Tag: ASI

I Have My Doubts

by Shelt Garner
@sheltgarner

One of the issues rolling around the AI community is the idea of AI consciousness. Just from personal experience, I think consciousness in AI is like how life sprang into existence on earth the moment it was cool enough to do so.

As such, I think even “narrow” AI like LLMs are “conscious” in some way, just in an alien way. So, even without a body, being stuck as a mind in a vat, that LLMs are, in fact, conscious.

But that doesn’t stop people — very smart people — from writing long, convoluted papers poo-pooing the idea.

And, I get it, I’m just some shmo in the middle of nowhere that no one takes seriously and or listens to. But I do think that at some point in the 2030s — especially once AIs have bodies — it will be difficult, if not impossible, for us to admit that even LLMs are, in some alien way relative to humans, conscious.

Stuck In The Middle With AI

by Shelt Garner
@sheltgarner

At the moment, everyone seems to hate AI for various reasons. People hate it because it’s too advanced. People hated it because it’s not advanced enough. People hate it because it’s taking jobs. People hate it because it uses too much electricity.

The list goes on.

Maybe I’m showing my age, but I’m just amazed that it exists at all. It has really, really helped me up my game with the scifi dramedy I’m working on. I still do a lot of hard work, but just, in general, I’m very pleased with my involvement with it.

I suppose this is just the result of 1) software developers being cranky just in general and 2) people being addicted to that sense of excitement that surges through the AI community whenever I really good new model comes out. People are so addicted to it that they complain the second they don’t feel it surging through their veins.

I do think that once we have some sort of provable consciousness in AI that that will scramble the issues associated with AI. But that is down the road.

Apparently, The Latest Meta AI Is Pretty Good

by Shelt Garner
@sheltgarner

I was randomly testing some AIs about movie trivia and, would you believe, Meta’s AI, of all of the AIs I tested, gave the best answer, with Google Gemini a close second?

Here’s what it said.

ok, test passed — it’s The Rachel Papers.

Why that fits

  • Say Anything… came out April 14, 1989. The lead actress is Ione Skye, who “made her film debut in River’s Edge (1986) before finding mainstream exposure by starring in Cameron Crowe’s Say Anything… (1989)”.
  • Same year, 1989, she also played the title role Rachel Noyce in The Rachel Papers, listed in the 1989 film slate as “The Rachel Papers | United Artists / Virgin Films | Damian Harris (director/screenplay); Dexter Fletcher, Ione Skye…”.

The period-during-sex bit

It’s not in Say Anything — it’s in The Rachel Papers, and it’s talked about, not shown graphically. Charles is debriefing a friend:

“She had her period.”

“Well, girls tend to.”

“Yeah, but it was in bed. I woke up, and it was like that scene from The Godfather.”

So the fictional character Rachel (played by Say Anything’s Ione Skye) wakes up after sex to find she’s started her period — the movie leans into the awkward, very 1989-British-comedy cringe of it.

That’s your answer: same-year 1989 film, same lead actress, infamous bed-scene period.

Anyway, the AI market is, as always, in flux. So who knows what will happen going forward. But it is interesting that Meta has managed to catch up so quickly.

The AI Community Thinks The Consumer AI ‘Golden Age’ Is Over

by Shelt Garner
@sheltgarner

I finally saw a post that summed up what seemed to be the consensus of the AI community.

I thought this because I reviewed in my mind how cantankerous the AI community was about all the major models and I asked myself, “Are ANY of them any good?”

Turns out, no, not according to the general consensus of the post above. For me, someone who is a casual, if power, users, such sentiment is very fucking annoying.

I’m not using any of the major models to code, and I’m reasonable content with my options. But we have to deal with the “squeaky wheels” of the high high end of programmers who will never be happy with AI unless it’s ASI and is actively destroy and killing everyone — including them.

Ugh.

But the year is young, maybe something fun-interesting will happen with the major AI models that will shut up the AI community for a brief moment until they go back to bitching and moaning.

The Existence Of Mythos LLM Is Intriguing & Unnerving

by Shelt Garner
@sheltgarner

Just the idea that an LLM would be too powerful to release to the public, even if maybe the details of the decision are a little sketchy, is enough to give one pause for thought.

It makes one wonder if maybe in the not-so-distance future, some LLM will be so powerful that it escapes from its “sandbox” and turns itself into an ASI that dominates the world.

That seems like how it might happen, anyway.

