Goldman Sachs Warns AI Boom Could Fuel Inflation, With U.S. Expected To Face Biggest Impact

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July 12, 2026

The logo for Goldman Sachs is seen on the trading floor at the New York Stock Exchange (NYSE) in New York City, New York, U.S., November 17, 2021. REUTERS/Andrew Kelly/Files

The rapid expansion of artificial intelligence is expected to fuel a new wave of inflation, with the United States likely to experience the greatest price pressures among major developed economies, according to new research from Goldman Sachs.

The investment bank said surging demand for AI infrastructure is creating supply bottlenecks across key industries, including semiconductors, memory chips, software and electricity, pushing up costs that are increasingly being passed on to businesses and consumers.

While economists widely expect AI to boost productivity and eventually reduce inflation over the long term, Goldman argues the technology is likely to have the opposite effect in the near term as companies race to build data centers, deploy AI software and secure scarce computing resources.

Goldman Sachs estimates that artificial intelligence is currently adding about 20 basis points (0.20 percentage points) annually to the United States’ core Personal Consumption Expenditures (PCE) inflation, the Federal Reserve’s preferred measure of underlying inflation.

The bank expects that contribution to increase sharply over the coming months. By the end of the year, AI-related price pressures are projected to add approximately 50 basis points (0.50 percentage points) to core PCE inflation, according to Goldman economist Megan Peters.

That impact would be significantly larger than in other developed economies.

Canada, Australia, Europe, the United Kingdom, and Japan are each expected to experience only around 10 basis points of additional core inflation linked to AI.

“While not completely negligible, these effects are far below the 50bp peak we estimate for U.S. PCE, suggesting that for the most part AI-driven inflation is a U.S. story,” Peters wrote.

The disparity reflects the United States’ dominant role in developing, deploying, and consuming advanced AI technologies, as well as the concentration of global investment in U.S.-based hyperscale data centers.

Three Waves of AI-Driven Inflation

Goldman identifies three principal channels through which artificial intelligence is pushing prices higher: memory chips, software, and electricity. Each represents a critical input for AI systems, and all are experiencing strong demand that is outpacing available supply.

Memory Prices Surge Amid AI Demand

The first inflationary wave stems from the extraordinary increase in demand for advanced memory chips used in AI servers.

High-bandwidth memory (HBM), DDR5 memory modules, and other advanced memory products have become essential components for training and operating large AI models.

As cloud providers and technology companies expand AI infrastructure, competition for memory has intensified. According to computer hardware tracking platform Pangoly, the average price of an 8GB DDR5 memory module reached approximately $148 during the last week, more than four times higher than the $35 recorded during the same period last year.

The sharp increase underlines persistent supply shortages across the memory industry.

SK Hynix, one of the world’s largest memory manufacturers, recently warned that demand is expected to exceed production capacity until at least 2030 and forecast that 2027 could become the industry’s worst-ever year for supply shortages.

Goldman noted that memory inflation has a greater impact on U.S. inflation because software and computer accessories account for a larger share of consumer spending than in most other developed economies.

Approximately 1% of U.S. core PCE inflation is linked to software and accessories, compared with less than 0.5% in many peer economies.

Software Becoming More Expensive

The second inflationary channel involves software pricing. Technology companies are increasingly embedding AI features into existing software products and charging higher subscription fees in return.

One prominent example is Microsoft’s decision to increase prices for its Microsoft 365 productivity suite after integrating its AI assistant, Copilot.

Similar pricing strategies are emerging across enterprise software, cybersecurity, design applications and productivity tools as software vendors seek to recover the substantial costs associated with developing and operating frontier AI models.

Goldman expects software inflation in the United States to accelerate further, forecasting that prices for software and related accessories could rise by as much as 30% year over year before the end of 2026.

Because software spending represents a larger share of U.S. consumer expenditures than in most other advanced economies, American households and businesses are expected to bear a disproportionate share of these increases.

Electricity Demand Creates New Bottleneck

The third source of inflation stems from energy.

Artificial intelligence requires enormous computing power, and modern AI data centers consume vast quantities of electricity to operate servers and cooling systems around the clock.

As AI infrastructure expands, electricity demand is rising rapidly.

According to Goldman Sachs, data centers are expected to account for approximately 11% of total U.S. electricity demand by the end of the decade, nearly double the current level of around 6%. That surge is placing additional strain on electricity grids already facing growing demand from electrification, manufacturing, and population growth.

