The global technology rally gathered remarkable momentum in the first half of 2026, but the biggest winners were no longer America’s trillion-dollar technology giants. Instead, investors increasingly rotated into semiconductor manufacturers, AI infrastructure suppliers and international technology stocks, signaling that the artificial intelligence investment boom is broadening well beyond Silicon Valley.

While U.S. Big Tech continued to deliver solid gains despite heightened volatility, emerging markets and Asian semiconductor stocks significantly outperformed, driven by an unprecedented global race to build AI data centers, expand chip manufacturing capacity and develop next-generation AI infrastructure.

The shift also reflects a maturing AI investment cycle. After two years dominated by companies such as Nvidia and Microsoft, investors are now directing capital toward the wider AI ecosystem, including memory chipmakers, semiconductor equipment manufacturers, power infrastructure companies, robotics firms and industrial automation suppliers.

Among MSCI’s sector-specific benchmarks, the Emerging Markets Information Technology Index emerged as the standout performer, soaring more than 90% during the first six months of 2026, according to a report by CNBC.

By comparison, the MSCI Europe Information Technology Index gained 44.8%, while the MSCI USA Information Technology Index, whose largest constituents include Nvidia, Apple, Microsoft, Broadcom and Micron, advanced a comparatively modest 19.4%.

The same pattern played out across broader technology benchmarks.

Europe’s STOXX 600 Technology Index climbed 23.4%, comfortably outperforming the S&P 500 Information Technology Index, which gained 19.4%.

Meanwhile, the technology-heavy Nasdaq 100 rose 19.9%, outperforming the broader U.S. market but still lagging several overseas indexes.

The broader U.S. equity market delivered respectable returns, with the S&P 500 rising 9.55%, the Nasdaq Composite advancing 12.79%, and the Dow Jones Industrial Average adding 8.85% during the first half.

Yet each was surpassed by several major international markets.

AI Investment Reshapes Global Leadership

Emerging markets continued their strong run as investors sought companies positioned to benefit from the global AI infrastructure build-out.

The MSCI Emerging Markets Index gained 24%, while South Korea’s Kospi surged an extraordinary 101.1%, making it one of the world’s best-performing major equity benchmarks. Japan’s Nikkei 225 also delivered exceptional returns, climbing approximately 39%.

The rally has been fueled by explosive growth in semiconductor manufacturers that have become central suppliers to the AI industry.

South Korea’s SK Hynix, now one of Nvidia’s largest suppliers of high-bandwidth memory (HBM) chips, soared roughly 300% during the first half of the year as demand for AI memory continued to outstrip supply. Taiwan Semiconductor Manufacturing Company (TSMC), the world’s largest contract chipmaker, rose 55.5%, supported by robust orders for advanced AI processors.

European semiconductor equipment manufacturers also enjoyed blockbuster gains. Dutch chip equipment maker ASMI surged 93.3%, while industry heavyweight ASML gained 86.8% as global chipmakers accelerated investments in advanced fabrication plants. BE Semiconductor Industries more than doubled in value, benefiting from booming demand for advanced semiconductor packaging technologies increasingly required for AI chips.

Although AI enthusiasm remained intact, investor leadership within the U.S. technology sector became more concentrated. Nvidia, whose chips remain the foundation of the AI revolution, gained 7.3% during the first half after an extraordinary rally over the previous two years.

Other members of the so-called Magnificent Seven experienced considerably greater volatility.

Microsoft shares declined 22.9% over the six-month period as investors questioned whether soaring AI-related capital expenditure would translate into equally rapid earnings growth.

Market participants increasingly scrutinized whether hyperscalers—including Microsoft, Amazon, Alphabet and Meta—could generate sufficient returns from hundreds of billions of dollars being invested in AI infrastructure.

That reassessment contributed to one of the most notable rotations in global equity markets this year.

European equities posted relatively modest gains overall. The pan-European STOXX 600 rose more than 8% during the first half. Britain’s FTSE 100 gained 5.7%, Germany’s DAX advanced about 1.9%, while France’s CAC 40 climbed just over 3%.

