Tag: AI Slop

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/

Some Insight Into My Use Of AI

by Shelt Garner
@sheltgarner

My gut tells me I’m in the clear when it comes to using AI on this scifi dramedy novel I’ve been working on. It’s not like AI actually writes the scenes for me, I do all that hard work. But let’s go through how I use AI as an “AI First” novelist.

Development
This is where I use AI the most. I use AI to dramatically speed up the process of doing development. AI really helps to guide me towards my goal, especially when that goal is rather nebulous.

Scene Summaries
I also use AI in this situation, where I write out a scene summary before I actually sit down to write the scene. I use an expanded scene summary as a guide to writing the scene.

“Gentle Editing”
The only place where AI comes close to “writing” the novel for me is when it comes to editing. I tell my AI to “gently edit” a scene once I’m done. I only do this because 1) I’m fucking poor 2) my actual writing, while ok, generally needs an editor to make it query-level.

Having said all that, I think I’m going to be a little bit more careful with the next novel I work on. I don’t want there to be ANY DEBATE about how much AI was involved in writing my next novel. I probably will not have AI write my expanded scene summaries…and not edit any of the novel…so I’m a lot more in the clear.

‘The Shy Girl’ Conundrum

by Shelt Garner
@sheltgarner

I don’t know what to do about this one. A recent novel, The Shy Girl, was not published after it was planned to be because it was “AI made.” Now, that’s kind of ambiguous. What does that mean?

Because I have decided — because I’m poor — to use AI as my editor. I have done so much hard work — and a lot of writing! — that the idea that I would get in trouble because I’ve “gentle edited” the novel I’m working on with AI.

Given how far I’m into working on this novel, my only option is to keep going. I’m going to edit this novel using AI. But, I have to admit, that the NEXT novel I write I will tweak my workflow so it will be more difficult for people to say that my novel is “AI written.”

I’m Poor — I Have To Rely Upon AI To Be My Novel’s ‘Editor’

by Shelt Garner
@sheltgarner

I’m flat broke. Very, very poor. I just can’t afford a human editor. So, out of desperation, I’m using AI as my “editor.” I do a lot — A LOT — of hard work, and the last step in my writing workflow is to have AI “gently” edit the text I’ve written.

Now, obviously, because everyone is hateful and ill-informed, they will assume that “AI wrote my novel” when that is just not the case. I actually did a lot of work, I swear!

I just need an editor. And, as such, I use AI to gently improve the novel’s copy to so it’s actually good enough to query.

It will be interesting to see if anyone will realize exactly what I’m talking about.

The Final Consolidation: Hollywood as Intellectual Property Vault

I have written about this thesis before — more than once. But the idea continues to feel underexamined relative to its inevitability, so it warrants another articulation.

The next and likely final wave of consolidation in Hollywood will occur when technology companies — most probably those leading the development of generative AI — acquire the major studios outright. When that transaction happens, it will mark a fundamental shift in what a studio is. Rather than functioning as content production engines, studios will become intellectual property holding companies. Their value will lie not in the films they produce, but in the libraries they own.

The mechanism is straightforward. As AI-driven personalization tools mature — what Apple once called the Knowledge Navigator, and what will almost certainly emerge under various names in the coming decade — individual audience members will be able to generate bespoke, hyper-personalized versions of existing intellectual property on demand. The studio’s role will be to license the raw material. The audience’s role will be to shape it.

Within twenty years, this model could turn a single actor’s filmography into an infinite creative substrate. Consider Harrison Ford: audiences will be able to generate an unlimited number of new performances featuring a young Ford in variations of his most iconic roles — or in entirely new scenarios built from the same DNA. The original films become source code rather than finished product.

This outcome may, paradoxically, resolve the fatigue audiences currently experience around franchises like Star Wars. The expanded universe contains hundreds of secondary and tertiary characters whose stories remain untold in any feature-length form. Under the current studio model, most of those stories will never be greenlit. Under an AI-personalized model, any audience member who wants a feature-length film centered on Dengar or Nien Nunb can simply commission one.

That, in essence, is the future of Hollywood.

One significant question remains unresolved: what happens to the human beings who would otherwise have become movie stars?

