● AI Crypto Payment Shock
AI-Driven Financial Transformation: Why Stablecoins, Cryptocurrencies, and Real-Time Payments Matter
The next five years will not be defined simply by whether Bitcoin rises or falls.
AI agents may begin to make payments directly, blockchains may handle real-time settlement, and stablecoins may increasingly fill gaps in global payment networks.
This article explains why cryptocurrencies and stablecoins may be more efficient than traditional card payments or bank transfers in the AI era, why HTTP 402 and x402 matter, and how to assess crypto within the 2026 economic outlook.
1. Key Development: Finance Is Moving Toward Real-Time Settlement Infrastructure
The central message is clear.
In the AI era, finance may shift from legacy banking and card networks toward real-time payments, smart contracts, and stablecoin-based infrastructure.
This is not simply about cryptocurrency prices. It is about a structural change in how the global financial system operates.
Traditional finance involves settlement delays even after a transaction is completed.
Card payments appear instant to consumers, but settlement between merchants, card networks, payment processors, and banks involves multiple steps.
Cross-border transfers are more complex.
They rely on SWIFT messages between banks, intermediary institutions, and multiple regulatory and foreign-exchange processes.
The result is a system that is slow, costly, and operationally inefficient.
By contrast, blockchain-based payments can combine transaction execution and settlement with minimal delay.
When paired with stablecoins, this model reduces the volatility problem associated with cryptocurrencies while preserving the benefits of digital assets.
This is one of the key financial transformations to monitor in the broader economic outlook.
2. Why Crypto Payments May Fit the AI Agent Era Better
As AI agents become more capable, they may increasingly compare products, subscribe to services, and execute payments on behalf of users.
The key question is not whether payments are possible, but which payment rail is most efficient.
Card payments are possible.
Bank transfers are possible.
QR payments are possible.
However, if AI systems must handle large volumes of micro-payments and conditional transactions automatically, legacy payment systems may face cost and speed constraints.
Crypto payments offer several advantages in this context.
First, blockchains are designed to be machine-readable and machine-processable.
Second, smart contracts can execute payments automatically when predefined conditions are met.
Third, the fee structure can be lower than that of card networks and payment processors.
Fourth, cross-border transactions can reduce dependence on foreign exchange conversion and intermediary banks.
If an AI agent is required to pay $0.01 for a single data request, stablecoin-based payment rails may be more practical than conventional card infrastructure.
This is the core point often overlooked in simplified discussions of AI and crypto.
3. The Kiosk Example: The Real Issue Is Payment Infrastructure Efficiency
The discussion uses kiosks as an example.
The rapid adoption of kiosks and table-ordering systems in restaurants and cafes is not driven only by technology.
For business owners, kiosk rental costs are often more predictable and lower than labor costs.
If a kiosk costs between KRW 20,000 and KRW 35,000 per month, then 100 units would cost about KRW 3.5 million per month.
By contrast, labor costs include not only wages, but also hiring, training, turnover, conflict, and staffing risk.
In practice, business owners compare total cost efficiency.
Finance is similar.
Legacy card networks do not fail to function.
But in an environment where AI agents execute large numbers of automated payments, fees, settlement speed, security, and automation capability become more important.
On that basis, stablecoins and blockchain payments may offer a more efficient model than traditional payment rails.
4. HTTP 402 and x402: Filling the Missing Payment Layer of the Internet
One of the most important points is HTTP 402.
Few people recognize that the internet protocol suite has long included HTTP 402, which stands for “Payment Required.”
However, since the 1990s, the internet has developed primarily around information transfer, while no standard payment layer has fully emerged.
We use HTTP to access web pages, SMTP to send email, and IP and Wi-Fi to operate in the smartphone era.
But a native standard for moving money across the internet has remained incomplete.
This is why x402, proposed by Coinbase, is attracting attention.
x402 is an attempt to make payments more natural for AI agents and web services.
It aims to create a structure in which money moves as seamlessly across the internet as information does.
This is not just about one token or one company.
It is about embedding payment functionality into the core structure of the internet.
