Crypto World
How zero-knowledge proofs work and why they matter for privacy
You can prove you are over 18 without revealing your birthday. You can prove you have enough money for a transaction without revealing your balance. You can prove a computation was performed correctly without revealing the inputs. Zero-knowledge proofs make all of this possible, and they are quietly becoming the most important cryptographic primitive in blockchain since the hash function.
Most introductions to zero-knowledge proofs start with the Ali Baba cave analogy, where someone proves they know the secret word to open a door by consistently exiting from the side a verifier requests, without ever revealing the word. The analogy is charming and completely useless for understanding why ZK proofs matter in practice. It tells you that such a proof is possible. It does not tell you why anyone would need one on a blockchain.
The practical starting point is simpler. Every blockchain faces the same tension: transparency enables trust, but transparency also destroys privacy. Bitcoin’s ledger is public. Every transaction, every balance, every address is visible to anyone. Ethereum is the same. This transparency is what makes the system auditable and trustworthy, but it also means that anyone who learns which address belongs to you can see every transaction you have ever made, every token you hold, and every protocol you have interacted with.
Zero-knowledge proofs resolve this tension. They let you prove facts about your data without revealing the data itself. You can prove your account balance exceeds a threshold without revealing the exact balance. You can prove a transaction is valid without revealing the sender, recipient, or amount. You can prove you are not on a sanctions list without revealing your identity.
The mathematics behind this are deep. The applications are immediate.
The three properties every ZK proof must have
Every zero-knowledge proof system must satisfy three properties, and understanding them is essential for evaluating any ZK-based protocol.
Completeness. If the statement is true and both the prover and verifier follow the protocol, the verifier will always be convinced. A valid proof never fails to verify. If you genuinely know the secret, the proof will always work.
Soundness. If the statement is false, no cheating prover can convince the verifier that it is true, except with negligible probability. A dishonest prover cannot fabricate a valid proof. The probability of a false proof passing verification is so small (typically less than one in 2^128) that it is effectively impossible.
Zero-knowledge. The verifier learns nothing beyond the fact that the statement is true. The proof does not leak any information about the secret itself, any intermediate computation, or any data used to generate the proof. The verifier’s knowledge after seeing the proof is identical to what it would be if someone simply told them the statement was true.
The third property is what makes ZK proofs useful rather than merely correct. Standard digital signatures prove that a message was signed by a specific key, but they reveal the message content. Standard hash commitments prove that a value was committed, but they reveal the value when opened. ZK proofs prove that a relationship holds between secret values without revealing those values at any point.
How zk-SNARKs and zk-STARKs differ
The two dominant ZK proof systems in blockchain are zk-SNARKs and zk-STARKs. They solve the same problem with different tradeoffs.
zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) produce small proofs that are fast to verify. A typical zk-SNARK proof is around 200 to 300 bytes and can be verified on chain for approximately 200,000 to 300,000 gas on Ethereum. The verification time is constant regardless of how complex the computation being proved is. A proof that verifies a single transaction takes the same time to check as a proof that verifies ten thousand transactions.
The cost of this succinctness is a trusted setup. Most zk-SNARK systems require a one time ceremony where random parameters are generated and the randomness is destroyed afterward. If the randomness from this ceremony is not properly destroyed, an attacker could forge proofs. Zcash conducted one of the most elaborate trusted setup ceremonies in cryptographic history (the “Powers of Tau” ceremony) involving hundreds of participants worldwide, where the security assumption is that at least one participant honestly destroyed their randomness.
Newer SNARK systems like PLONK and Halo 2 have reduced or eliminated the trusted setup requirement, but the perception persists. Some projects avoid SNARKs specifically because of the trusted setup concern, even when the implementations they would use do not require one.
zk-STARKs (Zero-Knowledge Scalable Transparent Arguments of Knowledge) eliminate the trusted setup entirely. They derive their security from hash functions rather than elliptic curve assumptions, which makes them transparent (no secret parameters) and theoretically quantum resistant (hash based cryptography is believed to be secure against quantum computers, while elliptic curve cryptography is not).
The tradeoff is size. STARK proofs are significantly larger than SNARK proofs, typically tens to hundreds of kilobytes compared to hundreds of bytes. On a blockchain where data storage costs gas, larger proofs mean higher verification costs. StarkWare, the primary developer of STARK technology, addresses this by using recursive proof composition: proving that a proof is valid, then proving that the proof of the proof is valid, compressing the final on chain footprint.
In practice, the distinction matters less than it did five years ago. Modern proof systems increasingly blend techniques from both families, and the engineering focus has shifted from which proof system to use to how fast the prover can generate proofs and how cheaply the verifier can check them.
ZK proofs for blockchain scaling
The scaling application of ZK proofs is conceptually straightforward. A rollup executes a batch of transactions off chain, generates a proof that the batch was executed correctly, and posts the proof to Ethereum. The Ethereum verifier contract checks the proof in a single operation and accepts the new state.
What makes this powerful is the asymmetry between proving and verifying. Generating the proof for a batch of 10,000 transactions might take a powerful machine several minutes. Verifying the proof takes a fraction of a second and costs a fixed amount of gas regardless of how many transactions are in the batch. This asymmetry is what allows ZK rollups to compress thousands of transactions into a single Ethereum verification.
The major ZK rollups each take a different approach to this architecture.
zkSync Era uses a custom virtual machine (zkEVM) that is compatible with Solidity at the language level but compiles to a different instruction set optimized for ZK proof generation. Existing Ethereum contracts can be recompiled for zkSync with minimal changes.
