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How AI can boost productivity and jump start growth

AI is a revolution, the optimists say. AI is a bubble, the skeptics reply.

We think AI’s impact could be truly transformative, as we discuss in our 2024 Mid-Year Outlook. The path ahead is uncertain. But powerful forces could drive AI forward.

In this article, we apply an economist’s perspective to an issue at the heart of the optimist-skeptic debate: How might AI impact the overall economy? Among the questions we tackle:

  • How much of a productivity boost could AI deliver? And how quickly might it arrive?
  • What jobs might be displaced, and how might policymakers respond?
  • Will AI be inflationary or disinflationary?
  • What will be the pace of corporate adoption?

Along with a macro outlook on the potential for AI, and particularly for generative AI,1 we also consider the prospects for investing in this powerful trend. Although valuations on AI stocks have had quite a run, we don’t see signs of a bubble. And while AI-related companies now account for a relatively high share of the overall U.S. market, we don’t think market concentration is necessarily a cause for concern. In short, you may find a wide range of AI-related investing opportunities to consider now and in the years ahead.

Productivity and growth: How soon, how fast?

In assessing AI’s economic prospects, productivity is a key metric. Faster productivity growth allows the economy to grow more rapidly, and living standards to rise more rapidly, without generating excess inflationary pressures.

The U.S. economy hasn’t experienced sustained productivity gains since the 1990s.2 A reprise of 1990s-style productivity gains could usher in a new era of economic growth.

To understand how AI might (or might not) transform the economy, we consider historical precedents.

In his latest shareholder letter, our Chairman and CEO Jamie Dimon likened AI to the steam engine, electricity and the personal computer. Looking at those episodes of technological innovation, we see that productivity benefits don’t appear overnight.

It took more than 60 years for the steam engine to deliver any observable economy-wide productivity benefit. With each subsequent technological innovation, productivity gains came more quickly.

If the trend holds, and we think it will, by the end of the 2020s, evidence of AI’s productivity boost could show up in U.S. economic data. Think of it this way: It took 15 years for the personal computer to increase the economy’s productivity. AI could do it in seven.

The timeline from innovation to productivity growth has been shrinking

Years from innovation to productivity growth

The chart describes years from innovation to productivity growth for innovations including steam engine, electricity, PCs/Internet and AI
Source: Crafts (2021), NBER, BEA. Data as of December 31, 2023.
Is AI the 21st century equivalent of the steam engine or electricity, each of which radically changed the economy? Probably not. But we do think AI has the potential to be as transformative as the web and personal computer, with the potential to deliver even more economic value over the next 20 years.3

The internet delivered a powerful productivity boost, but AI will likely surpass it

% labor productivity upside relative to no technology breakthrough baseline

The bar chart describes the percentage of macro productivity upside relative to no technology breakthrough baseline
Source: Crafts (2021), NBER, BEA, Haver, IMF, J.P. Morgan Private Bank. Data as of December 31, 2023.

Quantifying job displacement

To quantify the potential impact of AI, we modify an International Monetary Fund (IMF) framework. We conclude that AI’s impact could be much greater than the productivity assumptions baked into projections by government agencies such as the Congressional Budget Office.4

The IMF identifies which jobs could potentially be displaced by AI.5 We assume that half of the vulnerable jobs in the United States will be automated away over the next 20 years. The cumulative productivity gain would be about 17.5% or $7 trillion beyond the current Congressional Budget Office projection for GDP.6

What happens if half of vulnerable jobs in the United States are automated away over 20 years?

U.S. labor productivity, indexed at 100 for 2023

The chart describes U.S. labor productivity indexed at 100 for 2023 (CBO baseline and AI-induced potential upside).
Source: IMF, J.P. Morgan Private Bank. Data as of January 31, 2024. *Note: Congressional Budget Office (CBO) baseline calculated using real GDP projection divided by number of hours worked. AI induced potential upside estimated assuming 15% of jobs will be replaced by AI in the next 20 years which is half of what IMF suggested in research. 

It's important to remember that technological innovation tends to boost overall economic productivity only when it changes the amount of labor and capital needed to create a given service or product in the economy. So, for example, Uber did not change the labor-capital inputs (still one driver, one car). But driverless cars, if they eventually appear on our roads, could.

