Generative AI has ushered in a modern era of innovation, but faces challenges in user retention and value delivery.
Copyright: sequoiacap.com – BY SONYA HUANG, PAT GRADY, AND GPT-4 – “Generative AI’s Act Two”
Scientists, historians and economists have long studied the optimal conditions that create a Cambrian explosion of innovation. In generative AI, we have reached a modern marvel, our generation’s space race.
This moment has been decades in the making. Six decades of Moore’s Law have given us the compute horsepower to process exaflops of data. Four decades of the internet (accelerated by COVID) have given us trillions of tokens’ worth of training data. Two decades of mobile and cloud computing have given every human a supercomputer in the palm of our hands. In other words, decades of technological progress have accumulated to create the necessary conditions for generative AI to take flight.
ChatGPT’s rise was the spark that lit the fuse, unleashing a density and fervor of innovation that we have not seen in years—perhaps since the early days of the internet. The breathless excitement was especially visceral in “Cerebral Valley,” where AI researchers reached rockstar status and hacker houses were filled to the brim each weekend with new autonomous agents and companionship chatbots. AI researchers transformed from the proverbial “hacker in the garage” to special forces units commanding billions of dollars of compute. The arXiv printing press has become so prolific that researchers have jokingly called for a pause on new publications so they can catch up.
But quickly, AI excitement turned to borderline hysteria. Suddenly, every company was an “AI copilot.” Our inboxes got filled up with undifferentiated pitches for “AI Salesforce” and “AI Adobe” and “AI Instagram.” The $100M pre-product seed round returned. We found ourselves in an unsustainable feeding frenzy of fundraising, talent wars and GPU procurement.
And sure enough, the cracks started to show. Artists and writers and singers challenged the legitimacy of machine-generated IP. Debates over ethics, regulation and looming superintelligence consumed Washington. And perhaps most worryingly, a whisper began to spread within Silicon Valley that generative AI was not actually useful. The products were falling far short of expectations, as evidenced by terrible user retention. End user demand began to plateau for many applications. Was this just another vaporware cycle?[…]
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Read more: www.sequoiacap.com
Generative AI has ushered in a modern era of innovation, but faces challenges in user retention and value delivery.
Copyright: sequoiacap.com – BY SONYA HUANG, PAT GRADY, AND GPT-4 – “Generative AI’s Act Two”
This moment has been decades in the making. Six decades of Moore’s Law have given us the compute horsepower to process exaflops of data. Four decades of the internet (accelerated by COVID) have given us trillions of tokens’ worth of training data. Two decades of mobile and cloud computing have given every human a supercomputer in the palm of our hands. In other words, decades of technological progress have accumulated to create the necessary conditions for generative AI to take flight.
ChatGPT’s rise was the spark that lit the fuse, unleashing a density and fervor of innovation that we have not seen in years—perhaps since the early days of the internet. The breathless excitement was especially visceral in “Cerebral Valley,” where AI researchers reached rockstar status and hacker houses were filled to the brim each weekend with new autonomous agents and companionship chatbots. AI researchers transformed from the proverbial “hacker in the garage” to special forces units commanding billions of dollars of compute. The arXiv printing press has become so prolific that researchers have jokingly called for a pause on new publications so they can catch up.
But quickly, AI excitement turned to borderline hysteria. Suddenly, every company was an “AI copilot.” Our inboxes got filled up with undifferentiated pitches for “AI Salesforce” and “AI Adobe” and “AI Instagram.” The $100M pre-product seed round returned. We found ourselves in an unsustainable feeding frenzy of fundraising, talent wars and GPU procurement.
And sure enough, the cracks started to show. Artists and writers and singers challenged the legitimacy of machine-generated IP. Debates over ethics, regulation and looming superintelligence consumed Washington. And perhaps most worryingly, a whisper began to spread within Silicon Valley that generative AI was not actually useful. The products were falling far short of expectations, as evidenced by terrible user retention. End user demand began to plateau for many applications. Was this just another vaporware cycle?[…]
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Read more: www.sequoiacap.com
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