MARKET INSIGHTS

The landscape of tech stocks, AI, artificial intelligence stocks, and related indexes in Canada entered 2026 shaped by developments across the global AI market, ongoing AI boom, and evolving index methodologies. This overview explores how technology and AI exposure has been classified in index rules and documents sector notes on tech and AI companies and market segments that featured prominently in Canadian and global markets. All content reflects past performance, index methodologies, and publicly available trading data rather than forward-looking commentary.

Tech and AI have remained frequent topics in discussions of innovation, cloud computing, machine learning, data centers, generative AI, AI infrastructure, and enterprise software. Index exposure to these themes has varied, with products tracking broad technology sectors, robotics and AI thematic indexes, and AI infrastructure benchmarks. Canadian markets have seen both the participation of local AI companies and significant demand for global tech and AI stocks that contribute to the AI ecosystem.

This content is for educational purposes only and does not constitute investment advice or a recommendation.

Canada’s Tech Market Shape

Sector Composition: Tech Relative to Major Sectors

In broad Canadian equity indexes such as the S&P/Toronto Stock Exchange (TSX) Composite and S&P/TSX 60, Information Technology has historically represented a smaller portion of total market weight compared with sectors like financials, energy, and materials. Data from the S&P/TSX 60 index for December 2025 showed Information Technology near between 13.8% and 15.1% of sector weight in the large-cap index, with financials and energy making up a larger share of market capitalization.

Similarly, estimates for the full S&P/TSX Composite Index in late 2025 indicated financials near 30%, energy around 16%, materials 17%, and Information Technology approximately 10%, reflecting the broader Canadian public equity market’s composition.

These compositions have historically reflected Canada’s economic structure, where financial and resource sectors have been prominent, with technology representing a meaningful but smaller component by weight.

Canadian Information Technology Characteristics

Within the Information Technology sector on Canadian exchanges, characteristics have varied across subsectors:

• Software and Services: Enterprise software firms and IT services names have appeared among top technology contributors in major Canadian indexes, with reported revenue and operating figures from issuer filings providing historical context.
• Hardware and Equipment: Technology hardware and related services firms have contributed to sector representation, though often at lower individual index weights compared with software names. Index provider fact sheets have typically documented these subsector distributions.
• Canadian tech names have shown a range of trading liquidity, with larger constituents generally exhibiting higher exchange-traded volume and smaller names presenting more varied liquidity profiles, consistent with index methodology requirements that often emphasize minimum liquidity thresholds for inclusion.

Cross-Sector AI Exposure

Exposure to AI-related activities has sometimes appeared outside the pure Information Technology classification:

• Communication Services: May include firms with digital platforms that support AI workloads or generative AI features through software ecosystems.
• Industrials: Have historically included automation and robotics segments linked to technological applications.
• Real Estate/Utilities: Have been relevant where data centers and AI infrastructure underpin cloud and computational services, with REITs and infrastructure plays included in some thematic index constructs.
• Index providers typically categorize such exposures according to established classification standards such as the Global Industry Classification Standard (GICS), with sector weights disclosed in fact sheets.

These observations describe how Canada’s public market has been structured and how index methodologies have captured technology and related exposure based on historical data rather than current or forward-looking assumptions.

What Indexes Mean by “Tech” and “Artificial Intelligence”

Information Technology Sector (GICS) Definitions

Indexes that classify tech stocks, AI, and related exposure often rely on the Global Industry Classification Standard, an industry taxonomy developed jointly by MSCI and S&P Dow Jones Indices.

Under GICS, the Information Technology sector includes companies whose principal business activity is technology-related, with revenues often derived from software, hardware, IT services, semiconductors, or communications equipment.

Each company is assigned to a single GICS sector based on its primary business activity, which is determined primarily by revenue but may also consider earnings and market perception.

Indexes such as the MSCI ACWI Information Technology Index track a broad universe of global technology firms classified under GICS, spanning sub-industries from software and services to technology hardware and equipment. This classification provides a baseline definition for what “tech” exposure means within broad sector benchmarks, though it does not incorporate separate thematic rules for AI beyond the sector classification itself.

