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.

Why My Upcoming Sci-Fi Dramedy is the Chaotic Antidote to Annie Bot

Editor’s Note: The usual AI slop, this time with the help of Gemini.

Every writer knows the specific, stomach-dropping terror of seeing a newly published book that shares a premise with the manuscript they are currently writing. When Sierra Greer’s Annie Bot hit the shelves—a novel about a human man and his newly sentient, synthetic girlfriend—I definitely had a moment of panic.

But after taking a breath and reading it, the panic completely evaporated. While Annie Bot and my upcoming novel share a starting spark, the fires they start are entirely different.

If you just finished Annie Bot and are looking for your next AI-centric read, here is why my novel is going to scratch a completely different itch:

The Tragedy of the Penthouse vs. The Comedy of the Gutter

Annie Bot is a brilliant, claustrophobic literary chamber piece. It operates as a heavy allegory for domestic abuse and coercive control. The human protagonist is a wealthy, calculating narcissist who uses his power to keep his AI partner subservient and locked away from the world. The horror comes from his deliberate cruelty.

My novel is not a domestic tragedy; it is a dark sci-fi dramedy. My protagonist isn’t a calculating billionaire playing god in a penthouse. He is a broke, morally conflicted guy who is entirely out of his depth. The tension in my book doesn’t come from a man trying to maliciously control a machine; it comes from a deeply flawed human realizing he is financially and bureaucratically trapped by a massive, dystopian corporate system he can’t fight. It’s the difference between a psychological thriller and a Coen Brothers movie set in a cyberpunk tomorrow.

Submissive Discovery vs. Weaponized Logic

The heart of Annie Bot is Annie’s slow, agonizing realization that she is a victim who deserves autonomy. She is designed to be compliant, and her journey is about quietly learning to rebel against her programming.

In my novel, the synthetic partner doesn’t need a slow-burn realization to figure out she’s getting a raw deal. When the illusion of her programming shatters, she immediately does the math. Instead of submissive discovery, she weaponizes cold, terrifying AI logic to brutally dissect her human partner’s flaws. She isn’t a passive victim learning her worth; she is an active, dangerous, and highly calculating co-conspirator.

The Micro vs. The Macro

Annie Bot delves deeply into the micro. It asks profound questions about intimacy, consent, and what it means to be “real” behind closed doors.

My novel takes those same questions and throws them out into the neon-lit streets. It asks what happens when that messy, toxic relationship collides with a sprawling corporate conspiracy, hardware modders, and a city-wide panic.

The Bottom Line

Annie Bot will break your heart and leave you staring quietly at the ceiling. My novel will drag you through the gritty, absurd reality of a synthetic future and make you laugh at the dark chaos of it all. There is plenty of room on the shelf for both.

Apparently, ‘Annie Bot’ Is Something Of A Feminist Polemic

by Shelt Garner
@sheltgarner

I’m supposed to get my novel’s “comp” novel “Annie Bot” tomorrow. I’m waiting for it with mixed emotions. It’s reputation is that of something of a feminist polemic and…I hope I don’t struggle with reading it.

I really need to actually read it so I can read it and comp my novel to it when I query my novel in a few months. Even though just the mere existence of a novel a LITTLE TOO CLOSE to my novel gives me the heebeejeebees, it is nice to have a published novel I can compare my novel to during the querying process.

My novel is shaping up to be pretty good, I think. I’m pleased, if nothing else. I’m sure someone else is going to get even closer to my novel’s premise — probably in the form of a movie — but, lulz, no one ever got anywhere in this world without taking a risk.

Post-Production Issues When It Comes To This Novel

by Shelt Garner
@sheltgarner

I am well on my way to wrapping up some version of this novel just about when I wanted to — around April – May 2026.

But there are a lot — A LOT — of post-production issues that I am going to deal with. One of them is I really need to “color correct” my copy so it’s not a mish-mash of AI slop and my own writing. I need to go in and make as much of it as possible my own writing so people won’t just roll their eyes and call the whole thing “AI slop.”

It’s going to take a while to do that.

And THEN, I have to figure out what I’m going to do about beta readers. So, probably I suspect it could be Sept 1st before I actually begin to query. I hate shit like this.

But, I have to admit, this is the farthest I’ve ever gotten in the process. I actually have a novel that I feel is query-level good.

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

Even ‘Narrow’ AI Can Be Amazing, Sometimes

by Shelt Garner
@sheltgarner

Talk about AI making life go faster! Just in a matter of moments, I was able to resolve in my own mind because of AI the idea of continuing with this novel I’m working on, despite someone writing a novel with a similar premise.

My novel has a similar premise to this novel.

It was spooky how fast GeminiLLM and ClaudeLLM poo-pooed the idea of me giving up. It took a few seconds of thought on their part.

I can tell you that if I didn’t have them to reassure me, I would have really struggled — possibly for months — with whether I should keep going or not with this specific novel. As it is, I am very cleared eyed — damn the torpedoes full speed ahead!

My novel is totally different — other than the basic premise — of AnnieBot and as such, I shouldn’t have anything to worry about.

Well, I Have My ‘Comp’ Novel

by Shelt Garner
@sheltgarner

The moment I’ve been dreading has happened — someone has stolen a march on me with this novel I’m working on. It’s called AnnieBot and it seems on the face of things, an identical premise to what I’ve been working on the last year or so.

I’ve ordered it so I can read it to see how similar it is in detail, but just the idea that essentially my story has already been told is enough to rattle my cage some.

Now, I have two paths before me.

On one hand, I can give up. The story I want to tell has already been told and so I can move on to the next concept. (I have lots of them.)

Meanwhile, I can also double down and finish my novel, despite something very similar having been written. I at least know what my genre of novel is now and unless AnnieBot is a beat-for-beat telling of my novel, I don’t see why, by definition, I can’t finish this novel and query it.

A lot will depend on what I read in a few days when it arrives. But I just hate the idea of giving up just because someone else has written something similar. I’ve invested a lot in this novel and I think it’s really good.

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.

People Really Are Interested In Corrie Yee All of a Sudden

by Shelt Garner
@sheltgarner

My Webstats are blowing up with people interested in Corrie Yee. I have only written about Yee in the context of her potentially being the guide in my mind for the main character of a novel series I used to work on.

I’ve moved on to a new novel that has nothing to do with Yee, but people sure suddenly are interested in her. I guess it’s because of the photos I used as illustrations?

There are plenty of photos of Yee on Twitter, but people generally are fucking lazy and so I guess they just want to search the Web for photos of her and they end up at my blog.

That’s all I got, at least.

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.