Gonka Protocol Co-founder Anastasia: Whoever Controls Compute Power Implicitly Controls the Future of AI

Core Summary: Training large models requires building or upgrading data centers. However, centralized infrastructure is now facing hard physical limits. To enhance infrastructure capabilities, AI is being used to create greater scale and intelligent output.

Yet, control over computing power is becoming a critical power node within the AI industry.

At this moment, Gonka emerges. The Gonka protocol is a permissionless global network that anyone can join, with requests routed programmatically among distributed participants. In an exclusive conversation with Analytics Insight, Gonka co-founder and senior product manager Anastasia Matveeva discussed how they are innovating in the way compute power is accessed to build a more controllable and secure AI ecosystem.

Q: Public discussion about AI often focuses on the centralization of models, but there is less attention on the centralization of compute power. Why is control over compute becoming a critical power node in the AI industry? What risks does this concentration pose to innovation and the market as a whole?

A: Public discussion often focuses on models because they are visible. But the real center of power lies deeper—at the compute layer, which is the foundational layer determining who can build, deploy, and scale AI systems.

Control over compute has become critical due to economic and physical reasons. The primary bottleneck for modern AI is no longer algorithms, but the ability to access GPUs, electricity, and data center capacity.

Training large models increasingly requires building or upgrading data centers. However, centralized infrastructure is hitting physical limits: energy density, thermal constraints, and the maximum power supply a single location can handle. The industry is attempting extreme solutions—redesigning chips, cooling systems, and new energy sources.

This concentration brings systemic consequences.

First, it creates structural barriers to innovation. Access to compute becomes a privilege of infrastructure, not competition based on capability. Small teams, independent researchers, and even entire regions are priced out, the space for experimentation shrinks, and innovation becomes conservative.

Second, compute centralization reinforces a "rent extraction" model. AI has the potential to create "abundance"—intelligence is essentially replicable—but when the underlying infrastructure is scarce and controlled, this abundance is artificially suppressed. The market shifts towards subscriptions, lock-in effects, and pricing power, rather than cost reduction and broad accessibility.

Third, it introduces systemic fragility. When advanced compute is concentrated among a few operators and geographic locations, disruptions—regulatory, political, or physical—ripple through the entire AI ecosystem. Dependence becomes structural, not optional.

More importantly, compute is not neutral. Whoever controls compute implicitly decides what is feasible, permissible, and economically sustainable. When this control is centralized, AI governance defaults into existence, rather than being designed.

The risk is not just monopoly, but a long-term distortion of AI's development trajectory: fewer builders, less application diversity, slower hardware innovation, and infrastructure unable to match the ambitions of next-generation models.

Therefore, compute must be treated as foundational infrastructure—an architecture that can scale both economically and physically, crucial for the future of AI.

Q: Many AI compute platforms—whether centralized or decentralized—claim to be efficient. What metrics truly matter when evaluating the efficiency of an AI compute system? Where do these models typically encounter practical limitations?

A: Compute efficiency is often used as a marketing concept. In reality, only a few concrete metrics truly matter, covering user-side performance, provider operational efficiency, and the incentive structures governing both.

For users, efficiency means speed and cost transparency.

Speed refers to latency under real demand. Centralized hubs often have an advantage due to physical co-location. But decentralized architectures can achieve comparable performance if the blockchain acts only as a security layer and is not part of the real-time execution path. As long as requests are processed off-chain, the protocol itself does not add latency.

Cost transparency is equally critical. While "cost per token" is a common KPI, model integrity often lacks transparency. In centralized environments, the product can be a black box. During peak times, providers might adjust model configurations to maintain margins; these changes are often invisible but can affect output quality. True efficiency requires pricing to reflect consistent computational accuracy.

For providers, efficiency is a balance between GPU utilization and elasticity.

Centralized operators excel at utilization; GPUs in co-located environments can run near full capacity. But they lack elasticity, bearing idle costs during demand troughs.

Decentralized networks sacrifice some utilization for elasticity but must minimize consensus and verification overhead, allowing compute to be reallocated across different workloads as demand shifts.

