INSIDE THE MARKET

Are AI stocks in a bubble? 3 reasons we think not

Kristy Akullian, CFA Sep 16, 2026 EQUITY

Key takeaways

  • Tech stocks have risen amid growing excitement over artificial intelligence, but valuations have remained well below dot-com bubble era levels.
  • Unlike the speculative excesses of the late 1990s, today’s AI spending is grounded in real demand and strong Q2 earnings reinforced the long-term AI opportunity, with clear evidence that capex is translating to returns.
  • We believe investors should stay selective but engaged in the AI theme — focusing on companies with durable cash flow, pricing power, and real user demand.

Are we in an AI bubble the same way we were in the dot-com bubble?

 

Stock prices are near all-time highs. There’s enormous capital investment in infrastructure for demand that hasn't fully arrived yet. And suppliers are helping finance the customers who then spend that money back with them.

 

If you're looking for reasons to think we're going through a dot-com-like bubble happen again, you can probably find them.

 

So, let's take the comparison seriously — and then look at what the evidence actually shows.

 

I'm Faye Witherall, an Investment Strategist at BlackRock.

 

Our view is that AI stocks are not in a bubble. And that’s because we think this period looks fundamentally different from 2000 across three factors that help distinguish a boom from a bubble:

 

Valuations. The relationship between supply and demand. And just how early we think we actually are on the AI infrastructure build-out.

 

Let’s start with valuations.

 

At the peak of the dot-com boom, the four largest tech leaders of early 2000 — Microsoft, Cisco, Intel and Oracle — traded at nearly 70 times their two-year forward earnings.

 

Today, the four biggest AI hyperscalers — Microsoft, Alphabet, Amazon and Meta — trade at an average of about 18 times two-year forward earnings.2 Not the same bubble-like conditions that were in place during the dot-com boom.

 

What about semiconductors? It might come as a surprise to many that what is now the world’s largest industry, ranked by market cap, is cheaper than its been in nearly 4 years (outside of 2 short-lived dips in April ’25 and April ’26. The rally in semis has been an earnings rally.

 

Prices are higher today mainly because companies are earning much more, not because investors are willing to pay more for the same earnings. That distinction matters:

 

Back then: Prices ran ahead of profits.

But today: Profits have been doing the work.

 

Now let’s look at demand versus supply.

 

Here's the part of the dot-com comparison that gets the direction backwards.

 

In the late 1990s, years of broadband capacity were built before the demand to use it existed. Infrastructure waited for customers.

 

Today it's inverted. Demand is waiting for infrastructure.

 

Start with revenue. Together, that is more than $100 billion a year from products that didn’t exist four years ago. For scale, only one software company out there that currently brings in more than  $100 billion in annual revenue: Microsoft.

 

Another place to look is hardware pricing. Rental prices for NVIDIA GPUs have risen this year, even on chips that are about three years old. In technology markets, older hardware is supposed to get cheaper. The value of your used car doesn’t typically increase.

 

What’s happening right now is not normal. Aging hardware gets more expensive because demand is outrunning the industry's ability to build new capacity.

 

So, put simply:

 

Back then: Supply was built ahead of demand.

But today: Demand has been outrunning supply — and paying up front to secure it.

 

Capacity is scarce enough that customers are signing multi-year prepaid commitments just to lock in chips and data-center space.

 

And finally, we look at timing: how early are we?

 

This difference is the one that's easiest to miss, because AI already feels ubiquitous.

 

Consider what today's AI mostly does: it produces text and code. The frontier models available right now were built on infrastructure decisions made roughly two years ago. Investment has scaled enormously since then, which could mean the models arriving over the next few years could be substantially more capable than anything currently in use.

 

People often say: "Today's AI is the worst AI you will ever use."

 

And adoption is earlier than the headlines suggest. Widely available. Not yet deeply embedded in how companies operate.

 

We believe the economic applications with the most value attached — transportation, manufacturing, healthcare, scientific research — are largely ahead of us.

 

So, again, are we in an AI bubble the same way we were in the dot-com bubble?

 

We don’t believe we are. But there are real risks that we’re watching:

 

● Power constraints. The social and regulatory impact.
● Funding dynamics for AI infrastructure
● Increasing use of leverages strategies
● And Open-source models

 

The future is highly uncertain. But we still see tremendous opportunity in both the near-term and long-term when it comes to AI.

