument is that reported CPP EBITDA understates system value. If ownership unlocks engine deliveries, service revenue and technology development that would otherwise be delayed, the purchase can create returns unavailable to a financial buyer. The bear case is that GE pays upfront for synergies that prove slow, diffuse or already embedded in supplier contracts. At 26 times stand-alone EBITDA, the margin for disappointment is thin.

Why supply security is not the same as supply abundance

The GE Aerospace acquisition changes priorities, governance and information flow by bringing a supplier inside the group. It does not automatically produce more qualified parts. CPP will still need skilled employees, raw materials, tooling, maintenance, capital expenditure and disciplined quality systems. The acquisition may improve control over allocation, but it cannot bypass the physics or certification requirements of turbine manufacturing.

There is also a danger in optimizing too narrowly around the parent company. CPP serves multiple customers and end markets. That diversity can support scale, learning and utilization across cycles. If rival engine makers reduce orders because they distrust a GE-owned supplier, CPP could lose volume and technical breadth. If GE reserves capacity for its own programs, customers may accelerate qualification of alternatives. Either response could erode the target’s stand-alone value.

Successful integration therefore requires credible commercial firewalls. Customer data, pricing and program information must be protected. Capacity-allocation rules must be clear enough to reassure competitors without preventing GE from realizing strategic benefits. The challenge resembles the tension in financial-market infrastructure: a platform can be more valuable when many parties trust it, yet ownership by one participant can weaken that trust. Block2Learn explored a related issue in the Figure–Kiavi transaction, where owning origination capacity did not eliminate the need for external liquidity and counterparties.

Supply resilience also requires redundancy. Bringing CPP inside GE could reduce exposure to contract disputes or competing customer priorities, but concentrating more of the system in one corporate family creates its own failure modes. A plant outage, quality escape or delayed modernization program can still interrupt output. True resilience combines better control with multiple qualified routes, appropriate inventory and transparent risk management.

Antitrust is an operating risk, not a footnote

GE and CPP operate at different levels of the aerospace supply chain, so the GE Aerospace acquisition is principally vertical rather than a merger of direct engine competitors. Vertical deals can still attract close review when the acquired input is scarce and rivals depend on it. Regulators may ask whether GE could restrict access, raise costs, delay deliveries or obtain competitively sensitive information.

Reuters’ transaction report notes expectations of regulatory scrutiny, while its follow-up analysis raises the possibility of asset divestitures. Those risks affect valuation before closing. A remedy could remove precisely the facility, contract or technology that made the deal attractive. Behavioral commitments could preserve customer access but limit integration benefits. A long review can also delay capital spending and employee decisions.

The announced second-half 2027 closing window already implies patience. During that period, GE and CPP must remain separate businesses. GE cannot assume control early, and CPP must continue serving customers while employees face uncertainty. If aerospace demand keeps rising, a delayed close means the bottleneck must be managed through existing contracts for several more quarters.

Investors should therefore treat regulatory clearance as part of the operating model, not a binary legal checkbox. The relevant questions include which CPP facilities serve competing engine programs, how concentrated each casting market is, how easily customers can qualify alternatives and whether proposed safeguards preserve CPP’s neutrality. The GE Aerospace acquisition may have a strong industrial logic and still require concessions that reduce its economics.

What the deal signals across aerospace

The GE Aerospace acquisition sends a price signal through the entire supplier base. Scarce process capability is becoming strategically valuable again. Companies that control qualified forgings, castings, coatings and specialty materials may command higher valuations, especially when their output constrains a large aftermarket franchise.

That does not mean every engine maker should buy suppliers. Vertical integration consumes capital and can reduce flexibility. Long-term agreements, prepayments, joint ventures, equipment financing and dedicated capacity can secure output without full ownership. The right structure depends on how specific the asset is, how difficult the process is to contract and how much value comes from shared engineering.

For independent suppliers, the GE Aerospace acquisition is both validation and warning. It validates the economic value of hard-to-replicate manufacturing expertise. But it may also encourage customers to demand more visibility into capacity, yields and investment. Suppliers with weak balance sheets could face pressure to accept strategic capital or consolidate, while stronger companies may gain negotiating leverage.

