Global equity funds attracted fresh inflows for an eighth consecutive week through July 15 as easing U.S. inflation, robust corporate earnings and renewed optimism around artificial intelligence encouraged investors to increase exposure to risk assets, although the pace of buying slowed from the previous week.

According to LSEG Lipper data covering 28,904 funds, investors purchased a net $12.46 billion of global equity funds during the week, following a much stronger $48.35 billion of inflows the previous week.

The continued inflows suggest that investors remain constructive on equities despite elevated valuations and lingering geopolitical risks, including tensions involving Iran and uncertainty surrounding global trade. The moderation in weekly purchases appears to reflect profit-taking after the previous week’s surge rather than a broader deterioration in market sentiment.

Europe Leads Equity Inflows As Investors Rotate Globally

European equity funds emerged as the biggest beneficiaries of global investor flows, attracting $9.49 billion, while Asian funds drew $5.4 billion.

In contrast, U.S. equity funds experienced $4.8 billion in net outflows, indicating investors may be rotating geographically after a prolonged period of U.S. market outperformance.

The shift comes as European equities benefit from improving corporate earnings and relatively attractive valuations compared with U.S. stocks, where technology companies continue to dominate market performance and trade at historically elevated multiples.

The strong start to the second-quarter earnings season also helped reinforce confidence in global equities. Several major Wall Street banks, including Bank of America, JPMorgan Chase, and Morgan Stanley, reported stronger-than-expected results, easing concerns that higher interest rates and slowing economic growth would significantly weaken corporate profitability.

Investor sentiment also received a boost from Dutch semiconductor equipment maker ASML, whose quarterly earnings exceeded expectations. The company raised its 2026 outlook and announced plans to expand manufacturing capacity, boosting confidence that investment in artificial intelligence infrastructure remains robust despite earlier concerns that AI spending had outpaced fundamentals.

Technology remained the most popular sector among investors, attracting $3.37 billion during the week. Although that represented the sector’s smallest weekly inflow in three weeks, it demonstrates that AI-related investments continue to dominate equity allocations globally.

Financial sector funds received $567 million in net inflows, supported by strong earnings from major banks, while healthcare funds attracted $558 million as investors continued to seek defensive growth opportunities alongside cyclical exposure.

A key catalyst behind the improved market sentiment was softer-than-expected U.S. inflation data.

June consumer price figures showed headline inflation falling 0.4%, marking the first monthly decline since the COVID-19 pandemic, while core inflation remained unchanged. The report strengthened expectations that the Federal Reserve may not need to resume interest rate increases in the near term.

Lower inflation expectations pushed Treasury yields and the U.S. dollar lower earlier in the week, improving financial conditions and supporting equity valuations.

Although Federal Reserve policymakers cautioned that a single inflation report was insufficient to declare victory over inflation, investors interpreted the data as reducing the likelihood of additional monetary tightening this summer.

Bond Demand Remains Resilient As Cash Leaves Money Markets

Fixed-income funds also continued to benefit from shifting investor allocations. Global bond funds recorded $16.16 billion in net inflows, extending their buying streak to 15 consecutive weeks, underscoring persistent demand for high-quality fixed-income assets even as equity markets continue to rally.

Government bond funds attracted $3.38 billion, marking their strongest weekly inflow since April 8, as investors increased allocations to sovereign debt amid expectations that interest rates may have peaked in several major economies.

Short-duration bond funds also remained popular, drawing $4.17 billion, reflecting continued investor preference for lower-duration assets that offer attractive yields while limiting exposure to future interest rate volatility.

The bond inflows coincided with a sharp withdrawal from money market funds. Investors pulled $102.53 billion from cash funds, the largest weekly outflow since April 15, suggesting capital is increasingly moving out of defensive cash positions and back into both equities and fixed income as confidence in financial markets improves.

Commodity fund flows painted a mixed picture.

Gold and precious metals funds attracted $376 million, ending an eight-week streak of investor withdrawals. Renewed interest in precious metals likely reflected continued geopolitical uncertainty and a weaker U.S. dollar following the inflation data.

Energy funds, however, recorded $145 million in net outflows despite elevated oil prices, indicating that investors remain cautious about the sector’s longer-term outlook amid uncertainty over global economic growth and energy demand.

Emerging markets also showed signs of renewed investor confidence.

Emerging-market equity funds attracted $2.74 billion, ending an 11-week streak of outflows, while emerging-market bond funds recorded $795 million in fresh inflows.

The turnaround suggests investors are gradually rebuilding exposure to developing economies as expectations grow that the Federal Reserve may adopt a less restrictive monetary stance. Historically, easing U.S. monetary conditions tend to support emerging-market assets by reducing pressure on local currencies, lowering financing costs, and encouraging capital inflows.

Together, the latest fund flow data indicate that investors remain broadly optimistic about global markets. Strong corporate earnings, resilient AI-related investment, moderating inflation and expectations of a less aggressive Federal Reserve continue to underpin demand for both equities and bonds.

Databricks has cemented its position among the world’s most valuable private technology companies after announcing a new funding round that values the artificial intelligence and data software company at $188 billion, underscoring how successfully it has reinvented itself from a cloud data platform into a leading enterprise AI provider.

The financing, led by Coatue, comes just months after Databricks closed another multibillion-dollar funding round and highlights investors’ willingness to place larger bets on companies building the software infrastructure that enterprises use to deploy AI. Although Databricks did not disclose the size of the latest raise, the company said the transaction will close later this summer. Multiple media reports have estimated the funding at roughly $3 billion.

