Luke Ford

American workers are about the hardest-working and most productive workers in the world, and they can buy more stuff per time worked then most any other nation in the world.

It’s not surprising that American business is way ahead in adopting AI. Optimism makes it easier to innovate and Americans are about the most optimistic and enthusiastic people in the world.

These generalizations are based on my first-hand observations combined with what I have read and heard.

If your job includes writing and research, AI will make your writing and research sharper and better. If you jump on AI now, you may be the only person in your workplace who’s using it. In 18 months, in certain information sectors such as law and coding, you will either use AI or you don’t have a job. AI is already far better at making a medical diagnosis than doctors.

Grok says:

The piece paints a vivid picture of AI’s transformative impact on business and society, with the U.S. leading the charge. It’s a compelling narrative, grounded in data like the $109 billion in U.S. AI investment in 2024 and the stark contrast with China ($9.3 billion) and the UK ($4.5 billion). The argument about U.S. structural advantages—flexible labor markets, massive capital flows, and a relatively hands-off regulatory environment—holds water, especially when you consider historical tech adoption cycles like the internet boom and the app economy. The point about Trump’s budget bill preempting state-level AI regulation is intriguing, as it could indeed streamline deployment, though it risks glossing over ethical or safety concerns that fragmented regulation might otherwise address.
The mention of China’s DeepSeek and its open-source approach is a sharp counterpoint, highlighting a potential blind spot in U.S. dominance. Kai-Fu Lee’s perspective—that Chinese firms lag in enterprise spending but excel in consumer AI apps—adds nuance, suggesting the U.S. lead isn’t unassailable. The idea that open-source models could bypass U.S.-China tech decoupling is a critical insight, as it underscores the difficulty of controlling intangible digital flows compared to physical chips.
However, the piece leans heavily on optimism about productivity gains without fully grappling with the social fallout. The reference to Anthropic’s CEO warning of 50% entry-level job losses in one to five years is alarming, and the nod to youth unemployment linked to AI rollouts feels underexplored. Steve Bannon’s prediction about AI disruption fueling 2028 election tensions seems plausible, but it’s a bit speculative without more context on voter sentiment or political platforms.
The comparison to Europe’s slower AI adoption is well-supported, but it risks oversimplifying Europe’s challenges. “Tighter labour regimes” and “fragmented markets” are real, but cultural attitudes toward risk and innovation, plus GDPR-like regulations, also play a role. The piece could’ve dug deeper here.
Overall, it’s a strong analysis that captures the U.S.’s AI momentum and its economic implications while flagging real risks like job displacement and geopolitical competition. It’s bullish on American business but doesn’t shy away from China’s potential to disrupt via open-source AI.