Imagine trying to drive a Ferrari on crumbling roads. No matter how fast the car is, its full potential is wasted without a solid foundation to support it. That analogy sums up today’s enterprise AI landscape. Businesses often obsess over shiny new models like DeepSeek-R1 or OpenAI o1 while neglecting the importance of infrastructure to derive value from them. Instead of solely focusing on who’s building the most advanced models, businesses need to start investing in robust, flexible, and secure infrastructure that enables them to work effectively with any AI model, adapt to technological advancements, and safeguard their data.
With the release of DeepSeek, a highly sophisticated large language model (LLM) with controversial origins, the industry is currently gripped by two questions:
- Is DeepSeek real or just smoke and mirrors?Did we over-invest in companies like OpenAI and NVIDIA?
Tongue-in-cheek Twitter comments imply that DeepSeek does what Chinese technology does best: “almost as good, but way cheaper.” Others imply that it seems too good to be true. A month after its release, NVIDIA’s market dropped nearly $600 Billion and Axios suggests this could be an extinction-level event for venture capital firms. Major voices are questioning whether Project Stargate’s $500 Billion commitment towards physical AI infrastructure investment is needed, just 7 days after its announcement.
And today, Alibaba just announced a model that claims to surpass DeepSeek!
AI models are just one part of the equation. It’s the shiny new object, not the whole package for Enterprises. What’s missing is AI-native infrastructure.
A foundational model is merely a technology—it needs capable, AI-native tooling to transform into a powerful business asset. As AI evolves at lightning speed, a model you adopt today might be obsolete tomorrow. What businesses really need is not just the “best” or “newest” AI model—but the tools and infrastructure to seamlessly adapt to new models and use them effectively.
Whether DeepSeek represents disruptive innovation or exaggerated hype isn’t the real question. Instead, organizations should set their skepticism aside and ask themselves if they have the right AI infrastructure to stay resilient as models improve and change. And can they switch between models easily to achieve their business goals without reengineering everything?
Models vs. Infrastructure vs. Applications
To better understand the role of infrastructure, consider the three components of leveraging AI:
- The Models: These are your AI engines—Large Language Models (LLMs) like ChatGPT, Gemini, and DeepSeek. They perform tasks such as language understanding, data classification, predictions, and more.The Infrastructure: This is the foundation on which AI models operate. It includes the tools, technology, and managed services necessary to integrate, manage, and scale models while aligning them with business needs. This generally includes technology that focuses on Compute, Data, Orchestration and Integration. Companies like Amazon and Google provide the infrastructure to run models, and tools to integrate them into an enterprise’s tech stack.The Applications/Use Cases: These are the apps that end users see that utilize AI models to accomplish a business outcome. Hundreds of offerings are entering the market from incumbents bolting on AI to existing apps (i.e., Adobe, Microsoft Office with Copilot.) and their AI-native challengers (Numeric, Clay, Captions).
While models and applications often steal the spotlight, infrastructure quietly enables everything to work together smoothly and sets the foundation for how models and applications operate in the future. It ensures organizations can switch between models and unlock the real value of AI—without breaking the bank or disrupting operations.
Why AI-native infrastructure is mission-critical
Each LLM excels at different tasks. For example, ChatGPT is great for conversational AI, while Med-PaLM is designed to answer medical questions. The landscape of AI is so hotly contested that today’s top-performing model could be eclipsed by a cheaper, better competitor tomorrow.
Without flexible infrastructure, companies may find themselves locked into one model, unable to switch without completely rebuilding their tech stack. That’s a costly and inefficient position to be in. By investing in infrastructure that is model-agnostic, businesses can integrate the best tools for their needs—whether it's transitioning from ChatGPT to DeepSeek, or adopting an entirely new model that launches next month.
An AI model that is cutting-edge today may become obsolete in weeks. Consider hardware advancements like GPUs—businesses wouldn’t replace their entire computing system for the newest GPU; instead, they’d ensure their systems can adapt to newer GPUs seamlessly. AI models require the same adaptability. Proper infrastructure ensures enterprises can consistently upgrade or switch their models without reengineering entire workflows.
