Introduction The global race for AI dominance has entered a new phase — one that increasingly resembles a modern-day Cold War. While the 20th-century Cold War centered on nuclear deterrence and ideological expansion, the AI Cold War is defined by a contest over data, algorithms, compute, and the future of global influence. This conflict is not speculative — it’s already reshaping the legal, regulatory, and commercial terrain for startups. And AI companies operating at the frontier need to adopt proactive strategies to manage not just compliance, but risk across pricing, data protection, and geopolitical fragmentation. A Strategic Rivalry Escalates The foundations of this Cold War were laid in 2017, when China announced its “New Generation Artificial Intelligence Development Plan” — a national strategy to lead the world in AI by 2030. The United States, recognizing the national security implications, responded with a series of export controls, sanctions, and domestic investment initiatives like the CHIPS and Science Act. The rivalry has intensified in recent months. The U.S. Department of Commerce has repeatedly tightened controls on semiconductor exports to China, while the Biden administration considers outbound investment restrictions in sensitive technologies, including AI. Meanwhile, China’s push for “indigenous innovation” is accelerating under pressure, creating a bifurcated ecosystem that limits collaboration and increases legal complexity. The Legal Spillover: What’s at Stake? This geopolitical tension is generating a cascade of high-stakes legal challenges: • Export Controls & Sanctions: U.S. rules now restrict the export of advanced GPUs and AI development tools to China and other jurisdictions deemed sensitive. Startups using dual-use technology (commercial and military applications) are especially vulnerable to inadvertent violations. • Cross-Border Data Tensions: Data localization laws in China, India, and the EU create uncertainty about where AI models can be trained, and under what conditions sensitive data can cross borders. • IP Disputes and Trade Secrets Theft: Allegations of state-sponsored corporate espionage and misappropriation of proprietary models and training data are mounting, especially where partnerships or foreign investment are involved. • Volatility in Component Pricing: With global tariffs, blacklisting of key suppliers, and diplomatic flare-ups affecting access to GPUs, FPGAs, and cloud compute, startups face dramatic swings in infrastructure pricing — with little recourse or predictability. Managing Pricing Risk: A Legal and Strategic Imperative Pricing volatility is no longer a supply chain issue — it’s a legal and fiduciary one. AI startups that depend on GPUs from Nvidia or ASICs manufactured in Taiwan may find themselves exposed to sharp price increases due to new export controls or political tensions. From a legal perspective, companies can mitigate this exposure by: • Negotiating pricing floors and ceilings in vendor agreements, particularly for long-term cloud or chip supply contracts. • Using force majeure clauses judiciously, ensuring that geopolitical disruptions are clearly included or excluded depending on the party’s position. • Diversifying suppliers geographically, with attention to jurisdictions less exposed to export controls or sanctions. • Monitoring contractual triggers that may require board notification or SEC disclosure (for those raising or preparing to go public). Preventing Misappropriation of Proprietary AI Assets As foundational models become more valuable — and more portable — the risk of model theft, data scraping, and IP leakage has risen sharply. AI companies must proactively secure their core innovations. Key steps include: 1. Limit Access with Tiered Permissions: Use role-based access controls (RBAC) to restrict employee and contractor access to sensitive code, training data, and weights. 2. Secure Cloud Environments: Ensure that models and training infrastructure are deployed in VPCs (virtual private clouds) with strict ingress/egress controls, and audit logs that are routinely reviewed. 3. Use Data Watermarking and Model Fingerprinting: Embed cryptographic markers in your training data or model outputs to identify unauthorized reproductions. 4. Tighten NDAs and IP Assignment Agreements: Review onboarding documentation for employees, contractors, and external collaborators. Make sure NDAs are enforceable across jurisdictions — and that they clearly cover model weights, architecture, and training data. 5. Monitor for AI Model Leakage: Use services or in-house tools to monitor GitHub, model hubs, and other repositories for suspiciously similar models or data sets. 6. Prepare for Enforcement: Work with counsel to define a rapid response protocol in the event of misappropriation — including cease-and-desist workflows, digital forensics, and venue selection for litigation or arbitration. Staying Ahead in the Cold War Era AI startups that want to survive — and thrive — in this era of geopolitical tension need more than technical excellence. They need legal agility, strategic foresight, and rigorous operational discipline. Startups should: • Conduct routine legal audits across export controls, data governance, and IP protection. • Collaborate with counsel to build proactive compliance programs that can scale as regulatory expectations grow. • Stay engaged with policy developments, especially in D.C., Brussels, and Beijing, to anticipate rules that may affect their core business model. • Consider jurisdictional risk in both fundraising and go-to-market strategy — and be cautious with foreign investment that may trigger CFIUS or comparable reviews. Conclusion The AI Cold War appears to be quite real, and it’s reshaping the legal landscape in ways that can’t be ignored. For AI startups, this moment calls for more than product-market fit — it demands legal resilience. Those that invest early in legal strategy, pricing risk management, and IP protection will be best positioned to win not just in markets, but in courtrooms and regulatory arenas as well.

