Navigating IP in the Age of AI Innovations

Is code still intellectual property in a world where AI can generate it on demand? The rise of LLMs is transforming the foundation of software development, challenging traditional notions of intellectual property. Historically, IP was straightforward to identify: it was the code itself. Companies invested months or years building proprietary algorithms and implementations that represented their crystallized knowledge of how to solve complex problems. Today, LLMs can generate sophisticated, production-quality code in minutes rather than months. While this code still has inherent value in its utility, its nature as intellectual property has fundamentally changed. When multiple developers can prompt an LLM to generate similar solutions, the code itself no longer represents unique intellectual capital. This capability not only democratizes software development but raises a profound question: If code can be generated so easily, where does the real value in software development now lie? The answer is that intellectual property in the LLM era isn't in the code itself, but in the empirical knowledge of what actually works in practice. While LLMs can generate theoretically perfect code, they can't capture the practical insights that only come from deploying solutions in the real world. Consider a complex integration between enterprise systems - an LLM might generate syntactically correct code, but only practical experience reveals which approaches handle edge cases, maintain performance at scale, and remain maintainable. The true IP becomes this accumulated knowledge of real-world implementation challenges. This shift fundamentally changes how we build software businesses. Success is no longer about accumulating proprietary code, but about systematizing practical knowledge of making LLM-generated solutions work reliably at scale. When an LLM can generate ten different valid approaches to a problem, the competitive advantage lies in knowing which approach will actually succeed in production, how to implement it robustly, and how to maintain it efficiently. This knowledge, unlike code, can't be easily replicated or generated. We're entering an era where the winners won't be those who write the best code—that's becoming commoditized. Instead, they'll be those who most effectively accumulate and apply practical knowledge about making LLM-generated solutions work in production. This represents a profound shift: while LLMs can generate multiple, seemingly valid solutions to any problem, their non-deterministic nature means each generation might produce different code. This uncertainty actually increases the value of implementation knowledge - success requires understanding not just one solution, but the patterns and principles that work reliably across many possible implementations. The true intellectual property is the battle-tested knowledge of how to navigate from AI-generated possibilities to production-ready solutions.

Dr. Saju Skaria's Weekly Reflections: 20/2023 Intellectual Property (IP) issues and Generative AI The use of Generative AI has taken the industry by storm. I thought it’s imperative to touch on this critical issue, i.e., intellectual property, before we close the discussions on AI. AI technology, leveraging data lakes and question snippets to recover patterns and relationships, is helping immensely in creative industries. However, we have a critical issue that the legal fraternity is trying to address: copyright infringement, ownership of AI-generated work, and leveraging unlicensed content in training data. Trained AI tools can replicate copies of original work (for example, paintings or photographs), which is a copyright infringement. A further challenge is that the users might create copies of the original that need to be more transformative, thereby causing the credibility of the original work. These unauthorized derivatives can cause significant penalties, and the courts are already dealing with such issues. There is significant debate around the “fair use doctrine” that allows reviewing the copyrighted without the owner’s permission for purposes like criticism (including satire), comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research,” and for transformative use of the copyrighted material in a manner for which it was not intended. A word of caution for companies is how to use Generative AI and leverage content. It’s a tightrope walk. Not even accidentally using copyrighted content, directly or unintendedly, without adequate protection can cause significant penalties. How could we reduce the risk of getting stuck in an IP violation? Here are a few recommended steps. 1.     AI developers (individuals /organizations) must ensure that they comply with the law regarding acquiring data to train their models. 2.     Creators, both individual content creators and brands that create content, should take steps to examine risks to their intellectual property portfolios and protect them. 3.     Businesses should evaluate their transaction terms to write protections into contracts. As a starting point, they should demand terms of service from generative AI platforms that confirm proper licensure of the training data that feed their AI. Finally, with appropriate protection, businesses can build portfolios of works and branded materials, meta-tag them, and train their generative AI platforms to produce authorized, proprietary (paid-up or royalty-bearing) goods as sources of instant revenue streams. I welcome your thoughts and views on the topic. #AI #Leadership Bharat Amin, NACD.DC ML Kabir Sandeep (Sandy) M. Krishnan CA Randhir Mazumdar Dr. Swati Karve, PhD Psychology Ashish Saxena Shiny Skaria

AI is rewriting the rules of innovation. But who owns the future? The USPTO just unveiled its AI Strategy (January 2025), a blueprint for navigating AI’s role in intellectual property. With patents, trademarks, and copyrights at stake, this is about more than just technology—it’s about who controls the next era of innovation. Here are 5 takeaways: 1️⃣ AI Won’t Own Inventions—Yet AI can assist in innovation, but human inventors remain at the center of patent law. The USPTO is firm: AI can’t be listed as an inventor, but AI-generated work may influence patentability. The legal line is being drawn. 2️⃣ Patent Reviews Are Getting Smarter The USPTO is using AI to examine AI—leveraging machine learning for prior art searches, classification, and decision-making. This means faster approvals, better accuracy, and a more scalable patent process for the future. 3️⃣ IP Protection vs. AI Creativity: The Collision Generative AI is churning out text, images, music, and designs—but who owns it? Trademark and copyright laws weren’t built for AI-generated content. The USPTO is working to define rights and responsibilities in an AI-driven creative economy. 4️⃣ The U.S. is Playing for Global AI Leadership AI innovation is a geopolitical race. The USPTO is working with international partners to shape global AI patent standards, ensuring U.S. leadership in AI regulation, enforcement, and competition. The message? Innovation without protection is just an idea. 5️⃣ AI for All, Not Just Tech Giants The USPTO wants AI-driven innovation to be accessible, not just locked up by billion-dollar companies. From startups to underrepresented inventors, AI tools and patent protections need to be inclusive and equitable—or we risk leaving brilliant minds behind. What’s the bottom line? AI is not just a technology—it’s an economic force. The USPTO is positioning the U.S. to lead the next chapter of AI innovation while ensuring IP laws evolve to keep up. But will regulations accelerate AI’s potential—or slow it down?