For Airbus and Boeing, better engine-component flow could support aircraft deliveries, but only if improvements translate into complete engines. An airframer cannot deliver a plane without engines, and an engine maker cannot ship a finished unit if one certified component is missing. The industry’s backlog is large enough that relieving one constraint often exposes the next. Castings may be pivotal, but bearings, electronics, forgings, labor or maintenance capacity can still set the system’s pace.

The defense dimension matters as well. CPP supports military programs, and governments increasingly view aerospace supply chains through a national-security lens. Domestic process capability can attract policy support, yet it also raises questions about supplier independence and assured access. The deal will be evaluated not only as a corporate transaction but as a change in control over mission-critical production.

Five indicators that will decide the GE Aerospace acquisition

Because closing is distant and many benefits are operational, quarterly earnings alone will not provide an early verdict on the GE Aerospace acquisition. A more useful framework tracks five observable indicators.

1. Regulatory scope and remedies

The first signal is the review process itself. Requests for extensive data, prolonged timelines or divestiture discussions would show where regulators see foreclosure risk. Clean approval would preserve more of the strategic thesis; material remedies would require a new valuation using the assets and contracts that remain.

2. CPP customer retention

GE should disclose enough information to show whether non-GE customers continue ordering from CPP. Stable outside revenue would suggest that commercial safeguards are credible. A decline may reflect deliberate portfolio choices, but it could also indicate that rival manufacturers are moving work elsewhere.

3. Yield, cycle time and on-time delivery

The strongest evidence for the GE Aerospace acquisition would be operating metrics. Higher first-pass yield, lower scrap, shorter cycle time and better on-time delivery would demonstrate that Flight Deck is changing the economics of qualified capacity. Revenue growth without yield improvement may simply reflect more input spending and overtime.

4. Engine deliveries and services conversion

Investors should connect CPP performance to GE’s larger system. Do engine shipments rise with fewer supplier-related delays? Does the installed base grow as planned? Are service-shop visits supported by adequate parts? The strategic premium is justified only if foundry improvements unlock value beyond CPP’s own income statement.

5. Return on invested capital and balance-sheet discipline

Management’s double-digit return target by year five is the clearest financial marker. It should be assessed using the full purchase price, integration spending, incremental capital expenditure and working capital—not merely an adjusted synergy calculation. Debt reduction, interest expense, buybacks and other uses of cash will show whether the GE Aerospace acquisition preserves the flexibility promised at announcement.

Three scenarios for investors to understand

In the constructive scenario, regulators approve the transaction with limited remedies, CPP retains outside customers and GE improves yields faster than expected. Engine deliveries become more reliable, service revenue begins earlier and net synergies exceed $200 million. In that case, the apparent premium buys a scarce platform whose value to GE is substantially higher than its stand-alone EBITDA.

In the middle scenario, approval arrives on time and CPP grows with the market, but operational gains are gradual. Synergies reach management’s target while additional capital expenditure offsets part of the cash benefit. The deal remains strategically useful, yet returns only approach the lower end of expectations. Valuation then depends on whether supply stability deserves a lasting strategic premium.

In the adverse scenario, the review requires divestitures, rival customers migrate and manufacturing improvement proves harder than expected. Higher debt costs and integration spending compound the problem. GE still owns valuable facilities, but the purchase price capitalizes benefits that do not fully arrive. The GE Aerospace acquisition would then secure some supply at the expense of shareholder returns.

These scenarios are not price forecasts. They are a way to identify which assumptions drive value. The transaction’s outcome will be determined less by the next quarter’s market reaction than by years of plant-level learning, customer behavior and disciplined capital allocation.

Continue Through the Block2Learn Learning Path

The deeper lesson is that industrial capacity is not a commodity when process knowledge, qualification and yield determine usable output. GE Aerospace is paying for that difference. The GE Aerospace acquisition may convert control of CPP into faster learning, more reliable engine production and a larger lifetime service base. But the $11.75 billion price already assumes that ownership will create value a supply contract could not.

