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AI’s Race to the Bottom: How DeepSeek R1 Turned Innovation Into a Dollar Store Commodity – Or Did It?
2/1/25
Ivan Ruzic
Dario Amodei on DeepSeek, Commoditization, and AI’s Diverging Futures

Key Takeaways
DeepSeek’s R1 model has redefined AI economics, but its true cost remains a point of contention.
The overlooked significance of DeepSeek V3 as the base model reshapes how we view innovation.
Nevertheless, commoditization is reshaping artificial intelligence, but not without risks.
Two futures loom: a unipolar AI world dominated by democratic nations or a bipolar race between the U.S. and China.
In early 2025, DeepSeek unveiled its R1 model, sparking a frenzy in the AI world. Here was a Chinese startup claiming to offer reasoning capabilities comparable to OpenAI’s flagship systems at just 6% of the cost. Running on NVIDIA’s aging V100 GPUs and distributed as open-source software, R1 seemed to epitomize the commoditization of artificial intelligence—a process where cutting-edge innovations rapidly become widely accessible utilities.
But Dario Amodei, co-founder of Anthropic and one of AI’s leading voices, sees the story differently. While he acknowledges R1 as an impressive engineering feat, he argues that much of the hype surrounding its cost-effectiveness is overstated. According to Amodei, the real innovation wasn’t R1 itself but its predecessor, DeepSeek V3—a model that quietly laid the groundwork for R1’s success.
The Overlooked Importance of DeepSeek V3
Released just a month before R1, DeepSeek V3 was a pure pre-trained model that demonstrated exceptional reasoning capabilities across high-end tasks like advanced mathematics and coding. Amodei points out that V3 achieved near-parity with U.S. state-of-the-art models, but trained 7 to 10 months earlier—and it did so at a fraction of the cost. However, he cautions against viewing this as a revolutionary breakthrough.
“DeepSeek V3 represents an expected point on the ongoing cost-reduction curve,” Amodei explains. “It’s not some unique leap forward that fundamentally changes the economics of large language models.”
What makes V3 geopolitically significant isn’t its technical prowess but rather who created it—a Chinese company operating under U.S. chip sanctions. This marks the first time such a milestone has been achieved outside Western tech ecosystems, raising questions about global power dynamics in AI innovation.
V3’s engineering efficiency also deserves attention. It introduced advancements in key-value caching and optimized mixture-of-experts (MoE) architectures—techniques that allow models to dynamically activate only relevant subnetworks during inference. These innovations not only reduced computational costs but also set the stage for R1’s second phase of training: reinforcement learning fine-tuning for reasoning tasks.
R1: Building on V3’s Foundation
While R1 has captured headlines for its reasoning capabilities and open-source release, Amodei emphasizes that it owes much of its success to V3’s robust foundation. “R1 is essentially V3 with an additional reinforcement learning stage,” he notes. “Producing it was likely very cheap given how strong their pre-trained base model already was.”
This distinction matters because it reframes how we interpret R1’s cost claims. Critics have touted R1 as achieving what U.S. companies spend billions on for just $6 million—a figure that Amodei disputes. He argues that such comparisons overlook critical context: namely, that R1 was built on top of V3, which itself required significant investment and benefited from years of accumulated research in scaling laws and algorithmic progress.
To illustrate this point, Amodei draws comparisons to Anthropic’s own Claude 3.5 Sonnet model—a midsize system trained nine to twelve months ago without relying on larger models for distillation. Despite being older, Sonnet remains ahead of DeepSeek V3 in many internal and external evaluations while costing only tens of millions to train—far less than the billions often associated with frontier models.
“DeepSeek didn’t do for $6 million what costs U.S. companies billions,” Amodei asserts. “They produced something close to older U.S. models for less money but nowhere near the ratios people are suggesting.”
The Commoditization Debate: A Double-Edged Sword
Even if R1 doesn’t fundamentally rewrite AI economics, it undeniably accelerates commoditization - the process through which once-exclusive technologies become interchangeable utilities available at lower costs. Commoditization has transformed industries from steel production to cloud computing, but in AI, it introduces unique challenges.
At its core, commoditization lowers barriers to entry by making advanced tools accessible to smaller players with fewer resources. This democratization is evident in how quickly developers have repurposed R1 since its release; within 72 hours, 487 specialized derivatives appeared on platforms like Hugging Face, tackling tasks from legal contract analysis to protein folding simulations.
However, commoditization also erodes proprietary advantages that once protected incumbents like OpenAI and Anthropic. By open-sourcing R1 under an MIT license, DeepSeek has effectively leveled the playing field—empowering startups worldwide while challenging Silicon Valley’s dominance.
Amodei acknowledges this shift but warns against overstating its impact on training costs or hardware requirements. “Commoditization doesn’t mean we’ll spend less on AI,” he explains. “It means we’ll get more intelligence for the same budget—and then immediately reinvest those savings into building even smarter systems.”
This dynamic echoes what economists call the Jevons Paradox: efficiency gains often lead to increased consumption rather than savings. In AI, this translates into skyrocketing demand for compute power despite individual models becoming leaner and cheaper.
Export Controls and the Geopolitical Stakes
Amodei sees export controls as central to managing this new era of commoditized AI—particularly when it comes to China’s access to advanced chips like NVIDIA H100s. While DeepSeek has demonstrated that innovation can flourish even under constraints (as evidenced by their use of older V100 GPUs), he argues that allowing unrestricted access risks creating a bipolar world where both the U.S. and China wield equally powerful models.
