Brokers, hedge funds, and advisers push back hard on the SEC's proposed rules for AI used in financial advice, giving comments well past the October 10 deadline
Jennifer Hughes / Financial Times : LinkedIn: Piero Soave LinkedIn: Piero Soave : As regulators start implementing rules around #ai, some common pain points are emerging across jurisdictions. What do you include in the definition of the technology in scope? …
Context & Ripple Effects
The pushback follows the SEC’s earlier move to consider AI conflict-of-interest rules for advisers and brokerages, placing automated recommendations under the same scrutiny as other incentives that can shape client outcomes.
It also lands amid a wider dispute over how broadly AI rules should define systems in scope: divergent approaches to AI-model regulation were already raising concerns about compliance complexity across jurisdictions.
First-order effects
- Brokers, hedge funds, and advisers are using the comment process to challenge the SEC proposal, putting industry objections on the record even after the formal deadline.
- The SEC faces sharper pressure to justify how its AI rules would apply to financial-advice workflows and to distinguish problematic steering from legitimate technology use.
Second-order effects
- Firms deploying AI in advice and brokerage must weigh slower or more constrained rollouts against the compliance and documentation burden implied by the proposed rules.
- Industry resistance can turn technical definitions and conflict standards into the central battleground, encouraging other financial-market participants to seek narrower, more operationally specific requirements.
Third-order effects
- If sector-specific AI rules proceed through sustained consultation, financial services may develop a separate governance layer for algorithmic recommendations rather than relying on general AI regulation alone.
- The episode points to a broader regulatory pattern: adoption in high-stakes decisioning is likely to be shaped as much by accountability for incentives and outcomes as by whether a tool is labeled AI.
The trend: AI governance is moving from broad principles toward sector-specific controls over how automated systems influence consequential financial decisions.