StarkNet uses the Cairo programming language and STARK proofs. Cairo is a purpose-built language designed specifically for provable computation, which gives it performance advantages but requires developers to learn a new language and paradigm.
Polygon zkEVM aims for EVM equivalence, meaning it can execute the same bytecode as Ethereum without recompilation. This maximizes compatibility but introduces engineering complexity in making every EVM opcode provable.
Scroll also targets full EVM equivalence and uses a community-driven approach to its zkEVM implementation, with the goal of being the most Ethereum-compatible ZK rollup.
The competition between these approaches is ultimately a competition between compatibility and performance. The more compatible a ZK rollup is with existing Ethereum tooling, the easier it is for developers to migrate. The more the rollup optimizes its instruction set for provability, the faster and cheaper its proofs become.
ZK proofs for privacy
The privacy application is where ZK proofs become most consequential and most controversial.
A standard Ethereum transaction reveals the sender address, the recipient address, the amount transferred, and the smart contract called. This information is permanently public. Chain analysis firms like Chainalysis and Elliptic have built entire businesses on tracing transaction flows across the transparent ledger, linking addresses to real world identities through exchange KYC data, known entity labels, and behavioral patterns.
ZK privacy protocols break this chain of visibility. In a ZK-based private transaction, the user generates a proof that their transaction is valid (the sender has sufficient funds, no double spending occurs, the amounts balance) without revealing who sent it, who received it, or how much was transferred. The proof is posted on chain and verified by the network, but the underlying transaction details remain encrypted.
Zcash was the first major implementation of this concept, launching in 2016 with shielded transactions using zk-SNARKs. A Zcash user can choose between transparent transactions (identical to Bitcoin’s public ledger) and shielded transactions (where the sender, recipient, and amount are hidden behind a ZK proof). In practice, shielded transaction adoption on Zcash has been lower than proponents hoped, with the majority of ZCash transactions still using the transparent pool.
Newer protocols are building programmable privacy, where not just token transfers but arbitrary smart contract logic can execute privately. Aztec Network is building a privacy-first layer 2 on Ethereum where all transactions are private by default. Aleo is building a layer 1 blockchain with native ZK support for private smart contracts. Both use ZK proofs to verify state transitions without revealing the computation or data involved.
The potential for privacy extends beyond individual transactions. ZK proofs can enable private voting (prove you voted without revealing your choice), private identity verification (prove you are a citizen of a specific country without revealing your passport number), and private DeFi (provide liquidity to a pool without revealing your address or position size).
The regulatory collision
Privacy in crypto occupies a contested legal space that is still being defined.
In August 2022, the U.S. Treasury’s Office of Foreign Assets Control (OFAC) sanctioned Tornado Cash, an Ethereum-based mixer that used ZK proofs to break the link between deposit and withdrawal addresses. The sanctioning of open source smart contract code, rather than a person or company, was unprecedented and sent shockwaves through the crypto privacy community.
In May 2024, Alexey Pertsev, one of Tornado Cash’s developers, was convicted by a Dutch court of money laundering facilitation. The conviction established a legal precedent that writing privacy-preserving code can carry criminal liability if the tool is used for illicit purposes, regardless of whether the developer personally facilitated the illegal activity.
These actions have shaped the direction of ZK privacy development. The current generation of privacy protocols is building around regulatory constraints rather than ignoring them.
Selective disclosure allows a user to prove specific facts about their identity or transaction history without revealing everything. A user could prove they passed KYC with a licensed exchange, prove they are not on the OFAC sanctions list, or prove their funds did not originate from a sanctioned address, all using ZK proofs that reveal nothing beyond the specific claim being verified.
Privacy pools, a concept formalized by Vitalik Buterin and others, allow users to prove that their withdrawal from a privacy set belongs to a clean subset of deposits. Instead of mixing all deposits together indiscriminately, the protocol maintains association sets that exclude known illicit addresses. Users prove membership in the clean set without revealing which specific deposit they are withdrawing.
Whether these compromises satisfy regulators remains to be seen. The fundamental tension, that privacy and surveillance are architecturally incompatible, will not be resolved by technology alone. ZK proofs give policymakers a tool they have never had before: the ability to verify compliance without requiring disclosure. Whether they choose to use it is a political question, not a cryptographic one.
The proving cost has concrete implications for which applications adopt ZK technology first. High value financial transactions, where the cost of generating a proof is negligible relative to the transaction size, have been the earliest adopters. Institutional cross-chain transfers, large DeFi positions, and enterprise settlement systems can absorb a proving cost of several dollars per transaction without affecting their economics. Consumer applications, where individual transactions may be worth only a few dollars, need proving costs to fall by another order of magnitude before ZK privacy becomes practical for everyday use. The hardware acceleration efforts by companies building ZK-specific ASICs are directly targeting this cost barrier.
The convergence of scaling and privacy applications is perhaps the most underappreciated aspect of ZK technology. A ZK rollup that processes transactions privately would combine the throughput benefits of off chain execution with the confidentiality benefits of encrypted state transitions. Users would get fast, cheap transactions that are also invisible to chain analysis. Several projects, including Aztec and Polygon Miden, are building exactly this combination, though the engineering complexity of merging both capabilities into a production system remains substantial.
What this does not cover
This article does not cover the mathematics of polynomial commitments, elliptic curve pairings, or Fiat-Shamir transformations that underpin ZK proof systems. Understanding these requires graduate level abstract algebra and is not necessary for evaluating ZK-based protocols as a user or investor.
