Paul Oamen

Decentralized Compute is Criminally Underrated

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July 11, 2025 (2d ago)109 views

This article explores why decentralized compute is vastly underrated, how it can power the next generation of AI infrastructure, and why it may be the only way to keep intelligence scalable, auditable, and open.

Introduction

We are living in extraordinary times. Intelligence is now literally at everyone’s fingertips. In just the past few months, countless new systems have launched, iterated, and evolved at speeds that would have seemed impossible even a year ago. This growth will not stop. Each new advancement enables deeper research, which in turn fuels further breakthroughs, creating an endless cycle of improvement and discovery.

Beneath it all lies something concrete and limited: compute. Compute is the brain that drives these artificial intelligence systems, transforming raw data into reasoning, training new capabilities, and powering generative processes. As machine intelligence spreads across more domains of knowledge, research, and daily life, it is clear that the demand for compute will only grow. This opens a vast ocean of opportunities for enterprises that can scale computational resources.

So how do we scale compute?

1. Improving hardware performance

Improving hardware performance is both essential and inevitable. It is, in fact, one of the most remarkable forces of technological history. Just as we once transformed enormous mainframe systems from companies like International Business Machines into personal laptops and mobile devices that fit in our hands, advances in hardware will do the same for compute. They promise to compress what today requires vast data centers into chips that can process trillions of operations effortlessly.

Leading companies like NVIDIA, AMD, and others continue to push the boundaries of what is possible, developing GPUs and specialized accelerators that dramatically increase the throughput of machine learning workloads. At the same time, research into semiconductor physics is far from exhausted. New materials, fabrication techniques, and architectures promise even more breakthroughs ahead.

Yet while these improvements are profound, they also create their own feedback loop. Better hardware inevitably enables even larger and more complex models, which rapidly drive compute requirements higher. This is a natural acceleration. As intelligence grows more capable, it demands more resources to reach the next frontier. In this sense, hardware evolution, while powerful and transformative, also guarantees we will keep pushing quickly toward ever greater computational thresholds.

2. Vertical scaling by large entities

In this context, vertical scaling does not refer to the traditional technical notion of scaling infrastructure by adding more power to single machines versus distributing loads horizontally. Instead, it describes how individual organizations pursue growth by amassing enormous compute capacity within their own controlled domains. It is a model where scaling happens at the organizational level: building private data centers, negotiating exclusive energy contracts, and stockpiling specialized hardware to train increasingly sophisticated systems.

This is the dominant paradigm today. Massive hyperscalers and specialized AI labs pour billions into sprawling facilities, creating proprietary stacks that give them an outsized computational advantage. For now, this strategy undeniably works. It is why we see astonishing leaps in large language models, multi-modal agents, and autonomous reasoning systems.

But this approach carries serious structural consequences. As intelligence continues to grow in scope and complexity, only those with the deepest capital reserves and most expansive logistical reach can keep pace. This sets up a kind of organizational natural selection, concentrating the evolution of intelligence into the hands of a few dominant players.

More troubling is that the intelligence emerging from this model is often neither open nor transparent. The most powerful systems are trained behind closed doors on private data, governed by opaque internal policies, and rarely subject to meaningful third-party audits. This means critical decisions about alignment, safety, and long-term impact are made without broad oversight, potentially prioritizing narrow commercial goals over collective societal needs.

It is not merely that vertical scaling makes advanced intelligence costly or exclusive. It risks putting the steering wheel of our most transformative new capabilities firmly in the grip of a handful of corporate giants. Even more importantly, it is still deeply inefficient. In a world accelerating through layers of thought, recursive reasoning, and self-improving systems, we will reach computational ceilings faster than we can expand physical infrastructure. Vertical scaling may dominate today, but it is fundamentally ill-suited to sustain an ever-accelerating landscape of ideas building on ideas, thinking about thoughts, and generating yet deeper forms of cognition.

3. A fundamentally different path

There is a profoundly different approach to scaling compute that remains vastly underexplored. It involves building decentralized networks that aggregate computational capacity from countless independent participants. This is not traditional distributed computing directed by a single entity that leases machines across different regions. It is a truly collaborative model where individuals, small organizations, and large institutions contribute unused or idle hardware into a global infrastructure.

