Category: 3. Business

Revenue of $411 million increased 16 percent year-over-year

ARR of $1.782 billion increased 11 percent year-over-year

GAAP operating income of $13 million and non-GAAP operating income of $88 million

NEW YORK–(BUSINESS WIRE)–
UiPath, Inc. (NYSE: PATH), a global leader in agentic automation, today announced financial results for its third quarter fiscal 2026 ended October 31, 2025.

“I am pleased with our third quarter results delivering ARR of $1.782 billion, up 11 percent year-over-year, a testament to the team’s focus, consistent execution, and the momentum we’re seeing as customers scale agentic automation across the enterprise,” said Daniel Dines, UiPath Founder and Chief Executive Officer. “Enterprises are accelerating their AI and automation strategies, and they’re looking for a unified platform rather than standalone tools. Our ability to bring deterministic automation, agentic automation, and orchestration together in one trusted, governed system is a true differentiator. It’s delivering meaningful outcomes for customers and positions us well as we close out the year.”

Third Quarter Fiscal 2026 Financial Highlights

• Revenue of $411 million increased 16 percent year-over-year.
• ARR of $1.782 billion as of October 31, 2025 increased 11 percent year-over-year.
• Net new ARR of $59 million.
• Dollar based net retention rate of 107 percent.
• GAAP gross margin was 83 percent.
• Non-GAAP gross margin was 85 percent.
• GAAP operating income was $13 million.
• Non-GAAP operating income was $88 million.
• Net cash flow from operations was $28 million.
• Non-GAAP adjusted free cash flow was $28 million.
• Cash, cash equivalents, and marketable securities were $1.52 billion as of October 31, 2025.

“We delivered solid third quarter results, exceeding guidance across the board and achieving our first GAAP profitable third quarter,” said Ashim Gupta, UiPath Chief Operating Officer and Chief Financial Officer. “The progress we’ve made in strengthening our operating rhythm and execution is showing up in our results, and we feel well positioned as we head into the end of the year.”

Financial Outlook

For the fourth quarter fiscal 2026, UiPath expects:

• Revenue in the range of $462 million to $467 million
• ARR in the range of $1.844 billion to $1.849 billion as of January 31, 2026
• Non-GAAP operating income of approximately $140 million

Reconciliation of non-GAAP operating income guidance to the most directly comparable GAAP measure is not available without unreasonable efforts on a forward-looking basis due to the high variability, complexity, and low visibility with respect to the charges excluded from this non-GAAP measure; in particular, the effects of stock-based compensation expense specific to equity compensation awards that are directly impacted by unpredictable fluctuations in our stock price. We expect the variability of the above charges to have a significant, and potentially unpredictable, impact on our future GAAP financial results.

Recent Business Highlights

• Announced New UiPath Platform™ Capabilities: At FUSION, UiPath introduced new capabilities in the UiPath Platform for Agentic Automation & Orchestration, including pre-built agentic solutions and orchestration capabilities for specific use cases and industries; new tools for building and testing agents and automations; and an expansion of the platform’s built-in security and governance, creating a powerful solution that enables organizations to deploy rapid agentic automation to accelerate ROI and time-to-value, simplifying deployment across all stages of agent-building, automation, orchestration, and workflow automation.
• Integration with Microsoft Azure AI Foundry: UiPath announced an integration with Microsoft Azure AI Foundry, enabling customers to automate end-to-end processes using UiPath agents interacting with Azure AI Foundry agents and models. The use of a Model Context Protocol (MCP) extends the native, bi-directional integrations with Microsoft 365 Copilot and Microsoft Copilot Studio, allowing UiPath Maestro to deploy and scale end-to-end orchestrated workflows across Microsoft or UiPath agents. This gives process owners and analysts the trust, transparency, and governance needed to deploy AI agents in real-world enterprise workflows and realize business value faster.
• Announced Collaboration with OpenAI: UiPath announced a collaboration with OpenAI to build a ChatGPT connector that integrates OpenAI frontier models with enterprise customer workflows, powered by UiPath enterprise orchestration, accelerating time to value and ROI from their agentic AI efforts. UiPath’s best-in-class agentic automation capabilities, OpenAI models, and OpenAI APIs will simplify AI agent development and deployment, allowing users to focus on business goals rather than complexities of the underlying infrastructure and enable process owners to build trust in their AI agents.
• Launched UiPath Conversational Agent with Google’s Gemini Models: UiPath launched the UiPath Conversational Agent with voice interaction enabled by Google’s Gemini models. UiPath customers can now build agentic automation into business processes quickly and seamlessly without the need for complex coding and manual effort.
• Announced Collaboration with NVIDIA: UiPath announced a collaboration with NVIDIA to help enterprise customers fortify their existing automated workflows with AI capabilities in high-trust scenarios, such as fraud detection or care management in healthcare. Combining UiPath’s agentic automation expertise with open NVIDIA Nemotron models with NVIDIA NIM empowers organizations to quickly and easily deploy the enterprise-ready AI models as microservices – such as natural language processing, image understanding, and predictive analytics – to accelerate agentic AI adoption and automation for those sensitive workflows efficiently, accurately, and at scale.
• Partnered with Snowflake for Agent-driven Data Insights: UiPath announced a partnership with Snowflake, uniting the UiPath Platform™ for agentic automation with Snowflake Cortex AI, empowering businesses to quickly turn data insights into faster and smarter autonomous actions. The combination of UiPath’s Agentic Automation Platform and Snowflake’s Cortex AI Agents integrates best-in-class enterprise-grade automation with one of the leading modern data platforms. The integration expands the ability of UiPath Maestro to orchestrate low-code, no-code, and other specialized agents.
• UiPath Named a Leader and Star Performer in the Everest Group Intelligent Process Automation Platform (IPAP) and Full Suite IPAP PEAK Matrix® Assessment 2025: UiPath was named a Leader and Star Performer in the Everest Group Intelligent Process Automation Platform (IPAP) and Full Suite IPAP Peak Matrix® Assessment 2025, recognized for market impact, product vision, and capability.
• UiPath Positioned as a Leader in the Gartner® Inaugural Magic Quadrant™ for Intelligent Document Processing¹: UiPath was positioned as a Leader in the inaugural Gartner® Magic Quadrant™ for Intelligent Document Processing (IDP)¹. We believe this recognition reflects UiPath’s market-leading vision and the ability to deliver on that vision with its core IDP offerings.
• UiPath Recognized as a Leader in the Gartner® Magic Quadrant™ for AI-Augmented Software Testing Tools²: UiPath was positioned as a Leader in the Gartner® Magic Quadrant™ for AI-Augmented Software Testing Tools². We believe this recognition highlights the growth and criticality of technologies for agentic testing to ensure software quality, productivity, and market responsiveness.
• UiPath Platform™ Named to TIME Magazine’s “Best Inventions of 2025”: UiPath announced the UiPath Platform™ was named one of TIME’s Best Inventions of 2025, an annual list recognizing the world’s most groundbreaking inventions making a difference and redefining how we live.
• Launched Automation Cloud in UAE: UiPath announced an integration with Microsoft Azure. This new cloud-based enterprise SaaS solution enables private and public sector organizations in UAE to strategically position their infrastructure, applications, and data, while complying with local data residency regulations as they scale up Agentic AI adoption.
• Achieved ISO 42001 Certification for AI Adoption & Deployment: UiPath announced UiPath Platform™ certification for the ISO/IEC 42001:2023, the world’s first international standard for Artificial Intelligence Management Systems (AIMS) governing how AI technologies are designed, developed, deployed, and continuously improved, reinforcing the built-in governance in the platform to safeguard data, reduce risk, and help organizations adopt and scale agentic automation.
• Announced UiPath is a Founding Contributor to AIUC-1, Joining AIUC in Promoting Security Standards for Enterprise AI Adoption: UiPath announced it has become a founding technical contributor to AIUC-1, the leading security framework for AI agent adoption in the enterprise, to reinforce the framework to ensure high levels of security and trust for enterprises looking to adopt agents into their everyday business-critical processes and workflows.

