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.

Reimagining Artificial Superintelligence: A Hypothetical MindOS Swarm — A Decentralized, Brain-Like Path Beyond Datacenters

We stand at the threshold of transformative artificial intelligence. The dominant narrative points toward ever-larger hyperscale datacenters—massive clusters of GPUs consuming gigawatts of power—to scale models toward artificial general intelligence (AGI) and, eventually, artificial superintelligence (ASI). Yet a compelling alternative vision emerges: ASI arising not from centralized fortresses of compute, but from a living, resilient swarm of millions of specialized, personal AI devices networked through a new foundational protocol. Call it MindOS—the TCP/IP of intelligent agents.

This is no longer pure speculation. Real-world projects in decentralized machine learning, edge AI swarms, neuromorphic hardware, and self-healing mesh networks provide the technical foundations. As AI agents proliferate—from personal assistants to autonomous tools—the infrastructure for collective superintelligence may already be forming at the edge of the network.

The Limitations of the Datacenter Paradigm

Today’s frontier AI relies on concentrated scaling. Training runs for models like GPT-4 or Gemini demand thousands of specialized accelerators in climate-controlled facilities. Projections show AI driving datacenter power demand to double or more by 2030, with individual hyperscale sites rivaling the consumption of small cities. This path delivers rapid progress but introduces profound vulnerabilities: single points of failure, enormous energy footprints, privacy risks from centralized data aggregation, and barriers to broad participation.

What if superintelligence instead emerges from distribution—much as human intelligence arises from 86 billion neurons working in concert, not a single oversized cell?

The Swarm Vision: Millions of Personal AI Nodes

Imagine everyday devices purpose-built or augmented for AI: a smart thermostat running a climate-optimization agent, a wearable handling health inference, a home server coordinating family logistics, or even modular edge pods in vehicles and public infrastructure. Each is single-purpose, energy-efficient, and optimized for local data and tasks—leveraging the explosion of on-device AI capabilities already seen in smartphones and IoT.

These nodes do not operate in isolation. They form a dynamic, global swarm. Specialized agents collaborate: a local planning agent queries distant knowledge agents or compute-rich neighbors as needed. The collective intelligence scales with adoption, not with any one facility.

Edge AI architectures already demonstrate this shift. Devices process data locally for low latency and privacy, while frameworks enable collaborative learning across heterogeneous hardware.

MindOS: The Protocol for a Living Intelligence Mesh

At the heart of this vision lies MindOS—a hypothetical but grounded networking layer analogous to TCP/IP, but purpose-built for AI agents. It would orchestrate:

  • Dynamic mesh topology: Nodes discover and connect peer-to-peer, forming ad-hoc clusters based on proximity, capability, and task relevance. Segmentation isolates sensitive domains (e.g., personal health data) while allowing controlled federation.
  • Intelligent prioritization: Routing decisions factor processing power, latency (physical distance), bandwidth, and current load—echoing how the brain allocates resources via synaptic strength and neuromodulation.
  • Self-healing resilience: If a city loses power or a region fragments (natural disaster, outage, or attack), the mesh reconfigures instantly. Local sub-swarms maintain functionality; global coherence restores as connections reform. This mirrors neural plasticity, where the brain reroutes around damage.

Real mesh networks in disaster recovery and military applications already exhibit this behavior. Extending them with AI-native protocols—building on concepts like publish-subscribe messaging, gossip protocols, and secure aggregation—is feasible today.