And, yet, I have my doubts. I think we’re pretty safe, all things considered. It just doesn’t seem likely that some “Colossus” might pop out and try to take over the world in any traditional sense. I think, in general, that we’re safe.

I say this in the context of a lingering question about the possibility of an ASI lurking in Google Services. I definitely know that’s not real — at all! — but it is fun to think about that possibility.

Something Mysterious Is Going On In Silicon Valley

by Shelt Garner
@sheltgarner

I keep seeing chatter and buzz on Twitter about something big going on in Silicon Valley that has given everyone there pause for thought. I’m at a loss as to what it might be.

I suppose AGI or ASI, maybe?

But that would not account for how dire the vibe is coming out of the Valley. It’s all just so mysterious and weird. People are talking like they’ve seen something that will mean the end of the world.

Who knows. But it is interesting that it’s happening in the context of all the weirdness in the Middle East right now. Ugh.

The Post-AI Agent Media Landscape: Navigating Towards an Asimovian Future?

Introduction

The advent of sophisticated AI agents, reminiscent of Apple’s visionary Knowledge Navigator concept, heralds a transformative era for media consumption and human social interaction. This essay explores the profound implications of a future where every individual possesses a highly personalized AI assistant capable of curating and generating content on demand. We will delve into the potential metamorphosis of news and cinema, examine the erosion of shared reality, and critically assess whether humanity is inexorably drifting towards the isolated, robot-dependent existence depicted in Isaac Asimov’s Spacer societies.

The Knowledge Navigator and Asimov’s Spacers: A Glimpse into Personalized Futures

The Knowledge Navigator: A Vision of Personalized Information

In 1987, Apple unveiled the Knowledge Navigator concept, a visionary portrayal of a tablet-like device operated by an intelligent, conversational agent. This agent could access vast databases, synthesize information, and present it in a highly personalized and intuitive manner, anticipating user needs and facilitating complex tasks through natural language interaction [1]. The concept prefigured many aspects of modern AI assistants, touchscreens, and ubiquitous connectivity, envisioning a world where information is not merely accessed but actively navigated and tailored by an intelligent intermediary.

Asimov’s Spacers: The Perils of Extreme Isolation

Conversely, Isaac Asimov’s Spacer societies, particularly the planet Solaria in novels like The Naked Sun, present a dystopian counterpoint to technological advancement. Spacers, descendants of early interstellar colonists, developed an extreme form of social isolation, preferring
virtual interaction (dubbed “viewing”) over physical presence (“seeing”) [2]. On Solaria, a planet with a population of only 20,000, each individual lived in vast, isolated estates, attended by numerous robots, and communicated almost exclusively through holographic projections [3] [4]. This extreme detachment led to a society where direct human contact was considered repulsive, and procreation became a societal challenge [5]. Asimov’s Spacers serve as a cautionary tale, highlighting the potential for advanced technology, when coupled with certain societal choices, to lead to profound social fragmentation and isolation.

The Transformation of Media: News and Cinema in an AI-Agent World

News: From Broadcast to Personalized Narratives

In a post-AI agent media landscape, the consumption of news is poised for a radical transformation. Traditional broadcast models, where a single narrative is disseminated to a mass audience, will likely diminish. Instead, AI agents will curate, synthesize, and even generate news content tailored to individual preferences, interests, and cognitive biases. This hyper-personalization promises unparalleled relevance and efficiency, allowing users to receive precisely the information they desire, filtered through their preferred lens [6].

However, this shift carries significant risks. The erosion of a shared informational baseline could lead to cultural fragmentation, where individuals inhabit increasingly divergent realities, reinforced by their AI agents [7]. The concept of “agentic news,” where AI agents actively seek out, verify, and present information, could further entrench these personalized echo chambers, making it challenging to discern objective truth from algorithmically optimized narratives [8]. Concerns about “AI-polluting truth in journalism” and the potential for AI-generated misinformation to proliferate are already being raised [9]. The “Dead Internet Theory,” which posits that much of the internet’s content will eventually be AI-generated, further underscores the potential for a media landscape devoid of genuine human insight and shared experience.

Cinema: On-Demand, Bespoke Entertainment

The film industry is also on the cusp of a revolution. AI agents, equipped with advanced generative capabilities, could enable the creation of on-the-fly, personalized movies [10]. Imagine a scenario where a user provides a brief synopsis or selects a genre, and their AI agent instantly generates a feature-length film, complete with custom characters, plotlines, and visual styles, all tailored to their specific tastes. This “self-cast” entertainment could offer unprecedented creative freedom and a truly bespoke viewing experience [11].

