Tokenomics Will Wreck Your Margins: The AI Model Routing Workflow That Cuts Spend 50%

Who owns the intelligence derived from enterprise data fed into an AI model?

It is legally and contractually undefined. In the cloud era, providers stored data without inferring intelligence from it, so ownership was clean. Frontier models can parameterize data, meaning the derived understanding may escape even when the raw data is protected. Tarun Raisoni argues enterprises will treat owning that intelligence as their primary competitive moat over the next three to five years.

What is a hybrid AI strategy and why do enterprises need one?

Splitting workloads by IP density. Low-value, low-gravity tasks go to frontier models and public cloud. Genuinely proprietary work runs on a fine-tuned large open source model inside your own environment. Crown jewel workloads never expose data, metadata, or algorithms to an external model. There is no default router, so the classification work is the actual project.

Can frontier AI models become competitors to their own enterprise customers?

In enterprise contexts, yes, and the precedent is Amazon Basics: observe what sells, build your own version, undercut the seller. Tarun's caveat is that this risk is concentrated where deep operational IP lives, such as ERP data covering sourcing, pricing, and logistics. For internal learning and development, frontier models are largely beneficial.

Are AI costs going to increase for businesses?

Yes. Current pricing reflects a substantial investor subsidy that has been declining over time and will continue to decline. The bill enterprises see today is not the real cost of the compute they consume. Founders should model unit economics against future pricing rather than today's.

How do you sell enterprise software or services as a brand new company?

Gruve's approach was acquisition-led: buy services businesses to import talent, partnerships, and existing customers at once. From there, relationships, customer word of mouth, published technical application notes, and partner ecosystems carry the motion. Enterprise decision cycles have also compressed because C-suites now use AI directly for strategy instead of hiring consultants first.

Should companies use frontier models for security testing and red teaming?

Tarun recommends against pointing a frontier model directly at your environment. Use purpose-built security tools that sit on top of those models and provide guardrails, so you get the findings without your information flowing into the model. He considers AI-assisted security the area where applied AI will scale first.

Will AI eliminate jobs or transform them?

His view is transformation, not elimination. Gruve has continued adding headcount while the roles themselves changed. He points to rising demand for electricians, cooling technicians, and construction trades driven directly by AI infrastructure buildout, plus entirely new categories for managing AI systems. He also expects India's technology services job count peaked in 2026, because outsourcing to manage cost stops making sense when AI does the cost reduction.

What does an outcome-based services model look like in practice?

Clients pay for a defined result rather than consulting hours or headcount. Gruve started with security because outcomes there are unusually well defined. Delivering with AI agents rather than human hours is what makes the margin structure work, and Mayfield has publicly cited software-like gross margins in the seventy to eighty percent range as the reason venture capital is now willing to fund a services company.

How much data do enterprises need to clean before deploying AI?

Tarun's rough field number is that seventy percent of enterprise data is usable and thirty percent lacks proper governance or quality. That thirty percent has to be addressed before AI deployment, alongside establishing governance practices, or the outputs cannot be trusted.

Why does energy abundance matter more than AI abundance?

Cheap energy at scale addresses hunger, literacy, and homelessness directly, and makes AI inexpensive as a consequence. Tarun nearly founded a renewable energy company instead of Gruve and studied at the Stanford Doerr School before pivoting, citing dependence on government support. He notes China added more than 430 GW of wind and solar in 2025, roughly a third of total US installed capacity, and that the US generates power from only about three percent of its dams.