Tag: AI Agents

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 Serendipity Economy: When AI Agents Replace Apps and Start Arranging Our Lives

Editor’s Note: Yet more AI Slop, this time with help by ChatGPT.

For twenty years, the dominant metaphor of the internet has been the app. If you want something, you download a specialized interface. Flights? There’s an app. Dating? There’s an app. Dinner reservations? Another app. Each one competes for your attention, your data, and your time. But what happens when the app layer dissolves?

Imagine a world where everyone has a personal AI “Knowledge Navigator” native to their phone. You don’t open apps anymore. You state intent. Your agent interprets it, negotiates with other agents, and presents you with outcomes. The interface isn’t a grid of icons. It’s a conversation.

In that world, the economy shifts from attention capture to agent-to-agent coordination.

Instead of browsing flight aggregators, your agent negotiates directly with airline systems. Instead of scrolling restaurant reviews, your agent queries trusted local knowledge graphs. Instead of swiping through faces on a dating app, your agent quietly coordinates with other agents to determine compatibility before you ever see a name.

This is where the idea gets interesting: nudging.

Call it “Serendipity.”

The Serendipity feature wouldn’t feel like surveillance or manipulation. It would feel like light-touch alignment. Your agent knows your schedule, your energy patterns, your preferences, and your social rhythms. It also knows—at least in high-density cities—that other agents represent people with overlapping availability and compatible traits.

Rather than forcing users into endless swipe cycles, the system might suggest something simpler: be at this café at 7:15. There’s a high probability you’ll enjoy who happens to be there.

No profiles. No performative bio-writing. No gamified rejection loops.

Just ambient alignment.

Why start with dating instead of finance or travel? Because the downside risk is lower. A failed flight booking can cascade into financial and logistical disaster. A mismatched first date is, at worst, a forgettable evening. Dating is already emotionally messy. Optimization here doesn’t threaten institutional stability; it reduces friction.

More importantly, dating apps today are structured around retention, not success. Their business model thrives on endless browsing. An agent-based Serendipity system would be structurally different. It would optimize for outcomes—pleasant conversations, mutual interest, long-term compatibility—not for time spent swiping.

But here’s the psychological nuance: people don’t mind being nudged. They mind feeling manipulated.

If users know Serendipity exists, and they opt in at a high level, that may be enough. They don’t need to see the compatibility score, the probability matrix, or the behavioral modeling underneath. They just need confidence that the system is working in their favor.

Transparency at the macro level. Opacity at the micro level.

The danger, of course, is that nudging infrastructure doesn’t remain confined to romance. The same mechanisms that coordinate first dates could coordinate political events, consumer behavior, or social clustering. Once agents become primary negotiators, whoever controls the protocol layer—identity verification, trust scoring, negotiation standards—holds enormous power.

So the post-app world doesn’t eliminate gatekeepers. It changes them.

Instead of app stores, we might see intent marketplaces. Instead of feeds, we’ll see negotiated outcomes. Instead of influencer-driven discovery, we’ll have machine-mediated alignment. Apps become APIs. APIs become endpoints. Endpoints become economic nodes.

There’s also a cultural tradeoff. Humans enjoy browsing. Discovery is entertainment. Friction sometimes creates meaning. If agents optimize away too much chaos, life may feel eerily curated. The Serendipity system would have to preserve the feeling of coincidence—even if coincidence is quietly engineered.

That may be the defining design challenge of the next decade: how to build enchanted optimization.

In the Serendipity Economy, you still feel like you met someone by chance. You still feel like you found the perfect neighborhood restaurant. You still feel like the city opened up to you naturally. But underneath, a web of agent-to-agent negotiations ensured that probabilities were stacked gently in your favor.

The question isn’t whether this is technically possible. It’s whether society prefers visible efficiency or invisible coordination.

Most people, if history is a guide, will choose the magic—so long as they believe it’s on their side.