What jobs could be most at risk of AI displacement? Not surprisingly, white-collar professional service jobs such as budget analysis and technical writing look more vulnerable than child care work or pipelaying.7

We also expect to see an educational divide. A Pew Research Center study concludes that workers with bachelor’s degrees or higher are more than twice as likely to be in jobs exposed to AI than those with only high school diplomas.8 The chart below is illustrative:

Jobs in United States that are likely to have high, medium or low exposure to AI

The diagram describes jobs in the United States that are likely to have high, medium or low exposure to AI.
Source: Pew Research Center analysis of O*NET (Version 27.3). “Which U.S. Workers Are More Exposed to AI on Their Jobs?”

Across the global economy, AI will likely impact some economies more than others. The IMF has found that workers in advanced economies are more vulnerable to AI displacement than those in emerging market economies. For example, the IMF estimates that 30% of U.S. jobs could be displaced by AI versus less than 13% for India. 

All estimates and projections, including our own, should be taken with a grain of salt. No one can precisely predict AI’s economic trajectory, and estimates of AI’s economic impact vary widely. A specific uncertainty relates to the cost of implementing AI technologies in the workplace. Already we are seeing infrastructure costs related to the buildout of AI computer platforms spiral higher.9 Just because a particular job can be automated using AI technologies doesn’t mean it will be if it’s not cost-effective.

Among the optimistic prognosticators, Goldman Sachs projects a 15% boost in GDP from AI over the next 10 years. Our estimates are a little more tempered; we see an 8% to 9% increase to GDP over the next decade. MIT economics professor Daron Acemoğlu takes a much more circumspect view of AI’s potential macro impact, projecting only a 1%–1.5% boost to GDP over the same time span.10

Economists make varied estimates of how AI will impact growth over the next decade

Cumulative %pt upside to real GDP baseline by 2034

The chart describes AI’s potential impact on growth over the next decade and cumulative percentage upside to real GDP baseline by 2034.
Source: Acemoglu, JPM PB, Accenture, Mckinsey, Goldman, PWC. Data as of June 2024.

We should also remember that economies evolve in ways that may be best understood in hindsight. According to a study by economists at MIT, more than 60% of today’s U.S. job occupations didn’t even exist in 1940.11

New technologies explain much of that change. Through each successive technology transition, aggregate demand increased and the economy created jobs that didn’t previously exist. The chart below shows the pattern of that change:

History tells us that technology continuously creates new jobs as old ones disappear

Employment, by Pre-Existing and New Occupations, millions

The chart describes employment, by pre-existing and new occupations in millions.
Source: Autor et al. (2022). Data as of 2018.

Policymakers will need to respond to the societal challenges AI poses. Public policy focused on job training and transitioning vulnerable workers will likely be needed to minimize the upheaval from a greater pace of job displacement.

Challenges to corporate adoption

Next, we shift our perspective from the broader economy to sectors and companies. AI’s economic impact will depend on whether (and how) CEOs and management teams make AI a critical part of their business strategies and operations. Right now, it’s early days.

While most U.S. companies are considering how they could use AI, so far only about 4% of companies have actually adopted the new technology.12 We think adoption rates need to rise to 50% or higher before AI-driven productivity will start to impact the overall economy.13

Adoption faces many headwinds. These include: concerns around the supply of advanced semiconductors, legal and regulatory issues, potentially limited power and energy resources for data centers, and firms’ ability to optimize potential use cases.

Still, the technology makes possible a wide array of potential use cases across various industry sectors. That’s what gives AI the potential to have broad macro implications for the U.S. economy.

The table below highlights the potential use cases:

AI is likely to increase revenue sources, reduce time to market and lower costs

Sector applications of AI

The table describes sector applications of AI in seven sectors, which include healthcare, financials, advertising & digital content, retail & freight, energy & industrials, customer service, and business intelligence.
Source: J.P. Morgan, Morgan Stanley. Data as of March 31, 2024.

Investing in the infrastructure buildout

Many of these use cases will develop over time. But an immediate need is already evident: AI-related activities require extensive new infrastructure. That infrastructure falls into three categories: semiconductors, data centers and electricity power generation. (In an oft-cited example of AI’s thirst for power, a query on OpenAI’s ChatGPT LLM requires 6–10x more electrical power than a Google search.)14

The AI infrastructure buildout mapped out

The diagram describes AI infrastructure buildout. It starts from end users and investors to developers of AI tools. Then it moves to compute providers. It’s then split in three: data centers, chips and power.
Source: Bridgewater. Data as of May 20, 2024.

