The Business Impact of DeepSeek R1
For the first time, a non-Western model put in question the dominance of American LLM providers- not just in performance but in accessibility, cost, and infrastructure independence.
We had a community session on this topic and the following are some notes from that session along with some additional thoughts from me!
The LLM industry has been dominated by a few major players - OpenAI, Meta, Google DeepMind, and Anthropic, to name a few. A few models from the Middle East and Europe popped up here and there and took the headlines for a few days and then they disappeared as quickly as they showed up. Therefore, US based companies have controlled not only model development but also pricing, infrastructure, and the regulatory landscape around AI. This centralization may be partly due to where the capital for moonshot projects is most readily available, but it has certainly created a single point of failure for markets, entrepreneurs, and businesses alike. The dominance that we have experienced so far from the US companies in this market had also created relatively stable market dynamics at a relatively high price point for the end users; well, until a new kid on the block messed things up!
DeepSeek is a relatively young company and in our last two posts (this and this) we looked at the technical side of their newest model: R1, a reasoning model developed in China.
For the first time, a non-Western model put in question the dominance of American LLM providers- not just in performance but in accessibility, cost, and infrastructure independence. While there are still many open questions about deployment, security, and long-term impact, one thing is clear: mindset has shifted away from “only Americans can do it”, competition is here, and that’s good news for entrepreneurs.
Even if R1 itself doesn’t deserve the hype it created and even if it can’t hold up the promise like other models from non-US origins, its emergence signals a breakaway from the idea that all major AI breakthroughs must come from Western corporations. The availability of alternatives fosters competition, drives down costs, and increases the diversity of AI applications, creating new opportunities for businesses to build, experiment, and innovate on their own terms.
What has made me quite excited about LLMs in the past few years is the equalizing power that they bring to innovation. I know there is a lot of business potential around automating mundane tasks using LLM agents, but the part that gets me out of bed every day is building agentic applications that facilitate knowledge intensive workflows. Even with the earliest versions of LLM apps like ChatGPT we saw lowering of significant barriers to knowledge that was traditionally reserved to smaller portions of the population, think coding, business strategy, marketing tricks, and product ideation. This led into many more people trying out their crazy ideas or at least feeling more encouraged to explore their options. Now imagine with the lowering cost of operationalizing LLM systems, a serious competition that can impact pricing, and more powerful models, what kind of tools can we build to give more founders and founders-to-be a fair playing field!
The following section covers some select Qs and As from our session.
1. What makes DeepSeek R1 different from existing AI models?
DeepSeek R1 is a reasoning model that claims to be smaller and more efficient than existing alternatives. However, its real distinction lies in its origin. Unlike models from OpenAI or Anthropic, R1 was developed in China and optimized for deployment in non-Western infrastructure, such as Alibaba Cloud. This means businesses that previously had no choice but to rely on US-based AI providers now have an alternative. The model’s efficiency also raises questions about the future of AI computing, as it suggests that high-quality reasoning tasks may not require the enormous compute resources traditionally associated with GPT-O1-level models. For the western audience this might not even be an option, but imagine how many countries are out there, say the Middle East, Africa, and Eastern Europe who are more than happy to consider their options more broadly now that there are options available to them.
2. How does R1 impact the cost structure of AI-powered businesses?
Cost has always been a major factor in AI adoption. OpenAI’s most advanced models can cost up to $15 per million tokens for reasoning tasks, a price that makes AI-powered applications prohibitively expensive for many startups and small businesses. In contrast, DeepSeek R1 is priced at just $0.14 per million tokens - a staggering difference. While this might be an apples to pears comparison and these figures don’t necessarily reflect training or operational costs, they indicate that AI reasoning capabilities may soon become dramatically more affordable. This reduction in cost could allow small businesses to integrate advanced AI into their workflows without needing the budgets of Big Tech corporations.
3. Is DeepSeek a direct threat to Nvidia and Western AI infrastructure?
The release of R1 led to a short-term drop in Nvidia’s stock, highlighting the market’s reaction to potential shifts in AI computing demand. Investors had largely assumed that AI adoption worldwide would remain dependent on US cloud providers and American GPUs. However, the rise of R1 and similar models means businesses may increasingly turn to non-US cloud infrastructure and non-Nvidia chips, such as those developed by Huawei. While Nvidia and other Western AI players will likely continue to thrive, the assumption of American AI dominance is no longer a given. To a large extent this is a market correction because there was no reason to assume, this early in the game, that the dominance will remain in the West.
4. What challenges do businesses face in deploying R1?
Despite its advantages, R1 has proven difficult to deploy. Its official API has suffered frequent downtime, reportedly operating only 20% of the time due to DDoS attacks. Additionally, its architecture, built on the Mixture of Experts (MoE) framework, adds complexity to serving the model. MoE models have historically been difficult to scale and operate efficiently in production environments, which is why they have not been widely adopted despite their theoretical efficiency benefits. Entrepreneurs looking to integrate R1 into their products will need to consider these operational challenges.
5. Are there security risks associated with using R1?
Security is a major concern when adopting any AI model, and R1 is no exception. Some businesses may be hesitant to use a Chinese-developed AI system due to fears about data security and compliance. Those are fair concerns, but at the same time all those concerns can and should exist for any other players in the space. AI models, including R1, could be susceptible to training data poisoning, where adversaries inject subtle biases or vulnerabilities into a model’s responses. Additionally, LLMs can be manipulated to promote specific software libraries, including ones that contain hidden vulnerabilities. Businesses considering any LLMs, including R1 must weigh these risks against its cost and efficiency benefits.
6. How does R1 change the competitive landscape for AI startups?
Before R1, reasoning-capable AI was largely controlled by OpenAI and a few other firms, meaning startups had little choice but to pay high API fees for access. With R1 and potential future competitors, smaller businesses can now explore alternatives that are both cheaper and more flexible. This shift could make AI-powered startups more viable and profitable, especially in emerging markets where cost constraints previously limited access to high-quality models.
7. Is R1 a sign that China is overtaking the West in AI?
It’s too early to say that China is surpassing the US in AI innovation, but R1 demonstrates that the playing field is becoming more balanced. In 2023, 8 of the top 10 AI models were American, with only two exceptions - one from France (Mistral) and one from Canada (Cohere). By 2025, it is expected that at least half of the leading AI models will be Chinese. Companies like Alibaba, Baidu, and DeepSeek are rapidly catching up, and the assumption that only Western companies can build cutting-edge AI is no longer valid.