 

In our view, the bigger risk for investors is underestimating how large and how durable AI capital spending turns out to be.

 

So to recap, the three biggest differences that we think distinguish a boom from a bubble, and that makes today’s AI boom fundamentally different from the dot com bubble are:

 

Valuations, which are a fraction of dot-com peaks, and they're supported by real earnings.

 

Demand, which is running ahead of supply rather than trailing behind it.

 

And timing, where we think the technology's biggest applications are still in front of us.

 

That's why, in our view, this isn't a replay of 2000.

 

For investors looking at the theme, we spotlight two iShares ETF options: BAI, the iShares A.I. Innovation and Tech Active ETF, an actively managed concentrated portfolio of global AI and tech equities across market caps, selected through bottom-up fundamental research. And ARTY, the iShares Future AI & Tech ETF, which targets the full AI value chain — generative AI, data and infrastructure, software and services.

 

Explore our analysis below and bring us your questions directly on Reddit, at r/iShares.

 

 

Disclosures:

 

Carefully consider the Funds' investment objectives, risk factors, and charges and expenses before investing. This and other information can be found in the Funds' prospectuses or, if available, the summary prospectuses which may be obtained by visiting www.iShares.com or www.blackrock.com. Read the prospectus carefully before investing.

 

Investing involves risk, including possible loss of principal.

 

Funds that concentrate investments in specific industries, sectors, markets or asset classes may underperform or be more volatile than other industries, sectors, markets or asset classes and the general securities market. Technology companies may be subject to severe competition and product obsolescence. AI technology relies on large data sets, which can lead to inaccuracies. Companies in AI face competition, rapid obsolescence, and depend on demand from various industries. Regulatory scrutiny could limit AI development, with data collection facing closer examination and potential fines. Country-specific regulations could also impact AI and big data companies.

 

International investing involves risks, including risks related to foreign currency, limited liquidity, less government regulation and the possibility of substantial volatility due to adverse political, economic or other developments. These risks often are heightened for investments in emerging/developing markets and in concentrations of single countries.

 

Actively managed funds do not seek to replicate the performance of a specified index, may have higher portfolio turnover, and may charge higher fees than index funds due to increased trading and research expenses. There is no guarantee that an active fund will meet its investment objective.

 

This material represents an assessment of the market environment as of the date indicated; is subject to change; and is not intended to be a forecast of future events or a guarantee of future results. The opinions expressed are as of the date indicated and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and nonproprietary sources deemed by BlackRock to be reliable, are not necessarily all-inclusive and are not guaranteed as to accuracy. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by BlackRock, its officers, employees or agents. This material may contain “forward-looking” information that is not purely historical in nature. Such information may include, among other things, projections and forecasts. There is no guarantee that any of these views will come to pass. Reliance upon information in this material is at the sole discretion of the viewer.

 

The Funds are distributed by BlackRock Investments, LLC (together with its affiliates, “BlackRock”).

 

© 2026 BlackRock, Inc. or its affiliates. All Rights Reserved. BLACKROCK and iSHARES are trademarks of BlackRock, Inc. or its affiliates. All other trademarks are those of their respective owners.

 

MKTG0926-MH-5885659-EXP0927
Video 6:35

Today's AI boom vs. the dot-com bubble

Are we in an AI bubble the same way we were in the dot-com bubble?

 

Stock prices are near all-time highs. There’s enormous capital investment in infrastructure for demand that hasn't fully arrived yet. And suppliers are helping finance the customers who then spend that money back with them.

 

If you're looking for reasons to think we're going through a dot-com-like bubble happen again, you can probably find them.

 

So, let's take the comparison seriously — and then look at what the evidence actually shows.

 

I'm Faye Witherall, an Investment Strategist at BlackRock.

 

Our view is that AI stocks are not in a bubble. And that’s because we think this period looks fundamentally different from 2000 across three factors that help distinguish a boom from a bubble:

 

Valuations. The relationship between supply and demand. And just how early we think we actually are on the AI infrastructure build-out.

 

Let’s start with valuations.

 

At the peak of the dot-com boom, the four largest tech leaders of early 2000 — Microsoft, Cisco, Intel and Oracle — traded at nearly 70 times their two-year forward earnings.

 

Today, the four biggest AI hyperscalers — Microsoft, Alphabet, Amazon and Meta — trade at an average of about 18 times two-year forward earnings.2 Not the same bubble-like conditions that were in place during the dot-com boom.

