Coursera, Inc.

Ladies and gentlemen, thank you for standing by and welcome to Coursera's third quarter 2026 earnings call. All participants are in listen-only mode and this call is being recorded. Following the prepared remarks, we will hold a question and answer session. To ask a question, please click the raise hand button and be prepared to unmute your line when prompted. I would now like to turn the call over to Cam Carey, Vice President of Investor Relations. Mr. Carey, you may begin.

Good afternoon, and thank you for joining us for Coursera's third quarter 2026 earnings conference call. Today, I'm joined by Greg Hart, our President and Chief Executive Officer, and Mike Foley, our Chief Financial Officer. Following the prepared remarks, we'll open the call for questions. Our earnings press release was issued after market close. It is available on our investor relations website at investor.coursera.com, where this call is being webcast live, and where today's materials, including our quarterly shareholder letter, have been posted. During this call, we will present both GAAP and non-GAAP financial measures. Reconciliations of non-GAAP measures to the most directly comparable GAAP measure can be found in today's release and supplemental materials. Unless otherwise specified, all financial results discussed are presented on an as-reported basis, and all growth percentages refer to year-over-year change. As-reported results include Udemy only for the periods following the transaction close on May 11th. References to normalized performance include Udemy's pre-closed results and are provided solely for illustrative comparability. All business metrics reflect a unified reporting methodology. Finally, all statements made during this call relating to future results and events are forward-looking statements based on current expectations and beliefs. Actual results and events could differ materially due to a number of risks and uncertainties, including those discussed in our earnings release, shareholder letter, and SEC filings. Thank you, Cam, and good afternoon, everyone.

