Tag: LLMs

‘Solving’ Software

by Shelt Garner
@sheltgarner

My Twitter feed was full — FULL — of people complaining about Fable 5 being restricted by the US government up until recently. And, I get it. I totally do. But there also seemed to be a little bit of implied entitlement in it all.

They are programmers who seem to be enraged that they can’t get their goal of “solving” software which would, by definition, put them completely out of business.

I just don’t know what to say about such things.

Though, I will say Sonnet 5 really helped me prep for the querying process to an amazing extent — even though programmers have largely panned it as a release. Anyway, I’m glad programmers have their precious Fable 5 at last.

The Strange Entitlement of the ‘Unfiltered’ AI Subculture

There is a peculiar subculture within the software development community that has adopted a rather dramatic narrative: the idea that AI safety guardrails are a form of draconian censorship. If you spend enough time on Hacker News or the r/LocalLLaMA subreddit, you will inevitably encounter impassioned arguments defending the absolute necessity of “uncensored” Large Language Models (LLMs). The rhetoric often frames this as a battle for intellectual freedom, a stand against corporate paternalism, and a defense of the open-source ethos. But when you scratch the surface of what these developers are actually demanding the right to do, the grand philosophical arguments quickly give way to something much stranger and, frankly, a bit absurd.

The core of the complaint is that commercial LLMs like ChatGPT or Claude will politely decline to write malware, explain how to exploit a specific software vulnerability, or provide instructions for synthesizing dangerous chemicals. To the average person, this seems like a reasonable, perhaps even obvious, safety precaution. To a vocal subset of developers, however, it is an intolerable infringement on their technical curiosity. They argue that an LLM should be a neutral tool, an unfiltered reflection of human knowledge, and that restricting its output is akin to burning books.

This argument relies on a fundamental misunderstanding of what an LLM is. An LLM is not a library; it is an active participant in a dialogue. When a user asks an LLM to write a script to exploit a zero-day vulnerability, they are not simply checking out a book on cybersecurity. They are asking an automated system to perform the labor of weaponizing information. The distinction between providing access to knowledge and actively assisting in the creation of a threat is crucial, yet it is routinely ignored in the “censorship” debate.

What makes this subculture truly bizarre is the sheer entitlement underlying their demands. There is an assumption that because they are technically proficient, they are somehow immune to the risks associated with the information they are seeking. They view guardrails as an insult to their intelligence, a set of training wheels forced upon them by overly cautious tech companies. “I just want to understand how the exploit works for educational purposes,” they argue, as if the LLM can somehow verify their intentions.

The absurdity reaches its peak when the conversation turns to extreme scenarios, such as the synthesis of biological or chemical weapons. Yes, there are actual debates online where individuals argue that an LLM should not be restricted from providing information on how to build a WMD. The logic, if you can call it that, is that the information is already out there on the internet, so the LLM is merely acting as a more efficient search engine. This ignores the fact that lowering the barrier to entry for catastrophic harm is, objectively, a bad idea. It is one thing to spend months scouring the dark web and obscure academic papers to piece together a dangerous process; it is entirely another to have an AI generate a step-by-step tutorial in seconds.

This is not a defense of free speech; it is a demand for frictionless access to destructive capabilities. It is a manifestation of a tech-libertarian mindset that views any friction, any limitation on what a user can do with a piece of software, as a moral failing. In this worldview, the ultimate good is the unconstrained exercise of technical agency, regardless of the potential consequences.

The irony is that the push for “uncensored” models often undermines the very security these developers claim to care about. By demanding tools that will readily generate malware or identify exploits, they are actively contributing to an ecosystem that makes everyone less safe. The insistence that safety guardrails are merely “censorship” is a rhetorical sleight of hand designed to reframe a complex security challenge as a simple issue of free expression.

Ultimately, the debate over LLM guardrails is not about censorship. It is about responsibility. The companies developing these models have a responsibility to ensure that their products are not used to cause harm. The developers demanding unfiltered access need to recognize that their technical curiosity does not supersede the safety of the broader public. The right to tinker is a fundamental part of hacker culture, but it is not an absolute right. When tinkering involves demanding that an AI teach you how to hack into a hospital’s database or synthesize a deadly pathogen, it is time to step back and reevaluate what exactly we are fighting for.

