President Donald Trump signed an executive order on June 2, 2026, establishing a voluntary framework for AI companies to submit their frontier models to the federal government prior to public release, aiming to foster secure innovation and bolster the cybersecurity of critical infrastructure. The order acknowledges that while the US AI industry thrives due to minimal regulation, it also highlights the security risks accompanying advanced AI capabilities. Consequently, several federal agencies are tasked with developing a framework to evaluate the cyber prowess of AI models before their deployment. However, this decision raises questions about the practicality and effectiveness of pre-release assessments.

Firstly, the notion that AI companies will willingly submit their cutting-edge models for government review before market release is optimistic at best. Companies like OpenAI and Google often guard their algorithms fiercely to maintain competitive advantage, making voluntary submissions a tall order. Will these firms really share their proprietary models, or will they merely provide superficial data points to appease the administration? The order’s success hinges on industry participation, which may be limited by the cost and time required for thorough assessments.

Secondly, the assumption that pre-release evaluations significantly enhance cybersecurity overlooks the rapid evolution of AI. By the time a model is reviewed, it might already be outdated in terms of capabilities and vulnerabilities. AI models can be updated frequently, so a snapshot assessment might not capture future security risks. Moreover, the framework itself could become obsolete as new threats emerge, leading to a cycle where assessments are conducted but their relevance wanes quickly.

Additionally, the order’s emphasis on “secure innovation” suggests that regulation stifles creativity, yet it overlooks historical instances where stringent oversight spurred technological breakthroughs. For example, early aviation safety regulations led to innovations in aircraft design and navigation systems. Similarly, AI could benefit from structured reviews that push companies beyond incremental improvements, fostering truly transformative solutions.

Lastly, the execution of the framework may vary across agencies, resulting in inconsistent evaluations. If one agency is meticulous while another rushes through assessments, the overall effectiveness of the system will be compromised. Furthermore, the order doesn’t specify metrics or standards for evaluating AI models, leaving room for subjective interpretations and potential biases. Without clear criteria, the voluntary framework risks becoming more symbolic than substantive, with companies simply checking boxes to satisfy Trump’s directive.

In summary, while President Trump’s executive order is a bold step toward integrating federal oversight into AI development, its practicality depends on industry willingness, timely assessments, and consistent standards. The roasting here is that pre-release reviews might be too late, voluntary submissions could be superficial, and the framework’s metrics remain undefined—potentially turning secure innovation into a catchy slogan rather than a concrete achievement.