Crunch Lab raised $5M in a strategic round co-led by Galaxy Ventures and Road Capital to scale CrunchDAO’s crowdsourced AI network.Crunch Lab raised $5M in a strategic round co-led by Galaxy Ventures and Road Capital to scale CrunchDAO’s crowdsourced AI network.

Crunch Lab Secures $5M to Scale CrunchDAO’s Crowdsourced AI for Enterprises

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Crunch Lab, the core team behind CrunchDAO, just closed a $5 million strategic funding round this June as it races to build what it calls an “intelligence layer” for decentralized AI. The round was co-led by Galaxy Ventures and Road Capital and included participation from VanEck and Multicoin, bringing Crunch Lab’s total capital raised to roughly $10 million after a $3.5 million seed round last year.

At heart, Crunch Lab is trying to solve a familiar enterprise problem, access to elite AI talent, by crowdsourcing it. The protocol connects thousands of practitioners through structured modeling competitions known as “Crunches,” and the project now says its community includes more than 10,000 machine learning engineers and some 1,200 PhDs spread across 100-plus countries. That scale is central to the company’s pitch: instead of hiring one expensive in-house team, firms can tap a global, performance-driven network.

Measurable Gains

Crunch’s approach isn’t just theoretical. The network, the company and partners say, has already driven measurable gains for heavyweight institutions. The Abu Dhabi Investment Authority’s research arm has used Crunch challenges to improve cross-sectional asset-pricing predictions by double digits, and the Broad Institute of MIT and Harvard leveraged Crunch’s crowdsourced models for breakthrough results in cancer-gene research using computer vision.

Those case studies, and the competitions that produced them, have been central to Crunch’s effort to prove that decentralized, encrypted modeling contests can deliver research-grade outcomes. On the trading side, Crunch has built a low-latency tooling, branded Mid+One, aimed at the mid-market pricing problem in FX.

The Mid+One challenge and its engine are designed to produce ultra-fast predictions and, according to Crunch’s published case studies, the Mid+One algorithms have been integrated into live trading environments at a major global bank, producing tangible reductions in trading costs. The project frames that work as a proof point that crowdsourced models can move from leaderboard to production in capital markets.

“AI today is constrained by hiring bottlenecks, siloed teams and an inability to scale effectively,” said Jean Herelle, CEO of Crunch Lab and CrunchDAO. “We’ve flipped that model. Instead of competing for scarce talent, we give enterprises secure access to all of it through a decentralized network.” That message, access, scale and confidentiality, runs through Crunch’s public materials and the investor pitch behind the recent raise.

The Mechanics Matter

Crunch transforms forecasting problems into encrypted modeling challenges, where contributors submit models that are evaluated on blind test data and rewarded strictly by performance. That meritocratic structure is meant to incentivize continual improvement while keeping data and intellectual property protected, an important selling point for clients in finance and biomedicine.

Investors say they see a larger infrastructure opportunity. “Crunch Lab is building an intelligence layer for global enterprises,” said Will Nuelle, General Partner at Galaxy Ventures. “Whether predicting asset prices, optimizing energy demand, or advancing healthcare diagnostics, CrunchDAO’s crowdsourced models unlock smarter, faster decision-making.”

“We believe Crunch Lab represents one of the most compelling attempts to connect global quants with enterprises at scale,” added Thomas Bailey of Road Capital. “AI is a trillion-dollar market, and open protocols like Crunch are positioned to capture it.” With fresh funds in hand, Crunch Lab says it will accelerate product development and broaden industry coverage beyond finance and biomedical research.

The company is positioning itself at the intersection of collective intelligence and enterprise AI: a place where distributed teams, operating under encrypted rules and clear incentives, might outpace conventional R&D squads, and do so without compromising client confidentiality. Whether that promise scales to large swathes of enterprise IT remains the market’s next test, but for now, Crunch has the capital and a growing set of production stories to make its case.

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