Southern European markets delivered stronger performances. Spain’s IBEX 35 jumped 12.5%, Italy’s FTSE MIB gained 14.7%, and Portugal’s PSI Index added 10.5%.

Volatility Driven By AI, Geopolitics, and Monetary Policy

Global markets experienced significant turbulence throughout the period. Investors navigated uncertainty surrounding AI valuations, the U.S.-Iran conflict, rising geopolitical tensions and changing expectations for U.S. monetary policy.

The sharp sell-off that hit many technology stocks late in June reflected growing concern that elevated AI spending could weigh on corporate profitability before generating corresponding revenue.

In its mid-year investment outlook, BlackRock Investment Institute argued that artificial intelligence remains one of the most powerful long-term investment themes despite recent volatility.

“AI raises the prospect of a permanent growth breakout by accelerating innovation itself,” it said.

“Yet the route to abundance, if we get there, runs through scarcity. A similar tension is playing out across other investment themes—and reshaping portfolios.”

BlackRock said investors continue to face three major unanswered questions.

“Three questions remain unresolved: is AI becoming a bubble, how costly will it be, and who will capture the value?”

The firm maintained an overweight position on U.S. equities while recommending exposure to AI infrastructure rather than attempting to identify future model winners.

“We stay overweight U.S. equities and focus on bottleneck opportunities to participate in AI growth without picking model winners: power, grids, memory, chips and data centers. Physical AI—robots, autonomous systems and manufacturing—is the next frontier,” it added.

Interest Rates Could Dominate Second Half

While AI remains the structural driver of global markets, economists believe monetary policy could become the primary catalyst during the remainder of the year.

Anthony Willis, senior economist at Columbia Threadneedle Investments, said several headwinds that unsettled investors earlier this year appear to be easing.

“Encouragingly, some of the pressures that weighed on markets in the first half now appear to be easing.”

He said attention is increasingly shifting toward the Federal Reserve.

“As investors reassess whether the Fed may need to raise rates again—and how often—market pricing is likely to remain sensitive to incoming data and central bank communication.”

According to CME’s FedWatch Tool, markets are currently pricing a 66.3% probability that the Federal Reserve leaves interest rates unchanged at its July meeting, while assigning a 66.9% chance of at least a 25-basis-point rate increase at the September meeting.

Willis also warned that corporate earnings will become increasingly important as investors demand evidence that massive AI spending is generating sustainable returns.

“The critical question is whether companies can monetize that spending and generate an attractive return on investment,” he said.

“Expectations around AI-related capital expenditure, revenue growth and profitability are now high, which means earnings results could become an important source of market volatility.”

Analysts at Deutsche Bank believe June marked a turning point for market leadership. Strategist Jim Reid identified four major reasons behind the underperformance of the Magnificent Seven technology stocks during the month: the unwinding of crowded investor positioning, concerns about hyperscalers’ AI capital expenditure, a more hawkish Federal Reserve, and rising semiconductor costs.

“While ‘AI fever’ continues globally, with benchmarks like the KOSPI index up over 100% year-to-date, leadership in the market has shifted away from the Mag 7 for now,” Reid wrote.

The changing market leadership suggests investors are entering a new phase of the AI investment cycle, one where the beneficiaries extend far beyond America’s biggest technology companies to encompass semiconductor manufacturers, industrial automation firms, robotics suppliers, memory chipmakers and infrastructure providers across Asia and Europe. As global spending on AI continues to accelerate, analysts now expect the next leg of the rally to be driven by the companies supplying the hardware, power and industrial systems that make artificial intelligence possible.

Executives at large companies are becoming increasingly mindful of escalating AI-related expenses, with roughly 60% of enterprises now implementing some form of guardrails to manage or throttle their spending, according to recent conversations with IT leaders compiled by UBS analysts.

In a report released this week, UBS analysts Karl Keirstead, Timothy Arcuri, Taylor McGinnis, and their colleagues highlighted a noticeable shift in corporate attitudes toward AI investments. Token spending, the unit by which many AI services are priced, has emerged as a significant concern, particularly for larger organizations where chief financial officers and chief technology officers are seeing their AI budgets balloon without always delivering clear returns.

