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How can investors identify which companies are genuinely driving artificial intelligence innovation? This article looks at how a transparent, rules-based framework combining research and data helps define and track meaningful AI exposure across global markets, and how Bloomberg Thematic Indices apply that methodology to measure the theme consistently over time.
This article was written by Mike Pruzinsky, Equity Index Product Manager at Bloomberg Index Services Limited (BISL).
Welcome to the final part of our 3-part blog series looking at how AI is reshaping markets and how Bloomberg Indices can reflect this transformation in market structure and sector dynamics.
How can we determine which companies are genuinely participating in the development or deployment of AI and which are simply associated with it by narrative or proximity? This is not a trivial question. For example, traditional sector classifications often fail to capture how emerging technologies cut across established industry boundaries.
Active managers often look to fill the gap through research and judgment, but their approaches vary widely, making it difficult to replicate or compare results. The challenge lies not in the availability of data, but in establishing a consistent process to interpret it.
PRODUCT MENTIONS
Why do themes need taxonomy?
Thematic indices look to solve that problem by organizing the market around ideas rather than legacy categories. They aim to identify companies that derive meaningful economic value from participating in a structural trend and apply a transparent process to define and measure that exposure.
Nowhere is this more relevant than in the realm of AI, where rapid change defies traditional methods of classification. Note, as thematic investing is on the rise, we explored this broader shift in our previous article here.
In this piece, we explore how a rules-based thematic index framework developed by Bloomberg Indices and supported by Bloomberg Intelligence analyst teams, seeks to bring order to that complexity. By combining analyst insight with quantitative assessment, this approach helps to create a clearer, more objective view of how AI is reshaping companies and how investors can systematically track the theme as it evolves.
At the heart of this effort is the Bloomberg Thematic Protocol and that process begins with definition. Each theme is clearly scoped to avoid ambiguity. For AI, that means identifying companies engaged in machine learning, natural language processing, and related technologies that enhance perception, reasoning, and automation. Analysts then map the AI ecosystem, segmenting it into developers, enablers, service providers, and end-users. This creates a structured foundation that replaces guesswork with taxonomy.
“By uniting global equities, ETFs, and private markets through a thesis-driven lens, thematic strategy work redefines how investors discover opportunity, harnessing data-driven precision and collective insight to build agile, forward-looking research covering powerful secular trends like AI with a focus on where the world is moving next,” says Breanne Dougherty, Head of Thematic Strategy at Bloomberg Intelligence.
Finding signal in the noise
Once the scope is defined, Bloomberg’s natural language processing tools comb through company filings, presentations, and earnings transcripts to help identify where a theme or in this case AI exposure is mentioned in a meaningful way.
The data-driven screening process draws on signals from corporate disclosures and helps cast a wide net from the universe of companies. Human analysts then review the results to verify that exposure is authentic and strategically significant rather than superficial or promotional. This blend of technology and judgment aims to ensure that the theme remains grounded in reality rather than company promotion.
Each company is then evaluated across two key dimensions: revenue exposure and thematic relevance. Revenue exposure measures how much of a company’s future business derives from AI products or services, while thematic relevance assesses how strategically and competitively that company is positioned within the theme. Together, these combined assessments quantify both economic dependency and strategic importance.
As shown in Figure 3, Alphabet achieves the highest possible combined assessment, with 1’s in both its revenue and theme assessments, reflecting deep and diversified exposure to AI. Across its core businesses in search, cloud, YouTube, and advertising, Alphabet integrates AI to enhance relevance, automate operations, and drive user engagement.
Through Google Cloud’s AI infrastructure, the company also plays a pivotal role in enabling the broader AI ecosystem by supporting enterprises and developers with advanced machine learning and foundation model capabilities. This highlights how a rules-based thematic framework can provide a structured way to assess AI exposure in companies whose contributions are often overlooked or difficult to quantify.
Assessing the edge of the structure
The Bloomberg Thematic Protocol combines a balance between fundamental research and rules-based rigor. This combination provides atsystematic framework for thematic exposure,, offering investors clearer visibility into why each company is included and how it contributes to the theme over time.
“At Bloomberg, our goal is to translate deep thematic research into investable insights,” says Gaurav Pendse, Head of Equity Product Development at Bloomberg. “Bloomberg Intelligence systematically identifies companies with meaningful exposure to artificial intelligence. Building on that research, we developed the Bloomberg Artificial Intelligence Index, which tracks the 50 companies with the highest assessed AI involvement. Each constituent is weighted by a combination of market capitalization and revenue-based exposure, resulting in a rules-driven benchmark that reflects how AI is reshaping global equity markets.”
To assess thematic performance, we can compare the Bloomberg Artificial Intelligence Total Return Index (BAIT) against the broader technology sector. As shown in Figure 5, a Brinson attribution analysis highlights strong relative results. Over the past five years, the Bloomberg Artificial Intelligence Index has outperformed the Bloomberg World Technology Large & Mid Cap Index (WORLDTT), driven by allocations to AI application software, hyperscalers, infrastructure software, and semiconductors. Moreover, as
Figure 5 illustrates, companies with more participation to theme as measured by the combined assessments (revenue + theme assessment) have generally delivered stronger performance than those with less participation to theme.
This performance reflects the application of a transparent, rules-based approach designed to capture thematic trends beyond traditional classifications. By combining analyst insight with quantitative assessment, the Bloomberg Artificial Intelligence Index offers a forward-looking framework that helps investors understand how companies are positioned within the evolving AI landscape.
As AI and other transformative themes evolve, a rules-based thematic index brings order to constant change. It defines exposure, measures it consistently, and evolves with the narrative, showing that structure can enhance rather than constrain innovation. In doing so, it helps investors see how long-term themes translate into real economic value.
Mapping AI’s evolving market impact
This article is the third of a three-part series exploring AI’s investment landscape.
In part 1, we looked at how artificial intelligence (AI) is reshaping markets and how Bloomberg Indices can reflect this transformation in market structure and sector dynamics.
In part 2, focusing on mapping the AI value chain, we will explain how chips, cloud infrastructure, models, and applications create value, and how Bloomberg indices measure that opportunity.
Throughout this series, the insights presented aim to provide investors a clear understanding of AI’s impact on financial markets and demonstrate how Bloomberg Indices convert that innovation into measurable exposure.
To learn more about Bloomberg Indices, click here.
- First Trust has launched the First Trust Bloomberg Artificial Intelligence ETF based on the Bloomberg Artificial Intelligence Total Return Index (BAIT Index) under the ticker FAI.
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