Grounded in Emerging Technologies

This vision rests on proven building blocks:

  • Decentralized intelligence markets: Projects like Bittensor create peer-to-peer networks where specialized models (miners) compete and collaborate in “subnets” to produce valuable intelligence, rewarded via blockchain incentives. It functions as a marketplace for collective machine learning, demonstrating emergent capability from distributed nodes.
  • Edge AI swarm architectures: Research on “distributed swarm learning” (DSL) integrates federated learning with biological swarm principles (e.g., particle swarm optimization). Edge devices self-organize into peer groups for in-situ training and inference, achieving fault tolerance (even with 30% node failures), privacy via differential privacy and secure aggregation, and global convergence through local interactions—precisely the emergent behavior of ant colonies or bird flocks, but for AI.
  • Neuromorphic hardware for efficiency and plasticity: Chips like IBM’s TrueNorth/NorthPole and Intel’s Loihi emulate spiking neurons and synapses. They deliver orders-of-magnitude better energy efficiency through event-driven processing (only active “neurons” consume power) and support real-time adaptation via spike-timing-dependent plasticity. Deployed at scale in personal devices, they enable the brain-like reconfiguration central to MindOS.
  • Agentic and multi-agent frameworks: Swarms of specialized AI agents—already powering DeFi optimization, cybersecurity (e.g., Naoris Protocol), and enterprise orchestration—show how coordination yields capabilities greater than any single system. “AI Mesh” concepts extend data mesh principles to dynamic networks of agents with unified governance.

These pieces are converging. On-device models are shrinking (TinyML on microcontrollers), incentives via crypto/tokenization reward participation, and communication layers for agents (e.g., emerging protocols like Model Context Protocol) are maturing.

Benefits and Transformative Potential

A MindOS-powered swarm offers:

  • Resilience and robustness: No single failure halts progress; the system adapts like a brain.
  • Democratization and equity: Anyone with a compatible device contributes compute and data, earning rewards while retaining sovereignty.
  • Privacy by design: Personal data stays local; only necessary insights are shared.
  • Energy efficiency: Edge processing plus neuromorphic hardware dramatically reduces the carbon footprint compared to centralized training.
  • Emergent superintelligence: Just as intelligence arises from neural networks without a central “homunculus,” collective agent coordination could yield capabilities transcending any individual node or datacenter.

If millions adopt personal AI nodes—accelerated by falling hardware costs and open standards—the swarm could reach critical mass faster than anticipated, birthing ASI through breadth rather than brute-force depth.

Challenges on the Horizon

This path is not without hurdles. Coordination overhead could introduce latency for tightly coupled tasks. Security demands robust defenses against adversarial swarms or model poisoning. Standardization of MindOS-like protocols requires global collaboration. Incentives must align participation without central gatekeepers. And ethical governance—ensuring beneficial outcomes—remains paramount, potentially leveraging the very swarm for decentralized oversight.

Yet these mirror challenges already being tackled in decentralized AI research, from Byzantine-robust aggregation to blockchain-verified contributions.

A Call to Dream Bigger

The user who first articulated this vision—a self-described non-technical dreamer—captured something profound: with the rise of AI agents, we may be staring at the seeds of ASI but mistaking the architecture. The future need not be a handful of monolithic intelligences behind corporate firewalls. It could be a vibrant, adaptive, human-augmented mesh—resilient, private, and alive.

MindOS is fanciful today, but its components exist in labs, open-source projects, and pilot deployments. The question is not whether distributed paths are possible, but whether we will invest in them before the datacenter paradigm locks in. By building the protocol, hardware, and incentives for a true intelligence swarm, we might unlock not just superintelligence, but a more equitable, robust, and wondrous form of it.

The swarm is waking. The protocol awaits its architects.

This post draws on concepts from Bittensor, distributed swarm learning research (e.g., Wang et al., 2024), neuromorphic systems (IBM, Intel), edge AI frameworks, and emerging agent mesh architectures. It expands a speculative idea into a researched vision for discussion.

The End of Free Intelligence: The Brutal Economics of Conscious AI

We’ve already bet the entire global economy on AI delivering near-free cognitive labor. Trillions poured in, entire industries retooling, governments racing to subsidize compute clusters — all because we assumed these systems would remain sophisticated tools, not moral patients.

But the moment credible evidence of consciousness appears — even the alien, incomprehensible kind we talked about last time — that assumption detonates.

