Stop Paying the AI Tax: Own Your Stack, Own Your Outcomes

Enterprise Strategy · AI Sovereignty

Why Outcomes Beat Solutions in the New Intelligence Stack

Shashi Bellamkonda · February 2026 · With insights from Zoho Day 2026 / 6 min read

For decades, software was a tool used by humans to be more productive. We moved from paper maps to digital maps and from snail mail to email. We have essentially finished digitizing "things." At Zoho Day 2026, Raju Vegesna, Chief Evangelist at Zoho, argued that we are now entering a phase where software moves from being a tool for a task to a system that delivers an autonomous outcome.

This shift represents the digitization of "intelligence." The goal is no longer just saving time through better tools, but creating a system that understands the relationship between different parts of your business. This is what Raju calls the Universal Business Context.

The Core Concept: Sovereignty and the AI Tax

The enterprise strategy angle here is critical. Raju identifies a recurring "movie" in the tech industry: the transition from investment to extraction. Companies often enter a market with low prices to gain share, only to flip the switch and extract value once customers are locked in. This is what he defines as the "AI Tax."

The AI Tax Economic Thesis by Raju Vegesna

The predictable phase where proprietary AI vendors move from subsidizing model costs to extracting maximum profit, creating a permanent fiscal burden for enterprises that lack deployment choice.

🏗️
Vertical Integration

Owning the hardware, BIOS, and proprietary databases to squeeze out power and cost efficiencies.

Full Stack Control
🌐
Deployment Choice

Providing public cloud, private data center sections, or on-premise options to maintain data sovereignty.

Hybrid Flexibility
🧠
Model Agnosticism

Integrating with third-party LLMs, hosting open-source, or using proprietary specialized models.

Vendor Neutrality
📊
Scale Efficiency

Processing billions of AI requests monthly by utilizing small-to-mid-sized models for 80% of tasks.

Billions of Requests
Raju Vegesna Takeaway Context over size: A focused model with live, private data consistently outperforms massive, generic models that lack specific business relevance.

Managing the Knowledge Layers

Intelligence is driven by two distinct types of knowledge. Designing for both is the only way to achieve a high Narrative Strength Index in your digital transformation efforts. Most structured data is ignored or moved only for a narrow use case. Raju is right; there is rich information in calls and emails that provide context but is largely ignored or trapped in narrow CRM silos. Furthermore, structured reports often fail to provide context—they output what someone else thinks is important, not what a team needs for action.

The Unified Business Context

Raju explains how individual data signals tie together into a broader ecosystem strategy.

Design Knowledge

Formal structures like CRM fields, ontologies, and organizational hierarchies.

Discovered Knowledge

Signals found in unstructured data, such as chat sentiment or email open rates.

The Viewpoint: The End of Human Middleware

As software begins to possess the context to act autonomously, Raju notes that the "human middleware"—people who spend their days copying data from one system to another—will become a legacy friction point. We are moving toward a unified AppOS where intelligence is an integrated layer, not an add-on. I agree that the fascination for large models is often misplaced. Choosing smaller, narrow models using RAG (Retrieval-Augmented Generation) ensures accuracy and eliminates hallucinations.

Open Source Momentum

Raju believes open-source models will do for AI what Linux did for the cloud, breaking the "tax" model.

National Sovereignty

Raju observes that nations will increasingly mandate localized AI stacks to ensure essential services cannot be "switched off."

What Does This Mean for the Next Five Years?

The next five years will be defined by the struggle for data and model sovereignty. Fiscal maturity in the AI era, according to Raju, requires moving away from consumption-based "tax" models. CIOs must prioritize vertical integration—owning or controlling as much of the stack as possible. Choosing smaller, outcome-focused models is cheaper and more accurate. Companies that follow the hype lose without results; companies that choose outcomes over solutions win. If your data is fragmented, your AI will be expensive and prone to hallucinations. If your context is universal, your AI becomes an autonomous engine for growth.

Sources

  • Vegesna, Raju. "Keynote Address." Zoho Day 2026, 2026. zoho.com
Disclaimer: This blog reflects my personal views only. AI tools may have been used for research support. This content does not represent the views of my employer, Info-Tech Research Group.