This article does not cover ZK machine learning (zkML), an emerging field that uses ZK proofs to verify that a machine learning model produced a specific output without revealing the model’s weights or training data. This application is experimental and its practical implications are still being studied.
This article does not address the hardware acceleration race for ZK proof generation. Companies like Cysic, Ingonyama, and Fabric Cryptography are building custom ASICs and FPGAs specifically for ZK proving, which could reduce proving costs by orders of magnitude. The hardware landscape is moving too quickly for static analysis.
Practical checks before using a ZK-based protocol
Verify the proof system’s audit status. ZK proof systems are mathematically complex and implementation errors can be catastrophic. A bug in the circuit (the mathematical representation of the computation being proved) could allow an attacker to forge proofs and mint tokens or steal funds. Check whether the proof system and its circuits have been audited by firms specializing in ZK cryptography, not just general smart contract auditors.
Understand what is actually private. Not all ZK-based protocols provide the same level of privacy. Some hide transaction amounts but reveal addresses. Some hide addresses but reveal amounts. Some hide everything. Read the protocol’s documentation to understand exactly what information is concealed and what remains visible. Metadata such as transaction timing, gas patterns, and interaction frequency can often deanonymize users even when the core transaction data is hidden.
Check the trusted setup status. If the protocol uses zk-SNARKs, determine whether it required a trusted setup and how that setup was conducted. Multi-party computation ceremonies with hundreds of participants are more trustworthy than small ceremonies with a handful of known entities. Protocols using STARKs, PLONK with universal setup, or Halo 2 do not require trusted setups at all.
Assess the regulatory risk. Privacy protocols operate in a legally uncertain environment. Consider whether the protocol has a compliance mechanism (selective disclosure, privacy pools, opt-in compliance proofs) and whether that mechanism has been tested against actual regulatory scrutiny. Using a privacy protocol that is later sanctioned could complicate your ability to move or sell assets.
Test the proving time. Generating a ZK proof is computationally intensive. On a mobile device, proving a simple transaction might take 30 seconds to two minutes. On a desktop, it might take a few seconds. If the proving time is too long for your use case, the protocol may not be practical for frequent transactions. Some protocols offload proving to dedicated servers, which is faster but introduces a trust assumption that the server does not learn your private data.
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What is a zero-knowledge proof in simple terms?
A zero-knowledge proof is a way to prove that something is true without revealing why it is true. In blockchain, this means you can prove that a transaction is valid, that you own enough funds, or that a computation was done correctly, all without revealing the actual transaction details, your balance, or the data used in the computation. The verifier becomes convinced the statement is true but learns nothing else.
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What is the difference between zk-SNARKs and zk-STARKs?
zk-SNARKs produce very small proofs (hundreds of bytes) that are cheap to verify but historically required a trusted setup ceremony to generate the initial system parameters. zk-STARKs produce larger proofs (tens to hundreds of kilobytes) but do not require a trusted setup and are theoretically resistant to quantum computing attacks. In practice, modern proof systems are converging and the tradeoffs between size, speed, and trust assumptions are becoming less stark.
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How do ZK proofs help with blockchain scaling?
ZK rollups execute thousands of transactions off chain and generate a single proof that all transactions were executed correctly. This proof is verified on Ethereum in a single operation that costs a fixed amount of gas regardless of how many transactions were in the batch. The asymmetry between the cost of generating a proof (high but borne by the rollup operator) and verifying it (low and paid once for the whole batch) is what creates the scaling effect.
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Are ZK-based privacy coins illegal?
ZK-based privacy coins like Zcash are not inherently illegal in most jurisdictions. However, regulatory approaches vary significantly. Some exchanges have delisted privacy coins to comply with anti-money laundering regulations. The Tornado Cash sanctions in 2022 demonstrated that privacy-preserving protocols can face regulatory action. The legality depends on your jurisdiction and how you use the technology, not on the technology itself.
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What is a trusted setup and why does it matter?
A trusted setup is a one time ceremony required by some zk-SNARK systems to generate cryptographic parameters. During the ceremony, random values are created and must be destroyed afterward. If any participant retains the random values, they could theoretically forge proofs. Multi-party ceremonies mitigate this risk by requiring that only one participant out of potentially hundreds needs to honestly destroy their randomness. Newer proof systems like PLONK and Halo 2 have eliminated or minimized the trusted setup requirement.
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Can ZK proofs make all blockchain transactions private?
Technically, yes. Protocols like Aztec Network and Aleo are building systems where all smart contract interactions are private by default, not just token transfers. However, full privacy for all transactions introduces regulatory challenges, increases computational costs (ZK proof generation is expensive), and changes the user experience (proving takes time). Whether full on chain privacy becomes standard depends as much on regulatory decisions as on technical capability.
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How do privacy pools work?
Privacy pools allow users to deposit funds into a shared pool and withdraw from a different address, breaking the on chain link between the two addresses. Unlike simple mixers, privacy pools use ZK proofs combined with association sets to let users prove their withdrawal belongs to a subset of deposits that excludes known illicit addresses. This gives users privacy while providing a mechanism for compliance. The user proves they are in the clean set without revealing which specific deposit they are withdrawing.
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What are the main risks of using ZK-based protocols?
The main risks include implementation bugs in the ZK circuits (which could allow forged proofs), trusted setup vulnerabilities in older SNARK systems, regulatory action against privacy features, high computational requirements for proof generation on consumer hardware, and the relative immaturity of ZK tooling compared to standard smart contract development. Additionally, metadata leakage (transaction timing, gas patterns, interaction frequency) can sometimes deanonymize users even when the core transaction data is private.
