📰 Key Takeaways

MEGA, the organization behind Ethereum layer-2 network MegaETH, has confirmed it cut 20% of its staff. It’s the result of several months of restructuring, and the company is also expanding into AI products. Co-founder Shuyao Kong said on Friday that after the reorganization, some people who handled blockchain, wallets, ecosystem support, and PR and communications have left. She said the company integrated new capabilities and realigned its teams, and that process reduced headcount by 20%. These were people who had helped build MEGA since its earliest days. Kong didn’t disclose exactly how many people were affected or when the layoffs happened. She stressed that the founders, early employees, and core engineering team are all staying.

The cuts come as MEGA shifts its focus to building products on the MegaETH network, including AI services, and the organization has committed $30 million to that effort. Kong didn’t directly tie the layoffs to the new strategy, though. In their whitepaper, co-founders Yilong Li and Lei Yang laid out the idea of an “inference market,” where users can choose among multiple competing AI service providers. It tries to address two concerns: whether providers are really using the models they claim to use, and how they handle customer data. Kong revealed that MEGA plans to launch a public inference market in December.

💬 JudyAI Lab Take

MEGA, the organization behind Ethereum layer-2 network MegaETH, has confirmed it cut 20% of its staff while shifting its focus to AI products. It’s worth looking closely at the tradeoff itself: shrinking blockchain-related headcount while committing $30 million to AI.

From the original report, the people let go were some of those who handled blockchain, wallets, ecosystem support, and PR and communications, while the founders and core engineering team are staying. Co-founder Kong didn’t directly connect the layoffs to the new strategy, and we shouldn’t over-infer either. But at the same time, MEGA’s whitepaper proposes an “inference market” where users can choose among multiple competing AI service providers, and it tries to address two concerns: whether providers really use the models they claim, and how they handle customer data. For AI builders, these two questions are exactly the core trust gap when using third-party model services. Verifiability and data handling could become what AI services compete on, not just price and speed.

If your product plugs into multiple model providers, try listing this first: what can you actually verify today, and what do you only have the provider’s word for?

📅 Source Info

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