Themes in Motion: Examining Electrification & Smart Grid Themed ETFs

Themes in Motion is a research based editorial series that examines fundamental, macro and conceptual dynamics motivating around popular thematic ETF categories. 

Disclaimer:  This report is provided for informational purposes only and does not constitute investment advice, a recommendation, or an offer to buy or sell any ETF, security, strategy, or investment product. The attribution and earnings analysis is not GIPS compliant. Past performance and earnings estimates are not indicative of future results.

Electrification and smart grid ETFs are increasingly becoming a second-derivative AI infrastructure trade. The first wave of AI enthusiasm rewarded semiconductors, cloud platforms, and data-center operators. The next wave is reaching the physical economy: power generation, transmission equipment, grid automation, transformers, switchgear, storage, electrical contractors, and regulated utilities.

The supplied FactSet earnings data supports that shift. Across the ETF set, the most attractive fundamental signals are not simply high earnings growth, but positive estimate revisions paired with reasonable PEG ratios. ZAP, POWR, ELFY, and GRID show the clearest evidence that earnings expectations are being revised upward or stabilized as investors price in a longer grid-capex cycle. VOLT has the strongest positive revision breadth in the file, though its EPS history is incomplete. POW is the newest and most speculative of the group, with a higher P/E and shorter earnings record.

The central investment case is that AI compute demand is turning electricity from a slow-growth utility input into a strategic constraint. The IEA projects global data-center electricity consumption will roughly double to about 945 TWh by 2030, with accelerated servers, largely tied to AI adoption, growing far faster than conventional server demand. In the U.S., EIA’s latest Short-Term Energy Outlook expects electricity demand to rise, with commercial demand leading growth as data centers and cooling loads increase.

ETF Coverage Universe

FactSet Earnings Snapshot

The FactSet earnings data we reviewed is best interpreted as an earnings-estimate and valuation snapshot, not a full margin or operating-income dataset. Therefore, the operating leverage discussion below is inferred from EPS revisions, earnings-growth estimates, P/E changes, PEG ratios, and breadth of estimate revisions.

Earnings Quality and Operating Leverage Read-Through

The strongest positive signal in the FactSet file is that EPS estimates have generally moved higher while PEG ratios remain contained. That combination suggests investors are not merely paying higher multiples for a theme; in several ETFs, earnings expectations are also improving.

ZAP and POWR show the cleanest growth-adjusted earnings signal. ZAP’s EPS estimate is up 20.6% since December, with estimated earnings growth of 22.7%, a 22.9x P/E, and a 2.1x PEG. POWR has the strongest estimate reset, with EPS up 28.0% since December, estimated earnings growth of 25.4%, and the lowest P/E in the group at 20.5x. That gives POWR the most attractive valuation-growth mix in the dataset, though its revision breadth is neutral and its history appears more volatile than GRID or ZAP.

GRID remains the highest-quality pure smart grid benchmark. GRID’s EPS estimate is up 14.2% since December, and the revision count is broadly positive, with 60 companies revised up versus 35 revised down. Its earnings-growth rate is lower than ZAP or POWR, but the ETF offers the cleanest thematic exposure to grid modernization, meters, devices, networks, energy storage, management systems, and enabling software.

ELFY looks like a balanced infrastructure-first option. ELFY’s EPS estimate is up 20.3% since December, its earnings-growth estimate is 19.0%, and its PEG ratio is 1.9x. That makes it attractive as a diversified electrification infrastructure basket, especially because its stated strategy emphasizes utilities, transmission, materials, and equipment providers that enable electrification.

VOLT has the best revision breadth but incomplete EPS data. The file shows 17 upward revisions versus 5 downward revisions, producing the strongest positive revision breadth in the group. However, the EPS and earnings-growth fields are unavailable, so the thesis has to rely more on portfolio construction, valuation, and the ETF’s explicit focus on rising electricity demand from AI, EVs, digital assets, grid equipment, utilities, nuclear, and alternative energy.

POW is the highest-beta early-stage expression. POW has a short earnings history, a higher P/E of 32.1x, and a smaller asset base relative to more established funds. Its PEG ratio of 1.6x screens well, but the fund should be viewed as more speculative because the strategy is newer and more tightly linked to the “AI power infrastructure” narrative.

