Beyond Bitcoin: AI, Stablecoins, and the Future of Money

Part 3, The Invisible Guardian: Agentic AI Systems

Here is a scenario that sets the stage.

At 2 AM on a Monday the phone of an undergrad buzzes insistently. She’s got three notifications.

Security Alert: Unusual login attempt blocked

Security Alert: Password change request denied

Security Alert: Please verify – was this you?

Attackers just tried to break into her stablecoin wallet of $12,000 she’s been saving for grad school. The login came from a device in Eastern Europe, at 2 AM her time. Whoever they were knew her username and password (probably from a data breach six months ago), but something stopped them.

The security system had detected dozens of anomalies: wrong device, impossible location, typing pattern completely different from hers, mouse movements too mechanical. Within two seconds of the login attempt, the system blocked access, froze additional authentication attempts, and sent alerts to all her devices.

Her money is safe. The attackers failed. But no human was watching her account at 2 AM on a Monday. The system that protected her wasn’t following simple rules like “block logins from foreign countries.” It was something smarter that understood her patterns, recognized when someone wasn’t acting like her, and made an instantaneous judgment call.

This scenario describes why it matters that we’re rebuilding the entire financial system for protection.

The Problem is Created

When Circle launched USDC in 2018, they solved an old problem: how to move money across borders quickly and cheaply. Traditional wire transfers took three days and cost 6% in fees. USDC moved in minutes and cost pennies. Problem solved?

Not quite. They’d actually created a new problem, one that wouldn’t become obvious until the system scaled to billions of dollars. Speed without intelligence is dangerous.

Think about why traditional banking is slow. It’s not just technology; it’s checks and balances. When you wire $10,000 internationally, multiple humans review it. A compliance officer checks sanctions lists. A fraud analyst looks at your pattern. A manager approves large amounts. This takes time, but it provides safety. The slowness isn’t a bug. It’s a feature that catches mistakes and prevents fraud.

Now imagine collapsing that three-day process into three seconds. You can’t just remove the human oversight and hope for the best. You need something that can think as fast as the money moves.

This is where we introduce some basics because it’s where most people get confused by terminology.

Three Phrases, Three Different Meanings, Capabilities and Impact

How do we understand the critical role AI plays in an Agentic AI fraud detection deployment? Let’s start by clearing up the language, because even experts mix these up.

Imagine the tools utilized when driving cross-country.

Your first tool is a calculator (basic AI). You type in distance and average mileage, it tells you how many gallons of gas you need. Useful, but you have to do all the thinking. You decide when to calculate, what numbers to use, and what to do with the answer.

Your second tool is cruise control (an agent). Set it to 70 mph and it maintains that speed automatically. You’re still driving. You’re steering, watching the road, deciding when to exit. The cruise control just handles one specific task within the boundaries you set.

Your third tool is full self-driving (agentic AI – combining the 2^nd & 3^rd tools). You tell it to “drive me to Portland,” and it handles everything. It

• perceives the entire environment (other cars, weather, road conditions)

• reasons about optimal routes and safe speeds

• acts by steering, braking and accelerating, and

• learns from experience to become better at driving. You’re just along for the ride.

Most of the AI we use daily is closer to cruise control. It’s narrow, bounded, doing one thing well. What’s transforming finance right now is the self-driving version, systems that can perceive complex situations, reason about them, act, and learn from outcomes, all across multiple tasks, continuously, with minimal human intervention.

When the undergrad’s account was protected at 2 AM, it wasn’t cruise control. It was something perceiving dozens of signals simultaneously (device characteristics, typing patterns, behavioral history, transaction context), reasoning about whether this was her or an imposter, acting by blocking access and sending alerts, and learning from every attempted breach to recognize future attacks faster.

That’s agentic AI and here’s why nothing else works for digital money.

The Flash Crash Scare

In March 2023 Silicon Valley Bank collapsed on a Friday morning. By Friday afternoon, social media was exploding with a terrifying question: “If SVB held Circle’s reserves, is USDC still backed?”

Circle had $3.3 billion at SVB, about 13% of its reserves. The other $21.5 billion was safe at other banks. But markets don’t wait for nuance during panic.

2:00 PM: First wave of redemptions. $100 million in an hour—high, but not unprecedented.

2:15 PM: Social media spiral begins. “USDC losing its peg!” Posts spread faster than facts.

