How are the largest VCs viewing the early stages of the AI Era, from the perspective of investment, technology moats, economics, early adoption and future use-cases.  

SHOW: 879

SHOW TRANSCRIPT: The Cloudcast #879 Transcript

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SHOW NOTES:

• AI Eats the World (Presentation - Benedict Evans)

IS SILICON VALLEY STILL THE CENTER OF TECH INNOVATION?

• Companies are investing tons of money
• Breakthrough results haven’t emerged yet (business models, profits)
• It’s not clear that there is a technology moat; but maybe a capital moat
• Model training costs are expected to rise 5x to 10x - worse economics??
• Lots of VC investment and vendor 2nd-order investments
• LLM costs are creating marginal cost of software (been since the mainframe)
• Model quality vs. price is improving, but price of the services (e.g. ChatGPT-Pro) is increasing - how much extra value is being delivered?
• How will open source impact AI? 
• “If anything in life is certain, semiconductors are cyclical, commodity tech goes to marginal cost, and every new tech produces a bubble.”
• Today’s GenAI question - is it accurate and useful? How can we tell, and how can it improve (or does it need to)?
• Start with a simple concept - AI gives us unlimited interns - how can you extrapolate that? How would this have been extrapolated for the original internet (create content, translate language, write code, etc.)
• Use cases are still not easy to see beyond Chatbots (and variants), Coding Assistants
• Consulting revenue from GenAI is bigger than technology - and still most/many projects still in trials. 
• Technology can take a long time to adopt - Cloud still only has 30% of workloads (15yrs old)
• 66% of CEO’s don’t expect their first GenAI app in production until sometime in 2025, 50% at least 2H of 2025.
• [Shadow AI] SaaS AI will accelerate adoption, if it follows Cloud pattern - external forces are more motivated to attack business “change” than internal teams
• [Build vs. Ecosystem] Do the LLM vendors become the application vendors? Where does the LLM start and stop (infra, platform, API, apps, etc.)
• [Learning from the customers] Do the LLM vendors use their knowledge advantage to build the apps? 
• GenAI Apps Categories - Make something better, Replace something, Just do the thing
• “AI is just whatever is wrong/broken now” - How well does AI understand “broken”
• Will people be the biggest problem in AI progress? 
• [Decoupling] Looks at global markets for Internet today - ecommerce/retail, food delivery, advertising, media, autonomous driving, 
• [Elevator Example] Automation gets rid of people
• No real conclusion

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