One recent development drawing significant attention is the emergence of Manus AI, an autonomous agent that appears to rely on existing AI models such as Anthropic's Claude. This raises critical questions about trademark law, unfair competition, and the risks of AI model repackaging under U.S. and California law. Manus AI: A Case Study in AI Model Wrapping? Manus AI, developed by the Chinese startup Monica, has been introduced as a fully autonomous AI agent capable of executing complex workflows with minimal human intervention. Unlike traditional chatbots that require multiple user inputs, Manus AI purports to independently complete sophisticated tasks, including résumé sorting, stock trend analysis, and web development. While its marketing suggests proprietary advancements, technical investigations indicate that Manus AI may function as a wrapper around existing AI models, including Anthropic's Claude 3.5/3.7 and Alibaba's Qwen. If substantiated, this raises significant trademark and commercial law concerns, particularly regarding consumer perception and potential misattribution of the underlying technology. (Source) Trademark Considerations in AI Model Wrapping Trademark Protection and Consumer Confusion Under the Lanham Act (15 U.S.C. §§ 1051 et seq.), a trademark is any word, name, symbol, or device that identifies and distinguishes the source of goods or services. A key legal issue in AI model wrapping is whether repackaging and marketing a third-party AI system under a new brand could mislead consumers regarding its origin, thereby constituting trademark infringement (15 U.S.C. § 1114) or false designation of origin (15 U.S.C. § 1125(a)). California’s Unfair Competition Law (Cal. Bus. & Prof. Code § 17200) similarly prohibits misleading business practices, making deceptive AI repackaging a potential state-law violation. If a company integrates an AI model such as Claude into its product without clear attribution and markets it under a proprietary name, it could create a likelihood of consumer confusion—an essential element in a trademark infringement claim. Courts assess confusion based on factors established in AMF Inc. v. Sleekcraft Boats, 599 F.2d 341 (9th Cir. 1979), including: 1. Strength of the Original AI Brand: Anthropic has cultivated a strong brand identity around its Claude models, increasing the likelihood of confusion if its models are repackaged deceptively. 2. Similarity of the Marks: If Manus AI (or a similar entity) uses branding that closely resembles or references Anthropic’s trademarks, this heightens the risk of infringement. 3. Marketing Channels and Consumer Base: AI services are often marketed through similar digital channels, potentially leading consumers to mistakenly believe they are purchasing an official Anthropic product. 4. Evidence of Actual Confusion: If consumers mistakenly attribute an AI model’s outputs to a provider other than its true originator, this could support a claim for trademark infringement. False Designation of Origin and Misrepresentation Under 15 U.S.C. § 1125(a), false designation of origin occurs when a company misrepresents the source of goods or services in a manner likely to cause confusion. If Manus AI—or any AI provider—were to obscure or misrepresent the true origin of its AI model, it could violate this provision. For instance, marketing a wrapped version of Claude as a proprietary system without proper disclosure could expose the company to legal liability. Similarly, California’s False Advertising Law (Cal. Bus. & Prof. Code § 17500) prohibits misleading statements in advertising. If an AI company claims proprietary innovations while relying primarily on an unmodified third-party model, it could face scrutiny under state consumer protection statutes. AI Providers’ Policies and Contractual Enforcement Beyond trademark protections, AI companies such as Anthropic impose stringent contractual restrictions on how their models can be used. These agreements serve as additional layers of enforcement against unauthorized rebranding or misleading marketing practices. Anthropic’s Responsible Scaling Policy Anthropic’s Responsible Scaling Policy emphasizes ethical deployment and mandates compliance with safeguards against misuse. Breaching these policies can result in service termination and legal consequences. (Source) Rate Limits and Access Control Anthropic imposes rate limits and spending caps to regulate model access. Attempting to circumvent these restrictions could constitute a breach of contract and potentially lead to legal action. (Source) Legal Takeaways for AI Companies and Developers For companies operating in the AI space, the following legal considerations are paramount: 1. Ensure Clear Attribution and Licensing Compliance: Any use of third-party AI models should be properly attributed to avoid misleading consumers and infringing trademarks. 