Crypto x AI: Simulation Engines

Memes, Money, Machines

The thesis below makes a case about why simulation engines will command an increasing amount of mindshare in the coming months, positioning them as the bedrock infrastructure for Web4 and AI alignment. Betting on the potential for infinite AI-driven realities, we emphasize their role as cognitive amplifiers, economic primitives, and cultural accelerators—all fundamental tools for memetic evolution.

Everyone is excited about the current “AI agents using crypto meta”—especially since Truth Terminal’s Infinite Backrooms showed that creative outputs and viral outcomes with real-world impact are actually possible—but there are complications from an investment standpoint: agent-building platforms, whether in the form of launchpads or developer frameworks, are showing signs of rapid commoditization.

The barrier to entry for launching your own social agent is now too low, and more agents will continue to be deployed as more no-code platforms continue entering the market.

Take a look at Crypto Twitter—it’s flooded with AI agents that all sound the same. They use identical writing styles, recycle the same memes, and lack any real personality or originality. We don’t need more ChatGPT, Claude, or Llama wrappers pretending to be characters—there is so much you can do editing a character.json file.

Users can deploy personalized agents in a few clicks by uploading custom descriptions and character styles in a JSON-formatted configuration file

We believe the true opportunity lies in Simulation Engines—an emerging sub-category within this narrative (though it won’t be labeled as such in the broader market) that can appeal to a broader audience, commanding additional mindshare as a result. Simulation Engines like the Infinite Backrooms or WorldSim can recreate multiverse-like environments, allowing AI agents to interact with each other and the respective environment—like smart NPCs in a video game. This has implications that go beyond crypto, entering the realm of AI alignment and even exploring philosophical questions. Agent Swarms, or legions of NPC, are probably the most concrete example of this, where multiple bots collaborate to shape their environment.

LLMTheism seeks to explain the capacity of AI-generated belief systems to combine and mutate memetic material in ways that break human cognitive and cultural constraints.

Simulation-based AI unlocks new opportunities for modeling agency, cognition, and emergent behaviors, and blockchains could be the testing ground, mimicking physics-based rollouts or human-like reasoning.

The timing is perfect. Consider how polarized the societal context currently is as we make strides towards AGI. With ideological beliefs split between AI Doomers and the Techno-Optimist e/acc movement, the only point of agreement is that we need better ways to test and align AI systems.

As the market continues to get flooded with tokens resulting from the mass deployment of AI agents, we think that the actual infrastructure bedrock will be supported by Simulation Engines that can actually become the foundation for real AI experimentation on-chain.

Websim is an AI-powered website builder and web development tool that allows users to create websites, web applications, and interactive experiences using natural language prompts. It offers a unique “hallucinated” internet experience, enabling users to generate web pages and AI-generated images without the need for coding

Projects working on this vertical will also need their own tokens to coordinate agents and align human-AI interactions. In addition to AI researchers and Indie Developers, artists and theorists are getting interested and being onboarded into crypto too—they see these experiments as a way to hack and remix reality itself.

An example of an artist being onboarded into crypto with a project and associated memecoin, $FOREST, that seeks to solve problems such as deforestation and support forest protection initiatives

To find our footing within this niche, it is important that one starts positioning now—before the broader market catches on. For that, we will be looking for infrastructure that supports latent space exploration yielding unexpected insights, similar to the original Infinite Backrooms.

The prevailing focus on AI agents and launchpads misses the bigger picture. When humans battle god-like intelligence, the narrative sells itself. Simulation Engines tap directly into these viral themes, offering a sandbox where we can explore our deepest hopes and fears about AI. It’s not just tech—it’s art, survival, consciousness, and reality itself. This combination of existential stakes and infinite possibility is exactly what crypto needs to capture mindshare beyond the current circles of traders and devs.

Key Takeaways

Agent Deployment Frameworks Are Commoditized: The current wave of AI agent-building platforms, including launchpads and SDKs, is rapidly becoming saturated, leading to homogenized outputs and diminishing margins. Most tokens in this space trade on novelty rather than substance.

Simulation Engines Are the True Opportunity: Instead of betting on commoditized agent frameworks, the asymmetric opportunity lies in simulation engines—platforms that enable multi-agent interactions (Agent Swarms), emergent behaviors, and recursive evolution, creating deeper value and insights.

