According to @godofprompt, Mukund Jha’s experience interning at Google in 2009 revealed that true innovation in AI and tech is driven by engineering discipline rather than just speed. Google’s focus on building systems that are stable, scalable, and robust stands in contrast to the fast-and-loose approach often seen in startups. For AI startups, adopting rigorous engineering practices is critical for developing scalable AI solutions that can handle real-world demands and business growth. This lesson highlights a major opportunity for AI companies: prioritizing scalable architecture and reliability from the outset to ensure long-term success and market competitiveness (source: @godofprompt, Dec 9, 2025).

According to @godofprompt, Mukund’s experience at Google emphasizes that prioritizing robust AI infrastructure over rapid feature rollouts has given his team a decisive edge in the AI app-building space. Rather than following the 'move fast and break things' mantra, Mukund focused on building stable, production-grade systems that deliver reliability at scale. This approach has outperformed competitors who prioritized speed and flashy demos, highlighting a key business opportunity for AI startups: investing in foundational architecture ensures scalability, security, and long-term viability, which is now highly valued in enterprise AI deployments (source: @godofprompt, Dec 9, 2025).

According to @akshatgupta57, a major revision to their paper on Lifelong Knowledge Editing highlights that better regularization techniques are essential for maintaining consistent downstream performance in AI models. The research, conducted with collaborators from Berkeley AI, demonstrates that addressing regularization challenges directly improves the ability of models to edit and update knowledge without degrading previously learned information, which is critical for scalable, real-world AI deployments and continual learning systems (source: @akshatgupta57 on Twitter, May 23, 2025).