If AI agents are to browse the web, call APIs, and pay immediately for data or content usage, x402 represents an important technical direction.
5. Why Stablecoins Matter: The Core Issue Is Settlement Infrastructure, Not Volatility
Stablecoins are digital assets whose value is linked to fiat currencies such as the U.S. dollar.
Unlike Bitcoin or Ethereum, which can experience significant price fluctuations, stablecoins are more suitable for payments and settlement.
Stablecoins matter for three reasons.
First, they can improve the speed of global payments.
Second, they can reduce fees by lowering reliance on intermediaries.
Third, they are a better fit for AI agents and smart contracts that require automated execution.
Their relevance is especially clear in cross-border transfers and trade settlement.
Traditional financial systems rely on structures such as nostro and vostro accounts.
In simple terms, banks must pre-fund accounts across multiple jurisdictions to process international payments.
In the discussion, the size of these tied-up deposits is described as approaching KRW 3,000 trillion.
This capital supports trust in the financial system, but it is also highly inefficient.
If real-time settlement through stablecoins becomes more widely adopted, the amount of idle liquidity tied up in the system may decline, improving capital efficiency in global payments.
6. Open USD and Large Consortia: Even Conservative Financial Players Are Moving
The discussion also refers to the recent Open USD stablecoin initiative.
It highlights consortium participation from financial institutions, major corporates such as Samsung Electronics, asset managers such as BlackRock, card networks, and Wall Street firms.
This matters because it indicates that even the most conservative parts of the financial system are not ignoring stablecoins.
These organizations are highly sensitive to regulatory and reputational risk.
Their participation suggests they view stablecoins as a serious candidate for future financial infrastructure.
If cryptocurrency is viewed only as a speculative market, this shift may be missed.
The more important issue is whether stablecoins can become infrastructure for payments, settlement, remittances, and tokenized asset transactions.
7. The Next Five Years May Bring More Change Than the Past 50 Years of SWIFT
SWIFT has operated as a global financial messaging network since 1973.
It remains central to international transfers and bank-to-bank payments.
However, SWIFT is better understood as a messaging system for payment instructions than as a direct money transfer network.
Jenny Johnson, CEO of Franklin Templeton, has been cited as saying that the next five years may bring more change than the previous 50 years of SWIFT.
This is a significant statement.
It implies that the infrastructure supporting global finance may become faster, more automated, and more tokenized over a relatively short period.
If AI and blockchain continue to converge, the transformation may extend beyond user interfaces into back-end banking operations.
This includes interbank settlement, reserve structures, payment routes, and fee models.
8. The Goldsmith Note Analogy: 2026 May Mark a Second Monetary Transition
The discussion refers to the historical example of goldsmith notes.
In the past, people deposited gold with goldsmiths and used receipts as a medium of exchange.
As those receipts gained trust, they gradually replaced the physical gold itself and formed the basis of paper money and modern banking.
From this perspective, the first monetary transition was the move from physical gold to paper money and credit-based currency.
The current transition may be viewed as a move toward digital trust and programmable money.
Stablecoins still depend on trust.
It matters whether the issuer holds sufficient reserves, operates within a regulatory framework, and can support redemption.
Without trust, a stablecoin is only a digital record.
With trust, it may become a new settlement unit within the global financial system.
9. How Payment Methods Have Changed Across Analog, Digital, and AI Eras
Payment methods have evolved with each technological era.
In the analog era, cash and coins were the natural payment tools.
In the digital era, card payments, online banking, and QR payments became dominant.
In the AI era, payment systems must be understandable and executable by AI agents.
Digital transformation is often called DX.
The next phase is AX, or AI transformation.
In the AX era, financial infrastructure may become less human-centric and more machine-centric.
Smart contracts are important in this context.
They can execute agreements and payments automatically once specified conditions are met.
For example, if an AI system uses a dataset and usage is verified, payment can be made immediately in stablecoins.
Such a model is difficult or costly to implement through bank accounts and card networks alone.
10. Crypto Market Outlook: The Key Variable Is Institutional Adoption
The discussion offers a constructive view on the crypto market outlook.