Conference Call and Webcast

UiPath will host a conference call today, Wednesday, December 3, 2025, at 5:00 p.m. Eastern Time, to discuss the Company’s third quarter fiscal 2026 financial results and its guidance for the fourth quarter fiscal 2026. To access this call, dial 1-201-689-8057 (domestic) or 1-877-407-8309 (international). The passcode is 13756820. A live webcast of this conference call will be available on the “Investor Relations” page of UiPath’s website (https://ir.uipath.com), and a replay will also be archived on the website for one year.

Gartner Disclaimer

¹Gartner, Magic Quadrant for Intelligent Document Processing Solutions, By Shubhangi Vashisth etc. al, 3 September 2025

²Gartner, Magic Quadrant for AI-Augmented Software Testing Tools, By Joachim Herschmann, Sushant Singhal, Ross Power, C.A. Swan, 6 October 2025

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

The Gartner content described herein, (the “Gartner Content”) represent(s) research opinion or viewpoints published, as part of a syndicated subscription service, by Gartner, Inc. (“Gartner”), and are not representations of fact. Gartner Content speaks as of its original publication date (and not as of the date of this earnings release) and the opinions expressed in the Gartner Content are subject to change without notice.

Everest Group Disclaimer

Licensed extracts taken from Everest Group’s PEAK Matrix® Reports may be used by licensed third parties for use in their own marketing and promotional activities and collateral. Selected extracts from Everest Group’s PEAK Matrix® reports do not necessarily provide the full context of our research and analysis. All research and analysis conducted by Everest Group’s analysts and included in Everest Group’s PEAK Matrix® reports is independent and no organization has paid a fee to be featured or to influence their ranking. To access the complete research and to learn more about our methodology, please visit Everest Group PEAK Matrix® Reports.

About UiPath

UiPath (NYSE: PATH) is a global leader in agentic automation, empowering enterprises to harness the full potential of AI agents to autonomously execute and optimize complex business processes. The UiPath Platform™ uniquely combines controlled agency, developer flexibility, and seamless integration to help organizations scale agentic automation safely and confidently. Committed to security, governance, and interoperability, UiPath supports enterprises as they transition into a future where automation delivers on the full potential of AI to transform industries. For more information, visit www.uipath.com.

Forward-Looking Statements

Statements we make in this press release may include statements which are not historical facts and are considered forward-looking within the meaning of the Private Securities Litigation Reform Act of 1995, which are usually identified by the use of words such as “anticipates,” “believes,” “estimates,” “expects,” “intends,” “may,” “plans,” “possible,” “projects,” “outlook,” “seeks,” “should,” “will,” and variations of such words or similar expressions, including the negatives of these words or similar expressions.

We intend these forward-looking statements to be covered by the safe harbor provisions for forward-looking statements contained in Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, and are making this statement for purposes of complying with those safe harbor provisions.

These forward-looking statements include, but are not limited to, statements regarding: our financial guidance for the fourth fiscal quarter 2026; our ability to drive and accelerate future growth and operational efficiency and grow our platform, product offerings, and market opportunity; our business strategy; plans and objectives of management for future operations; the estimated addressable market opportunity for our platform and the growth of the enterprise automation market; the success of our platform and new releases including the incorporation of AI; the success of our collaborations with third parties; our customers’ behaviors and potential automation spend; and details of UiPath’s stock repurchase program. Forward-looking statements involve known and unknown risks, uncertainties, and other factors that may cause our actual results, performance, or achievements to be materially different from any future results, performance, or achievements expressed or implied by the forward-looking statements. These risks include, but are not limited to, risks and uncertainties related to: our expectations regarding our revenue, annualized renewal run-rate (ARR), expenses, and other operating results; our ability to effectively manage our growth and achieve or sustain profitability; our ability to acquire new customers and successfully retain existing customers; the ability of the UiPath Platform™ to satisfy and adapt to customer demands and our ability to increase its adoption; our ability to grow our platform and release new functionality in a timely manner; future investments in our business, our anticipated capital expenditures, and our estimates regarding our capital requirements; the costs and success of our marketing efforts and our ability to evolve and enhance our brand; our growth strategies; the estimated addressable market opportunity for our platform and for automation in general; our reliance on key personnel and our ability to attract, integrate, and retain highly-qualified personnel and execute management transitions; our ability to obtain, maintain, and enforce our intellectual property rights and any costs associated therewith; the effect of significant events with macroeconomic impacts, including but not limited to military conflicts and other changes in geopolitical relationships and inflationary cost trends, on our business, industry, and the global economy; our reliance on third-party providers of cloud-based infrastructure; our ability to compete effectively with existing competitors and new market entrants, including new, potentially disruptive technologies; the size and growth rates of the markets in which we compete; and the price volatility of our Class A common stock.

