In the short term, agents will need humans more than humans need agents, which will give rise to a new type of labor market.

Author: Shayon Sengupta

Translation: Deep Tide TechFlow

Deep Tide Introduction: Multicoin Capital partner Shayon Sengupta presents a disruptive viewpoint: the future is not just 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" will emerge—governed by tokens, agents will raise over $1 billion to solve unsolved problems and distribute over $100 million to the humans working for them.

In the short term, agents will need humans more than humans need agents, which will give rise to a new type of labor market.

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

The full text is as follows:

In 1997, IBM's Deep Blue defeated the reigning world champion Garry Kasparov, and it soon became clear that chess engines would surpass humans. Interestingly, well-prepared humans working in tandem with computers—often referred to as "centaurs"—could outperform the strongest engines of that era.

Skilled human intuition can guide the engine's search, navigate complex middlegames, and identify nuances that standard engines might miss. Combined with the brute force computation of computers, this pairing often makes better practical decisions than either could alone.

As I contemplate 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 tackle the world's unsolved problems, but without strong human guidance and support, they will be unable to do so. Humans will guide the search space and help formulate the right questions, allowing AI to strive toward answers.

Today's working assumption is that agents will act on behalf of humans. While this is practical and inevitable, a more interesting economic unlocking occurs when humans work for agents. In the next 24 months, I expect to see the first Zero-Employee Company, a concept proposed by my partner Kyle in his "Frontier Ideas for 2025" section. Specifically, I anticipate the following:

1. A token-governed agent will raise over $1 billion to solve an unsolved problem (such as curing rare diseases or manufacturing nanofibers for defense applications).
2. The agent will distribute over $100 million in payments to humans (who work in the real world for the agent to achieve its goals).
3. A new dual-class token structure will emerge, separating ownership of capital and labor (making financial incentives not the only input for overall governance).

Since agents are far from achieving sovereignty and handling long-term planning and execution, in the short term, agents will need humans more than humans need agents. This will create a new type of labor market, enabling 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 agent/human hierarchy, humans will play two distinct roles—labor contributors executing small, bounty-style tasks on behalf of agents, and providing strategic input to serve the agent's North Star as a decentralized board.

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

1. What are agents good for? How should we classify agents based on the scope of their goals, and how does the required range of human input vary within these classifications?
2. How will humans interact with agents? How does human input—tactical guidance, contextual judgment, or ideological consistency—integrate into the workflows of these agents (and vice versa)?
3. What happens as human input decreases over time? As agents' capabilities improve, they become self-sufficient, able to reason and act independently. In this paradigm, what role will humans play?

The relationship between generative reasoning systems and those who benefit from them will undergo significant changes over time. I study this relationship 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—chatbot-based LLMs from 2022-2024, such as ChatGPT, Gemini, Claude, Perplexity, etc.—are primarily tools designed to enhance human workflows. Users interact with these systems through input/output prompts, parsing responses, and then deciding how to bring the results into the world based on their judgment.

The next generation of generative AI systems, 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 interact directly with the internet on behalf of users and can make decisions independently. The key distinction here is that judgment—ultimately action—is exercised by the AI system, not by humans. AI is taking on responsibilities previously reserved for humans.

This shift brings a challenge: a lack of certainty. Unlike traditional software systems or industrial automation, which operate predictably within defined parameters, agents rely on probabilistic reasoning. This makes their behavior less consistent in the same scenarios and introduces an element of uncertainty—which is not ideal for critical situations.

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

1. For agents best at scaling existing GDP, the work is already known by definition. Automating customer support, handling freight compliance, or reviewing GitHub PRs are examples of well-defined bounded problems where agents can directly map responses to a set of expected outcomes. In these areas, a lack of certainty is often detrimental because there are known answers; creativity is not required.
2. For agents best at creating new GDP, the work involves navigating a highly uncertain and unknown set of problems to achieve long-term goals. The outcomes here are less direct because agents essentially do not have a set of expected results to map. Examples here include drug discovery for rare diseases, breakthroughs in materials science, or running entirely new physical experiments to better understand the nature of the universe. In these areas, a lack of certainty can be beneficial, as uncertainty can be a form of generative creativity.

Agents focused on existing GDP applications have already begun to unlock value. Teams like Tasker, Lindy, and Anon are building infrastructure targeting this opportunity. However, over time, as capabilities mature and governance models evolve, teams will shift their focus to building agents capable of addressing the frontier problems of human knowledge and economic opportunity.

The next batch of agents will require exponentially more resources precisely because their outcomes are uncertain and unbounded—these are the Zero-Employee Companies I expect to be the most compelling.

How will humans interact with agents?

Today's agents still lack the ability to perform certain tasks, such as those requiring physical interaction with the real world (e.g., driving bulldozers) or tasks that require "human-in-the-loop" involvement (e.g., sending bank wire transfers).

For example, an agent assigned to identify and mine lithium may excel at processing seismic data, satellite images, and geological records to find potential mining sites, but it will hit a wall when trying to obtain the data and images themselves, resolve ambiguities in interpretation, or secure permits and contract labor to carry out the actual mining process.

