Important risk factors that could affect our operations and financial performance, or that could cause results or events to differ from current expectations, are described in “Part I, Item 1A – Risk Factors” to the 2025 Form 10-K, as supplemented by the information set forth below: An emerging component of our growth strategy involves the adoption, integration, and effective utilization of AI technologies across our products, services, and internal operations, which introduces significant and evolving risks. We currently incorporate AI into certain existing and planned products, as well as our internal operations. For example, some of our marketing, customer service and anti-fraud efforts are currently enhanced by AI. Further, our internal technology development efforts are utilizing AI in expanding ways, and other internal operational functions are beginning to use AI to improve effectiveness and efficiency. Achieving consistent, secure, and compliant AI adoption across departments—including Product & Engineering, Marketing, Trust & Safety, Customer Support, Finance, and Legal/Compliance—requires ongoing investment in training, governance, and change management. Failure by any function to adopt or appropriately use these tools or failure to monitor and control the results of the adoption of the tools could reduce profitability, productivity, impair product quality, or cause compliance or security issues. AI technologies are complex, resource-intensive, and rapidly evolving. Market demand and acceptance of AI-driven customer-facing offerings, such as n2p AI Agent and n2p Coach AI, remain uncertain, and our product development efforts may not achieve widespread adoption or may be outpaced by competitors. Competitors with greater financial, technical, data, or distribution resources may gain an advantage in attracting and retaining AI talent and in acquiring training data and compute capacity, which could impair our ability to maintain competitive AI capabilities. If our AI solutions, or those of others in our industry, draw controversy due to their perceived or actual societal impact—such as generating biased, harmful, or misleading content—we may experience brand or reputational harm, competitive harm, or legal liability, which could slow user adoption of our products. The use of AI also raises ethical, reputational, and legal concerns. AI-based or AI-enhanced systems can generate or amplify content that is inaccurate, misleading, biased, discriminatory, harmful, or otherwise controversial, or be misused by third parties. If our AI tools produce, or are perceived to produce, such outputs, or if we fail to implement adequate human oversight, testing, and safeguards (including data governance, evaluation, and post-deployment monitoring), our brand and competitive standing could be harmed and we could face complaints, investigations, or litigation. Potential litigation or government regulation related to AI may increase the burden and cost of research and development, further subjecting us to reputational harm, competitive harm, or legal liability. Failure to address perceived or actual technical, legal, compliance, privacy, security, or ethical issues could undermine public confidence in AI, slowing customer adoption of our AI-driven products and services. 44 Table of Contents Laws and regulations focused on the development, use, and provision of AI technologies and other digital products and services are proliferating in many jurisdictions around the world. Staying compliant with evolving laws, regulations, and industry standards pertaining to AI may impose significant operational costs and constrain our ability to develop, deploy, or employ AI technologies profitably or at all. Failing to adapt appropriately to this evolving regulatory environment could result in legal liability, regulatory actions, monetary penalties and damage to our brand and reputation. Operationally, AI models depend on the quality, provenance, and security of data and on reliable third-party infrastructure. Inadequate, outdated, biased, or compromised datasets can produce flawed outputs and “model drift.” Our reliance on third-party models, APIs, datasets, and cloud providers exposes us to outages, cost volatility, performance degradation, or changes in licensing or acceptable-use terms, which could disrupt our operations if these services become unavailable or are no longer offered on commercially reasonable terms. Integrating AI introduces new cybersecurity risks, including prompt-injection, data exfiltration, model poisoning, and supply-chain vulnerabilities, as well as the risk that employees inadvertently input confidential or personal data into external systems. Intellectual property ownership surrounding AI technologies has not been fully addressed by U.S. or foreign courts or federal, state or foreign laws, nor by international legal frameworks. Our ongoing development and use of generative AI tools may result in copyright infringement claims, disputes over ownership and licensing, and potential patent infringement claims, among other things. These legal challenges could be costly to defend against, leading to substantial financial obligations and reputational damage. The evolving regulatory environment and uncertain legal precedents in this field further increase our exposure to litigation risks, which could materially affect our business, financial condition, and results of operations. Additionally, laws and regulations focused on the development and use of AI are proliferating globally and continue to evolve (for example, comprehensive AI frameworks in the EU and emerging federal and state guidance in the United States). Compliance may require significant documentation, transparency and record-keeping, risk assessments, model governance, content provenance or watermarking, impact assessments, vendor oversight, and restrictions on certain use cases. Noncompliance could result in investigations, fines, injunctions, remediation obligations, or other sanctions. Cross-border data transfer rules, sanctions, and export controls may affect access to datasets, models, or compute resources in some jurisdictions. Further, our use of generative AI in aspects of our platforms may present risks and challenges that could increase as AI solutions become more prevalent. AI algorithms may be flawed. Datasets may be insufficient or contain biased information. These deficiencies and other failures of AI systems could have negative impacts on our users’ experience and subject us to competitive harm, regulatory action, legal liability, and brand or reputational harm. Contractual indemnities from vendors may be unavailable or insufficient. We may also face claims related to privacy (including the processing of personal or biometric information), publicity rights, deceptive practices, or content moderation failures. Defending such claims can be costly and time-consuming, could require changes to our products or processes, and could harm our reputation and financial results. Finally, AI-related development and inference can increase energy consumption and costs, and investor or regulatory focus on sustainability may impose additional constraints. If we fail to implement robust AI governance, align employee practices with our policies, maintain sufficient human oversight, and continuously evaluate and improve our systems, the risks described above could materially and adversely affect our business, financial condition, results of operations, and reputation. 45 Table of Contents