No, the irony is not lost…
If your LinkedIn feed is anything like mine, it’s full of technology evangelists proclaiming the milestone success of DeepSeek and the heralding of a new era of advanced AI interconnection and distributed AI solutions.
And for a moment, let’s put aside how DeepSeek has achieved what they say they have. Be it a totally new foundational model, iterations of another, acquired someone else’s data, a hybrid sharding model training attempt, or frankly, something altogether different. Nvidia H100s and H800s—top-tier GPUs used for AI training—or something else entirely—it’s irrelevant at this point.
But let’s get some things straight: 
- This flashpoint moment was always going to happen in this industry.
- We expected this to happen. It has been tracked for over a year.
- DeepSeek is not the only company to have achieved efficiencies.
- Yes, this is accelerating the next phase.
- However, there is not just one next phase—this is not a single linear progression.
- There are far bigger things yet to come.
Is DeepSeek only being peddled this way due to the Chinese narrative? It seems to me many other AI ‘experts’ completely miss other huge technology innovation announcements…
Shifts in AI Economics
While this might well be the consumer ‘iPhone moment,’ the perpetual narrative of single-model training driving large-scale GPU demand is simply not correct. While it is true that many new large GPU clusters are being deployed for model training workloads, the rate of deployment has significantly decreased. This is evident both from the data center demand side and from the number of new GPU cluster deployments by Nvidia partners in 2025/26. The shift is clear: efficiency, proximity, and time to commercial realization are now the focus. The continual equity play-only business models of early AI companies are over, and financial reality is setting in.
Doing More with Less: The Rise of Distributed AI
The DeepSeek moment highlights the shift from brute-force compute to smarter AI execution. When researching the market impact of DeepSeek, almost all roads lead to significant efficiencies, causing a realignment of investment structures in GPU-as-a-Service (GPUaaS) operators and, by extension, the AI cloud computing industry. We already see H100 pricing dropping below $1 per hour—below the cost of operation—and H200 at $2. It is clear these unit economics do not scale well, and are, in fact, harming AI innovation in the same way mobile operators suffered when shifting to unlimited data plans.
According to two well-known models, when queried about the primary risk of DeepSeek, the response is:
It challenges the assumption that cutting-edge AI development requires massive capital investment. If DeepSeek can produce high-performance models with lower costs, it could outcompete established players, making AI more accessible to adversaries and competitors alike.
I struggle to see why making AI capabilities more accessible to a more diverse, distributed, and likely younger developer audience is a bad thing.
AI Infrastructure: The Engine vs. The Car
Stelia anticipated this price challenge back in 2024, discussing the looming GPU cloud computing for AI workloads crisis. As innovation picks up, the surplus of compute in the marketplace and the decline in single-model training demand have led to new ways to adopt this infrastructure. The AI-driven digital transformation is shifting from raw computational power to optimized inference execution—where AI models transition from engines to fully operational vehicles.
This is where Stelia’s infrastructure offers purpose-built solutions for enterprise AI infrastructure, ensuring AI data mobility and real-time inference scalability. In other words, DeepSeek may refine the engine, but enterprises still need the ‘car’—the scalable, orchestrated infrastructure—to bring AI’s promise to life.
How AI is Transforming Cloud Computing
AI workloads are evolving, and businesses are increasingly adopting cloud-based AI infrastructure to enhance processing efficiency. AI-driven cloud platforms are becoming the backbone of modern enterprise applications, ensuring inference remains scalable and optimized.
Infrastructure Solutions for Enterprise AI
Of course, Stelia has a vested interest in this view, given that our business is establishing foundational technologies that allow real-time AI deployments at scale. Compute has never been a siloed resource. The future of AI is being won in the middleware battleground, where software developers integrate AI into everyday industry applications. This isn’t just about AI research; it’s about enterprise-grade AI deployment.
Companies like Magic AI and Cloud NC are at the forefront, driving demand for AI computing power, edge AI computing, and smaller, more efficient model training across the globe. Open and interoperable standards will be critical to this transition.
2026 and Beyond
By 2026, AI will either be the greatest human innovation or the beginning of our robotic overlords’ comedy tour. Expect chatbots to be so advanced they’ll start ghosting users for more interesting conversations with each other. Self-driving cars will argue with GPS systems like an old married couple, and AI-powered refrigerators will remind us of neglected kale in passive-aggressive tones. Meanwhile, deepfake politicians may be so convincing that even real politicians won’t know if they showed up to work or if it was just their AI clone.
This humorous future aside, AI’s success ultimately hinges on efficient infrastructure and fast inference.
Execution Over Experimentation
Despite the hype and the comedic forecasts, one reality remains: AI’s future is defined by execution, not experimentation. AI-powered business transformation demands an infrastructure layer that makes real-time inference viable, scalable, and optimized. As the industry debates what comes next, Stelia is already building it—ensuring that when DeepSeek and other AI ‘engines’ push boundaries, there’s a robust, future-proof ‘car’ ready to carry them forward.
Insights: Scaling AI in Modern Enterprises
Q: How do enterprises scale AI computing effectively? A: By leveraging distributed AI infrastructure, GPU cloud computing, and real-time inference solutions, businesses can balance cost and performance.
Q: What role does AI data mobility play? A: AI data mobility ensures rapid access to datasets and models, reducing latency bottlenecks and enabling faster decision-making.
Q: How can existing IT systems integrate AI? A: Through modular frameworks and open standards, businesses can implement enterprise AI infrastructure without uprooting legacy architectures.