While most investors focus largely on the need for more semiconductors, demand is likely to increase for other parts of the AI infrastructure supply chain as well. These include: data center real estate, engineering and construction firms, copper wire to transmit electrical signals, nuclear and renewable power to support energy requirements, cooling technologies to offset heat produced by the servers, and the electrical components used to connect it all.

In the short run, AI adoption will likely pressure inflation rates higher. But we think the inflationary effects across the entire economy will be modest. That’s mainly because the AI infrastructure buildout doesn’t look to be that large compared to the broader economy.

By our estimates, the cumulative rise in AI capex spend over the next five years will total about $400 billion, which is just 0.3% of expected cumulative GDP over the same time span.15 That’s a far cry from a recent spending surge that did prove inflationary. Cumulative excess government spending on fiscal transfers to households during the COVID years of 2020 and 2021 totaled over $2 trillion, or more than 4% of GDP.

In the long run, we believe, AI’s impact should be more disinflationary than inflationary, but that benefit isn’t likely to be realized until later in the decade.

It’s early days for capex spending on AI infrastructure

U.S. AI Capex (Billions) by calendar year—Microsoft, Meta, Amazon (AWS), Oracle and Alphabet (left); Extra spending as % of cumulative GDP (right)

Source: J.P. Morgan Asset Management, Bloomberg Finance L.P. Data as of March 31, 2024. Forecasts, projections and other forward-looking statements are based upon current beliefs and expectations. They are for illustrative purposes only and serve as an indication of what may occur. Given the inherent uncertainties and risks associated with forecasts, projections and other forward statements, actual events, results or performance may differ materially from those reflected or contemplated.
Source: J.P. Morgan Asset Management, Bloomberg Finance L.P., AMECO, CBO, Haver Analytics. Data as of May 30, 2024. 

Currently, AI-related capital investment (capex) is being funded by highly profitable companies with low levels of debt. This was not the case in past periods of technology-driven capex, such as the internet buildout of the 1990s.

The 20 largest capex spenders in the S&P 500 today report EBITDA (earnings before interest, taxes, depreciation and amortization) margins of 26% and net debt to EBITDA (one way of measuring debt leverage) of below 1x. Compare this to the top 20 capex spenders in 1999: They reported 16% EBITDA margins and a net debt to EBITDA ratio of 2.45x.

Today’s capex spenders boast strong cash flows and manageable debt

Top 20 S&P 500 Capex Spenders

This chart shows the top 20 spenders with regard to capital expenditures in 1999, 2007 and 2024.
Source: Bloomberg Finance L.P. Data as of April 2024.

What’s more, the hyperscalers (large cloud service providers) that are investing 50% or more of the overall AI capex are sitting on huge piles of cash, and their businesses generate high levels of free cash flow. These companies are not issuing excessive amounts of debt to fund their capex. They don’t need to. Given hyperscalers’ ability to self-fund their AI initiatives, it is less likely that today’s high interest rates will curtail their investments.

The absence of excess leverage is important because it means that AI-related capex is not, in our view, creating a worrisome debt imbalance in the economy. This makes for a notable contrast with the dot.com bubble of the late 1990s. That bubble expanded in part through excess leverage—and ended in a bust.

A discussion of leverage circles back to inflation. While today’s AI capex spend may result in pockets of sector-specific inflation (e.g., in the power sector, where we are already seeing a fierce bidding competition by hyperscalers for long-term contracts16), we don’t anticipate that AI will produce meaningful inflationary pressures at the macro level.

What this means for your portfolio

How might you think about AI investments in your own portfolio? AI has already catalyzed a wave of excitement, investment and earnings growth. We want to invest for the long term across the value chain.

For now, we suggest clients focus on the AI infrastructure buildout: semiconductors, data centers and new power generation—including the many smaller and less well-known companies. Facing increased demand for their products and services, companies in these sectors have been already meaningfully boosting their sales outlook. 

The below chart shows 12 months forward sales expectations for companies in the three infrastructure categories, indexed to when ChatGPT was released in November 2022. Only one category, data center sales, lags the overall market, while the other two have seen substantial upward revisions.

Revenues are on the rise for AI infrastructure-related companies

12 months ahead forward sales, indexed so that November 2022=100

The chart describes 12 months ahead forward sales indexed so that November 2022 = 100.
Source: Bloomberg Finance L.P. Data as of June 10, 2024. Note: Chips data calculated using ICE Semiconductor Index. S&P 500 data calculated using S&P 500 Index. Data Centers data calculated using a group of equities (EQIX US, DLR US, AMT US, CCI US, SBAC US). Power data calculated using a group of equities (NEE US, VST US, PEG US, AES US, CEG US).

