The authors argue that generative AI is now entering its second act, focusing on solving human problems end-to-end and creating more comprehensive solutions. They also outline a shared playbook for companies to follow, including emerging reasoning techniques, transfer learning, retrieval-augmented generation, new developer tools, and AI-first infrastructure companies. Despite the current challenges, the authors remain optimistic about the future of generative AI, citing the emergence of killer applications and the magnitude of end-user demand.
Key takeaways:
- The article discusses the rapid growth and challenges of generative AI, highlighting its potential as a profound platform shift in technology.
- Despite initial hype and success, generative AI has faced criticism and skepticism, particularly around its actual usefulness and the legitimacy of machine-generated IP.
- However, the authors argue that generative AI has already had a more successful start than SaaS, with over $1 billion in revenue from startups alone, and applications like ChatGPT, Midjourney, and Character becoming household names.
- Looking forward, the authors predict a shift from "Act 1" of generative AI, which was technology-out, to "Act 2", which will be customer-back, solving human problems end-to-end and introducing new editing interfaces and multi-modal applications.