Stable-value Microeconomics: Sub-Penny Transactions in the AI Age

The End of an Era: Why the Death of the Penny Matters More Than You Think

The US Treasury is phasing out production of the penny and will soon stop putting new one-cent coins into circulation, the department said in a statement Thursday. The last of the new pennies will enter circulation early next year. This isn’t just about saving money on copper and zinc - though the cost of making a penny was 3.7 cents in Fiscal Year 2024 and Treasury says halting the production of pennies will save $85 million. It’s about recognizing a fundamental shift in how value moves through our economy.

When physical pennies disappear from circulation, something fascinating happens to the precision of economic transactions. The Treasury told WSJ businesses will need to start rounding up or down to the nearest 5 cents when there’s not enough pennies to use in everyday cash transactions. But here’s the thing: digital systems don’t need to round. They can handle fractions of fractions of pennies with mathematical precision that would make your calculator jealous.

This creates an interesting economic paradox. At the exact moment when our physical currency is becoming less precise, our digital economy is demanding more precision than ever before. The elimination of the penny isn’t just about cost savings - it’s inadvertently highlighting why sub-penny transactions represent the future of economic efficiency.

Real-World Use Cases: Where Every Fraction Counts

The demand for sub-penny precision isn’t theoretical. It’s happening right now, across multiple industries, in ways that demonstrate why artificial intelligence systems will increasingly need access to ultra-precise value transfer.

Content Monetization and Micropayments

Consider online content consumption. Micropayments are small transactions or payments usually of less than a dollar—and, in some cases, only a fraction of a cent—that are mainly made online. Micropayments are seen as a way to leverage the internet to facilitate the immediate distribution of digital rights, royalties, in-game purchases, online tipping.

Imagine reading a news article that costs 0.3 cents, watching a video worth 0.7 cents, or accessing a database query that provides value equivalent to 0.15 cents. Traditional payment systems can’t handle these transactions economically because the processing fees exceed the transaction value. But in a world of stable, metal-backed digital currency with sub-penny precision, these micro-transactions become not just possible, but profitable.

The publishing industry has struggled for decades with the “all or nothing” problem - either content is free (and ad-supported) or behind expensive subscription paywalls. Sub-penny transactions enable true pay-per-use models where consumers pay exactly for what they consume, when they consume it.

AI-to-AI Economic Interactions

This is where things get really interesting. Global research and brokerage firm Bernstein has joined the growing list of experts identifying blockchain and digital assets as the key to unlocking artificial intelligence (AI) utility. Bernstein researchers noted that the legacy financial system limits AI as it relies on centralized systems and restricted networks like SWIFT and payment processors like Mastercard and Visa.

AI systems operating autonomously need to conduct business with each other. An AI analyzing market data might pay another AI 0.02 cents for a specific data point. A machine learning model might compensate a distributed computing network 0.001 cents per calculation cycle. AI systems use machine learning to analyze data, predict outcomes, and determine optimal terms based on predefined objectives, such as cost, efficiency, or resource allocation, automating negotiation and execution.

These aren’t hypothetical scenarios. They’re already happening in limited forms, but they’re constrained by the granularity of current payment systems. In the legacy system, opening an account requires identification, which these agents lack. “How would AI agents obtain bank accounts and credit cards without identity?” the researchers pose.

Internet of Things and Resource Optimization

The Internet of Things represents another massive use case for sub-penny transactions. Smart devices could autonomously purchase exactly the computational resources, data, or services they need in real-time. A smart thermostat might pay 0.005 cents for a precise weather forecast. An autonomous vehicle could pay 0.03 cents for real-time traffic optimization data.

When AI agents connect to IoT devices via decentralized infrastructure networks, they can autonomously manage resources, optimize processes, and conduct business transactions. This integration could revolutionize operations across industries.

Cross-Border Efficiency

Traditional international payments are notoriously expensive and slow. But AI systems operating globally need to conduct thousands of micro-transactions across borders instantly. A machine learning model trained in Singapore might need to pay for data processing in Germany, storage in Canada, and validation in Japan – all in amounts less than a penny, all happening simultaneously.

