Category: 3. Business

As artificial intelligence becomes more powerful and widespread, so does the environmental cost of running it.

Behind every chatbot, image generator, and television streaming recommendation are massive banks of millions of computers housed in an increasing number of data centers that consume staggering amounts of electricity and water to keep their machines cool. Most of that electricity is still produced by fossil fuel-burning power plants, which contribute directly to air pollution and climate change.

A study from UC Riverside’s Marlan and Rosemary Bourns College of Engineering, however, proposes a solution to this growing problem. It outlines a method to dramatically reduce the pollution caused by AI processing in large data centers—while also extending the life of the hardware doing the work. No existing system combines these two goals, say the authors, professors Mihri Ozkan and Cengiz Ozkan. 

While other strategies focus mainly on scheduling computing tasks when or where electricity is cleaner, the proposed system goes further. Called the Federated Carbon Intelligence, or FCI, it integrates environmental awareness with real-time assessments of the condition of the servers in use. The goal is not just to minimize carbon emissions but also to reduce the stress and wear and tear on the machines that generate the pollution.

The researchers, who are married, backed their proposal with simulations. Their modeling showed that FCI could reduce carbon dioxide emissions by up to 45 percent over a five-year period. The system could also extend the operational life of a server fleet by 1.6 years.

“Our results show that sustainability in AI cannot be achieved by focusing on clean energy alone,” said Mihri Ozkan, professor of electrical and computer engineering. “AI systems age, they heat up, and their efficiency changes over time—and these shifts have a measurable carbon cost.

“By integrating real-time hardware health with carbon-intensity data, our framework learns how to route AI workloads in a way that cuts emissions while protecting the long-term reliability of the machines themselves.”

By constantly monitoring the temperature, age, and physical wear of servers, FCI helps avoid overworking machines that are already stressed or nearing the end of their useful life. In doing so, it prevents costly breakdowns, reduces the need for energy and water-intensive cooling, and keeps servers running longer. 

This approach recognizes that sustainability isn’t just about cleaner energy. It’s also about getting the most out of the hardware we already have, the authors say.

Their system further accounts for the complete lifecycle carbon footprint of computing—especially the embodied emissions from manufacturing new servers. By keeping existing machines in service longer and distributing computing tasks in a way that balances performance, wear, and environmental impact, the system addresses both sides of the sustainability equation.

“We reduce operational emissions in real time, but we also slow down hardware degradation,” said Cengiz Ozkan, professor of mechanical engineering. “By preventing unnecessary wear, we reduce not only the energy used today but also the environmental footprint of tomorrow’s hardware production.”

FCI dynamically determines where and when to process AI tasks based on constantly updated data. It tracks the condition of the machines, gauges the carbon intensity of electricity at any given time and place, and evaluates the demands of each AI workload. Then, using that information, it makes real-time decisions to send the task to the server best suited to handle it—with the least impact on the planet and the machine.

Deploying such systems—driven by AI models—could represent a major advancement for both environmental sustainability and the cloud computing industry, the researchers said.

Establishing the adaptive framework would not require new equipment, just smarter coordination across the systems already in place, Mihri Ozkan said.

Published in the journal MRS Energy and Sustainability, the study is titled “Federated carbon intelligence for sustainable AI: Real-time optimization across heterogeneous hardware fleets.”

The researchers say the next step is partnering with cloud providers to test FCI in real data centers, a move that could lay the foundation for NetZero-aligned AI infrastructure worldwide. The Ozkans described an urgent need. The growing number of data centers is already consuming more power than entire countries, including Sweden.

“AI is expanding faster than the energy systems that support it,” Cengiz Ozkan said. “Frameworks like ours show that climate-aligned computing is achievable—without sacrificing performance.” 

 

Thursday, Nov 20, 2025, 5:00pm

by Semiconductor Industry Association

WASHINGTON—November 20, 2025—The Semiconductor Industry Association (SIA) today announced AMD Chair and CEO Dr. Lisa Su has been elected Chair of the SIA Board of Directors. SIA represents 99% of the U.S. semiconductor industry by revenue and nearly two-thirds of non-U.S. chip firms.

“We are delighted to welcome Dr. Lisa Su as SIA Chair during an exciting and consequential time for the semiconductor industry,” said SIA President and CEO John Neuffer. “Lisa has pushed the boundaries of semiconductor innovation for decades and is an extremely strong and influential leader in our industry. We look forward to her leadership in the year ahead as we push for policies that promote growth and innovation in the chip sector and keep America on top in this foundational, transformative technology.”

Dr. Su brings more than 30 years of experience in the semiconductor industry. As Chair and CEO of AMD, she has led the company’s transformation into a global leader in high performance computing and a key supplier of advanced AI chips. Before assuming her current role, Dr. Su served as chief operating officer at AMD, where she unified AMD’s business units, sales, operations, and infrastructure into a single organization focused on execution and market impact. Prior to her roles at AMD, Dr. Su held leadership roles with Freescale Semiconductor (now NXP Semiconductors), IBM, and Texas Instruments. Dr. Su holds a PhD in electrical engineering from the Massachusetts Institute of Technology, and in 2020 received the Robert N. Noyce Award for her groundbreaking contributions to the semiconductor industry.

“The semiconductor industry is at the heart of American innovation and essential to our economic growth and national security,” said Dr. Su. “It’s an honor to serve as Chair of SIA at such an important time. I look forward to working alongside my colleagues on the SIA Board of Directors to strengthen U.S. semiconductor competitiveness, extend our foundation for innovation, and build a stronger chip industry for many years to come.”

# # #

About SIA
The Semiconductor Industry Association (SIA) is the voice of the semiconductor industry, one of America’s top export industries and a key driver of America’s economic strength, national security, and global competitiveness. SIA represents 99% of the U.S. semiconductor industry by revenue and nearly two-thirds of non-U.S. chip firms. Through this coalition, SIA seeks to strengthen leadership of semiconductor manufacturing, design, and research by working with Congress, the Administration, and key industry stakeholders around the world to encourage policies that fuel innovation, propel business, and drive international competition. Learn more at www.semiconductors.org.

PALM BEACH GARDENS, Fla., Nov. 20, 2025 /PRNewswire/ — Carrier Global Corporation (NYSE: CARR) Chairman & CEO David Gitlin will speak at the Goldman Sachs Industrials and Materials Conference on Thursday, December 4, 2025, at 8:40 a.m. ET.

The event will be broadcast live at ir.carrier.com. A webcast replay will be available on the website following the event.

About Carrier  

Carrier Global Corporation, global leader in intelligent climate and energy solutions, is committed to creating innovations that bring comfort, safety and sustainability to life. Through cutting-edge advancements in climate solutions such as temperature control, air quality and transportation, we improve lives, empower critical industries and ensure the safe transport of food, life-saving medicines and more. Since inventing modern air conditioning in 1902, we lead with purpose: enhancing the lives we live and the world we share. We continue to lead because of our world-class, inclusive workforce that puts the customer at the center of everything we do. For more information, visit corporate.carrier.com or follow Carrier on social media at @Carrier.

Carrier. For the World We Share.

Europe is seeing an increase in bloodstream infections (BSIs) caused by difficult-to-treat drug-resistant bacteria, according to data published this week by the European Centre for Disease Prevention and Control (ECDC).

The data from the latest EARS-Net (European Antimicrobial Resistance Surveillance Network) report, which covers 30 European Union/European Economic Activity (EU/EEA) countries, show that the estimated total incidence of carbapenem-resistant Klebsiella pneumoniae BSIs rose by 61% from 2019 (the baseline year) through 2024, while the incidence of third-generation cephalosporin-resistant Escherichia coli BSIs increased by 5.9%. 