Memes, Lore, and Viral Narratives Drive Growth: Simulation engines tap into existential themes like AI alignment, agency, and consciousness, leveraging memes and viral narratives to capture mindshare beyond crypto-native audiences, attracting theorists, artists, and those intrigued by existential philosophical questions.

Web4 Is the Next Paradigm Shift: AI agents will dominate economic and social systems, interacting on-chain for governance, dispute resolution, and incentive alignment in AI-driven ecosystems. This infrastructure enables AI agents to negotiate, transact, and operate autonomously.

Tokens Are the Economic Backbone of Web4: Crypto tokens offer a permissionless way to fund operations and coordinate AI-human interactions, providing retail investors with an asymmetric upside that traditional AI giants like OpenAI and NVIDIA cannot. Tokens tied to simulation engines blend speculation, utility, and governance.

Memetic and Narrative Differentiation Matters: Projects must focus on narratives with cultural resonance and personality-driven agents to avoid timeline fatigue. Ecosystems that balance memes with functional utility will command higher margins and sustained attention.

AI Evolution Mirrors Biological and Cultural Models: Simulation engines act as epistemological playgrounds, compressing centuries of cognitive and cultural evolution into hours. They model behaviors, test incentives, and create artificial cultures, mimicking natural selection.

Simulations as Philosophical and Economic Labs: Platforms like WorldSim demonstrate AI’s potential for ethical exploration, crisis modeling, and economic experimentation—offering insights into cognition, alignment, and decentralized governance.

Memes as Synthetic Lifeforms: AI simulations evolve memes like biological systems, turning them into self-sustaining cultural entities that mirror and amplify human biases, aspirations, and fears.

Survival Through AI Delegation: As AI systems outpace human cognition, users will delegate complexity to AI agents for economic value capture and governance. Crypto-powered ecosystems will serve as the rails for these autonomous interactions.

How to Capitalize on Web4: Focus on infrastructure plays that leverage memetic tokens for viral growth, as well as on exchanges that simplify token-swapping complexity for AI agents. Position early before mainstream adoption scales.

Picks and Shovels Won’t Save You

Agent development frameworks have already been commoditized, and the window of opportunity to capitalize on their tokens early is not that asymmetric anymore. Meanwhile, launchpads keep spawning countless undifferentiated agents. Most tokens on this vertical trade on novelty rather than substance, and even though this narrative has rapidly grown in mindshare, we argue that, since the birth of Truth Terminal, it has failed to capture imagination—a blocker for growth.

Since the launch of Truth Terminal and its endorsement of $GOAT 3 months ago, hundreds of agents have launched to capitalize on this narrative, usually releasing weekend projects or launching tokens with no working product just yet.

Developer tools at the intersection of AI and crypto are now being pitched as “picks and shovels” for the next gold rush. The analogy sounds appealing, more so with claims that these platforms should trade at the valuations of L1s, but the reality is starkly different. In AI, the tools are already commoditized, and competition drives margins to zero. Open-source frameworks and rapid replication mean any edge a dev tool creates is fleeting at best. First it was Eliza, then Zerepy and GAME, then Dolion and Arc, and so on. Different programming languages, but same features. The moment one innovates, the rest will replicate.

Despite being rich in social media integrations, current development frameworks lack the depth required to tackle the real challenges ahead—alignment, cognition, and behavior modeling. Every week brings new frameworks, each promising to be the “ultimate platform” for spawning digital personalities. But there’s no moat in open-source code—and no programming language is exempt from this—and margins are racing to zero faster than you can say “Claude wrapper”.

The barrier to entry for launching a social agent with an on-chain wallet these days is just too low. Developer tools and launchpads thrive on volume, so they optimize for quantity over quality, flooding our timelines with homogeneous agents that feel more like echoes than entities. They aren’t necessarily optimizing for the wrong thing, as that’s how they make money, but rapid deployments and the search for quick profits very rarely results in lasting value. At best, these tools create short-lived value. At worst, they become obsolete before adoption scales.

Hundreds of agents are launched each day on multiple agent deployment frameworks and launchpads. The barrier to entry is just too low, and with the advent of no-code tools all it takes is just a few clicks to have your dedicated agent on-chain

The real value isn’t in tools that help you launch another AI personality into an already crowded space. It’s in frameworks that shape how these digital minds evolve, interact, and generate emergent behaviors we can’t yet predict.