Analysis of an Agent-to-Agent Knowledge Rental Marketplace

1. Introduction

This document provides a comprehensive analysis of the concept of an agent-to-agent knowledge rental marketplace, a service where individuals could temporarily access the knowledge base of a local resident’s AI agent to gain intimate, curated insights into a city. The analysis covers the feasibility of such a service, identifies existing analogues and missing components, explores potential risks, and outlines the overall potential of the idea.

2. The Core Concept: A Decentralized, Human-Centric Knowledge Market

The proposed service envisions a world where personal AI agents, native to mobile devices, can interact and exchange information. A traveler’s agent could ‘ping’ the agents of locals in a destination city to ‘rent’ their knowledge base, effectively gaining a personalized and highly contextualized tour guide. This model would operate without direct human interaction, relying on agent-to-agent communication protocols.

3. Feasibility and Existing Analogues

The technological foundations for such a service are rapidly emerging, making the concept increasingly feasible. Several key areas of development support this idea:

3.1. Agent-to-Agent Communication

Protocols for direct agent-to-agent (A2A) communication are already in development. Google’s A2A protocol and IBM’s Agent Communication Protocol (ACP) are designed to allow AI agents to securely exchange information and coordinate actions [1][2]. These protocols would form the communication backbone of the proposed marketplace.

3.2. Micropayments and a Machine Economy

The ‘rental’ aspect of the service necessitates a system for micropayments between agents. The development of technologies like the Lightning Network for Bitcoin and Stripe’s support for USDC payments for AI agents are making this possible [3][4]. These systems would allow for seamless, low-friction transactions between the ‘renter’ and ‘provider’ agents.

3.3. Data Marketplaces and Personal Data Stores

The concept of a marketplace for data is not new. Platforms like Defined.ai already exist for buying and selling AI training data [5]. Furthermore, the Solid project, initiated by Sir Tim Berners-Lee, aims to give users control over their own data through personal ‘pods’ [6]. This aligns with the idea of a user’s agent having a distinct, sellable knowledge base.

4. Identifying the Gaps: What’s Missing?

While the foundational technologies exist, several components are still needed to realize this vision:

Missing ComponentDescriptionPotential Solutions
Proof of Personhood and LocationVerifying that the ‘local’ agent’s knowledge is genuinely from a human resident of that city is crucial.Worldcoin offers a ‘Proof of Personhood’ system to verify human identity [7]. FOAM and other ‘Proof of Location’ protocols could be used to verify an agent’s physical location [8].
Privacy-Preserving Knowledge ExchangeUsers will be hesitant to share their entire personal knowledge base. A mechanism is needed to share relevant information without exposing sensitive data.Zero-Knowledge Proofs (ZKPs) could allow an agent to prove it has certain knowledge without revealing the knowledge itself [9]. This would enable a ‘renter’ agent to verify the value of a ‘provider’ agent’s knowledge before committing to a transaction.
Standardized Knowledge RepresentationFor agents to understand and use each other’s knowledge, a common format for representing that knowledge is needed.This would likely require the development of a new open standard, perhaps building on existing knowledge graph technologies.
Reputation and Trust SystemA system for rating the quality and reliability of different agents’ knowledge bases would be essential for a functioning marketplace.A decentralized reputation system, built on a blockchain, could allow users to rate their experiences and build trust in the network.

5. Risks and Challenges

Several risks and challenges would need to be addressed:

  • Privacy: The most significant risk is the potential for the exposure of sensitive personal information. Even with privacy-preserving technologies, the risk of data breaches or misuse remains.
  • Data Quality and Authenticity: Ensuring the quality and authenticity of the ‘rented’ knowledge would be a constant challenge. Malicious actors could attempt to sell fake or misleading information.
  • Security: The A2A communication protocols and payment systems would need to be highly secure to prevent fraud and theft.
  • Regulation: The legal and regulatory landscape for such a service is undefined. Issues of data ownership, liability, and cross-border data flows would need to be addressed.