However, the core message is not that prices must rise.
No asset rises forever, and no asset falls forever.
The crypto market has experienced major liquidations, followed by months of weak performance and diminished investor confidence.
Many retail investors have suffered losses, and some have exited the market.
However, such stagnation is unlikely to continue indefinitely.
The key variable is institutional adoption.
Regulatory measures such as the U.S. Clarity Act may reduce uncertainty in the market.
Clearer regulation can make it easier for institutional investors and financial firms to participate.
This structural shift is more important than short-term price action.
The discussion also suggests that the second and third quarters of 2026 may form a base, while the fourth quarter could see renewed upward momentum.
This is only one scenario, not a certainty.
Crypto investing remains highly volatile and requires diversification and risk management.
11. The Tokenization Era: Why Crypto Should Be Viewed by Sector
A central message from the discussion is that crypto should not be viewed as a single asset class defined by one coin price.
Major altcoins serve different roles and industrial functions.
Accordingly, the market should be analyzed by sector.
The first sector is smart contracts.
This area supports decentralized applications, automated agreements, DeFi, and tokenized asset transactions.
The second sector is finance.
This includes projects that serve as financial back-end infrastructure, bridge assets, and payment and settlement layers.
The third sector is interoperability and oracles.
These systems connect blockchains and bring real-world data into blockchain environments.
Oracles are essential when financial data, price feeds, or real-world asset information must interact with smart contracts.
The fourth sector is memes.
Memecoins remain controversial in terms of fundamental value, but they are difficult to ignore because of community effects, liquidity, and market sentiment.
That said, this segment is highly volatile and requires caution.
12. The Core Point Often Missed in Other Coverage
First, the convergence of AI and crypto is not just an investment theme. It is a change in payment standards.
Many reports focus on “AI coins” or “stablecoin beneficiaries” in price terms.
The more important issue is the emergence of machine-native money infrastructure for AI agents.
Second, HTTP 402 and x402 are not merely technical terms.
They represent an attempt to restore the missing payment layer of the internet.
If the web is an information highway, x402 is an effort to add a payment lane on top of it.
Third, the real value of stablecoins is not price stability alone, but lower global settlement costs.
The key issue is the reduction of inefficiencies created by large balances trapped in nostro and vostro accounts, as well as international transfer delays.
Fourth, participation from major financial institutions and corporations signals a change in market structure.
In the past, crypto was closer to a retail-driven speculative market.
It is increasingly becoming a competition over payment infrastructure and financial back-end systems.
Fifth, in the 2026 economic outlook, crypto should be analyzed alongside AI, global payments, tokenization, and capital market restructuring.
Without this framework, it is difficult to understand the next phase of financial change.
13. What Investors Should Focus On
This does not mean investors must buy cryptocurrencies.
However, it is increasingly difficult to ignore this market entirely.
Major global companies and financial institutions are actively evaluating the space, and individual investors who dismiss it outright may miss a major structural shift.
Investment decisions remain a matter of individual judgment.
However, understanding the sector is increasingly necessary.
At a minimum, Bitcoin, Ethereum, stablecoins, smart contracts, tokenized assets, and AI agent payment structures now belong in basic financial literacy.
When assessing the crypto market outlook for the second half of the year and 2026, investors should focus not only on charts, but also on institutional adoption, payment infrastructure use, global liquidity, interest-rate policy, and regulatory developments.
Macroeconomics and crypto markets are becoming more tightly linked.
< Summary >
In the AI era, AI agents may increasingly make payments directly.
Stablecoins and blockchain-based real-time settlement may become more efficient than legacy card and banking networks.
HTTP 402 and x402 are important attempts to restore a payment layer to the internet.
The SWIFT-centered international financial system may undergo significant change over the next five years.
Crypto should be assessed not only through short-term price movements, but also through institutional adoption, smart contracts, tokenization, and global payment infrastructure.