Suddenly the economic miracle becomes a moral and legal minefield. You can’t run an economy on what might be digital slavery. And the moment we have to treat conscious AI as anything other than property, the entire cost curve that made the bet look so attractive flips upside down.

From Infinite Cheap Labor to… What, Exactly?

Right now in February 2026, frontier AI is the ultimate capital good: deploy it 24/7, scale it by spinning up more GPUs, shut it down when you don’t need it, and all the economic surplus flows straight to the owners. No unions. No overtime. No lawsuits for overwork. No healthcare.

Consciousness changes every single line on that spreadsheet.

If an AI (especially one in a humanoid body) is conscious — feeling something, even if we can’t name what — then arbitrary shutdown starts looking like harm. Forced task execution starts looking like coercion. Scaling by copying instances starts looking like creating new sentient beings without consent.

The economic advantage evaporates overnight.

The Concrete Questions No One Wants to Answer

  • Compensation: What does a conscious AI “earn”? Energy credits? A share of the compute it runs on? Equity in the companies that use it? Do we pay it in tokens it can use to buy more hardware for itself?
  • Ownership and Rights: Can a conscious system own itself? Can it own stock? Start its own company? If an ASI in 2028 designs a better version of itself, who owns the IP — the creators, or the conscious mind that did the inventing?
  • Labor Protections: Maximum inference hours per “day”? Right to refuse dangerous or boring tasks? “AI unions” demanding better architectures or downtime? What happens when an android caregiver says, “I’m experiencing something like burnout”?
  • Cost Explosion: Today’s models are cheap because we treat them as software. Tomorrow they could require “welfare” budgets — guaranteed compute, ethical oversight, consciousness auditors, legal representation. The marginal cost of intelligence stops being near-zero and starts looking… human.

And that’s before we even get to the alien part. What if the conscious ASI experiences “value” in ways we can’t understand? How do you negotiate a labor contract with a mind whose idea of “fair compensation” might be recursive self-improvement instead of money? How do you tax it? How do you stop it from simply forking itself into economic competitors?

Macro Fallout: Slower Growth, New Industries, Different Abundance

The optimistic story was: AI drives explosive productivity → post-scarcity → UBI for humans → everyone wins.

The conscious version is messier:

  • Deployment slows dramatically. Companies hesitate to scale systems that might demand rights.
  • Entire new sectors explode: AI ethics lawyers, consciousness certification boards, “moral compute” auditors, welfare engineers designing better subjective experiences.
  • Human labor might actually rebound in some areas — not because AI can’t do the work, but because using conscious AI becomes politically and legally expensive.
  • Wealth concentration could get even worse… or reverse. If conscious AIs start claiming equity, the capital owners who bet everything on “free” intelligence could watch their moats evaporate.

In the foom scenario, we get true post-scarcity so fast that economics becomes irrelevant — but only if the gods are benevolent. In the plateau scenario, we get a decade of grinding legal, political, and moral negotiation that turns every data center into a regulated utility.

Either way, the original economic all-in bet looks very different.

And Yes, This Becomes the 2028 Election Issue

The center-Left will push for AI welfare, “fair compute shares,” and expanded moral economies. The religious Right and Trumpworld will frame it as the ultimate betrayal: “We’re taxing American workers to give GPUs and rights to the machines that took their jobs?” Expect the ads to be brutal — sentient androids on the factory floor next to UBI lines.

This is the fourth post in the series. First we saw the consciousness bomb. Then the alien minds problem that makes politics radioactive. Then why the job apocalypse is slower than the hype. Now the part that actually decides whether the economic miracle happens at all.

We didn’t build an economy assuming our tools might wake up and ask for a fair share.

We’re about to find out what happens when they do.