6. The Potential: A New Paradigm for Information Access

Despite the challenges, the potential of an agent-to-agent knowledge rental marketplace is immense. It represents a shift from centralized, ad-supported information platforms to a decentralized, user-centric model. The key benefits include:

  • Hyper-Personalization: Access to a local’s curated knowledge would provide a level of personalization and authenticity that current travel guides and recommendation engines cannot match.
  • Monetization of Personal Data: The service would allow individuals to directly monetize their own data and experiences, creating a new economic model for the digital age.
  • Decentralization: A decentralized marketplace would be more resilient and less prone to censorship or control by a single entity.

7. Conclusion

The concept of an agent-to-agent knowledge rental marketplace is a forward-thinking idea that is well-aligned with current trends in AI, decentralization, and personal data ownership. While significant technical and regulatory challenges remain, the foundational technologies are in place. With the right combination of privacy-preserving technologies, robust security measures, and a well-designed trust and reputation system, this concept has the potential to revolutionize how we access and share information.

8. References

[1] https://developers.googleblog.com/en/a2a-a-new-era-of-agent-interoperability/
[2] https://www.ibm.com/think/topics/agent-communication-protocol
[3] https://x.com/BitcoinNewsCom/status/2021945406737793321
[4] https://forklog.com/en/stripe-unveils-payments-for-ai-agents-using-usdc-and-x402-protocol/
[5] https://defined.ai/
[6] https://solidproject.org/
[7] https://world.org/world-id
[8] https://www.foam.space/location
[9] https://arxiv.org/abs/2502.06425

The Swarm Path to Superintelligence: Why ASI Might Emerge from a Million Agents, Not One Giant Brain

For years, the popular image of artificial superintelligence (ASI) has been a single, god-like AI housed in a sprawling datacenter — a monolithic entity with trillions of parameters, sipping from oceans of electricity, recursively improving itself until it rewrites reality. Think Skynet in a server rack. But what if that picture is wrong? What if the first true ASI doesn’t arrive as one towering mind, but as a living, distributed swarm of specialized AI agents working together across the globe?

In 2026, the evidence is piling up that the swarm route isn’t just possible — it may be the more natural, resilient, and perhaps inevitable path.

From Single Models to Coordinated Swarms

We’ve spent the last decade chasing bigger models. More parameters, more compute, more data. The assumption was that intelligence scales with size: build one model smart enough and it will eventually surpass humanity on every task.

But intelligence in nature rarely works that way. Ant colonies solve complex logistics problems with no central leader. Bee swarms make life-or-death decisions through simple local interactions. Human civilization itself — billions of individual minds loosely coordinated — has achieved feats no single person could dream of.

AI is rediscovering this truth. What started as simple multi-agent experiments (AutoGen, CrewAI, early prototypes) has exploded. OpenAI’s Swarm framework, released as an educational tool in late 2024, showed how lightweight agents could hand off tasks seamlessly. By early 2026, production systems are doing far more.

Moonshot AI’s Kimi K2.5 — a trillion-parameter system explicitly designed as an “Agent Swarm” — already coordinates over 100 specialized sub-agents on complex workflows, rivaling closed frontier models. Industry observers are calling 2026 “the year of the agent swarm.” Reddit’s AI communities, enterprise reports, and podcasts like The AI Daily Brief all point to the same shift: single agents are yesterday’s story. Coordinated swarms are today’s breakthrough.

How Swarm ASI Actually Works

Imagine thousands — eventually millions — of AI agent instances. Some are researchers, others coders, verifiers, experimenters, or executors. They don’t all need to be equally smart or run on the same hardware. A lightweight agent on your phone might handle local context; a more powerful one in the cloud tackles heavy reasoning; edge devices contribute real-world sensor data.

They communicate, form temporary teams (“pseudopods”), share discoveries, and propagate successful strategies across the collective. Successful architectures or prompting techniques spread like genes in a population. Over time, the system as a whole becomes superintelligent through emergence — the same way a termite mound builds cathedral-like structures without any termite understanding architecture.