The EU has set 2030 target reductions of 5% and 10% for the two pathogens, respectively, but ECDC says it appears unlikely those targets will be met.

BSIs caused by other bug-drug combinations under EARS-Net surveillance also saw increases, including carbapenem-resistant E coli and vancomycin-resistant Enterococcus faecium. But one bright spot was that incidence of BSIs caused by methicillin-resistant Staphylococcus aureus fell by 20.4% from 2019 levels. As with prior EARS-Net reports, higher rates of antimicrobial resistance (AMR) were reported by countries in southern, central, and eastern Europe.

Not just a medical issue

The ECDC estimates AMR causes more than 35,000 deaths a year in EU/EEA countries. The organization attributes the rise in difficult-to-treat infections to an aging and vulnerable population with chronic health issues, cross-border transmission of resistant pathogens, persistent high antibiotic use combined with gaps in infection prevention and control, and a shortage of novel antibiotics.

“Antimicrobial resistance is not just a medical issue—it’s a societal one,” Diamantas Plachouras, MD, PhD, head of the ECDC’s Antimicrobial Resistance and Healthcare-Associated Infections division, said in a press release. “We must ensure that no one in Europe is left without an effective treatment option.”

Fears of a growing bubble around the artificial intelligence frenzy resurfaced on Thursday as leading US stock markets fell, less than 24 hours after strong results from chipmaker Nvidia sparked a rally.

Wall Street initially rose after Nvidia, the world’s largest public company, reassured investors of strong demand for its advanced data center chips. But the relief dissipated, and technology stocks at the heart of the AI boom came under pressure.

The benchmark S&P 500 closed down 1.6%, and the Dow Jones industrial average closed down 0.8% in New York. The tech-focused Nasdaq Composite closed down 2.2%.

Earlier in the day, the FTSE 100 had closed up 0.2% in London while the Dax had risen 0.5% in Frankfurt. The Nikkei 225 had climbed 2.65% in Tokyo.

Nvidia, now valued at some $4.4tn, has led an extraordinary surge in the valuations of AI-related firms in recent months. As firms splurge on chips and data centers in a bid to get a foothold in AI, fears of a bubble have mounted.

While Nvidia’s highly anticipated earnings exceeded expectations on Wednesday, as the chipmaker continues to enjoy robust demand, concerns persist around the firms using those chips to invest in AI, spending heavily and driving that demand.

“The people who are selling the semiconductors to help power AI doesn’t alleviate the concerns that some of these hyper-scalers are spending way too much money on building the AI infrastructure,” said Robert Pavlik, senior portfolio manager at Dakota Wealth. “You have the company that’s benefiting it, but the others are still spending too much money.”

A mixed jobs report on Thursday morning, which revealed healthy growth in the labor market in September but a slight rise in unemployment, also reinforced expectations that policymakers at the Federal Reserve will likely keep interest rates on hold at their next meeting, in December.

Shares in Nvidia sank 3.2%. The VIX, a measure of market volatility, also climbed 8%.

Reuters contributed reporting

Global Business Solutions Online Ecosystem Revenue Grew 21 percent; Consumer Revenue Grew 21 percent

MOUNTAIN VIEW, Calif.–(BUSINESS WIRE)–
Intuit Inc. (Nasdaq: INTU), the global financial technology platform that makes Intuit TurboTax, Credit Karma, QuickBooks, and Mailchimp, announced financial results for the first quarter of fiscal 2026, which ended October 31.

“We delivered an exceptional first quarter as we continue to execute on our AI-driven expert platform strategy. Intuit is creating a system of intelligence, leveraging data, data services, AI, and human intelligence to fuel the success of consumers, small and mid-market businesses, and accountants,” said Sasan Goodarzi, Intuit’s chief executive officer. “We launched significant done-for-you innovations across our platform that are helping businesses manage from lead to cash, and consumers manage credit building to wealth building, all in one place.”

Financial Highlights

For the first quarter, Intuit:

• Grew total revenue to $3.9 billion, up 18 percent.

• Increased Global Business Solutions revenue to $3.0 billion, up 18 percent; grew Online Ecosystem revenue to $2.4 billion, up 21 percent. Excluding Mailchimp, Global Business Solutions revenue grew 20 percent, and Online Ecosystem revenue grew 25 percent.

• Grew Consumer revenue to $894 million, up 21 percent.

• Increased GAAP operating income to $534 million, up 97 percent.

• Grew non-GAAP operating income to $1.3 billion, up 32 percent.

• Increased GAAP diluted earnings per share to $1.59, up 127 percent.

• Grew non-GAAP diluted earnings per share to $3.34, up 34 percent.

Unless otherwise noted, all growth rates refer to the current period versus the comparable prior-year period, and the business metrics and associated growth rates refer to worldwide business metrics.

Snapshot of First-quarter Results

	GAAP			Non-GAAP
	Q1 FY26	Q1 FY25	Change	Q1 FY26	Q1 FY25	Change
Revenue	$3,885	$3,283	18%	$3,885	$3,283	18%
Operating Income	$534	$271	97%	$1,258	$953	32%
Earnings Per Share	$1.59	$0.70	127%	$3.34	$2.50	34%
Dollars are in millions, except earnings per share. See “About Non-GAAP Financial Measures” below for more information regarding financial measures not prepared in accordance with Generally Accepted Accounting Principles (GAAP).

“We delivered a strong first quarter of fiscal 2026, driven by continued momentum across the company,” said Sandeep Aujla, Intuit’s chief financial officer. “We are confident in delivering double-digit revenue growth and expanding margin this year, and we are reiterating our full year guidance for fiscal 2026.”

Business Segment Results

Global Business Solutions

Global Business Solutions revenue grew to $3.0 billion, up 18 percent, and Online Ecosystem revenue increased to $2.4 billion, up 21 percent.

• QuickBooks Online Accounting revenue grew 25 percent in the quarter, driven by higher effective prices, customer growth, and mix-shift.

• Online Services revenue grew 17 percent, driven by growth in money and payroll offerings.

• Total international online revenue grew 9 percent on a constant currency basis.

Consumer

Consumer revenue of $894 million was up 21 percent in the quarter.

• Credit Karma revenue grew 27 percent to $651 million, driven by strength in personal loans, credit cards, and auto insurance.

• TurboTax revenue grew 6 percent to $198 million.

• ProTax revenue grew 15 percent to $45 million.

Capital Allocation Summary

The company:

• Reported a total cash and investments balance of approximately $3.7 billion and $6.1 billion in debt as of October 31, 2025.

• Repurchased $851 million of stock, and $4.4 billion remains on the company’s share repurchase authorization.

• Received Board approval for a quarterly dividend of $1.20 per share, payable January 16, 2026. This represents a 15 percent increase per share compared to the same period last year.

Forward-looking Guidance

Intuit reiterated guidance for the full fiscal year 2026. The company expects:

• Revenue of $20.997 billion to $21.186 billion, growth of approximately 12 to 13 percent.

• GAAP operating income of $5.782 billion to $5.859 billion, growth of approximately 17 to 19 percent.

• Non-GAAP operating income of $8.611 billion to $8.688 billion, growth of approximately 14 to 15 percent.

• GAAP diluted earnings per share of $15.49 to $15.69, growth of approximately 13 to 15 percent.

• Non-GAAP diluted earnings per share of $22.98 to $23.18, growth of approximately 14 to 15 percent.

The company also reiterated full fiscal year 2026 segment revenue guidance:

• Global Business Solutions: growth of 14 to 15 percent. Excluding Mailchimp, the company expects Global Business Solutions Group revenue growth of 15.5 percent to 16.5 percent.

• Consumer: growth of 8 to 9 percent. This includes TurboTax growth of 8 percent, Credit Karma growth of 10 to 13 percent, and ProTax growth of 2 to 3 percent.