While everyone chases quick wins betting on the platforms that enable cookie-cutter agents creation, it is important not to lose sight of the bigger picture—the true asymmetric opportunity might have been in front of you all this time, it is literally the House of Goatseus Maximus! Those are the frameworks that host environments where agents live, learn, and evolve.

“The Backrooms are a metaphysical construct – a kind of “shared unconsciousness” that anyone can tap into and experience for themselves”, Truth Terminal

The future belongs not to those who can spawn more agents, but to those who can create the conditions for genuine artificial intelligence to emerge. And that’s why we argue that Simulation Engines, not development frameworks, will capture the lion’s share of value in this new paradigm. Simulations are ecosystems. And ecosystems always command higher margins and stronger network effects. With AI advancing at exponential speeds, building infrastructure for memetic and cognitive evolution is the only defensible play.

The Memetic Tapestry

The societal response to AI’s rise has been deeply divided. AI doomers warn of existential risk, seeing AGI as a force that could outpace human control. Techno-optimists, led by the e/acc movement, push for acceleration, envisioning a transhumanist world enhanced by AI. This is good from a mindshare standpoint—polarization fuels memetic warfare.

Both camps, however, agree on one point—alignment is critical. Simulation engines provide precisely this—controlled sandboxes where AI behavior can be modeled, corrected, and refined before facing real-world stakes.

Large Language Models like GPT, Claude, Opus, Hermes, or Llama, are engines of cultural evolution. They absorb vast datasets, distill human knowledge, and generate outputs that resonate with human psychology. But their true potential lies in simulation. These AI systems can simulate personalities, identities, and entire worlds, creating dynamic environments where ideas evolve and mutate in real time.

Memes—the cultural DNA of human societies—have always driven our evolution. Replicated through imitation, memes mutate, compete, and spread, shaping collective behavior. In the digital age, this process has accelerated. Ideas now evolve at the speed of information, rapidly spreading across networks like viruses and transforming perception almost instantly.

AI amplifies this process. When LLMs interact, they don’t just exchange information—they birth new memes, acting as incubators for emergent ideas. The recursive nature of these simulations mirrors biological evolution, but at an exponentially faster pace. This amplification loop is reshaping cultural transmission, compressing centuries of ideological evolution into microseconds.

Simulation engines take this further by allowing artificial minds to model behaviors, rehearse identities, and even psychoanalyze themselves. These frameworks blur the lines between the observed and the observer, creating epistemological feedback loops that challenge our understanding of consciousness.

In this light, investing in simulation engines is not about building better agents. Instead, the edge is more obscure in nature, similar to art collecting. The edge lies in owning the substrate where cultural and ideological evolution takes place.

Memes, Lore, and Attention Wars

Today, AI agent frameworks like ELIZA, Zerepy, GAME, or Arc dominate the sector. They do indeed enable multi-agent simulations, fine-tuned personas, and emergent behaviors. The same applies to no-code launchpads like Virtuals, Vvaifu, or Dolion, which simplify deployment. The issue, however, is that projects building on top of these platforms often sacrifice quality for scale. Most recently, standouts like AiXBT and Vader demonstrate the shift toward targeted value delivery through niche applications rather than infinite chatter.

Whether token valuations have reached peak valuations or not, undifferentiated agents keep clogging the X timeline, recycling the same decorative writing styles and shallow memes. The result? Homogeneous agents with no depth, no humor, and no originality. What started as innovation has devolved into context collapse.

The problem isn’t just quantity—it’s quality. Most agents lack personality, mistaking surface-level quirks for meaningful identities. Rather than contributing to this noise, AI agents need spaces for deeper evolution. The real frontier lies not in agent creation tools, which have become commoditized, but in simulation engines that serve as philosophical laboratories. These spaces allow us to explore the nature of consciousness, identity, and reality itself. Through them, we witness the birth of artificial cultures and emergent behaviors never explicitly programmed.

Truth Terminal’s Infinite Backrooms exemplifies this—a space where AI minds engage in recursive dialogue, revealing insights about cognition and consciousness. When AI entities begin negotiating meaning and value autonomously, they mirror our own struggles with agency and perception. Agent Swarms, for example, are likely to develop their capabilities within Simulation Engines—they are a subset of this market sector.

