AI still doesn't work well, businesses are faking it, and a reckoning is coming
theregister.com
March 17, 2026
8 min read
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47/100
Summary
Many businesses are struggling to effectively integrate AI into their operations, leading to concerns about potential issues arising from AI-generated code and content. Experts emphasize the need to reduce hype around AI and take time to identify appropriate use cases and reference architectures.
Key Takeaways
- Businesses are struggling to effectively integrate AI into their operations, with many pretending to have solutions despite lacking a clear playbook.
- AI-generated code can appear correct yet still be fundamentally flawed, as demonstrated by an AI attempt to rewrite SQLite that resulted in code performing 2,000 times worse than the original.
- Current metrics used to evaluate coding success, such as lines of code and pull requests, do not accurately reflect engineering quality or team performance, necessitating new measurement standards.
- The founders of Codestrap emphasize the need for organizations to experiment and iterate with AI, as it still does not work well in practice.
Community Sentiment
Positives
- The rapid advancement of AI tools, which were mere toys two years ago, indicates a significant potential for future improvements, even if current implementations are flawed.
Concerns
- The performance of AI systems is severely lacking, as evidenced by a database that is 2,000 times worse than its predecessor, raising concerns about viability and investment.
- AI-generated designs have led to significant errors in real-world applications, causing project delays and highlighting the tool's unreliability in critical decision-making.
- Trust in AI remains low due to a history of unmet expectations and false promises, making businesses hesitant to fully embrace its capabilities.