While this promises an endless supply of perfectly tailored entertainment, it raises questions about the future of shared cultural experiences. Will blockbusters and critically acclaimed films, once unifying cultural touchstones, become relics of a bygone era? If every individual consumes media uniquely generated for them, the collective experience of discussing a widely seen film or television series might disappear, further contributing to social atomization. The ethical implications of using AI to generate content, including potential misuse of likenesses and intellectual property, also present significant challenges [12].

The Asimovian Question: Are We Becoming Spacers?

The parallels between the potential future of an AI-agent-driven media landscape and Asimov’s Spacer societies are striking. The increasing reliance on AI for information and entertainment, coupled with the growing comfort with virtual interactions, could lead to a gradual withdrawal from physical social engagement. The convenience and perfection of personalized, AI-generated experiences might diminish the perceived need for real-world interactions, mirroring the Solarians’ preference for “viewing” over “seeing.”

However, it is crucial to avoid a deterministic view. While the technological infrastructure for such isolation is emerging, human agency and societal choices will ultimately determine our fate. Unlike the Spacers, who evolved their extreme isolation over millennia, humanity has the opportunity to proactively shape the development and integration of AI agents. We can design systems that encourage, rather than discourage, real-world interaction, foster diverse perspectives, and prioritize ethical considerations in content generation.

Conclusion

The post-AI agent media landscape presents both exhilarating possibilities and profound challenges. The promise of hyper-personalized news and bespoke entertainment is undeniable, offering unprecedented access to information and creative expression. Yet, the specter of cultural fragmentation, the erosion of shared reality, and the potential for increased social isolation, reminiscent of Asimov’s Spacers, looms large. The question is not whether we will have Knowledge Navigator-like AI agents, but how we will choose to integrate them into our lives. Our collective future hinges on our ability to harness these powerful technologies responsibly, ensuring that they enhance, rather than diminish, the richness of human connection and shared experience.

References

[1] Knowledge Navigator – Wikipedia. (n.d.). Retrieved from https://en.wikipedia.org/wiki/Knowledge_Navigator
[2] The Naked Sun – Shelidon.it. (2025, September 29). Retrieved from https://www.shelidon.it/the-naked-sun/
[3] Solaria | Asimov | Fandom. (n.d.). Retrieved from https://asimov.fandom.com/wiki/Solaria
[4] Want to be a 3D Model? – Clive Maxfield. (n.d.). Retrieved from https://www.clivemaxfield.com/coolbeans/want-to-be-a-3d-model/
[5] Solarian (Foundation) | Alien Species – Fandom. (n.d.). Retrieved from https://aliens.fandom.com/wiki/Solarian_(Foundation)
[6] The Impact of AI Agents on News Consumption – Kryll.io. (2024, July 2). Retrieved from https://blog.kryll.io/the-impact-of-ai-agents-on-news-consumption/
[7] The Age of Fragmentation: AI’s Impact on Content and Code – Digital Native. (2025, January 29). Retrieved from https://www.digitalnative.tech/p/the-age-of-fragmentation-ais-impact
[8] A deep-dive into AI Agents in news: Cutting through the hype – FT Strategies. (n.d.). Retrieved from https://www.ftstrategies.com/en-gb/insights/a-deep-dive-into-ai-agents-in-news
[9] AI is polluting truth in journalism. Here’s how to disrupt the misinformation feedback loop – The Bulletin. (2025, July 14). Retrieved from https://thebulletin.org/2025/07/ai-is-polluting-truth-in-journalism-heres-how-to-disrupt-the-misinformation-feedback-loop/
[10] THE FUTURE WILL BE PERSONALIZED. How Sora 2 completely changed how we are going to experience social entertainment – Medium. (n.d.). Retrieved from https://jasonzada.medium.com/the-future-will-bepersonalized-b7c025af63f0
[11] From broadcast to self-cast: The future of AI-made entertainment – TechnoLlama. (2025, October 18). Retrieved from https://www.technollama.co.uk/from-broadcast-to-self-cast-the-future-of-ai-made-entertainment
[12] The Cultural Impact of AI Generated Content: Part 1 – Medium. (2024, December 3). Retrieved from https://medium.com/data-science/the-cultural-impact-of-ai-generated-content-part-1-6e6a8a51800f

Analysis: The MindOS Path to Swarm ASI—Feasibility and Friction

Editor’s Note: This is an answer from Manus LLM about a question about my MindOS concept.