Much of the current enterprise tooling is not built with AI in mind. Most data tools—like those that are part of the traditional analytics stack—are designed for code-heavy, manual data manipulation. Retrofitting AI into these existing tools often creates inefficiencies and limits the potential of advanced models.
AI-native tools, on the other hand, are purpose-built to interact seamlessly with AI models. They simplify processes, reduce reliance on technical users, and leverage AI’s ability to not just process data but extract actionable insights. AI-native solutions can abstract complex data and make it usable by AI for querying or visualization purposes.
Core pillars of AI infrastructure success
To future-proof your business, prioritize these foundational elements for AI infrastructure:
Data Abstraction Layer
Think of AI as a “super-powered toddler.” It’s highly capable but needs clear boundaries and guided access to your data. An AI-native data abstraction layer acts as a controlled gateway, ensuring your LLMs only access relevant information and follow proper security protocols. It can also enable consistent access to metadata and context no matter what models you are using.
Explainability and Trust
AI outputs can often feel like black boxes—useful, but hard to trust. For example, if your model summarizes six months of customer complaints, you need to understand not only how this conclusion was reached but also what specific data points informed this summary.
AI-native Infrastructure must include tools that provide explainability and reasoning—allowing humans to trace model outputs back to their sources, and understand the reason for the outputs. This enhances trust and ensures repeatable, consistent results.
Semantic Layer
A semantic layer organizes data so that both humans and AI can interact with it intuitively. It abstracts the technical complexity of raw data and presents meaningful business information as context to LLMs while answering business questions. A well nourished semantic layer can significantly reduce LLM hallucinations. .
For instance, an LLM application with a powerful semantic layer could not only analyze your customer churn rate but also explain why customers are leaving, based on tagged sentiment in customer reviews.
Flexibility and Agility
Your infrastructure needs to enable agility—allowing organizations to switch models or tools based on evolving needs. Platforms with modular architectures or pipelines can provide this agility. Such tools allow businesses to test and deploy multiple models simultaneously and then scale the solutions that demonstrate the best ROI.
Governance Layers for AI Accountability
AI governance is the backbone of responsible AI use. Enterprises need robust governance layers to ensure models are used ethically, securely, and within regulatory guidelines. AI governance manages three things.
- Access Controls: Who can use the model and what data can it access?Transparency: How are outputs generated and can the AI’s recommendations be audited?Risk Mitigation:Preventing AI from making unauthorized decisions or using sensitive data improperly.
Imagine a scenario where an open-source model like DeepSeek is given access to SharePoint document libraries . Without governance in place, DeepSeek can answer questions that could include sensitive company data, potentially leading to catastrophic breaches or misinformed analyses that damage the business. Governance layers reduce this risk, ensuring AI is deployed strategically and securely across the organization.
Why infrastructure is especially critical now
Let's revisit DeepSeek. While its long-term impact remains uncertain, it’s clear that global AI competition is heating up. Companies operating in this space can no longer afford to rely on assumptions that one country, vendor, or technology will maintain dominance forever.
Without robust infrastructure:
- Businesses are at greater risk of being stuck with outdated or inefficient models.Transitioning between tools becomes a time-consuming, expensive process.Teams lack the ability to audit, trust, and understand the outputs of AI systems clearly.
Infrastructure doesn’t just make AI adoption easier—it unlocks AI’s full potential.
Build roads instead of buying engines
Models like DeepSeek, ChatGPT, or Gemini might grab headlines, but they are only one piece of the larger AI puzzle. True enterprise success in this era depends on strong, future-proofed AI infrastructure that allows adaptability and scalability.
Don’t get distracted by the “Ferraris” of AI models. Focus on building the “roads”—the infrastructure—to ensure your company thrives now and in the future.
To start leveraging AI with flexible, scalable infrastructure tailored to your business, it’s time to act. Stay ahead of the curve and ensure your organization is prepared for whatever the AI landscape brings next.
The post DeepSeek Distractions: Why AI-Native Infrastructure, Not Models, Will Define Enterprise Success appeared first on Unite.AI.