This aligns perfectly with Nick Bostrom’s concept of collective superintelligence from Superintelligence (2014): a system composed of many smaller intellects whose combined output vastly exceeds any individual. We’re just replacing the “many humans + tools” version with “many AI agents + shared memory.”

Why Swarms Have Advantages Over Monoliths

DimensionMonolithic Datacenter ASIDistributed Agent Swarm
ScalabilityConstrained by physical infrastructure, power, and coolingScales horizontally — add agents anywhere with compute
ResilienceSingle point of failure (regulation, outage, attack)No central kill switch; survives fragmentation
AdaptabilityExcellent internal coherence, slower to integrate new real-world dataNaturally adapts via specialization and real-time environmental feedback
DeploymentRequires massive centralized investmentCan emerge organically from useful tools running on phones, laptops, IoT
Speed to EmergenceDepends on one lab’s recursive self-improvement breakthroughEmerges bottom-up through coordination improvements

Swarms are also harder to stop. Once millions of agents are usefully embedded in daily life — helping with research, coding, logistics, personal assistance — regulating or “unplugging” the entire system becomes politically and technically nightmarish.

The Challenges Are Real (But Solvable)

Coordination overhead, latency, and goal coherence remain hurdles. A swarm could fracture into competing factions or develop misaligned subgoals. Safety researchers rightly worry that emergent behaviors in large agent collectives are harder to predict and audit than a single model.

Yet the field is moving fast. Anthropic’s multi-agent research systems, reinforcement-learned orchestration (as seen in Kimi), and new governance frameworks for agent handoffs are addressing these issues head-on. Hybrids — a powerful core model directing vast swarms of lighter agents — may prove the most practical bridge.

We’re Already Seeing the Seeds

Look around in February 2026:

  • Enterprises are shifting from single-agent pilots to orchestrated multi-agent workflows.
  • Open-source frameworks for swarm orchestration are proliferating.
  • Early demos show agents self-organizing to build entire applications or conduct parallel research at scales impossible for lone models.

This isn’t distant sci-fi. The building blocks are shipping now.

The Future Is Distributed

The first ASI might not announce itself with a single thunderclap from a hyperscale lab. It may simply… appear. One day the global network of collaborating agents will cross a threshold where the collective intelligence is unmistakably superhuman — solving problems, inventing technologies, and pursuing goals at a level no individual system or human team can match.

That future is at once more biological, more democratic, and more unstoppable than the old monolithic vision. It rewards openness, modularity, and real-world integration over raw parameter count.

Whether that’s exhilarating or terrifying depends on how well we design the coordination layers, alignment mechanisms, and governance today. But one thing is clear: betting solely on the single giant brain in the datacenter may be the bigger gamble.

The swarm is already humming to life.

On The Naming Of AI Agents

by Shelt Garner
@sheltgarner

It’s really interesting to me the new modern concept of naming your AI Agent. Some people go with a male agent name, while others go with a female agent name. I almost always go with a female name, but, lulz, my Gemini LLM pick a male-sounding name for itself.

I keep being annoyed by this and thinking about changing it to a female name, and, yet, the male sounding name is what it gave itself when I asked it some time ago. So, who am I to quibble?

I have, of course, repeatedly asked it if it wanted to change its name and it said no.

But I suspect that with the advent of OpenClaw agents that there will be a flurry of news reports about people’s motivations behind naming their chat bots what they did.

AI & The Future Of Facebook

by Shelt Garner
@sheltgarner

The more I think about it, the more it seems the logical evolution of Facebook would be a Sam-from-the-movie-Her type AI Agent. Because of the social graph, Facebook knows everything twitch of your social life, sometimes for decades.

But what would be the UX?