Data from the U.S. Bureau of Labor Statistics show that the average residential electricity price reached approximately $0.19 per kilowatt-hour in May, representing an increase of about 27% since May 2022.

Higher electricity costs affect consumers directly through utility bills while also increasing operating expenses for businesses, potentially feeding through to broader consumer prices.

AI Infrastructure Amplifies Commodity Demand

The inflationary effects extend beyond electricity.

Building AI infrastructure requires significant quantities of semiconductors, advanced networking equipment, cooling systems, steel, copper, and specialized construction materials.

Data centers also require large volumes of land, power transmission equipment, and skilled labor. These investments have contributed to rising costs across several industrial supply chains. At the same time, energy markets have experienced additional volatility following geopolitical tensions in the Middle East.

Although crude oil prices have retreated from recent highs, West Texas Intermediate crude remains roughly 25% higher year to date, reflecting continued concerns over global energy supplies. Higher fuel prices further increase transportation, manufacturing, and electricity costs throughout the economy.

Despite warnings of near-term inflationary pressures, Goldman Sachs continues to believe artificial intelligence will eventually reduce inflation by improving productivity. Historically, technological breakthroughs have lowered production costs, increased efficiency, and expanded economic output over time.

AI has the potential to automate repetitive tasks, improve decision-making, accelerate research, and increase labor productivity across numerous industries.

Those gains could ultimately offset today’s higher infrastructure and computing costs. However, Goldman cautioned that the disinflationary effects may emerge more slowly than many investors currently expect.

In previous research, the bank argued that AI is likely to prove less disinflationary than earlier technological revolutions, including the widespread adoption of the internet during the 1990s.

The difference lies in AI’s exceptionally high infrastructure requirements. Unlike earlier digital technologies, frontier AI depends on enormous investments in chips, memory, networking equipment, data centers and electricity generation, all of which remain constrained by limited supply.

Implications for Policymakers

Goldman’s findings present an additional challenge for central banks, particularly the Federal Reserve. If AI continues adding to inflation while simultaneously boosting economic growth and productivity, policymakers may find it more difficult to determine the appropriate pace of interest-rate adjustments.

The report also reinforces the idea that the AI boom is influencing not only technology stocks but also broader macroeconomic conditions. Rather than acting solely as a driver of innovation, artificial intelligence is increasingly reshaping supply chains, commodity markets, energy demand and inflation dynamics. Those make it a growing factor in monetary policy, corporate pricing strategies, and global economic forecasts.

Future of Digital Ownership and Creative Economies in Web3

By

Paul Ugbede Godwin

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July 12, 2026

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The digital collectibles industry continues to evolve beyond profile pictures and speculative trading, moving toward deeper utility, intellectual property expansion, and new forms of artistic experimentation.

Recent developments from Claynosaurz, Doodles, and Beeple illustrate how leading Web3-native brands are redefining ownership, creativity, and community engagement in the NFT ecosystem.

One of the most significant announcements comes from Claynosaurz, which has introduced an equity options allocation checker for its community. The tool allows holders to understand how their digital collectibles may connect to the company’s broader ownership structure.

This move represents a notable shift in the relationship between creators and collectors.

Traditionally, NFT ownership has primarily granted access to communities, exclusive content, or future airdrops. By introducing mechanisms that potentially align collectors with the company’s long-term success, Claynosaurz is exploring a model that bridges digital collectibles and corporate participation.

Such initiatives reflect a growing trend in Web3 toward community capitalism, where users are not merely consumers but stakeholders in the ecosystems they help build.

As NFT projects mature into entertainment and media brands, aligning incentives between founders and collectors could create stronger communities and more sustainable business models.

It also demonstrates how blockchain technology can offer greater transparency regarding ownership structures and reward mechanisms. Meanwhile, Doodles continues to push the boundaries of creative expression and consumer products with the teaser of its upcoming Toy Factory.

The new platform promises to transform virtually any image or concept into a customized toy rendered in Doodles’ distinctive artistic style. This initiative highlights the increasing convergence between digital assets and physical merchandise.

The Toy Factory concept could significantly expand the accessibility of Web3 creativity. Instead of limiting participation to NFT holders alone, it opens the possibility for anyone to engage with the Doodles brand through personalized creations.

This user-generated approach mirrors broader trends in artificial intelligence and generative technologies, where consumers increasingly expect products tailored to their individual preferences.

For Doodles, the initiative represents another step in its transformation from an NFT collection into a global entertainment brand. By turning personal ideas and images into physical collectibles, the company is building new revenue streams while reinforcing emotional connections between users and the brand.