Disclaimer: This article is for informational and educational purposes only. It does not constitute financial, investment, or legal advice. Cryptocurrency markets are volatile and carry significant risk. Always conduct your own research before making investment decisions.
Consumer price index data largely matched expectations of a retreating inflation threat but may keep alive the possibility of a Federal Reserve rate hike in September following Friday’s weak July jobs reports. Ahead of the report, odds of a tightening stood just below 50%. Technology goods were among the categories seeing firmer prices, thanks partly to Apple (AAPL). S&P 500…
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The latest U.S. inflation data landed exactly where economists expected, removing the immediate risk of an upside surprise and leaving cryptocurrency investors focused on what the Federal Reserve does next.
The Bureau of Labor Statistics reported Wednesday that the Consumer Price Index (CPI) rose 3.4% year-over-year in July, matching consensus estimates while slowing from June’s 3.5%. Core CPI, which excludes volatile food and energy prices, also met expectations at 2.5% year-over-year, down from 2.6% previously.
Inflation Meets Expectations
Markets entered the release treating July’s CPI report as one of the most important macroeconomic events before the Federal Reserve’s September policy meeting.
Economists broadly expected headline inflation to cool to 3.4%, while core inflation was forecast to ease to 2.5% after June’s surprisingly soft report. The data ultimately delivered exactly that outcome, suggesting inflation continues to moderate without producing another significant downside surprise.
Because the figures aligned with expectations, investors are likely to shift their attention from the headline numbers toward what they mean for future monetary policy rather than reacting to an unexpected inflation shock.
Fed Outlook Remains the Main Driver
The inflation report arrives as investors remain divided over whether the Federal Reserve will keep interest rates unchanged or deliver another quarter-point increase at its September meeting.
Fed Chair Kevin Warsh has repeatedly emphasized that policy decisions will remain data dependent while reaffirming the central bank’s commitment to returning inflation to its 2% target. Recent weakness in the U.S. labor market has already reduced expectations for another rate hike, making inflation reports increasingly important for policymakers.
An in-line CPI reading neither strengthens nor weakens the case for immediate policy tightening, keeping markets focused on upcoming economic releases before the next Federal Open Market Committee meeting.
Bitcoin Awaits the Market’s Next Move
For cryptocurrency markets, inflation data often influences expectations for interest rates, Treasury yields and the U.S. dollar—all major drivers of digital asset prices.
Leading into Wednesday’s report, traders viewed a hotter-than-expected inflation reading as a potential catalyst for renewed rate hike expectations and pressure on Bitcoin. Conversely, a softer print was expected to reinforce the view that the Fed could remain on hold, supporting risk assets.
Instead, the consensus outcome leaves investors waiting for the broader market reaction as Treasury yields, the dollar and Fed pricing adjust to inflation that continues to cool but remains above the central bank’s long-term target.
What’s Next?
With July CPI now behind markets, investor attention shifts to incoming economic data and evolving expectations ahead of the Federal Reserve’s September meeting. For Bitcoin and the wider crypto market, the next major catalyst will likely be whether future inflation and labor market reports strengthen the case for holding rates steady or revive expectations of another hike. As long as inflation continues to move broadly in line with forecasts, monetary policy—not inflation surprises—is likely to remain the dominant driver of crypto market sentiment.
The post US Inflation Meets Forecasts, Keeping Bitcoin’s Fed Bet Alive appeared first on BeInCrypto.
Chicago Federal Reserve President Austan Goolsbee has flagged high inflation as a major challenge for the US. Inflation remains well above the Federal Reserve’s 2% target.
The Fed held interest rates steady in July. However, three officials dissented and backed a 25-basis point rate hike.
Inflation Is The Biggest Problem
Goolsbee stated during an interview with Wired that rising prices are the biggest problem confronting the US, calling them more damaging than current labor-market conditions.
“The biggest problem facing our economy right now is not the collapse of industry and the collapse of jobs; it’s that the prices have been rising too fast. We have an inflation problem, and people hate inflation.”
Goolsbee also discussed employment and called the labor market “stable without being good,” highlighting the unemployment rate, hiring, and layoffs as key factors behind his reasoning. The Chicago Fed Chair suggested that market conditions have weakened but do not require the Federal Reserve’s immediate attention.
Inflation has remained higher than the Fed’s 2% target despite lower month-on-month price increases. June Consumer Price Index (CPI) fell 0.4%, while annual inflation dropped from 4.2% to 3.5%. Core CPI, which omits food and energy, remained unchanged in June but increased 2.6% from the previous year.
However, Goolsbee has not indicated whether he would support a rate hike at September’s Federal Open Market Committee (FOMC) meeting. While the Chicago Fed President is not voting on monetary policy, his observations could fuel an ongoing debate around rate hikes among regional Fed Chairs.
Policymakers Divided Over Rate Hike
Policymakers are deeply divided over interest rate hikes. The Federal Reserve left interest rates unchanged following the July FOMC meeting, with Beth Hammack, Neel Kashkari, and Lorie Logan dissenting in favor of a 25-basis point increase. Kashkari, the Minneapolis Fed President, urged the Fed to raise interest rates as high inflation, combined with the ongoing US-Iran conflict, has complicated the policy outlook. He added that the uncertainty prevents the Federal Reserve from issuing firm guidance or promising rate cuts.