2:30 PM: $1.2 billion in redemption requests. This is now 12 times normal. The pattern matches every other stablecoin bank run in history.

2:35 PM: USDC price on exchanges drops to $0.994. Small deviation, but the trend is wrong.

2:40 PM: $0.987. Now we’re in dangerous territory.

In a traditional financial crisis, executives would be scrambling to conference rooms, dialing into emergency calls, trying to coordinate a response. But 2:40 PM on a Friday is too late when money moves at internet speed. By the time humans convened a meeting, USDC could have broken its $1 peg, triggering a cascade that destroyed confidence in all stablecoins.

Here’s What Actually Happened:

At 2:31 PM, Circle’s systems detected the pattern. Not just “redemptions are high” (simple rule), but “redemptions are accelerating at a rate consistent with previous stablecoin death spirals, social media sentiment is 85% negative and spreading, similar pattern preceded Terra/UST collapse [described later under heading A Real-Life Example], but our fundamentals are sound because we have $21.5B available immediately.”

The system didn’t panic. It reasoned. This is fear, not insolvency. We need to restore confidence immediately.

By 2:32 PM, a comprehensive response was already deploying.

• Communications blast: Tweet published showing real-time reserves. Blog post with detailed breakdown. Emails to major institutional holders. All synchronized.

• Operational response: $5 billion emergency credit line activated. Redemption processing accelerated from 24-hour standard to 30-minute guarantee. Exchanges contacted to ensure arbitrage traders could quickly buy “cheap” USDC and redeem at full price (which brings the price back to $1).

• Market intervention: Assets shifted from longer-term T-bills to instant-access bank accounts. If USDC dropped below $0.975, Circle itself would buy USDC on the open market, creating a price floor.

• 3:00 PM: Redemptions slowed. Still elevated, but no longer accelerating.

• 3:15 PM: USDC price recovered to $0.993.

• 4:00 PM: Back to $0.999. Crisis contained.

By Monday morning: Full recovery and trust maintained.

The agentic system didn’t just “detect a problem and alert someone.” It perceived a complex situation, distinguished between panic and insolvency, reasoned about the appropriate response, coordinated multiple simultaneous actions across communications/operations/markets, and executed everything before human executives even finished dialing into the emergency call.

This is why the old way doesn’t work anymore. You can’t have a committee meeting when the crisis unfolds in minutes.

Now, back to our undergrad’s 2 AM wake-up call. How did the system actually know it wasn’t her?

The attackers had done their homework. They had her username and password. They knew her email address. They even knew she lived in Chicago, so they routed their access through a Chicago VPN to make the location look right. In 2015, this would have worked. Username + password = access granted. Maybe a text message code would be needed if they were being extra careful, but the attacker had tools to intercept that too.

In 2025, authentication isn’t about knowing a password. It’s about being the person. And you can’t fake being someone across multiple dimensions simultaneously. What the system saw in those two seconds:

The device fingerprint was all wrong. She uses an iPhone, screen resolution 2532×1170, iOS 17.2, has 47 specific fonts installed, battery typically at 60-80% this time of day, connects via her home WiFi network with specific characteristics. The attacker? Android phone, different resolution, different OS, different fonts, different everything.

The typing pattern was wrong. She types at 67 words per minute with a distinctive rhythm, (fast at the start of sentences, slower as she thinks through what she’s writing), frequent use of backspace because she edits as she goes. The attacker typed at 42 WPM with mechanical consistency, and almost no corrections. Not her natural style.

The behavior was wrong. When she logs in, she typically checks her balance first, then maybe looks at transaction history, then decides whether to do anything. The attacker went straight for “Send Money” without looking at anything else. Wrong pattern.

The timing was wrong. She logs in during daytime hours, usually afternoon after classes. 3 AM would be unusual even if everything else checked out.

The context was wrong. Just yesterday, someone had tried (and failed) to reset her password. That attempt came from a different IP address but had similar characteristics. Pattern of coordinated attack.

None of these signals alone proves fraud. She could have gotten a new phone (device change). Could be typing differently because she’s tired (behavior change). Could be checking at odd hours because of insomnia (timing change). But all together? The probability this is an attacker reaches 98%. Two seconds after the login attempt began, access was blocked.

No human reviewer was awake at 2 AM to make this call. No human could have correlated all these signals fast enough anyway. The decision had to happen in the space between login attempt and account access, less than three seconds in a fast system.