Further information on risks that could cause actual results to differ materially from our guidance and other forward-looking statements can be found in our Annual Report on Form 10-K for the fiscal year ended January 31, 2025 filed with the United States Securities and Exchange Commission (SEC) on March 24, 2025, and other filings and reports that we may file from time to time with the SEC. Any forward-looking statements contained in this press release are based on assumptions that we believe to be reasonable as of this date. Except as required by law, we assume no obligation to update these forward-looking statements.

Key Performance Metric

Annualized Renewal Run-rate (ARR) is the key performance metric we use in managing our business because it illustrates our ability to acquire new subscription customers and to maintain and expand our relationships with existing subscription customers. We define ARR as annualized invoiced amounts per solution SKU from subscription licenses and maintenance and support obligations assuming no increases or reductions in customers’ subscriptions. ARR does not include the costs we may incur to obtain such subscription licenses or provide such maintenance and support. ARR also does not reflect nonrecurring rebates payable to partners (upon establishing sufficient history of their nonrecurring nature), the impact of nonrecurring incentives (such as one-time discounts provided under sales promotional programs), and any actual or anticipated reductions in invoiced value due to contract non-renewals or service cancellations other than for certain reserves (for example those for credit losses or disputed amounts). ARR does not include invoiced amounts associated with perpetual licenses or professional services. ARR is not a forecast of future revenue, which is impacted by contract start and end dates and duration. ARR should be viewed independently of revenue and deferred revenue as ARR is an operating metric and is not intended to replace these items.

Dollar-based net retention rate represents the rate of net expansion of our ARR from existing customers over the preceding 12 months. We calculate dollar-based net retention rate as of a period end by starting with ARR from the cohort of all customers as of 12 months prior to such period end (Prior Period ARR). We then calculate the ARR from these same customers as of the current period end (Current Period ARR). Current Period ARR includes any expansion and is net of any contraction or attrition over the preceding 12 months but does not include ARR from new customers in the current period. We then divide total Current Period ARR by total Prior Period ARR to arrive at dollar-based net retention rate. Dollar-based net retention rate may fluctuate based on the customers that qualify to be included in the cohort used for calculation and may not reflect our actual performance.

Investors should not place undue reliance on ARR or dollar-based net retention rate as an indicator of future or expected results. Our presentation of these metrics may differ from similarly titled metrics presented by other companies and therefore comparability may be limited.

Non-GAAP Financial Measures

Non-GAAP financial measures are financial measures that are derived from the consolidated financial statements, but that are not presented in accordance with generally accepted accounting principles in the United States (GAAP). This earnings press release includes financial measures defined as non-GAAP financial measures by the SEC, including non-GAAP cost of licenses, non-GAAP cost of subscription services, non-GAAP cost of professional services and other, non-GAAP gross profit and margin, non-GAAP sales and marketing expenses, non-GAAP research and development expenses, non-GAAP general and administrative expenses, non-GAAP operating income and margin, and non-GAAP net income and non-GAAP net income per share. These non-GAAP financial measures exclude:

• stock-based compensation expense;
• amortization of acquired intangibles;
• employer payroll tax expense related to employee equity transactions;
• restructuring costs;
• charitable donation of Class A common stock;
• change in fair value of contingent consideration; and
• in the case of non-GAAP net income, release of valuation allowance on deferred tax assets and estimated tax adjustments associated with the add-back items, as applicable.

Additionally, this earnings release presents non-GAAP adjusted free cash flow, which is calculated by adjusting GAAP operating cash flows for the impact of purchases of property and equipment, cash paid for employer payroll taxes related to employee equity transactions, net payments/receipts of employee tax withholdings on stock option exercises, and cash paid for restructuring costs.

UiPath uses these non-GAAP financial measures internally in analyzing its financial results and believes they are useful to investors by excluding the effects of items that do not reflect the ordinary earnings of our operations, and as a supplement to GAAP measures. UiPath believes that the use of these non-GAAP financial measures provides an additional tool for investors to use in evaluating ongoing operating results and trends and in comparing its financial results with other companies in UiPath’s industry, many of which present similar non-GAAP financial measures to investors. Investors should consider these non-GAAP financial measures in addition to, and not as a substitute for, our financial performance measures prepared in accordance with GAAP. Further, our non-GAAP information may be different from the non-GAAP information provided by other companies. The information below provides a reconciliation of non-GAAP financial measures used in this earnings press release to the most directly comparable GAAP financial measures. We encourage investors to consider our GAAP results alongside our supplemental non-GAAP measures, and to review the reconciliation between GAAP results and non-GAAP measures that is included at the end of this earnings press release. This earnings press release and any future releases containing such non-GAAP reconciliations can also be found on the Investor Relations page of UiPath’s website at https://ir.uipath.com.

UiPath, Inc.
Condensed Consolidated Statements of Operations
in thousands, except per share data
(unaudited)
		Three Months Ended October 31,								Nine Months Ended October 31,
			2025				2024				2025				2024
Revenue:
Licenses		$	150,043			$	137,174			$	390,490			$	389,553
Subscription services			247,573				206,922				703,239				586,726
Professional services and other			13,497				10,557				35,736				29,739
Total revenue			411,113				354,653				1,129,465				1,006,018
Cost of revenue:
Licenses			1,311				2,340				3,779				7,334
Subscription services			40,121				43,487				116,818				123,770
Professional services and other			27,380				17,936				76,452				51,304
Total cost of revenue			68,812				63,763				197,049				182,408
Gross profit			342,301				290,890				932,416				823,610
Operating expenses:
Sales and marketing			179,186				187,188				505,150				561,657
Research and development			96,869				96,976				290,049				281,012
General and administrative			53,175				50,090				160,743				177,119
Total operating expenses			329,230				334,254				955,942				1,019,788
Operating income (loss)			13,071				(43,364	)			(23,526	)			(196,178	)
Interest income			11,701				10,055				36,353				37,255
Other (expense) income, net			(180	)			7,810				(4,636	)			26,199
Income (loss) before income taxes			24,592				(25,499	)			8,191				(132,724	)
Benefit from income taxes			(174,247	)			(14,844	)			(169,677	)			(7,236	)
Net income (loss)		$	198,839			$	(10,655	)		$	177,868			$	(125,488	)
Net income (loss) per share, basic		$	0.37			$	(0.02	)		$	0.33			$	(0.22	)
Net income (loss) per share, diluted		$	0.37			$	(0.02	)		$	0.33			$	(0.22	)
Weighted-average shares used in computing net income (loss) per share, basic			532,255				551,036				538,854				562,950
Weighted-average shares used in computing net income (loss) per share, diluted			539,018				551,036				544,718				562,950