AI Stocks/Thematic Index Constructions

Indexes designed to capture AI market exposure or artificial intelligence stocks typically apply additional rules beyond the GICS sector classification. Thematic indexes may screen for companies with measurable revenue exposure to AI hardware, software, or services, drawing on business activity data to assess relevance to AI themes. Some indexes use criteria that evaluate how much of a company’s revenue comes from AI-related segments or integrate research-based exposure scores that reflect participation in the AI ecosystem. These methodologies often include periodic rebalances whereby eligible companies are reevaluated by the index provider and the list of constituents is adjusted on a scheduled basis (e.g., quarterly).

Indexes focused on AI infrastructure or robotics and AI thematic exposure commonly identify eligible firms that contribute to AI development and deployment across hardware, analytics, or platform services, expanding beyond traditional tech subsectors.

NASDAQ-Style Growth and Broad Benchmarks

Other indexes that provide tech-oriented exposure, such as NASDAQ large-cap growth proxies, define technology and AI relevance through quantitative rules that select companies based on market capitalization, liquidity, and thematic relevance to innovation trends. Unlike GICS-based sector indexes, thematic indexes may include firms outside the Information Technology sector if they exhibit significant involvement in AI workloads, AI infrastructure, or generative AI applications.

NASDAQ thematic index descriptions note that these constructs are rules-based and may incorporate cross-sector inclusion, recognizing that AI-linked activities can appear in communication services, industrials, or consumer sectors as part of broader technological innovation.

Summary of Index Interpretation

Overall, when index methodologies refer to “tech,” they often rely on established industry classification frameworks like GICS to define the broad Information Technology sector. When indexes reference AI themes specifically, they tend to incorporate additional selection rules based on revenue or exposure to AI hardware, software, or services, periodically rebalancing to reflect updated data. While these rules aim to systematize exposure to AI stocks and related areas, they remain defined by historical classifications and documented index methodologies rather than forward-looking assumptions.

Tech/AI Sector & Theme Cards: Facts, Disclosures & Risks

This section summarizes how several key technology and AI-related sectors have been defined in index methodologies and in issuer fact sheets for Canadian-listed products. Each card includes an overview of index methods, typical inclusions/exclusions, common disclosures such as management expense ratio (MER) and replication approach, and risk notes drawn from available documentation (e.g., issuer fact sheets and index rulebooks).

Canadian Information Technology (Broad)

Index Method:

• The S&P/TSX Capped Information Technology Index serves as a benchmark for Canadian technology exposure. The index uses GICS to identify companies primarily classified in the Information Technology sector and applies a capped market-capitalization methodology to limit the weight of any single constituent, typically around 25% to mitigate concentration. Quarterly reviews and rebalancing adjust constituent weights in line with the cap rule.

Common Inclusions/Exclusions:

• Included firms are those listed on the TSX with primary business activities in tech sub-industries such as software & services, hardware & equipment, and related IT services. The capping rule implicitly limits domination by large individual issuers but does not impose thematic screens beyond sector classification.

Disclosures Often Reviewed:

• Issuer fact sheets typically disclose MER, replication approach (full replication of index constituents), quarterly reconstitution cadence, and any securities lending policies. The number of holdings and sector breakdown are also commonly reported.

Risk Notes:

• Risk sections in fact sheets have noted that narrower sector focus and limited number of constituents can contribute to higher volatility relative to broader equity benchmarks. Concentration risk due to large weights in a few companies may also be mentioned.

Global Large-Cap Growth Proxy

Index Method:

• Indexes such as the NASDAQ-100 Index are constructed to represent a large group of non-financial companies listed on the NASDAQ exchange. Eligibility criteria include minimum liquidity and seasoning periods, and financials are excluded based on sector classification systems like the Industry Classification Benchmark (ICB). The index employs modified market-capitalization weighting with provisions to cap individual weight influence.

Common Inclusions/Exclusions:

• Constituents typically include large-capitalization technology, consumer discretionary, communication services, and other sectors except dedicated financial firms. The modified weighting and annual reconstitution processes reflect rules designed to capture market dynamics over time.

Disclosures Often Reviewed:

• Canadian-listed ETFs that track NASDAQ proxies generally disclose indexed benchmark names (e.g., NASDAQ-100), MERs, replication techniques (often full replication or representative sampling), and annual rebalancing schedules as part of listing documents and fund facts.