Why Operating Leverage May Be Improving

The operating leverage case rests on a simple idea: electricity demand is rising faster than the existing grid was built to absorb. When demand visibility improves, companies that sell electrical equipment, transformers, switchgear, grid automation, utility services, engineering, construction, and power infrastructure can convert backlog into revenue with better utilization of fixed costs.

The clearest corporate evidence is coming from grid contractors and power equipment suppliers. Quanta Services reported first-quarter 2026 adjusted diluted EPS of $2.68, up from $1.78 a year earlier, and raised its full-year outlook, which supports the idea that grid modernization and utility infrastructure demand are flowing through to earnings. Caterpillar also raised revenue expectations, citing AI-driven power and construction demand, with a record backlog and a plan for power-generation equipment sales to triple by 2030 from 2024 levels.

For ETF investors, this matters because operating leverage changes the character of the theme. Electrification is no longer only a capex story; it is becoming an earnings revision story. When companies can push more volume through existing service teams, factories, engineering platforms, and utility project backlogs, earnings can grow faster than revenue. That is the key reason the FactSet data’s EPS revisions are important.

AI Compute as the Core Tailwind

AI compute is creating a power bottleneck. Data centers are being built faster than power generation, transmission, and interconnection capacity can be expanded. The IEA’s base case projects data-center electricity use to double to roughly 945 TWh by 2030, with AI-driven accelerated servers growing around 30% annually.

The U.S. is one of the most important markets for this theme. EIA expects U.S. electricity demand to rise, with commercial-sector demand leading growth and summer commercial demand expected to accelerate into 2027. EIA also modeled a high-demand scenario around data-center-heavy regions, highlighting the risk that AI load growth could materially exceed baseline forecasts in certain power markets.

This changes the investable universe. The AI trade is not limited to Nvidia, cloud platforms, or semiconductor capital equipment. It now includes:

This is why smart grid ETFs should be viewed as part of the AI infrastructure value chain, not just as clean energy or utility-adjacent products.

Macro Conditions: Supportive Demand, More Complicated Costs

The macro setup is mixed but broadly constructive for the theme.

Demand is strong. Electricity demand is rising from data centers, electrification, manufacturing reshoring, cooling demand, EV adoption, and digital infrastructure growth. DOE recently announced an approximately $1.9 billion funding opportunity for critical grid infrastructure upgrades aimed at meeting rising demand and improving resource adequacy.

Rates are a headwind. The Fed held the federal funds target range at 3.50%–3.75% at its April 29, 2026 meeting and said inflation remains elevated, partly reflecting higher global energy prices. Higher rates can pressure utilities, infrastructure equities, and long-duration growth multiples. They also raise financing costs for grid projects, renewable generation, data centers, and transmission expansion.

Inflation is a two-sided force. Inflation and energy-price uncertainty can push policymakers and utilities to accelerate grid reliability investment. However, the same inflation can hurt margins if steel, copper, aluminum, labor, and transformer costs rise faster than contracts allow. Tariff policy adds another layer: the White House’s April 2026 tariff framework included tariffs on derivative articles made substantially of steel, aluminum, or copper, while setting a lower tariff rate for certain metal-intensive industrial and electrical grid equipment through 2027.

Utilities are becoming AI-infrastructure enablers. Entergy increased its four-year capital spending plan by 33% to support data-center-related demand, including infrastructure tied to Meta’s Louisiana data-center operations. Dominion also beat quarterly profit estimates on higher Virginia power demand, with data-center capacity commitments reinforcing the AI-grid connection.

ETF Ranking by Fundamental Attractiveness

Investment Conclusion

Electrification and smart grid ETFs have moved from a niche clean-energy subtheme to a central expression of the AI infrastructure buildout. The investment case is no longer only about decarbonization or EV adoption. It is increasingly about the physical bottlenecks created by AI compute: power availability, grid reliability, transmission capacity, substation buildouts, electrical equipment supply chains, and utility capex.

