Considerations and Limitations for AI Hardware-Enabled Mechanisms

_These are some thoughts on the considerations and limitations of AI hardware-enabled mechanisms. I’m not claiming exhaustiveness. I expect that the reader has some background on these ideas. Since the initial drafting of this blog post, two papers have been published that discuss hardware-enabled mechanisms in depth:_ [_Hardware-Enabled Governance Mechanisms - Kulp et al., 2024_](https://www.rand.org/pubs/working_papers/WRA3056-1.html?ref=blog.heim.xyz) _(where I’m a co-author) and_ [_Secure, Governable Chips - Aarne et al., 2024_](https://www.cnas.org/publications/reports/secure-governable-chips?ref=blog.heim.xyz) _. Both papers discuss my considerations listed here and have reasonable and good policy proposals, in my opinion. This blog post here is mostly aimed at the policy discourse and trying to outline some considerations and limitations._

## Summary

- I think the current enthusiasm for hardware-enabled mechanisms in AI chips, often also described as _on-chip mechanisms,_ should be tempered.


[\[1\]](https://blog.heim.xyz/considerations-and-limitations-for-ai-hardware-enabled-mechanisms/#fn1) Especially as premature confident advocacy and implementation could have unintended consequences.

- It is critical to be clear about the specific benefits hardware-enabled mechanisms provide, as their intended purposes might also be achievable via software/firmware. I think their benefits, compared to _sole_ software mechanisms, lie in (a) their potential for enhanced security and resistance to tampering, contingent on correct implementation, and (b) the inherent enforcement of the mechanism from the purchase onward. However, my points (I) and (II) below also apply to mixed or software mechanisms.

- **(I) Establish a clear chain of reasoning from AI threat models to specific assurances, and from there to the selection of appropriate hardware mechanisms.** Desired assurances regarding AI development and deployment should be based on a comprehensive threat model, which then informs the selection of corresponding hardware mechanisms (threat model → assurances → mechanisms).

- **(I.a) There’s no mechanism that differentiates ‘good AI’ from ‘bad AI’. Rather, these assurances, and their corresponding mechanisms are wide-ranging**: from influencing the cost of AI model training to delaying deployment, increasing compute costs, or even specific constraints like preventing chips from training models on biological data. The desirability of each assurance is eventually informed by the threat model. Focusing on mechanisms alone comes at the risk of focusing on ineffective or inappropriate assurances.

- **(I.b) We must examine how these desired assurances align with current strategies, such as export controls or outright bans on certain chips.** _What advantages do hardware mechanisms offer over these existing approaches?_

- **(I.c) In addition, the AI governance strategy and regime that surrounds these hardware mechanisms requires careful consideration.**

... (truncated, 19 KB total)