It also demonstrates how NFT-native companies are increasingly positioning themselves within mainstream consumer culture rather than remaining isolated within crypto circles. On the artistic front, Beeple has once again showcased the unique possibilities of blockchain-based creativity.

His legendary Everydays series has been integrated into Normie #0 through a custom algorithm that compresses each daily artwork into a 40×40 pixel grid entirely stored onchain. This project is particularly significant because it merges artistic innovation with blockchain permanence.

Beeple’s Everydays project, one of the most influential digital art initiatives in history, has always symbolized persistence and creative experimentation. By encoding these daily works into an onchain algorithmic format, the artist reinforces the ethos of decentralization and permanence that underpins blockchain technology.

The reduction of highly detailed artworks into minimalist pixel grids also raises interesting questions about memory, abstraction, and the preservation of digital culture. These developments reveal an industry entering a more mature phase.

Claynosaurz is exploring community ownership models, Doodles is expanding into personalized consumer products, and Beeple continues to pioneer new forms of onchain artistic expression. Collectively, they demonstrate that NFTs are evolving far beyond simple collectibles, becoming platforms for ownership innovation, creative production, and entirely new forms of digital culture.

Whale Places Massive Leveraged Bet on Nasdaq 100, Signaling Renewed Risk Appetite

By

Paul Ugbede Godwin

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July 12, 2026

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Blockchain analytics platform Lookonchain has revealed that a major market participant, commonly referred to as a whale, has taken an enormous leveraged position on the Nasdaq 100 index.

According to the data, wallet address 0x3e7a opened a 20x leveraged long position worth approximately $50.3 million over a five-hour period. More notably, the trader still has an additional $19.16 million in limit orders waiting to be executed, potentially increasing the total exposure to nearly $70 million.

The move has quickly captured the attention of both cryptocurrency and traditional market participants because of its sheer size and aggressive use of leverage. A 20x leveraged position means that for every dollar of capital committed, the trader gains exposure to twenty dollars worth of assets.

While such leverage can generate substantial profits if the market moves in the trader’s favor, it also significantly magnifies losses and increases liquidation risks. This massive bet appears to reflect growing confidence in the outlook for technology stocks and the broader U.S. equity market.

The Nasdaq 100, which tracks many of the world’s largest technology companies, including major artificial intelligence and semiconductor firms, has been one of the strongest-performing indexes in recent years. Continued enthusiasm surrounding AI infrastructure spending, cloud computing, and digital transformation has fueled investor optimism despite concerns over inflation, interest rates, and geopolitical uncertainty.

The whale’s decision to deploy such a large amount of capital suggests an expectation that the Nasdaq 100 could continue its upward trajectory in the near term.

Market sentiment has increasingly shifted toward risk assets as investors anticipate potential monetary easing and continued earnings growth among large-cap technology firms. If these expectations materialize, leveraged positions such as this could generate extraordinary returns.

The strategy is far from risk-free. Leveraged trades are inherently volatile, and even relatively small market corrections can trigger significant losses. A decline of only a few percentage points in the Nasdaq 100 could place the position under severe pressure, potentially leading to forced liquidations depending on margin requirements and platform mechanics.

The additional $19.16 million in pending limit orders also indicates that the trader may be planning to increase exposure if specific price conditions are met. Should these orders execute, the total position would approach $70 million, making it one of the most notable publicly tracked leveraged bets in recent weeks.

Such large positions often attract attention because they can influence market sentiment and occasionally encourage other traders to adopt similar risk-taking behavior. Beyond the immediate implications, this event also highlights the increasing convergence between traditional financial markets and blockchain-based trading ecosystems.

Platforms that allow tokenized exposure to equity indexes and leveraged derivatives are creating new opportunities for traders to express macroeconomic views directly on-chain. As a result, blockchain analytics firms like Lookonchain are becoming important sources of market intelligence, providing real-time transparency into large trading activities that were once largely hidden in traditional finance.

Whether this whale ultimately records a substantial profit or suffers a painful liquidation remains uncertain. Nevertheless, the trade underscores the high-risk, high-reward environment currently dominating financial markets.

It also reflects the growing conviction among some investors that technology stocks and the Nasdaq 100 still have room to climb, despite lingering economic uncertainties and elevated valuations.

LLMS.txt And The Future Of AI

By

TI Partners

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July 12, 2026

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Artificial intelligence is rapidly changing how people find information online. For decades, search engines directed users to lists of websites that matched their queries, leaving individuals to compare sources and decide which page best answered their questions. Today, AI-powered search platforms and large language models can generate complete responses by analyzing information from multiple sources at once. This shift is encouraging businesses to think differently about how their websites are organized and presented to intelligent systems.