Follow the Block2Learn Learning Path to build a structured framework for evaluating mergers, capital allocation and supply-chain constraints. For the GE Aerospace acquisition, keep the analysis anchored to evidence: regulatory remedies, customer retention, casting yields, engine deliveries and return on invested capital. Those measures will show whether GE bought a bottleneck—or merely paid a bottleneck premium.

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-token
Reserve Rights (RSR) $ 0.001381 5.99%
arbitrum-bridged-weth-arbitrum-one
Arbitrum Bridged WETH (Arbitrum One) (WETH) $ 2,265.06 3.52%
zcash
Zcash (ZEC) $ 1,189.68 5.42%
tether-gold
Tether Gold (XAUT) $ 4,343.65 1.23%
ether-fi-staked-btc
Ether.fi Staked BTC (EBTC) $ 76,722.00 4.00%
ai16z
ai16z (AI16Z) $ 0.000496 5.87%
ether-fi-staked-eth
ether.fi Staked ETH (EETH) $ 2,317.47 1.05%
apecoin
ApeCoin (APE) $ 0.13499 5.91%
coredaoorg
Core (CORE) $ 0.019698 7.26%
helium
Helium (HNT) $ 0.49798 11.04%
frax
Legacy Frax Dollar (FRAX) $ 0.990832 0.15%
akash-network
Akash Network (AKT) $ 0.539261 8.70%
compound-governance-token
Compound (COMP) $ 19.66 4.81%
meow
MEOW (MEOW) $ 0.000004 4.26%
usdx-money-usdx
Stables Labs USDX (USDX) $ 0.008732 4.47%
ecash
eCash (XEC) $ 0.000007 2.00%
chiliz
Chiliz (CHZ) $ 0.013626 5.02%
wormhole
Wormhole (W) $ 0.009527 7.15%
amp-token
Amp (AMP) $ 0.000436 4.23%
ultima
Ultima (ULTIMA) $ 2,020.47 2.89%
eigenlayer
EigenCloud (prev. EigenLayer) (EIGEN) $ 0.197649 8.87%
pumpbtc
pumpBTC (PUMPBTC) $ 76,077.00 2.54%
deep
DeepBook (DEEP) $ 0.014843 10.34%
resolv-usr
Resolv USR (USR) $ 0.113891 4.34%
pancakeswap-token
PancakeSwap (CAKE) $ 2.11 7.98%
pax-gold
PAX Gold (PAXG) $ 4,344.20 1.28%
gigachad-2
Gigachad (GIGA) $ 0.002116 8.94%
mina-protocol
Mina Protocol (MINA) $ 0.088459 4.54%
gnosis
Gnosis (GNO) $ 114.20 2.89%
pendle
Pendle (PENDLE) $ 1.93 7.16%
bitcoin-avalanche-bridged-btc-b
Avalanche Bridged BTC (Avalanche) (BTC.B) $ 76,260.00 3.16%
beldex
Beldex (BDX) $ 0.076803 0.32%
echelon-prime
Echelon Prime (PRIME) $ 0.222507 6.53%
zksync
ZKsync (ZK) $ 0.009458 9.90%
paypal-usd
PayPal USD (PYUSD) $ 0.99989 0.00%
havven
Synthetix (SNX) $ 0.207898 5.01%
coinbase-wrapped-staked-eth
Coinbase Wrapped Staked ETH (CBETH) $ 2,539.40 3.57%
true-usd
TrueUSD (TUSD) $ 0.999206 0.01%
stakestone-berachain-vault-token
StakeStone Berachain Vault Token (BERASTONE) $ 2,424.48 3.58%
axelar
Axelar (AXL) $ 0.042136 7.50%
tbtc
tBTC (TBTC) $ 70,942.00 7.49%
apenft
AINFT (NFT) $ 0.000000233846 0.63%
snek
Snek (SNEK) $ 0.000446 12.21%
mog-coin