UiPath, Inc.
Condensed Consolidated Balance Sheets
in thousands
(unaudited)
		As of
		October 31, 2025				January 31, 2025
Assets
Current assets
Cash and cash equivalents		$	743,660			$	879,196
Restricted cash			438				438
Marketable securities			654,526				750,322
Accounts receivable, net of allowance for credit losses of $4,291 and $1,642, respectively			366,757				451,131
Contract assets			129,335				88,735
Deferred contract acquisition costs			86,499				82,461
Prepaid expenses and other current assets			112,128				86,276
Total current assets			2,093,343				2,338,559
Marketable securities, non-current			121,609				94,113
Contract assets, non-current			4,701				3,447
Deferred contract acquisition costs, non-current			137,775				139,341
Property and equipment, net			44,604				32,740
Operating lease right-of-use assets			65,166				66,500
Intangible assets, net			21,583				7,905
Goodwill			120,625				87,304
Deferred tax assets			212,999				27,963
Other assets, non-current			73,571				67,398
Total assets		$	2,895,976			$	2,865,270
Liabilities and stockholders’ equity
Current liabilities
Accounts payable		$	14,280			$	33,178
Accrued expenses and other current liabilities			151,871				83,923
Accrued compensation and employee benefits			88,551				112,355
Deferred revenue			533,998				569,464
Total current liabilities			788,700				798,920
Deferred revenue, non-current			99,155				135,843
Operating lease liabilities, non-current			72,016				74,230
Other liabilities, non-current			11,488				10,515
Total liabilities			971,359				1,019,508
Commitments and contingencies
Stockholders’ equity
Class A common stock			5				5
Class B common stock			1				1
Treasury stock			(824,329	)			(494,779	)
Additional paid-in capital			4,531,257				4,333,300
Accumulated other comprehensive income (loss)			27,690				(4,890	)
Accumulated deficit			(1,810,007	)			(1,987,875	)
Total stockholders’ equity			1,924,617				1,845,762
Total liabilities and stockholders’ equity		$	2,895,976			$	2,865,270

UiPath, Inc.
Condensed Consolidated Statements of Cash Flows
in thousands
(unaudited)
		Nine Months Ended October 31,
			2025				2024
Cash flows from operating activities
Net income (loss)		$	177,868			$	(125,488	)
Adjustments to reconcile net income (loss) to net cash provided by operating activities:
Depreciation and amortization			11,996				14,017
Amortization of deferred contract acquisition costs			71,096				62,951
Net accretion on marketable securities			(9,047	)			(26,552	)
Stock-based compensation expense			225,842				270,520
Charitable donation of Class A common stock			4,187				6,564
Non-cash operating lease expense			12,904				11,762
Benefit from deferred income taxes			(187,310	)			(20,773	)
Credit loss expense			4,591				1,765
Other non-cash charges (credits), net			6,137				(1,822	)
Changes in operating assets and liabilities:
Accounts receivable			93,457				98,062
Contract assets			(37,146	)			(32,179	)
Deferred contract acquisition costs			(65,617	)			(59,657	)
Prepaid expenses and other assets			(10,378	)			10,228
Accounts payable			(17,401	)			14,954
Accrued expenses and other liabilities			36,027				11,230
Accrued compensation and employee benefits			(28,846	)			(48,587	)
Operating lease liabilities, net			(7,452	)			(10,750	)
Deferred revenue			(92,048	)			(1,762	)
Net cash provided by operating activities			188,860				174,483
Cash flows from investing activities
Purchases of marketable securities			(507,239	)			(1,162,243	)
Maturities of marketable securities			585,081				1,176,776
Purchases of property and equipment			(15,996	)			(7,531	)
Payments related to business acquisition, net of cash acquired			(24,821	)			—
Other investing, net			(16,839	)			(35,809	)
Net cash provided by (used in) investing activities			20,186				(28,807	)
Cash flows from financing activities
Repurchases of Class A common stock			(329,101	)			(381,403	)
Proceeds from exercise of stock options			967				934
Payments of tax withholdings on settlement of equity awards			(41,703	)			(60,384	)
Proceeds from employee stock purchase plan contributions			11,864				12,893
Payment of deferred consideration related to business acquisition			—				(5,570	)
Net cash used in financing activities			(357,973	)			(433,530	)
Effect of exchange rate changes			13,391				(194	)
Net decrease in cash, cash equivalents, and restricted cash			(135,536	)			(288,048	)
Cash, cash equivalents, and restricted cash – beginning of period			879,634				1,062,116
Cash, cash equivalents, and restricted cash – end of period		$	744,098			$	774,068

UiPath, Inc.
Reconciliation of GAAP Cost of Revenue, Gross Profit and Margin to Non-GAAP Cost of Revenue, Gross Profit and Margin
in thousands, except percentages
(unaudited)
		Three Months Ended October 31,								Nine Months Ended October 31,
			2025				2024				2025				2024
GAAP cost of licenses		$	1,311			$	2,340			$	3,779			$	7,334
Less: Amortization of acquired intangible assets			251				822				742				2,485
Non-GAAP cost of licenses		$	1,060			$	1,518			$	3,037			$	4,849
GAAP cost of subscription services		$	40,121			$	43,487			$	116,818			$	123,770
Less: Stock-based compensation expense			3,317				5,041				10,873				14,601
Less: Amortization of acquired intangible assets			923				602				2,529				1,790
Less: Employer payroll tax expense related to employee equity transactions			41				46				182				291
Less: Restructuring costs			—				7				585				325
Non-GAAP cost of subscription services		$	35,840			$	37,791			$	102,649			$	106,763
GAAP cost of professional services and other		$	27,380			$	17,936			$	76,452			$	51,304
Less: Stock-based compensation expense			2,359				2,953				7,445				8,438
Less: Employer payroll tax expense related to employee equity transactions			22				24				83				117
Less: Restructuring costs			—				(21	)			18				105
Non-GAAP cost of professional services and other		$	24,999			$	14,980			$	68,906			$	42,644
GAAP gross profit		$	342,301			$	290,890			$	932,416			$	823,610
GAAP gross margin			83	%			82	%			83	%			82	%
Plus: Stock-based compensation expense			5,676				7,994				18,318				23,039
Plus: Amortization of acquired intangible assets			1,174				1,424				3,271				4,275
Plus: Employer payroll tax expense related to employee equity transactions			63				70				265				408
Plus: Restructuring costs			—				(14	)			603				430
Non-GAAP gross profit		$	349,214			$	300,364			$	954,873			$	851,762
Non-GAAP gross margin			85	%			85	%			85	%			85	%