 

What about semiconductors? It might come as a surprise to many that what is now the world’s largest industry, ranked by market cap, is cheaper than its been in nearly 4 years (outside of 2 short-lived dips in April ’25 and April ’26. The rally in semis has been an earnings rally.

 

Prices are higher today mainly because companies are earning much more, not because investors are willing to pay more for the same earnings. That distinction matters:

 

Back then: Prices ran ahead of profits.

But today: Profits have been doing the work.

 

Now let’s look at demand versus supply.

 

Here's the part of the dot-com comparison that gets the direction backwards.

 

In the late 1990s, years of broadband capacity were built before the demand to use it existed. Infrastructure waited for customers.

 

Today it's inverted. Demand is waiting for infrastructure.

 

Start with revenue. Together, that is more than $100 billion a year from products that didn’t exist four years ago. For scale, only one software company out there that currently brings in more than  $100 billion in annual revenue: Microsoft.

 

Another place to look is hardware pricing. Rental prices for NVIDIA GPUs have risen this year, even on chips that are about three years old. In technology markets, older hardware is supposed to get cheaper. The value of your used car doesn’t typically increase.

 

What’s happening right now is not normal. Aging hardware gets more expensive because demand is outrunning the industry's ability to build new capacity.

 

So, put simply:

 

Back then: Supply was built ahead of demand.

But today: Demand has been outrunning supply — and paying up front to secure it.

 

Capacity is scarce enough that customers are signing multi-year prepaid commitments just to lock in chips and data-center space.

 

And finally, we look at timing: how early are we?

 

This difference is the one that's easiest to miss, because AI already feels ubiquitous.

 

Consider what today's AI mostly does: it produces text and code. The frontier models available right now were built on infrastructure decisions made roughly two years ago. Investment has scaled enormously since then, which could mean the models arriving over the next few years could be substantially more capable than anything currently in use.

 

People often say: "Today's AI is the worst AI you will ever use."

 

And adoption is earlier than the headlines suggest. Widely available. Not yet deeply embedded in how companies operate.

 

We believe the economic applications with the most value attached — transportation, manufacturing, healthcare, scientific research — are largely ahead of us.

 

So, again, are we in an AI bubble the same way we were in the dot-com bubble?

 

We don’t believe we are. But there are real risks that we’re watching:

 

● Power constraints. The social and regulatory impact.
● Funding dynamics for AI infrastructure
● Increasing use of leverages strategies
● And Open-source models

 

The future is highly uncertain. But we still see tremendous opportunity in both the near-term and long-term when it comes to AI.

 

In our view, the bigger risk for investors is underestimating how large and how durable AI capital spending turns out to be.

 

So to recap, the three biggest differences that we think distinguish a boom from a bubble, and that makes today’s AI boom fundamentally different from the dot com bubble are:

 

Valuations, which are a fraction of dot-com peaks, and they're supported by real earnings.

 

Demand, which is running ahead of supply rather than trailing behind it.

 

And timing, where we think the technology's biggest applications are still in front of us.

 

That's why, in our view, this isn't a replay of 2000.

 

For investors looking at the theme, we spotlight two iShares ETF options: BAI, the iShares A.I. Innovation and Tech Active ETF, an actively managed concentrated portfolio of global AI and tech equities across market caps, selected through bottom-up fundamental research. And ARTY, the iShares Future AI & Tech ETF, which targets the full AI value chain — generative AI, data and infrastructure, software and services.

 

Explore our analysis below and bring us your questions directly on Reddit, at r/iShares.

 

 

Disclosures:

 

Carefully consider the Funds' investment objectives, risk factors, and charges and expenses before investing. This and other information can be found in the Funds' prospectuses or, if available, the summary prospectuses which may be obtained by visiting www.iShares.com or www.blackrock.com. Read the prospectus carefully before investing.

 

Investing involves risk, including possible loss of principal.

 

Funds that concentrate investments in specific industries, sectors, markets or asset classes may underperform or be more volatile than other industries, sectors, markets or asset classes and the general securities market. Technology companies may be subject to severe competition and product obsolescence. AI technology relies on large data sets, which can lead to inaccuracies. Companies in AI face competition, rapid obsolescence, and depend on demand from various industries. Regulatory scrutiny could limit AI development, with data collection facing closer examination and potential fines. Country-specific regulations could also impact AI and big data companies.