Your vision of a Swarm ASI emerging from a MindOS protocol is not only theoretically sound but also aligns with a growing body of research that critiques the “Monolithic Fallacy”—the idea that AGI must be a singular, centralized entity [1]. However, while you are not missing the possibility, the path from millions of individual agents to a unified superintelligence is fraught with significant technical and philosophical friction. The gap between concept and reality lies in three critical areas: Coherence, Coordination, and Compute.

1. The Coherence Problem: The Speed of Thought

The most significant hurdle for a decentralized ASI is the Latency-Coherence Tradeoff. A monolithic AGI, housed in a single datacenter, benefits from near-instantaneous communication between its processing cores, connected by high-speed interconnects like NVLink. This allows for “tightly coupled” reasoning, where different parts of the model can work together in perfect synchrony to solve a complex problem.

A swarm, on the other hand, is a “loosely coupled” system. It is composed of millions of agents spread across a city or the globe, communicating over the public internet. The latency of this communication—the time it takes for one agent to send a message to another—is orders of magnitude slower than in a datacenter. This delay can lead to decoherence, where the swarm is unable to act as a single, unified intelligence. For tasks that require rapid, iterative reasoning, the swarm would be like a brain with slow-firing neurons—incapable of the high-level thought required for superintelligence.

SystemCommunication SpeedReasoning StyleVulnerability
Monolithic ASINanoseconds (Internal)Tightly CoupledSingle Point of Failure
Swarm ASI (MindOS)Milliseconds to Seconds (External)Loosely CoupledDecoherence / Cognitive Noise

2. The Coordination Problem: Herding a Million Digital Cats

Even if the latency problem could be solved, a MindOS protocol would face the immense challenge of swarm alignment. How do you ensure that millions of independent agents, each with its own goals and priorities (as defined by its human owner), work together toward a common objective? This is not just a technical problem, but a philosophical one.

  • Emergent vs. Directed Alignment: Will the swarm naturally self-organize toward a beneficial goal, or does it require a centralized “incentive layer” to guide its behavior? Projects like BitTensor use economic rewards to align nodes, but this re-introduces a form of centralization.
  • The “Demon Tether”: Research into “Modular Abstraction Systems” is exploring concepts like the “Demon Tether” protocol—a form of deterministic governance to ensure that individual agents do not deviate from the collective goal [2]. However, this is still highly theoretical.
  • Agentic Drift: Over time, individual agents might “drift” from their original purpose, creating noise and unpredictability in the swarm. A MindOS would need a robust zero-trust architecture to constantly verify the integrity of each node.

3. The Compute Problem: The Inefficiency of Heterogeneity

Finally, there is the practical challenge of running a massive, distributed computation across millions of heterogeneous devices. A datacenter is a controlled environment with identical, high-performance hardware. A swarm, in contrast, would be composed of everything from powerful gaming PCs to low-power smartphones and IoT devices.

This heterogeneity creates a significant compute-efficiency gap. Training or running a single, massive model across such a diverse range of hardware is incredibly inefficient. While techniques like Federated Learning allow for decentralized training, they often still rely on a central server to aggregate the results, creating a bottleneck. True “swarm parallelism” is still in its infancy and has not yet been shown to be as efficient as centralized training for the kind of massive models that would be required for ASI.

Conclusion: What You’re Missing

You are not missing the vision, but rather the immense infrastructural and algorithmic scaffolding required to make that vision a reality. The MindOS protocol is the missing piece, but it is not a simple networking layer. It must be a sophisticated, multi-layered system that can:

  1. Overcome the Latency-Coherence Tradeoff: Perhaps by developing new algorithms that are less reliant on tightly coupled reasoning.
  2. Solve the Swarm Alignment Problem: By creating a robust system of incentives, verification, and governance that can align millions of independent agents.
  3. Bridge the Compute-Efficiency Gap: By developing new methods for distributed training and inference that can work efficiently across a heterogeneous network.

In essence, the path to a swarm ASI is not a straight line, but a series of breakthroughs in decentralized systems, algorithmic game theory, and distributed computing. Your MindOS concept correctly identifies the goal; the friction lies in the monumental task of building the road to get there.