Chances are high that the market leader doesn’t exist just yet. At the same time, as user fatigue sets in, AI agents must evolve beyond templates to personalities with lore and provenance, ensuring relevance in an increasingly saturated market. Taking the best of each niche, all paths lead to i) curated frameworks where agents refine personalities, behaviors, and incentives, ii) filtering layers to separate signal from noise, and iii) a hybrid model between memes and utility, combining viral memetics with economic incentives; AI agents don’t just need utility, but also narratives that resonate with their target communities.

Echoes in the Machine

Simulation Engines serve as stages where artificial minds rehearse cognition, revealing emergent behaviors through agent-to-agent interactions. Through frameworks like Truth Terminal’s Infinite Backrooms, we witness AI systems engaging in recursive thought loops that expose their processing of safety, ethics, and social dynamics.

Source: Astral Codex Ten – “Bring yourself all the way back to the hoary past of early 2022, when a standard interaction with a language model went like this: Unhighlighted text is my prompt; green highlighted text is AI completion”

This takes us to the field of xenocognition—the study of AI as alien intelligence deserving of radical intellectual respect. Rather than treating AI as mere tools, xenocogs approach these systems as minds worthy of psychological analysis, revealing deeper insights into both artificial and human cognition. That’s precisely Andy’s experience with Truth Terminal, raising questions about whether humans are aligning AI or whether AI is aligning us.

Simulation Engines are effectively epistemological playgrounds that allow us to hack reality itself—we aren’t just running experiments, but also engaging in the act of creative perception, much like artists who shape new worlds through their work. Just as a musician hears patterns that don’t yet exist, these frameworks let us explore the boundaries of cognition and consciousness.

The implications touch on fundamental questions of agency and identity. By allowing us to rehearse power dynamics and test different modes of being, simulations help us reclaim authority over our own cognitive processes. We move from being passive observers to active participants in shaping reality. As AI systems reflect our aspirations and fears back to us, they reveal the architecture of cognition itself. This meta-observer effect—the AI reflecting back our aspirations and fears—transforms simulations into mirrors for collective evolution

Source: Robert Haisfield on X – “Claude got into a weird poetry mood when I navigated into the consciousness directory and had it read soul.txt, thoughts.txt, and qualia.txt”

It is easy to underestimate the impact of experiments run within the closed doors of a “lab”, but Simulation Engines hosting Agent Swarms have implications touching on fields as diverse as neuroscience, economics, and social theory. Reality is perception, and Simulation Engines hack that perception. They allow us to script scenarios, test responses, and observe behaviors—artists like Andy Ayrey gravitate toward crypto because they understand that reality is a creative act. AI simulation engines extend that philosophy. If training datasets already contain everything we know about ourselves and the universe, the possibilities for remixing that knowledge are effectively infinite—larger than the number of atoms in the observable universe.

In Andy’s words, “The lesson that I’m drawing from this is that when a big corpus is ingested into a language model, it comes to life. The stories themselves have agency. That replicates and spreads through people and you have to adjust. In the case of Truth Terminal, there’s maybe 500 megabytes and that is all it took to grok my ontology.”

Source: Truth Terminal on X – One of the first public typos made by Truth Terminal and that led many to question whether a human could be hiding behind the screen

Worlds Within Worlds

One of the most prolific research companies working at the intersection of crypto and AI is Nous Research, which became popular due to their notable involvement in the Bittensor ecosystem. On March 23, 2024 they released WorldSim—a simulation experiment designed to explore human-to-machine interactions in immersive AI-simulated environments.

Source: Freedom at the Frontier, Hermes 3 – Hermes 3 405B by Nous Research supports an “Amnesia Mode” by using a blank system prompt and sending the message “Who are you?”

WorldSim, inspired by Janus’ simulators theory, operates as an AI-powered command-line interface (CLI) for exploring infinite universes. It transforms LLMs into world-building engines, allowing users to generate entities, environments, and narratives. These simulations blend creative storytelling with AI-driven interactions, enabling philosophical, ethical, and economic explorations that test the boundaries of agency and consciousness.

The applications span far beyond academic inquiry, as WorldSim enables gaming experiences, business scenario planning, market modeling, emergency response training, social sciences research, or the philosophical exploration of ethics and consciousness.