In such a scenario, China could leverage its centralized governance structure and industrial base to focus disproportionately on military applications—a prospect Amodei finds deeply concerning.
Alternatively, effective export controls could lead to a unipolar world where democratic nations maintain dominance in AI development. This temporary lead could snowball into a lasting advantage if Western companies use their head start to develop even smarter systems.
But achieving this outcome requires vigilance. Amodei notes that while DeepSeek likely smuggled some banned chips into their operations, most of their hardware remains legal under current sanctions—highlighting gaps in enforcement that need urgent attention.
The Path Forward: Balancing Innovation and Responsibility
As commoditization reshapes AI development, policymakers and industry leaders face dual challenges: fostering innovation while mitigating risks related to security, sustainability, and geopolitical stability.
Amodei believes that sustainability efforts must move beyond optimizing individual models like R1 toward reimagining entire value chains—from chip production to energy consumption during inference tasks. Similarly, he calls for stronger safeguards around open-source releases to prevent misuse while preserving their benefits for academic research and small-scale innovation.
Ultimately, he argues that true leadership in AI will belong not to those who simply build cheaper models but to those who create ecosystems capable of balancing innovation with accountability.
“DeepSeek V3 was impressive; R1 is significant,” Amodei concludes. “But what matters most is how we navigate what comes next—because we’re no longer just building tools; we’re shaping futures.”
DeepSeek-R1 represents both the promise and peril of commoditized innovation: it democratizes access while destabilizing markets; it drives efficiency while straining sustainability efforts; it empowers startups while challenging incumbents’ control over technology's trajectory.
The stakes couldn’t be higher—and as Amodei reminds us, we are indeed playing for all the marbles now.
FAQs
Q. Why does Dario Amodei emphasize the importance of DeepSeek V3 over R1?
A. While R1 has garnered significant attention for its open-source release and reasoning capabilities, Dario Amodei argues that DeepSeek V3 is the true innovation. V3’s efficiency in pre-training and its engineering breakthroughs, such as key-value caching and optimized mixture-of-experts architecture, laid the foundation for R1. Amodei believes that R1’s success is largely built on V3’s advancements, which represent a predictable step in the ongoing cost-reduction curve rather than a groundbreaking leap.
Q. What are the broader implications of commoditized AI models like R1 for smaller nations?
A. Commoditization enables smaller nations and startups to access advanced AI capabilities without requiring massive investments in infrastructure. This could democratize innovation across sectors like healthcare, agriculture, and education. However, it also raises concerns about security, as open-source models can be exploited by malicious actors or authoritarian regimes to develop unregulated or harmful applications.
Q. *ow does reinforcement learning fit into the scaling curve of AI development?
A. Reinforcement learning (RL) represents a new paradigm in training large language models, focusing on reasoning and problem-solving tasks. By using RL as a second stage of training, models like R1 can achieve significant performance gains with relatively small investments compared to traditional pre-training methods. Amodei highlights this as an early-stage opportunity with massive potential for scaling up, suggesting that RL could soon dominate how future AI systems are developed.
Q. What risks does open-sourcing powerful AI models like R1 pose to global security?
A. Open-sourcing models like R1 introduces transparency but also creates vulnerabilities. With a 73% jailbreak success rate in red-teaming exercises, R1 demonstrates how easily such models can be manipulated for unintended purposes. This raises questions about whether the benefits of open access outweigh the risks of misuse, especially in critical areas like cybersecurity and misinformation campaigns.
Q. Could DeepSeek’s reliance on older hardware challenge the dominance of companies like NVIDIA?
A. DeepSeek’s ability to achieve high performance using outdated NVIDIA V100 GPUs challenges the industry’s reliance on cutting-edge hardware like H100 chips. While this may appear to undermine NVIDIA’s premium pricing model, it also opens opportunities for optimizing legacy hardware. In the short term, this could benefit NVIDIA through partnerships with companies like DeepSeek. However, long-term trends toward efficiency-focused architectures might reduce demand for next-generation chips, reshaping the GPU market entirely.
Sources:
[1] https://huggingface.co/blog/m-ric/dario-amodei-on-deepseek-r1
[2] https://ca.indeed.com/career-advice/career-development/commoditization
[3] https://smallbusiness.chron.com/examples-commoditization-36973.html
[4] https://builtin.com/artificial-intelligence/deepseek-r1
[5] https://www.technologyreview.com/2025/01/24/1110526/china-deepseek-top-ai-despite-sanctions/
[6] https://builtin.com/artificial-intelligence/what-deepseek-means-for-tech
[7] https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2018.00026/full
[8] https://www.timpaul.co.uk/posts/automation-and-the-jevons-paradox/
[10] https://en.wikipedia.org/wiki/Jevons_paradox?a=1
[11] https://blog.gorozen.com/blog/dr-jevons-paradox-energy-demand
[13] https://vast.ai/article/deepseek-r1-open-source-disruptor-or-overhyped-upstart
[14] https://fireworks.ai/blog/deepseek-r1-deepdive
[15] https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it
[16] https://www.amitysolutions.com/blog/deepseek-r1-ai-giant-from-china
[17] https://hiddenlayer.com/innovation-hub/deepsht-exposing-the-security-risks-of-deepseek-r1/
[18] https://seekingalpha.com/article/4753270-nvidia-deepseek-could-trigger-gpu-commoditization
[19] https://poly.ai/blog/reimagining-ai-innovation-lessons-from-deepseeks-r1-release/
Sources