Fair enough, the skeptics say. But isn’t all that promise already priced in? Some of our clients worry that AI stocks are too expensive, and the market too concentrated, to justify investing new money in the trend. AI, they worry, is currently in a bubble.

Certainly, the biggest mega-cap stocks, many AI-related, have surged since the S&P 500 hit its cycle low in October 2022. Yet, when comparing the market of today to the 1990s bubble, we see significant differences. During the last five years, the Nasdaq 100 rose just over 200%. That is nowhere near the >1,000% appreciation that preceded the 2000 market peak. Further, while valuations of the AI leaders today are not undervalued, they trade at nearly half the PE multiple of the leaders in 2000.

Today’s market is a far cry from the dot.com bubble

Cumulative rise in NASDAQ during dot.com bubble and now

This chart compares the price performance of the NASDAQ 100 and AI leaders of today over the last 5 years versus the performance of the NASDAQ 100 for the 5 years leading up to the bubble.
Source: Bloomberg Finance L.P. Data as of January 2024.

AI skeptics often point out that the market is considerably more concentrated than the historical norm. The top 10 stocks currently make up just under 35% of the total market capitalization of the S&P 500 versus an average of about 20% during the 2005–2019 pre-COVID period. Importantly, though, these 10 companies also account for a greater portion of the index’s earnings, now at 26%, than at any time over that period. In 2000, not long before the dot.com bubble burst, the top 10 stocks in the S&P 500 made up 27% of the index’s market cap, but only 17% of its earnings.

We think today’s high valuations for AI-related stocks are justified, given the potential productivity benefits—and the resulting corporate earnings growth—that AI could deliver.

In other words, AI is not a speculative bubble, in our view. It is indeed a revolution, and it has only just begun.

We can help

Your J.P. Morgan team can help you consider what kinds of AI-related investing make the most sense for you and your family.

1Generative AI” refers to large language models (LLMs). LLMs are computer programs that learn and generate text, images, audio, software code or other media using an architecture trained on large pools of data. Examples of LLMs include Open AI’s ChatGPT, Google’s Bard, Microsoft’s Copilot, and many others.

2The natural trend labor productivity growth rate of the U.S. economy is about 1.5% annually. That rate picked up to nearly 3% in the 1990s and early 2000s. This allowed GDP per capita to accelerate even as inflation was falling. That combination—rising productivity, falling inflation—occurred in the U.S. economy only once on a sustained basis in the past 50 years, which underscores how important and rare AI’s impact could be. 

3We estimate that the full productivity benefits from AI will take effect in 20 years. The comparable timeline for the prior technological breakthroughs varied, from 43 years for the steam engine to 20 years for electricity, to 17 years for PCs and the internet.

4This is not a knock on the CBO projections, which are conservative in nature and serve a valuable purpose in policy planning. However, our framework suggests that private sector economists might entertain further upside GDP potential from AI. When we looked at CBO projections in the early 1990s, we saw they completely missed the macro productivity boom from the internet.

5Mauro Cazzaniga, Florence Jaumotte, Longji Li, Giovanni Melina, Augustus J. Panton, Carlo Pizzinelli, Emma Rockall, and Marina M. Tavares, “Gen-AI: Artificial Intelligence and the Future of Work,” International Monetary Fund, January 2024.

6More specifically: We assume AI labor market displacement follows a classic “S” curve peaking at 15% of U.S. employment, and we make the crucial assumption that aggregate demand follows potential growth through the entire 20-year period, and that displaced workers can easily find new jobs throughout the adoption process. In reality, AI will likely cause some increase in structural unemployment. As a result, our estimates are probably on the optimistic end of the spectrum. To calculate our estimate of cumulative real GDP gains ($7 trillion), we apply our productivity results from AI labor displacement to the CBO’s baseline path of nominal GDP and then net out the cumulative expected (PCE) inflation. We don’t think the AI capex buildout will have a meaningful impact on GDP or inflation, as we explain later in the article.

7That is, at least when it comes to the initial impact of the technology. Much further into the future, AI may interact with robotics to displace manual labor or blue collar professions.