Current foreign exchange systems with their spreads, fees, and delays make such transactions economically impossible. But stable, metal-backed digital currency with global acceptance eliminates currency conversion costs and political volatility, making true global AI commerce feasible.

Technical Challenges: Why Current Systems Fall Short

The technical barriers to implementing true micropayments are more complex than simply making payments smaller. They represent fundamental limitations in how our financial infrastructure thinks about value transfer.

The Fixed Cost Problem

Traditional payment systems have fixed costs that don’t scale down. A user can open an account with PayPal and deposit say, $150. Later, if this same user spends $7.99 in a digital store such as iTunes, the funds would be debited from the PayPal account and used to pay for the purchase. PayPal defines micropayments as transactions that are less than $10. But even with PayPal’s approach, there’s still a floor below which transactions become uneconomical.

Credit card processing fees typically start at around 2.9% plus 30 cents per transaction. For a $1 purchase, you’re paying about 32.9% in fees. For a $0.10 purchase, you’re paying 329% in fees. For a $0.01 purchase, the math breaks down entirely - you’d pay 3,029% in fees.

This isn’t just inefficiency; it’s economic impossibility. Unfortunately, however, this remains just a vision. Current payment systems generally do not favor micropayments; required minimum purchases plus fees cost too much to make small transfers worthwhile.

The Identity and Authentication Bottleneck

Current financial systems require human identity verification for account creation. This creates a fundamental barrier for AI systems. The legacy financial system is limiting AI as it doesn’t enable micropayments, programmability or non-human entity involvement, according to Bernstein researchers.

Even if we solve the technical payment issues, how does an AI system prove its identity to open a bank account? How does it provide a social security number, proof of address, or pass a know-your-customer check? The entire framework of traditional finance assumes human participants with legal identities.

Scalability and Network Effects

The Bernstein report acknowledged that most digital asset networks are not ready for the era of microtransactions as they still can’t scale, and their fees are prohibitively high. Bitcoin, for example, has transaction fees that can easily exceed $1 during periods of high network congestion. Ethereum’s gas fees can make a simple transaction cost $50 or more.

This creates a chicken-and-egg problem. We need massive scale to make micropayments economically viable, but we can’t achieve massive scale without solving the micropayment problem first.

Precision and Rounding Errors

Here’s a technical challenge that might seem minor but becomes critical at scale: how do you handle fractions of fractions of pennies? If an AI system processes 10 million transactions per day, each involving 0.0001 cents, how do you prevent rounding errors from accumulating into significant amounts?

Traditional accounting systems use various rounding strategies, but these were designed for human-scale transactions. When you’re dealing with AI systems processing billions of micro-transactions, tiny rounding errors compound into major discrepancies.

Economic Efficiency Gains: The Math of Frictionless Commerce

The economic implications of solving micropayment challenges extend far beyond just enabling small transactions. They represent a fundamental shift toward more efficient resource allocation and value creation.

Eliminating Transaction Cost Deadweight Loss

Transaction cost theory, formally proposed by Ronald Coase in 1937 to explain the existence of firms. He theorised that transactions via market mechanisms incur cost, particularly the costs of searching for exchange partners and making and enforcing contracts. When transaction costs approach zero, economic efficiency increases dramatically.

If we can reduce transaction costs by better institutional design, then fewer resources would be wasted and more resources would be able to be transacted, thereby increasing economic efficiency. This isn’t just theory – it’s measurable. KPMG (2016) reports that the transaction costs of using cash and checks amount to 0.52% of Singapore’s GDP.

Imagine if we could reduce transaction costs not just for large transactions, but for all economic activity down to the smallest conceivable units. The efficiency gains compound exponentially.

Time Value and Opportunity Cost Optimization

With the median wage in the US being $23.80 per hour in 2024, it takes less than 2 seconds of work to earn one cent. Thus, it is not worthwhile for most people to deal with a penny. If it takes only 2 seconds extra for each transaction that uses a penny, the cost of time wasted in the US is about $3.65 per person annually, or about one billion dollars for all Americans.

This calculation becomes even more dramatic when applied to AI systems that can process thousands of transactions per second. If an AI system spends even microseconds on transaction overhead, the opportunity cost quickly becomes enormous when multiplied by scale.