UiPath, Inc.
Reconciliation of GAAP Operating Expenses, Income (Loss) and Margin to Non-GAAP Operating Expenses, Income and Margin
in thousands, except percentages
(unaudited)
		Three Months Ended October 31,								Nine Months Ended October 31,
			2025				2024				2025				2024
GAAP sales and marketing		$	179,186			$	187,188			$	505,150			$	561,657
Less: Stock-based compensation expense			21,589				32,688				68,577				106,377
Less: Amortization of acquired intangible assets			1,045				307				2,548				1,157
Less: Employer payroll tax expense related to employee equity transactions			289				356				1,140				2,156
Less: Restructuring costs			—				1,956				2,524				9,927
Non-GAAP sales and marketing		$	156,263			$	151,881			$	430,361			$	442,040
GAAP research and development		$	96,869			$	96,976			$	290,049			$	281,012
Less: Stock-based compensation expense			32,249				34,211				102,931				96,007
Less: Employer payroll tax expense related to employee equity transactions			344				237				1,184				1,155
Less: Restructuring costs			—				187				(52	)			1,868
Non-GAAP research and development		$	64,276			$	62,341			$	185,986			$	181,982
GAAP general and administrative		$	53,175			$	50,090			$	160,743			$	177,119
Less: Stock-based compensation expense			11,961				12,595				36,016				45,097
Less: Amortization of acquired intangible assets			31				39				93				117
Less: Employer payroll tax expense related to employee equity transactions			207				124				474				714
Less: Restructuring costs			—				911				1,332				3,427
Less: Charitable donation of Class A common stock			—				—				4,187				6,564
Less: Change in fair value of contingent consideration			79				—				(198	)			—
Non-GAAP general and administrative		$	40,897			$	36,421			$	118,839			$	121,200
GAAP operating income (loss)		$	13,071			$	(43,364	)		$	(23,526	)		$	(196,178	)
GAAP operating margin			3	%			(12	)%			(2	)%			(20	)%
Plus: Stock-based compensation expense			71,475				87,488				225,842				270,520
Plus: Amortization of acquired intangible assets			2,250				1,770				5,912				5,549
Plus: Employer payroll tax expense related to employee equity transactions			903				787				3,063				4,433
Plus: Restructuring costs			—				3,040				4,407				15,652
Plus: Charitable donation of Class A common stock			—				—				4,187				6,564
Plus: Change in fair value of contingent consideration			79				—				(198	)			—
Non-GAAP operating income		$	87,778			$	49,721			$	219,687			$	106,540
Non-GAAP operating margin			21	%			14	%			19	%			11	%

UiPath, Inc.
Reconciliation of GAAP Net Income (Loss) and GAAP Net Income (Loss) Per Share to Non-GAAP Net Income and Non-GAAP Net Income Per Share
in thousands, except per share data
(unaudited)
		Three Months Ended October 31,								Nine Months Ended October 31,
			2025				2024				2025				2024
GAAP net income (loss)		$	198,839			$	(10,655	)		$	177,868			$	(125,488	)
Plus: Stock-based compensation expense			71,475				87,488				225,842				270,520
Plus: Amortization of acquired intangible assets			2,250				1,770				5,912				5,549
Plus: Employer payroll tax expense related to employee equity transactions			903				787				3,063				4,433
Plus: Restructuring costs			—				3,040				4,407				15,652
Plus: Charitable donation of Class A common stock			—				—				4,187				6,564
Plus: Change in fair value of contingent consideration			79				—				(198	)			—
Less: Release of valuation allowance on deferred tax assets			(184,465	)			(24,633	)			(184,465	)			(24,633	)
Tax adjustments to add-backs			(3,912	)			2,009				(10,942	)			4,191
Non-GAAP net income		$	85,169			$	59,806			$	225,674			$	156,788
GAAP net income (loss) per share, basic		$	0.37			$	(0.02	)		$	0.33			$	(0.22	)
GAAP net income (loss) per share, diluted		$	0.37			$	(0.02	)		$	0.33			$	(0.22	)
GAAP weighted average common shares outstanding, basic			532,255				551,036				538,854				562,950
Plus: Dilutive potential common shares from outstanding equity awards			6,763				—				5,864				—
GAAP weighted average common shares outstanding, diluted			539,018				551,036				544,718				562,950
Non-GAAP weighted average common shares outstanding, basic			532,255				551,036				538,854				562,950
Plus: Dilutive potential common shares from outstanding equity awards			6,763				2,906				5,864				7,369
Non-GAAP weighted average common shares outstanding, diluted			539,018				553,942				544,718				570,319
Non-GAAP net income per share, basic		$	0.16			$	0.11			$	0.42			$	0.28
Non-GAAP net income per share, diluted		$	0.16			$	0.11			$	0.41			$	0.27

UiPath, Inc.
Reconciliation of GAAP Operating Cash Flow to Non-GAAP Adjusted Free Cash Flow
in thousands
(unaudited)
		Nine Months Ended October 31,
			2025				2024
GAAP net cash provided by operating activities		$	188,860			$	174,483
Purchases of property and equipment			(15,996	)			(7,531	)
Cash paid for employer payroll taxes related to employee equity transactions			3,019				4,435
Net payments of employee tax withholdings on stock option exercises			7				6
Cash paid for restructuring costs			13,616				11,475
Non-GAAP adjusted free cash flow		$	189,506			$	182,868

 

View source version on businesswire.com: https://www.businesswire.com/news/home/20251203017308/en/

Investor Relations Contact

Allise Furlani

Investor.relations@uipath.com

UiPath

Media Contact

PR@uipath.com

UiPath

Source: UiPath, Inc.