8Rakesh Kochar, “Which U.S. Workers Are More Exposed to AI on Their Jobs?” Pew Research Center, 2023. https://www.pewresearch.org/social-trends/wp-content/uploads/sites/3/2023/07/st_2023.07.26_ai-and-jobs.pdf

9https://www.itpro.com/technology/artificial-intelligence/the-costs-of-building-generative-ai-platforms-are-racking-up

10 https://www.project-syndicate.org/commentary/ai-productivity-boom-forecasts-countered-by-theory-and-data-by-daron-acemoglu-2024-05

11https://news.mit.edu/news-clip/fast-company-202

12Based on data from the U.S. Census Bureau’s Business Trends and Outlook Survey.

13AI adoption rates are higher for specific sectors. For example, a recent survey found a nearly 40% adoption rate of AI technologies in the contact center industry, where productivity gains from AI are already manifesting (https://www.nojitter.com/ai-automation/generative-ai-already-embedded-contact-centers?secureweb=AcroRd32). However, contact center employment in the U.S. represents only about 0.3% of total jobs. Ultimately, the economy-wide adoption rate will matter most for gauging overall macro productivity benefits from AI.

14https://www.epri.com/research/products/3002028905

15The AI projected capex spending numbers are taken from J.P. Morgan Asset Management estimates for the so-called “hyperscalers” (Microsoft, Meta, Amazon, Oracle and Alphabet). We scaled up these estimates to represent the total U.S. economy, and considered the capex ramp-up in excess of the pre-2023 trend.

16Just recently one hyperscaler purchased a 960MW data center from a power provider along with a 10-year commitment to provide a minimum of 480 MWs of power and an additional payment for carbon-free energy sales. https://www.ans.org/news/article-5842/amazon-buys-nuclearpowered-data-center-from-talen/

Our analytic framework quantifies the potential economic impact of AI. We believe it could be transformative.

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Morgan SE Brussels Branch is also supervised by the National Bank of Belgium (NBB) and the Financial Services and Markets Authority (FSMA) in Belgium; registered with the NBB under registration number 0715.622.844. In Greece, this material is distributed by J.P. Morgan SE—Athens Branch, with its registered office at 3 Haritos Street, Athens, 10675, Greece, authorized by the Bundesanstalt für Finanzdienstleistungsaufsicht (BaFin) and jointly supervised by the BaFin, the German Central Bank (Deutsche Bundesbank) and the European Central Bank (ECB); J.P. Morgan SE—Athens Branch is also supervised by Bank of Greece; registered with Bank of Greece as a branch of J.P. Morgan SE under code 124; Athens Chamber of Commerce Registered Number 158683760001; VAT Number 99676577. In France, this material is distributed by J.P. Morgan SE—Paris Branch, with its registered office at 14, Place Vendôme 75001 Paris, France, authorized by the Bundesanstaltfür Finanzdienstleistungsaufsicht (BaFin) and jointly supervised by the BaFin, the German Central Bank (Deutsche Bundesbank) and the European Central Bank (ECB) under code 842 422 972; J.P. Morgan SE—Paris Branch is also supervised by the French banking authorities the  Autorité de Contrôle Prudentiel et de Résolution (ACPR) and the Autorité des Marchés Financiers (AMF). In Switzerland, this material is distributed by J.P. Morgan (Suisse) SA, with registered address at rue du Rhône, 35, 1204, Geneva, Switzerland, which is authorized and supervised by the Swiss Financial Market Supervisory Authority (FINMA) as a bank and a securities dealer in Switzerland.

This communication is an advertisement for the purposes of the Markets in Financial Instruments Directive (MIFID II) and the Swiss Financial Services Act (FINSA). Investors should not subscribe for or purchase any financial instruments referred to in this advertisement except on the basis of information contained in any applicable legal documentation, which is or shall be made available in the relevant jurisdictions (as required).

In Hong Kong, this material is distributed by JPMCB, Hong Kong branch. JPMCB, Hong Kong branch is regulated by the Hong Kong Monetary Authority and the Securities and Futures Commission of Hong Kong. In Hong Kong, we will cease to use your personal data for our marketing purposes without charge if you so request. In Singapore, this material is distributed by JPMCB, Singapore branch. JPMCB, Singapore branch is regulated by the Monetary Authority of Singapore. Dealing and advisory services and discretionary investment management services are provided to you by JPMCB, Hong Kong/Singapore branch (as notified to you). Banking and custody services are provided to you by JPMCB Singapore Branch. The contents of this document have not been reviewed by any regulatory authority in Hong Kong, Singapore or any other jurisdictions. You are advised to exercise caution in relation to this document. If you are in any doubt about any of the contents of this document, you should obtain independent professional advice. For materials which constitute product advertisement under the Securities and Futures Act and the Financial Advisers Act, this advertisement has not been reviewed by the Monetary Authority of Singapore. JPMorgan Chase Bank, N.A., a national banking association chartered under the laws of the United States, and as a body corporate, its shareholder’s liability is limited.