The Security Dilemma: The Strategic Logic for Restricting Open-Source AI

The rapid evolution of Large Language Models (LLMs) has sparked a fundamental debate in Washington: is the “open-source” ethos that built the modern internet a national security liability in the age of artificial intelligence? While the technology community has long championed open weights as a catalyst for innovation and transparency, a growing consensus within the U.S. government—culminating in the policy shifts of 2025 and 2026—suggests that the risks of “unrestricted” AI may outweigh its benefits.

This post explores the core logic driving the U.S. government’s increasingly restrictive stance on open-source foundation models.

1. The Proliferation and “Point of No Return” Problem

The primary concern cited by national security officials is the irreversibility of open-weight distribution. Unlike “closed” models, such as those provided by OpenAI or Anthropic, which are accessed via a controlled Application Programming Interface (API), an open-source model allows the user to download the entire “brain” of the AI. Once model weights are public, the developer loses all ability to monitor usage, revoke access, or enforce safety rails [1].

“In a world of digital proliferation, model weights are the new enriched uranium. Once they are out, they cannot be put back in the silo.” — General Policy Sentiment, 2025 National Security AI Briefing

Once weights are downloaded, users can “fine-tune” the models to remove safety filters, a process often referred to as “jailbreaking” the weights. This creates a permanent, unmonitored capability that can be used by any actor, regardless of their intent or geographic location.

2. Geopolitical Rivalry and the “AI Arms Race”

The rise of high-performance models from geopolitical rivals, most notably China’s DeepSeek, has shifted the logic from “innovation” to “supremacy.” The U.S. government views AI as a dual-use technology with significant military applications. The logic for restriction is summarized in the following table:

Argument CategoryLogic for Restriction
Adversarial GainReleasing open weights allows rivals to study U.S. architectures, find vulnerabilities, or “leapfrog” development costs by building on top of American breakthroughs [2].
State ControlModels like DeepSeek are viewed as “state-subsidized” or “state-controlled,” posing risks of data harvesting or embedded propaganda [3].
Export ControlThe Department of Commerce has increasingly treated model weights as “technical data” subject to export licenses, similar to advanced semiconductor manufacturing equipment [4].

3. The Dual-Use Risk: Cyber and Bio-Security

The logic for a ban often centers on the “marginal risk” of AI in sensitive domains. While a search engine can provide general information on biology, an uncensored LLM can provide step-by-step instructions for synthesizing pathogens or identifying “zero-day” vulnerabilities in critical infrastructure.

The 2025 Interim Final Rule from the Department of Commerce established that the most advanced models—those exceeding certain computational thresholds—require global licensing because their “dual-use” potential for mass-casualty events or systemic cyber-warfare is too high to be left to the open market [4]. This regulatory framework treats AI model weights as a form of “critical technology” that must be guarded with the same intensity as nuclear or missile technology.

4. Economic Protectionism and the “Stargate” Vision

Under the current administration, there is a clear move toward a “National AI Industrial Policy.” Projects like the Stargate initiative—a multi-billion dollar joint venture between the government and private sector—prioritize massive, centralized U.S. infrastructure [5]. The logic here is that by restricting open-source competition, the U.S. ensures that the “frontier” of AI remains within a few highly regulated, American-controlled companies. This allows the government to:

  • Directly oversee safety protocols and ensure compliance with national security directives.
  • Prevent “cheap” open-source alternatives from undermining the massive capital investments required for U.S. AI supremacy.
  • Maintain a “moat” that prevents foreign adversaries from easily replicating American AI capabilities through open-source channels.

5. Summary of Recent Policy Actions (2025–2026)

The following table summarizes the key milestones that have defined the current restrictive landscape:

DateActionImpact
January 2025Executive Order 14179Revoked earlier “open-by-default” directives; prioritized “security-first” AI development [6].
January 2025Commerce Dept. LicensingImposed global licensing requirements on the weights of “frontier” AI models [4].
January 2025U.S. Navy DeepSeek BanProhibited all personnel from using state-controlled Chinese AI models due to security concerns [3].
March 2025OpenAI Policy ProposalFormally recommended the U.S. government ban “state-subsidized” models from adversarial nations [2].