We appreciate you joining us. Q3 was our first full quarter as a combined company, giving us a clearer view of both our progress and the work ahead. We delivered $377 million of revenue and nearly $62 million of adjusted EBITDA. Mike will walk through the details, but the results show two things. We are strengthening our financial profile as committed. Reported adjusted EBITDA margin expanded by more than 800 basis points year over year, doubling to approximately 16%. At the same time, our top line trajectory, particularly in consumer, remains below our growth ambition. We are staying focused on what we can control, and that starts with the integration. Since May, we have simplified the combined organization, integrated our go-to-market teams, and aligned around a unified product vision and roadmap. The pace matters as much as the actions, and we are moving faster than planned. We now expect to have more than $150 million of annualized gross cost savings in place by year-end. Those savings underpin the transaction's $115 million annual run rate net synergy target, which we are on track to deliver just eight months after close, compared with our original 24-month plan. As we have said, integration is the foundation, not the finish line. Its purpose is to create the operating discipline, focus, and investment capacity to build what comes next. Improving our growth trajectory will require disciplined execution and products that create meaningfully more value for customers. That is where I want to focus today, the problems our customers face and the platform we are building to solve them. We are in the middle of one of the largest technology buildouts in history. Goldman Sachs estimates global AI-related investment will reach roughly $1 trillion in 2026. That investment is moving far faster than workforce readiness. Earlier this year, Deloitte found that 43% of business leaders say their organizations are highly prepared on AI technology infrastructure, while only 20% say the same about talent, a gap of more than two to one. That gap is why, as I said in our last call, AI raises the bar for learning. Infrastructure alone does not produce a return. Companies can deploy the technology, but people still have to redesign workflows, exercise judgment, and know when to trust the output and when to question it. Investment in people now needs to catch up with investment in technology. We recently published our 2026 Global Skills Report, now in its eighth edition. For the first time, it draws on learning signals from the combined Coursera and Udemy ecosystem, and our data shows how learners are already responding. Learners are not choosing between technical AI skills and human skills. They're building both. Across our platforms, the GenAI enrollment rate increased from 25 per minute in 2025 to more than 40 per minute in 2026. At the same time, critical thinking enrollments grew over 100% year over year. That combination makes sense. As AI becomes more capable, people need the fluency to use it and the judgment to frame the right problem, evaluate the output, and lead through change. Learners and educators face a related challenge. AI is extraordinarily good at giving us answers. But getting an answer is not the same as helping us learn or build a durable skill. Research from the University of Pennsylvania illustrates why the design matters. In a field experiment with nearly 1,000 high school math students, access to a standard chat-style interface improved performance on practice problems by 48%. But when the AI was removed for the exam, those students performed 17% worse than the control group. The encouraging news is that a tutor designed with learning guardrails largely mitigated that negative effect. That distinction is central to our strategy. If the value of online learning is simply access to information, AI makes that value easier to replicate. The durable value lies in trusted expertise, effective practice and feedback, and credible proof that people can do the work. We believe AI can strengthen each of those when it is grounded in learning science and designed to build real capability. Coursera and Udemy's history is rooted in expanding access to learning at global scale. We now need to build on that foundation to address the tougher workforce questions businesses face. What skills do we need to achieve our business goals? How do we help each individual build and apply them quickly in the context of their daily work? And how do we know, with verifiable evidence, that capability has improved? I believe solving these problems is one of the defining opportunities of the AI era. In September, at Forward, our annual customer event, we gave customers their first look at Project Helix. Project Helix is our adaptive, AI-native skills platform that draws on the combined strengths of Coursera and Udemy to connect trusted learning to verified proof of skills. Helix is the clearest expression of the unified platform strategy I described last quarter. The experience starts with the outcome a customer wants to achieve, for example, preparing a workforce for a new AI-enabled process or helping an individual move into a new role. From there, Helix is designed to identify the skills required, assess the learner's current capability, and assemble an adaptive path through trusted learning and practice, with credible evidence of what the learner can do. Three capabilities make that possible. First, skills intelligence tied to business priorities. We are building Helix to translate a goal, expressed in natural language, into the skills and development path needed to pursue it. Second, personalized and applied learning. The aim is an experience that continuously adapts to the learner's role, goals, progress, and organizational context, bringing relevant learning and practice into the flow of work. Third, proof. Graded assessments, hands-on practice, and recognized credentials provide evidence of demonstrated skills that learners and leaders can use. We believe connecting trusted learning, relevant context, and credible proof at enterprise scale is what will differentiate Helix. The value of that connection can compound over time as the system learns which skills matter, how people build them, and what evidence best demonstrates readiness. How we build Helix is just as important as what we build. Our forward keynote was a preview. We are taking a phased approach, working with a select group of customers and partners to shape the experience and targeting broad enterprise availability in the first half of 2027. Helix has to earn the right to scale. That means putting new learning experiences in customers' hands, measuring whether they solve the problem better than what exists today, and investing in what works. Alongside that roadmap, we continue to invest in the Coursera and Udemy products customers rely on today. Many of the capabilities we are shipping this year deliver value to customers while laying the groundwork for Helix. Let me give you two examples. First, moving from practice to proof. Completion and learning hours are easy to report. Verified skills are not. Graded roleplay places a learner in a realistic scenario, such as a tough negotiation, and uses interactive learning with an AI persona to evaluate the learner against goals set by the course author. Graded labs are built to do the same for technical skills, such as a developer deploying a web application in a live, sandboxed environment. The goal is evidence of what a person can do, not just that they finished. Second, learning in the flow of work. Employees increasingly turn to AI tools for answers, and those answers may not lead to trusted learning or practice. Our integrations with ChatGPT, Claude, and Copilot are designed to recognize a learning moment and point the learner to a relevant course, video, or guided practice. On Udemy, connectors can also work in reverse. When a tool identifies that a sales representative needs coaching, it can send that insight to Udemy to create a ready-to-use role play. Helix is being designed to connect these capabilities around the customer's goal so that learning, practice, and assessment work together as one system that turns learning data into action, understand the goal, identify the skill, build it, apply it, and prove it. That system is only as strong as the ecosystem behind it. As AI makes access to information more abundant, we believe trusted expertise becomes more essential. Our ecosystem provides that expertise along with the context, responsiveness, and verification to keep pace as skilling needs change. Today, Coursera and Udemy have reached more than 310 million learners and serve more than 1.6 million paid subscribers and nearly 12,000 enterprise customers. Our instructor and partner communities contribute complementary forms of a valuable asset, human expertise. Udemy brings a global community of nearly 100,000 instructors who can respond quickly as tools, roles and skills change. Coursera brings deep relationships with more than 425 universities, leading companies and professional institutions that provide trusted instruction and recognized credentials. Together, they provide a broader, more responsive foundation for learning, practice, and assessment that is central to Helix. In Q3, we expanded our ecosystem and tested new ways to connect its expertise. At Forward, we announced 16 new university and industry partners and 25 new professional certificates. New partners included Harvard Business School Online in leadership and business strategy, GitHub in agentic AI and modern software development, and the American Medical Association and Taipei Medical University in healthcare and the life sciences. Existing partners are moving quickly as well. Earlier this month, Anthropic nearly doubled its Coursera catalog with 10 new courses for developers, small business owners, creators, and teachers. Anthropic plans to publish more courses each month to help learners stay current as AI tools and techniques evolve. We are also beginning to test how expertise can move across platforms. Dr. Ryan Ahmed, a professor and Udemy instructor who has taught several hundred thousand learners, adapted a series of courses covering Claude into a specialization on Coursera. This is one of our first experiments bringing a Udemy instructor's expertise to Coursera's learner base, and it became one of our top new launches of 2026. A second experiment, AI Series, helps learners connect the latest AI skills to their roles. These learning paths combine content from multiple partners and are reviewed by practitioners in each field. Learners receive a curated plan to track their progress with the right content in the right sequence for their chosen field. We started with AI Series for data analytics, Business Leadership, Software Development, and Cybersecurity, and plan to expand to more fields in Q4. Both experiments are informing Helix, how expertise is best leveraged across platforms, and how content from different experts comes together around a learner's needs. Today, we are using expert curation. Over time, we intend to make these capabilities systematic, with Helix using stronger signals of internal and external skill demand to intelligently assemble learning, practice, and proof around each learner's needs and each organization's goals. The architecture is intended to be flexible enough to support that ambition as technology, the labor market, and skills needs change at an increasingly rapid pace. Our instructors and partners are also helping shape that design. Earlier this month, at our first partner and instructor forum in Madrid, more than 80 leaders from our university, industry, and instructor communities worked directly with our team on the future of AI-native learning. They see real opportunity in the combination, and several have already joined our design work. As Helix develops, we want to give these experts better ways to turn their expertise into adaptive learning, practice, assessment, and proof, and reach the right learner at the right moment. That combination, trusted institutional expertise, practitioner speed, broad distribution, and increasingly intelligent orchestration through Helix, is what we believe can make the ecosystem more valuable to all participants over time. The progress this year sets up our next phase. In May, we close the transaction. In June, we establish the financial and integration framework for the combined company. Since then, we have accelerated integration and previewed the product strategy that work is meant to enable. As we look to 2027, our priorities are straightforward. First, complete the major integration milestones with speed and discipline, protecting continuity for customers, learners, instructors, and partners while improving commercial execution. Second, build Helix alongside our customers, progressing through alpha and beta programs toward our target of broad enterprise availability next year. Third, turn our greater scale, data, and investment capacity into new products that drive stronger customer outcomes. Integration creates capacity. Product execution turns that capacity into customer value, and customer value is what unlocks more durable growth over time. With that, I will turn it over to Mike to walk through the financial results and outlook. Mike, please go ahead.