America Is Winning the Wrong AI Race

 

"The U.S. is winning an AI race -- but it's the wrong one. American policymakers have assumed that artificial general intelligence, or AGI, is achievable relatively soon, and so the aim has been to maintain an 18-month lead over China in reaching it. Washington has restricted Beijing's access to advanced chips, built AI energy infrastructure, and imposed export controls on other components. This has kept the U.S. ahead in the sprint for AGI, but it's a contest that can't be won. America should turn to a different, winnable AI race.

Experts shift the goal post for AGI, or "true intelligence" such as you'd see in a person, with each AI advance. Mastering chess and writing a coherent essay were once held out as AGI benchmarks. AI can now do both, but clear, obvious gaps with human capabilities persist. AGI is a philosophical goal -- a perpetually receding horizon -- rather than a practical target for strategic victory.

But even if experts could arrive at a stable definition for AI technological supremacy, trying to be the first nation to hit that goal isn't a smart policy priority. Because of how AI advances, foreign competitors will quickly catch up and likely using far fewer resources.

Model capabilities increase logarithmically with the hardware resources used to train them. In effect, this means you can make a model 90% as good as the model on the current frontier of AI performance with only 10% of the hardware. This is why limiting access to graphics processing units won't stop America's competition. Foreign companies and governments, even those with a fraction of the resources, will still be able to push neck-and-neck with U.S. companies. It was inevitable that a Chinese model like DeepSeek -- open-source, cheaply trained -- would come along to challenge American pre-eminence in AI, regardless of how tightly Washington controlled chip exports.

Moreover, key AI hardware and software are rapidly becoming more efficient. Something like Moore's Law -- the observation that CPUs double in capacity about every two years -- has proved roughly true for GPUs, too. At the same time, algorithmic improvements are driving model efficiency hard enough that smaller models can quickly catch up to those on the cutting edge of AI capability. The sort of advanced AI that today requires historic data-center investments will become accessible to more global players with moderate infrastructure tomorrow.

While America can't stop global AI model competition, what we can do is lead the race for AI implementation.

What will determine if a nation is ahead on AI isn't if it has the best models first, but if it is translating AI into widespread benefits for society. This means bringing the best models into organizations' core missions and processes, from the factory floor to the operating room to the battlefield.

Consider healthcare. Hospitals collect reams of data. The ability of a nurse or doctor to synthesize critical factors in a patient's case rapidly can be the difference between his getting better and worse, or even life and death. Leading hospitals have started using AI-driven systems to capture bed capacity, diagnoses, staffing patterns, surgical operations and more. Decisions that would have taken hours or even days can be made in seconds.

Nascent AI uses have their challenges. In healthcare, for instance, lawmakers will have to clarify carefully medical data-privacy laws. Patients need to be confident that their private information isn't being stored or used improperly, while healthcare practitioners need to avoid getting bogged down in useless box-checking and paperwork that does little to protect patients' rights. Lawmakers will have to craft similarly nuanced domain-specific legislation as AI usage spreads to different fields.

In manufacturing, AI can encourage progress toward what may seem like opposing goals -- efficiency and resiliency. On the factory floor, AI models can detect defects, letting companies catch faulty parts before they ship. AI can also more quickly identify the root causes of mistakes and prevent future faults. Outside factory walls, AI can capture every link in a supply chain, monitoring real-time disruptions and allocating resources accordingly.

On the battlefield, AI can make militaries faster and more lethal than ever before. The Maven Smart System, backed by Palantir's platform, has been able to improve targeting efficiency 100-fold by integrating weapons systems, satellite data, intelligence, force-readiness data and force tracking. Before, finding and verifying military targets could require as many as 2,000 soldiers, but with this AI-driven platform the U.S. military can accomplish the same objective with 20 soldiers. Now, soldiers can collaborate with models and work together much more efficiently with other service members across different functions and the chain of command. All this data comes together for a final decision that's still left up to a human.

The Maven Smart System helped turn a unit that was large enough to be easily visible to enemies into one so small that it's nearly invisible, and it will grow in capability as the government and commercial collaborators make further investments. This radically improves deterrence, even in the face of numerical disadvantage.

This is the sort of AI competition that matters. There isn't one goal but many spread across the economic and policy worlds. To make progress, the U.S. must focus on carefully smoothing the path for AI integration in each sector and rapidly invest in unleashing the technology's transformative power to improve lives and solve real-world problems. That's what winning the AI race looks like.

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Mr. Alhassani is Palantir's head of government affairs and public policy. He served as a special assistant at the National Security Council, 2009-12. Mr. Bak is Palantir's head of AI implementation. He serves on the National AI Advisory Committee's Subcommittee on Law Enforcement.” [1]

We lost this race already. Most of the work in the military today is done by drones. Part of these drones are already using AI to avoid electronic jamming.

Ability to mass produce such drones is decisive. We outsourced our mass production ability to China.

Therefore nothing could be done. Those 20 soldiers are just 20 clowns looking at computer screens, not able to do anything in the battle fields. Zelensky’s army is not using our drones. Zelensky’s army is using Chinese drones. What we are giving him, is not working. He is laughing at our tanks and F-16s. This is why Zelensky is playing a circus with us, and directing what president Trump has to do next: come to Turkey, sit right there, do more sanctions...

1. America Is Winning the Wrong AI Race. Alhassani, Mehdi; Bak, Anthony.  Wall Street Journal, Eastern edition; New York, N.Y.. 17 May 2025: A15.