Experts Warn AI Regulation is Obsolete Due to Post-Training Innovation
Why It Matters
If regulation focuses solely on foundational models, it misses the true source of modern AI capabilities and risks found in tool-use and inference scaling.
Key Points
- Most AI gains now come from post-training, tool use, and inference scaling rather than just initial model training.
- Safety mitigations like filters are typically part of the software scaffold, making model-level regulation largely ineffective.
- Horizontal, one-size-fits-all AI laws struggle to address domain-specific risks like biological or cybersecurity threats.
- Regulators should prioritize outcomes-based rules and prove that existing negligence laws are insufficient before passing new ones.
- Effective oversight requires deep domain expertise within specific verticals rather than centralized 'entity-based' approaches.
Tech policy experts are sounding alarms that current and proposed AI legislation is fundamentally mismatched with the current state of the industry. While early regulatory efforts like the EU AI Act focused on large-scale model training by labs, recent breakthroughs are increasingly driven by post-training techniques, scaffolding, and inference compute scaling. Critics argue that because many safety mitigations, such as filters and oversight mechanisms, are applied at the software layer rather than the model weights, model-centric laws are becoming ineffective. This shift suggests that abstract horizontal requirements may fail to address specific harms like cybersecurity threats or fraud, which are often facilitated by how models are deployed rather than how they were initially trained. Experts are now calling for a transition toward outcomes-based regulation and domain-specific oversight that accounts for the complex chain of actors involved in AI deployment.
Imagine passing a law about how powerful a car engine can be, only to realize people are now building rocket boosters and smart-steering kits to attach to them later. That is what is happening with AI regulation. Lawmakers are obsessed with 'training' the big models, but the real power (and danger) now comes from 'scaffolding'—the extra software and tools we wrap around the AI after it's built. Because regulators are looking at the 'engine' instead of the 'car,' they are missing the risks. We need laws that focus on what the AI actually does in the real world, not just how it was made.
Sides
Critics
Argues that current AI bills are obsolete because they focus on model-level risks rather than the software scaffolding and deployment reality.
Defenders
Proponents of the 'entity-based' and horizontal approach to AI risk through frameworks like the AI Act.
Noise Level
Forecast
Legislators will likely face increasing pressure to amend 'horizontal' AI acts as they realize foundational model restrictions don't prevent application-level harms. We will see a shift toward 'vertical' regulation where specific sectors like finance or healthcare create their own AI safety standards.
Based on current signals. Events may develop differently.
Timeline
Policy Critique Issued
Policy experts highlight the growing gap between legal 'path dependencies' and the technical reality of AI scaffolding.
Inference Scaling Breakthroughs
Major labs shift focus toward compute-heavy inference and 'system 2' thinking models, changing the risk profile of AI tools.
EU AI Act Implementation Begins
The European Union begins the rollout of the world's first comprehensive horizontal AI regulation framework.
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