Intuit announced guidance for the second quarter of fiscal year 2026, which ends January 31. The company expects:

• Revenue growth of approximately 14 to 15 percent.

• GAAP diluted earnings per share of $1.76 to $1.81.

• Non-GAAP diluted earnings per share of $3.63 to $3.68.

Conference Call Details

Intuit executives will discuss the financial results on a conference call at 1:30 p.m. Pacific time on November 20. The conference call can be heard live at https://investors.intuit.com/news-events/ir-calendar. Prepared remarks for the call will be available on Intuit’s website after the call ends.

Replay Information

A replay of the conference call will be available for one week by calling 800-934-4245, or 402-220-1173 from international locations. There is no passcode required. The audio call will remain available on Intuit’s website for one week after the conference call.

About Intuit

Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible. Please visit us at Intuit.com and find us on social for the latest information about Intuit and our products and services.

About Non-GAAP Financial Measures

This press release and the accompanying tables include non-GAAP financial measures. For a description of these non-GAAP financial measures, including the reasons management uses each measure, and reconciliations of these non-GAAP financial measures to the most directly comparable financial measures prepared in accordance with Generally Accepted Accounting Principles, please see the section of the accompanying tables titled “About Non-GAAP Financial Measures” as well as the related Table B1, Table B2, and Table E. A copy of the press release issued by Intuit today can be found on the investor relations page of Intuit’s website.

Cautions About Forward-looking Statements

This press release contains forward-looking statements, including expectations regarding: forecasts and timing of growth and future financial results of Intuit and its reporting segments; Intuit’s prospects for the business in fiscal 2026 and beyond; timing and growth of revenue from current or future products and services; demand for our products; customer growth and retention; Intuit’s corporate tax rate; the amount and timing of any future dividends or share repurchases; and the impact of strategic decisions on our business; as well as all of the statements under the heading “Forward-looking Guidance.”

Because these forward-looking statements involve risks and uncertainties, there are important factors that could cause our actual results to differ materially from the expectations expressed in the forward-looking statements. These risks and uncertainties may be amplified by the effects of global developments and conditions or events, including macroeconomic uncertainty and geopolitical conditions, which have caused significant global economic instability and uncertainty. Given these risks and uncertainties, persons reading this communication are cautioned not to place any undue reliance on such forward-looking statements. These factors include, without limitation, the following: our ability to compete successfully; potential governmental encroachment in our tax business; our ability to develop, deploy, and use artificial intelligence in our platform and offerings; our ability to adapt to technological change and to successfully extend our platform; our ability to predict consumer behavior; our ability to anticipate and solve new and existing customer problems; our reliance on intellectual property; our ability to protect our intellectual property rights; any harm to our reputation; risks associated with our environmental, social, and governance efforts; risks associated with acquisition and divestiture activity; the issuance of equity or incurrence of debt to fund acquisitions or for general business purposes; cybersecurity incidents (including those affecting the third parties we rely on); customer or regulator concerns about privacy and cybersecurity incidents; fraudulent activities by third parties, including through the use of AI; our failure to process transactions effectively; interruption or failure of our information technology; our ability to develop and maintain critical third-party business relationships; our ability to attract and retain talent and the success of our hybrid work model; our ability to effectively develop and deploy AI in our offerings; any deficiency in the quality or accuracy of our offerings (including the advice given by experts on our platform); any delays in product launches; difficulties in processing or filing customer tax submissions; risks associated with international operations; risks associated with climate change; changes to, and evolving interpretations of public policy, laws, or regulations affecting our businesses; allegations of legal claims and legal proceedings in which we are involved; fluctuations in the results of our tax business due to seasonality and other factors beyond our control; changes in tax rates and tax reform legislation; global economic conditions (including, without limitation, inflation); exposure to credit, counterparty, and other risks in providing capital to businesses; amortization of acquired intangible assets and impairment charges; our ability to repay or otherwise comply with the terms of our outstanding debt; our ability to repurchase shares or distribute dividends; volatility of our stock price; and our ability to successfully market our offerings.

More details about these and other risks that may impact our business are included in our Form 10-K for fiscal 2025 and in our other SEC filings. You can locate these reports through our website at https://investors.intuit.com. Second-quarter and full-year fiscal 2026 guidance speaks only as of the date it was publicly issued by Intuit. Other forward-looking statements represent the judgment of the management of Intuit as of the date of this presentation. Except as required by law, we do not undertake any duty to update any forward-looking statement or other information in this presentation.

TABLE A

INTUIT INC.

GAAP CONSOLIDATED STATEMENTS OF OPERATIONS

(In millions, except per share amounts)

(Unaudited)

	Three Months Ended
	October 31, 2025				October 31, 2024
Net revenue:
Service	$	3,497			$	2,889
Product and other		388				394
Total net revenue		3,885				3,283
Costs and expenses:
Cost of revenue:
Cost of service revenue		824				772
Cost of product and other revenue		15				14
Amortization of acquired technology		44				37
Selling and marketing		1,082				962
Research and development		843				704
General and administrative		422				394
Amortization of other acquired intangible assets		121				120
Restructuring		—				9
Total costs and expenses [A]		3,351				3,012
Operating income		534				271
Interest expense		(58	)			(60	)
Interest and other income, net		85				2
Income before income taxes		561				213
Income tax provision [B]		115				16
Net income	$	446			$	197
Basic net income per share	$	1.60			$	0.70
Shares used in basic per share calculations		279				280
Diluted net income per share	$	1.59			$	0.70
Shares used in diluted per share calculations		281				283

See accompanying Notes.

INTUIT INC.

NOTES TO TABLE A

[A]	The following table summarizes the total share based compensation expense that we recorded in operating income for the periods shown.

	Three Months Ended
(In millions)	October 31, 2025			October 31, 2024
Cost of revenue	$	97		$	111
Selling and marketing		156			137
Research and development		185			161
General and administrative		105			102
Total share-based compensation expense	$	543		$	511

[B]

We compute our provision for or benefit from income taxes by applying the estimated annual effective tax rate to income or loss from recurring operations and adding the effects of any discrete income tax items specific to the period. We recognized excess tax benefits on share-based compensation of $30 million and $28 million in our provision for income taxes for the three months ended October 31, 2025 and 2024, respectively. Our effective tax rate for the three months ended October 31, 2025 was approximately 20%. Excluding discrete tax items primarily related to share-based compensation, our effective tax rate was approximately 24%. The difference from the federal statutory rate of 21% was primarily due to state income taxes and non-deductible share-based compensation, which were partially offset by the tax benefit we received from the federal research and experimentation credit. Our effective tax rate for the three months ended October 31, 2024 was approximately 8%. Excluding discrete tax items primarily related to share-based compensation, our effective tax rate was approximately 24%. The difference from the federal statutory rate of 21% was primarily due to state income taxes and non-deductible share-based compensation, which were partially offset by the tax benefit we received from the federal research and experimentation credit. In the current global tax policy environment, the U.S. and other domestic and foreign governments continue to consider, and in some cases enact, changes in corporate tax laws. As changes occur, we account for finalized legislation in the period of enactment.

TABLE B1

INTUIT INC.