 

International investing involves risks, including risks related to foreign currency, limited liquidity, less government regulation and the possibility of substantial volatility due to adverse political, economic or other developments. These risks often are heightened for investments in emerging/developing markets and in concentrations of single countries.

 

Actively managed funds do not seek to replicate the performance of a specified index, may have higher portfolio turnover, and may charge higher fees than index funds due to increased trading and research expenses. There is no guarantee that an active fund will meet its investment objective.

 

This material represents an assessment of the market environment as of the date indicated; is subject to change; and is not intended to be a forecast of future events or a guarantee of future results. The opinions expressed are as of the date indicated and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and nonproprietary sources deemed by BlackRock to be reliable, are not necessarily all-inclusive and are not guaranteed as to accuracy. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by BlackRock, its officers, employees or agents. This material may contain “forward-looking” information that is not purely historical in nature. Such information may include, among other things, projections and forecasts. There is no guarantee that any of these views will come to pass. Reliance upon information in this material is at the sole discretion of the viewer.

 

The Funds are distributed by BlackRock Investments, LLC (together with its affiliates, “BlackRock”).

 

© 2026 BlackRock, Inc. or its affiliates. All Rights Reserved. BLACKROCK and iSHARES are trademarks of BlackRock, Inc. or its affiliates. All other trademarks are those of their respective owners.

 

MKTG0926-MH-5885659-EXP0927
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Are AI stocks in a bubble?

AI bubble concerns have ebbed and flowed since the release of ChatGPT in late 2022. But AI stocks are not in a bubble, in our view. Gains for AI and related stocks are supported by earnings, AI infrastructure is serving real demand, and we believe the technology’s largest economic applications are still ahead.

Still, comparisons to the late-1990s dot-com era are easy to draw. Stock prices are near all-time highs, driven by AI exuberance and the capital investment fueling the build out of datacenters.

For many, today’s AI economy rhymes with the dot.com era when capital investments were being made to pave the way for the internet. There are also parallels being drawn between the vendor financing arrangements of the dot-com era and so-called “circular financing” today — deals where a supplier helps finance a customer who then spends money back with that supplier (either directly or via a partner) — as the scale of the datacenter build has led to unconventional financial agreements among major companies across the AI stack.

But we see three key differences between today’s environment and the late 1990s:

  • AI-related stock valuations are relatively low. Today’s valuations are far below levels reached at the peak of the dot-com bubble when the top four tech leaders of early 2000 (MSFT, CSCO, INTC, ORCL) traded nearly 70 times 2-year forward earnings.1
  • Today, the average 2-year forward Price/Earnings (P/E) is about 18x for the biggest AI datacenter spenders: Microsoft, Alphabet, Amazon and Meta — also known as hyperscalers.2
  • The demand for AI far exceeds the supply today. Demand for AI compute is growing exponentially with no observed slowdown.3 AI datacenter capacity is constrained, driving multi-year, prepaid commitments to lock in scarce AI chip and datacenter supply. The demand curve has no clear cap: every leap in capability has been creating appetite for more compute.
  • We may be in the very early stages of an industrial revolution-type transformation. AI has the potential to transform almost every sector of the economy, including transportation, manufacturing, and healthcare.

Are AI stock valuations too high?

AI-related stock valuations are not egregiously high, in our view. The forward P/E ratio on the PHLX Semiconductor Index (SOX) is about 20x, the same level it was trading in January 2023, less than two months after ChatGPT launched. Since that date (November 30, 2022), semiconductor stocks are up 47% on an annualized basis.

SOX Index — 1Yr forward P/E — last 5 years

Line chart showing the 1-year forward price-to-earnings (P/E) ratio of the PHLX Semiconductor Sector Index (SOX) over the past five years.

Source: Bloomberg, BlackRock as of Aug 24, 2026. PHLX Semiconductor Sector Index (SOX) 1-year forward price-to-earnings ratio. Five-year average calculated over the period shown.

Chart description: Line chart showing the 1-year forward price-to-earnings (P/E) ratio of the PHLX Semiconductor Sector Index (SOX) over the past five years. Semiconductor valuations fell to roughly 13x forward earnings in 2022 before rising sharply during the generative AI investment cycle, reaching approximately 30x at their peak in June 2024. More recently, the forward P/E has declined to around 20x, slightly below its five-year average of approximately 22x.