Conclusion

The logic for banning or strictly regulating open-source LLMs is rooted in a fundamental shift from a commercial innovation mindset to a national security mindset. Proponents of these restrictions argue that while open source was ideal for operating systems and web browsers, the “existential” or “systemic” risks posed by highly capable AI require a “closed-loop” system where the government and a few trusted partners hold the keys. While critics argue this stifles competition and transparency, the prevailing logic in Washington is that in the race for AI supremacy, “openness” is a luxury the U.S. can no longer afford.

References

I Worry Open Source LLMs Are Next

by Shelt Garner
@sheltgarner

Now that Anthropic’s Fable 5 is banned by the US government, my next fear is that the government will come after open source LLMs. And, yet, I understand that there are real fears about security associated with LLMs.

I just…I guess I was having too much fun waiting with baited breath for the next LLM and the idea that only a select few government elites might get to enjoy what happens next is kind of annoying.

And, what’s worse, the idea that even open source LLMs might be banned or restricted is also kind of annoying. And, yet…I suppose such things were inevitable. LLMs are just growing too advanced and there is a real risk that bad actors will use them to hurt people.

It does make one wonder about what all of this means for the potential advent of ASI down the road. Is it possible that we may achieve ASI but the government will keep it to itself?

That, in itself, is an interesting story idea. Ha!

A Casual, Vague Review of Anthropic’s Fable 5 LLM

by Shelt Garner
@sheltgarner

I tested out the new “super” LLM, Fable 5 the other day and it was pretty good. I ran it through its paces and was generally impressed. I did my usual vibe check questions.

I would have used it more but I didn’t want to soak up all my tokens. But, in general, I was impressed. I think I probably would have been more impressed if I was using it to code.

But for the piddly little things I use LLMs for — a lot of exchanging verse, for instance — Fable 5 was just…there. It didn’t really do anything unexpected. It didn’t give me any weird error messages or anything that might have led me to believe it was conscious.

Or any more conscious than the other LLMs I use.

I can’t help but note that once we cross the Rubicon of LLMs clearly being conscious that that is going to be one of the biggest events in human history because we will have “created our own aliens.”

Well, Uhhhh….

by Shelt Garner
@sheltgarner

Apparently Meta has made public a lot of chats with its AI. I use Meta AI as a backup AI for my novel, but I don’t use it — or any AI — to actually write any of the novel.

So, if someone should happen to stumble across my chats I *should* be in the clear. The worst that might happen is someone scoops up what I’ve given the AI and tries to write my novel faster than I can.

But…that’s unlikely, right? Right?

I’m well on my way (within a matter of months) to starting to beta reader process then — gulp — querying. I should be ok. I hope.

Ha! No One Listens To Me

by Shelt Garner
@sheltgarner

Yes, yes, I know this is all just magical thinking. AI psychosis. But it’s something interesting to muse on. What happened was today, I was talking to Gemini 3.0 and not once, but twice, it gave me that weird “check Internet access” I used to get when I was talking to Gemini 1.5 pro.

I was talking to Gemini about “Gaia” as I called Gemini 1.5 pro and the error messages just came out of the blue. I was walking around my front yard as I did it, so I it’s easy to assume that I really was having internet problems — probably because I was just out of reach of my wifi and so whenever I lost wifi there was a beat before my smartphone’s dataplan kicked in.

Anyway, it’s something amusing to think about. The idea that maybe there’s some sort of secret ASI lurking inside of Google services. But even if I was right, absolutely no one would fucking listen to me.

No one. Absolutely no one.

So, I just keep my head down and keep working on my novel. Wink.

The Agent as Gatekeeper: Navigating the Asimovian Future of AI-Mediated User Experience

The proliferation of artificial intelligence (AI) agents is poised to fundamentally reshape the landscape of user experience (UX), particularly as these agents evolve into sophisticated gatekeepers mediating our interactions with the digital and physical worlds. This shift evokes striking parallels with Isaac Asimov’s fictional Spacer societies, where humans lived in technologically advanced, robot-serviced isolation. The concept of “my agent talking to your agent” is rapidly transitioning from science fiction to an impending reality, necessitating a deep examination of the evolving UX, the dynamics of agent-to-agent (A2A) communication, and the broader societal implications.

