DeepSeek Projects 545% AI Profit—Is It Realistic or Just Hype?

Chinese AI startup DeepSeek claims it could achieve a 545% profit margin—but only if every user currently using its AI models switched to a paid plan. In a recent GitHub post, the company revealed that its V3 and R1 models cost approximately $87,072 per day to run, based on leasing Nvidia H800 chips at $2 per hour. If all users paid according to DeepSeek-R1’s pricing, the company estimates it could generate $562,027 in daily revenue, surpassing $200 million in annual earnings.

However, DeepSeek acknowledges that a large portion of its AI services remain free, meaning these numbers are purely theoretical. While the company has started monetizing some of its products, it has not yet achieved full adoption of paid plans—something no AI company has been able to do at scale.

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Statistics of DeepSeek's Online Service:
⚡ 73.7k/14.8k… — DeepSeek (@deepseek_ai) March 1, 2025

DeepSeek’s Revenue Projections Depend on Unrealistic Assumptions

While DeepSeek’s potential profitability looks impressive on paper, it depends on every free user converting to a paid plan, which is a highly unlikely scenario. AI companies worldwide, including OpenAI and Google DeepMind, have struggled to move free-tier users onto premium subscriptions. The AI industry has yet to find a fully sustainable monetization strategy, as most users prefer free access over paid plans.

Another challenge is DeepSeek’s tiered pricing model, which includes lower-cost plans for its V3 model. Since V3 is priced significantly lower than R1, the company’s actual revenue would likely be lower than the theoretical projection based only on R1 pricing. These pricing inconsistencies cast doubt on whether DeepSeek can ever achieve its estimated profit margins.

DeepSeek’s Discounted Pricing Reduces Real Profits

DeepSeek has designed its operations to cut costs through dynamic pricing and off-peak resource allocation. During peak hours, its Nvidia H800 chips handle inference tasks—where AI models generate responses. However, during lower-demand nighttime hours, DeepSeek shifts its hardware to focus on research and training, allowing for cost savings.

To attract developers, DeepSeek applies automatic nighttime discounts, reducing the cost for users who run AI workloads during off-peak hours. While this strategy helps balance computing power and reduce waste, it also lowers actual revenue compared to what could be earned under standard pricing. The company's financial model relies on maximizing peak-hour revenue, but with automatic discounts and variable pricing, achieving a consistent 545% profit margin remains unlikely.

DeepSeek’s Low-Cost AI Model Shocked the Tech Industry

DeepSeek recently disrupted the AI market with claims that it trained its AI model using less than $6 million in Nvidia chips. This number is far below the billion-dollar training budgets of competitors like OpenAI, Microsoft, and Google, which have spent vast sums building their AI infrastructure.

The announcement triggered a sell-off in tech stocks, as investors worried that if DeepSeek could develop powerful AI at a fraction of the cost, then companies investing billions into AI infrastructure might be overpaying. This raised questions about whether major AI players have been inefficient in their spending or if DeepSeek has found a way to optimize costs better than the rest of the industry.

Experts Challenge DeepSeek’s Cost Efficiency Claims

Despite DeepSeek’s bold claims, industry experts and competitors are skeptical of its financial model and cost efficiency. Google DeepMind CEO Demis Hassabis called the hype surrounding DeepSeek “exaggerated”, arguing that its $5.6 million training cost likely accounts only for the final phase of model training and ignores broader development, infrastructure, and data acquisition expenses.

AI training is a multi-stage process that requires years of investment in research, computing power, and human expertise. Many researchers believe that DeepSeek's cost claims omit significant hidden expenses, making its model seem more efficient than it actually is.

DeepSeek Faces Challenges in Turning Free Users Into Paying Customers

While DeepSeek’s projections provide a rare look into AI economics, its biggest challenge remains turning free users into paying customers. AI companies have struggled to balance affordable pricing with the high operational costs of running advanced AI models.

DeepSeek must also compete with established AI giants like OpenAI, Microsoft, and Google, which have brand recognition, larger infrastructure, and diverse monetization strategies. If DeepSeek cannot successfully convert free users into paying customers at scale, its financial model may prove unsustainable.

For now, DeepSeek’s claims have certainly shaken up the AI industry, but whether it can live up to its financial projections remains to be seen.

Also Read: Tech Stocks Drop as Chinese AI Model DeepSeek Challenges U.S. Dominance