Perfect Price Discovery

Sub-penny precision enables perfect price discovery in ways that were previously impossible. Instead of being forced to round prices to the nearest cent, markets can find true equilibrium points. This is particularly important for high-frequency, low-margin activities where precision matters.

Consider algorithmic trading. The difference between 0.001 cents and 0.002 cents might seem trivial, but when you’re processing millions of transactions, that precision determines profitability. The SEC introduced Rule 612, the Sub-Penny Rule, in 2005 to address the increment issue. The rule states that the minimum price increments for stocks over $1 must be $0.01. Stocks under $1 can increment by $0.0001.

The financial markets have already recognized that sub-penny precision matters for efficiency. Now we need to extend that recognition to the broader economy.

Network Effects and Velocity of Money

When transaction costs approach zero and precision becomes infinite, the velocity of money increases dramatically. Resources can be allocated and reallocated in real-time based on immediate needs and opportunities.

This creates positive network effects. The more participants who can engage in frictionless micro-transactions, the more valuable the network becomes for everyone. An AI system that can efficiently buy and sell computational resources in real-time creates opportunities for other AI systems to do the same.

AI Systems and Stable Value Transfer: The Perfect Match

The convergence of AI systems and stable, divisible value transfer represents more than just a technological advancement – it’s the foundation for an entirely new economic paradigm.

Computational Economics

AI systems think in computational terms. They optimize for efficiency, minimize waste, and maximize utility with mathematical precision. Traditional currency systems, with their political volatility and arbitrary denominations, are fundamentally incompatible with how AI systems operate.

Token-based payment systems in AI services create a new frontier, pushing micropayments from theory into practice. But current token systems still suffer from volatility and conversion complications. A stable, metal-backed currency provides the mathematical stability that AI systems require for long-term planning and optimization.

Autonomous Economic Agents

Do you know AI-to-AI crypto transactions involve two artificial intelligence systems to conduct financial operations? This technology enables AI agents to buy and sell assets without any need for human intervention. This isn’t science fiction - it’s happening now in limited forms.

But truly autonomous economic agents need more than just the ability to transact. They need stable purchasing power over time. If an AI system budgets 1,000 units for a specific computational task, it needs confidence that those 1,000 units will have consistent purchasing power whether the transaction happens today, tomorrow, or next month.

Metal-backed currency provides this stability. Unlike fiat currencies that can be devalued by political decisions, or cryptocurrencies that fluctuate based on speculation, a properly structured metal-backed system provides the mathematical stability that AI systems require.

Global Interoperability

AI systems don’t respect national borders. A machine learning model might be trained using data from dozens of countries, processed on servers across multiple continents, and applied to problems that span the globe. These systems need a truly global currency that works identically everywhere.

Current financial systems create friction at every border crossing. Currency conversion costs, regulatory differences, and settlement delays make global AI commerce needlessly complicated. A stable, metal-backed digital currency that operates on blockchain infrastructure eliminates these friction points.

Programmable Money for Programmable Systems

Perhaps most importantly, AI systems need programmable money. They need to be able to set up automatic payments, create contingent transactions, and optimize spending in real-time based on changing conditions.

DigiByte’s exceptionally low transaction fees, often amounting to fractions of a cent, effectively eliminate this barrier. This cost efficiency is indispensable for AI agents that engage in high-frequency, low-value transactions. But the solution isn’t just about low fees - it’s about predictable, stable value that can be programmed into long-term AI systems.

The Efficiency Multiplier Effect

When you combine stable value with infinite divisibility and zero transaction costs, you get an efficiency multiplier effect that compounds over time. AI systems can optimize resource allocation in real-time, leading to reduced waste, improved productivity, and more innovative applications.

For instance, a decentralized application (dApp) could integrate multiple AI agents where each provider of a specialized function pays or is paid per task. The convenience of managing hundreds or thousands of these microtransactions per minute is achieved directly through the blockchain, democratizing AI services and creating a truly autonomous economic system.

The Technical Implementation: Making It Real

The theoretical benefits of sub-penny transactions are compelling, but implementation requires solving specific technical challenges that traditional payment systems were never designed to handle.