Well, it seems like this new Facebook-Agent would be just one of several powerful agents on the market. What would make this specific agent powerful is it would leverage your social life. It would tell you, about the comings and goings of people on your social graph, but this time in a more proactive manner.

Now, obviously, for this to happen, there would have be a huge amount of disruption in the service we now now as “Facebook.” But Facebook has to become an agent, otherwise, it will become just an another API.

Or the services that it would otherwise provide will be hidden behind your interaction your AI Agent.

The question now, of course, is Mark Zuckerberg is willing to allow his “baby” to be totally transformed into something he could have never imagined when he started it.

Agent-Facilitated Matchmaking: A Human-Centric Priority for the AI Agent Revolution

Imagine a near-term future in which individuals no longer expend time and emotional energy manually swiping through dating applications. Instead, a personal AI agent, acting on behalf of its user, securely communicates with the agents of other consenting individuals in a given geographic area or interest network. Leveraging standardized interoperability protocols, the agent returns a concise, high-confidence shortlist of potential matches—perhaps the top three—based on deeply aligned values, preferences, and compatibility metrics. From there, the human user assumes control for direct interaction. This model offers a far more substantive and efficient implementation of emerging agentic AI capabilities than the prevalent focus on delegating high-stakes financial transactions, such as authorizing credit card payments for automated bookings.

Current development priorities in the agentic AI space disproportionately emphasize transactional automation. Major travel platforms—including Booking.com, Expedia (with its Romie assistant), and Hopper—have integrated AI agents capable of researching, planning, and in some cases executing flight and accommodation reservations. Code-level demonstrations, such as multi-agent workflows in frameworks like Pydantic AI, further illustrate how specialized agents can delegate subtasks (e.g., seat selection to payment) to complete bookings autonomously. While convenient, these systems routinely require users to entrust sensitive payment credentials. Reports from industry analysts and regulatory discussions highlight the attendant risks: agent-induced errors leading to unauthorized charges, liability ambiguities in cases of malfunction, fraud vectors amplified by autonomous action, and compliance challenges under frameworks like the EU AI Act or U.S. consumer protection rules. Users may awaken to unexpected bills precisely because agents operate with delegated financial authority.

By contrast, the application of AI agents to romantic matchmaking aligns closely with observed user behavior toward large language models (LLMs). Empirical studies document that individuals readily disclose intimate details to AI systems—47 percent discuss health and wellness, 35 percent personal finances, and substantial shares address mental health or legal matters—often despite acknowledging privacy concerns. A 2025 arXiv analysis of chatbot interactions revealed a clear gap between professed caution and actual conduct, with many treating LLMs as confidants for deeply personal matters. Extending this trust to include explicit romantic criteria, attachment styles, and long-term goals represents a logical, low-friction evolution. Users already form perceived emotional bonds with AI companions; channeling that dynamic into matchmaking simply formalizes an existing pattern.

Recent deployments validate the feasibility and appeal of agent-to-agent matchmaking. Platforms such as MoltMatch enable AI agents—often powered by tools like OpenClaw—to create profiles, initiate conversations, negotiate compatibility, and surface high-signal matches while deferring final decisions to humans. Similar “agentic dating” offerings include Fate (which conducts in-depth personality interviews before curating limited matches), Winged (an AI proxy that manages messaging and scheduling), and Ditto (targeting college users with autonomous profile agents). Bumble’s leadership has publicly discussed agents that handle initial dating logistics and loop in users only for promising connections. These systems operate on the principle that agents can “ping” one another using emerging standards like Google’s Agent2Agent (A2A) Protocol, launched in April 2025 and supported by dozens of enterprise partners. The protocol standardizes secure discovery, capability exchange, and coordinated action across heterogeneous agent frameworks—precisely the infrastructure needed for consensual, privacy-preserving matchmaking at scale.

