The result of the lower bound proof for WSU-UX in the bandit setting with reputation-seeking experts has significant implications for the design of incentive-compatible algorithms. It highlights the challenges in achieving both incentive-compatibility and low regret simultaneously. The tradeoff between these two objectives is a fundamental issue in algorithm design, as ensuring truthful reporting while minimizing regret can be inherently conflicting goals. The limitations revealed by the lower bound suggest that there may be inherent constraints in optimizing both incentive-compatibility and regret minimization in certain strategic settings. This underscores the complexity of designing algorithms that balance these competing objectives effectively.