Risk Notes:

• Documentation has noted that concentration in growth-oriented technology names and related sectors could correspond with greater variability in historic returns relative to broader market indexes.

AI Infrastructure (Semiconductors, Compute, Cloud)

Index Method:

• Some AI-infrastructure thematic indexes, such as the Indxx Artificial Intelligence & Big Data Index, have been referenced in fact sheets for products like the Global X Artificial Intelligence & Technology Index ETF. These indexes use systematic eligibility criteria to identify companies that have measurable involvement in AI development, utilization, or hardware support (e.g., processors, data analytics infrastructure).

Common Inclusions/Exclusions:

• Indexes in this theme may include firms involved in high-performance computing, semiconductors, cloud infrastructure, and other technologies facilitating AI workloads. Specific revenue or business activity thresholds are applied as specified by the index provider.

Disclosures Often Reviewed:

• Issuer documents list the underlying benchmark, MER, index provider, holdings structure, and replication approach. Periodic rebalancing frequency and index reconstitution rules are often summarized in fact sheets.

Risk Notes:

• Risk descriptions have cited potential for heightened volatility given focused exposure to infrastructure segments and reliance on global technology cycles. Historical volatility metrics may be included to portray how returns have varied in the past.

Software & Services (AI-Enabled Applications)

Index Method:

• Indexes targeting software and services exposure typically rely on industry classification systems like GICS to define sub-industries including application software, systems software, and IT services. While specific thematic AI screens may not be part of broad sector indexes, these classifications capture companies where AI-enabled applications contribute to business activities.

Common Inclusions/Exclusions:

• Companies that derive most revenue from software development or technology services are included. Excluded firms are those outside these classifications, and non-operating entities or firms with limited software relevance are typically omitted.

Disclosures Often Reviewed:

• Fact sheets for equity products referencing these classifications report MER, underlying benchmark, replication approach, sector breakdown, and top holdings. Software exposure levels and historical sector performance information may be part of periodic disclosures.

Risk Notes:

• Risk summaries often note that concentration in software and services may lead to performance patterns that differ from broad market indexes, particularly during periods of sector-specific shifts in demand or innovation cycles.

Data Centers/Connectivity Adjacent

Index Method:

• Where applicable in Canadian-listed ETFs, data centre and connectivity adjacent exposures have been captured through thematic benchmarks that include infrastructure and cloud-related companies. In products like AI or big data indexes, methodology descriptions may list eligibility rules for companies that contribute to data centre operations, network infrastructure, and associated cloud services.

Common Inclusions/Exclusions:

• Included firms are those with significant business lines related to data processing infrastructure, cloud networking, and connectivity platforms. Exclusions generally occur when companies lack material involvement in these areas as defined by index criteria.

Disclosures Often Reviewed:

• Issuer fact sheets summarize the index tracked, MER, replication style, reconstitution frequency, and any securities lending programs. Holdings reflecting data-centre REITs or networking infrastructure providers may also be detailed.

Risk Notes:

• Fact sheet risk sections for these themes have historically mentioned that concentrated exposures to infrastructure or connectivity sectors may correspond with return profiles that differ from broader indexes and may show pronounced historical volatility.

Summary

Each of these sector and theme cards reflects index methodologies and disclosures documented in publicly available rulebooks and issuer fact sheets. Definitions rely on structured criteria such as GICS classifications, revenue exposure, or systematic eligibility screens. Common disclosures include management expense ratios, replication techniques, index reconstitution schedules, and holdings breakdowns. Risk notes, as presented in fact sheets, have typically referenced concentration, sector-specific exposure, and historical variability in returns.

Implementation Considerations for Tech Stocks/AI Stocks

Replication Approaches in ETFs

Issuer disclosures for Canadian-listed ETFs and index funds often describe how the product replicates its underlying benchmark. Common replication types include:

• Full Replication: The fund holds all of the securities in the index in proportion to index weights, with the aim of closely tracking the reference index.
• Sampling/Optimized Replication: In some cases, particularly where an index contains a large number of constituents or thinly traded securities, the fund may use a sampled or optimized subset of the underlying securities to approximate index exposure. Such approaches have been noted as factors that can affect tracking difference and tracking error, which measure how closely an ETF’s return aligns with its index over time. Factors influencing tracking difference can include replication methodology, expenses, and portfolio turnover.