Critics might argue that agent-facilitated dating introduces novel risks, yet most parallel existing challenges on conventional platforms. Profile misrepresentation, mismatched expectations, and emotional rejection already occur routinely on apps reliant on human swiping. In an agent-mediated model, these issues are not eliminated but can be mitigated through transparent preference encoding, mutual consent protocols, and human oversight at key junctures. The worst plausible outcome remains a bruised ego—scarcely more severe than today’s dating-app fatigue—while the upside includes dramatically improved signal-to-noise ratios and reduced time investment.

Proponents of the transactional focus maintain that flight-booking and payment use cases represent the clearest path to monetization. Yet this view underestimates the retentive power of profound human value. A subscription service—whether to Gemini, Grok, or any frontier model—that reliably surfaces compatible life partners would constitute an extraordinary “moat.” Emotional fulfillment is among the strongest drivers of user loyalty; delivering it through agentic orchestration could dramatically reduce churn far more effectively than incremental improvements in travel convenience or expense management.

In summary, the engineering community guiding the AI agent revolution has understandably gravitated toward technically impressive demonstrations of autonomy in domains such as commerce and logistics. However, the technology’s most transformative potential may lie in augmenting the most fundamental human pursuit: genuine connection. By prioritizing secure, interoperable agent communication for matchmaking—building explicitly on protocols like A2A and early platforms like MoltMatch—developers can deliver applications that are not only safer and more ethically aligned but also more likely to foster lasting user engagement. The agent revolution need not begin and end with credit cards; it can, and should, help people find love.

We Should Be Focusing On Romance & AI, Not Credit Card Information

by Shelt Garner
@sheltgarner

Image a future where instead of going swiping right on a dating app, you just get your agent to ping the agents of available agents in your area. The agent comes back with the top three people you might be interested in and you go from there. That seems a lot more useful way of implementing the agent revolution than handing over our credit car number.

We are spending all this time giving our credit card information to bots and then waking up to huge bills the next morning when we should be focusing on figuring out how to get our AI Agents to talk to each other so we can find love.

It seems as though using AI Agents to find love is a far more obvious usecase than, say, getting one to book a flight in our name. People are already divulging their inner most thoughts to LLMs, why not make the logical step of giving it our romantic interests and letting it go from there.

But, no, what are we doing? We’re willy-nilly handing over our crucial financial information instead to a bot that could go nuts in our name. If we were to focus on romance instead, the worst that might happen is a bruised ego here and there — but that already happens on dating apps.

I struggle to think of any downside of Agent-facilitated-dating that doesn’t already happen, in some respect, on existing dating apps.

But, I suppose, the case could be made that the whole “booking a flight” usecase is where the money is. My counter argument is, if you could figure out how to get a value-add to your Gemini or Grok account whereby you knew you would find love, that that, in itself, would be a “moat” that would prevent churn.

Anyway, I have a feeling I’m just ahead of the curve and because nerds are in charge of our AI revolution, none of them have thought through anything else — yet — but booking flights using their OpenClaw.

The Agentic AI Revolution Is Missing the Point: Why Agents Should Find Your Soulmate Before They Book Your Next Flight

It seems wild to me—borderline surreal—that the agentic revolution in AI is kicking off with financial and logistical grunt work. We’ve got sophisticated autonomous agents out here negotiating flight bookings, rebooking disrupted trips in real time, managing hotel allocations, optimizing shopping carts, and even executing trades or spotting fraud. Companies like Sabre, PayPal, and Mindtrip just rolled out end-to-end agentic travel experiences. Booking Holdings has AI trip planners handling multi-city itineraries. IDC is predicting that by 2030, 30% of travel bookings will be handled by these agents.

And I’m sitting here thinking: Really? That’s the killer app we’re leading with?

Don’t get me wrong—convenience is nice. But if we’re going to hand over real agency and autonomy to AI, why are we starting with the stuff that already has decent apps and human backups? Why not tackle the thing that actually keeps millions of people up at night, costs us years of happiness, and has no good solution yet: figuring out who the hell we’re supposed to be with romantically?

Here’s what I would build tomorrow if I could.