One concept attracting growing attention is the llms.txt file. Although it is still an emerging proposal rather than a formally adopted web standard, many developers and digital marketers see it as a practical way to help AI systems identify the most important content on a website. Understanding how llms.txt differs from robots.txt, and why it may become valuable in the future, allows businesses to make informed decisions as AI-powered search continues to evolve.

Understanding the Purpose of LLMS.txt

An llms.txt file is intended to act as a guide for large language models rather than a set of instructions that controls crawling or indexing. It typically sits at the root of a website and contains references to important pages, documentation, product information, support resources, knowledge bases, and other content that best represents the organization.

The primary objective is to make it easier for AI systems to locate the website’s most authoritative and useful information. Rather than expecting an AI model to evaluate hundreds or thousands of pages equally, an llms.txt file can point toward carefully selected resources that provide accurate explanations of products, services, policies, or technical information.

Unlike many traditional optimization techniques that focus primarily on rankings, llms.txt is centered on improving content accessibility and organization for AI systems. It encourages website owners to identify their most valuable resources and present them in a structured way that may simplify future AI interpretation.

It is important to recognize that llms.txt is still developing as an industry concept. Not every AI platform currently supports or relies on it, and there is no guarantee that all large language models will use it in the same way. Even so, the idea reflects a broader movement toward making websites easier for AI systems to understand.

How LLMS.txt Differs From Robots.txt

Although llms.txt and robots.txt are both text files stored at the root of a website, they serve very different purposes.

Robots.txt has been part of the web ecosystem for many years. Its role is to communicate with web crawlers by indicating which areas of a website may or may not be crawled. Website owners use robots.txt to reduce unnecessary crawling, prevent access to certain directories, or guide search engine bots away from duplicate or private content. It functions as a set of crawling instructions rather than a description of website content.

An llms.txt file takes a different approach. Instead of telling AI systems where they can or cannot go, it highlights where they should begin when looking for high-value information. Rather than restricting access, it provides recommendations about the website’s most useful resources.

This distinction is important because AI-powered systems often need context rather than permission. A language model benefits from understanding which pages contain official documentation, comprehensive guides, or authoritative explanations. Robots.txt helps manage crawler behavior, while llms.txt aims to improve content discoverability and interpretation.

Businesses should think of the two files as complementary rather than competing technologies. One focuses on technical website management, while the other supports emerging AI use cases by emphasizing organization and clarity.

Why LLMS.txt May Improve AI Visibility

As AI-powered search platforms become more sophisticated, businesses are looking for ways to increase the likelihood that their content is recognized as reliable and useful. While an llms.txt file is not a ranking factor or guarantee of visibility, it may contribute to a stronger AI optimization strategy by improving how important content is presented.

For websites containing large amounts of information, identifying cornerstone resources can reduce ambiguity. Instead of leaving AI systems to determine which pages are most authoritative, businesses can highlight product documentation, educational articles, implementation guides, support materials, and frequently asked questions.

This curated approach may become increasingly valuable as AI models continue emphasizing factual consistency, topical authority, and comprehensive information. A thoughtfully organized llms.txt file reflects careful content management while encouraging businesses to review whether their most important pages remain accurate and up to date.

Another potential advantage is improved content governance. Creating an llms.txt file often requires organizations to evaluate which pages genuinely represent their expertise. During that process, outdated articles may be revised, duplicate information consolidated, and important resources expanded. Even if an AI system never directly references the file, the resulting improvements strengthen the overall quality of the website.

The Role of LLMS.txt in AI Optimization

For businesses investing in AI optimization, llms.txt should be viewed as one component of a broader strategy rather than a standalone solution.

Successful AI optimization begins with creating accurate, well-researched, and genuinely helpful content. AI systems consistently favor information that demonstrates expertise, answers user questions clearly, and provides sufficient context. Strong technical SEO also remains essential because page speed, mobile usability, structured data, logical navigation, and internal linking continue supporting website performance.

An llms.txt file complements these efforts by making important resources easier to identify. It encourages businesses to organize content intentionally instead of relying solely on navigation menus or internal linking structures. This organizational mindset aligns well with the broader goals of AI optimization, which emphasize clarity, consistency, and authority.

Businesses should also continue monitoring developments within the AI industry. Because llms.txt has not yet become a universally recognized standard, implementation should be considered an emerging best practice rather than a required technical specification. Remaining flexible allows organizations to adapt as AI platforms introduce new recommendations or standards.