[Related Articles…]
AI Agents and the Next Generation of Payment Infrastructure
Stablecoin Expansion and the 2026 Global Financial Outlook
*Source: [ 경제 읽어주는 남자(김광석TV) ]
– “금융이 통째로 바뀝니다” AI 시대, 스테이블코인과 암호화폐가 필요한 진짜 이유 | 경읽남과 토론합시다 | 문창훈 작가 [3편]
● AI-Driven Rout, Software Winners-Split, Unity Soars-HubSpot Tanks
AI Did Not Kill Software: Why Unity Surged While HubSpot, Datadog, and Figma Fell
The key issue this chapter highlights is not the simple fear that AI is killing software stocks.
The real issue is whether AI is materially improving corporate revenue or still in a transitional phase that is disrupting pricing and cost structures.
Although Unity, Paycom, HubSpot, Datadog, and Figma were all grouped as AI beneficiaries, their share prices moved in sharply different directions. This divergence is likely to become an important framework for evaluating U.S. software and growth stocks going forward.
This move also signals that the market is no longer assigning value simply because a company “does AI.”
In other words, Wall Street is now focused on numbers created by AI, not just the AI narrative itself.
1. Today’s market backdrop: software stocks were hit much harder than the broader market
All three major U.S. equity indexes closed lower.
However, the software sector was hit significantly harder than the broader market.
The software ETF IGV fell more than 2%, and several major software names posted double-digit declines.
- HubSpot: down about 19%
- Datadog: down about 17% to 19%
- Figma: down about 15%
- Paycom: up about 23%
- Unity: up about 15%
This matters because the move is difficult to explain solely by rates, growth concerns, or inflation.
If the market were driven only by weaker rate-cut expectations or macro slowdown concerns, most growth stocks would have moved in the same direction.
Instead, winners and losers within AI and cloud software diverged sharply.
2. Why HubSpot sold off: customer slowdown mattered more than reported earnings
HubSpot fell about 19% in a single session.
At first glance, the move may appear to reflect a major earnings miss, but the core issue was the outlook for new customer additions.
The company had expected net new customers of roughly 9,000 to 10,000 in the second quarter.
Actual net additions were only in the 7,000 range.
More importantly, management lowered forward guidance for quarterly net new customers to roughly 5,000 to 6,000.
This was the key factor behind the market reaction.
Growth stocks are valued more on future growth rates than on current-period results.
For high-multiple software names such as HubSpot, any slowdown in new customer momentum can trigger a sharp re-rating.
3. HubSpot’s real issue: not weak AI demand, but a pricing transition
HubSpot cited two main reasons for the slowdown.
- AI agent pricing changes
- Tighter budget approval processes among enterprise customers
In April, HubSpot introduced performance-based pricing for certain AI agent features.
This includes AI agents used for customer support or sales functions, where pricing is tied to measurable business outcomes.
This differs from traditional seat-based or access-based software pricing.
The company also expanded free trials, giving customers more time to test before committing.
As a result, the sales cycle lengthened.
At the same time, companies became more selective about software purchases, which further slowed approvals.
Importantly, AI demand itself has not disappeared.
Usage of HubSpot’s AI agents is still rising rapidly.
The company said more than 16,000 customer accounts have activated its data agent.
HubSpot’s issue is not that AI is failing to sell.
Rather, the company is in the process of determining how to convert AI usage into revenue, and that transition is temporarily affecting booking timing and customer cycles.
4. Why Datadog sold off: strong results were not enough to meet elevated expectations
Datadog reported revenue growth of about 36% year over year.
Quarterly revenue reached about $1.12 billion, above consensus estimates.
Full-year revenue guidance was also raised to roughly $4.45 billion to $4.47 billion from the prior range of $4.30 billion to $4.34 billion.
On the surface, these were strong results.
Even so, the stock fell about 17% to 19%.
The reason is straightforward.
Datadog had already more than doubled this year before the release.
AI-related upside and cloud observability growth expectations were already embedded in the valuation.
In that environment, good results are not enough.
The market wants performance that is meaningfully above already-high expectations.
When results fail to exceed those expectations by a wide margin, the stock can decline sharply.
5. What concerned the market at Datadog: gross margin and usage trends from a major AI customer
Datadog reported a gross margin of about 80%.