With respect to countries in Latin America, the distribution of this material may be restricted in certain jurisdictions. We may offer and/or sell to you securities or other financial instruments which may not be registered under, and are not the subject of a public offering under, the securities or other financial regulatory laws of your home country. Such securities or instruments are offered and/or sold to you on a private basis only. Any communication by us to you regarding such securities or instruments, including without limitation the delivery of a prospectus, term sheet or other offering document, is not intended by us as an offer to sell or a solicitation of an offer to buy any securities or instruments in any jurisdiction in which such an offer or a solicitation is unlawful. Furthermore, such securities or instruments may be subject to certain regulatory and/or contractual restrictions on subsequent transfer by you, and you are solely responsible for ascertaining and complying with such restrictions. To the extent this content makes reference to a fund, the Fund may not be publicly offered in any Latin American country, without previous registration of such fund’s securities in compliance with the laws of the corresponding jurisdiction.

JPMorgan Chase Bank, N.A. (JPMCBNA) (ABN 43 074 112 011/AFS Licence No: 238367) is regulated by the Australian Securities and Investment Commission and the Australian Prudential Regulation Authority. Material provided by JPMCBNA in Australia is to “wholesale clients” only. For the purposes of this paragraph the term “wholesale client” has the meaning given in section 761G of the Corporations Act 2001 (Cth). Please inform us if you are not a Wholesale Client now or if you cease to be a Wholesale Client at any time in the future.

JPMS is a registered foreign company (overseas) (ARBN 109293610) incorporated in Delaware, U.S.A. Under Australian financial services licensing requirements, carrying on a financial services business in Australia requires a financial service provider, such as J.P. Morgan Securities LLC (JPMS), to hold an Australian Financial Services Licence (AFSL), unless an exemption applies. JPMS is exempt from the requirement to hold an AFSL under the Corporations Act 2001 (Cth) (Act) in respect of financial services it provides to you, and is regulated by the SEC, FINRA and CFTC under U.S. laws, which differ from Australian laws. Material provided by JPMS in Australia is to “wholesale clients” only. The information provided in this material is not intended to be, and must not be, distributed or passed on, directly or indirectly, to any other class of persons in Australia. For the purposes of this paragraph the term “wholesale client” has the meaning given in section 761G of the Act. Please inform us immediately if you are not a Wholesale Client now or if you cease to be a Wholesale Client at any time in the future.

This material has not been prepared specifically for Australian investors. It:

  • May contain references to dollar amounts which are not Australian dollars;
  • May contain financial information which is not prepared in accordance with Australian law or practices;
  • May not address risks associated with investment in foreign currency denominated investments; and
  • Does not address Australian tax issues.

References to “J.P. Morgan” are to JPM, its subsidiaries and affiliates worldwide. “J.P. Morgan Private Bank” is the brand name for the private banking business conducted by JPM. This material is intended for your personal use and should not be circulated to or used by any other person, or duplicated for non-personal use, without our permission. If you have any questions or no longer wish to receive these communications, please contact your J.P. Morgan team.

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JPMorgan Chase Bank, N.A. and its affiliates (collectively "JPMCB") offer investment products, which may include bank-managed accounts and custody, as part of its trust and fiduciary services. Other investment products and services, such as brokerage and advisory accounts, are offered through J.P. Morgan Securities LLC ("JPMS"), a member of FINRA and SIPC. Insurance products are made available through Chase Insurance Agency, Inc. (CIA), a licensed insurance agency, doing business as Chase Insurance Agency Services, Inc. in Florida. JPMCB, JPMS and CIA are affiliated companies under the common control of JPMorgan Chase & Co. Products not available in all states.

 

Please read the Legal Disclaimer for key important J.P. Morgan Private Bank information in conjunction with these pages.

INVESTMENT AND INSURANCE PRODUCTS ARE: • NOT FDIC INSURED • NOT INSURED BY ANY FEDERAL GOVERNMENT AGENCY • NOT A DEPOSIT OR OTHER OBLIGATION OF, OR GUARANTEED BY, JPMORGAN CHASE BANK, N.A. OR ANY OF ITS AFFILIATES • SUBJECT TO INVESTMENT RISKS, INCLUDING POSSIBLE LOSS OF THE PRINCIPAL AMOUNT INVESTED

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