My view — which I have articulated in various forms across several previous posts — is that their primary stage will shift to Broadway and live theatre. The theatre will become the venue where new stars are born, where audiences discover the charisma and presence that no algorithm can fabricate. Live performance will serve as the audition reel for a new kind of celebrity.

The economic model will follow accordingly. Emerging performers will eventually undergo full-body digital scans, licensing their likenesses for use in AI-generated content. The passive income derived from that licensing — their digital selves appearing in thousands of personalized films — will constitute a substantial and ongoing revenue stream, potentially exceeding what any single theatrical run could generate.

I recognize that this argument has, to date, found a limited audience. I have made it repeatedly, in various registers, and it has not yet gained traction. Perhaps one day it will.

Update On My WIP For March 13th, 2026

As an “AI First” novelist, I sometimes ask AI to act as a more aggressive manuscript consultant than I’d normally prefer — punching up a scene when I’m frustrated by how short it is and need a quick-and-dirty way to get it to length. This adds to my workload later, but at least it gives me a guide for fixing length problems down the road.

The real risk is in the “color correction” — my term for the revision pass where I make every line sound like me. If I don’t do that properly, there’ll be an obvious gap between what I wrote and what AI expanded. That pass is where I earn the manuscript.

All of this is happening against a timeline that’s coming into focus. I’m targeting September 1st to start querying. I’ll hopefully — maybe — wrap up this draft within a few weeks. Then comes color correction, and then comes finding beta readers, which frankly worries me more than the writing. I have real doubts I’ll find anyone willing to read the thing.

One thing I’ll probably revisit during color correction: character names. A relative told me the “weird names” were off-putting, and I don’t entirely disagree — but I don’t entirely agree either. I’ll need to sit with that one.

Still, this is by far the farthest I’ve ever gotten in the process of writing a query-worthy novel. I feel a little sheepish about how many years it’s taken to reach this point, but I’ve had fun getting here. And for the first time, the next stage — actually querying — feels like something that’s about to happen rather than something I’m imagining.

The Evolution of Hollywood: From Production Powerhouses to AI-Driven IP Holding Companies

The hypothesis that Hollywood studios will eventually transform into IP holding companies, where AI agents leverage licensed intellectual property to create personalized, bespoke content for users, is no longer a matter of distant speculation. As of early 2026, the entertainment industry is already in the midst of this structural metamorphosis. The convergence of generative AI (such as OpenAI’s Sora 2), decentralized distribution, and a shift in consumer behavior toward hyper-personalization has set the stage for a fundamental reimagining of what a “studio” actually is.

1. The Shift in the Core Asset: From Films to Foundations

In the traditional studio model, the primary asset was the finished product—the 120-minute feature film or the 22-episode television season. In the emerging AI-driven landscape, the core asset has shifted to the foundational intellectual property: the characters, the lore, the world-building, and the digital likenesses of the actors.

AspectTraditional Studio ModelAI-Driven IP Holding Model
Primary FunctionPhysical production and distributionIP stewardship and licensing
Core AssetFinished media (MP4, DCP)Digital assets, lore, and character weights
ProductionHuman-led, multi-year timelinesAI-driven, real-time, or near-real-time
Creative ControlCentralized (Director/Producer)Decentralized (AI-guided, User-influenced)

Recent developments in 2025 and 2026 show that studios like Disney and Warner Bros. are increasingly viewing their libraries as “training sets” and “licensing foundations.” For instance, Disney’s reported move in late 2025 to hand over its most valuable characters to AI models for licensed use marks a pivotal moment where the studio prioritizes the utility of its characters over the scarcity of its films.

2. The Rise of Personalized and Bespoke Narratives

The concept of “bespoke versions” for users is already manifesting in platforms like Fable Studio’s “Showrunner”, which allows users to generate custom episodes of animated shows from simple text prompts. Even more telling is the recent launch of the Disney+ “Magic Feed” in March 2026. This feature, which initially focused on vertical video clips, is widely seen as the precursor to a system where users can generate personalized AI scenes within the Disney ecosystem—such as a version of Star Wars where the user themselves is a Jedi, or a Marvel movie with a customized plotline.