The announcement is unusual because companies typically reveal funding rounds only after the capital has been received. In this case, Databricks disclosed the deal before closing, reflecting strong investor demand. According to TechCrunch, investors were competing aggressively for allocations, giving the company little reason to keep its latest valuation confidential.

The latest fundraising continues one of Silicon Valley’s most remarkable valuation trajectories.

In December 2024, Databricks raised what was then a record $10 billion at a $62 billion valuation. That was followed by a $1 billion fundraising in September 2025, valuing the company at $100 billion, before another $5 billion Series L round closed in February 2026 at a $134 billion valuation.

Just five months later, the company’s valuation has surged another 40% to $188 billion, representing more than a threefold increase in less than two years.

The pace of fundraising has become so frequent that it has sparked jokes across the venture capital industry about Databricks eventually exhausting the traditional alphabetical naming convention for financing rounds.

One social media user quipped: “Turning on alerts for when we get a Series AA.”

Behind the soaring valuation is a fundamental transformation of Databricks’ business.

Founded in 2013 by the creators of Apache Spark, the company originally built its reputation during the big data era by helping enterprises store massive datasets in cloud environments while enabling fast analytics. Long before generative AI became mainstream, Databricks had already become deeply embedded in many large organizations’ data infrastructure.

That existing position proved highly advantageous once companies began deploying generative AI.

Unlike startups building frontier AI models, Databricks already sat on the enterprise data that organizations wanted AI systems to access securely. Rather than competing directly with OpenAI or Anthropic, the company positioned itself as the platform enabling businesses to integrate multiple AI models with their proprietary data while maintaining governance, compliance and security.

Its evolution has accelerated over the past two years.

Databricks has introduced a succession of AI-focused products, including Lakebase, a database designed specifically for AI agents, Unity, an AI gateway for enterprise model management, and Omnigent, a “meta-harness” that orchestrates multiple AI agents working together across complex workflows.

Those offerings place Databricks squarely at the center of one of enterprise AI’s fastest-growing markets: helping businesses deploy agentic AI systems capable of performing autonomous tasks.

The company’s transformation also mirrors a broader shift occurring across enterprise AI. Rather than relying exclusively on expensive proprietary models from OpenAI or Anthropic, many enterprises are increasingly adopting powerful open-weight models to reduce operating costs while maintaining flexibility.

Databricks has emerged as one of the industry’s strongest advocates of that strategy. The company has become a prominent supporter of Z.ai’s GLM 5.2, arguing that the Chinese-developed open-weight model delivers competitive coding performance at significantly lower cost than proprietary alternatives.

Last week, Chief Executive Ali Ghodsi published the results of internal benchmarking designed to optimize AI usage across Databricks’ roughly 3,000 software engineers. Instead of relying solely on conventional benchmark scores, the company evaluated AI systems using the real-world programming tasks performed by its own engineering teams.

The results highlighted a rapidly changing competitive industry. According to Databricks, GLM 5.2 successfully handled even the company’s most demanding software engineering tasks while delivering substantially lower inference costs than leading proprietary models from Anthropic and OpenAI.

Perhaps more significant was another finding.

Databricks concluded that selecting the AI model itself is only part of determining overall costs. The company found that the surrounding agentic coding harness—the software layer responsible for managing prompts, context windows and interactions with the underlying model—can have an equally significant effect on efficiency and operating expenses.

Its research identified the open-source harness Pi as one of the strongest performers, demonstrating that effective context management could reduce AI costs without sacrificing output quality.

“The lesson here isn’t that one harness is always cheaper or that native harnesses are worse,” Databricks wrote in its blog post.

“Instead, model choice is only one piece of the puzzle.”

The findings boost one of the defining trends emerging across enterprise AI in 2026. Competitive advantage is increasingly shifting away from simply possessing the largest frontier models toward building sophisticated software platforms that intelligently orchestrate models, agents, enterprise data and workflows.

That positioning has become a major driver of Databricks’ rising valuation. Unlike companies whose fortunes depend on training ever-larger foundation models, Databricks generates recurring enterprise software revenue while benefiting from growing corporate AI adoption regardless of which underlying model customers ultimately choose.

Its platform effectively allows enterprises to mix proprietary and open-weight models based on performance, security and cost considerations, making Databricks relatively insulated from rapid shifts in the foundation model landscape.

The valuation also reveals broader investor enthusiasm for enterprise AI infrastructure. While much public attention has focused on AI chipmakers such as Nvidia and cloud providers building massive data centers, investors are increasingly directing capital toward software companies enabling businesses to operationalize AI at scale.

Databricks’ ability to reinvent itself from a cloud analytics company into a core enterprise AI platform shows that the generative AI boom is reshaping not only technology products but also investor perceptions. What was once viewed primarily as a data engineering company is now valued alongside the world’s most important AI businesses.

The company’s rapid ascent also demonstrates the powerful valuation premium investors continue to assign to firms viewed as central to enterprise AI adoption. As TechCrunch noted, the so-called “AI halo” has become so influential that companies across industries increasingly emphasize artificial intelligence in investor communications. In one notable example, sandwich chain Jersey Mike’s referenced AI 22 times in its IPO filing, highlighting how closely capital markets have tied corporate growth narratives to artificial intelligence.

Databricks’ latest funding round suggests that, for companies able to convincingly position themselves at the center of enterprise AI, investor appetite remains far from exhausted.