RECONCILIATION OF NON-GAAP FINANCIAL MEASURES

TO MOST DIRECTLY COMPARABLE GAAP FINANCIAL MEASURES

(In millions, except per share amounts)

(Unaudited)

	Fiscal 2026
	Q1				Q2			Q3			Q4			Year to Date
GAAP operating income (loss)	$	534			$	—		$	—		$	—		$	534
Amortization of acquired technology		44				—			—			—			44
Amortization of other acquired intangible assets		121				—			—			—			121
Net (gain) loss on executive deferred compensation plan liabilities		16				—			—			—			16
Share-based compensation expense		543				—			—			—			543
Non-GAAP operating income (loss)	$	1,258			$	—		$	—		$	—		$	1,258
GAAP net income (loss)	$	446			$	—		$	—		$	—		$	446
Amortization of acquired technology		44				—			—			—			44
Amortization of other acquired intangible assets		121				—			—			—			121
Net (gain) loss on executive deferred compensation plan liabilities		16				—			—			—			16
Share-based compensation expense		543				—			—			—			543
Net (gain) loss on debt securities and other investments [A]		(34	)			—			—			—			(34	)
Net (gain) loss on executive deferred compensation plan assets		(15	)			—			—			—			(15	)
Income tax effects and adjustments [B]		(182	)			—			—			—			(182	)
Non-GAAP net income (loss)	$	939			$	—		$	—		$	—		$	939
GAAP diluted net income (loss) per share	$	1.59			$	—		$	—		$	—		$	1.59
Amortization of acquired technology		0.16				—			—			—			0.16
Amortization of other acquired intangible assets		0.43				—			—			—			0.43
Net (gain) loss on executive deferred compensation plan liabilities		0.05				—			—			—			0.05
Share-based compensation expense		1.93				—			—			—			1.93
Net (gain) loss on debt securities and other investments [A]		(0.12	)			—			—			—			(0.12	)
Net (gain) loss on executive deferred compensation plan assets		(0.05	)			—			—			—			(0.05	)
Income tax effects and adjustments [B]		(0.65	)			—			—			—			(0.65	)
Non-GAAP diluted net income (loss) per share	$	3.34			$	—		$	—		$	—		$	3.34
Shares used in GAAP diluted per share calculations		281				—			—			—			281
Shares used in non-GAAP diluted per share calculations		281				—			—			—			281

[A]	During the three months ended October 31, 2025, we recognized $34 million in net gains on other long-term investments.
[B]	As discussed in “About Non-GAAP Financial Measures – Income Tax Effects and Adjustments” following Table E, our long-term non-GAAP tax rate eliminates the effects of non-recurring and period-specific items. Income tax adjustments consist primarily of the tax impact of the non-GAAP pre-tax adjustments and tax benefits related to share-based compensation.
See “About Non-GAAP Financial Measures” immediately following Table E for information on these measures, the items excluded from the most directly comparable GAAP measures in arriving at non-GAAP financial measures, and the reasons management uses each measure and excludes the specified amounts in arriving at each non-GAAP financial measure.

TABLE B2

INTUIT INC.

RECONCILIATION OF NON-GAAP FINANCIAL MEASURES

TO MOST DIRECTLY COMPARABLE GAAP FINANCIAL MEASURES

(In millions, except per share amounts)

(Unaudited)

	Fiscal 2025
	Q1				Q2				Q3				Q4				Full Year
GAAP operating income (loss)	$	271			$	593			$	3,720			$	339			$	4,923
Amortization of acquired technology		37				37				38				44				156
Amortization of other acquired intangible assets		120				120				120				121				481
Restructuring		9				4				1				1				15
Professional fees for business combinations		—				—				2				—				2
Net (gain) loss on executive deferred compensation plan liabilities		5				8				(7	)			21				27
Share-based compensation expense		511				498				469				490				1,968
Non-GAAP operating income (loss)	$	953			$	1,260			$	4,343			$	1,016			$	7,572
GAAP net income (loss)	$	197			$	471			$	2,820			$	381			$	3,869
Amortization of acquired technology		37				37				38				44				156
Amortization of other acquired intangible assets		120				120				120				121				481
Restructuring		9				4				1				1				15
Professional fees for business combinations		—				—				2				—				2
Net (gain) loss on executive deferred compensation plan liabilities		5				8				(7	)			21				27
Share-based compensation expense		511				498				469				490				1,968
Net (gain) loss on debt securities and other investments [A]		42				3				2				(2	)			45
Net (gain) loss on executive deferred compensation plan assets		(4	)			(7	)			7				(20	)			(24	)
Income tax effects and adjustments [B]		(208	)			(196	)			(172	)			(260	)			(836	)
Non-GAAP net income (loss)	$	709			$	938			$	3,280			$	776			$	5,703
GAAP diluted net income (loss) per share	$	0.70			$	1.67			$	10.02			$	1.35			$	13.67
Amortization of acquired technology		0.13				0.13				0.13				0.16				0.55
Amortization of other acquired intangible assets		0.42				0.42				0.43				0.43				1.70
Restructuring		0.03				0.01				—				—				0.05
Professional fees for business combinations		—				—				0.01				—				0.01
Net (gain) loss on executive deferred compensation plan liabilities		0.02				0.03				(0.02	)			0.07				0.10
Share-based compensation expense		1.80				1.76				1.66				1.74				6.95
Net (gain) loss on debt securities and other investments [A]		0.15				0.01				0.01				(0.01	)			0.16
Net (gain) loss on executive deferred compensation plan assets		(0.02	)			(0.02	)			0.02				(0.07	)			(0.09	)
Income tax effects and adjustments [B]		(0.73	)			(0.69	)			(0.61	)			(0.92	)			(2.95	)
Non-GAAP diluted net income (loss) per share	$	2.50			$	3.32			$	11.65			$	2.75			$	20.15
Shares used in GAAP diluted per share calculations		283				283				282				282				283
Shares used in non-GAAP diluted per share calculations		283				283				282				282				283

[A]	During the three months ended October 31, 2024, we recognized a $42 million net loss on other long-term investments.
[B]	As discussed in “About Non-GAAP Financial Measures – Income Tax Effects and Adjustments” following Table E, our long-term non-GAAP tax rate eliminates the effects of non-recurring and period-specific items. Income tax adjustments consist primarily of the tax impact of the non-GAAP pre-tax adjustments and tax benefits related to share-based compensation.
See “About Non-GAAP Financial Measures” immediately following Table E for information on these measures, the items excluded from the most directly comparable GAAP measures in arriving at non-GAAP financial measures, and the reasons management uses each measure and excludes the specified amounts in arriving at each non-GAAP financial measure.

TABLE C

INTUIT INC.

CONDENSED CONSOLIDATED BALANCE SHEETS

(In millions)

(Unaudited)

	October 31, 2025			July 31, 2025
ASSETS
Current assets:
Cash and cash equivalents	$	3,506		$	2,884
Investments		190			1,668
Accounts receivable, net		579			530
Notes receivable held for investment		1,519			1,403
Notes receivable held for sale		48			—
Income taxes receivable		31			50
Prepaid expenses and other current assets		630			496
Current assets before funds receivable and amounts held for customers		6,503			7,031
Funds receivable and amounts held for customers		3,918			7,076
Total current assets		10,421			14,107
Long-term investments		92			94
Property and equipment, net		965			961
Operating lease right-of-use assets		596			541
Goodwill		13,980			13,980
Acquired intangible assets, net		5,136			5,302
Long-term deferred income tax assets		1,173			1,222
Other assets		828			751
Total assets	$	33,191		$	36,958
LIABILITIES AND STOCKHOLDERS’ EQUITY
Current liabilities:
Short-term debt	$	749		$	—
Accounts payable		670			792
Accrued compensation and related liabilities		479			858
Deferred revenue		1,045			1,019
Other current liabilities		658			625
Current liabilities before funds payable and amounts due to customers		3,601			3,294
Funds payable and amounts due to customers		3,918			7,076
Total current liabilities		7,519			10,370
Long-term debt		5,391			5,973
Operating lease liabilities		643			597
Other long-term obligations		316			308
Total liabilities		13,869			17,248
Stockholders’ equity		19,322			19,710
Total liabilities and stockholders’ equity	$	33,191		$	36,958

TABLE D

INTUIT INC.