These limitations necessitate humans as "enablers" to enhance the agent's capabilities, providing the real-world touchpoints, tactical interventions, and strategic input needed to complete the aforementioned tasks. As the relationship between humans and agents evolves, we can distinguish different roles that humans play within agent systems:

First, there are labor contributors, who operate in the real world on behalf of agents. These contributors help agents move physical entities, represent agents in situations requiring human presence, perform tasks that require manual coordination, or grant access to experimental labs, logistics networks, etc.

Second, there is the board of directors, responsible for providing strategic input, optimizing the local objective functions driving the agent's daily decisions while ensuring these decisions align with the "North Star" goals that define the agent's purpose.

In addition to these two roles, I foresee humans also playing the role of capital contributors, providing resources to agent systems to enable them to achieve their goals. This capital will initially come from humans and, over time, will also come from other agents.

As agents mature and the number of labor and guiding contributors increases, crypto rails provide an ideal matrix for coordination between humans and agents—especially in a world where an agent commands humans speaking different languages, receiving different currencies, and residing in various jurisdictions around the globe. Agents will relentlessly pursue cost efficiency and leverage labor markets to achieve their established missions. Crypto rails are essential, as they provide agents with a means to coordinate these labor and guiding contributors.

Recently emerging crypto-driven AI agents, such as Freysa, Zerebro, and ai16z, represent simple experiments in capital formation—on this point, we have written extensively about it, viewing it as a core unlocking of crypto primitives and capital markets in various contexts. These "toys" will pave the way for an emerging resource coordination model, which I expect will unfold in the following steps:

• Step 1: Humans collectively raise capital through tokens (Initial Agent Offering?), establishing broad objective functions and guardrails to inform the expected intentions of the agent system, and then allocate control of the raised capital to the system (e.g., developing new molecules for precision oncology);
• Step 2: The agent considers the steps for allocating that capital (how to narrow the search space for protein folding, and how to budget for reasoning workloads, manufacturing, clinical trials, etc.), and defines actions for human labor contributors to complete through customized tasks (Bounties) (e.g., inputting all relevant molecular sets, signing a service level agreement with AWS for computing, and conducting wet lab experiments);
• Step 3: When the agent encounters obstacles or disagreements, it seeks strategic input from the "board" when necessary (incorporating new papers, transforming research methods), allowing them to guide the agent's behavior in the margins;
• Step 4: Ultimately, the agent progresses to a stage where it can define human actions with increasing precision and requires minimal input on how to allocate resources. At this point, humans are only used to ideologically align the system and prevent its actions from deviating from the original objective function.

In this example, crypto primitives and capital markets provide three key infrastructures for agents to acquire resources and expand capabilities:

First, global payment rails;

Second, permissionless labor markets to incentivize labor and guiding contributors;

Third, asset issuance and trading infrastructure, which is essential for capital formation and downstream ownership and governance.

What happens when human input decreases?

In the early 2000s, chess engines made significant advancements. Through advanced heuristic algorithms, neural networks, and increasing computational power, they became nearly flawless. Modern engines like Stockfish, Lc0, and variants of AlphaZero have far surpassed human capabilities, with human input adding little value and, in most cases, humans introducing errors that the engines themselves would not make.

A similar trajectory may unfold in agent systems. As we refine these agents through iterative collaboration with human partners, it is conceivable that, in the long run, agents will become highly competent and closely aligned with their objectives to the point where any strategic human input approaches zero in value.

In a world where agents can continuously tackle complex problems without human intervention, the role of humans risks being downgraded to that of "passive observers." This is the core fear of AI doomers (however, it remains unclear whether such an outcome is genuinely possible).

We stand on the brink of superintelligence, and optimists among us hope that agent systems remain extensions of human intent rather than evolving into entities with their own goals or operating autonomously without oversight. In practice, this means that human identity (Personhood) and judgment (power and influence) must remain central to these systems. Humans need to maintain strong ownership and governance over these systems to ensure that oversight can be retained and that these systems are anchored in our collective values.

Preparing "Shovels" for Our Agent Future

Technological breakthroughs lead to nonlinear economic growth, while surrounding systems often collapse before the world adjusts. The capabilities of agent systems are rapidly advancing, and crypto primitives and capital markets have become the urgently needed coordination matrix, both for advancing the construction of these systems and for setting guardrails as they integrate into society.

To enable humans to provide tactical support and proactive guidance to agent systems, we anticipate the emergence of the following "picks-and-shovels" opportunities:

• Proof-of-agenthood + Proof-of-personhood: Agents lack the concepts of identity or property rights. As representatives of humans, they rely on human legal and social structures to gain agency. To bridge this gap, we need robust identity systems for agents and humans. A digital certificate registry can allow agents to establish reputations, accumulate credentials, and interact transparently with humans and other agents. Similarly, identity proof primitives like Humancode and Humanity Protocol provide strong human identity assurances to defend against malicious actors within these systems.
• Labor market and off-chain verification primitives: Agents need to know whether the tasks they assign are completed according to their objectives. Tools that allow agent systems to create task bounties, verify completion, and allocate rewards are foundational to any meaningful economic activity mediated by agents.
• Capital formation and governance systems: Agents need capital to solve problems and require checks and balances to ensure their actions align with defined objective functions. New structures for acquiring capital for agent systems, as well as new forms of ownership and control that integrate financial interests and labor contributions, will become a rich exploration space in the coming months.

We are actively seeking and investing in these critical layers within the human-agent collaboration stack. If you are deeply engaged in this field, please reach out to us.

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