Artificial intelligence has become one of the defining forces in global equity markets, with AI-linked companies substantially outperforming broader benchmarks since the beginning of 2024. The Bloomberg Global AI Index has gained approximately 118% over the period shown in the source graphic, compared with roughly 57% for the MSCI World Index, illustrating how strongly capital has concentrated around companies positioned to benefit from the AI investment cycle.

AI Performance Has Created a Wide Gap With Global Equities

The scale of the divergence is significant. Based on the data presented in the attached Bloomberg-sourced graphic, the Bloomberg Global AI Index has generated an approximately 61-percentage-point cumulative advantage over the MSCI World Index since the start of 2024. The MSCI World Index represents large- and mid-cap companies across developed markets and covers approximately 85% of free-float-adjusted market capitalization in each country, providing a broad reference point for developed-market equities.

The comparison demonstrates how the AI investment cycle has influenced the composition of global equity returns. Bloomberg’s AI index is designed to capture companies that develop, facilitate or use artificial intelligence solutions, including areas such as machine learning, natural language processing and related technologies. Bloomberg’s methodology spans multiple parts of the AI ecosystem rather than focusing exclusively on chip manufacturers or software companies.

Emerging Markets Have Also Participated in the Rally

The performance gap is not simply an AI-versus-everything-else story. The attached data shows the MSCI Emerging Markets Index gaining approximately 70% since the beginning of 2024, placing it between the AI index and the broader developed-market benchmark. MSCI’s current index data confirms that emerging markets have delivered substantial recent gains, with the MSCI Emerging Markets Index recording a 31.39% one-year return as of September 25, 2026.

Emerging markets also have meaningful exposure to the technology supply chain, particularly through Asian semiconductor, hardware and technology companies. This creates an important distinction: the global AI cycle can benefit markets outside the United States, but the distribution of those benefits depends on where companies sit within the AI value chain, their exposure to global technology spending and the strength of domestic demand.

Why the AI Premium Matters for Global Markets

The widening performance differential reflects more than enthusiasm surrounding a single technology. AI development requires substantial spending on semiconductors, data centers, networking equipment, cloud infrastructure, software and electricity, creating revenue opportunities across several industries. Bloomberg has described AI as an ecosystem spanning infrastructure and applications, while its more recent index research emphasizes that investors are increasingly examining the broader AI value chain rather than only the most visible technology companies.

At the same time, the strength of the rally creates a more important market question: how much future AI growth is already reflected in equity prices? Bloomberg reported in 2026 that the AI theme had continued to outperform broader equity benchmarks while valuation dispersion remained substantial across semiconductors, hyperscalers and infrastructure software. The research also highlighted the increasing importance of AI monetization as markets move beyond the initial investment phase and begin assessing whether enormous capital expenditures can generate sufficiently strong earnings and cash flows.

For sophisticated investors in Israel and global markets, the divergence is relevant because AI exposure is increasingly influencing global benchmark performance, sector allocation and cross-border capital flows. It also means that headline index performance can conceal substantial differences between companies that supply critical AI infrastructure, businesses monetizing AI applications and companies whose valuations are more dependent on future adoption.

Going forward, the key indicators will be AI-related revenue growth, corporate capital expenditure, semiconductor demand, data-center investment, productivity gains and valuation levels. If earnings growth continues to broaden across the AI ecosystem, the current market leadership could become more diversified; if spending rises faster than monetization, the performance gap could become more volatile. The next phase of the AI cycle will therefore be measured increasingly by financial results and cash-flow generation rather than technology adoption alone.