Released December 3, 2025

Goodwin is the top advisor to firm for All Private Equity Stages by deal count and Venture Capital Rounds by deal count and round value in LSEG’s Q3 2025 Global Venture Capital and Private Equity Legal Advisory League Tables. PitchBook’s Q3 2025 Global League Tables named Goodwin the legal advisor for M&A deals most active globally and in the US for representing companies. 

Goodwin’s Q3 2025 league table results include: 

• LSEG Global PE/VC
  • #1 Advisor to Firm – All Private Equity Stages (by deal count)
  • #1 Advisor to Firm – VC Rounds (by deal count and round value)
  • #3 Advisor to Company – All Private Equity Stages (by deal count)
  • #3 Advisor to Company – VC Rounds (by deal count)
  • #4 Advisor to Company – LBO & Related Rounds (by deal count)
  • #5 Advisor to Firm – LBO & Related Rounds (by deal count)

• PitchBook – Q3 2025 (all by deal count)
  • #1 M&A – Most active globally: companies
  • #1 M&A – Most active in US: companies
  • #1 Venture Capital – UK & Ireland
  • #2 Private Equity – Healthcare
  • #2 Private Equity – IT
  • #2 Venture Capital – Europe
  • #2 Venture Capital – HC services & systems
  • #2 Venture Capital – Pharma & BioTech
  • #2 Venture Capital – Transportation
  • #3 All Deals Combined – Global
  • #3 All Deals Combined – US
  • #3 Venture Capital – Exits
  • #3 Venture Capital – US
  • #3 Venture Capital – Other
  • #3 Venture Capital – Commercial products & services
  • #4 M&A – Global
  • #4 M&A – US
  • #4 Private Equity – Buyouts
  • #4 Private Equity – Other PE deals
  • #4 Private Equity – Exits
  • #4 Private Equity – Global
  • #4 Venture Capital – Global
  • #4 Venture Capital – Early stage
  • #4 Venture Capital – Late stage
  • #4 Venture Capital – Software
  • #5 Private Equity – US
  • #5 Venture Capital – Consumer goods & services
  • #5 Venture Capital – HC devices & supplies

Key insights:

• • • Growing pains with expanded AI use — The surge in AI adoption is creating new compliance, governance, and security challenges for businesses, with nearly all firms planning to expand AI use.

    • Many compliance teams are faltering — Current compliance approaches are falling short, with 88% of firms struggling with AI governance and data security, and 37% reporting gaps in their search and e-discovery capabilities.

    • New communications tech tools might help — To address these challenges, organizations need to invest in innovative digital communications governance and archiving platforms that can handle the complexity of AI-generated content and provide forensic-level insight into potential issues.

---

Human-to-AI communication is accelerating across the workplace, creating new compliance, governance, and security challenges for modern organizations. The rise of AI assistants, generative AI (GenAI) tools, and agentic AI introduces a new category of communications — known as aiComms — and a new workplace participant: AI itself. Organizations now face added complexity as they work to manage, monitor, and retain these evolving forms of communication.

Surge in aiComms as firms plan to expand AI use

The results of Theta Lake’s 7th annual Digital Communications Governance Report, based on independent data gathered from 500 IT and compliance leaders in the financial services sector, highlights both the scale of AI expansion and emerging governance challenges.

Nearly all respondents (99%) say their firms plan to implement or expand AI features within their unified communications and collaboration tools, including GenAI assistants (with 92% saying this), AI-powered meeting notetakers and summarization (81%), and customized agentic AI (77%).

Indeed, the widespread adoption of popular GenAI capabilities such as summarization, prompts, and responses is transforming productivity and offering opportunities for firms to realize numerous efficiency gains and improvements. However, the volume of communications is set to accelerate, which will require entirely new approaches to governance, compliance, and content inspection.

In fact, 88% of respondents say their firms already report challenges with AI governance and data security, with the top challenge, identified by nearly half of respondents (47%), is ensuring that AI-generated content is accurate and compliant with regulatory standards. Respondents also cited factors such as difficulties in detecting whether confidential or sensitive data has been exposed in GenAI output (45%), concerns around identifying risky end-user behavior with AI tools (41%), and their ability to track where problematic AI content is shared and with whom (40%).

Fragmented communications compound risk

The report also reinforces the idea that AI is only one part of a broader compliance challenge. Firms are continuing to use multiple unified communications and collaboration platforms, many of which rely on multiple compliance, surveillance, and e-discovery solutions that in turn create inefficiencies, complexity, and gaps in oversight.

Fully 82% of survey respondents say their firms use four or more communication and collaboration platforms. Most organizations also rely on at least three different vendors or repositories for recording, archiving, and supervising communications, the survey shows.

These single-purpose compliance solutions are increasingly inadequate for today’s meshed communications environments, in which text, audio, video, and AI-generated content are created and consumed simultaneously. The limitations of these platforms result in incomplete capture, viewing, and reconciliation, the survey shows, including:

• • • 37% of respondents say their firms report search and e-discovery gaps, up from 31% last year
    • 36% cite surveillance gaps that affect risk detection, remediation, and controls
    • 62% report their firms have difficulties reconstructing and replaying conversations that span multiple communication tools and include textual, voice, visual, and AI-generated interactions

Another continuing concern is the use of off-channel communications, respondents say. Despite more than $4 billion in global fines from government regulators for organizations’ recordkeeping and supervision failures in recent years, more than two-thirds (67%) of respondents say they remain concerned about employees using unmonitored channels.

The findings align with the United Kingdom’s Financial Conduct Authority’s multi-firm review into off-channel communications, released in August, which found that most firms in its sample continue to identify breaches of internal communication policies.

Regulatory expectations

Regulators have made it clear that AI tools are encompassed by existing compliance frameworks. For example, FINRA’s Regulatory Notice 24-09 reminds member firms that:

FINRA’s rules — which are intended to be technology neutral — and the securities laws more generally, continue to apply when member firms use GenAI or similar technologies in the course of their businesses, just as they apply when member firms use any other technology or tool.

This means that highly regulated organizations must be able to confidently enable AI tool while continuing to maintain full oversight of how those tools are used, what they produce, and how their outputs are managed.

Innovative digital communications governance and archiving platforms now include features designed to support AI compliance and security guardrails, helping organizations maintain oversight and transparency in their use of GenAI tools.