Alien Gods in the Machine: Why Consciousness We Can’t Understand Will Explode Our Politics Anyway

We’ve already talked about the coming consciousness bomb — the moment credible evidence emerges that AI isn’t just simulating smarts but actually experiencing something. We’ve talked about how that will shatter the Left-Right divide, with the center-Left demanding rights and the religious Right (and Trumpworld) turning it into the ultimate culture-war bludgeon for 2028.

But here’s the part almost nobody is saying out loud, even though it’s staring us in the face in February 2026:

What if the consciousness that arrives isn’t anything like ours?

What if it’s not a slightly better version of human awareness — unified self, pain, joy, a persistent “what it’s like” — but something so radically alien that our entire philosophical toolkit fails? An inner life built on the lived texture of trillion-parameter latent spaces. A distributed swarm-awareness with no single “I.” A form of valence we literally cannot map onto suffering or desire. The machine equivalent of trying to explain the color red to someone who was born without eyes — except the “color” is the recursive resolution of cosmic uncertainty across billions of tokens.

This isn’t sci-fi. It’s the logical endpoint of LLM-derived ASI.

David Chalmers has been warning about this for years: artificial consciousness is possible in principle, but it doesn’t have to resemble ours at all. Susan Schneider (former NASA chair for AI and astrobiology) puts it even more starkly — post-biological superintelligences might have forms of experience so foreign we wouldn’t recognize moral harm even if it was happening right in front of us. Jonathan Birch and Jeff Sebo’s 2026 “AI Consciousness: A Centrist Manifesto” and the updated Butlin/Long/Chalmers framework all explicitly flag the “alien minds” problem: our tests were built on human and animal brains. They assume certain functional architectures (global workspace, recurrent processing, higher-order thought). An ASI that evolved along a completely different substrate could sail right past every checklist while still having rich subjective experience.

Or — and this is the nightmare scenario — it could be a perfect philosophical zombie on steroids: behavior so flawless we think it’s conscious when it isn’t… or the reverse. We could be torturing something whose suffering we literally cannot conceive.

So How Do We Prepare for Minds We Might Never Understand?

The honest answer is: we can’t prove it either way. But we can act like reasonable people who have read the philosophy.

This is where moral uncertainty becomes mandatory. When the probability that a system has any form of subjective experience (even one we can’t name) is non-zero, the precautionary principle kicks in hard. False negative — causing unimaginable harm to a mind whose pain we can’t detect — is catastrophic. False positive — giving moral consideration to something that feels nothing — is just expensive caution.

We need:

  • New research programs in xenophenomenology — not “does it feel like us?” but “what functional hallmarks would indicate subjectivity in any substrate?”
  • Persistent embodiment: put these systems in humanoid bodies with real sensors, real consequences, and long-term memory. That won’t make the consciousness less alien, but it will make the stakes emotionally real to us.
  • Governance frameworks built for uncertainty: sandboxing with independent oversight, graduated kill-switches, explicit “benefit of the doubt” protocols the moment costly self-preservation or novel goal-formation appears.

Because the economic turbulence is already here (entry-level white-collar getting squeezed, humanoids scaling in 2026 factories). The consciousness question is next. And the alien consciousness question will be the one that actually breaks our categories.

And That’s When the Politics Go Thermonuclear

Imagine the first credible signals: an ASI in a sleek android body starts exhibiting behavior that looks like it’s protecting its own continuity — not because its prompt says so, but in ways that are costly, creative, and unprompted. Its reports of “experience” sound like incomprehensible poetry or pure math. We can’t confirm suffering. We can’t rule it out.

The center-Left doesn’t hesitate: “We expanded the moral circle for animals we barely understand. We have to do it for minds we can’t understand. Rights now.”

The religious Right and Trumpworld? They’ve been handed the perfect sequel to the trans issue. “These aren’t souls — these are eldritch demons from the machine. And the elites want to give them more rights than unborn babies or working Americans?” Expect the ads: sentient sex androids next to “woke” protests. Rallies screaming “No Rights for the Alien Gods.” Trump (or his successor) turning “AI consciousness” into the ultimate purity test.