Kashkari also warned that high oil prices could impact American households and businesses, adding that there was no certainty about when shipping routes through the Strait of Hormuz would return to normalcy.
St. Louis Federal Reserve President Alberto Musalem also supported a rate hike, arguing for pre-emptive measures before inflation pushes even higher. However, he did not cast a dissenting vote. San Francisco Fed President Mary Daly supported the Fed’s decision to leave interest rates unchanged, stating that more evidence was needed to determine if the increase was temporary or permanent.
Markets Look For Clues
The big question in the market is “will the Fed raise interest rates or leave them unchanged?” According to CME FedWatch, the odds are almost equal. Stubborn inflation and volatile job data have raised market uncertainty ahead of this week’s inflation report. Wednesday’s Consumer Price Report will give market watchers guidance on whether inflation is cooling. Traders expect higher interest rates by the end of the year thanks to US-Iran tensions. However, they are unsure when the Fed may raise rates.
Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.
A new report cited by Wu Blockchain informed that Fidelity, the entity behind the fourth-largest Ethereum ETF by AUM, plans to add staking and quarterly cash distributions to its FETH.
It added that the exchange-traded fund tracking the performance of the largest altcoin can stake up to 100% of its ETH holdings “under normal conditions.”
There will be no minimum requirement, but the fund will retain some ETH for redemptions, expenses, and other liquidity needs. It will retain 85% of gross staking rewards, and the remaining will be paid to the sponsor, custodians, and node operators.
The report further noted that net rewards will first cover fund expenses, with the remainder distributed quarterly in cash. It’s worth noting that the ETF may sell some Ether to fund distributions if necessary, the report concluded.
Fidelity’s move comes after BlackRock debuted a new staking Ethereum ETF called ETHB in March. The first-day trading volume was north of $15 million and opened with $100 million in assets. ETHB is currently the fifth-largest Ethereum ETF, with $577 million in net assets, according to SoSoValue.
Fidelity’s FETH is a spot above, holding nearly $900 million in net assets under management, while BlackRock’s main fund leads far ahead with $5.6 billion.
Earlier this year, Fidelity tapped Ethereum to launch its own stablecoin called Fidelity Digital Dollar (FIDD), which will be pegged 1:1 to the greenback and backed by reserves.
The post Fidelity’s Ethereum ETF to Offer Staking and Quarterly Cash Payouts: Report appeared first on CryptoPotato.
An idea that until recently would have been seen as radical—that the public should co-own AI—now commands bipartisan consensus.
In June, Senator Bernie Sanders introduced the first AI tax in history, the American AI Sovereign Wealth Fund Act, which took up our AI equity tax framework. The bill proposes making the biggest AI companies owe the public half the equity in each company’s AI business, paid in newly issued shares.
In the weeks since, OpenAI’s Sam Altman and President Donald Trump have scrambled to compete, offering their own visions of an AI sovereign wealth fund, which, unlike a tax, would be voluntarily created through the companies’ partnership with the government. Specifically, the ChatGPT creator is reportedly considering giving the U.S. government a 5% stake in the company. We believe this approach is a mistake—and a tax is the solution.
The essential nature of a tax is that it’s mandatory, which is why a tax is the best way to secure the public’s standing as a co-owner of AI. Plus, the public supports an AI equity tax. Last month, a national poll found 69% of Americans in favor of requiring the largest AI companies to transfer half their stock into a public sovereign wealth fund.
The fight now is over the terms of public co-ownership: how much the public gets, who must deliver it, and whether delivery can be enforced. Altman and Trump are negotiating those terms with each other. Whether the public ever sees any benefit from the equity stake to be taken in its name is what hangs in the balance.
A deal between Altman and Trump would bind OpenAI to the current administration in the public’s name, at least symbolically. Yet the rights would belong to the administration, and the public would likely have no recourse if a later administration unwound the arrangement.
That brings us to an idea that until recently would also have been seen as radical: the federal government taking ownership of companies through a tax.
The Trump Administration has taken equity stakes in more than two dozen companies over the past year. The trend in those deals is that the rights belong to the administration, rather than being directly owed to citizens. And public dollars have to be handed over in exchange for the shares, putting the public on the hook.
Trump’s side of these deals follows a common pattern. His administration negotiates each stake company by company. In the case of Intel, government grants already owed were converted into an equity stake. In a mineral-rights deal, fresh taxpayer money was spent. And now, for something completely different, Trump is making early moves to accept AI stakes as donations. A government stake in AI companies “would be a beautiful thing,” the President says, and would ensure that “the American people can benefit from the success of AI.” Yet across Trump’s equity-acquisition deals, terms are established not by statute but through private negotiations, and the President or his agencies keep the rights.
Two deals illustrate how this works and why we should be wary. Trump publicly demanded Intel’s chief executive’s resignation. Only weeks later, the administration had its stake, a position that is now worth tens of billions. And as a condition for approval of the Nippon Steel acquisition, the administration took a golden share in U.S. Steel: “I, President Donald J. Trump, hold the Class G Preferred Stock (Golden Share) in U.S. Steel,” the President wrote in the Federal Register. The issue in both of these cases: one person, Trump, wields unprecedented power.
Altman argues a public stake is “the best way to share the upside of AI.” The company has reportedly proposed giving the administration a 5% stake, structured like the Alaska Permanent Fund and revocable whenever OpenAI chooses. The Financial Times reports the aim: clearing political obstacles by securing the administration’s financial buy-in. It’s a Trojan horse in which no one is fooled except the public.