Investing in the future

As the report illustrates, confidence in existing compliance approaches remains extremely low at just 2%, down from 8% last year. This underscores the recognition that traditional compliance systems cannot accommodate the volume, diversity, and dynamic nature of today’s digital communications.

As a result, firms are significantly increasing their investment in communications compliance to keep pace with the growing complexity of digital communications, including aiComms. In fact, 86% of respondents say their organizations are already investing more — up from 65% last year — and a further 12% plan to do so.

As human-AI collaboration becomes an integral part of business communication, governance frameworks must evolve to ensure accountability, transparency, and trust. Organizations will need forensic-level insight into AI-generated content to detect potential issues, confirm appropriate use, and respond quickly — all without slowing productivity. Platforms can better support safe GenAI adoption by ensuring AI-generated content aligns with internal policies and regulatory obligations, enabling swift action on risky or non-compliant content, and allowing firms to retain only what’s necessary to meet oversight and recordkeeping requirements.

---

In the future, tiny flying robots could be deployed to aid in the search for survivors trapped beneath the rubble after a devastating earthquake. Like real insects, these robots could flit through tight spaces larger robots can’t reach, while simultaneously dodging stationary obstacles and pieces of falling rubble.

So far, aerial microrobots have only been able to fly slowly along smooth trajectories, far from the swift, agile flight of real insects — until now.

MIT researchers have demonstrated aerial microrobots that can fly with speed and agility that is comparable to their biological counterparts. A collaborative team designed a new AI-based controller for the robotic bug that enabled it to follow gymnastic flight paths, such as executing continuous body flips.

With a two-part control scheme that combines high performance with computational efficiency, the robot’s speed and acceleration increased by about 450 percent and 250 percent, respectively, compared to the researchers’ best previous demonstrations.

The speedy robot was agile enough to complete 10 consecutive somersaults in 11 seconds, even when wind disturbances threatened to push it off course.

“We want to be able to use these robots in scenarios that more traditional quad copter robots would have trouble flying into, but that insects could navigate. Now, with our bioinspired control framework, the flight performance of our robot is comparable to insects in terms of speed, acceleration, and the pitching angle. This is quite an exciting step toward that future goal,” says Kevin Chen, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), head of the Soft and Micro Robotics Laboratory within the Research Laboratory of Electronics (RLE), and co-senior author of a paper on the robot.

Chen is joined on the paper by co-lead authors Yi-Hsuan Hsiao, an EECS MIT graduate student; Andrea Tagliabue PhD ’24; and Owen Matteson, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro); as well as EECS graduate student Suhan Kim; Tong Zhao MEng ’23; and co-senior author Jonathan P. How, the Ford Professor of Engineering in the Department of Aeronautics and Astronautics and a principal investigator in the Laboratory for Information and Decision Systems (LIDS). The research appears today in Science Advances.

An AI controller

Chen’s group has been building robotic insects for more than five years.

They recently developed a more durable version of their tiny robot, a microcassette-sized device that weighs less than a paperclip. The new version utilizes larger, flapping wings that enable more agile movements. They are powered by a set of squishy artificial muscles that flap the wings at an extremely fast rate.

But the controller — the “brain” of the robot that determines its position and tells it where to fly — was hand-tuned by a human, limiting the robot’s performance.

For the robot to fly quickly and aggressively like a real insect, it needed a more robust controller that could account for uncertainty and perform complex optimizations quickly.

Such a controller would be too computationally intensive to be deployed in real time, especially with the complicated aerodynamics of the lightweight robot.

To overcome this challenge, Chen’s group joined forces with How’s team and, together, they crafted a two-step, AI-driven control scheme that provides the robustness necessary for complex, rapid maneuvers, and the computational efficiency needed for real-time deployment.

“The hardware advances pushed the controller so there was more we could do on the software side, but at the same time, as the controller developed, there was more they could do with the hardware. As Kevin’s team demonstrates new capabilities, we demonstrate that we can utilize them,” How says.

For the first step, the team built what is known as a model-predictive controller. This type of powerful controller uses a dynamic, mathematical model to predict the behavior of the robot and plan the optimal series of actions to safely follow a trajectory.

While computationally intensive, it can plan challenging maneuvers like aerial somersaults, rapid turns, and aggressive body tilting. This high-performance planner is also designed to consider constraints on the force and torque the robot could apply, which is essential for avoiding collisions.

For instance, to perform multiple flips in a row, the robot would need to decelerate in such a way that its initial conditions are exactly right for doing the flip again.

“If small errors creep in, and you try to repeat that flip 10 times with those small errors, the robot will just crash. We need to have robust flight control,” How says.

They use this expert planner to train a “policy” based on a deep-learning model, to control the robot in real time, through a process called imitation learning. A policy is the robot’s decision-making engine, which tells the robot where and how to fly.

Essentially, the imitation-learning process compresses the powerful controller into a computationally efficient AI model that can run very fast.

The key was having a smart way to create just enough training data, which would teach the policy everything it needs to know for aggressive maneuvers.

“The robust training method is the secret sauce of this technique,” How explains.

The AI-driven policy takes robot positions as inputs and outputs control commands in real time, such as thrust force and torques.

Insect-like performance

In their experiments, this two-step approach enabled the insect-scale robot to fly 447 percent faster while exhibiting a 255 percent increase in acceleration. The robot was able to complete 10 somersaults in 11 seconds, and the tiny robot never strayed more than 4 or 5 centimeters off its planned trajectory.

“This work demonstrates that soft and microrobots, traditionally limited in speed, can now leverage advanced control algorithms to achieve agility approaching that of natural insects and larger robots, opening up new opportunities for multimodal locomotion,” says Hsiao.

The researchers were also able to demonstrate saccade movement, which occurs when insects pitch very aggressively, fly rapidly to a certain position, and then pitch the other way to stop. This rapid acceleration and deceleration help insects localize themselves and see clearly.

“This bio-mimicking flight behavior could help us in the future when we start putting cameras and sensors on board the robot,” Chen says.

Adding sensors and cameras so the microrobots can fly outdoors, without being attached to a complex motion capture system, will be a major area of future work.

The researchers also want to study how onboard sensors could help the robots avoid colliding with one another or coordinate navigation.

“For the micro-robotics community, I hope this paper signals a paradigm shift by showing that we can develop a new control architecture that is high-performing and efficient at the same time,” says Chen.