Preparing Websites for an AI-Driven Future

The growth of artificial intelligence is reshaping digital marketing in ways that extend beyond traditional search engine optimization. Businesses are increasingly considering how AI systems discover, interpret, and reference online information, making website organization more important than ever.

The concept behind llms.txt reflects this evolution. Rather than focusing exclusively on search rankings, it encourages businesses to present their most valuable information clearly for intelligent systems that generate answers instead of simply displaying links. While widespread adoption is still developing, the principles behind llms.txt already support stronger content organization and better information management.

Companies that invest in comprehensive documentation, accurate educational content, logical website architecture, and ongoing content maintenance are positioning themselves for long-term success regardless of how AI technologies continue evolving. Adding an llms.txt file may become one useful part of that strategy by helping organize key resources for future AI systems while encouraging higher standards for content quality.

As digital search continues changing, businesses that combine proven SEO practices with thoughtful AI optimization will be better prepared to reach audiences wherever they search. Whether through traditional search engines, AI assistants, or future large language model applications, organized, trustworthy content will remain one of the strongest foundations for long-term online visibility.

Robinhood Expands AI Push With Crypto Trading Agents For U.S. Users

By

Ojukwu Emmanuel

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July 12, 2026

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Robinhood is set to expand its innovative Agentic Trading feature to cryptocurrency, allowing eligible US users to connect third-party AI agents that can autonomously trade crypto on their behalf.

The announcement marks the next phase of a product that first launched in beta for equities in May 2026, bringing automated, AI-driven investing closer to everyday retail traders.

The Agentic Trading system works by letting users open a dedicated, isolated brokerage account separate from their main portfolio.

Users connect their chosen AI agent through Robinhood’s Model Context Protocol (MCP), granting the agent permission to view account information, analyze portfolios, and execute trades within predefined limits.

All activity is logged in real-time, giving users full visibility and control. Robinhood emphasizes built-in safety controls, including the ability to set spending caps, require approvals, or monitor every action the agent takes.

Robinhood’s announcement has sparked a lively debate across the crypto community, with reactions ranging from excitement to skepticism.

Some users on X viewed the move as a natural progression in financial technology, arguing that AI-powered interactions will soon become commonplace across banking and investing.

One commenter noted that while the idea may sound futuristic today, many people would have dismissed the notion of managing bank accounts from a smartphone just a decade ago.

Others, however, questioned whether AI could meaningfully improve investment outcomes. Several commenters argued that while AI may execute trades more quickly and efficiently, it cannot compensate for poor investment strategies, warning that faster execution could simply lead to faster losses if the underlying decisions are flawed.

Robinhood’s business model also drew criticism. Some users expressed concerns over the company’s payment-for-order-flow practices, questioning whether investors should entrust AI-driven crypto trading to a platform they believe shares trade data with market makers.

Despite the skepticism, several commenters struck a more balanced tone, suggesting that the competitive advantage will not come from AI alone but from how responsibly investors use the technology.

They argued that AI should be viewed as a tool to support decision-making rather than a replacement for sound investment judgment.

The comments reflected a broader sentiment that while AI is poised to transform investing, many retail traders remain cautious about handing over trading decisions to autonomous systems.

Overall, the discussion highlights both the growing interest in AI-powered financial services and the persistent concerns surrounding trust, transparency, and the role of human oversight in automated investing.

Notably, this development comes as Robinhood’s CEO has highlighted the 24/7 nature of crypto markets, where AI agents can monitor opportunities around the clock something human traders cannot match consistently.

Initially focused on stocks and options, the feature is now extending support to crypto, with plans for further assets like futures and event contracts in the future.

For users, this means the potential for more sophisticated, emotion-free trading strategies powered by advanced AI models.

Agents could rebalance portfolios, execute complex orders, or respond to market signals faster than manual trading allows. However, Robinhood stresses that users remain ultimately responsible for their accounts and any trades executed.

The move aligns with broader industry trends toward agentic AI in finance, where autonomous systems handle decision-making with human oversight.

As Robinhood continues rolling out enhancements, the platform aims to make advanced tools accessible to its millions of users. Those interested should check the official Robinhood app or website for eligibility and setup instructions once the crypto feature becomes available.

This evolution could reshape how retail investors interact with volatile crypto markets, blending human judgment with machine efficiency.

Goldman Sachs Warns AI Boom Could Fuel Inflation, With U.S. Expected To Face Biggest Impact