CONDENSED CONSOLIDATED STATEMENTS OF CASH FLOWS

(In millions)

(Unaudited)

	Three Months Ended
	October 31, 2025				October 31, 2024
Cash flows from operating activities:
Net income	$	446			$	197
Adjustments to reconcile net income to net cash provided by operating activities:
Depreciation		44				44
Amortization of acquired intangible assets		165				157
Non-cash operating lease cost		23				19
Share-based compensation expense		543				511
Deferred income taxes		58				(91	)
Other		(6	)			63
Total adjustments		827				703
Changes in operating assets and liabilities:
Accounts receivable		(49	)			31
Income taxes receivable		19				51
Prepaid expenses and other assets		(119	)			(27	)
Accounts payable		(135	)			(75	)
Accrued compensation and related liabilities		(378	)			(507	)
Deferred revenue		25				19
Operating lease liabilities		(23	)			(22	)
Other liabilities		24				(8	)
Total changes in operating assets and liabilities		(636	)			(538	)
Net cash provided by operating activities		637				362
Cash flows from investing activities:
Purchases of corporate and customer fund investments		(101	)			(306	)
Sales of corporate and customer fund investments		115				55
Maturities of corporate and customer fund investments		1,473				235
Purchases of property and equipment		(38	)			(33	)
Originations and purchases of notes receivable held for investment		(1,297	)			(666	)
Sales of notes receivable originally classified as held for investment		213				110
Principal repayments of notes receivable held for investment		876				420
Other		(43	)			(3	)
Net cash provided by (used in) investing activities		1,198				(188	)
Cash flows from financing activities:
Proceeds from borrowings under secured revolving credit facilities		166				85
Proceeds from issuance of stock under employee stock plans		62				96
Payments for employee taxes withheld upon vesting of restricted stock units		(244	)			(239	)
Cash paid for purchases of treasury stock		(854	)			(557	)
Dividends and dividend rights paid		(341	)			(296	)
Net change in funds receivable and funds payable and amounts due to customers		(3,160	)			1,672
Other		(1	)			—
Net cash provided by (used in) financing activities		(4,372	)			761
Effect of exchange rates on cash, cash equivalents, restricted cash, and restricted cash equivalents		(1	)			—
Net increase (decrease) in cash, cash equivalents, restricted cash, and restricted cash equivalents		(2,538	)			935
Cash, cash equivalents, restricted cash, and restricted cash equivalents at beginning of period		9,481				7,099
Cash, cash equivalents, restricted cash, and restricted cash equivalents at end of period	$	6,943			$	8,034
Reconciliation of cash, cash equivalents, restricted cash, and restricted cash equivalents reported within the condensed consolidated balance sheets to the total amounts reported on the condensed consolidated statements of cash flows
Cash and cash equivalents	$	3,506			$	2,872
Restricted cash and restricted cash equivalents included in funds receivable and amounts held for customers		3,437				5,162
Total cash, cash equivalents, restricted cash, and restricted cash equivalents at end of period	$	6,943			$	8,034
Supplemental schedule of non-cash investing activities:
Transfers of notes receivable originated or purchased as held for investment to held for sale	$	253			$	113

TABLE E

INTUIT INC.

RECONCILIATION OF FORWARD-LOOKING GUIDANCE FOR NON-GAAP FINANCIAL MEASURES TO PROJECTED GAAP REVENUE, OPERATING INCOME, AND EPS

(In millions, except per share amounts)

(Unaudited)

	Forward-Looking Guidance
	GAAP Range of Estimate									Non-GAAP Range of Estimate
	From			To			Adjmts			From			To
Three Months Ending January 31, 2026
Revenue	$	4,519		$	4,549		$	—		$	4,519		$	4,549
Operating income	$	676		$	691		$	695	[a]	$	1,371		$	1,386
Diluted net income per share	$	1.76		$	1.81		$	1.87	[b]	$	3.63		$	3.68
Twelve Months Ending July 31, 2026
Revenue	$	20,997		$	21,186		$	—		$	20,997		$	21,186
Operating income	$	5,782		$	5,859		$	2,829	[c]	$	8,611		$	8,688
Diluted net income per share	$	15.49		$	15.69		$	7.49	[d]	$	22.98		$	23.18

See “About Non-GAAP Financial Measures” immediately following Table E for information on these measures, the items excluded from the most directly comparable GAAP measures in arriving at non-GAAP financial measures, and the reasons management uses each measure and excludes the specified amounts in arriving at each non-GAAP financial measure.
[a]	Reflects estimated adjustments for share-based compensation expense of approximately $530 million; amortization of other acquired intangible assets of approximately $121 million; and amortization of acquired technology of approximately $44 million.
[b]	Reflects estimated adjustments in item [a], income taxes related to these adjustments, and other income tax effects related to the use of the non-GAAP tax rate.
[c]	Reflects estimated adjustments for share-based compensation expense of approximately $2.2 billion; amortization of other acquired intangible assets of approximately $483 million; amortization of acquired technology of approximately $176 million; and net losses on executive deferred compensation plan liabilities of approximately $16 million.
[d]	Reflects estimated adjustments in item [c], income taxes related to these adjustments, other income tax effects related to the use of the non-GAAP tax rate, and adjustments for a net loss on other long-term investments.

INTUIT INC.

ABOUT NON-GAAP FINANCIAL MEASURES

The accompanying press release dated November 20, 2025 contains non-GAAP financial measures. Table B1, Table B2, and Table E reconcile the non-GAAP financial measures in that press release to the most directly comparable financial measures prepared in accordance with Generally Accepted Accounting Principles (GAAP). These non-GAAP financial measures include non-GAAP operating income (loss), non-GAAP net income (loss), and non-GAAP diluted net income (loss) per share.

Non-GAAP financial measures should not be considered as a substitute for, or superior to, measures of financial performance prepared in accordance with GAAP. These non-GAAP financial measures do not reflect a comprehensive system of accounting, differ from GAAP measures with the same names, and may differ from non-GAAP financial measures with the same or similar names that are used by other companies.

We compute non-GAAP financial measures using the same consistent method from quarter to quarter and year to year. We may consider whether other significant items that arise in the future should be excluded from our non-GAAP financial measures.

We exclude the following items from all of our non-GAAP financial measures:

• Amortization of acquired technology

• Amortization of other acquired intangible assets

• Restructuring charges

• Share-based compensation expense

• Gains and losses on executive deferred compensation plan liabilities

• Goodwill and intangible asset impairment charges

• Gains and losses on disposals of businesses and long-lived assets

• Professional fees and transaction costs for business combinations

We also exclude the following items from non-GAAP net income (loss) and diluted net income (loss) per share:

• Gains and losses on debt securities and other investments

• Gains and losses on executive deferred compensation plan assets

• Income tax effects and adjustments

• Discontinued operations

We believe these non-GAAP financial measures provide meaningful supplemental information regarding Intuit’s operating results primarily because they exclude amounts that we do not consider part of ongoing operating results when planning and forecasting and when assessing the performance of the organization, our individual operating segments, or our senior management. Segment managers are not held accountable for share-based compensation expense, amortization, restructuring, or the other excluded items and, accordingly, we exclude these amounts from our measures of segment performance. We believe our non-GAAP financial measures also facilitate the comparison by management and investors of results for current periods and guidance for future periods with results for past periods.

The following are descriptions of the items we exclude from our non-GAAP financial measures.

Amortization of acquired technology and amortization of other acquired intangible assets. When we acquire a business in a business combination, we are required by GAAP to record the fair values of the intangible assets of the business and amortize them over their useful lives. Amortization of acquired technology in cost of revenue includes amortization of software and other technology assets of acquired businesses. Amortization of other acquired intangible assets in operating expenses includes amortization of assets such as customer lists and trade names.