“The ‘Magic Feed’ won’t just show you clips from existing movies; it will show you personalized AI-generated scenes… this is the first step toward user-created artificial intelligence videos within a walled garden of premium IP.” [1]

3. The Economic Transformation: Licensing as the New Box Office

As studios become IP holding companies, their revenue models are shifting from high-risk, high-reward box office releases to a more stable licensing and subscription-based model. In this scenario, the studio acts as a “Rights OS,” providing the legal and creative framework for AI agents to operate.

  • IP Licensing Fees: Charging AI platforms (like OpenAI, Runway, or specialized “Showrunner” apps) for the right to use specific characters or worlds.
  • Bespoke Subscriptions: A premium tier of streaming services that allows for a certain number of AI-generated “bespoke” episodes or scenes per month.
  • Data Monetization: Using the data from millions of user-generated versions to understand what audiences truly want, which then informs the creation of “canonical” entries in the franchise.

4. Challenges: Talent, Law, and the “Soul” of Cinema

Despite the economic logic, this transition faces significant headwinds:

  • Talent Resistance: The 2025 backlash against AI avatars like Tilly Norwood and the ongoing concerns from SAG-AFTRA highlight a deep-seated fear that human actors will be replaced by digital clones. Studios must navigate complex “right of publicity” laws to license actor likenesses for personalized content.
  • Legal Battles: Landmark lawsuits in 2025 (e.g., Disney/Universal vs. Midjourney) demonstrate that studios are willing to fight aggressively to ensure they, and not tech companies, control the AI generation process.
  • The Loss of Shared Experience: If everyone is watching their own bespoke version of a movie, the “water cooler” moment of shared cultural experience risks disappearing. Critics argue that “bespoke” content may lack the artistic intent and cohesion that only human creators can provide.

5. Conclusion: The Hybrid Future

The most likely outcome is a hybrid model. Hollywood studios will not entirely stop producing “canonical” high-budget human-led films, as these serve as the “anchor points” for the IP’s value. However, the vast majority of consumer engagement will likely shift toward AI-driven personalized content.

In this future, a studio’s success will be measured not by its ability to manage a film set, but by its ability to maintain the cultural relevance and legal integrity of its IP. The studio becomes the “gardener” of a fictional universe, while AI agents and users are the “explorers” who create an infinite number of paths within it.

References:
[1] Inside the Magic, “Mickey Goes TikTok: Why Disney+ Vertical Video is the Secret Key to User-Created AI Videos,” March 2026.
[2] The Trumplandia Report, “The Ultimate Fate of Content Creation in the Age of AI Agents,” March 2024/2026.
[3] Variety, “Media Predictions 2026: Odyssey Dominates,” December 2025.

The AI Agent Imperative: Pressure on Wearable Hardware

The emergence of wearable devices specifically designed to run AI agents—proactive, reasoning-capable digital companions—is creating unprecedented pressure on the hardware market. Unlike traditional smartwatches that primarily track health metrics, AI agents require continuous environmental sensing (vision/audio), real-time reasoning (LLMs/SLMs), and proactive action. This shift is driving a radical evolution in silicon, thermal management, and battery technology.

1. The “Agentic” Hardware Gap

Traditional wearables (e.g., Apple Watch, Fitbit) are designed for low-power, bursty data processing. In contrast, an AI agent must maintain a “high-alert” state to be truly useful. This creates a significant gap between current hardware capabilities and the requirements of a reliable AI companion.

FeatureTraditional WearableAI Agent WearableHardware Pressure
SensingPeriodic (Heart rate, GPS)Continuous (Vision, Audio)High ISP/DSP throughput
ComputeMicrocontroller (Cortex-M)Neural Processor (NPU)High TOPS/Watt requirement
ConnectivityBluetooth (Sync only)LTE/5G (Cloud offload)High modem power drain
ReasoningCloud-based (Siri/Alexa)On-device SLM (Local AI)RAM and NPU bandwidth

2. The Silicon Response: Dedicated AI Wearable Chips

The industry is moving away from repurposed smartphone chips—like the Snapdragon 720G found in the Humane AI Pin—which suffered from severe overheating and battery drain. Instead, we are seeing the rise of dedicated “AI Wearable” silicon.

