In a recent feature interview with Capital, Bulgaria’s leading business publication, DiscreteStack Founder and CEO Hristo Todorov addressed a question that is quietly reshaping enterprise strategy:
If AI becomes central to how a business operates, who actually owns it?
The interview, titled “The Future with AI: Do You Own It or Rent It?”, marks a significant moment – not just for DiscreteStack, but for the broader conversation about how enterprises are building their AI foundations.
From Adoption to Dependency
For the past several years, enterprise AI strategy was largely about adoption. Which tools to use. Where to start. How fast to move.
That phase is changing.
As Hristo Todorov explained in the Capital interview, AI is no longer sitting at the edge of business operations. It is moving into software development pipelines, customer workflows, internal decision systems, and operational processes. The further it moves toward the core, the more the question shifts from “should we use AI?” to “what is our relationship with the AI we depend on?”
That distinction sounds philosophical. The business consequences are not.
When AI is an experiment, convenience is the right priority. When AI is infrastructure – when it shapes how the business runs, scales, and competes – the calculus changes. Control, predictability, and long-term dependency become the variables that matter most.
The Rented Land Analogy
One of the most memorable observations from the Capital interview was Hristo Todorov’s framing of how most enterprises currently relate to AI:
“Building your business on AI owned by someone else is like building on rented land.”
The analogy resonates precisely because it is accurate. The models are owned by the provider. The infrastructure is owned by the provider. The pricing, the rules of access, the future product roadmap – all of it sits outside the enterprise’s control. The business can use the AI. But it does not own it.
And rented land works, until the owner changes the terms.
Three Risks Behind the Current Model
Todorov identified three interconnected risks for enterprises running critical operations on third-party AI platforms.
The first is data exposure. Every prompt, document, and internal workflow processed through an external AI system leaves the organization’s perimeter. For companies in regulated industries – finance, aviation, healthcare, legal – this creates governance and compliance questions that go well beyond technology.
The second is loss of control. When a critical business workflow depends on a model owned and operated by another company, any change to that model’s behavior, pricing, or access terms becomes a business risk. Organizations may have no recourse and no warning.
The third is cost unpredictability. As Todorov noted in the interview, usage-based pricing looks efficient at the start. But as AI becomes more integrated into operations, the costs compound – token metering scales with every prompt, every agent step, every workflow – regardless of whether that usage is creating value or burning budget.
This is why AI economics are no longer a technology conversation. They are a finance and procurement conversation.
Why the Ownership Question is Urgent Now
For much of the AI boom, enterprises accepted these trade-offs because there was no credible alternative. The best models were proprietary. The performance gap between closed and open-weight AI was real.
That gap has closed.
As Todorov explained in Capital, the rise of capable open-weight models is making AI ownership a practical option – not a theoretical one. Enterprises can now run powerful, frontier-grade AI systems inside environments they control, on hardware they own, under governance they define. The open models driving this shift now match or exceed proprietary alternatives on key benchmarks, with only a matter of weeks separating open from closed.
The choice is no longer between building everything from scratch or renting intelligence from hyperscalers. A third path exists: deploying owned AI infrastructure, at enterprise grade, without the complexity of building it alone.
As the Stanford HAI 2026 AI Index confirms, the intelligence gap between best-in-class open and closed models has shrunk to an average of three months – and in several domains, it has vanished entirely. The performance premium that once justified vendor dependency is disappearing. What remains is the dependency itself.
What DiscreteStack Was Built to Solve
The Capital interview explored the origin of DiscreteStack in direct terms.
Todorov described how the company emerged from working with enterprise clients who wanted to adopt AI seriously – but could not accept the trade-offs of routing sensitive workflows and proprietary data through external platforms. The compliance exposure was too high. The cost structure was too unpredictable. The control was too limited.
DiscreteStack was built to close that gap – a private AI operating system deployed on infrastructure controlled by the customer, running frontier open-weight models, with fixed annual licensing instead of token-based billing. The data never leaves the enterprise’s perimeter. The economics don’t scale with usage.
As Todorov framed it in the interview, the hyperscalers want companies to rent AI. DiscreteStack was built for the ones that want to own it.
That disctinction defines more that positioning. It defines the architecture, the pricing model, and the customer relationship.
The Strategic Choice Ahead
The Capital interview captures a broader inflection point in enterprise AI.
The first phase of the market was about access. The next phase is about control. As AI embeds itself into the systems and workflows that define how businesses operate, the ownership question stops being a vendor comparison and becomes a strategic decision.
For many use cases, renting AI will remain sufficient. But for workflows where sensitive data is involved, where cost predictability matters, where governance and regulatory compliance cannot be outsourced – the case for ownership is strengthening every quarter.
The question Todorov posed in Capital is one every enterprise leadership team will eventually face:
Do you own your AI, or are you renting it?
The answer will shape how enterprises govern, scale, and compete for the next decade.
This article draws on ideas discussed in the interview “The Future with AI: Do You Own It or Rent It?” published by Capital, featuring DiscreteStack CEO Hristo Todorov. The original interview is in Bulgarian.
Explore how DiscreteStack helps enterprises move from metered intelligence to owned AI infrastructure – start a free 30-day trial or compare ownership vs. renting.