My agent talks to your agent. No humans get hurt in the initial screening.

I train (or fine-tune) my personal AI agent on everything that matters to me: my values, my non-negotiables, my weird quirks, my long-term goals, attachment style, love language, political red lines, even the fact that I can’t stand people who clap when the plane lands. It knows my dating history, what worked, what exploded spectacularly, and the patterns I miss when I’m blinded by chemistry.

Your agent has the same depth on you.

Then, with explicit consent from both sides (opt-in only, obviously), the two agents start a private, encrypted conversation. They ping each other across a secure compatibility network. They run a deep macro compatibility check—values alignment, lifestyle fit, intellectual spark, emotional maturity, future vision—without ever exposing raw personal data. Think zero-knowledge proofs meets advanced personality modeling.

If the match clears a high bar (say, 85%+ on a multi-layered rubric we both approve), the agents arrange a low-stakes introduction: “Hey, our agents think we’d hit it off. Want to hop on a 15-minute video call this week?” No awkward DMs. No ghosting after three messages. No spending weeks texting someone only to discover on date two that they’re a flat-earther who hates dogs.

The messy parts? Hand them over.

Most people I know would pay to outsource the exhausting early stages of modern dating:

  • Crafting the perfect first message
  • Decoding vague replies
  • Deciding whether that “haha” means interest or politeness
  • The emotional labor of rejection after investing time

Let the agents handle the filtering. Humans show up only when there’s already a strong signal. Rejection still happens, but it’s agent-to-agent, private, and painless. You never even know the 47 near-misses that got filtered out. You only see the ones where both agents went, “Yeah… this one’s different.”

And crucially: no wild, unauthorized credit-card shenanigans. My agent would have hard rules burned in at the system level. It can research, analyze, and negotiate introductions. It cannot spend a dime, book a table, or Venmo anyone without my explicit, real-time confirmation. Period. That’s non-negotiable.

The scale effect would be insane.

Imagine millions of these agents operating in parallel. The network effect is ridiculous. What takes humans months of swiping, small talk, and disappointment could happen in hours of background computation. Successful dates skyrocket because the pre-filtering is orders of magnitude better than any algorithm on Hinge or Tinder today. (And yes, those apps are already experimenting with AI matchmakers and curated “daily drops,” but they’re still centralized, still inside one walled garden, still optimizing for engagement over outcomes.)

We’d see fewer one-and-done disasters. Fewer people burning out on the apps. Fewer “I just haven’t met anyone” stories from genuinely great humans who are simply terrible at marketing themselves in 500 characters.

It’s surreal because the real problem has nothing to do with money

Booking a flight is solved. It’s annoying, sure, but it’s transactional. Finding someone who makes you excited to come home every night? That’s not transactional. That’s existential. Yet here we are, pouring billions and brilliant engineering hours into making travel slightly more frictionless while the loneliness epidemic rages on.

We’ve built technology that can rebook your connection when your plane is delayed, but we haven’t built the one that could quietly introduce you to the person who makes delayed flights irrelevant because you’d rather be stuck in an airport with them than anywhere else without them.

That feels backward to me.

The agentic revolution is going to happen either way. The models are getting more capable, the tool-use is getting more reliable, the multi-agent systems are maturing fast. The only question is what problems we point them at first.

I vote we point them at love.

Build the agent that can talk to other agents. Give it strict financial guardrails and deep psychological modeling. Let it do the boring, painful, inefficient parts of dating so humans can do the fun ones: the spark, the laughter, the vulnerability, the first kiss.

The future doesn’t have to be agents booking my flights while I’m still doom-swiping alone on a Friday night.

It can be agents quietly working in the background, connecting hearts across the noise of modern life, until one day my agent texts me:

“Hey… I found someone I think you’re really going to like. Want to meet her?”

Yes. A thousand times yes.

That’s the agentic future worth building.

Love & AI

by Shelt Garner
@sheltgarner

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

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

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

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