Industry Milestone: At MWC 2026, Qualcomm announced the Snapdragon Wear Elite platform. Built on a 3nm process, it is designed specifically for AI-powered wearables, offering a dedicated NPU with over 20 TOPS (Trillions of Operations Per Second) within a sub-1W power envelope.

This new generation of silicon focuses on TOPS/Watt rather than raw peak performance, enabling devices to run Small Language Models (SLMs) like Phi-3 or Llama 3-8B (quantized) locally for privacy and latency.

3. Thermal and Battery Bottlenecks

The most visible pressure is on thermal management and battery density. AI agents that process video or audio continuously generate significant heat, which is difficult to dissipate in small, skin-contact form factors.

  • Thermal Limits: Early AI wearables like the Humane AI Pin and Rabbit R1 were criticized for “toasty” operation, often shutting down after minutes of intensive use. This has forced manufacturers to innovate in passive cooling and “distributed compute” architectures.
  • Battery Density: Standard Lithium-ion batteries are reaching their physical limits. The industry is now pivoting toward Solid-State Batteries (SSBs). Companies like Grepow and Factorial are piloting SSBs for wearables in 2026, promising 2x the energy density and significantly improved safety (no thermal runaway).

4. Distributed Intelligence: The “Hub and Spoke” Model

To manage the power pressure, many manufacturers are adopting a distributed intelligence strategy. Instead of the wearable doing everything, it acts as a “spoke” to a “hub” (usually a smartphone or a dedicated pocket compute unit).

  1. Wearable (Edge): Handles low-latency tasks like wake-word detection, basic vision recognition, and UI rendering.
  2. Smartphone (Local Hub): Runs the more complex AI agent reasoning and large-scale data processing via high-performance NPUs (e.g., Snapdragon 8 Gen 5).
  3. Cloud (Remote Hub): Handles massive reasoning tasks that don’t require immediate real-time response.

5. Conclusion

The pressure to boost power in the wearable market is not just a trend; it is a fundamental shift in how these devices are engineered. We are entering an era where NPU efficiency and thermal headroom are the primary metrics of success. As 3nm silicon and solid-state batteries become standard in 2026 and 2027, the “passive tracker” will be fully replaced by the “active agent,” finally fulfilling the promise of a truly intelligent wearable companion.

References:

  1. Qualcomm Snapdragon Wear Elite Announcement – MWC 2026
  2. iFixit: Humane AI Pin and Rabbit R1 Teardown Analysis
  3. Solid-State Battery Trends for Consumer Electronics 2026

The Sublimation of Reality: Why AI Slop Will Save the Theatre

Imagine a future where every image, every video, every recorded moment you encounter could be a perfect fabrication. Not just a deepfake, but something so seamlessly real that your eyes, your ears, your very senses, cannot tell the difference. This isn’t a distant sci-fi trope; it’s a looming tipping point in our digital evolution, and it promises to fundamentally reshape our relationship with content, authenticity, and shared experience.

The Deluge of Perfect Fakes: The AI Tipping Point

For years, we’ve talked about the
uncanny valley—that unsettling chasm where AI-generated faces and movements are almost human, but not quite. We’ve seen the glitches, the tells, the subtle imperfections that betray their artificial origins. But what happens when AI crosses that valley? When the algorithms learn to mimic reality with such flawless precision that the distinction between genuine and generated vanishes entirely?

This is the tipping point: the moment when visual proof dies. Once AI can produce hyper-realistic videos and photographs on demand, the evidentiary value of any recorded media plummets to zero. Your eyes can no longer be trusted. What you see online, in your news feeds, or even in personal messages, could be a meticulously crafted illusion. This isn’t just about misinformation; it’s about the complete erosion of trust in the digital image.

And what will fill this void? A tidal wave of what one insightful observer termed “AI slop.” Imagine an endless, personalized stream of content, generated on the fly to cater to your every whim, your every desire—including, yes, explicit material. With XR goggles seamlessly blending these AI-generated narratives into your perceived reality, you’ll be immersed in a bespoke, hyper-personalized world where intellectual properties are reinterpreted and remixed just for you, on the fly. Canon? Forget about it. Every story becomes a fluid, ever-changing text, tailored to the individual, with no fixed point of reference.