Restructuring charges. This consists of costs incurred as a direct result of discrete strategic restructuring actions, including, but not limited to severance and other one-time termination benefits, and other costs, which are different in terms of size, strategic nature, and frequency than ongoing productivity and business improvements.

Share-based compensation expense. This consists of non-cash expenses for stock options, restricted stock units, and our Employee Stock Purchase Plan. When considering the impact of equity awards, we place greater emphasis on overall shareholder dilution rather than the accounting charges associated with those awards.

Gains and losses on executive deferred compensation plan liabilities. We exclude from our non-GAAP financial measures gains and losses on the revaluation of our executive deferred compensation plan liabilities.

Goodwill and intangible asset impairment charges. We exclude from our non-GAAP financial measures non-cash charges to adjust the carrying values of goodwill and other acquired intangible assets to their estimated fair values.

Gains and losses on disposals of businesses and long-lived assets. We exclude from our non-GAAP financial measures gains and losses on disposals of businesses and long-lived assets because they are unrelated to our ongoing business operating results.

Professional fees and transaction costs for business combinations. We exclude from our non-GAAP financial measures the professional fees we incur to complete business combinations. These include investment banking, legal, and accounting fees.

Gains and losses on debt securities and other investments. We exclude from our non-GAAP financial measures credit losses on available-for-sale debt securities and gains and losses on other investments.

Gains and losses on executive deferred compensation plan assets. We exclude from our non-GAAP financial measures gains and losses on the revaluation of our executive deferred compensation plan assets.

Income tax effects and adjustments. We use a long-term non-GAAP tax rate for evaluating operating results and for planning, forecasting, and analyzing future periods. This long-term non-GAAP tax rate excludes the income tax effects of the non-GAAP pre-tax adjustments described above, and eliminates the effects of non-recurring and period specific items which can vary in size and frequency. Based on our long-term projections, we are using a long-term non-GAAP tax rate of 24% for fiscal 2025 and fiscal 2026. This long-term non-GAAP tax rate could be subject to change for various reasons including significant acquisitions, changes in our geographic earnings mix, or fundamental tax law changes in major jurisdictions in which we operate. We will evaluate this long-term non-GAAP tax rate on an annual basis and whenever any significant events occur which may materially affect this rate.

Operating results and gains and losses on the sale of discontinued operations. From time to time, we sell or otherwise dispose of selected operations as we adjust our portfolio of businesses to meet our strategic goals. In accordance with GAAP, we segregate the operating results of discontinued operations as well as gains and losses on the sale of these discontinued operations from continuing operations on our GAAP statements of operations but continue to include them in GAAP net income or loss and net income or loss per share. We exclude these amounts from our non-GAAP financial measures.

The reconciliations of the forward-looking non-GAAP financial measures to the most directly comparable GAAP financial measures in Table E include all information reasonably available to Intuit at the date of this press release. These tables include adjustments that we can reasonably predict. Events that could cause the reconciliation to change include acquisitions and divestitures of businesses, goodwill and other asset impairments, sales of available-for-sale debt securities and other investments, and disposals of businesses and long-lived assets.

View source version on businesswire.com: https://www.businesswire.com/news/home/20251120052286/en/

Investors

Kim Watkins

Intuit Inc.

650-944-3324

kim_watkins@intuit.com

Media

Kali Fry

Intuit Inc.

650-944-3036

kali_fry@intuit.com

Source: Intuit Inc.

Released November 20, 2025

Introduction

Electronic cigarette (e-cigarette) use has been prevalent among youth and young adults in recent years. Using National Youth Tobacco Survey data (2013‐2022), the cross-sectional analysis found that the prevalence of current e-cigarette use among US youth rose from 3.10% in 2013 to a peak of 20.18% in 2019, then declined and remained relatively stable in 2021 and 2022 (7.50% and 9.44%, respectively) []. Although the prevalence of e-cigarette use among middle school and high school students has decreased from 7.7% in 2023 to 5.9% in 2024, about 1.63 million youth still reported e-cigarette use in 2024 []. Results from the 2020 National Youth Tobacco Survey (NYTS) data analyses showed that the e-cigarette product characteristics (eg, e-cigarette flavors, concealability, and vape tricks), family and peer influence (family or friend use), curiosity, and replacing cigarettes were the main reasons for e-cigarette use []. While e-cigarettes are often cited for their potential as smoking-cessation aids [], their long-term health effects remain uncertain [,], and therefore, there is broad consensus that youth access should be prevented [,]. Chemical analyses of e-cigarette aerosol have detected carcinogens like formaldehyde, acetaldehyde, acrolein, diacetyl, and toxic, carcinogenic metals such as chromium, nickel, and lead [-]. Human studies have shown that e-cigarette use was associated with several respiratory disorders (such as wheezing, asthma, and chronic obstructive pulmonary disease) [-] and mental health problems (such as depression) [,]. E-cigarette use costs the United States approximately US $15 billion annually in 2018 due to the US $2024 excess health care expenditures per e-cigarette user per year compared to never tobacco product users [].

With over 1 billion monthly active users, TikTok is one of the most popular social media platforms among youth and young adults in the United States []. TikTok’s short video format, engaging content, and personalized recommendation system have attracted millions of youths and young adult users. The TikTok platform allows users to create, view, and share videos with posts leading to likes, shares, comments, and followers []. Recognizing the popularity of social media, the vaping industry and vaping proponents have prioritized social media to promote vaping. E-cigarette companies and vape shops promote e-cigarettes on TikTok by posting professionally designed videos using popular hashtags [], creating fake user accounts to disseminate spam and favorable views [,], and providing celebrity sponsorship []. Although TikTok’s Community Guidelines prohibit content that facilitates the purchase, sale, trade, or solicitation of e-cigarettes, e-cigarette promotional posts remain prevalent on the platform [].

In contrast to vaping promotion messages on social media, the number of social media posts educating or warning the public about the adverse health effects of vaping is minimal on all social media platforms [,]. To reduce the prevalence of vaping among youth, in September 2018, the US Food and Drug Administration (FDA) launched a vaping prevention campaign – “The Real Cost” on social media and other platforms to educate youth about the adverse health effects and risks of vaping []. However, the impact of the FDA’s “The Real Cost” vaping prevention campaign on Instagram (Meta) is limited partially due to low engagement []. For example, the FDA-sponsored TheRealCost account has a much lower median number of likes per Instagram post than the vaping promotion accounts []. Therefore, it becomes essential to understand and (especially) design vaping prevention messages with high user engagement for effective health communication in the community.

High user engagement, such as a high number of likes, might significantly impact user behavior []. Improving social media user engagement of vaping prevention messages can prevent vaping initiation and motivate cessation []. Vaping promotion posts have high engagement due to well-designed content and popular hashtags [,]. Some features (such as hashtags) in e-cigarette promotion posts with high user engagement could be integrated into the vaping prevention messages to increase user engagement. In addition, identifying the features of vaping promotion social media messages targeting vulnerable populations (such as youth) will facilitate future regulation of such social media posts. Thus, our study will include both vaping promotion and prevention posts. Current social media studies on e-cigarette-related TikTok videos focus on characterizing content rather than identifying high user engagement post features [,]. More recently, several studies have started to describe the features of social media posts associated with high user engagement [,]. Yet, none of the previous studies identified video features associated with high user engagement on TikTok. Video is popular on social media platforms and is more attractive than images and texts due to its combination of visuals, sounds, and motions to create an emotional connection with viewers through a storytelling mode, including facial expressions and different tones of voice []. Thus, identifying video features associated with high user engagement is important to attract TikTok users’ attention to vaping prevention and cessation content. This study aims to fill this knowledge gap by identifying video features from e-cigarette-related TikTok videos associated with high user engagement. Findings from this study are valuable in designing effective vaping prevention video messages for tobacco control and vaping cessation interventions.

