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US AI Dominance Is Over: Here's Why

2026-07-27 · 14:00 UTC ·AI News & Strategy Daily | Nate B Jones ·1 min read

Brief

Nate B Jones' July 27, 2026 presentation (video/podcast) runs a practical bakeoff of Chinese LLMs (DeepSeek, Qwen, GLM, Kimi, MiniMax), compares token and finished-work economics (Kimi K3 $15, DeepSeek $0.87), explains mixture-of-experts and serving costs, summarizes CAISI findings on DeepSeek V4 Pro, and recommends Chinese models for bounded, high-volume, checkable work but not where errors are costly.

Why it matters

Nate B Jones (AI News & Strategy Daily) published July 27, 2026, shows Kimi K3 priced at $15 and DeepSeek at $0.87 and demonstrates Chinese models deliver real money on bounded, high-volume, checkable tasks where lower cost-per-accepted-result matters.

Key details

  • He ran a bakeoff evaluating DeepSeek, Qwen, GLM, Kimi, and MiniMax and reports the CAISI evaluation found DeepSeek V4 Pro offers favorable economics for high-volume endpoints (see chapters on 'Where DeepSeek earns high-volume work' and 'Cost per accepted result').
  • Mixture-of-experts changes your hardware burden because only active experts need serving, downloading weights doesn’t solve serving costs, and distillation allegations matter for how quickly capability spreads—prompting his recommendation to choose API, third-party host, or self-host only after his four pre-commit questions.
Source evidence

Should you use Chinese AI models? How I test DeepSeek, Qwen, GLM, Kimi, and MiniMax before I put any of them on real work.

How to run a Chinese-model bakeoff (Guide):
https://natesnewsletter.substack.com/p/chinese-ai-models-test?r=1z4sm5&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true

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What's really happening inside the Chinese model conversation?

The common story is that Chinese models are cheaper, more open, and easy to run
locally — but the real question is which specific job, endpoint, and check you are
buying, because price, license, hardware burden, and data path all move independently.

In this video, I share the inside scoop on how I actually evaluate Chinese models:

  • Why token price and finished work cost point in opposite directions
  • How mixture-of-experts changes what your hardware actually has to hold
  • What the CAISI evaluation found about DeepSeek V4 Pro economics
  • Where the distillation allegations matter for how fast capability spreads

Chinese models are worth real money on bounded, high-volume, checkable work, and
they are still the wrong default anywhere a plausible error is expensive to catch.

Chapters:
00:00 Why Chinese models is not one thing
00:47 Kimi K3 at $15, DeepSeek at $0.87
02:29 Where DeepSeek earns high-volume work
04:17 Testing GLM, Kimi, Qwen, and MiniMax
05:39 Five labs, five different strategies
06:57 Mixture of experts and your hardware burden
08:04 Why downloading weights does not solve serving
09:10 What the CAISI evaluation found
10:19 Cost per accepted result
13:15 Distillation, and where the fight starts
17:08 API, third-party host, or self-host
20:05 The four questions I run before committing

Listen to this video as a podcast.

Spotify: https://open.spotify.com/show/0gkFdjd1wptEKJKLu9LbZ4
Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-news-strategy-daily-with-nate-b-jones/id1877109372

Channel: AI News & Strategy Daily | Nate B Jones
Published: 2026-07-27
Video URL: https://www.youtube.com/watch?v=JBzz53HqMEs