DeepSeek R1: Hype vs. Reality — A Deeper Look at AI’s Latest Disruption

The release of DeepSeek R1 has sparked intense discussions across the AI community, marking a significant moment in the evolution of large language models (LLMs). This open-source reasoning model, backed by a Chinese hedge fund, quickly made waves — outpacing ChatGPT in app downloads, causing fluctuations in NVIDIA’s stock price, and triggering reactions from major AI players like OpenAI and Google. But beyond the buzz, what does DeepSeek R1 really mean for the future of AI development?

In a recent episode of the ODSC Ai X Podcast, mathematician and AI expert Sinan Ozdemir dissected the technical breakthroughs and market implications of DeepSeek R1, separating hype from reality.

You can listen to the full podcast on Spotify, Apple, and SoundCloud.

Breaking Down the DeepSeek R1 Model: A Mixture of Experts Approach

DeepSeek R1 is built on a mixture of experts (MoE) model, a technique where different “expert” subnetworks are activated selectively depending on the input. This allows the model to be efficient in handling different types of tasks without using all its parameters at once. OpenAI’s GPT-4 has long been speculated to use a similar approach, but DeepSeek R1 is among the first openly available models to incorporate MoE at a high level of performance.

While the mixture of experts approach isn’t new, DeepSeek R1’s unique training methodology has drawn attention. Unlike OpenAI’s reinforcement learning with human feedback (RLHF), DeepSeek R1 is trained using reinforcement learning without human feedback (RL) — a key distinction. Instead of relying on human evaluators, it iterates on math and coding problems, learning to refine its reasoning autonomously. This shift suggests a new pathway for LLM improvement without the bottleneck of human annotation.

Does Reasoning Matter? The Benchmark Debate

One of the biggest discussions surrounding DeepSeek R1 is its reasoning capability. Reasoning models like OpenAI’s GPT-4 Turbo and DeepSeek R1 are designed to handle complex, multi-step problems better than standard LLMs. However, Sinan points out that reasoning does not always translate to better results.

In his testing, DeepSeek’s V3 model, which lacks explicit reasoning, sometimes outperformed R1 when simple prompting techniques (like chain-of-thought) were applied. This raises the question: Is a dedicated reasoning model always necessary, or can smaller, more efficient models achieve similar results with the right guidance?

While DeepSeek R1 scores highly on math-heavy benchmarks, its performance on general knowledge and common sense reasoning tasks remains mixed. The disparity between mathematical rigor and real-world problem-solving capabilities shows that reasoning-focused AI models still have limitations.

The Cost and Accessibility of Open Source AI

DeepSeek R1 is being hailed as a major step forward for open-source AI. But does “open-source” truly democratize access? While the model’s weights are publicly available, deploying it remains a significant challenge. Running DeepSeek R1 requires expensive cloud infrastructure — AWS Bedrock estimates the cost of hosting it at around $35,000 per month. This raises an important point: open access does not necessarily mean widespread usability.

Additionally, Sinan highlights that OpenAI and other leading companies subsidize model access through venture capital funding. While DeepSeek R1 is available for free, users may still opt for cost-effective, proprietary alternatives due to the high cost of self-hosting.

China’s Growing AI Influence and Global Reactions

A key aspect of the DeepSeek R1 discourse is its geopolitical implications. The model’s rapid adoption underscores China’s ability to develop competitive AI systems — a reality that many in the Western AI community are only now fully acknowledging. The U.S. government’s response, including discussions on banning DeepSeek, reflects the broader tensions in AI dominance between global powers.

However, Sinan argues that the reaction to DeepSeek R1’s success is largely a perception issue rather than a technical one. If another Western company had released a model with similar capabilities, the panic would have been significantly lower. In reality, AI breakthroughs build upon years of incremental research, and competition in the space is both expected and necessary.

What’s Next for AI? The Future of Reasoning Models

DeepSeek R1’s release has sparked an arms race in AI development, with companies like OpenAI, Google, and Anthropic scrambling to release counter-updates. However, the real question is whether reasoning models like R1 will become industry standard or remain niche tools.

Sinan remains skeptical that reasoning models in their current form are game-changers. Speed and efficiency are still major challenges — reasoning models often force unnecessary steps, making them slower than their non-reasoning counterparts. For real-world applications, companies may favor lightweight models that can be fine-tuned with prompting rather than relying on heavy, slow reasoning architectures.

Final Verdict: Is DeepSeek R1 Overhyped or Worth the Attention?

Sinan’s take? DeepSeek R1 is overhyped — but still significant. The model represents a meaningful step forward in AI, particularly in open-source reasoning models. However, its impact is more about what it signals (China’s growing AI capabilities, cost-efficient training, and reinforcement learning advances) than what it actually delivers today.

For AI practitioners and businesses, the takeaway is clear: Don’t get caught up in the hype — evaluate models based on your specific needs. Whether reasoning models will drive the next wave of AI innovation or simply become another tool in the LLM toolkit remains to be seen. But one thing is certain — the pace of AI evolution isn’t slowing down anytime soon.