Earnings have pushed semiconductor stocks higher but the market isn’t rewarding the fundamentals, in our view, let alone putting outsized P/E multiples on semi stocks.

Semiconductor earnings: What analysts expected in January 2023 vs actual results

Area chart comparing what analysts expected in January 2023 for aggregate SOX semiconductor earnings against what companies actually delivered in each year since.

Source: Bloomberg, as of 8/24/2026. Semiconductors as represented by the PHLX Semiconductor Index (SOX). "What was expected" is consensus estimate data dated 1/1/2023; "what was delivered" is reported actuals, with the final year showing the current consensus estimate. Figures are aggregated on a like-for-like basis across the index constituents that have a complete set of expectations on that date, in US dollars. Texas Instruments' expected earnings for the final year uses the earliest estimate available on that basis. Earnings only; sales are not shown.

Chart description: Area chart comparing what analysts expected in January 2023 for aggregate SOX semiconductor earnings against what companies actually delivered in each year since. Actual earnings came in ahead of the January 2023 expectation in every year shown, and the gap between the two widens progressively across the chart.

A P/E multiple, high or low, still begs the question: a multiple of what earnings? In January 2023, the SOX traded at roughly 17x forward earnings5, but consensus was pricing an earnings path that ultimately proved far too conservative, especially beyond the first year.

At the start of 2023, semis traded at 17 times the next 12 months of expected earnings and 13 times what the market expected them to earn this year.

The near-term number wasn’t a major surprise, but the gap between expectations and reality for 2026 earnings is large. Semiconductor earnings in 2026 are now forecast to be 3.4 times what the market expected at the beginning of 2023.

Portfolio risk may rise if a few AI-related names stumble, as detailed in our 2026 Fall Investment Directions, but we believe this isn’t proof of mispricing considering those names are producing the bulk of the earnings growth.

Is AI demand keeping pace with investment?

Demand for AI is greater than supply. Not only is demand greater than supply, demand is growing exponentially while supply growth is limited physically. This is in stark contrast with the dot-com era, where years of broadband supply was added before demand existed.

The clearest evidence is revenue. Anthropic is reportedly generating $65 billion in annualized revenue, while OpenAI is now reportedly generating revenue at a $40 billion run rate.7 That’s over $100 billion in revenue from products that didn’t exist four years ago. For context, there’s only one software company in the world (Microsoft) that makes over $100 billion a year in revenue.8

The largest providers of AI infrastructure are all reporting the same trend: accelerating cloud growth. In just one year, the combined cloud backlog, which represents signed customer commitments, of Amazon, Alphabet, Microsoft, and Oracle nearly tripled, rising from $800 billion to $2.3 trillion.9 AI investment is translating into revenue. The hyperscalers also have continued raising capex guidance and describe an environment where customer demand exceeds available infrastructure.

Cloud revenue by calendar year — actual/latest estimates

Column chart comparing the current combined cloud revenue path, actuals for the reported years and consensus estimates thereafter, against four earlier semiannual estimate vintages.

Source: Bloomberg, as of 8/24/2026. Combined cloud revenue represents AWS plus Microsoft Intelligent Cloud plus Google Cloud, in US dollars, on a reported-actuals basis. Calendar years are constructed from four calendar quarters; Microsoft's June fiscal year end is offset by two fiscal quarters, while Amazon and Alphabet fiscal years equal calendar years.

Chart description: Column chart comparing the current combined cloud revenue path, actuals for the reported years and consensus estimates thereafter, against four earlier semiannual estimate vintages, with each vintage beginning only in the years it covers.

AI compute remains scarce enough that rental prices for NVIDIA H100 GPUs have continued rising10 despite the chips being roughly three years old. In most technology markets, older hardware becomes cheaper over time. Rising prices for aging AI hardware is clear evidence that demand is growing faster than new capacity can be brought online.

Silicon Data H100 Rental Index (SDH100RT) — NVIDIA H100 GPU hourly rental rate, last 12 months

Line chart showing the daily hourly rental rate for NVIDIA H100 GPUs over the trailing twelve months.

Source: Bloomberg and Silicon Data, as of 8/24/2026. Silicon Data H100 Rental Index, daily closing price in US dollars per GPU-hour, shown over the trailing twelve-month window. The index tracks the performance and pricing trends of key GPU models across the Silicon Data ecosystem.