The FactSet earnings data supports that evolution. ZAP, POWR, ELFY, and GRID all show evidence of improving or resilient earnings expectations, while VOLT shows strong positive revision breadth despite incomplete EPS detail. POW provides a more speculative, high-beta way to express the same theme.

For investors, the strongest message is that earnings streams are becoming more attractive where companies have pricing power, backlog visibility, and exposure to mission-critical grid infrastructure. The risk is that valuations have already begun to reflect the AI power narrative, while higher rates, tariffs, labor shortages, and equipment bottlenecks could pressure margins or delay project conversion.

The best-positioned ETFs are likely those that combine AI power demand, utility capex, grid modernization, and earnings revision breadth without relying too heavily on one speculative subtheme. On that basis, ZAP, POWR, ELFY, and GRID have presence across those categories, while VOLT and POW offer more concentrated thematic upside with higher data and valuation risk.

Source List

1. FactSet Earnings and Revisions Data — Used for ETF-level EPS estimates, earnings-growth estimates, P/E, PEG, EPS revisions, and upward/downward estimate revision counts.
2. First Trust — First Trust Nasdaq Clean Edge Smart Grid Infrastructure Index Fund (GRID). Used for GRID’s smart-grid exposure, including electric grids, meters, devices, networks, storage, energy management, and enabling software.
3. Nasdaq — Nasdaq OMX Clean Edge Smart Grid Infrastructure Index. Used for index methodology and the definition of smart-grid infrastructure exposure behind GRID.
4. Tema ETFs — Tema Electrification ETF (VOLT). Used for VOLT’s electrification, AI power demand, EV, digital asset, nuclear, and grid-infrastructure positioning.
5. Global X — Global X U.S. Electrification ETF (ZAP). Used for ZAP’s focus on U.S. electricity demand, generation, transmission, distribution, grid infrastructure, and smart-grid technology.
6. iShares / BlackRock — iShares U.S. Power Infrastructure ETF (POWR). Used for POWR’s exposure to U.S. power infrastructure, including power generation, transmission, distribution, storage, electrification, and AI-driven energy needs.
7. ALPS Funds — ALPS Electrification Infrastructure ETF (ELFY). Used for ELFY’s electrification infrastructure strategy, including utilities, transmission, materials, equipment providers, grid modernization, AI, EVs, and reshoring exposure.
8. VistaShares — VistaShares Electrification Supercycle ETF (POW). Used for POW’s AI power infrastructure, energy storage, smart grid, and electrification supply-chain positioning.
9. International Energy Agency — Energy and AI Report. Used for global data-center electricity demand projections, including the estimate that data-center electricity consumption could roughly double to around 945 TWh by 2030.
10. U.S. Energy Information Administration — Short-Term Energy Outlook. Used for U.S. electricity-demand forecasts, including growth in total electricity demand and commercial-sector demand tied partly to data centers and cooling needs.
11. U.S. Department of Energy — Critical Grid Infrastructure Funding / SPARK Program. Used for the approximately $1.9 billion funding opportunity for grid upgrades, reconductoring, advanced transmission technologies, and electricity infrastructure modernization.
12. Federal Reserve — April 29, 2026 FOMC Statement. Used for the macro-rate backdrop, including the Fed’s decision to maintain the federal funds target range at 3.50%–3.75%.
13. Quanta Services — First Quarter 2026 Results. Used for evidence of grid-infrastructure demand, backlog strength, adjusted EPS improvement, and earnings momentum in utility and power infrastructure services.
14. Caterpillar — First Quarter 2026 Results. Used for evidence of AI-driven demand flowing into power generation, construction equipment, revenue growth, backlog strength, and operating trends.
15. Reuters — Entergy Q1 2026 / Data Center Demand. Used for utility capex expansion tied to Meta data-center demand and broader AI-related power infrastructure needs.
16. Reuters — Dominion Energy Q1 2026 / Virginia Power Demand. Used for evidence of data-center-driven electricity demand supporting utility earnings and contracted capacity growth.
17. Reuters — Caterpillar AI-Driven Power and Construction Demand. Used as a market-news source supporting the AI power infrastructure and power-equipment demand narrative.