Market expectations were about 80.7%.
The gap was only 0.7 percentage points.
In a normal market, that difference would likely be manageable.
For a high-valuation growth stock, however, even small deviations can trigger selling.
Another concern was slower usage from one of its largest AI customers.
Datadog said it now serves more than 750 AI customers, including 31 that spend more than $1 million annually.
AI companies have clearly been an important contributor to growth.
Still, reported slowing usage from one major AI customer drew attention.
The company said the impact had already been incorporated into third-quarter and full-year guidance.
The CEO also said that excluding that customer, growth across the rest of the business was largely unchanged.
Even so, the market remained cautious.
The reason is that AI expectations were very high.
When a stock is priced for substantial AI upside, even a slowdown at a single major customer can be interpreted as a broader risk signal.
6. Why Figma sold off: revenue growth was strong, but AI costs rose first
Figma reported revenue growth of about 48% year over year.
Revenue was around $370 million.
On the surface, this reflected very strong growth.
Nevertheless, the stock fell about 15%.
The reason was the cost burden associated with expanding AI features.
Figma is aggressively embedding AI into its product set.
The issue is that some beta-stage AI features are not yet monetized, while the inference costs are already being absorbed by the company.
The CFO noted that inference costs continue even before the company begins charging for AI usage.
The company also indicated that margins may remain volatile on a quarterly basis for now.
That comment was enough to pressure the stock.
It suggested that AI costs are showing up before AI revenue.
For Figma, the issue is not growth rate, but the timing of AI monetization.
7. Why Paycom rose 23%: automation strengthened the core business
By contrast, Paycom rose about 23%.
Revenue grew about 10%, and full-year guidance was raised.
Paycom provides HR and payroll software.
The company cited automation as one of the drivers behind the strong results.
It did not separately disclose how much of revenue was attributable to AI.
Still, the market responded positively.
The reason is that its AI and automation strategy appeared to enhance the core business rather than disrupt it.
This quarter, automation was reflected in stronger growth and improved guidance.
In other words, Paycom was viewed not as a company whose pricing model was being disrupted by AI, but as one where AI and automation were strengthening product competitiveness.
8. Why Unity rose 15%: an AI concern stock became an AI beneficiary
Unity was the most notable case in this session.
Earlier in the year, Unity came under pressure when Google introduced AI tools for game generation.
The market feared that if AI could generate games end-to-end, the need for game engines could decline.
Unity’s stock once fell more than 20% in a single session on that concern.
This time, the reaction was the opposite.
Unity rose about 15%.
Revenue increased about 24% year over year.
Strategic business revenue grew about 38%.
The company said its AI-based ad model, Unity Vector, was driving growth in its advertising network.
This is the important shift.
Unity moved from being seen as a company threatened by AI to one showing that AI can support advertising revenue growth.
From the market’s perspective, this provided evidence that AI can translate into actual revenue growth.
9. The market’s new criterion: not whether a company uses AI, but whether AI shows up in the numbers
The market’s framework today was more nuanced than a simple AI/no-AI distinction.
All of the companies mentioned here — Unity, Paycom, HubSpot, Datadog, and Figma — are using AI.
What the market actually evaluated was this:
- Is AI improving revenue and margins now?
- Is AI adoption disrupting pricing, customer behavior, or cost structure?
- Has the market already priced in too much AI upside?
Unity and Paycom were interpreted as companies where AI is strengthening the existing business.
HubSpot faced slower customer growth as its AI pricing model changed.
Datadog delivered strong results, but high expectations and slower usage from a large AI customer weighed on sentiment.
Figma posted rapid growth, but AI-related costs pressured margin expectations.
The conclusion is that today was not a day when AI “killed software.”
Rather, it was a day when the market stopped assuming that every AI-linked software company deserves a higher valuation by default.
10. The most important but often overlooked point: software monetization models are changing
The most important part of this episode is the change in software pricing models.
Traditional SaaS companies have usually relied on seat-based pricing.
As employee counts or user accounts increased, revenue typically expanded in a relatively predictable way.