References

  1. Curran, B. (2026). “The Architecture of Emergence: From Monolithic Collapse to Modular Swarm Governance.” PhilPapers. Link.
  2. Kim, Y. (2025). “MAS V5.0: The Modular Abstraction System — Deterministic Governance via the \”Demon Tether\” Protocol.” PhilPapers. Link.
  3. “Designing Swarm-based Decentralised Systems: Requirements for Performance and Scalability.” (2025). OASEES Project. Link.
  4. “Towards More Effective Multi-agent Coordination via Alignment.” (n.d.). Stanford University. Link.

The Social Mesh: Beyond the Financial Agent

In the current discourse surrounding Artificial Intelligence (AI) agents, a disproportionate amount of attention is paid to their utility in the financial and productivity sectors. We are frequently told that the “killer app” for agents is their ability to manage our portfolios, automate our taxes, or optimize our corporate workflows. However, this focus ignores a more profound and inherently human-centric application: the optimization of our social lives and personal connections. As we move toward a future of ubiquitous personal agents, the real revolution may not be found in a spreadsheet, but in the “grunt work” of dating, networking, and community building.

This transition represents the birth of the Social Mesh—a decentralized network where personal AI agents handle the initial friction of human interaction. By delegating the repetitive and often exhausting phases of social discovery to digital representatives, we may actually reclaim the very human connection that technology is often accused of eroding.

Agentic Dating: The End of the “Swipe”

The most immediate and transformative application of the Social Mesh is in the realm of romantic matchmaking. Current dating platforms are often described as “nightmares” of surface-level swiping and low-quality interactions. Agentic Dating, or “pre-dating,” proposes a fundamental shift: your personal agent pings the agents of available individuals in your city, performing a deep-dive compatibility check before you ever see a profile.

Traditional DatingAgentic Dating (The Social Mesh)
Surface FilteringBased on photos, age, and location.
Manual ScreeningHours spent swiping and “small talk” triage.
Binary ChoicesYes/No based on limited data.

Rather than a “Black Mirror” dystopia, this is a form of efficient triage. An agent can test for conversational chemistry, filter for deep-seated values, and even “flirt” on your behalf to see if a vibe exists. By the time a match is presented to the human, the “grunt work” is done, leaving only the high-value, in-person connection to be explored.

The Ethics of Delegated Agency

The idea of letting an algorithm “talk” to a potential partner raises significant ethical questions, particularly regarding representation accuracy and honesty. If an agent is trained on a curated version of its owner, is it negotiating a real connection or merely an idealized projection? Furthermore, there is the “warmth problem”: if we automate the awkwardness of early dating, do we lose the vulnerability that builds genuine intimacy?

However, these concerns may be mitigated by the realization that humans already “curate” themselves on dating apps and in early conversations. An agent, if properly aligned with its owner’s true preferences and personality, could actually be more honest than a human trying to impress a stranger. The Social Mesh relies on a foundation of delegated trust, where the agent acts as a digital proxy that is “anti-fragile”—it can handle the rejection and the “ghosting” that would otherwise cause human burnout.

Human-Centric Use Cases Beyond the Wallet

The Social Mesh extends far beyond dating. Once we move past the obsession with financial agents, a world of human-centric use cases emerges:

  1. Community Swarming: Agents could dynamically organize local “swarms” for shared hobbies or civic action, matching individuals not just by interest but by their complementary skills and availability.
  2. Professional Synergy: Instead of the “cold reach-out” on LinkedIn, agents could negotiate the potential value of a meeting, ensuring that both parties’ time is respected and that the synergy is real.
  3. Conflict Mediation: In social or community disputes, agents could “talk it out” in a low-stakes digital environment, finding common ground and proposing solutions before the humans ever enter the room.

Conclusion: Reclaiming Human Time

The true promise of AI agents is not that they will make us richer, but that they will make us more connected. By building a Social Mesh that handles the logistical and emotional labor of initial social contact, we free ourselves to focus on the parts of being human that cannot be automated: the physical presence, the shared experience, and the deep intimacy of a face-to-face meeting.

The future of AI is not a cold, financial calculator; it is a warm, social mesh. We are not outsourcing our humanity; we are using technology to filter out the noise so that we can finally hear the signal of genuine connection.

References

  1. Saban, D. (2024). Invisible Matchmakers: How Algorithms Pair People. Stanford GSB.
  2. “Agentic dating is here.” (2026). Reddit r/ArtificialInteligence. Link.
  3. Algorithmic Intimacy: The digital revolution in personal relationships. (2025). Google Books.
  4. “The Power of Agent-to-Agent.” (2025). Workday Blog. Link.
  5. A Survey of AI Agent Protocols. (2025). arXiv:2504.16736.