This is agentic AI in action: perceiving across multiple data streams simultaneously, reasoning about the combination of signals, acting faster than human thought, and learning from every attack attempt to recognize the next one sooner.

Traditional security might have caught this...eventually…by Monday morning when she checked her account and saw her money was gone. When transactions are irreversible and settlement is instant, “eventually” is too late.

The Three Faces of Digital Money

Not all stablecoins are created equal, and they don’t all need the same kind of intelligence watching over them.

1. The Simple One: Fiat-Backed (USDC, EUROC)

In this straightforward version every digital token is backed by an actual dollar (or euro or other country currency), sitting in a bank account or government bond. One token in, one dollar out. Simple math.

But even simple designs need smart guardians. Those $25 billion in reserves? They’re not just sitting there. Every day, the system must decide: Keep money in liquid bank accounts where it earns 4.2%, or shift some into Treasury bills earning 5.3%? Too much in T-bills and you can’t meet redemption requests fast enough. Too much in cash and you’re leaving $275 million per year on the table.

A human treasury manager might make these decisions once per day, balancing safety and yield using rules of thumb. An agentic system makes thousands of micro-adjustments, continuously rebalancing based on real-time redemption patterns, interest rate movements, market conditions, and risk forecasts. The result? An extra $100 million in annual yield on $25 billion in reserves, which is more than enough to fund the entire AI system with $55 million left over.

2. The Volatile One: Crypto-Backed Decentralized Autonomous Organization (DAI)

This is where things get complicated and AI becomes absolutely critical. Instead of dollar backing, these stablecoins use cryptocurrency as collateral. Since crypto is volatile, you need over-collateralization: deposit $150 of Ethereum to mint $100 of stablecoin. This might sound safe, until Ethereum drops 40% in a market crash. Suddenly your $150 collateral is worth $90, and your $100 stablecoin is under-backed. The system automatically liquidates your position to protect the stablecoin’s value and you lose everything.

This happens quickly. Crypto markets never sleep. A flash crash can occur at 3 AM on Sunday. If you’re not monitoring your collateral ratio 24/7, you could wake up to find your entire position liquidated.

Agentic AI solves this by never sleeping. It watches every crypto-backed position continuously, forecasts liquidation risk based on market volatility, and acts before disaster strikes. If your collateral ratio is approaching danger zone, it alerts you or, if you’ve pre-authorized, automatically adds more collateral to keep you safe.

Importantly, it manages systemic risk. If too many positions get liquidated simultaneously (because everyone’s collateral is Ethereum and Ethereum is crashing), those forced sales drive prices down further, triggering more liquidations, more forced sales, more price drops. A death spiral. The AI monitors system-wide leverage, adjusts interest rates to incentivize voluntary deleveraging before crisis hits, and prevents cascades that would destroy the entire system.

A Real-Life Example: Algorithmic (Terra/UST)

In May 2022, $40 billion vanished in less than a week. Terra/UST, an algorithmic stablecoin with no actual backing, collapsed in a death spiral that destroyed wealth and shook confidence in all digital assets.

Could AI have prevented it? Probably not entirely, because the design itself was flawed. The peg was maintained through an algorithm that created perverse incentives under stress. When selling pressure exceeded the system’s capacity to absorb it, the algorithm made things worse instead of better.

But AI could have, should have, warned against launching this design in the first place. Every previous attempt at purely algorithmic stablecoins had failed. The economic game theory was suspect. Under stress-testing scenarios, cascading failures were inevitable.

An agentic system analyzing the design pre-launch would have flagged it as extremely high-risk. During the collapse, it would have recognized the death spiral pattern within hours (not days) and warned users to exit immediately. Thousands of people who lost their life savings might have been spared if they’d received that warning soon enough. The lesson isn’t that AI can fix anything. It’s that AI makes well-designed systems work better and can warn when designs are fundamentally broken. But it can’t turn a flawed architecture into a safe one.

3. The Complex One: Synthetic

Imagine a stablecoin backed not by dollars or crypto, but by a basket: 40% fiat-backed stablecoins, 30% short-term Treasury bonds, 20% corporate bonds, 10% gold-backed tokens. Diversification is normally recommended as protection from loss. But it’s more complicated here.

The complexity is staggering. As those underlying assets fluctuate in value (bonds change price, gold rises and falls), the basket must be constantly rebalanced to maintain the $1.00 value. Not once per day, but thousands of times per day, across multiple markets, optimizing for transaction costs and timing.

