Anthropic Faces Challenges From Pentagon Requirements

Posted on February 28, 2026 by Stephen Packard

I have been critical of Anthropic before. The company rose quickly and is run primarily by founders who do not come from a conventional business leadership background. The company is governed with a strong spirit of ethics and stewardship.

While I have found their message to be a bit unprofessional, speculative and over the top at times, there’s no doubt that it’s honorable for a company to put its own ethics above lucrative business deals. As so many large corporations support ICE actions and government overreach, it’s nice to see a company that still is willing to stand up and do the right thing.

When it comes to using technology by militaries, the ethics get dicey fast. Is it okay to use technology for purely defensive roles? What if it is offensive, but in a justified conflict? Is it okay if it results in more deaths on the other side? What if a weapon is powerful but its impacts are based on how it is used? Should our commanders be trusted to use technology ethnically? Is it patriotic to provide tech to the military, because it may save out servivcepeople?

These are not easy questions, and companies grapple with them all the time. Some companies are card carrying defense contractors, and that’s just what they do. But war is an unusual situation: The aim is to kill people and cause maximum destruction. That’s at odds with most corporate ethics.

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New Youtube Channel and Focus on AI

Posted on February 26, 2026 by Stephen Packard

Artificial Intelligence has more mysticism than just about any other subject out there. I’ve never seen any subject so poorly understood and so sensationalized. It’s a technology that everyone seems to realize is big, revolutionary and important. But that’s only resulted in a huge amount of mythology.

Few people understand AI from a technical perspective, but just about everyone *thinks* they understand it, because it seems so intuitive. It seems like you can just talk to it and it understands, so the implications are obvious, right?

Right now the world has a deficiency in AI experts who understand the tech, and even fewer are mature risk managers. That’s resulted in a lot of skewing toward sensationalism. Most AI leaders are not even knowledge about the tech and the media rewards a high drama narrative. There are a few ways it skews: one of the most ridiculous messages is AI doomerism, the idea that AI might wipe out humanity. It’s cartoonish but it receives more attention than it should. There are also claims of permanent unemployment. On the other end is AI utopianism. There are also those insisting AI might become conscious or a moral patient. Yes, this is also being taken seriously.

It’s really a subject that attracts all kinds. But few people realize that like any technology, AI and ML have fundamental limits and capabilities. They’re not magic. But the recent AI summit in India would have you think otherwise, with ubiquitous claims of being close to superintelligence.

And so, as one of the few AI technical experts willing to address this problem I have launched a new YouTube Channel and will be focusing primarily on this topic. AI risks, mitigations, technology and truth: AI Sanity, on Youtube.

Where We Really Stand In AI Capabilities

Posted on February 23, 2026 by Stephen Packard

The recent talk of AGI, as if it is some kind of impending certainty, and now talk about “Superintelligence” is really causing a great deal of confusion. The reality is that we are nowhere near the point of human level intelligence in all domains, the idea of artificial super intelligence, is entirely speculative and nowhere near foreseeable capabilities, and you can’t scale past the limits of current AI systems. The truth has been lost in a sea of sensational rhetoric.

The modern public discourse around artificial intelligence began with a fundamental shift in frame of reference. For decades, AI systems were narrow, technical, and largely invisible to the general public. Then, quite suddenly, natural language processing systems emerged with startling fluency. For the first time, people could interact with a machine through conversational language that resembled human dialogue.

This single development reset public intuition overnight.

Instead of being understood as statistical systems operating within defined computational constraints, large language models were immediately interpreted through the lens of science fiction archetypes: conversational minds, digital assistants, synthetic intellects. The resemblance in surface behavior was compelling enough to override the underlying reality of how these systems actually function.

But fluency is not cognition. Simulation of reasoning is not reasoning itself.

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A Risk-Oriented Hierarchy of Intervention in the Deployment and Customization of Large Language Models

Posted on February 22, 2026 by Stephen Packard

A practical and pragmatic discussion of the levels of risk and complexity in the customization of large language models. Many organizations are using LLM technology to build customized chatbots, RAG tools and content generators. However, many organizations do not have a full understanding of the options and levels of risk and development complexity that come from LLM customization and deployment.

In the contemporary landscape of artificial intelligence deployment, a structural shift is occurring: base models are becoming increasingly capable out of the box. Instruction-following performance, contextual reasoning, retrieval integration, and domain adaptability have improved to such a degree that many historical justifications for invasive model modification are steadily eroding. This evolution necessitates a corresponding philosophical and governance framework—one grounded in the principle that greater customization introduces greater uncertainty, greater liability, and a proportionally greater need for validation and risk controls.

At its core, the responsible deployment of large language models should be guided by a hierarchy of invasiveness. Each successive layer of intervention introduces deeper system coupling, increased behavioral unpredictability, and escalating regulatory, operational, and reputational risk. Accordingly, risk management should not begin at the level of model alteration, but rather at the least invasive layers of interaction and configuration.

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