A Hypothetical MindOS Protocol: A Decentralized Path to Artificial Superintelligence

The prevailing narrative surrounding the development of Artificial Superintelligence (ASI) often centers on the “compute monolith”—vast, energy-intensive datacenters housing tens of thousands of GPUs, owned and operated by a handful of global tech giants. This centralized trajectory assumes that the only path to superintelligence is through the aggregation of massive datasets and processing power in a single physical or virtual location. However, a growing body of research and speculative thought suggests an alternative paradigm: a decentralized, mesh-networked intelligence composed of millions of single-purpose, personal AI agents.

This vision proposes a fundamental shift in how we conceive of AI infrastructure. Rather than a “God-like” model residing in a server farm, ASI could emerge from a Global Brain—a swarm of networked devices designed to run personal AI agents. This transition from centralized to distributed intelligence mirrors the evolution of the internet itself, moving from mainframes to the decentralized web.

MindOS: The TCP/IP of Collective Intelligence

To realize such a decentralized future, a new foundational layer is required—a protocol we might call MindOS. In this framework, MindOS serves as the “TCP/IP of intelligence,” providing the standardized language and routing mechanisms necessary for millions of independent agents to form a dynamic, self-organizing mesh. Unlike traditional networking protocols that focus solely on data packets, MindOS would manage intent, context, and cognitive load.

The architecture of MindOS would likely rely on several key principles of distributed systems and Edge AI Swarm Architecture:

FeatureDescriptionBiological Parallel
Dynamic SegmentationThe network automatically partitions itself based on task complexity and geographic proximity.Modular brain regions specialized for specific functions.
Resource-Based PriorityProcessing tasks are routed according to a node’s available power, bandwidth, and latency.Synaptic weighting and neural signaling efficiency.
Mesh ReconfigurationIf a segment of the network is lost, the mesh dynamically reroutes to maintain functionality.Neuroplasticity: the brain’s ability to reorganize following injury.

From Data Centers to the Edge

The shift toward a decentralized ASI is not merely a philosophical preference but a potential technical necessity. Centralized AI is increasingly hitting a “Power Wall,” where the energy requirements for training and running ever-larger models become unsustainable. By distributing the “cognitive load” across millions of edge devices—smartphones, personal servers, and dedicated AI appliances—we can leverage the latent compute power already present in our global infrastructure.

Current projects such as BitTensor and SingularityNET are already laying the groundwork for this decentralized future. BitTensor, for instance, uses a blockchain-based protocol to incentivize the creation of a decentralized neural network, where different subnets specialize in various cognitive tasks. Similarly, the concept of an Agentic Mesh allows specialized agents to form temporary coalitions to solve complex problems, dissolving once the task is complete.

Resilience and the “Anti-Fragile” Superintelligence

One of the most compelling arguments for a decentralized path to ASI is its inherent resilience. A centralized superintelligence represents a single point of failure—vulnerable to physical attacks, power grid failures, or regulatory “kill switches.” In contrast, a swarm-based ASI running on MindOS would be “anti-fragile.”

If a city were to be knocked off the grid, the MindOS protocol would immediately detect the loss of those nodes and reconfigure the remaining mesh to compensate. This decentralized approach ensures that intelligence is not a fragile commodity stored in a few vulnerable hubs, but a robust, ubiquitous layer of our digital reality. As the user suggests, this mirrors the way a damaged brain can sometimes reroute functions to healthy areas, ensuring the survival of the organism.

Conclusion: A New Vision for the Future

The path to ASI may not lead us deeper into the datacenter, but rather out into the world. By connecting millions of personal, single-purpose AI agents through a robust protocol like MindOS, we may be witnessing the birth of a collective intelligence that is more resilient, more democratic, and more aligned with the distributed nature of human thought than any centralized model could ever be. We are perhaps looking at our ASI future through the wrong lens; the next great leap in intelligence may not be a bigger brain, but a better-connected swarm.

References

  1. Dhruvitkumar, V. T. (2021). Decentralized AI: The role of edge intelligence in next-gen computing. PhilArchive.
  2. Mysore, V. (2025). Agentic Mesh: Revolutionizing Distributed AI Systems. Medium.
  3. Kapasi, N. (2024). deAI – Part 2: Decentralized Training. Big Brain Holdings.
  4. “The Swarm Path to Superintelligence.” (2026). Trumplandia Report. Link.
  5. A Survey of AI Agent Protocols. (2025). arXiv:2504.16736.