Most critical is incentive design.

When rewards are tied to faster, cheaper, verifiable AI workloads, optimization becomes structural. Participants are incentivized to improve hardware efficiency, reduce latency, and experiment with specialized chips.

Conversely, if rewards or governance weight are primarily tied to capital holdings, optimization shifts away from infrastructure performance, and inefficiency becomes entrenched.

In Gonka, efficiency is embedded at the protocol layer: nearly 100% of compute is used for real AI workloads (primarily inference). Rewards and governance weight are based on measured compute contribution, not capital holdings.

True efficiency only emerges when most compute is used for real tasks, incentives reward verified contributions, and internal overhead does not grow uncontrollably with network scale.

Q: Is it possible for decentralized AI compute networks to dedicate most of their compute to real AI workloads, rather than maintaining the network itself? What are the key architectural choices?

A: It is possible—but only if overhead is treated as a core architectural constraint, not an inevitable byproduct of decentralization.

Most decentralized compute networks spend significant resources on maintaining consensus and security, not on AI workloads. This is because productive work and security mechanisms are separated, leading to duplicated computation.

To dedicate most compute to real AI tasks, several key principles are needed:

First, security and measurement mechanisms must be "time-bound," not continuously running. Proof mechanisms should be concentrated within defined, short periods, not consuming resources constantly. In Gonka, this is achieved through Sprints (structured, time-bound cycles). Outside these cycles, hardware resources are available for real AI workloads.

Second, reduce duplication through selective and reputation-based dynamic verification, rather than fully replicating verification for every task. Work from new participants might be 100% verified; as reputation builds, the verification ratio can drop to around 1%. Overall verification compute can be kept below ~10% while maintaining security.

Participants attempting to cheat do not receive rewards, making cheating economically irrational.

Third, rewards and governance weight must be tied to verified compute contribution, not capital holdings.

When consensus is lightweight, verification is adaptive, and incentives are aligned with productive computation, decentralized compute can truly serve practical workloads.

Q: Decentralized AI compute networks often emphasize open participation, but infrastructure requirements can create high barriers to entry. How can such systems scale while remaining accessible to participants with vastly different levels of compute power?

A: While decentralized networks aim to lower the barrier to entry for AI infrastructure, long-term survival also requires competing with centralized providers and meeting real-world demand. Hardware constraints ultimately boil down to one core requirement: the ability to run models that have genuine market demand.

To scale while maintaining accessibility, several principles are crucial.

First, permissionless infrastructure access. Any GPU owner—whether a single-device operator or a large data center—should be able to join the network without an approval process or centralized gatekeeping. This removes structural entry barriers.

Second, proportional rewards and influence based on verified compute. In a compute-weighted model, higher computational contribution naturally leads to a larger share of tasks, rewards, and governance weight. This does not make small participants completely equal to large ones—nor should it. The key is uniform rules: influence is determined by actual compute contribution, not by capital, delegation mechanisms, or financial leverage.

Third, the role of compute pools. In systems with real infrastructure requirements, resource aggregation emerges naturally. Pools allow smaller participants to consolidate resources, reduce volatility, and participate in larger-scale workloads.

However, the architecture must avoid granting structural advantages to large pools or incentivizing excessive concentration of influence. Pools should exist as coordination tools, not as re-centralization mechanisms.

Ultimately, scaling a decentralized AI compute network should not mean raising the entry barrier. It should mean increasing the overall compute capacity while maintaining neutral, transparent, and consistent participation rules, and preserving the real economic value the network creates for users. Open access, proportional economic mechanisms, and controlled concentration determine whether a system remains decentralized as it grows.

Q: Why has the issue of decentralized AI compute become particularly urgent at this moment? If this problem is not solved in the coming years, what do you think the long-term consequences for the industry will be?

A: This urgency reflects AI's transition from an experimental phase to an infrastructure phase.

As mentioned, compute has become a physical bottleneck. Scalability is increasingly constrained not just by capital, but by energy, power density, and data center limitations. Simultaneously, access to advanced GPUs and hyperscale infrastructure is influenced by long-term contracts, corporate consolidation, and national strategic priorities.

