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.

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.