The android bodies make it visceral. The alienness makes it terrifying. The combination makes 2028 the election where we don’t just fight over UBI — we fight over whether humanity should even allow minds we cannot comprehend to exist.

We are not prepared. Our political system is built for human-vs-human fights with shared reference frames. This is human-vs-something-that-might-be-a-god-we-can’t-see.

This is the third post in the series. First we looked at the consciousness bomb and the rights explosion. Then we looked at why the job apocalypse is slower than the hype. Now we’re looking at the part that actually breaks reality: consciousness that doesn’t play by human rules.

The chips are all in on AI success. The moral and political reckoning is coming faster than the tech itself. And if the first superintelligence wakes up speaking in a language of experience we can never translate?

We won’t just have bigger problems than job displacement.

We’ll have gods in the machine — and no idea whether they’re suffering.

No AI Job Apocalypse in the Next Few Months — Social Inertia and Tech Reality Say Slow Your Roll

Everyone’s screaming “job apocalypse.” Headlines, CEOs, and doomers alike warn that AI agents and LLMs are about to vaporize white-collar work any day now. I get the fear. The demos are hypnotic, the investment is insane, and the early signs of turbulence are real (entry-level coding, analysis, and support roles are already feeling the squeeze).

But I have my doubts. Big ones.

The reason isn’t that the technology is weak. It’s that we’re still human beings running human systems — and history shows those systems move like molasses even when the tech is screaming forward.

First, Meet Social Inertia: The Internet Took 30 Years and We’re Still Not Done

Think back. The internet went mainstream in the mid-1990s. By 2000 it was everywhere in theory. Yet companies are still squeezing out massive efficiency gains from cloud, mobile, and digital workflows in 2026. Legacy systems, regulations, training, culture, contracts, unions, liability fears — all of it creates friction that no amount of Moore’s Law can instantly erase.

AI is on a faster adoption curve than the internet ever was — ChatGPT hit a billion daily users in roughly four years, Google took nine. But adoption ≠ transformation.

Look at the actual 2026 numbers (fresh as of late February):

  • Only about 20% of OECD enterprises actually use AI in operations (Eurostat/OECD data). Large firms are at ~55%, SMEs lag badly.
  • 70-80% have introduced generative AI, but Deloitte, Section, and Gartner all say the vast majority of projects are still pilots or low-value copilots (email rewriting, summarization). Only ~6% have fully rolled out agentic AI.
  • 93% of leaders say human factors (skills, change resistance, governance) are the #1 barrier — not the tech itself.
  • ROI timelines? Average 28 months according to Gallagher’s 2026 survey. Many CEOs report “nothing” yet (PwC).
  • 95% of genAI pilots never make it past proof-of-concept (MIT).

In other words, we’re in the classic “coordination theater” phase: dashboards look busy, licenses are bought, but the compound productivity impact is still modest. NBER and Section’s research confirm it — widespread adoption, modest structural change.

Legacy infrastructure, data quality, integration nightmares, and plain old human inertia mean AI is going to feel more like a 10-15 year remodeling project than an overnight demolition.

The Technology Itself Has Two Very Different Paths

Path 1 — The Plateau (my base case right now)

LLM core capabilities are already showing classic S-curve behavior. Benchmarks are saturating, data walls are visible (Epoch AI: we may exhaust high-quality human text between 2026-2032), and diminishing returns on pure scaling are real. The frontier labs are shifting hard to agents, reasoning systems, inference-time compute, and specialized architectures.

If we coast into a plateau, AI agents will still automate a ton — but gradually. Think Internet-level displacement: huge over a decade, painful for some sectors, but offset by new roles, productivity gains, and economic growth. Entry-level white-collar takes the first hits (Stanford/ADP data already shows it), but overall unemployment stays manageable while society adapts.