Chart description: Line chart showing the daily hourly rental rate for NVIDIA H100 GPUs over the trailing twelve months.

Every incremental unit of AI compute is being rapidly absorbed by revenue-generating workloads. Supply, meanwhile, remains limited by the physical constraint of chips, memory, power and data-center capacity. In the dot-com era, infrastructure waited for demand. Today, demand is waiting for infrastructure.

NVIDIA's latest earnings call reflects this trend as the company gave a revenue growth outlook for next year (fiscal 2028) of “approximately 70%” vs. consensus estimates of 45%. “This is a supply-constrained outlook,” NVDIA's CFO said on a conference call, noting that demand was actually growing much faster than supply.11

How may AI transform the broader economy?

We believe we are in the very early stages of an AI era with the potential to transform the global economy at a scale similar to the industrial revolution. As excited as everyone seems to be about AI, it is still mainly limited to printing text and code. In the coming years, the economy may see profound efficiency gains as modern AI moves more deeply into transportation, manufacturing, healthcare and scientific research.

The most advanced frontier models available today were built on infrastructure investments made roughly two years ago. Investment has scaled substantially since then, supporting the development of models that are expected to be far more capable than those we use today. In 2023, Wharton professor Ethan Mollick coined an oft-repeated remark, “Today’s AI is the worst AI you will ever use.”12 It is still true. We are only scratching the surface.

Adoption is also much earlier than the headlines suggest. According to the 2026 Stanford AI Index, 88% of organizations used AI in some form, but the deployment of AI agents remained in the single digits across nearly every business function.13 AI is widely available, but it is not yet deeply embedded in how most companies operate.

What risks could challenge the AI investing outlook?

Our optimistic thesis could be challenged by any of the following scenarios:

  1. Power constraints. If AI builders can’t secure consistent, 24/7 power, then our constructive view may weaken. Interconnection queues and transmission bottlenecks in hubs like Northern Virginia and Texas already suggest power constraints could slow the AI datacenter build-out.
  2. Rising idle capacity or sustained utilization slippage. Evidence that new datacenters aren’t filling, or that booked capacity is being canceled, would challenge the “insatiable” view.
  3. Public opposition and regulation, which could slow adoption.
  4. Circular financing could pull demand forward and concentrate losses if expected revenues do not materialize.
  5. Greater reliance on debt financing: repayment obligations are fixed, so even ultimately viable investments can run into liquidity or solvency problems if revenues arrive later or lower than expected.
  6. Open-source models could pressure pricing and shift profits across the AI ecosystem. But cheaper models can also make more applications economical and increase total usage.

These risks will create winners and losers, but they do not negate the growth in earnings, revenue and end demand already visible today. Investors risk underestimating the magnitude and durability of AI capital spending.

While vigilance is warranted as the landscape evolves, we feel that current evidence points to a growth story that is fundamentally distinct from past bubbles. Investors willing to remain disciplined and discerning may find the AI theme remains one of the most compelling opportunities of the decade.

Frequently asked questions

AI stocks are not in a bubble, in our view. Gains for AI and related stocks are being supported by earnings, AI infrastructure is serving real demand, and we believe the technology’s largest economic applications are still ahead.

The dot-com bubble was characterized by excessive valuations on unprofitable companies and debt-financed infrastructure built for anticipated future demand.

The AI stock market is being supported by earnings, AI infrastructure is serving real demand, which continues to outpace supply, and we believe the technology’s largest economic applications are still ahead.

Investing in AI carries risks such as data inaccuracy, intense competition, rapid product obsolescence, and dependency on consumer base and end-user demand. Companies may also face sector-specific market and business risks, affecting overall performance.

There’s no single level of AI exposure that’s right for every investor. It depends on your investment goals, time horizon and comfort with risk. You may consider starting by first looking at how much AI-related exposure you may already have through broad equity and technology holdings. From there, investors may consider whether a thematic AI ETF could complement their existing portfolio. Depending on how targeted you want that exposure to be, some funds provide access across the AI stack, while others focus on individual layers, such as semiconductors. Investors may also want to consider how that exposure fits within a diversified portfolio, including assets beyond equities, such as commodities or alternative strategies.

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Photo of Kristy Akullian, CFA

Kristy Akullian, CFA

Head of iShares Investment Strategy

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Samuel McClellan, CFA

Investment Strategist

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