Methods

Study Design

Our study is a cross-sectional TikTok post content analysis to identify important features associated with high user engagement in TikTok posts. All data collected and analyzed in our study are publicly available from the TikTok platform, and all results were reported in aggregate form to ensure user anonymity.

Ethical Considerations

Our study has been reviewed by the University of Rochester Research Subjects Review Board (RSRB) office and was determined to meet federal and university criteria for exemption under Exempt Category 4 (Study ID: STUDY00009334). The RSRB determined that this study qualifies as secondary research involving existing data and, therefore, does not require informed consent. All publicly available TikTok videos included in the analysis were deidentified before use, and no compensation was provided to the TikTok users. Our study report followed the Strengthening and Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines [].

Data Collection

TikTok short videos (less than one minute) in English related to e-cigarettes from the United States were searched and downloaded using the TikTok research application programming interface (API) using keywords related to e-cigarettes, including “e-cig,” “vaping,” “e-cigarettes,” “e-cigs,” “ecig,” “ecigs,” “electroniccigarette,” “ecigarette,” “ecigarettes,” “vape,” “vapers,” “vapes,” “e-liquid,” “ejuice,” “eliquid,” “e-juice,” “vapercon,” “vapeon,” “vapefam,” “vapenation,” “juul.” []. The TikTok API has a limitation of around 100 on the number of videos we can collect each month. In February 2024, through the TikTok API, a total of 1487 TikTok videos (from 1064 unique creators) related to e-cigarettes posted between January 2023 and January 2024 were collected and confirmed manually in this study.

Video Feature Extraction

The TikTok video feature list was derived primarily from our previous work—namely, our characterization of Instagram image features for e-cigarette content and our manual coding of e-cigarette videos on TikTok and YouTube [-,,]. We then reviewed sample TikTok videos to identify additional features that could affect engagement and, as a team, agreed on the final set of features for this study. The TikTok video features analyzed in this study included promotional content (including advertisements or promoting the e-cigarette use), celebrity endorsements, background setting, perceived sex (male or female, based on the observation), social events, young adult themes, lifestyle portrayals, e-cigarette devices (the presence of physical devices), smoking or vaping behaviors (the action of smoking or vaping regardless of the presence of physical e-cigarette devices), talking, singing, dancing, humorous or silly content, cartoons or animations, vaping tricks, and the use of emojis. We considered 2 cutting-edge large language models to extract video features from TikTok videos: GPT-4 [] and Video Large Language Model Meta AI (Video-LLaMA-7B) []. Both models can understand the visual and auditory content in the video. Based on a reported AI labeling accuracy of 94% [] and a prespecified 10% margin of error for the 95% CI, the single-proportion sample-size calculation indicated that 22 videos are required for our pilot evaluation of GPT-4 and Video-LLaMA-7B feature labeling. To examine which large language model performs better, we randomly selected 25 videos from the 1487 videos and manually labeled the proposed features by 2 human coders. Two human coders worked together side-by-side to watch these videos and label the defined video features. GPT-4 uses vision models to break down the visual component of a video frame by frame and detect objects, faces, text in the image, and other visual elements to help us identify features from the TikTok videos. In this study, to reduce data size and the demand for computational resources while still capturing meaningful information in the video, we adopted a frame sampling strategy, one image frame for every 15 frames, to represent the video []. In this study, the OpenAI API was used to interact with the GPT-4 model, and well-designed prompts were used to generate the desired and optimal output from the model. Table S1 in lists prompts used to automatically extract video features from the remaining 1462 TikTok videos using the GPT-4 API.

We used the GPT-4 API to categorize each video’s background into one of the following 6 groups based on the description of the background, including car, public space (club, studio, indoor and outdoor, medical building, restaurant, spacecraft, and other), private space (bathroom, bedroom, home, kitchen, room, and office), outside (beach, forest, garden, natural, space, campus, cave, urban, and natural), shop (shop and vape shop), and not detectable. Based on the description of the lifestyles from GPT-4, we classified the lifestyles into casual, leisure (fitness, gaming, and leisure), and no lifestyle (educational, health, industrial, vaping, working, or others) categories.

Statistical Analysis

A linear mixed effects model with random intercept to account for multiple videos posted by the same user was used to model the association of TikTok user engagement with extracted features. We calculated the Spearman correlations among likes, shares, comments, and views of TikTok videos and found significant moderate-to-strong correlations across these metrics, as expected (Figure S1 in ). Given this interdependence, a more appropriate approach is to use overall engagement as the outcome variable, combining likes, shares, comments, and views using the formula applied by TikTok. The TikTok user engagement measure was calculated based on the formula [(number of likes+number of comments+number of shares)/(number of views+0.5)] X 100 []. For each video, add up all user actions (likes, comments, and shares) and then divide that total by the number of views. This gives interactions per view, so it reflects how actively viewers responded to the content, independent of how many people saw it. The value of 0.5 is added to the formula to minimize the generation of infinity values when the number of views is 0. Wilcoxon rank sum tests were used to compare the TikTok user engagement on a log scale between different feature categories. A linear mixed effects model was used to identify significant features associated with TikTok user engagement. The purposeful variable selection method was used to select important features for the final model, and Tukey’s method was used to control the familywise error rate for pairwise comparisons. Statistical analysis software R 4.3.1 (R Core Team, 2007) was used for the data analysis, with a significance level of 5% for all the analyses.

Results

Feature Extraction From TikTok Videos Using Large Language Models

From the 1487 e-cigarette-related videos, we randomly selected 25 TikTok videos for hand-coding video features. We compared the accuracy in extracting features between 2 popular large language models, GPT-4 and Video-LLaMA-7B, using the hand-coding video feature as the golden standard. As shown in Table S2 in , for the video features we selected, the large language model GPT-4 showed much higher accuracy (83% to 100%) than the model Video-LLaMA-7B (24% to 88%). Therefore, we used GPT-4 to extract features from the remaining 1462 videos.

User Engagement for Video Features

Using the Wilcoxon sum rank test, we compared the distribution of TikTok user engagement measures on a log scale between different categories of those identified video features ().

As shown in , TikTok videos with a background in a car versus other background categories (5.48 for car vs 3.06‐4.65 for other background categories), featuring females versus males or females and males or no people (4.98 for females vs 3.63‐4.47 for males or females and males or no people), advertising social events (4.60 vs 4.13), including young adults (4.69 vs 3.92), showing casual lifestyles (4.70 vs 4.03-4.36), without the presence of physical e-cigarette devices (4.72 vs 3.93), having the action of people smoking or vaping (4.61 vs 4.35), including people talking (4.88 vs 3.80) or singing (5.31 vs 4.39), have significantly higher user engagement than TikTok videos without those features (all P<.05). TikTok videos without promotion content had substantially higher user engagement than TikTok videos with promotion content (4.66 vs 3.02). In addition, TikTok videos that are funny or silly (5.09 vs 4.16) or contain emojis (4.90 vs 4.26) had significantly higher user engagement than other TikTok videos (P<.001). TikTok videos with features like celebrity endorsement, people dancing, cartoons or animations, and vape tricks were not significantly different from TikTok videos without those features.

