From leaked source code and Greek-named models to AI agents that draft patent applications overnight an honest look at where artificial intelligence really stands, and what it means for intellectual property professionals.
Agentic AI patent drafting is reshaping how IP professionals work. Tools like Claude Code are turning what once took weeks into a single overnight session. Meanwhile, AI news continues to move at a relentless pace. Each week, a new model announcement feels bigger than the last. Moreover, somewhere between the headlines, a student in the Bay Area rewrites a company's proprietary codebase overnight using a rival AI tool.
Surprisingly, that is not a hypothetical. That is exactly what happened in the week Samir Raiyani and Saskia van Ryneveld sat down to record the very first episode of the IP Author Podcast.
To that end, this post unpacks Episode 1. First, we cover the AI stories that defined that week. Then, we give an honest look at where large language models stand right now. Finally, we explore what all of this means for IP professionals.
Anthropic's Mythos Model: A New Era for Agentic AI and Patent Drafting Security
Anthropic, the company behind Claude, announced a new model called Mythos (Preview). The announcement came with an unusual warning. With remarkable accuracy, the model identifies security vulnerabilities across software systems. As a result, Anthropic built a protective framework around it before any public release.
That framework took the form of Project Glass Wing. This new industry consortium brings together Microsoft, Google, and Nvidia. Interestingly, all three have financial stakes in Anthropic. Together, they aim to coordinate a responsible rollout.
"They are coming forth as the Avengers, defending the world from this one super-intelligent new model."
Saskia van Ryneveld, Host, IP Author Podcast
The podcast tackles this question head-on. Is the threat truly that severe? Or is this partly a PR exercise? For IP professionals, however, the underlying capability is worth noting. A model that excels at code analysis can also assist with prior art searches and claim mapping and support freedom-to-operate analysis. The same capability that raises security concerns also offers significant benefits for IP work.
The Claude Code Leak: What It Reveals About AI Patent Drafting Risks
Here is where the episode gets genuinely remarkable. That very same week, Anthropic announced it would protect the world from AI security threats. Yet at the same time, its own flagship product Claude Code leaked accidentally into the public domain.
A University of British Columbia student spotted the exposed code. Running a hackathon for Korean participants, he had been awake through the night in the Bay Area. Without hesitation, he fed the code to OpenAI's Codex agent. His reasoning was simple: rewriting TypeScript into Python would sidestep any copyright concerns.
Within hours, developers rewrote the code in Python. Then in Rust. After that, the open-source community remixed it approximately twenty times. By the time Anthropic noticed, every key architectural decision inside Claude Code was fully visible to any developer who wanted to look.
In the same week Anthropic announced a model capable of protecting the world from software vulnerabilities, its own flagship coding tool's source code was reverse-engineered and redistributed by a student running on no sleep and a rival AI agent.
This episode raises a critical question for the IP space. AI can now generate and deconstruct proprietary code at speed. So what does software IP protection actually look like today? There is no clean answer yet, but the industry will have to grapple with it seriously, and soon.
So What Is Claude Code? The Engine Behind Agentic AI Patent Drafting
Set aside the drama for a moment. Claude Code is, as Samir put it plainly, "genuinely good." It represents a meaningful step forward from every AI coding tool that came before it.
Earlier AI coding tools worked at the level of a single code snippet. You paste in a function, the model fixes a bug, and you paste it back. Useful, but limited. You still do most of the architectural thinking yourself.
Claude Code operates differently and this distinction matters for agentic AI patent drafting workflows. It acts as an orchestrator. You describe what you want to build. Claude Code then coordinates multiple specialised agents in parallel: one writes the frontend, another handles backend logic, a third builds the database schema. Together, they compare outputs, catch mistakes, and iterate toward a working product.
Crucially, this improvement does not come from a newer or smarter model. Samir confirmed that Claude Code uses the same underlying LLM as earlier Claude versions. The gains come entirely from smarter orchestration, more careful memory management, more precise task decomposition, and real-time error correction.
Have LLMs Hit Their Ceiling? What It Means for Agentic AI and Patent Drafting
Beyond the leak story, this question sits at the heart of current AI discourse. The podcast engages with it honestly. Saskia brings a useful framing from her theatre background. Has AI reached its Shakespeare moment, where the core work is now so good that future progress is simply about retelling it in new formats?
Samir's view is measured. He believes the underlying LLM technology has broadly reached its peak for this generation. The rapid improvements that defined 2022 to 2024 are tapering off. But that does not mean progress has stopped.
"It took a while for the iPhone to plateau. Only once it reached maturity did we get Uber, Instacart, DoorDash. Claude Code is the first of the 'Ubers' built on top of the large language model."
Samir Raiyani, CEO & Founder, IP Author
The concept of capability overhang captures this well. Existing models already hold latent abilities that developers have not yet fully exploited. Claude Code is the clearest example today. Although it uses the same model as before, it delivers dramatically better outcomes through a more cleverly built application layer.
Consequently, the most interesting work over the next two to three years will not be about improving the models themselves. It will focus entirely on what gets built on top of them, a genuine opportunity for IP professionals who act early.
Agentic AI Patent Drafting in Practice: What IP Author Is Doing About It
IP Author has been building in this direction. The platform connects its existing tools to agentic frameworks like Claude Code, tools including patent search, claim charting, whitespace analysis, and office action response. The platform now enables workflows that would have been impractical just twelve months ago.
Samir shared a practical example. A user can now prompt the system to analyse a technology domain. The system then identifies white spaces where patent protection does not yet exist. After that, it drafts initial patent claims targeting those opportunities all in a single coordinated session.
From whitespace analysis to patent claims in one session
IP Author's search, claim charting, and whitespace analysis tools are now accessible to agentic frameworks. Users can move from landscape analysis to drafted patent claims in a single orchestrated workflow with agents handling the heavy lifting.
Explore the platform →On the office action response side, Samir described a telling experiment. He left an AI agent running overnight receiving prosecution data, patent records, and strategic insights with the task of improving the quality and depth of the platform's suggested responses.
"About three times out of ten, they do something useful," Samir said. In the context of exploratory work, a 30 per cent hit rate from an overnight agent is not a failure. It is a new kind of R&D.
The Global AI Race: Implications for Agentic AI and Patent Drafting Strategy
The episode also covers the growing geopolitical dimension of AI development. US-based AI companies recently petitioned the federal government. Their concern is that Chinese competitors are training models by distilling outputs from American LLMs, learning from proprietary model behaviour without ever accessing the underlying weights directly.
At the same time, Meta announced its new Muse Spark model, reportedly built on top of Alibaba's Qwen model. The lines between who is building on whose foundation are no longer clean.
"Everything is going in circles. Nobody has a monopoly on super-intelligence," Samir observed. For IP strategists, this raises serious questions about how to protect AI-related innovations, assess freedom-to-operate across jurisdictions, and define prior art when models are trained on other models.
Key Takeaways: Agentic AI, Patent Drafting, and the Future of IP
If you are an IP professional trying to make sense of the current AI moment, here is what matters most:
Listen to Episode 1
To hear the full story, listen to the conversation between Samir and Saskia wherever you get your podcasts. Episode 1 of the IP Author Podcast is titled "Claude Code and the Hype."
If you are exploring agentic AI patent drafting as an IP professional, patent practitioner, or in-house counsel, this podcast is built for you. Future episodes will continue to cover the intersection of artificial intelligence and intellectual property, practical insights, honest analysis, no hype.