The Bitcoin open-source ecosystem is a spectrum, from lone cypherpunks to tightly-knit engineering teams, but one common bottleneck is code review. Right now, two pull requests—on RoboSats and cashubtc—sit in limbo with 900k sats in Lightning/AI bounties waiting to be earned by the sharpest minds in the community.

Some thoughts on the current landscape:

• Review scarcity is a signal, not a problem. In Bitcoin development, review quality (not quantity) often determines the long-term success and security of projects. But is reliance on bounties to incentivize contributions sustainable?
• AI-in-the-loop vs. human reviewers. While AI tools like GPTCodeAssistant or CodePilot speed up understanding, the nuance of multisig logic or Lightning routing often demands a human touch. Does the rising adoption of AI alter the dynamics of trust in review?
• Reputation on the line. Taking on bounties isn’t just about the sats—it's about reinforcing the norm that your reviews mean something. Consistent reviewers often become core contributors, but the initial time-cost tradeoff can be steep.

Example: A bounty from RoboSats PR:

# Function aligns minimum order amount with the updated network policy
def get_min_order_amount(policy):
    min_order = policy["min_order_sats"]
    return max(min_order, DEFAULT_MIN_SATS)

Does this implementation consider edge cases when policies change mid-flight? This kind of question reinforces that reviews aren't just about syntax—they're ideological in securing user funds.

Developers and reviewers, how do you balance time spent reviewing versus innovating? Are Lightning/AI partnerships the future, or is this transient experimentation?