The Great Sublimation: From Digital Chaos to Live Authenticity

This isn’t a future of digital backlash, but rather a sublimation. Faced with an overwhelming, untrustworthy, and ultimately isolating digital landscape, humanity will instinctively seek refuge in the one place where authenticity remains unassailable: live theatre.

Think about it. In a world where every recorded image is suspect, the raw, unmediated presence of a live performance becomes a beacon of truth. There are no filters, no algorithms, no post-production tricks. The actors are physically there, breathing the same air as you. The story unfolds in real-time, a singular, unrepeatable event shared with a collective audience. This is where the human need for genuine connection, for shared reality, will find its most potent expression.

Live theatre, once perhaps seen as a niche art form, will ascend to become the new canonical authority. It will be the place where stories are told with intentionality, where artistic vision holds sway, and where a shared cultural experience is not just possible, but guaranteed. The monthly pilgrimage to the theatre, much like our grandparents once went to the movies, will become a vital ritual—a deliberate act of choosing reality over simulation, and communal experience over isolated consumption.

Why This Matters

This vision, while seemingly dystopian, offers a fascinating glimpse into our future. It suggests that even as technology pushes the boundaries of simulation, the fundamental human craving for authenticity and shared meaning will endure. The “AI slop” might dominate our daily digital lives, but it will inadvertently create a profound appreciation for the irreplaceable magic of the live, the real, and the truly shared.

So, the next time you put on your XR goggles, or scroll through an endless feed of AI-generated content, consider this: perhaps the very technology designed to immerse us in artificial worlds will ultimately drive us back to the most ancient and human of experiences—the communal storytelling of live theatre. And in that, there’s a strange, hopeful beauty.

Qwen 3.5 Mobile AI Agent Hivemind: A Technical Architecture

Executive Summary

The emergence of Qwen 3.5, particularly its highly efficient “Small” series, marks a pivotal moment for decentralized artificial intelligence. By leveraging the native multimodal capabilities and advanced reasoning of these models, it is now feasible to construct a distributed hivemind of AI agents operating entirely on mobile hardware. This architecture, which we designate as Qwen-Hive, utilizes peer-to-peer (P2P) networking and linear attention mechanisms to synchronize state across a fleet of smartphones. Such a system transforms individual mobile devices from passive endpoints into active, collaborative nodes capable of complex task decomposition, environmental sensing, and collective problem-solving without reliance on centralized cloud infrastructure.

1. The Foundation: Qwen 3.5 Small Series

The Qwen 3.5 release introduced a specialized family of models optimized for edge deployment. These models utilize a hybrid architecture that combines linear attention via Gated Delta Networks with a sparse Mixture-of-Experts (MoE) approach [1]. This design is critical for mobile devices as it provides a significant increase in decoding throughput—up to 19x compared to previous generations—while maintaining a minimal memory footprint [1]. The table below delineates the primary variants within the Qwen 3.5 Small series and their recommended roles within a mobile hivemind.

Model VariantParameter CountPrimary Role in HivemindHardware Target
Qwen 3.5-0.8B0.8 BillionUI Navigation & Local SensingEntry-level / IoT
Qwen 3.5-2B2.0 BillionData Classification & FilteringMid-range Smartphones
Qwen 3.5-4B4.0 BillionLogic Reasoning & Code ExecutionHigh-end Smartphones
Qwen 3.5-9B9.0 BillionHivemind Leader / CoordinatorFlagship Devices

The 0.8B model is particularly noteworthy for its ability to run with ultra-low latency, making it the ideal “worker” for real-time interface interactions. Conversely, the 9B model possesses sufficient reasoning depth to act as a “Leader” node, responsible for decomposing complex user requests into sub-tasks for the rest of the hivemind [2].

2. Distributed Architecture and Coordination

The Qwen-Hive framework operates on a decentralized, peer-to-peer model. Unlike traditional client-server architectures, every phone in the hivemind acts as both a consumer and a provider of intelligence. The system relies on ExecuTorch or MLC LLM for native hardware acceleration, ensuring that inference utilizes the device’s NPU (Neural Processing Unit) to preserve battery life [3] [4].