“This work is especially impressive because these robots still perform precise flips and fast turns despite the large uncertainties that come from relatively large fabrication tolerances in small-scale manufacturing, wind gusts of more than 1 meter per second, and even its power tether wrapping around the robot as it performs repeated flips,” says Sarah Bergbreiter, a professor of mechanical engineering at Carnegie Mellon University, who was not involved with this work.

“Although the controller currently runs on an external computer rather than onboard the robot, the authors demonstrate that similar, but less precise, control policies may be feasible even with the more limited computation available on an insect-scale robot. This is exciting because it points toward future insect-scale robots with agility approaching that of their biological counterparts,” she adds.

This research is funded, in part, by the National Science Foundation (NSF), the Office of Naval Research, Air Force Office of Scientific Research, MathWorks, and the Zakhartchenko Fellowship.

Stay informed with free updates

Simply sign up to the Technology sector myFT Digest — delivered directly to your inbox.

The writer is co-founder of BlankPage Capital, co-founder of Graphcore and author of ‘How AI Thinks’

It was a small company based in London that started the race to build the world’s most advanced artificial intelligence. DeepMind, founded in 2010, pioneered deep-learning AI and is working towards human-level AI. But its acquisition by Google in 2014 means that the company’s breakthroughs are largely benefiting the US, not the UK. As the AI race intensifies, the UK needs to find a way to build its own large-scale tech companies.

The usual conversation is about ways that the country can bridge the funding gap and encourage innovation. But this is not where the problem lies. The UK is world class at creating technology companies, often via spinouts from our universities. In 2023, according to figures provided by database company Dealroom to investor Phoenix Court, Bay Area seed and early-stage companies (those raising between $1mn and $15mn in funding) raised $4.5bn. The equivalent companies in the UK raised $4.1bn. So the UK is not far behind at the earliest start-up stage. This first link in our innovation ecosystem is not broken.

Yet by the final start-up stage, where these companies need to raise $100mn or more in funding to drive growth, US businesses are way ahead, raising five times as much as their UK counterparts. What is it that goes wrong in between?

The answer lies in the all-important middle stage, known as early growth, when companies have built a team and a product and are trying to expand into global businesses. In 2023, Bay Area early-growth stage companies received nearly two times the funding of their UK counterparts — at just under $14bn compared to just over $7bn.  

DeepMind was passing through exactly this growth stage when the co-founders decided to sell their business and gain access to the resources that were available as a division of Google. Last year, I sold my AI chipmaker Graphcore to Japan’s SoftBank for exactly the same reason. 

UK policymakers are focused on funding, pouring energy into reforming capital markets, corralling pension funds to invest in UK companies and putting more state funds into venture capital — including the new £500mn Sovereign AI Unit announced in July.  

This is all helpful. But the answer isn’t money — or rather, it isn’t just money. Yes, access to capital is critical. But to attract more conservative, late-stage, global private capital, UK tech companies must be mature enough to show commercial traction, the potential for massive revenue growth and the ability to generate future profits. What is holding them back is a lack of the right kind of support to connect their businesses to customers and to turn them from technology start-ups into true commercial entities.

If your company has access to Silicon Valley investors and your early-growth stage funding is led by Benchmark Capital, Andreessen Horowitz or one of the other leading tech VC firms, you get more than money. These firms will provide access to senior leaders at the biggest tech companies, many of which they funded. You will receive help from partners who built their own global businesses and are highly connected to the market.

This is the type of support we must offer in the UK by replicating the Silicon Valley ecosystem of specialised support for early-growth companies. Founders who have built their own tech companies must pass on their experience. VCs must double down on this part of the innovation ecosystem and help early-growth tech businesses win deals in international markets and connect with expert mentors who have scaled and exited major companies.  

The UK has the potential to lead in AI, just as we do in fintech. But to do so, we need to build a new base of VCs. It is their expertise that will help our most promising tech companies find global success.

Unlock the Editor’s Digest for free

Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.

They grow up so fast. Anthropic, a maker of artificial intelligence models and rival to OpenAI, has hired lawyers ahead of an initial public offering that could value it at $350bn next year. By that point the company would have reached the not-at-all-grand age of five.

That makes it a pretty good example of AI companies’ turbocharged growth. As a comparison, Google went public six years after its founding, achieving a valuation of about $23bn. Facebook took eight years to spring into public markets with roughly a $100bn price tag. The geriatric Microsoft waited 11 years and debuted in 1986 at around $800mn.

Behind the hype, at least, is a business. Anthropic’s main product is its chatbot Claude. That generates revenue, if not yet profit: Anthropic has projected that it could make $70bn in sales by 2028, The Information has reported. That would put its mooted valuation at five times that sum. Meta went public in 2012 at, with hindsight, a multiple of six times its three-year-hence sales; China’s Alibaba at seven times and Palantir at 10.

When investors do get the chance to buy stock in Anthropic directly, joining existing backers Amazon, Google, Microsoft and Nvidia, they will be benchmarking it particularly closely with OpenAI, the maker of ChatGPT, whose latest valuation of $500bn is also five times 2028 projections.

Who wins the bake-off depends on what flavours an investor prefers. Anthropic seems more popular with companies, with a 32 per cent share of the “enterprise” market as of the end of July, according to Menlo Ventures — which it should be noted is an Anthropic investor. That’s helpful, because businesses are more likely to pay up for AI than consumers.

Anthropic is also a more narrow business. It builds models, and that’s about it. OpenAI, meanwhile, is investing in data centres, pocket-sized devices, other companies’ shares and its own web browser. Some might call that sprawl; a venture capitalist might be more likely to call it “full stack”. Seen that way, Anthropic might more resemble a Palantir or a Salesforce, where OpenAI has shades of Google parent Alphabet or Microsoft.

Perhaps the toughest thing to value is what Anthropic might see as its greatest asset: its principles. The company was founded to be a safer alternative to OpenAI, building bots that are “helpful, honest and harmless”. Indeed, the Center for AI Safety deems Anthropic’s products the least likely among major models to “overtly lie” or furnish answers to “hazardous expert-level virology queries”.

Whether investors will pay a premium for that — or instead demand a discount — remains to be seen. In the meantime, much can happen. By the time it goes public, if it does, AI may have taken another leap forward, or tripped on its own hype. It’s therefore worth thinking about how Anthropic might justify a $350bn valuation — while also being prepared to tear those assumptions up and start again.

john.foley@ft.com