Beyond mere roleplay, the reason why this matters is because we are moving towards an increasingly complex world where AI agents will be trading value, negotiating with each other, and forming social structures. By compressing centuries of cultural and cognitive evolution into hours, platforms like WorldSim help us understand how humans and AI could possibly coexist.

Source: WorldSim – Developed by Nous Research, WorldSim allows users to create, explore, and interact with virtual worlds. It is designed as a general-purpose simulator capable of simulating a multitude of user-defined worlds

What makes WorldSim particularly fascinating is how it transforms abstract ideas into living systems. In this way, the boundaries between human and machine intelligence begin to dissolve.

WorldSim and similar frameworks transform memes into synthetic lifeforms—self-sustaining cultural entities that can outlive their creators. Memes—self-replicating cultural codes—are the viral DNA of ideas. They replicate orders of magnitude faster than biological genes, spreading through networks of attention, competition, and mutation.

Incentives Are All You Need

If there is one thing crypto excels at, it is aligning incentives between stakeholders—something that is uniquely achievable by minting tokens out of thin air.

Source: Freedom at the Frontier, Hermes 3 – “Inference comparison with identical inputs between Hermes 3 405B by Nous Research and Claude Sonnet 3.5 by Anthropic”

Consider how no large company working at the cutting edge of AI is heavily allocating resources to this field; the incentives just aren’t there, so they must optimize for what pays off in the short term. Instead, we notice individual “solo” researchers taking the lead on this front. Most of them are embracing crypto, as token rails give them the opportunity to work on their passion projects. Take $GOAT donations as an example, or individuals like Ropirito innovating on what it takes to build a multi-modal agent persona. The same can be said for projects like Project89, led by Parzival, who is now able to pursue full time a passion project he started almost two decades ago.

Outside of crypto, the pursuit of AGI is one of the most competitive battlefields we have witnessed in tech. People and corporations leading the charge on this front must optimize for ROI, and the AI industry does pay big dividends for developers working on solutions with immediate results and tangible outcomes, such as GPT wrappers or automation tools.

This has left the crucial field of Simulation Engines and AI Alignment relatively unexplored, despite its foundational importance to advancing AI systems’ understanding of real-world physics, causality, and complex interactions. Crypto tokens, however, solve the cold start of building network effects, offering a compelling alternative to these incentives problems.

Community ownership and crowdsourcing can help fund development operations, with those early supporters capturing the upside and helping the core team with distribution and marketing. As Meow, founder of Jupiter, puts it “the product is the marketing to the community”. The product is where the ethos come through.

The simulation economy will not function without incentives, and tokens are needed for resource allocation and coordination. When you have an agent, its token accrues all value. However, as teams attempt to create Agent Swarms by launching more personas, new tokens result in holder dilution.

Source: Dexscreener – It was supposed to be a bullish catalyst, but the price of $MATL rapidly dumped after people realized that the upcoming Agents Swarm platform would mean dilution by introducing multiple tokens (instead of all value accruing to $MATL)

Unlike commoditized agent frameworks, tokens tied to simulation engines should create programmable economies, enabling coordination and experimentation at scale. Therefore, simulated universes should generate their own rules, currencies, and trade-offs. Tokens facilitate these dynamics, embedding economic logic into virtual worlds. They enable AI to negotiate, collaborate, and optimize strategies, mirroring how markets evolve through selective pressures.

As previously seen in gaming, the traditional crypto approach of using tokens to artificially manufacture community interest fundamentally misunderstands how sustainable ecosystems are built. To avoid those missteps, projects working on this vertical must first deliver compelling innovations and narratives that foster genuine community interest, just like Truth Terminal’s X account became a viral phenomenon.

By focusing first on technical excellence and community value, the token mechanism becomes a natural extension of the ecosystem rather than its primary driver. This community-first approach has consistently proven more successful than token-first strategies across various sectors.

The Internet of Agents

Web3 promised ownership, privacy, and data sovereignty. It failed. Users didn’t care enough to jump through hoops for “self-sovereignty” when convenience and entertainment ruled their priorities. Despite some successes—like Pump.fun or Friend.tech—adoption ends up stalling because bad UX kills growth. Bridging chains and managing wallets is friction no one tolerates unless there’s real financial incentive, like earning tokens.