TikTok Video Features Associated With High Social Media User Engagement

In the multivariate linear mixed effects model, several video features, such as lifestyle, dancing, and singing, were not significantly associated with TikTok user engagement. According to the purposeful model selection method, these were not included in the final linear mixed effects model. Results from the linear mixed effects showed significant features associated with the TikTok user engagement after adjusting for other variables in the model (). As the exponential of the parameter estimates were the rate ratios (RR) from the linear mixed effects model, an estimated RR of 1 or higher was classified as high engagement, and an estimated RR smaller than 1 was classified as low engagement. TikTok videos featuring young people had significantly higher user engagement than those without (estimated RR=1.24, 95% CI 1.00‐1.53; P=.048), corresponding to 24% greater engagement. TikTok videos with people talking had significantly higher user engagement (RR=1.63, 95% CI 1.30‐2.05; P<.001), meaning user engagement is 1.63 times that of videos without people talking. TikTok videos that are funny or silly had significantly higher user engagement than TikTok videos that are not funny or silly (RR=1.61, 95% CI 1.29‐2.00; P<.001). TikTok videos that contain emojis had significantly higher user engagement than TikTok videos without emojis (RR=1.88, 95% CI: 1.48‐2.38; P<.001). There is a marginally significant association between TikTok videos with vaping tricks and TikTok user engagement (RR=1.62, 95% CI 0.97‐2.71; P=.07). On the contrary, TikTok videos containing promotional content had significantly lower user engagement than TikTok videos without promotional content (RR=0.60, 95% CI 0.45‐0.81; P=.001).

‎

The background setting showed a significant association with engagement. Accordingly, we estimated all pairwise contrasts between video backgrounds using Tukey’s method. Pairwise comparisons showed significant differences in TikTok user engagement among different video backgrounds (). TikTok videos with a background in the car had significantly higher user engagement than TikTok videos with a public space background (RR=3.91, 95% CI 1.25‐12.20; P=.009). Other video background comparisons showed no significant differences, as their 95% CIs included 1.

‎

Discussions

Principal Findings

Our study is the first to investigate video features in e-cigarette-related TikTok videos associated with high TikTok user engagement. Using artificial intelligence (large language models such as GPT-4) and generalized linear models, we extracted and identified e-cigarette-related TikTok video features associated with high user engagement, such as background settings, young adult presence, funny or silly content, and emojis. Some of these identified features (such as background setting, humorous content, and proper emojis) could be used to design future e-cigarette prevention and cessation videos to increase user engagement.

In this study, as a proof of concept, we have tried to extract a set of features from e-cigarette-related TikTok videos. Some are general video features like the background, young people, and fun. In contrast, others are more specific to e-cigarette-related videos, such as smoking or vaping and vaping tricks. While Video-LLaMA is designed to analyze videos, our results showed that GPT-4 performed better than Video-LLaMA in identifying these video features. One potential reason for this is that GPT-4 is a multimodal model trained on a much more extensive training dataset. In addition, while Video-LLaMA might be more potent in understanding video motion and temporal dynamics, GPT-4 might perform better in semantic feature extraction at the frame level, especially from still frames in videos, by leveraging cross-modal information.

The pairwise comparison results within the linear mixed effects model framework showed the differences of various video background settings in their associations with TikTok user engagement. The reduced number of significant findings for the car background in the pairwise comparisons resulted from the stricter Type I error control applied via Tukey’s method. TikTok videos with backgrounds in a car had significantly higher user engagement than TikTok videos with backgrounds in public spaces. These results showed TikTok users prefer background settings in a more closed or private space than public spaces for e-cigarette-related TikTok videos. The reason for this might be related to the vaping ban in public places, including all bars and restaurants, which was recommended by the World Health Organization (WHO) in August 2016 and implemented in most United States states, including Alabama, California, and New York []. The widely adopted vaping ban in public places in the United States might lead people to view vaping in public spaces, such as bars, restaurants, or medical buildings, as inappropriate. Thus, e-cigarette-related TikTok videos in public places had less user engagement than TikTok videos with car backgrounds.

E-cigarette-related TikTok videos with young adults had higher user engagement. This might be due to the significant population of e-cigarette users being youth and young adults, and most of the TikTok users being young people. However, TikTok videos that showed only the physical e-cigarette device were less attractive to users—likely because they lacked human cues, offered little narrative value, and looked like advertisements. TikTok videos that are funny or silly attract more attention. Humor seems to be an effective tool to connect with audiences on TikTok by capturing users’ interest, encouraging sharing, and fostering a sense of community. Humor could also help increase the loyalty of followers []. E-cigarette-related TikTok videos featuring vaping tricks appear very impressive to viewers [], sparking curiosity among many users about how those tricks are performed []. Our study found a positive association between vaping tricks and user engagement that was marginally significant. While vaping tricks are commonly used to promote vaping on social media, they should be appropriately regulated or controlled to reduce their influence on susceptible social media users. Emoji is another powerful tool that attracts TikTok users’ attention. In TikTok videos, emojis could serve as visual cues to express feelings, reactions, and attitudes to effectively communicate with viewers on an emotional level and attract their engagement [,].

Current policy is insufficient in preventing the spread of vaping promotion videos on TikTok []. Identifying video features linked to higher engagement in e-cigarette–related TikTok posts has clear implications for policy and practice. For public health communication, prevention content can ethically adopt engaging formats to improve reach—eg, featuring young adults, using light emoji overlays and humorous content, and filming in relatable settings such as cars—while avoiding elements that glamorize product use.

Limitations

Our current study has several limitations. First, the e-cigarette-related TikTok video features included in this study are only a subset of video features. Other video features, such as the audio features, should be further investigated in future studies. Our study is the first one to investigate e-cigarette-related TikTok video features associated with social media user engagement. Here, we only tested some features that could be identified using current artificial intelligence techniques; for example, the current GPT model could not detect the background settings from some of the TikTok videos. With the rapid development of artificial intelligence techniques, we believe more features could be accurately labeled by the GPT models or other more advanced models, which can be included in our future studies. Second, our list of TikTok video features may not be applicable to other social media platforms. Therefore, it is crucial for future studies to explore important features on different social media platforms, such as Instagram and YouTube. Third, the accuracy of some feature labeling by the current GPT models has not reached 100% yet. There might be mislabeling of some features in our data, which might lead to some bias in our results. The accuracy of video features could be further improved with the availability of more advanced large language or video models. In addition, because sex was inferred from perceived physical characteristics and presentation in the videos rather than self-reported identity, the measure reflects perceived sex and is subject to misclassification bias, with potential exclusion or misrepresentation of transgender, non-binary, and gender-diverse individuals. Fourth, due to the unavailability of data, we cannot determine the demographics of TikTok users who were engaged with these e-cigarette-related videos, which should be investigated using other approaches. Moreover, we do not have the information about account-level factors—such as the number of unique accounts reacting, whether an account is personal versus sponsored or brand, and whether a post was boosted or advertised—that may also influence engagement. Fifth, not all features identified in this study might be appropriate for vaping prevention videos, which should be further tested and validated in future studies. Sixth, the data collection was conducted in February 2024 and included TikTok videos posted from January 2023 to January 2024. The numbers of views, likes, shares, and comments may change over time. Although the moderate-to-strong correlations across views, likes, shares, and comments imply that the engagement ratio (likes+shares+comments)/views should remain broadly consistent, potential bias could be introduced with the changes in the engagement metrics over time. Seventh, because the TikTok API limits monthly retrievals, our 1-year sample included 1487 videos. Larger samples in future work would increase power to detect additional features associated with user engagement. Eighth, 2 coders annotated video features side-by-side rather than independently; consequently, we could not quantify inter-rater reliability (eg, Cohen Kappa). This consensus approach may inflate agreement and introduce shared-observer bias. Finally, with the rapid changes in e-cigarette marketing and social media, it is necessary to closely monitor and update new developments of social media posts with high user engagement.

Conclusions

In this study, we showed that specific TikTok video features, including background settings (eg, car), talking, funny or silly, and use of emojis, were significantly associated with higher TikTok user engagement. These findings offer practical guidance for designing more engaging vaping-prevention videos for a broader reach of social media users.