Mog Coin (MOG) $ 0.000000101279 8.75%
telcoin
Telcoin (TEL) $ 0.001768 1.78%
toshi
Toshi (TOSHI) $ 0.000111 8.03%
dydx
dYdX (ETHDYDX) $ 0.115861 4.75%
kava
Kava (KAVA) $ 0.062809 4.52%
polygon-pos-bridged-weth-polygon-pos
Polygon PoS Bridged WETH (Polygon POS) (WETH) $ 2,261.63 3.58%
newton-project
AB (AB) $ 0.000909 3.75%
notcoin
Notcoin (NOT) $ 0.000431 6.51%
chex-token
Chintai (CHEX) $ 0.010314 10.11%
bridged-usdc-polygon-pos-bridge
Polygon Bridged USDC (Polygon PoS) (USDC.E) $ 0.99972 0.00%
vethor-token
VeThor (VTHO) $ 0.000664 49.64%
frax-ether
Frax Ether (FRXETH) $ 2,262.16 2.20%
1inch
1INCH (1INCH) $ 0.088515 4.89%
trust-wallet-token
Trust Wallet (TWT) $ 0.56694 1.20%
quantixai
Quantix Finance (QFI) $ 18.27 3.89%
grass
Grass (GRASS) $ 0.335999 7.47%
stader-ethx
Stader ETHx (ETHX) $ 2,455.55 2.19%
superfarm
SuperVerse (SUPER) $ 0.116939 6.21%
terra-luna
Terra Luna Classic (LUNC) $ 0.00005 5.86%
sweth
Swell Ethereum (SWETH) $ 2,521.55 3.25%
safe
Safe (SAFE) $ 0.093694 8.64%
livepeer
Livepeer (LPT) $ 1.38 7.05%
hashnote-usyc
Circle USYC (USYC) $ 1.14 0.01%
usdb
USDB (USDB) $ 0.993439 0.62%
creditcoin-2
Creditcoin (CTC) $ 0.094972 2.39%
theta-fuel
Theta Fuel (TFUEL) $ 0.00916 3.91%
oasis-network
Oasis (ROSE) $ 0.006549 6.66%
super-oeth
Super OETH (SUPEROETH) $ 2,263.65 2.59%
aixbt
aixbt (AIXBT) $ 0.020136 11.37%
kusama
Kusama (KSM) $ 3.81 5.40%
bio-protocol
Bio Protocol (BIO) $ 0.025201 7.68%
layerzero
LayerZero (ZRO) $ 1.03 8.95%
blur
Blur (BLUR) $ 0.01664 7.06%
dash
Dash (DASH) $ 56.63 11.85%
cat-in-a-dogs-world
cat in a dogs world (MEW) $ 0.000395 7.13%
ordinals
ORDI (ORDI) $ 4.00 5.89%
solayer-staked-sol
Solayer Staked SOL (SSOL) $ 112.14 4.30%
io
io.net (IO) $ 0.129371 9.19%
ondo-us-dollar-yield
Ondo US Dollar Yield (USDY) $ 1.14 0.00%
freysa-ai
Freysa AI (FAI) $ 0.002311 3.66%
arkham
Arkham (ARKM) $ 0.09781 9.96%
turbo
Turbo (TURBO) $ 0.000928 8.65%
popcat
Popcat (POPCAT) $ 0.048196 7.20%
binance-peg-busd
Binance-Peg BUSD (BUSD) $ 1.00 0.05%
olympus
Olympus (OHM) $ 20.15 0.92%
dog-go-to-the-moon-rune
Dog (Bitcoin) (DOG) $ 0.001054 11.91%
nervos-network
Nervos Network (CKB) $ 0.001147 1.02%
astar
Astar (ASTR) $ 0.00585 4.09%
just
JUST (JST) $ 0.103395 0.45%
compound-wrapped-btc
cWBTC (CWBTC) $ 1,534.90 2.99%
mx-token
MX (MX) $ 1.77 0.45%
zilliqa
Zilliqa (ZIL) $ 0.002654 7.12%
verus-coin
Verus (VRSC) $ 0.186307 7.85%
melania-meme
Melania Meme (MELANIA) $ 0.100477 10.27%
holotoken
holo (HOLO) $ 0.000011 5.46%
ai-rig-complex
AI Rig Complex (ARC) $ 0.073616 0.63%
origintrail
OriginTrail (TRAC) $ 0.31016 5.02%