This combination deepens structural asymmetry. Entities controlling large-scale infrastructure continue to solidify their advantage, while entry barriers for small teams and emerging regions keep rising. The risk is not just market concentration, but the widening of a global compute divide.

If this trend continues, innovation will depend more on infrastructure access than on ideas themselves. The AI market could solidify into a rent-based model where intelligence is accessed under conditions set by a few dominant providers.

Therefore, decentralized compute is not an ideological debate. It is a response to visible structural constraints—and a choice that will shape the long-term architecture of the AI industry.

Q: AI agents are increasingly autonomously booking GPU resources. How does Gonka's architecture support seamless integration for a self-regulating AI compute economy?

A: The rise of agentic AI means systems are increasingly making autonomous decisions—including acquiring computational resources. In this model, compute becomes a core asset in economic interactions among agents.

Such an ecosystem requires programmatic access, transparent economic mechanisms, and reliability.

First, integration must be seamless. Gonka provides an OpenAI-compatible API, enabling most AI agents to connect without changing their architecture or workflow.

Second, the compute economy must be transparent and system-driven. Pricing adjusts dynamically based on network load, not fixed by contracts. In the network's early stages, inference costs are designed to be significantly lower than centralized providers because participants are compensated not only through user fees but also through rewards from a Bitcoin-like issuance mechanism proportional to available compute capacity.

This structure allows AI agents operating within budgets to execute workloads efficiently. As the network evolves, pricing parameters will remain subject to community governance.

Third, reliability is reinforced at the protocol level. In centralized environments, reliability comes from certification and SLAs. In decentralized infrastructure, reliability is supported by open-source code, third-party audits, and on-chain verifiable proofs of computational completion and network performance.

Together, these elements enable AI agents to request compute and allocate budgets within a transparent framework. In this way, Gonka provides the infrastructural foundation for a self-regulating AI compute economy, allowing agents not only to execute tasks but also to dynamically optimize the resources they depend on.

Q: Regulatory uncertainty around decentralized technologies is intensifying. How is Gonka proactively addressing data sovereignty and AI governance compliance in a fragmented global market?

A: In the context of decentralized compute, the main challenge is balancing network openness with diverse and evolving jurisdictional requirements.

Gonka is a permissionless global network—anyone can join, and requests are routed programmatically among distributed participants. At the current stage, users cannot deterministically control the geographic location where their request is processed. For use cases with strict data residency or regional processing requirements, this may currently be a limitation.

However, from a privacy perspective, this architecture reduces data centralization. Each request is processed by a randomly selected participant and routed independently, preventing the accumulation of complete user histories. So far, this model has covered most practical use cases while allowing the network to scale.

As the network grows and market demands become clearer, the governance mechanism allows participants to propose and vote on architectural changes to support specific regulatory requirements. These could include dedicated subnets with additional participation criteria, operational constraints for specific jurisdictions, or hardware-level guarantees for enterprise workloads, such as Trusted Execution Environments (TEEs).

Decentralization does not eliminate compliance obligations. It provides architectural flexibility. Gonka is designed to allow the network to evolve in response to regulatory and market demands, rather than being locked into a single compliance model from the outset.

About Gonka.ai

Gonka is a decentralized network designed to provide efficient AI compute, aiming to maximize the use of global GPU compute for meaningful AI workloads. By eliminating centralized gatekeepers, Gonka offers developers and researchers permissionless access to compute resources, while rewarding all participants with its native token, GNK.

Gonka was incubated by the US AI developer Product Science Inc. The company was founded by Web 2 industry veterans, former Snap Inc. core product directors, the Liberman siblings, and successfully raised $18 million in 2023, with an additional $51 million raised in 2025. Investors include OpenAI backer Coatue Management, Solana backer Slow Ventures, Bitfury, K5, Insight and Benchmark partners, among others. Early contributors to the project include leading enterprises in the Web 2-Web 3 space such as 6 blocks, Hard Yaka, and Gcore.

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