Uber’s operations chief, Andrew Macdonald, captured the growing sentiment in May when he noted it was becoming harder to justify rising costs given the relatively modest return on investment observed so far.

The analysts, drawing from more than a dozen discussions with enterprise IT executives over recent weeks, described a “modest emerging headwind” in AI spending patterns. Follow-up conversations reinforced this view, though the degree of impact varies considerably across organizations.

“Token spend optimization has become a key issue in most organizations, resulting in a big spending speed bump for some organizations, but a smaller speed bump for others that are either too early in their AI deployments or are far deeper but are unwilling to throttle users because they see the offsetting ROI or have an organizational priority in place to drive innovation and hence AI use,” the analysts wrote.

AI model providers such as OpenAI and Anthropic are likely to feel the effects most acutely in the near term, the report suggested. Open-source and Chinese models, such as DeepSeek, could emerge as notable beneficiaries, especially for enterprises seeking cost-effective solutions for non-coding tasks.

Despite the emerging restraint, the analysts emphasized they are not sounding any alarms, viewing the trend as “a healthy problem” that reflects more disciplined management rather than a fundamental retreat from AI.

“Some measure of AI spend optimization is normal, no one is hitting the brakes on AI deployment and it is likely that we’re sitting in front of new models trained on next-gen chips that might drive token costs down further,” they wrote.

Leading AI companies have already begun highlighting improved token efficiency in their latest models. Google has its Gemini 3.5 Flash offering, while Anthropic recently launched Claude Sonnet 5, which the company said “runs autonomously at a level that just a few months ago required larger and more expensive models.”

From Experimentation to Engineering Discipline

Conversations with executives reveal a clear evolution in how companies approach AI. One organization told UBS analysts the industry is moving beyond the initial phase of broad experimentation.

“The question isn’t whether to use tokens, it’s how to use them efficiently,” the analysts quoted one executive as saying. “As a result, optimization becomes an ongoing engineering discipline rather than a reaction to a budget crisis.”

Another company described a situation where its CTO had enthusiastically embraced multiple AI tools early on, only to face budget constraints later.

“We have 5 AI tools internally and all of the LLM products. Like others, we ran into the issue where we have already used most of our token budget for the entire year,” the analysts recounted. “Now we’re only using 2 AI tools and being careful around usage.”

This shift toward greater cost consciousness does not necessarily signal a slowdown in overall AI adoption. Instead, it points to a more mature phase where organizations are becoming savvier about where and how they deploy the technology. Companies are increasingly prioritizing high-impact use cases while optimizing spending on lower-value applications.

The analysts noted that while some organizations are actively throttling usage, others, particularly those earlier in their AI journeys or those with strong innovation mandates, continue to invest aggressively. This variation suggests the overall AI spending trajectory remains upward, albeit with greater scrutiny and efficiency measures in place.

Implications for AI Providers and the Broader Ecosystem

For AI model makers, this development is expected to reshape competitive dynamics. Providers offering more token-efficient models or flexible pricing structures may gain an edge, while those relying on premium pricing for cutting-edge capabilities could face margin pressure if enterprises become more selective.

Open-source alternatives and models from Chinese developers are particularly well-positioned to capture share in cost-sensitive segments. Analysts expect this to accelerate the commoditization of certain AI capabilities and intensify competition across the industry.

At the same time, the focus on optimization is expected to drive innovation in areas such as model compression, efficient inference, and specialized AI agents designed for specific business functions. Companies that can deliver strong performance at lower token costs may find themselves better positioned as enterprises move from experimentation to scaled deployment.

However, the broader takeaway from UBS’s conversations is one of pragmatic maturation rather than disillusionment. Enterprises are not abandoning AI — they are becoming more sophisticated in how they implement and manage it. This evolution could ultimately strengthen the technology’s long-term adoption by ensuring investments are more closely tied to measurable business outcomes.