Sex, Data, And Reason: Updates At Revenant Research

Are you ready for 2025?

It’s been a wild start to 2025. I think it’s only going to heat up. As a result, I’ve been pushing out a lot of work. So I figured I’d switch it up and send out a newsletter on the latest developments rather than inundate you with multiple emails.

Revenant AI R&D

First, I’d like to highlight my recent report on AI’s Cambrian Explosion For Enterprise Creativity because it isn’t just an academic exercise. I am building out Revenant AI as a Phase V software architecture platform. Why? Because you’re paying way too much for software. Native-AI platform can replace most of your third-party software workflows for 75% cheaper.

I’ll be releasing more of this work soon but I wanted to tease the Revenant AI workflow reasoning engine. The workflow reasoning engine is designed to intake a complex task or set of instructions and…

1. Develop a detailed plan of action

2. Identify what agents need to be called to complete the task. Revenant Agents act autonomously in the background and can dynamically query databases, call APIs, and execute algorithms.

3. Orchestrate the AI agents in sequential order to complete the workflow.

Why this matters?

• Both resoning models and agents are becoming key themes of 2025.

• Despite their advancements they are still constrained by limited context windows (memory). This is why it feels like ChatGPT has dementia the longer your chat thread goes.

• None of these solutions are viable on their own for complex workflows that businesses rely on.

Revenant AI’s Workflow Reasoning solves this problem, reduces code complexity by over 90%, and allows for easy integration into business operations.

For more information, demo, or consultation on how Revenant AI can automate your workflows email me at nathan@revenantai.com

Case Law

Whether it’s sex or data, AI is being taken to court over and over again for non-consensual applications.

Nearly all AI researchers and developers ignore one of the most fundamental layers of the AI stack: law and governance. How AI is shaped by case law, particualry as governments still struggle to define and codify AI regulation, is critical to understanding how to invest in and adopt AI.

I’ve been tracking AI-related cases for this reason. I’ve added four new AI cases for January 2025.

1. The New York Times Co. v. Microsoft et al.

2. Concord Music et al. v. Anthropic

3. Thomson Reuters et al. v. ROSS Intelligence

4. Kohls v. Ellison

In total, some themes have begun to emerge…

The most common legal disputes revolve around intellectual property, deepfake and misinformation risks, and the fairness of AI-driven decision-making.

A key theme throughout these cases is the clash between AI innovation and existing legal frameworks. Courts are increasingly asked to determine whether AI’s use of copyrighted materials constitutes fair use, with cases like NYT v. Microsoft & OpenAI and Authors Guild v. Anthropic poised to define how AI models can be trained on existing content. Similarly, deepfake technology is facing heightened legal scrutiny, particularly in cases involving non-consensual content (City of San Francisco v. AI “Undressing” Websites, Megan Thee Stallion v. Milagro Gramz).

The judiciary is also confronting AI’s role in governance, with cases like Elon Musk v. OpenAI, which questions whether AI companies should be held accountable for shifting from nonprofit missions to for-profit ventures. At the same time, legal professionals are grappling with the ethics of AI-generated legal documents (Minnesota Voters Alliance v. Minnesota), raising concerns about hallucinated citations and misinformation in legal filings.

These cases suggest courts and lawmakers will soon need to establish clearer regulatory frameworks governing AI training data, digital identity protection, and AI-generated misinformation. If courts take a strict stance on AI copyright infringement and deepfake regulation, AI companies may need to invest in more scalable licensing models and stronger content moderation. Conversely, a favorable ruling for AI companies in fair use cases could reinforce broader, less restrictive training practices.

For further reading see the U.S. Copyright Office’s newly released report on AI.

Primers

I also published two primers…

A Primer On U.S. Semiconductor Export Controls and Entity Bans discusses the evolution of U.S. policies aimed at restricting China's access to advanced semiconductor technologies. Initially, U.S. efforts were reactive, targeting specific entities like Semiconductor Manufacturing International Corporation (SMIC) due to its ties to China's military-industrial complex. However, in 2022, the U.S. adopted a more proactive approach, implementing broad export controls to limit China's access to advanced AI chips and semiconductor manufacturing tools. These measures also restricted U.S. persons from supporting Chinese semiconductor manufacturing, even in areas not directly covered by export controls. These restrictions represent a new reality for AI and global commerce.

This primer supports Revenant Research’s theme on AI Sovereignty in the Second Cold War. Now that AI applications and business investments in AI are becoming a tool of geopolitical positioning, investors and pundits are wading into the backwaters of export controls and…getting it wildly wrong. As someone who invests heavily in AI technology, develops AI technology, and has dug documents out of dumpsters of a Russian shell company violating export controls, I think I’ll have more to say here soon.

Also see: my supply chain audits of almost all comanpanies in the AI Supply Chain.

A Primer On Model Level Architecture explores the evolution of artificial intelligence (AI) model architectures, emphasizing key developments that have enhanced efficiency and performance. It begins by discussing early AI systems that utilized fully connected neural networks, which applied uniform computation across all inputs. A significant advancement occurred in 2017 with the introduction of the Transformer model, which replaced recurrent networks with self-attention mechanisms, enabling parallel sequence processing and greater scalability. Further innovations, such as Mixture of Experts (MoE) and Test-Time Compute (TTC), have optimized computational resource allocation by dynamically activating relevant model components.

This primer gives you everything you need to know to know about what’s happening in AI labs. It mostly matters because the pace of software commoditization, business and application consolidation, and destruction and displacement begins here.

I talk to companies every day about AI and the wave of automation and change is rapidly outpacing their strategies. 2025 is the year these companies need to get serious about AI. Everyone is comfortable talking about job displacement now. No one is comfortable talking about company displacement.

With that said, I’d love to hear some feedback. What do you want to see more of?