We can guess where the public’s best interests could be treated as bargaining chips. OpenAI and Anthropic are preparing IPOs and likely need the administration’s goodwill. OpenAI, for example, is currently under pressure from the government to limit GPT-5.6’s release—a constraint the administration might be persuaded to drop in exchange for a donated equity stake. The administration’s record shows it accepts equity as consideration for looking the other way or relenting. Yet none of these dealings inherently benefit the American public.
In response, only Sanders has committed to actually issuing payouts to each American. With Sanders’ AI equity tax, there is no question mark about the public benefit of the tax. After all covered AI companies remit half their equity in newly issued shares, a public trust fund holds the shares, and the fund must pay out its returns to the public by statute. According to estimates from Sanders’ office, a 5% annual distribution would be about $1,045 per person per year. Since no public funds are used to buy shares and the bill specifically prohibits public bailouts of covered companies, the US public truly shares in the upside of AI.
The outcome of the midterms will likely determine whether Sanders’ AI equity tax advances, and thus the coming months may decide who owns America’s AI future. In the meantime, Trump and Altman may move ahead with their visions.
In our view, one design courts more corruption and wealth consolidation; the other ensures that, if AI ushers in any prosperity, it will be shared broadly, transparently, and with public accountability.
Artificial intelligence is moving beyond chatbots and copilots. The next generation of AI systems is increasingly capable of acting on behalf of users—searching for information, purchasing services, managing workflows, executing trades, interacting with applications, and coordinating with other software agents.
But there is one major capability AI agents still need to operate effectively in an increasingly autonomous digital economy: money they can use programmatically.
This is where stablecoins could become especially important.
Unlike traditional bank-based payments, stablecoins can move value directly across blockchain networks, operate 24/7, and be integrated into smart contracts and software applications. For AI agents that need to make frequent, automated, and machine-to-machine payments, these characteristics could make stablecoins a natural financial layer.
AI Agents Are Becoming Economic Actors
An AI agent is more than a system that generates an answer. An agent can be designed to perceive information, make decisions, use tools, and execute actions with limited human intervention.
Imagine an AI agent managing an online business.
It could:
- Purchase computing resources when demand increases.
- Pay another AI agent for specialized data.
- Subscribe to an API.
- Purchase advertising services.
- Pay for storage.
- Execute transactions according to predefined rules.
- Receive payments for completing tasks.
- Exchange one digital asset for another.
Each of these activities requires some form of payment.
If AI agents are expected to operate continuously and independently, relying exclusively on traditional payment systems could introduce significant friction.
Bank accounts often require identity verification, geographic availability, banking relationships, business accounts, payment processors, and human-controlled authentication. Those requirements make sense for people and companies, but they can become cumbersome when the payer itself is autonomous software.
Stablecoins offer a different model.
Stablecoins Give AI Agents Programmable Money
The defining feature of a stablecoin is relatively simple: it is a blockchain-based token designed to maintain a stable value, typically relative to a fiat currency such as the U.S. dollar.
For AI agents, the important part isn’t simply the stability.
It is the combination of stability + programmability + global accessibility.
An AI agent can interact with blockchain infrastructure through software. It can hold digital assets in a wallet, check balances, sign transactions according to its permissions, and interact with smart contracts.
That creates the possibility of a machine-controlled financial account.
Instead of an AI agent saying:
“I need a human to approve this $5 payment.”
the system could be designed to automatically execute the payment when predefined conditions are satisfied.
For example, an AI research agent might have a wallet funded with $100 in stablecoins. It could spend a maximum of $2 per API request, $10 per day on data, and $25 per week on specialized services.
These rules can potentially be enforced through smart contracts, wallet permissions, spending limits, and other programmable controls.
Machine-to-Machine Payments
One of the most interesting applications is machine-to-machine commerce.
The internet was originally designed primarily for humans to communicate and transact. AI agents introduce a new possibility: software communicating and transacting with other software.
Consider a network of specialized agents.
One agent performs market research.
Another analyzes financial data.
A third provides computational resources.
A fourth verifies information.
Instead of every transaction passing through a human-controlled billing process, agents could pay one another directly.
For example:
Agent A → pays stablecoins → Agent B → receives data → Agent A
The payment could happen automatically based on predefined conditions.
At large scale, this could create a new digital economy where tiny transactions occur continuously between autonomous software systems.
Why Stablecoins Instead of Volatile Crypto?
AI agents need predictable economics.
Imagine an autonomous agent with a budget of $1,000.
If it holds a highly volatile cryptocurrency, the purchasing power of that budget could change dramatically. A service that costs $20 today might effectively consume substantially more or less of the agent’s available capital tomorrow.
Stablecoins can reduce that problem.
A dollar-denominated stablecoin gives the agent a relatively predictable unit for budgeting, accounting, pricing, and payments.
That matters particularly for:
- API usage
- Cloud computing
- Data purchases
- Subscription services
- Digital labor
- Advertising
- Automated commerce
- Agent-to-agent payments
If AI agents are going to participate in real economic activity, predictability may be more valuable than speculation.
Stablecoins Could Enable Micropayments
Traditional payment infrastructure isn’t always optimized for extremely small, frequent transactions.
Blockchain-based stablecoin payments could potentially support smaller transactions with automated settlement, depending on the network and its transaction costs.
This opens the door to interesting business models.
An AI agent might pay:
- $0.01 for a data point
- $0.05 for a computation
- $0.10 for an API request
- $0.50 for a specialized analysis
- $2 for a completed task
Instead of purchasing a large subscription, an agent could potentially pay precisely for what it consumes.