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) $ 0.100531 7.41%
baby-doge-coin
Baby Doge Coin (BABYDOGE) $ 0.00000000037254 6.79%
ether-fi
Ether.fi (ETHFI) $ 0.615285 0.32%
safepal
SafePal (SFP) $ 0.260889 7.92%
staked-frax-ether
Staked Frax Ether (SFRXETH) $ 2,589.68 3.62%
aethir
Aethir (ATH) $ 0.00453 10.28%
golem
Golem (GLM) $ 0.106283 2.04%
basic-attention-token
Basic Attention (BAT) $ 0.071924 6.13%
swissborg
SwissBorg (BORG) $ 0.170469 5.01%
skale
SKALE (SKL) $ 0.003707 4.85%
wemix-token
WEMIX (WEMIX) $ 0.189889 2.74%
mocaverse
Moca Network (MOCA) $ 0.008631 4.63%
xyo-network
XYO Network (XYO) $ 0.003429 2.67%
gas
Gas (GAS) $ 1.21 5.14%
celo
Celo (CELO) $ 0.074248 6.63%
benqi-liquid-staked-avax
BENQI Liquid Staked AVAX (SAVAX) $ 12.58 0.25%
qtum
Qtum (QTUM) $ 0.872811 3.47%
spell-token
Spell (SPELL) $ 0.000081 6.06%
would
would (WOULD) $ 0.027846 81.61%
vine
Vine (VINE) $ 0.007402 5.86%
zencash
Horizen (ZEN) $ 6.79 8.32%
woo-network
WOO (WOO) $ 0.010725 6.73%
iotex
IoTeX (IOTX) $ 0.002922 7.15%
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) $ 0.000818 3.70%
bybit-staked-sol
Bybit Staked SOL (BBSOL) $ 112.08 4.42%
plume
Plume (PLUME) $ 0.013016 6.74%
osmosis
Osmosis (OSMO) $ 0.033594 10.74%
vana
Vana (VANA) $ 0.905605 6.24%
griffain
GRIFFAIN (GRIFFAIN) $ 0.011113 1.14%
zetachain
ZetaChain (ZETA) $ 0.03263 7.03%
uxlink
UXLINK (UXLINK) $ 0.000823 6.87%
ethereum-pow-iou
EthereumPoW (ETHW) $ 0.253639 6.76%
ankr
Ankr Network (ANKR) $ 0.004158 5.03%
akuma-inu
Akuma Inu (AKUMA) $ 0.000000081206 6.98%
tribe-2
Tribe (TRIBE) $ 0.381507 0.96%
ravencoin
Ravencoin (RVN) $ 0.002391 23.18%
enjincoin
Enjin Coin (ENJ) $ 0.02586 5.85%
peanut-the-squirrel
Peanut the Squirrel (PNUT) $ 0.046215 9.05%
elixir-deusd
Elixir deUSD (DEUSD) $ 0.000977 0.00%
memecoin-2
Memecoin (MEME) $ 0.000505 10.33%
aelf
aelf (ELF) $ 0.05931 0.56%
anime
Animecoin (ANIME) $ 0.003035 2.52%
constellation-labs
Constellation (DAG) $ 0.006995 0.97%
polymesh
Polymesh (POLYX) $ 0.035827 5.24%
convex-finance
Convex Finance (CVX) $ 2.06 8.24%
drift-protocol
Drift Protocol (DRIFT) $ 0.011569 4.21%
sats-ordinals
SATS (Ordinals) (SATS) $ 0.000000010314 8.38%
venice-token
Venice Token (VVV) $ 23.36 15.51%
qubic-network
Qubic (QUBIC) $ 0.000000410289 1.54%
coinex-token
CoinEx (CET) $ 0.006533 12.94%
peaq-2
peaq (PEAQ) $ 0.02586 12.30%
threshold-network-token
Threshold Network (T) $ 0.004172 4.76%
stepn
GMT (GMT) $ 0.007041 7.32%
usda-2
USDa (USDA) $ 0.967102 0.00%

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