Worse, diversification only works if the assets don’t fail together. The 2008 financial crisis showed what happens when supposedly diversified mortgage bonds all crashed simultaneously because they shared one hidden risk. An agentic system must monitor not only individual asset prices, but also correlations among them, watching for scenarios where “diversified” suddenly becomes “everything fails at once.”

This complexity is impossible for humans to manage manually. The AI isn’t just executing trades faster; it’s perceiving relationships across multiple asset classes, reasoning about correlation risk, acting with microsecond timing, and learning from market patterns to predict when diversification might break down.

The Invisible Guardian

Most people who use stablecoins never think about what’s protecting them. They just want to send money quickly and cheaply. They don’t see the system running in the background, making thousands of decisions per day.

• Every 12 seconds: verifying that reserves match the number of tokens outstanding. Not monthly, like traditional audits, but every 12 seconds. If there’s ever a mismatch, even for a single block, there is an immediate alert, automatic hold on new issuance, investigation launched.

• Every transaction: analyzing patterns across millions of addresses to detect organized crime networks. Recognizing when 47 people sending money to the same address aren’t making legitimate investments but are victims of an elaborate pig butchering scam.

• Every redemption request: checking not just “Is this a legitimate customer?” but “Is this the real customer, or someone who stole their credentials?” by analyzing behavioral biometrics that are nearly impossible to fake.

• Every market movement: distinguishing between normal volatility and the early stages of a coordinated attack designed to break the peg and profit from the chaos.

All of this happens invisibly, at machine speed, 24/7/365. When it works well, you don’t notice it at all. You just know your money arrives in minutes, costs almost nothing, and when someone in Eastern Europe tries to steal it at 2 AM, the system has your back.

But What Happens When Agentic AI Doesn’t Work Well?

This is why guardrails, monitoring, and human oversight are emphasized throughout this series. When agentic AI fails, it typically fails in one of three ways, each with different consequences and safeguards.

1. False Positives (Blocking Legitimate Users)

• What happens: Undergrad tries to make a normal transaction, but AI incorrectly flags it as fraud and blocks it

• User impact: Frustrating but temporary inconvenience

• Safeguard: Human appeal process—undergrad proves identity via video call, transaction approved within 30 minutes

• Learning: System updates to avoid this mistake with undergrad (and similar users) in future

• Cost: User friction, support overhead, but no money lost

2. False Negatives (Missing Actual Fraud)

• What happens: Fraudster’s attack is sophisticated enough that AI doesn’t catch it, transaction proceeds

• User impact: Money stolen, customer loses funds

• Safeguard: Insurance, liability protections, fraud reimbursement policies

• Learning: Post-incident analysis, AI updated to catch this pattern next time, attack method shared across industry

• Cost: Financial loss (covered by institution), reputation damage

3. System Malfunction (AI Makes Catastrophically Wrong Decisions)

• What happens: Bug in code, corrupted data, or compromised system causes AI to make dangerous decisions at scale

• User impact: Could affect thousands of users simultaneously

• Safeguard: This is why kill switches, bounded authority, and continuous monitoring are critical

• Response: Humans detect anomaly via monitoring dashboard, shut down AI immediately, revert to manual processing or previous version

• Learning: Root cause analysis, system redesign, enhanced testing before redeployment

These are not theoretical concerns. They are anticipated failure modes requiring comprehensive and vetted engineered responses. Good stablecoin Agentic AI systems are designed assuming failure will happen, with safeguard failover processes that activate when it does.

Why is This Technology Happening Now? The Convergence

If this technology is so important, why are we only seeing it deployed at scale in 2024-2025? Why not five or ten years ago? Because three separate technological and regulatory developments had to mature simultaneously.

1. The AI Breakthrough (2022-2024)

In 2020, AI could do narrow tasks well: recognize faces, translate languages, play chess better than humans. But it couldn’t reason across domains. It couldn’t understand context. It couldn’t make the kind of nuanced judgments humans make constantly.