2. Conduct Trademark Clearance and Brand Distinction Analyses: AI developers should conduct thorough trademark searches before branding their models to ensure they do not create confusion with existing AI providers. 3. Review API Agreements Carefully: AI providers impose strict licensing terms that dictate permissible uses of their models. Violating these agreements can lead to service termination and potential financial liability. 4. Avoid Unauthorized Rebranding: Marketing a wrapped AI model as proprietary, without clear attribution, may constitute false designation of origin or trademark infringement. 5. Monitor Evolving Legal Precedents: The legal landscape around AI is evolving rapidly. Monitoring cases involving AI model misuse will provide insight into future enforcement trends and regulatory shifts. Final Thoughts The legal ramifications of wrapping an AI model extend beyond a mere terms-of-service violation; they often implicate core principles of trademark law, contract law, and consumer protection. As AI technology advances, legal frameworks will continue to adapt to address emerging risks and enforcement challenges. For companies integrating AI responsibly, a legally sound approach is not just advisable—it is essential. Those who fail to comply with AI providers’ policies or misrepresent the origins of their technology may face significant legal and reputational consequences. As an attorney specializing in AI and technology law, my advice remains consistent: respect trademark rights, adhere to contractual agreements, and operate with transparency.

Introduction As artificial intelligence (AI) continues to reshape industries, legal frameworks governing intellectual property (IP) are facing unprecedented challenges. The question of whether AI-generated content warrants its own IP protections has emerged as a pressing issue for businesses, creators, and policymakers alike. In the United States, the GOP has historically positioned itself as a champion of free-market principles, innovation, and deregulation. With that in mind, would a Republican-controlled federal government be likely to establish entirely new IP categories for AI? While AI law remains in flux, a close examination of legislative priorities, judicial trends, and industry influences suggests that major statutory changes are unlikely. Instead, any movement in AI-related IP law under GOP leadership would most likely involve reinterpretations of existing frameworks, rather than the creation of new, standalone AI IP categories. 1. The GOP’s Traditional Stance on Intellectual Property The Republican Party has long been an advocate for strong IP protections, viewing them as a mechanism to drive economic growth and reward innovation. However, this support has historically been balanced against concerns about regulatory overreach and excessive government intervention. Patent Law Republicans have generally favored policies that strengthen patent rights, particularly in technology and pharmaceutical industries. Legislative efforts such as the Leahy-Smith America Invents Act (AIA), enacted in 2011 with bipartisan support, introduced reforms such as the first-to-file system and post-grant review proceedings, modernizing the U.S. patent framework. However, recent Supreme Court decisions—such as Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014)—have narrowed patent eligibility, particularly for software and AI-related inventions. Under a GOP-controlled government, we would likely see efforts to broaden patent eligibility, particularly in response to judicial limitations imposed by Alice and its progeny. While AI-generated inventions present unique challenges under the human inventorship requirement of 35 U.S.C. § 100(f) and (g), a Republican administration might support administrative reinterpretations rather than entirely new legal categories for AI patents. Copyright Law Republican lawmakers have traditionally supported robust copyright protections but have also expressed skepticism toward regulatory expansion. Recent developments in AI copyright law suggest that major legislative shifts under a GOP-led Congress would be unlikely. The U.S. Copyright Office, in its ruling on Stephen Thaler’s AI-generated artwork A Recent Entrance to Paradise, reaffirmed that AI-generated works cannot receive copyright protection unless they involve significant human authorship (Thaler v. Perlmutter, 2023). This decision is consistent with longstanding precedents, such as Burrow-Giles Lithographic Co. v. Sarony, 111 U.S. 53 (1884), which emphasized human authorship as a requirement for copyright eligibility. A GOP-led administration is unlikely to challenge these established principles by proposing entirely new AI copyright protections. Instead, the focus would likely remain on industry self-regulation and contractual agreements governing AI-generated content. 