When you look at where AI is headed, it is hard not to notice a familiar pattern. It looks a lot like cloud computing in its early and mid-stages. A few players dominate the market, racing to abstract complexity, while enterprises struggle to comprehend it all. The similarities are not superficial. The architecture, ecosystem dynamics, and even the blind spots we are beginning to see mirror the path we walked with cloud.
Just like cloud computing eventually became a utility, general-purpose AI will too.
From First-mover Advantage to Oligopoly
OpenAI had a distinct advantage, not only in terms of model performance but also in terms of brand affinity; even my non-technical mother was familiar with ChatGPT. That advantage, though, is shrinking, as we witnessed during the ChatGPT 5 launch. We now see the rise of other foundation model providers: Anthropic, Google Gemini, Meta’s Llama, Mistral, Midjourney, Cohere, Grok, and the fine-tuning layer from players like Perplexity.This is the same trajectory that cloud followed: a few hyperscalers emerged (AWS, Azure, and GCP), and while niche providers still exist, compute became a utility over time.
Enter Domain-Specific, Hyper-Specialized Models
This abstraction will not be the end. It will be the beginning of a new class of value creation: domain-specific models. These models will be smaller, faster, and easier to interpret. Think of LLMs trained on manufacturing data, healthcare diagnostics, supply chain heuristics, or even risk-scoring for cybersecurity.
These models won’t need 175B parameters or $100 million training budgets: they will be laser-focused and context-aware and deployable with privacy and compliance in mind. Most importantly, they will produce tailored outcomes that align tightly with organizational goals.
The outcome is similar to containerized microservices: small, purpose-built components operating near the edge, orchestrated intelligently, and monitored comprehensively. It is a back-to-the-future moment.
All the lessons from Distributed Computing …. Again
Remember the CAP theorem? Service meshes? Sidecars? The elegance of Kubernetes versus the chaos of homegrown container orchestration? Those learnings are not just relevant; they are essential again.
In our race to AI products, we forgot a key principle: AI systems are distributed systems.
Orchestration, communication, and coordination: these core tenets of distributed computing will define the next wave of AI infrastructure. Agent-to-agent communication, memory systems, vector stores, and real-time feedback loops need the same rigor we once applied to pub/sub models, API gateways, and distributed consensus.
Even non-functional requirements like security, latency, availability, and throughput have not disappeared. They’ve just been rebranded. Latency in LLMs is much a performance metric as disk IOPS in a storage array. Prompt injection is the new SQL injection. Trust boundaries, zero-trust networks, and data provenance are the new compliance battlegrounds.
Why This Matters
Many of us, in our excitement to create generative experiences, often overlook the fact that AI didn’t emerge overnight. It was enabled by cloud computing: GPUs, abundant storage, and scalable compute. Cloud computing itself is built on decades of distributed systems theory. AI will need to relearn those lessons fast.
The next generation of AI-native products won’t just be prompt-driven interfaces. They will be multi-agent architectures , orchestrated workflows, self-healing pipelines, and secure data provenance.
To build them, we will need to remember everything we learned from the cloud and not treat AI as magic but as the next logical abstraction layer.
Final thought
AI isn’t breaking computing rules; it’s reminding us why we made them. If you were there when cloud transformed the enterprise, welcome back. We’re just getting started.