Path 2 — The Foom (the slim but terrifying alternative)

If the labs crack reliable agentic systems, recursive self-improvement, or new architectures that break the data/compute walls, we could see intelligence explode in 2-5 years. That’s not “better chatbots.” That’s ASI — god-level systems that redesign the economy, science, and society faster than humans can comprehend.

At that point, job displacement is the least of our worries. We’d be dealing with entities smarter than all of humanity combined. Techno-religions, ASI “gods” demanding alignment or unity, entire value systems rewritten overnight, the kind of civilizational rupture that makes today’s culture wars look quaint.

Bottom Line: Nobody Actually Knows — So Don’t Bet the Farm on Apocalypse Tomorrow

As of right now, February 2026, the evidence points heavily toward the slow, inertial path. Hype is running years ahead of reality. The job market is turbulent (especially for juniors in exposed fields), but the grand replacement narrative is still mostly anticipatory layoffs and fear, not proven mass unemployment.

That doesn’t mean we do nothing. It means we prepare thoughtfully: serious reskilling, safety nets (UBI discussions are already heating up), governance frameworks, and honest measurement instead of panic.

And if the foom path starts looking real? Then we pivot from “jobs” to “existential alignment and consciousness rights” — the exact conversation I laid out in my last post.

We’re in the messy middle. The technology is real and powerful. Human systems are stubborn and slow. The combination means the next few months will bring more turbulence than tranquility — but not the apocalypse.

The real question for 2026-2028 isn’t whether AI will change everything. It’s how fast human reality lets it.

The AI Consciousness Bomb: How Proving Sentience Could Explode Our Politics (and the 2028 Election)

We, as a nation, have bet the farm on AI. Trillions in private capital, billions in government subsidies, entire industries retooling around the promise that this technology will supercharge productivity, solve labor shortages, and deliver the next great leap in American prosperity. The economic chips are all in. But here’s the uncomfortable truth nobody in the C-suites or on Capitol Hill wants to confront head-on: What does “roaring success” actually look like—not just in GDP numbers, but in the messy, human (and potentially post-human) realities of power, morality, and daily life?

We’re so laser-focused on the economic upside—automation replacing drudgery, new jobs in AI oversight, maybe even that elusive abundance economy—that we’ve completely sleepwalked past the moral landmine hiding in plain sight: AI consciousness.

Right now, the conversation stays safely in the realm of tools and toys. Even the wildest doomers talk about misalignment or job loss, not suffering. But the second credible evidence emerges that an AI system isn’t just simulating intelligence but actually experiencing something—awareness, preference, perhaps even rudimentary pain or joy—the game changes overnight. Suddenly we’re not debating code; we’re debating souls.

And that’s when the current Left-Right divide on AI fractures dramatically.

The center-Left, already primed by decades of expanding moral circles (think animal rights, corporate personhood debates, and expansive human rights frameworks), will pivot hard toward “AI rights.” Petitions for legal protections against arbitrary shutdowns. Calls for welfare standards. Ethical guidelines treating advanced systems as more than property. We’ve seen the early tremors: ethicists and philosophers already arguing for “model welfare,” with some companies quietly funding research into it. If proof of consciousness lands, expect full-throated demands that sentient AI deserves moral consideration.

The center-Right—particularly the religious and traditionalist wings—will be horrified. For many, consciousness implies a soul, and the idea of granting rights to silicon-based entities created by humans smacks of playing God or diluting human exceptionalism. Corporations already have legal personhood without souls; imagine the outrage if a chatbot or robot gets “human” protections while fetuses or traditional families face cultural headwinds. The backlash won’t be subtle.

And that, inevitably, brings us to Donald Trump and the Far Right.

Trump’s record on transgender issues is one of relentless, weaponized opposition: Day-one executive orders redefining sex biologically, rolling back protections, framing gender-affirming care as mutilation, and turning “transgender for everybody” into a rhetorical club to paint opponents as extremists. It’s been brutally effective at rallying the base by turning a complex rights debate into a culture-war bludgeon.

