The Tokenomics Trap: Why Your Agentic AI Investments Are Bleeding Cash

 The AI revolution is taking enterprises by storm, but behind the hype, agentic AI projects are stalling not because of a lack of technology, but because token-based AI is not scaling nearly as effectively. Tokenomics is effective for proofs of concept, but the maths breaks down when the complexity of agentic systems comes into play. Agentic systems plan, act, verify, and learn constantly.

When token-based pricing models are scaled to accommodate agentic AI, every action burns tokens, every verification multiplies cost, and every scaling attempt becomes a budgeting nightmare. This is the hidden tax of tokenomics: the illusion of affordability that breaks down at real-world scale.

The maths doesn’t add up

With agentic systems, a single, seemingly simple user query can trigger a complex chain of sub-agents, each consuming hundreds, if not thousands, of tokens. What looks like a $0.50 query can quickly become $10. Costs skyrocket when scaled across an enterprise’s daily operations. Token-based pricing makes budget forecasting nearly impossible. In fact, a staggering 80% of enterprises miss their AI infrastructure forecasts by more than 25%, with 84% reporting significant gross margin erosion tied to AI workloads. This financial turbulence erodes investor confidence and stalls scaling efforts.

Compounding this issue, overall investment in AI is accelerating without proper cost control. Research shows that the average monthly spend on AI tools is projected to rise by 36%, from $63,000 to over $85,000. This trajectory is unsustainable without a fundamental shift in how AI is deployed and priced.

Open-source models beat token-based cloud AI

Open-source models are rapidly catching up to expensive proprietary alternatives. Today, a 70-billion-parameter open-source model rivals proprietary APIs in reasoning and retrieval. It offers complete control over fine-tuning, optimisation, and deployment. Coupled with sovereign, on-prem infrastructure, it enables enterprises to transform agentic AI initiatives from cost centres into revenue-generating assets. This approach enables a sovereign AI stack, where every token generated, every action a model takes, and every prompt it responds to remains entirely under the organisation’s control.

The agentic AI revolution ultimately boils down to economics at scale and AI governance. An enterprise that controls its token pipeline controls its costs, compliance posture, and speed of innovation.

A case for sovereign AI

A sovereign AI architecture, deployed entirely on-prem at the edge, offers better ROI than cloud AI. It moves organisations from a variable, consumption-based model to a predictable annual licensing structure, converting variable operational expenditure (OpEx) into manageable capital expenditure (CapEx) or fixed OpEx.

With sovereign AI, data never leaves an organisation’s environment. Crucially, proprietary process IP, including custom prompts, workflows, and evaluation logic that define a company’s competitive edge, remains fully owned and governed internally, mitigating data leakage and audit risks.

Sovereign, private AI is purpose-built to execute complex, multi-agent workflows efficiently. The result is a dramatic reduction in the computational overhead of agentic reasoning. Leading private AI platforms have demonstrated the ability to reduce AI run costs by 30–50%, enabling higher throughput for the same workload.

The ROI mandate: from token burn to business return

For enterprise leaders, adopting sovereign AI is not just a technology upgrade but a direct path to measurable ROI. It restructures agentic AI investments from experimental cost centres into strategic value-creation engines.

Just a few years ago, many AI projects failed to deliver focused, high-impact deployments. Today, successful initiatives are achieving substantial returns. Analysis from 2025 shows that organisations with clear strategic focus achieve average cost savings of 15.2% and productivity improvements of 22.6% when targeting well-defined objectives.

Private, sovereign AI directly eliminates key operational inefficiencies, shifting resources away from firefighting and towards strategic growth. Variable token costs are replaced with predictable annual licensing, stabilising budgets and gross margins. With the DPDP Act and GDPR imposing severe penalties for data breaches, cloud AI presents increasing risk. Sovereign AI ensures regulatory compliance while minimising cyber exposure. It also consolidates fragmented tools, eliminating shadow AI costs.

A truly autonomous organisation is sovereign. To lead this shift, business leaders must recognise that the greatest impediment is not the technology itself, but the outdated economic model of tokenomics. Investing in sovereign AI is the decisive step required to unlock sustainable value. It is the only strategy that ensures AI investment is not a gamble in a consumption-based system, but a predictable, high-yield asset securing enterprise autonomy and long-term profitability. True autonomy begins when you own every token your AI consumes.

By Praveer Kochhar, Co-Founder & CPO, KOGO AI

(The views expressed in this article are those of the author and do not necessarily reflect the views or positions of CyberMedia )