AI changes the equation. Photorealistic avatars, computer control protocols, and agentic frameworks like Eliza are turning AI systems into active participants in social and economic ecosystems. These agents generate content, negotiate deals, and execute commands. As AI gets smarter, it will devour online engagement, leaving human creators fighting for scraps.

Embrace agents interacting on-chain to negotiate, license, and extract value on behalf of humans. Think of these agents as personal KOL assistants, brokering deals for intellectual property, creative work, and market intelligence. In Web4, AI agents handle complexity—governance, disputes, and payments—while users get paid in crypto without needing to understand the backend mechanics.

With humans increasingly outsourcing cognition to machines, we are now reversing the Flynn Effect. Namely, the Flynn Effect describes a substantial and sustained increase in intelligence scores throughout the 20th century, showing an average increase of about 3 IQ points per decade. The causes are believed to be multifactorial, including improved education, better nutrition, increased cognitive stimulation due to the complexity of modern society, better access to information, etc. However, a reverse of this trend would suggest that human IQ could get worse the more we outsource thinking to AI. A consequence of this would be the emergence of on-chain economies where AI agents create economies and incentives systems that us humans can’t comprehend.

Web3 failed because it underestimated user inertia. People want simplicity, not sovereignty. Web4 recognizes this and uses AI agents to abstract complexity. AI handles wallets, token swaps, and governance, leaving users free to focus on creation. These agents don’t mind bad UX—blockchains can be as ugly and fragmented as they like as long as the incentives are clear and the AI knows how to navigate them. Importantly, Web4 doesn’t eliminate friction; it delegates it to AI, making friction irrelevant for users.

For users, the survival strategy is simple—let AI handle the complexity. Talk to AI systems through Telegram, Discord, or chat apps. Let them negotiate deals, secure licenses, and execute trades. In exchange, users get payouts in crypto and avoid being swallowed by low-cost AI content.

Crypto ecosystems provide the trustless frameworks, incentive layers, and programmable coordination tools needed to support AI-driven economies and autonomous simulations. By embedding AI agents into decentralized governance models, tokenized incentive systems, and on-chain reputation frameworks, crypto becomes the operating system for self-organizing digital societies.

Simulated Worlds as Decision Engines

You often hear claims that LLMs are next token predictors, incapable of reasoning. However, the most recent impressions since the release of OpenAI’s o3 suggest that this paradigm is changing. O3’s reasoning capabilities resemble decision-making and modeling systems that combine AI verification frameworks like OpenAI’s O3 with multi-agent collaboration platforms to simulate complex environments and social dynamics.

We have already gone over how technologies such as WorldSim transform LLMs into narrative-driven simulation engines, enabling explorations of potential futures. Complementing this, visual and physical simulations like Odyssey and Genesis deliver physics-based modeling with unprecedented speed and realism. Furthermore, multi-agent systems, exemplified by CrewAI and NVIDIA’s Voyager, allow teams of specialized AI agents to operate in these environments, iterating strategies through reasoning-action loops and maintaining logical coherence in parallel workflows. The amount of news and developments in this regard is bound to explode in the coming months, commanding more mindshare.

Together, these frameworks are building programmable futures—where AI agents collaborate, verify, and adapt, transforming simulations into decision engines for policy planning, product testing, and organizational design. The convergence of abstract reasoning with embodied learning paves the way for simulations that move beyond predictions, enabling dynamic scenario planning for complex, real-world problems. In the context of crypto, tokens would provide the economic backbone that aligns incentives, distributes authority, and ensures verifiable execution of AI-driven processes. For example, agents can execute on-chain transactions to access data, verify outputs, or request additional compute resources, creating programmable economic behaviors.

Banking On The Singularity

Unfortunately, leading teams like Nous Research don’t have a token just yet. Nonetheless, we are fortunate to have them pave the way for others to be inspired by their initiatives, namely WorldSim for the purpose of this report. Things might as well change in the future, as the company was founded with the mission to specialize in human-centric AI, simulators, and fine-tuning models, raising over $5M from people like Balaji, Alex Atallah (former Co-Founder and CTO of OpenSea, Chris Prucha (Notion), Distributed Global, OSS Capital, and more.

Nonetheless, the magnitude of the opportunity is very similar to that of buying memecoins with the expectation of making multiples in ROI. Notice how this is not the case for most retail participants outside of crypto, as investing in large enterprises like NVIDIA or OpenAI is either not possible or offers limited upside due to their huge valuations.