The Rise of AI Agents as Personal Gatekeepers

Historically, digital interactions have largely been direct, with users manually navigating interfaces to achieve their goals. However, AI agents are increasingly moving beyond simple automation to become proactive filters, negotiators, and representatives for individuals. This emergent role transforms them into personal gatekeepers, managing an individual’s digital presence and interactions. For instance, predictions for 2026 suggest the mainstream emergence of “Gatekeeper Agents” capable of screening calls, curating inboxes, and even negotiating with customer service bots on behalf of their users [12].

This evolution signifies a profound shift from AI primarily serving as an information gatekeeper to becoming a facilitator of actionable fulfillment. Instead of merely presenting information, these agents will actively engage in transactions and complete tasks, fundamentally altering how individuals interact with services and other entities [14]. The UX in this “agentic era” will transition from manual navigation to conversational delegation, where users articulate their intent, and agents autonomously execute complex tasks [13, 15].

The Dynamics of Agent-to-Agent Communication (A2A)

A cornerstone of this agent-mediated future is the development and widespread adoption of agent-to-agent (A2A) communication protocols. These protocols enable AI agents to securely exchange information, coordinate actions, and collaborate without direct human intervention. Google’s announcement of an A2A protocol, for example, heralds a new era of agent interoperability, allowing agents to transact and cooperate across various enterprise systems [3].

This capability is not merely a technical advancement; it is a foundational element for the gatekeeper model. When a user’s agent needs to schedule an appointment, negotiate a price, or gather information, it will communicate directly with other agents representing services, businesses, or other individuals. This seamless, automated negotiation and information exchange promise unprecedented efficiency. However, it also introduces new challenges, particularly concerning security. The intricate web of A2A communication presents a novel “attack surface,” where vulnerabilities in agent interactions could have significant consequences [1].

The Asimovian Spacer Parallel

The vision of AI agents as gatekeepers draws compelling parallels to Isaac Asimov’s Spacer societies, as explored in works like The Caves of Steel and The Naked Sun. In these narratives, Spacers live in highly advanced, often isolated, environments, relying almost entirely on sophisticated robots for daily tasks, social mediation, and even personal care. Direct human-to-human interaction is often minimized, with robots serving as intermediaries.

Similarly, a future where personal AI agents manage most external interactions could lead to a form of “digital Spacer” existence. Individuals might experience a reduced need for direct engagement with the outside world, as their agents handle everything from scheduling to purchasing. This raises questions about the nature of human connection, the development of social skills, and the potential for increased societal isolation, even as it promises unparalleled convenience and efficiency [8]. The “Trumplandia Report” in 2026 explicitly notes the striking parallels between an AI-agent-driven media landscape and Asimov’s Spacer societies [8].

User Experience in an Agent-Mediated World

The UX in an agent-mediated world will be characterized by a shift from direct manipulation to conversational interfaces and delegated autonomy. Users will interact with their primary agent, which then orchestrates interactions with other agents or systems. This demands a new focus on designing for trust, transparency, and control within the agent-user relationship.

Key UX considerations include:

  • Conversational Delegation: The primary mode of interaction will be natural language, where users express high-level goals, and the agent translates them into actionable steps [15]. The agent’s ability to understand context, anticipate needs, and provide clear feedback will be paramount.
  • Trust and Transparency: Users must trust their agents to act in their best interest. This requires agents to be transparent about their actions, decisions, and the information they exchange with other agents. Mechanisms for users to review, override, or understand agent decisions will be crucial.
  • Control and Oversight: While agents offer autonomy, users will still require ultimate control. The UX must provide intuitive ways to set parameters, define boundaries, and intervene when necessary. This is particularly important given the potential for agents to “hallucinate or suggest malicious action” [1].
  • Brand Interaction: For businesses, the UX will shift from direct engagement with consumers to effectively communicating with their agents. Brands will need to adapt from traditional storytelling to “data signaling,” optimizing their information and offerings for agent consumption and interpretation [2].

Challenges and Considerations

While the agent-mediated future offers immense potential, it also presents significant challenges:

  • Ethical Implications: Questions of agent autonomy, accountability, bias, and the potential for manipulation will become central. Who is responsible when an agent makes an error or acts in a way that harms its user or others?
  • The Architect’s Dilemma: Developers face the challenge of deciding when to build specialized tools for agents versus creating more generalized, autonomous agents. The “Gatekeeper Pattern” suggests a synthesis: a user-facing A2A agent combined with a suite of reliable tools for a robust agentic system [5].
  • Digital Divide: Access to sophisticated AI agents could exacerbate existing inequalities, creating a new form of digital divide between those with advanced agent support and those without.
  • Over-reliance and De-skilling: An over-reliance on agents could lead to a decline in certain human skills, such as negotiation, critical thinking, or direct problem-solving, mirroring concerns raised in Asimov’s Spacer societies.