Blockchain Infrastructure as the Foundation

Traditional banking infrastructure simply cannot handle the volume and precision required for AI-driven micropayments. Banks process transactions in batches, often with delays measured in days. Unfortunately, however, blockchain technology fell short of its promise. Firstly, the average user cannot handle the technical complexity of managing wallets and security keys on their own.

But AI systems don’t have this problem. They can manage cryptographic keys with perfect security and never lose their passwords. The technical complexity that makes blockchain challenging for humans becomes trivial for AI systems.

The key is choosing the right blockchain architecture. With its uncapped block sizes that respond to user demand, BSV has been setting the scaling standard since 2018. BSV is set to go to another level this year with the upcoming Teranode upgrade, pushing the network to over a million transactions per second.

Stable Value Anchoring

The biggest challenge isn’t technical - it’s economic. How do you create a currency system that provides the stability AI systems need while maintaining the divisibility and efficiency they require?

Metal backing provides the answer. By anchoring digital tokens to a basket of precious metals, you get mathematical stability without political interference. The value becomes predictable over time, which is exactly what AI systems need for long-term planning.

But the implementation must be done carefully. The metal backing needs to be verifiable, transparent, and mathematically robust. Blockchain technology enables real-time auditing of metal reserves, ensuring that every digital token is backed by actual physical assets.

Precision Management

Handling sub-penny precision at scale requires careful mathematical design. You need to prevent rounding errors from accumulating while maintaining computational efficiency. This means designing systems that can handle arbitrary precision arithmetic while still processing millions of transactions per second.

The solution involves using fixed-point arithmetic with sufficient precision to handle the smallest conceivable transaction while maintaining computational efficiency. Modern computers can easily handle arithmetic with hundreds of decimal places, making sub-penny precision technically trivial.

Cross-Border Interoperability

One of the biggest advantages of blockchain-based systems is that they’re natively global. A transaction between an AI system in Tokyo and another in New York is technically identical to a transaction between systems in the same data center.

This eliminates the currency conversion complexity that currently plagues international commerce. Instead of converting between yen and dollars, both AI systems can transact in the same stable, metal-backed units regardless of their physical location.

Conclusion: The Inevitable Future of Frictionless Commerce

The elimination of penny production isn’t just about saving $85 million in manufacturing costs. It’s a signal that our economy is evolving beyond the constraints of physical currency toward something more precise, more efficient, and more aligned with how digital systems actually operate.

We’re entering an era where artificial intelligence systems will conduct billions of autonomous transactions, optimizing resource allocation in real-time with mathematical precision. These systems don’t need the artificial constraints of rounding to the nearest five cents. They need access to true sub-penny precision backed by stable, predictable value.

The technical solutions exist today. Blockchain infrastructure can handle the scale. Metal backing can provide the stability. Cryptographic systems can ensure the security. What we need now is the economic architecture to bring these pieces together.

The implications extend far beyond micropayments:

Perfect Resource Allocation: When transaction costs approach zero and precision becomes infinite, resources flow to their most efficient uses in real-time.

Global AI Commerce: Stable, divisible value transfer enables true global commerce between autonomous AI systems without currency conversion friction.

New Business Models: Sub-penny precision enables entirely new categories of economic activity that were previously impossible due to transaction cost constraints.

Economic Efficiency Gains: The elimination of artificial minimum transaction thresholds removes economic friction and increases overall system efficiency.

The death of the penny isn’t ending precision in our economy – it’s highlighting why digital precision matters more than ever. As we transition from physical currency to digital value transfer, we have an opportunity to build something better: a monetary system that matches the mathematical precision of the digital systems that increasingly run our world.

The question isn’t whether we’ll need sub-penny transactions in an AI-driven economy. The question is whether we’ll build the infrastructure to support them efficiently, or whether we’ll artificially constrain our economic potential by clinging to the limitations of physical currency systems that were designed for a simpler world.

The mathematics are clear. The technology is ready. The only question is whether we have the vision to implement solutions that match the precision our digital future demands.

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Note: This analysis is based on current technological capabilities and emerging trends in AI systems and digital payments. Specific implementation details would require additional technical validation and regulatory consideration.