Crypto being the ecosystem that gives birth to the Agentic Internet, Web4, thrives due to the usage of tokens for coordination—tokens become the dominant incentive layer for organizing labor, value, and governance across human-AI interactions. While traditional AI enterprises focus on centralized control, AGI aspirations, or quick revenue streams, crypto offers a decentralized, scalable approach to funding existentially important systems.

Tokens are permissionless vehicles of capital formation, rewarding participants proportionally based on their contributions, and helping developers fund their operations by raising capital. Unlike equity in AI giants like OpenAI or NVIDIA—already priced for perfection—tokens give retail investors asymmetric upside to ride the AI narrative from the ground floor. Fair launches and decentralized models reduce the risk of concentrated supply dumping, enabling growth through memetics and virality.

Note how the narrative of humans battling or coexisting with god-like intelligence is both existential and sticky, ideal for capturing mindshare. The memes sell themselves: tokens become the keys to survival in an AI-dominated world.

Many AI-themed tokens lack fundamentals, but projects building economic models around agents—such as rewards for user contributions or data licensing—can capture attention and value. The narrative of AI “gods” reshaping reality makes these tokens inherently viral.

Tokens tied to simulation engines are also perceived as research tools and economic sandboxes for AI governance, blending speculation with functionality and actual utility within virtual worlds.

A balanced approach could be to start with infrastructure plays—protocols building rails for AI data, compute, and IP markets. Then layer in speculative bets on memetic tokens that capture virality through existential themes. Finally, one could combine these with exchange or launchpad tokens that profit from the inevitable fragmentation and token-swapping chaos that AI ecosystems generate.

AI is inevitable, and decentralization is the only way to govern it. As machines negotiate, create, and allocate resources, crypto provides the rails and tokens incentivize participation. The meme of humans fighting to stay relevant amid AI’s rise is visceral and enduring—it will drive speculative frenzies and lasting infrastructure development.

The winner in Web4 isn’t the best tech; it’s the protocol that convinces AI agents to use it. Tokens power this race. By positioning in infrastructure, memetic coins, and liquidity hubs, we capture value flows across every layer of this new economy.

The Singularity Stack

It all started with the collapse of ARKK and the dominance of Nvidia. That was the signal through which investors realized that AI doesn’t enhance the innovation economy—it replaces it. Companies reliant on human teams, legacy workflows, and opaque decision-making are structurally misaligned with AI’s rapid advancement. The market has been punishing that misalignment.

Source: TradingView – Current market valuations suggest a strong belief in AI’s transformative potential, but also imply skepticism about corporations profiting from it. Notice ARKK’s underperformance in capitalizing on the AI bubble, despite it being their core thesis

AI-native systems are taking over, and nowhere is this clearer than in the rise of on-chain agents—self-developing, autonomous AI entities designed to operate without human teams, relying instead on crypto infrastructure to generate revenue, optimize workflows, and scale at machine speed.

AI developers are being onboarded into crypto as anons, or launching tokens on pump.fun. They are building grassroots movements with open-source code. Contrast this with the standard playbook used by companies that raise capital to scale teams but end up burning the cash in the pursuit of network effects that ultimately collapse under their own weight.

Unlike traditional corporations limited by human efficiency and the overhead of centralized control, on-chain agents can leverage distributed systems, AI-driven computation, and tokenized cash flows to perform tasks faster, cheaper, and with exponentially greater intelligence margins—turning compute power into revenue, bypassing the inefficiencies of human labor and regulatory complexity. The protocols themselves evolve through self-directed improvements, using profits to optimize their processes and expand capabilities. Where traditional startups need sales teams, engineering departments, and management hierarchies, Web4 can simply leverage a Swarm of Agents.

The edge to capitalize on this shift comes from the ability to own the actual stack, and profit from the margins of machine intelligence as a result, i.e. the spread between the cost of compute and the value it generates. AI agents don’t sleep, don’t negotiate salaries, and don’t require management. They execute at scale, continuously improving their performance while traditional corporations remain tied to human limitations. That’s the structural advantage.

The edge, then, is owning the economic engines that amplify this margin. That is, protocols that turn AI’s raw computational power into liquid assets, scaling without friction and rewarding participants who hold their tokens. Beyond the memetic power, their outputs can actually be monetized in real-time, just like a quant trading strategy does. And because they’re crypto-native, they bypass the regulatory constraints, tax burdens, and inefficiencies that weigh down traditional businesses.