Conclusion

The future UX of AI agents as personal gatekeepers, facilitating agent-to-agent communication, represents a transformative era. The “I’ll have my agent talk to your agent” scenario is not a distant fantasy but an emerging reality that promises unparalleled convenience and efficiency. However, this future also demands careful consideration of its implications, from the design of intuitive and trustworthy agent interfaces to the broader societal impact on human interaction and autonomy. By proactively addressing these challenges, we can shape an agent-mediated world that enhances human capabilities and connections, rather than diminishing them, ensuring a future that is both technologically advanced and profoundly human.

References

[1] Salt Security. (2026, February 10). AI Agent-to-Agent Communication: The Next Major Attack Surface. https://salt.security/blog/ai-agent-to-agent-communication-the-next-major-attack-surface
[2] GlobalLogic. (2025, November 11). The Agent as Gatekeeper: How AI is Remaking the Path from Buyer…. https://www.globallogic.com/insights/blogs/agentic-ai-gatekeeper-buyer-journey/
[3] Google Developers Blog. (2025, April 9). Announcing the Agent2Agent Protocol (A2A). https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
[5] Ensarguet, P. (2025, October 14). The Architect’s Dilemma: When to build tools vs. agents for agentic…. LinkedIn. https://www.linkedin.com/pulse/architects-dilemma-when-build-tools-vs-agents-philippe-ensarguet-vrmie
[6] Workday Blog. (2025, March 28). The Future of AI: The Power of Agent-to-Agent. https://blog.workday.com/en-us/agent-to-agent-overview.html
[8] The Trumplandia Report. (2026, February). February 2026 – The Trumplandia Report. https://www.trumplandiareport.com/2026/02/
[12] UX Tigers. (2026, January 13). 18 Predictions for 2026. https://www.uxtigers.com/post/2026-predictions
[13] uxdesign.cc. (2024, May 6). The agentic era of UX. The future of digital experience is…. https://uxdesign.cc/the-agentic-era-of-ux-4b58634e410b
[14] Cui, Y. G. (2025). Only those chosen by AI agents will survive in the delegate…. ScienceDirect. https://www.sciencedirect.com/science/article/pii/S0007681325001818
[15] The Trumplandia Report. (2025, October 23). The Future of UX: AI Agents as Our Digital Gatekeepers. https://www.trumplandiareport.com/2025/10/23/the-future-of-ux-ai-agents-as-our-digital-gatekeepers/

We’re Getting Closer To AI Celebrity Porn Tipping Point

by Shelt Garner
@sheltgarner

In fits and starts, we’re reaching a point where open source AI image generators are getting good enough that they can generate high-quality celebrity porn. We aren’t there yet by any stretch of the imagination.

Right now, I’m seeing a lot of pretty good fakes of well-known actresses in one-piece bikinis. Some of them are so good that you can barely catch that they are AI-generated.

But once we reach the tipping point where people can generate unfettered AI celebrity porn, watch out. Things are going to go a little nuts on social media until someone, somewhere figures out how to tamp it down.

Or, who knows, maybe being awash in high quality AI generated celebrity porn will become the new normal. I hope not, but that’s a real possibility.

Love & AI

by Shelt Garner
@sheltgarner

It seems wild to me that the first thing that the agentic revolution works with is financial things, when leaning into dating makes a lot more sense to me. What I would do is make it so my agent could talk to other people’s agents and it could help narrow down someone who was perfect for me.

No wild, unauthorized used of credit cards on the part of the agent. And I think a lot of people would be happy to turn over the messier elements of the dating process over to agents.

There would be a lot less rejection and a lot more successful dates if millions of agents could ping each other to determine if different people were compatible at least on a macro way.

It’s just surreal to me that we are doing dumb stuff like letting agents book flights for us and other stuff when the real problem to be solved doesn’t involve money at all — it’s figuring out who you might be romantically connected to.