Then came the transformer architecture breakthroughs, large language models (LLM) like GPT-4 and Claude, and multimodal AI that could analyze text and images and data simultaneously. Suddenly AI could look at a passport photo and not just say, “This photo matches government ID format,” but instead say, “This is an AI-generated fake because the eye highlights are physically impossible and the skin texture shows artifacts of neural network generation.” The jump from pattern matching to actual reasoning made sophisticated fraud prevention possible. Before this breakthrough, AI could flag simple rule violations. Now it can understand sophisticated social engineering attacks, recognize coordinated criminal networks across millions of addresses, and distinguish panic from genuine crisis.

2. The Data Infrastructure (Blockchain Maturity)

Traditional banking keeps transaction data locked in silos. Your bank knows your transactions, but can’t always see where the money came from before it reached you, or where it goes after leaving. After two or three hops, the trail goes cold. Blockchain flipped this completely. Every transaction was permanently recorded on a public ledger. Want to know where that $10,000 originated? Trace it back through 47 transactions across six months. Want to know if this wallet is part of a criminal network? Analyze connections across millions of addresses.

This transparency gives AI the data it needs to detect sophisticated fraud patterns. The same openness that privacy advocates worry about makes advanced fraud prevention possible. It’s a tradeoff: being better protected from scams at the cost of perfect transaction privacy.

3. The Regulatory Clarity (2023-2025)

In 2019, banks looked at stablecoins and asked: “Is this even legal? Will regulators shut us down tomorrow?” That’s too much uncertainty for institutional money. Then came Europe’s Markets in Crypto Assets (MiCA) regulation in 2024, providing a clear framework for stablecoin operation. The US moved toward stablecoin legislation. Singapore, UK, Hong Kong issued clear guidance. Regulators signaled: “This can work, under proper oversight, with appropriate safeguards.” Suddenly the risk calculation changed from “We might build this and then be forced to shut down” to “We know the rules, let’s invest.” That regulatory certainty unlocked hundreds of millions in development funding, which paid for the AI infrastructure, which made stablecoins safe enough for mainstream adoption.

All three pieces had to align. Without AI capability, stablecoins would be too vulnerable. Without blockchain data, AI couldn’t see the patterns. Without regulatory clarity, institutions wouldn’t invest.

They aligned in 2024-2025. That’s why Money20/20 shifted from “Can this work?” to “How do we deploy at scale?” That’s why Western Union, about as traditional as financial services gets (at 170 years old), is launching a stablecoin in 2026. That’s why this transformation is happening now.

What This Means for Our Future

Whether your expertise is computer science or literature, finance or philosophy, this transformation will shape your world.

If you are a technologist, you might design these agentic systems, building the AI that protects billions of dollars while maintaining the delicate balance between security and usability.

If your focus is business, you’ll make decisions about when to deploy AI, how much to invest, which vendors to trust. Understanding the $100 million difference between good AI and mediocre AI in reserve management could determine whether your financial institution thrives or struggles.

If your work has an international context, you’ll use these systems daily, sending money across borders for a fraction of what it costs today, receiving payments instantly instead of waiting days, managing finances without being penalized for working in a global economy.

If you are an educator, you’ll help the next generation understand these systems, not just how they work, but what they mean for society, for equity, for trust in institutions, for the balance between innovation and stability.

And if someone tries to steal your money at 2 AM on a Monday, you’ll want that invisible guardian watching over you, making split-second decisions that protect you while you sleep.

The old financial system was built for a world where humans made all the decisions and three-day settlement times provided natural safety buffers. That world is disappearing. The new system must operate at digital speed with digital intelligence, not instead of humans, but as a tireless partner that never blinks, never sleeps, and learns from every attempted breach to recognize the next one faster.

This isn’t the future. It’s operational reality today. The question isn’t whether this transformation happens, it’s whether we build it well, deploy it responsibly, and ensure it serves everyone, not just the privileged few.

Understanding what’s possible, what’s at stake, and what can go wrong positions technologists and financiers to build systems that are not just faster and cheaper, but genuinely better – safer, more trustworthy, and more accessible than what came before.

That’s the opportunity. That’s also the responsibility.

The technology that protected our undergrad at 2 AM is the same technology managing billions in reserves, preventing death spirals, and making global money movement as easy as sending an email. Understanding it isn’t optional. It’s literacy for participating in the financial system being built right now, whether you’re designing it, deploying it, regulating it, teaching about it, or simply using it to pay for coffee.

The guardian is invisible, but essential. This article explained why it exists, how it works, and why it matters that we build it well.

If our AI research sparked something valuable for you, consider leaving a tip to help us keep processing the future, with our thanks in return!