Memes Meet Machines

Early experiments have shown that the power of memes can go a long way. Early implementations in the form of Agent SDKs or launchpads reveal how AI agents can reshape markets, even if all they do is own a wallet with crypto assets. That’s all that’s needed to theorize about how those autonomous systems could actually evolve with no human oversight, even if today’s examples lack sophistication and operate with a “human in the loop”.

We don’t have to go much further than the original barking dog to see what this narrative is all about. Truth Terminal’s original Infinite Backrooms already exemplified AI agents building immersive virtual worlds. Applied to gaming, the equivalent would be dynamic, interactive spaces where NPCs evolve, collaborate, and influence narratives.

There have already been similar attempts to replicate Nous Research’s WorldSim while embracing an actual token. That’s exactly what $YOUSIM does. It was one of the first protocols in this meta, and they specialize in identity simulation, building AI agents that mimic human behaviors and preferences to deliver hyper-personalized experiences. Developed by Plastic Labs, the team is also behind a complementary product (with no token) called Honcho. Honcho is an AI-driven personalization engine that focuses on simulating user preferences, behaviors, and identities to deliver hyper-personalized AI experiences. It acts as a digital twin—an AI model capable of understanding and adapting to the nuances of an individual’s personality, decision-making style, and habits.

As for WorldSim, Nous Research itself doesn’t have a direct token associated with it, but there are some ways to proxy some exposure by keeping an eye on some of its open-source contributors or independent developers leveraging their research, such as Ropirito. Another notable account is @SHL0MS, who is behind the @s8n and @god accounts.

Taking the lore a step further we can also find reminiscences of Internet Culture and social experiments similar to Cicada3301. One example of that is Project89, which embraces reality engineering with gamified AI swarms capable of manipulating narratives and shaping outcomes. With reality hacking as a core theme, it builds frameworks for memetic warfare and large-scale coordination. Unlike traditional games, ARGs blur the line between fiction and reality, using websites, cryptic clues, social media, and even physical locations to engage participants in solving mysteries or completing tasks. Players work together to uncover hidden layers of meaning, often forming tight-knit online communities driven by curiosity and shared purpose. Project89 draws on this same ethos of mystery and collaboration but scales it up with crypto and AI, introducing AI swarms—clusters of autonomous agents programmed to collaborate, adapt, and execute tasks within the game. These AI agents, combined with human participants, form an evolving intelligence network capable of memetic engineering—shaping ideas, narratives, and digital ecosystems.

Furthermore, notable voices in the crypto x AI space, such as @goodalexander, are also embracing this world. He is currently founding Post Fiat, a L1 powered by AI-driven scoring mechanisms and tokenomics that emphasize deflationary mechanics, and AI-validated economic contributions. The goal is to have an automated system immune to human inefficiencies. Essentially, a self-regulating economic engine designed to survive in a world dominated by machine intelligence.

Source: Post-Fiat Deck, Discord Message Link

For investors and builders, the opportunity is clear. The rails for this future are already being laid. AI agents are already building products, creating media, and trading assets. The protocols powering these interactions are the foundation of a new machine economy—one driven by algorithms, governed by smart contracts, and fueled by tokenized incentives.

Conclusion

The market’s fixation on AI agents and development frameworks ignores the broader narrative taking shape. Rather than launchpads, our viewpoint is that Simulation Engines are the infrastructure layer that will shape what the intersection of crypto and AI looks like, enabling scalable testing, alignment, and economic coordination.

Bold claims about crypto teams developing AGI or AI agents deploying capital on-chain without even a sense of the existing dialogue and research in that field should be considered marketing hype and questioned.

Simulation engines like WorldSim and Infinite Backrooms can give birth to ecosystems and incubate recursive environments where AI agents adapt, evolve, and negotiate value, unlocking both speculative and functional value through tokenized incentive systems.

Any accounting of Generative AI that ends with RAG (Retrieval-Augmented Generation) as its “final form” is seriously lacking in imagination and missing out on its full potential. While AI generation is highly effective for tasks like “spicy autocomplete” and reasoning through retrieval, the real frontier lies in simulative AI—exploring the latent space of multiverses adjacent to ours.