Crypto’s Next Chapter: How ‘Internet Labor Markets’ Could Bring Millions of New Users On-Chain

For most of crypto’s existence, the entry point for every participant has been consistent: convert traditional money into digital tokens and begin trading. That model has defined the industry’s growth - and its reputation for speculation.

But a new thesis is quietly gaining traction in investment circles. 

Multicoin Capital, a multi-billion-dollar crypto investment firm, believes the next major wave of adoption will not come from people buying tokens but from people earning them.

The firm calls this emerging model Internet Labor Markets (ILMs).

Key Takeaways

• Multicoin Capital’s Internet Labor Markets model proposes that users earn crypto by completing verifiable tasks, shifting participation away from speculation-driven token purchases.
• ILM systems use blockchain infrastructure to verify work and settle payments instantly, removing traditional employment friction like invoices and approval delays.
• The convergence of ILMs with AI could generate demand for distributed human contributors in data labeling, quality judgment, and real-world task execution globally.

What Are Internet Labor Markets?

The ILM concept is straightforward. Rather than purchasing crypto as an entry point, users contribute work, resources, or judgment to decentralized networks and receive tokens as payment.

“The reason people get their first crypto in the future won’t be because they bought it,” said Multicoin Capital’s Rahul Sengupta. “It’ll be because they earned it.”

In practice, that labor can take many forms, including:

• Bandwidth sharing: Contributing unused internet capacity to support data tasks
• Data labeling: Tagging and organizing datasets to train AI models
• Bug identification: Flagging errors in code for developer networks
• Real-world task completion: Performing physical or location-based assignments tied to decentralized infrastructure
• Quality judgment: Applying human discretion to tasks that automated systems cannot reliably perform

The model flips the traditional crypto dynamic: instead of buying in first and then participating, users participate first and receive tokens as a result.

Building on DePIN - But Going Further

The ILM concept draws from an earlier generation of blockchain experiments, most notably Decentralized Physical Infrastructure Networks (DePIN) - a category that emerged largely from the Solana ecosystem. These networks reward participants for contributing hardware resources, such as wireless coverage or geographic mapping data.

But Multicoin Capital’s thesis argues the next phase moves beyond passive hardware contributions.

“The system moves from just plugging in hardware to people doing more active work - contributing judgment, effort and time,” Sengupta explained.

One real-world example already in motion is ‘Grass’, a network that allows users to install software, share spare bandwidth, and earn tokens in return. That bandwidth is then used for web-scraping tasks that help train AI systems - creating a direct link between distributed human infrastructure and artificial intelligence development.

What’s The Blockchain Advantage?

A key reason blockchain infrastructure makes this model viable is its ability to verify and settle work automatically, without intermediaries.

Traditional employment or freelance structures rely on:

• Manual invoicing
• Multi-step approval chains
• Delayed payment processing

ILM systems replace that friction with deterministic verification - confirming a task was completed and issuing payment immediately through crypto rails. For global contributors, particularly in markets where financial infrastructure is limited, this represents a meaningful shift in access.

How Does AI Fit in This?

Rather than positioning AI as a threat to human contributors, Sengupta sees it as a driver of demand for the ILM model. As companies automate more of their core operations, they increasingly rely on human judgment for tasks that algorithms cannot yet perform reliably.

“The next phase is not just scraping data, but humans applying discretion - labeling data, judging quality - in ways that only humans can,” he said.

In this framing, humans and AI systems become collaborators rather than competitors, with ILM networks serving as the coordination layer that sources, verifies, and compensates contributors at scale.

The Bottomline

If the ILM thesis proves correct, the implications for crypto adoption are quite significant. 

Rather than requiring users to first understand exchanges, wallets, and asset purchases, these networks offer a more intuitive entry point: complete a task, receive payment. “Someone starts a company to source something the market needs, and 50,000 people around the world can get paid for producing that labor,” Sengupta said.

For Multicoin Capital, which raised $422 million for a venture fund focused on early-stage blockchain startups back in January 2022, the ILM model represents one of the more credible paths toward bringing the next generation of users on-chain.

Crypto’s next users, in this vision, may not arrive through speculation at all - and rather through work.

Frequently Asked Questions (FAQs)

1. How is the ILM model different from DePIN? 

DePIN networks primarily reward users for contributing hardware or passive infrastructure resources. The ILM model extends this to active human work, such as data labeling, quality assessment, and judgment-based tasks.

2. How does AI connect to Internet Labor Markets? 

AI systems increase demand for human contributors by requiring ongoing data labeling, quality checks, and judgment-based input that automated tools cannot reliably perform - tasks well-suited to ILM networks.

3. What is an example of an ILM system operating today? 

Grass is a current example, allowing users to share spare internet bandwidth through software, which is then used for AI-related data scraping. Contributors earn tokens in return for their participation.

4. Which blockchain ecosystem is most active in developing ILM projects? 

Solana has emerged as a leading ecosystem for ILM and DePIN experimentation, owing to its low transaction costs and high throughput.