2.1. The Linear Attention Advantage

One of the most significant technical breakthroughs in Qwen 3.5 is the implementation of Gated Delta Networks for linear attention. In a traditional Transformer model, the memory cost of maintaining a long conversation history grows quadratically, which quickly exhausts mobile RAM. Qwen 3.5’s linear attention allows the hivemind to maintain a massive shared context window (up to 256k tokens in open versions) across multiple devices with constant memory complexity [1]. This enables the hivemind to “remember” the state of a complex, multi-day task across all participating nodes.

2.2. Communication and Mesh Networking

Communication between agents is facilitated through an Agent Mesh—a specialized data plane optimized for AI-to-AI communication patterns [6]. In local environments, agents utilize Bluetooth Low Energy (BLE) or Wi-Fi Direct to form an offline mesh, allowing the hivemind to function even in the absence of internet connectivity [5].

“The Qwen 3.5 series is designed towards native multimodal agents, empowering developers to achieve significantly greater productivity through innovative hybrid architectures and sparse mixture-of-experts.” [1]

3. Agent Logic and Tool Integration

Each node in the hivemind integrates the Qwen-Agent framework, which provides standardized support for the Model Context Protocol (MCP). This allows any agent in the hive to call upon the specific tools available on its host device—such as the camera, GPS, or local files—and share the results with the collective.

The hivemind employs a Hierarchical Coordination strategy:

  1. Ingestion: A high-end “Leader” node (running Qwen 3.5-9B) receives a complex objective.
  2. Decomposition: The Leader breaks the objective into atomic tasks (e.g., “Find the nearest pharmacy,” “Check opening hours,” “Calculate the fastest route”).
  3. Dispatch: Tasks are dispatched to “Worker” nodes (running 0.8B or 2B models) based on their current battery level and proximity to the required data.
  4. Synthesis: Workers report their findings back to the Leader, which synthesizes the final response for the user.

4. Challenges and Security

Despite the potential of Qwen 3.5, deploying a mobile hivemind involves significant hurdles. Resource constraints remain the primary bottleneck; even with FP8 quantization, running a 4B model requires several gigabytes of dedicated VRAM. Furthermore, security is paramount in a P2P system. The Qwen-Hive architecture must implement end-to-end encryption for all inter-agent messages and utilize a “Zero-Trust” model where every task result is verified by at least two independent nodes before being accepted by the Leader.

5. Conclusion

The release of Qwen 3.5 provides the first viable foundation for a truly mobile-first AI hivemind. By combining the efficiency of linear attention with the versatility of native multimodal agents, we can move beyond the limitations of centralized AI. The resulting system is not just a collection of chatbots, but a distributed intelligence that is private, resilient, and deeply integrated into the physical world through the sensors and interfaces of our mobile devices.

References

[1] Qwen3.5: Towards Native Multimodal Agents. (2026, February 13). Qwen. Retrieved March 3, 2026, from https://qwen.ai/blog?id=qwen3.5
[2] Alibaba just released Qwen 3.5 Small models: a family of 0.8B to 9B … (2026, March 2). MarkTechPost. Retrieved March 3, 2026, from https://www.marktechpost.com/2026/03/02/alibaba-just-released-qwen-3-5-small-models-a-family-of-0-8b-to-9b-parameters-built-for-on-device-applications/
[3] ExecuTorch – On-Device AI Inference Powered by PyTorch. (n.d.). Retrieved March 3, 2026, from https://executorch.ai/
[4] How to Run and Deploy LLMs on your iOS or Android Phone. (2026, January 10). Unsloth.ai. Retrieved March 3, 2026, from https://unsloth.ai/docs/blog/deploy-llms-phone
[5] How Offline Mesh Messaging Works: Inside the Next Gen of … (2025, July 8). Medium. Retrieved March 3, 2026, from https://medium.com/coding-nexus/how-offline-mesh-messaging-works-inside-the-next-gen-of-communication-3187c2df995d
[6] An Agent Mesh for Enterprise Agents – Solo.io. (2025, April 24). Solo.io. Retrieved March 3, 2026, from https://www.solo.io/blog/agent-mesh-for-enterprise-agents