The next challenge in software development will be ensuring the verification of code generated by AI, as simply improving code writing won't significantly speed up engineering processes.

The bottleneck for AI development isn't the algorithms or technical limitations; it's actually the availability of data.

AI can assist in fact-checking and improving the accuracy of information, but we must also develop tools to verify AI outputs responsibly.

The future of AI will likely involve developing numerous routine tests to assess the truthfulness of generated content, similar to how image recognition evolved.

The more testing AI models undergo, the more reliable they become, similar to software development practices.

The more testing AI models undergo, the more reliable they become, similar to software development practices.

Trust in AI systems involves verifying their reliability and consistency, which is crucial for user acceptance and enterprise success.

The reliability of AI systems is a major concern as we transition from consumer applications to mission-critical systems like healthcare and defense.

If AI can develop a sense of viability for proof strategies, it could potentially collaborate with human mathematicians, enhancing the problem-solving process.