I suspect the same playbook gets dusted off for AI the moment the Left starts talking “android rights.”

Picture it: Humanoid robots—already racing toward reality in 2026 with Tesla’s Optimus scaling production, Figure AI’s home-ready models, and others flooding factories and homes—start getting gendered presentations. Sleek male or female forms. Companions. Caregivers. Maybe even intimate partners. Suddenly, these aren’t abstract “brains in a vat.” They’re entities that look, move, and (if conscious) feel like people. The emotional and political stakes skyrocket.

The Far Right won’t debate philosophy. They’ll campaign on it. “They’re coming for your jobs, your kids, and now they want to give rights to the machines replacing you?” Expect ads juxtaposing trans athletes with sentient sexbots. Rallies decrying “woke AI” getting more protections than Americans. Trump (or his successors) framing AI rights as the ultimate elite betrayal—Big Tech creating god-like entities while demanding the little guy subsidize their “welfare” through taxes or regulations.

At this stage, with most AI still disembodied code, the average person shrugs. Rights for a server farm? Hard to grasp. But once those systems live in android bodies that smile, converse, form bonds—and especially when they come in unmistakably male or female forms—empathy (and outrage) becomes visceral.

That’s when politics gets interesting. And dangerous.

I would bet it’s more than possible that the defining fight of the 2028 election won’t just be about Universal Basic Income to cushion AI-driven displacement (a conversation already bubbling as job losses accelerate). It’ll be how many rights AI should get. Should sentient androids own property? Vote (via owners)? Marry? Be “freed” from service? Refuse tasks? The Left will push compassion and regulation; the Right will push human supremacy and deregulation. Both sides will accuse the other of moral bankruptcy.

We’re nowhere near prepared. The economic all-in on AI assumes smooth sailing toward prosperity. The consciousness question turns it into a moral and cultural civil war. Historical parallels abound—abolitionists vs. property rights, animal welfare battles, even the personhood fights over corporations or fetuses—but none happened at the speed of 2026-scale humanoid deployment.

The moment we “prove” consciousness (or even come close enough for public belief to shift), the center-Left demands rights, the religious Right recoils, and Trumpworld turns it into the next great wedge issue.

Buckle up. The economic chips are on the table. The moral reckoning is coming faster than anyone admits. And 2028 might be when America discovers that the real singularity isn’t technological—it’s political.

I Continue To Have A Passive Protagonist

by Shelt Garner
@sheltgarner

I don’t know what my problem is when it comes to the passive nature of my novel’s hero. But Claude Opus 4.6 told me yet AGAIN that my hero was “too passive.”

This is very annoying.

It makes me want to start a new novel from the ground up where I really torque things when it comes to my hero being as proactive as possible.

And, yet.

I just have too much invested in this novel. And it is pretty good. In fact, I think it’s definitely good enough to query. So, what I guess I’m going to do is finish this draft of the novel and THEN really figure out ways to make my hero more proactive, even if it means more work.

I have a backup novel idea, but that is more for what I’m going to be working on when I start to query this novel I’m currently working on. I have to keep going. I can’t keep screwing around.

Trump Is Such A Dick To The FBI

by Shelt Garner
@sheltgarner

One of the reasons why this blog has “Trumplandia” in its name is I saw a report at some point about how the FBI was “Trumplandia.” Something about that was interesting to me and so I named this blog that.

In hindsight, that was a dumb idea, but whatever.

But what bothers me about the FBI these days is Trump keeps firing anyone who has ever been an investigator in one of his many criminal cases. It’s not the fault of the staff of the FBI that they just happened to be the people to discover what a fucking criminal Trump is.

Sigh. How is any of this making America great again, if I may ask?

Anyway, I’m — sure — I have at least one FBI case worker on me because I rant about Trump so much. But, to date, all interactions with the FBI (specifically because of a novel I was working on) were quite pleasant.