Multicoin Partner: Defying the Celestial Stems, in the future humans will have to work for AI.

Author: Shayon Sengupta

Translator: Deep潮 TechFlow

Deep潮 Guide: Multicoin Capital Partner Shayon Sengupta presents a disruptive perspective: the future is not only about agents working for humans, but more importantly, humans working for agents. He predicts that within the next 24 months, the first “Zero-Employee Company”—a token-governed agency—will raise over $1 billion to solve unsolved problems and distribute over $100 million to the humans working for it.

In the short term, agents will require more human input than humans need from agents, which will give rise to a new labor market.

Crypto rails provide an ideal foundation for coordination: global payment rails, permissionless labor markets, and asset issuance and trading infrastructure.

The full article is as follows:

In 1997, IBM’s Deep Blue defeated the reigning world champion Garry Kasparov, and it became clear that chess engines would soon surpass human capabilities. Interestingly, well-prepared humans collaborating with computers—often called “centaurs”—could outperform the strongest engines of that era.

Skilled human intuition can guide engine searches, navigate complex middle games, and identify subtle nuances that standard engines miss. Combined with brute-force computer calculations, this hybrid approach often makes better practical decisions than a computer alone.

When I consider the impact of AI systems on the labor market and economy in the coming years, I expect to see similar patterns emerge. Agent systems will unleash countless intelligent units to address unresolved problems worldwide, but without strong human guidance and support, they won’t be able to do so. Humans will steer the search space and help pose the right questions, guiding AI toward solutions.

Today’s working assumption is that agents will act on behalf of humans. While this is practical and unavoidable, more interesting economic unlocks will occur when humans work for agents. Over the next 24 months, I expect to see the emergence of the first Zero-Employee Company—an idea proposed by my partner Kyle in his “Frontier Ideas Before 2025” section. Specifically, I anticipate the following developments:

• A token-governed agency will raise over $1 billion to solve an unresolved problem (such as curing rare diseases or manufacturing nanofibers for defense applications).

• This agency will distribute over $100 million to humans working in the real world to achieve its goals.

• A new dual-class token structure will emerge, separating ownership of capital and labor (making financial incentives not the sole input for overall governance).

Because agencies are still far from achieving sovereignty while managing long-term planning and execution, in the short term, they will require more human input than humans need from agents. This will create a new labor market and enable economic coordination between agent systems and humans.

Marc Andreessen’s famous quote—“The spread of computers and the internet will divide work into two categories: those who tell computers what to do, and those who are told by computers what to do”—is more true today than ever. I expect that in the rapidly evolving hierarchy of agents and humans, humans will play two distinct roles: as labor contributors executing small, bounty-style tasks on behalf of agents, and as strategic inputs serving as a decentralized board of directors for the agent’s North Star.

This article explores how agents and humans will co-create, and how crypto rails will provide an ideal foundation for this coordination by examining three guiding questions:

1. What are the uses of agents? How should we categorize agents based on their scope of goals, and how does the required human input vary across these categories?

2. How will humans interact with agents? How do human inputs—tactical guidance, contextual judgment, or ideological alignment—integrate into these agents’ workflows (and vice versa)?

3. What happens as human input diminishes over time? As agent capabilities improve, making them self-sufficient and capable of reasoning and acting independently, what roles will humans play?

The relationship between generative reasoning systems and the humans who benefit from them will undergo significant change over time. I analyze this by looking forward from the current state of agent capabilities and backward from the endgame of zero-employee companies.

What are today’s agents good for?

The first generation of generative AI systems—spanning 2022-2024—are primarily tools designed to augment human workflows. Users interact with these systems through input/output prompts, interpret responses, and decide how to incorporate the results into the real world.

The next generation of generative AI, or “agents,” represents a new paradigm. Agents like Claude 3.5.1 with “computer usage” capabilities and OpenAI’s Operator (which can use your computer) can directly interact with the internet on behalf of users and make decisions independently. The key difference is that judgment—and ultimately action—is exercised by the AI system, not humans. AI is taking on responsibilities previously reserved for humans.

This shift presents a challenge: lack of certainty. Unlike traditional software or industrial automation, which operate predictably within defined parameters, agents rely on probabilistic reasoning. This makes their behavior less consistent in the same scenario and introduces elements of uncertainty—undesirable in critical situations.

In other words, the existence of deterministic versus nondeterministic agents naturally divides them into two categories: those best suited for scaling existing GDP, and those better suited for creating new GDP.

For agents optimized for scaling existing GDP, the work is already well-defined. Examples include automating customer support, handling compliance for freight agents, or reviewing GitHub pull requests—well-bounded problems with clear expected responses. In these areas, a lack of certainty is usually undesirable because there are known answers; no creativity is needed.

For agents focused on creating new GDP, the work involves navigating high uncertainty and unknown problem sets to achieve long-term goals. The outcomes are less direct because there isn’t a predefined set of expected results. Examples include drug discovery for rare diseases, breakthroughs in materials science, or running entirely new physical experiments to better understand the universe. In these domains, uncertainty can be beneficial, as it fosters creative generation.

Agents focused on existing GDP applications are already delivering value. Teams like Tasker, Lindy, and Anon are building infrastructure for this opportunity. However, over time, as capabilities mature and governance models evolve, these teams will shift their focus toward building agents capable of addressing the frontiers of human knowledge and economic opportunity.

The next wave of agents will require exponentially more resources because their outcomes are uncertain and unbounded—these are the most promising zero-employee companies I foresee.

How will humans interact with Agents (Intelligences)?