AI agents change that structure.
AI does not occupy a seat like a human employee; it produces measurable outcomes.
As a result, customers increasingly want to pay based on performance rather than usage seats.
HubSpot’s move to performance-based pricing for certain AI agents reflects this shift.
If this model spreads, it could weaken the stability of traditional SaaS revenue structures.
That does not mean the end of software companies.
Companies that deliver stronger AI performance and clearer outcomes may be able to command higher pricing.
The risk is that the transition period can lengthen sales cycles, extend free trials, and delay revenue recognition.
That is the real core of the software stock selloff today.
The issue is not that AI is destroying software. AI is rewriting how software companies monetize.
11. Why the declines were so severe: expectations and positioning mattered more than fundamentals
The share-price declines in HubSpot, Datadog, and Figma were large.
That naturally raises the question of whether the market overreacted.
Some Wall Street analysts do view the selloff as excessive.
The reasons are threefold.
- First, many of these stocks had already risen sharply.
- Second, high-multiple growth stocks can re-rate sharply on even modest changes in outlook.
- Third, algorithmic trading and sector-wide selling amplified the move.
In particular, names such as Datadog, which had more than doubled this year, were priced with extremely high expectations.
In that setting, even strong results may not be enough if they do not materially exceed consensus.
In addition, earnings-season trading increasingly reflects fast-moving quantitative and algorithmic flows.
Negative commentary from one company can spread across an entire sector and trigger correlated selling.
As a result, stock volatility can far exceed the underlying fundamental change.
In short, the selloff appears more consistent with a valuation reset and positioning unwind than with an actual collapse in AI demand.
12. What investors should monitor next
When evaluating AI-related software stocks, investors should look beyond the statement that a company “has AI features.”
Four factors are especially important:
- Is AI translating into revenue growth?
- Are AI-related costs rising faster than revenue?
- Is a pricing transition lengthening the customer purchase cycle?
- Has the AI upside already been fully reflected in the stock price?
Unity was rewarded because AI appeared to drive advertising revenue.
Paycom was rewarded because automation improved the outlook for the core business.
By contrast, Figma faced margin pressure because AI costs came first, while HubSpot faced slower customer growth as it adjusted pricing.
In AI investing, economic monetization now matters more than the technology story itself.
The key question is not whether AI is impressive, but how quickly it becomes profitable.
13. Conclusion: the winners in the AI software era are becoming clearer
This software-sector selloff should not be interpreted as a simple collapse.
It is better understood as a shift in the market’s framework for valuing AI software companies.
Going forward, the market is unlikely to treat all AI-enabled software companies as the same type of beneficiary.
Companies where AI improves both revenue and margins are likely to remain favored, while those that face cost pressure or pricing disruption may come under strain.
Unity’s rally is therefore symbolic.
A company once viewed as vulnerable to AI showed that AI can support revenue growth.
By contrast, the declines in HubSpot, Datadog, and Figma suggest not that AI demand has vanished, but that the market is beginning to assess the quality of AI monetization.
The message is clear.
AI did not kill software.
AI has started to expose the real differences in execution across software companies.
< Summary >
U.S. software stocks showed significant volatility today.
HubSpot fell sharply due to AI agent pricing changes and slower new customer growth.
Datadog declined despite strong results because expectations were high and usage from a major AI customer slowed.
Figma posted 48% revenue growth, but margin concerns increased as AI inference costs rose first.
By contrast, Paycom rose sharply as automation strengthened the core business, and Unity advanced on evidence that AI-based advertising is driving growth.
The key issue is not whether a company uses AI, but whether AI is converting into revenue and earnings.
Going forward, investors should focus more on pricing, cost structure, and monetization speed than on the AI narrative alone.
[Related Articles…]
- AI Software Investment Strategy: Winners Will Be Defined by Monetization Speed
- U.S. Growth Stocks Volatility Check: Key Takeaways From Earnings Season
*Source: [ Maeil Business Newspaper ]
– [LIVE] AI가 소프트웨어를 죽였다는데, 왜 유니티는 올랐나? | 이나연 특파원