Reconstitution and Rebalance Frequency

Index rulebooks and issuer fact sheets commonly disclose schedules for reconstitution and rebalancing, such as quarterly or semi-annual adjustments. Those schedules define when changes in index composition or weightings are implemented. Frequent rebalancing or turnover may influence trading costs and tracking difference, as periodic trades can have execution costs.

Liquidity Screens and Secondary Market Considerations

Issuer documents often reference liquidity criteria used in index selection or inclusion requirements. Liquidity thresholds may help ensure that underlying securities can be traded efficiently, which in turn may support more stable bid-ask spreads on the secondary market. Research by Canadian regulators has examined ETF market liquidity and the role of the arbitrage mechanism in aligning net asset value (NAV) and market prices, noting the importance of secondary market liquidity in overall ETF functioning.

Costs: MER, TER, and Tracking Differences

Disclosure requirements in ETF fact sheets include management expense ratios (MER), which encompass management fees and operational costs. Many Canadian ETFs also report trading expense ratios (TER), which capture portfolio transaction and operational costs beyond the MER. Combined, these can contribute to tracking differences between an ETF and its index. Canadian ETF documents may include these metrics in interim and annual reports as part of regulated disclosure.

Foreign Withholding Tax Considerations

For ETFs holding non-Canadian securities, withholdings on dividends or other income from foreign jurisdictions may be applied at the source. For example, U.S. dividend withholding tax may be withheld on income from U.S. equities held within Canadian ETFs, potentially reflected in net distributions reported to holders. Disclosure in fact sheets and tax reporting documents typically identifies how underlying foreign taxes are treated.

Risk & Disclosure Information When Investing in Tech & AI Companies

Valuation Dispersion and Earnings Sensitivity

Canadian tech and growth ETF fact sheets often note that companies in these segments can display a wide range of valuations. Differences in price-to-earnings, price-to-sales, or other metrics across constituents may result in greater historical volatility. Earnings announcements, sector-specific developments, or broader economic conditions can influence returns disproportionately, particularly for technology or AI-related holdings.

Currency and International Exposure

Funds holding non-Canadian securities may be exposed to foreign exchange fluctuations. Disclosures frequently highlight that changes in currency values can affect net asset value and distributions.

Concentration and Thematic Risks

Single-theme or sector-focused ETFs may show concentration risk, where a small number of holdings or subsectors represent a large portion of assets. Fact sheets often state that such concentration can contribute to more pronounced performance variation compared with broader, diversified indexes.

Reconstitution Turnover and Capacity

Index rebalancing and reconstitution events can lead to portfolio turnover, potentially affecting liquidity and transaction costs. Fact sheets typically provide historical turnover rates and note any considerations regarding fund capacity, emphasizing that higher turnover may impact cost efficiency.

Other Considerations

Additional risk disclosures often reference market volatility, liquidity of underlying securities, and regulatory changes. Historical language emphasizes that investors may observe short-term fluctuations and differences between ETF returns and the underlying index.

Conclusion: Observations on Tech & AI ETFs in Canada

Canadian-listed technology and AI-focused ETFs provide a range of exposures, from broad Information Technology indexes to thematic AI infrastructure or software-oriented funds. Index methodologies and issuer disclosures highlight how constituents are selected, e.g., through GICS classifications, revenue exposure to AI, or sector tilts, and how replication, rebalancing, and reconstitution are implemented.

Investor-facing documents, including factsheets and prospectuses, commonly report management expense ratios, replication methods, turnover, holdings breakdown, and risk factors such as valuation dispersion, concentration, and foreign currency exposure. Historical trading activity, as captured in TMX reports, reflects which securities and ETFs have exhibited higher trading value or volume but does not indicate future performance.

Across these ETFs, disclosures are standardized to provide transparency on structure, costs, and risk. By consulting publicly available issuer and index documentation, observers can gain an understanding of how tech and AI exposure are operationalized in the Canadian public market, without implying suitability or future outcomes.