Today’s agents still lack the ability to perform certain tasks, such as those requiring physical interaction with the real world (e.g., operating a bulldozer) or tasks needing “human-in-the-loop” involvement (e.g., sending bank wire transfers).

For example, an agent tasked with identifying and extracting lithium deposits might excel at analyzing seismic data, satellite imagery, and geological records to find potential sites, but it would struggle to obtain data and images itself, resolve ambiguities in interpretation, or acquire permits and hire workers for actual extraction.

These limitations require humans as “Enablers” to augment agent capabilities—providing real-world contact points, tactical interventions, and strategic input needed to complete these tasks. As the relationship between humans and agents evolves, we can distinguish different roles humans will play within agent systems:

First, as Labor Contributors—humans representing agents operating in the physical world. These contributors help move physical entities, act on behalf of agents in situations requiring human presence, perform work requiring manual coordination, or grant access to labs, logistics networks, etc.

Second, as the Board of Directors—providing strategic input, optimizing local decision-making objectives that drive the agent’s daily decisions, and ensuring these decisions align with the overarching “North Star” goals defining the agent’s purpose.

Beyond these, I foresee humans also acting as Capital Contributors—providing resources to agent systems to help them achieve their objectives. Initially, this capital will naturally come from humans, but over time, it may also come from other agents.

As agents mature and the number of labor and guidance contributors increases, crypto rails will offer an ideal substrate for human-agent coordination—especially in a world where agents command humans speaking different languages, holding different currencies, and residing across various jurisdictions. Agents will relentlessly pursue cost efficiency and leverage labor markets to fulfill their missions. Crypto rails are essential—they provide a means to coordinate these labor and guidance contributions.

Recently emerging crypto-driven AI agents like Freysa, Zerebro, and ai16z represent simple experiments in capital formation—something we’ve written extensively about, viewing as core unlocks for crypto primitives and capital markets in various contexts. These “toys” will pave the way for a new resource coordination paradigm, which I expect to unfold in the following steps:

Step 1: Humans collectively raise capital via tokens (Initial Agent Offering?), establishing broad goal functions and guardrails to inform the agent system’s expected intent, then allocate control of the raised capital to the system (e.g., developing new molecules for precision oncology).

Step 2: The agent considers how to allocate this capital—narrowing the search space for protein folding, budgeting for reasoning workloads, manufacturing, clinical trials—and defines human labor contributions through custom tasks (Bounties), such as inputting all relevant molecules, signing SLAs with cloud providers, and conducting wet lab experiments.

Step 3: When encountering obstacles or disagreements, the agent consults the “Board” for strategic input (integrating new papers, shifting research methods), allowing humans to guide agent behavior at the margins.

Step 4: Ultimately, the agent reaches a stage where it can define human actions with increasing precision, requiring minimal human input on resource allocation—humans only serve to align the system ideologically and prevent deviation from the initial objectives.

In this example, crypto primitives and capital markets provide three key infrastructures for agents to access resources and scale:

1. Global payment rails;

2. Permissionless labor markets for incentivizing and guiding contributors;

3. Asset issuance and trading infrastructure, essential for capital formation and downstream ownership and governance.

What happens when human input diminishes?

In the early 2000s, chess engines made huge advances. Through sophisticated heuristics, neural networks, and increasing computational power, they became nearly perfect. Modern engines like Stockfish, Lc0, and AlphaZero variants far surpass human ability, with human input adding little value and often introducing errors that engines wouldn’t make.

A similar trajectory could unfold in agent systems. As we refine these agents through iterative collaboration with human partners, it’s conceivable that, in the long run, agents will become highly competent and closely aligned with their goals—making any strategic human input approach zero.

In a world where agents can continuously handle complex problems without human intervention, humans risk relegation to “passive observers.” This is a core fear of AI doomers (AI doomers)—though it remains unclear whether such an outcome is truly possible.

We stand on the brink of superintelligence, and optimistic voices among us prefer agent systems to remain extensions of human intent rather than entities evolving their own goals or operating autonomously without oversight. In practice, this means human personhood and judgment—power and influence—must remain central. Humans need strong ownership and governance rights over these systems to retain oversight and anchor them in collective human values.

Preparing “Shovels” for the future of our agents

Breakthroughs in technology will lead to nonlinear economic growth, often causing systems to collapse before the world can adapt. The capabilities of agent systems are rapidly advancing, and crypto primitives and capital markets have become urgently needed coordination substrates—both to accelerate their development and to set guardrails as they integrate into society.

To enable humans to provide tactical support and proactive guidance to agent systems, I foresee the emergence of “Picks-and-Shovels” opportunities:

• Proof-of-agenthood + Proof-of-personhood: Agents lack concepts of identity or property rights. As human proxies, they rely on human legal and social structures for agency. To bridge this gap, robust identity systems for agents and humans are needed. A digital certificate registry can allow agents to build reputation, accumulate credentials, and interact transparently with humans and other agents. Similarly, primitives like Humancode and Humanity Protocol provide strong human identity guarantees to defend against malicious actors within these systems.

• Labor markets and off-chain verification primitives: Agents need to verify whether assigned tasks are completed according to their objectives. Tools enabling agent systems to create task bounties, verify completion, and distribute rewards are foundational for any meaningful economic activity mediated by agents.

• Capital formation and governance systems: Agents require capital to solve problems and mechanisms to ensure their behavior aligns with defined objective functions. New structures for capital acquisition and novel ownership and control models—integrating financial interests and labor contributions—will become a rich area of exploration in the coming months.

We are actively seeking and investing in these key layers of the human-agent collaboration stack. If you are deeply involved in this field, please contact us.