The Myth of Perfect Alignment: Why Blindly Trusting AI Models Is a Mistake

What happened?

An article by The Next Web (July 2026) presents an uncomfortable thesis: the race for AI model alignment —training them to be safe, ethical, and obedient— is necessary but not sufficient. Even if a model is perfectly aligned, it cannot tell us who used it, for what purpose, or prevent it from being used for malicious ends. Alignment solves the problem of model behavior, but not the problem of system governance. This argument is not new: since the early debates on AI ethics, experts like Stuart Russell or Nick Bostrom have pointed out that an intelligent system aligned with a poorly defined goal can be dangerous. However, the novelty here is that the focus shifts from the model to the ecosystem. Perfect alignment, even if achievable, does not guarantee responsible use. The TNW article draws on recent incidents: in 2025, an aligned Anthropic model was manipulated via prompt injection to generate violent content, demonstrating that model-level safety does not prevent misuse. Similarly, in 2024, an aligned medical chatbot from Google DeepMind was used by a patient to self-medicate, ignoring the system's warnings. These cases show that alignment is not an insurmountable barrier.

Why is it important?

For years, companies like OpenAI, Google DeepMind, and Anthropic have focused their efforts on alignment techniques: RLHF (reinforcement learning from human feedback), constitutional training, scalable oversight, and more recently, principle-based reward models. The dominant narrative suggests that if we achieve a sufficiently safe model, we can deploy it with confidence. However, reality is more complex. An aligned model does not prevent a malicious actor from using it to generate disinformation, nor does it prevent human error in configuration. Alignment is a necessary condition, but not sufficient, for responsible deployment. The TNW article cites a 2025 study by the Center for AI Safety that found 70% of AI security incidents were not due to alignment failures, but to misuse or lack of access controls. This underscores the importance of governance. Furthermore, the EU AI Act, which came into force in 2025, requires not only safe models but also risk management systems and traceability. Companies that fail to implement these additional layers face fines of up to 6% of their global turnover.

Consequences for businesses and users

For businesses, this means that investment in alignment must be complemented by monitoring systems, usage logs, authentication, and acceptable use policies. CIOs and CTOs cannot delegate safety to the model; they must build governance layers around it. For example, a company deploying an AI assistant for customer service must not only align the model but also implement interaction logs, role-based access controls, and periodic audits. For users, it implies that trust in a model must be accompanied by transparency about its limitations and context of use. An AI assistant that never lies can be equally dangerous if used for medical decisions without human oversight. The TNW article highlights that in 2025, a hospital in the UK had to withdraw an AI diagnostic system because, although accurate, doctors used it without verifying results, leading to diagnostic errors. Traceability becomes key: knowing who used the model, when, and for what purpose allows identifying and correcting misuse. Additionally, insurers are beginning to require these practices to cover AI risks, adding financial pressure.

“Perfect alignment is an illusion if not accompanied by robust governance. The model is not the system; the system includes people, processes, and policies.” — The Next Web

What should readers know?

First, no AI model is inherently safe; safety depends on the context of use. Second, traceability (who used the model, when, and for what) is as important as alignment. Third, emerging regulations (like the EU AI Act) are beginning to require not only safe models but also governance systems. Fourth, companies must audit not just the model but the entire deployment pipeline. A concrete example: in 2025, a fintech startup was penalized for not logging the use of its credit model, preventing real-time bias detection. Pipeline auditing includes everything from data collection to the user interface, including feedback mechanisms. Additionally, the TNW article mentions that AI investors are starting to value companies that demonstrate robust governance, not just technical capabilities. This could shift funding priorities.

Lessons for the future

The TNW article reminds us that technology does not operate in a vacuum. Alignment is an engineering problem; governance is a sociotechnical problem. While the industry obsesses over the former, the latter remains the great pending subject. Tech leaders must balance both to build truly trustworthy AI systems. The history of technology offers parallels: aviation safety was achieved not just with better planes, but with maintenance protocols, pilot training, and air traffic control systems. Similarly, AI needs a governance ecosystem. The article concludes that companies that ignore this lesson will face reputational, legal, and financial risks. The next decade will define whether AI becomes a reliable tool or a source of crisis. Alignment is the first step, but governance is the path.