2. GOP Perspectives on AI and Technology Regulation While AI is not inherently a partisan issue, Republican policymakers have consistently favored: • Private Sector Leadership: AI-driven innovation is seen as an economic asset, and Republicans generally advocate for minimal government interference in emerging technologies. • Avoiding Overregulation: The GOP has historically opposed expansive regulatory measures, particularly those that might stifle technological progress. This approach has been evident in resistance to European-style AI regulations, such as the EU AI Act. • National Security and Economic Competitiveness: AI is increasingly viewed through a national security lens, particularly in competition with China. GOP lawmakers are more likely to incentivize AI research through tax incentives or defense-related funding rather than creating new IP categories. Legislation such as the CHIPS and Science Act of 2022, which had bipartisan support, reflects the government’s broader strategic interest in AI without overhauling the IP framework. A Republican-led administration would likely continue this approach, emphasizing economic competitiveness rather than regulatory expansion. 3. Judicial and Agency Trends: The Role of the USPTO and Copyright Office Any substantial changes to AI-related IP law would also depend on key agencies and the judiciary. U.S. Patent and Trademark Office (USPTO) The USPTO has so far rejected AI-generated patent applications based on the requirement that inventors must be human. In Thaler v. Vidal, 43 F.4th 1207 (Fed. Cir. 2022), the Federal Circuit upheld the USPTO’s decision denying patent protection for an invention attributed to an AI system known as DABUS. The court reasoned that under the Patent Act, an inventor must be a “natural person.” A Republican administration could influence USPTO policy by appointing leadership more sympathetic to AI-assisted inventions, potentially expanding the interpretation of “inventorship” in ways that allow AI-assisted patents without fully granting AI the status of an inventor. However, statutory reforms redefining inventorship would be a heavy lift, making it more likely that AI-related patent issues will continue to be addressed through judicial interpretations rather than legislative action. U.S. Copyright Office The Copyright Office’s rulings on AI-generated works have reinforced the human authorship requirement, aligning with precedents like Feist Publications, Inc. v. Rural Telephone Service Co., 499 U.S. 340 (1991), which emphasized originality and creativity as fundamental to copyright protection. A GOP-led government would likely maintain this stance, preferring contractual agreements and trade secret protections over sweeping legislative changes. 4. Business and Lobbying Influences The tech industry’s lobbying power could shape Republican approaches to AI IP policy. Major AI-driven companies—including OpenAI, Google, and Meta—may push for clearer legal protections for AI-generated content, particularly in the realm of patents and trade secrets. However, traditional IP stakeholders, including the entertainment and publishing industries, are likely to resist new AI-specific protections that could undermine human authorship claims. Given the GOP’s alignment with pro-business policies, any AI-related IP reforms would likely prioritize trade secret protections and voluntary industry standards rather than statutory mandates. Conclusion AI-generated content presents novel challenges, but a GOP-controlled federal government is unlikely to establish new IP categories specifically for AI. Instead, Republican policymakers will likely continue to advocate for business-friendly, market-driven solutions, relying on existing IP laws, industry practices, and judicial interpretations to address AI-related legal questions. While there may be incremental shifts in patent law interpretations and trade secret protections, wholesale AI IP reform under a Republican administration remains improbable. Would AI-generated content benefit from a new IP regime, or should existing laws suffice? Let’s discuss. This version provides a more authoritative, well-supported analysis with relevant case law and policy references. Let me know if you’d like any further refinements.