My fellow pro-growth/progress/abundance Up Wingers in America and around the world:
Artificial intelligence is expected to transform the economy, but what about the AI industry itself? Companies across the AI supply chain are now valued in the trillions of dollars. But do the data and current demand justify such lofty expectations, or are investors getting ahead of reality? And when will the AI economy finally begin to live up to the hype?
Today on Faster, Please!—The Podcast, I am joined by Azeem Azhar, a Digital Fellow at Stanford’s Digital Economy Lab, the founder of Exponential View, a research platform focused on helping leaders understand emerging technologies and their impact on society. He is also the author of The Exponential Age and the co-author of The State of the AI Economy, one of the first serious attempts to measure the size and trajectory of the AI economy from the demand side and know what customers are actually paying for.
We discuss where the AI economy stands today, why businesses are still struggling to realize AI’s full potential, who stands to capture the greatest gains from the technology, and the roles different countries could play in the “AI Race” between the United States and China.
In This Episode:
The AI Economy (0:44)
AI’s Return on Investment (6:40)
Will AI Bring an Entrepreneurial Wave? (12:41)
When AI Hits the Bottom Line (17:53)
Who Wins the AI Economy? (23:48)
Europe in the AI Race (30:20)
A lightly edited transcript of our conversation will appear in my Week in Review issue on Saturday. (Another option is using the Substack auto transcript function.)
But here are some edited highlights from the chat:
On AI’s creation of abundance…
It may look like abundance when you’re on the other side before the inflection, but when you’re in it, it really looks like a day-to-day grind where you still have to fight for the things that you care about. The notion of abundance in general, I think, defies the realities of physics. You always need energy to get something done.
On why AI adoption has been slower than expected…
Companies are going through the similar type of process that Paul David describes around electrification, which is that you can put the light bulbs in the workshop—that is the copilot. But to move to the moving assembly line requires a lot more work and a lot more expertise.
On AI’s challenge for businesses…
No business was ever about, “Can we summarize an email quicker than before?” The question is: At what point do AI tools meaningfully come in to make those decisions happen more effectively—either at higher quality, at lower cost, or at higher speed?
On why AI may not lead to immediate job losses…
Companies realize that there was so much tacit knowledge in their workforce that they just let walk out the door. I think CEOs will start to want to understand: What are we losing when we cut an entire function for an LLM?
On whether we know who will win the AI race…
If you’d asked me two years ago, the winner was OpenAI, and Anthropic was really just playing around doing God knows what. So I don’t think we necessarily know who the biggest winners are. Does Anthropic or OpenAI have a significant role to play in several years time? I’m absolutely sure that will be the case. That doesn’t preclude there being other, bigger winners.
On Europe’s AI opportunity…
There’s things that states can do, even if they’re smaller states like the UK, because they can clear paths, they can help vertical integration, they can get access to the best specific talent. And this is not all about large language models. It’s also about models that can stimulate physics or models that can discover new materials.
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