The primary problem for AI creators isn't convincing people to trust their product, but stopping them from trusting it too much in areas where it's not yet reliable. This "low trustworthiness, high trust" scenario is a danger zone that can lead to catastrophic failures. The strategic challenge is managing and containing trust, not just building it.

Leaders must resist the temptation to deploy the most powerful AI model simply for a competitive edge. The primary strategic question for any AI initiative should be defining the necessary level of trustworthiness for its specific task and establishing who is accountable if it fails, before deployment begins.

The emphasis on long-term, unprovable risks like AI superintelligence is a strategic diversion. It shifts regulatory and safety efforts away from addressing tangible, immediate problems like model inaccuracy and security vulnerabilities, effectively resulting in a lack of meaningful oversight today.

The primary danger in AI safety is not a lack of theoretical solutions but the tendency for developers to implement defenses on a "just-in-time" basis. This leads to cutting corners and implementation errors, analogous to how strong cryptography is often defeated by sloppy code, not broken algorithms.

AI leaders aren't ignoring risks because they're malicious, but because they are trapped in a high-stakes competitive race. This "code red" environment incentivizes patching safety issues case-by-case rather than fundamentally re-architecting AI systems to be safe by construction.

Security expert Alex Komorowski argues that current AI systems are fundamentally insecure. The lack of a large-scale breach is a temporary illusion created by the early stage of AI integration into critical systems, not a testament to the effectiveness of current defenses.

AI companies engage in "safety revisionism," shifting the definition from preventing tangible harm to abstract concepts like "alignment" or future "existential risks." This tactic allows their inherently inaccurate models to bypass the traditional, rigorous safety standards required for defense and other critical systems.

Public fear focuses on AI hypothetically creating new nuclear weapons. The more immediate danger is militaries trusting highly inaccurate AI systems for critical command and control decisions over existing nuclear arsenals, where even a small error rate could be catastrophic.

Even when air-gapped, commercial foundation models are fundamentally compromised for military use. Their training on public web data makes them vulnerable to "data poisoning," where adversaries can embed hidden "sleeper agents" that trigger harmful behavior on command, creating a massive security risk.