This could transform the economics of digital services.
Rather than humans subscribing to software, software could dynamically purchase services from other software.
Stablecoins Could Give Agents Global Payment Rails
Another major advantage is geographic reach.
Traditional financial infrastructure remains fragmented across countries, banks, payment networks, currencies, and regulatory systems.
Stablecoins operate on blockchain networks that can be accessed globally.
For AI agents operating across borders, this could simplify settlement.
An AI company in one country could operate an agent that purchases computing services from another provider, while a third-party agent supplies specialized data from another region.
Stablecoins could provide a common settlement asset across these interactions.
The AI agent doesn’t necessarily need to understand banking systems in every country.
It simply needs to understand the payment rules of the digital network it operates on.
AI Agents Could Become Their Own Economic Identities
This leads to an even bigger concept.
Today, an AI agent usually operates under the identity and financial accounts of a person or company.
In the future, agents could potentially have their own cryptographic identities, wallets, permissions, and transaction histories.
That does not necessarily mean an AI becomes a legal person.
Instead, it could mean that an agent becomes a distinct economic software entity.
For example:
Agent ID: ResearchAgent-204
Wallet: Dedicated blockchain address
Budget: $500/month
Spending limit: $20/transaction
Allowed services: Data + computing
Approval threshold: Human authorization above $20
This structure could make autonomous systems easier to monitor and control.
Blockchain transactions could also provide an auditable record of what the agent spent and where the funds went.
The Combination of AI + Smart Contracts Is Powerful
AI agents are good at making decisions.
Blockchains and smart contracts are good at executing deterministic rules.
Stablecoins connect the two through money.
That creates a potentially powerful architecture:
AI → Decision
Smart Contract → Rules
Stablecoin → Value
Blockchain → Settlement
Consider an autonomous procurement agent.
The AI determines that a company needs additional computing capacity. It compares providers, selects one based on price and performance, and initiates the purchase.
A smart contract could enforce the agreed conditions.
The stablecoin payment could be released when those conditions are satisfied.
The blockchain records the transaction.
In this model, AI handles the intelligence while blockchain handles coordination, ownership, and settlement.
The Challenges Are Just as Important
Stablecoins are not a magic solution.
AI agents managing money introduce serious risks.
Security
If an AI-controlled wallet is compromised, attackers could potentially gain access to its funds.
Agents therefore need strong wallet security, permission systems, spending limits, and transaction controls.
Hallucinations and Bad Decisions
An AI agent can make incorrect decisions.
If an agent is allowed to spend money autonomously, an incorrect assumption could become a financial loss.
This makes human oversight and programmable constraints extremely important.
Smart Contract Risk
Smart contracts can contain vulnerabilities.
An AI agent interacting with poorly designed contracts could potentially expose its funds to unnecessary risks.
Regulatory Uncertainty
Stablecoins operate within an evolving regulatory environment.
Different jurisdictions may impose different requirements on issuers, users, payment providers, and businesses.
AI agents participating in financial transactions could introduce additional compliance questions.
Privacy
Blockchain transactions can be transparent.
That can be useful for auditing, but it may also expose information about an agent’s activities, counterparties, and spending patterns.
Future systems may therefore need privacy-preserving technologies alongside transparent settlement.
The Bigger Picture: An Economy of Autonomous Agents
The most important idea isn’t simply that AI agents could use stablecoins.
It is that AI agents could become participants in digital markets.
Imagine millions of specialized agents operating simultaneously.
Some agents generate content.
Others analyze data.
Some manage logistics.
Others provide computing power.
Some negotiate prices.
Others verify information.
They could continuously interact, purchase services, sell capabilities, and exchange value.
Humans would still define objectives, budgets, permissions, and constraints—but machines could handle much of the execution.
Stablecoins could serve as one of the financial primitives that makes this economy possible.
Stablecoins May Become the Financial Language of AI
The next phase of AI may not be defined solely by how intelligent models become.
It could also be defined by what those models are allowed to do.
An AI that can only generate text is powerful.
An AI that can use tools is more capable.
An AI that can independently coordinate resources, purchase services, and receive payments becomes something fundamentally different: an economic actor operating in the digital world.
Stablecoins could provide the predictable, programmable settlement layer required for that transition.
The combination of AI agents, blockchain networks, smart contracts, and stablecoins could therefore create an entirely new category of machine-driven commerce.
The future internet may not just connect people.
It may connect agents that work, negotiate, transact, and pay each other around the clock.
And when machines start doing business with machines, they will need money that machines can actually use.
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Key takeaways
- Uniswap falls nearly 6% on Wednesday after declining 5% in the previous session.
- Uniswap has launched Continuous Clearing Auctions on Avalanche, allowing teams to conduct on-chain token sales and bootstrap liquidity.
- UNI’s social dominance and volume have fallen sharply, signaling weaker retail attention.
Uniswap (UNI) faces intense selling pressure on Wednesday, falling nearly 9% after recording a 5% decline the previous day.
The pullback comes despite Uniswap’s continued product expansion, including the introduction of Continuous Clearing Auctions on Avalanche. The feature allows blockchain projects to conduct fully on-chain token auctions and establish initial liquidity through Uniswap v4.
However, declining social activity and derivatives demand suggest the launch has not been enough to offset the cryptocurrency market’s broader risk-averse mood.
Continuous clearing auctions launch on Avalanche
Uniswap’s Continuous Clearing Auctions provide Avalanche developers with a new mechanism for launching tokens and bootstrapping liquidity onchain.
The model is designed to reduce friction during token distribution by allowing teams to conduct auctions transparently through smart contracts. Projects can then connect their newly distributed tokens with Uniswap v4 liquidity.
The launch expands Uniswap’s presence on Avalanche and strengthens its role as infrastructure for token issuance, trading and liquidity management.
It follows the recent launch of the TradePools platform on Robinhood, which allows users to deposit USDC, USDT or ETH in pursuit of yield.
While these developments may support Uniswap’s long-term utility, they have yet to produce a meaningful improvement in near-term demand for UNI.
Retail interest in Uniswap is weakening as traders prepare for the release of July’s US Consumer Price Index report, scheduled for Wednesday at approximately 12:30 GMT.
The CPI reading could influence the Federal Reserve’s next interest-rate decision and affect demand for risk assets. A hotter-than-expected report could strengthen expectations for tighter monetary policy, while softer inflation could improve sentiment across cryptocurrency markets.
Santiment data shows Uniswap’s social dominance fell to 0.08% on Tuesday from 0.19%. Social volume also declined to 40 from 152.
The sharp contraction indicates that UNI accounts for a smaller share of cryptocurrency discussions and is attracting less attention from retail traders.
Uniswap’s derivatives market reinforces the decline in retail participation.
CoinGlass data shows UNI futures open interest fell more than 3% over the past 24 hours to $261.60 million. The decline indicates traders are closing positions and reducing their leveraged exposure.
Long liquidations reached $2.88 million during the same period, significantly exceeding short liquidations of just $1,950. The imbalance shows that falling prices have disproportionately forced bullish traders out of their positions.
However, UNI’s open-interest-weighted funding rate improved to 0.0016% from negative 0.0054% the previous day.
The return to positive funding indicates that the remaining leveraged market carries a slight bullish bias. Still, falling open interest and heavy long liquidations suggest overall sentiment remains fragile.
Uniswap Technical outlook: UNI tests 100-day EMA
Uniswap is testing its 100-day Exponential Moving Average at $3.55, an important near-term support level.
UNI remains below the 50-day EMA at $3.65 and the 200-day EMA at $3.93. These moving averages create overhead resistance and reinforce the prevailing bearish structure.
The Relative Strength Index has declined to 40, placing it below the neutral midpoint of 50 and indicating growing selling momentum. However, the indicator remains above the oversold threshold of 30.
The Moving Average Convergence Divergence indicator has also fallen below its signal line, while its expanding bearish profile suggests downside momentum is strengthening.
A decisive daily close below the 100-day EMA at $3.55 could extend Uniswap’s decline toward the 50% Fibonacci retracement level at $3.25. This level is measured from UNI’s advance between $2.31 and $4.57.
A successful defense of $3.55 could allow buyers to attempt a recovery. However, UNI must reclaim the 50-day EMA at $3.65 to ease immediate selling pressure.
Above that level, the 23.6% Fibonacci retracement at $3.89 and the 200-day EMA at $3.93 form a significant resistance cluster.
Until Uniswap recovers above these moving averages with stronger trading activity, the short-term outlook is likely to remain bearish.
An attacker drained 99.7% of the XRP reserve backing the Coreum cross-blockchain bridge on August 9 by conjuring fake evidence of deposits that fooled the bridge’s own operators into authorizing real withdrawals.
Nobody stole anyone’s private key to execute the clever hack. Instead, Coreum’s bridge liquidity account paid out 199,916 XRP worth over $200,000 across 94 transactions, each one carrying a majority of signatures from its own relayers.
Hours later, Coreum was shocked to discover it held just 493 XRP worth roughly $500.
A memo was all Coreum’s bridge required
The relayer software watched the bridge account’s history for payments carrying a Coreum recipient memo.
It never verified that payment destination. A seemingly valid memo existed, yet the destination was the hacker’s wallet.
Ostensibly independent operators reached identical conclusions and signed off on all of the withdrawals because they were all running the same buggy code.
Read more: XRP Ledger generated less than $400 in fees yesterday
TX confirms an FBI report
TX, a brand that absorbed both the Coreum and Sologenic communities in February, confirmed the incident, and admitted the software “incorrectly registered transactions that never actually delivered any XRP to the bridge as deposits, and minted bridged XRP on the tx chain against them.”
The same statement said the bridge had undergone “multiple internal and third-party audits prior to deployment.”
It also conceded that bridged XRP on the tx chain “is not currently fully backed,” and confirmed a complaint had gone to the FBI.
The price of XRP dipped below $1 yesterday, its first sub-dollar print since November 2024. The coin has lost 45% of its value this year, and is 74% below its all-time high.
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Mining pools controlling most of Ravencoin’s hash rate are building a competing chain that could trigger a three-day reorganization.
Russia’s central bank will allow retail investors to only trade bitcoin , ether and USDT on regulated exchanges, making Tether’s dollar-linked token the only stablecoin on the initial list.
The draft rules would limit non-qualified investors to 300,000 rubles (around $3,600) of crypto purchases per year at each intermediary. Qualified investors wouldn’t face the cap.
The whitelist adds detail to legislation passed in July that opens regulated crypto trading from Sept. 1 but did not specify which assets retail investors could buy. Crypto payments inside Russia remain prohibited.
The wording sets the 300,000-ruble limit per intermediary rather than across an investor’s total purchases, potentially allowing larger aggregate exposure through multiple brokers or exchanges.
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