The incentives for participation are immediate and straightforward. Owners of GPUs and other accelerators anywhere in the world can earn by putting their underutilized resources to work. Developers and researchers can draw from a pool of compute that grows organically with participation, without needing to negotiate access through centralized intermediaries. With the right cryptographic proofs and orchestration protocols, these networks can verify computations in a way that requires no trust in any single participant and remains open to rigorous external auditing. Systems built on this principle are naturally more resilient, because they do not depend on the stability or policies of a single provider. They can also be designed to operate with full transparency, exposing the processes of training, reasoning, and inference to public verification.

A typical incentive structure in such a system might allocate rewards based on a participant’s reliability and the actual computational work they contribute. A simplified formulation is:

where R_i is the reward allocated to participant i, w_i reflects a reliability or uptime factor, c_i is the computational work performed by i, N is the total number of contributors, and T is the total reward pool for that cycle.

There are important technical challenges. Maintaining reliable uptime across a globally distributed network, protecting against malicious or dishonest nodes, and safeguarding the system from coordinated attacks all require careful engineering. Yet these are problems of implementation, not of fundamental structure. Unlike vertical scaling, which inherently narrows who can participate to only those with immense financial and logistical capabilities, decentralized compute invites broad and organic expansion. It is a model that grows in step with worldwide participation, turning idle hardware into a collective engine for advancing intelligence.

Most crucially, this approach aligns precisely with a world that is accelerating through ever deeper levels of abstraction and recursive reasoning. As each generation of intelligence builds tools to further extend its own reach, only an open, verifiable, and widely accessible infrastructure can sustain this accelerating progression of ideas building on ideas and thoughts expanding upon thoughts.

We did it for money with Bitcoin. We can do it even better for intelligence

This may be the clearest path yet to fulfilling what Satoshi Nakamoto began. Bitcoin demonstrated how decentralized economic incentives could bootstrap a secure, global system for managing money. It transformed finance, but it did not permeate everyday transactions for most people, largely because it touched domains that governments and regulatory institutions have a natural interest in supervising. In the same way, many decentralized projects that followed have struggled to gain mainstream adoption. They were often met with the common objections of being high-risk, unregulated, or tied to volatile speculative markets.

This cautious attitude toward decentralization has quietly limited many otherwise promising initiatives. I have seen it firsthand. I have studied and worked on decentralized energy networks and collaborative mining systems that aimed to pool resources and distribute value fairly. Many of these efforts ran into not only technical hurdles, but also the deeper societal skepticism that decentralization is difficult to oversee, or that it cannot be trusted without familiar centralized guarantees.

But artificial intelligence has now become mainstream. It is rapidly integrating into every dimension of work, learning, and daily life. This makes it perhaps the first domain where decentralization is not just feasible but may become absolutely essential. I strongly believe that this will also open the door for other decentralized applications that until now have remained overshadowed by Bitcoin’s regulatory controversies or the broader skepticism around financial uncertainty. In practice, most people care far more that these intelligent systems work and improve their lives than about who maintains the underlying infrastructure. Decentralized compute provides a way to embed cryptographically verifiable, economically aligned, transparent infrastructure directly into the next generation of intelligence.

And it is not only about scale. Decentralized compute brings inherent advantages for privacy, auditability, and trust. It allows us to build systems that keep data local, train models collaboratively across private datasets, and verify every computational step through cryptographic proofs. This is exactly where federated learning, zero-knowledge proofs for machine learning, and secure multi-party computation integrate naturally. These are no longer speculative concepts. They are advancing rapidly and pairing precisely with decentralized compute backbones to support a future that is open, transparent, and aligned with broad societal needs.

Conclusion

Decentralized compute is not just an alternative way to scale. It may be the only practical foundation for an intelligent future that remains resilient, transparent, and truly aligned with broad human interests. In a world racing toward more powerful and increasingly opaque systems, concentrating this computational backbone in the hands of a few is neither secure nor sustainable. In contrast, a decentralized infrastructure that is open, verifiable, and globally participatory offers a path where intelligence can grow in ways that stay accountable and accessible to all. As we stand at the beginning of an era defined by recursive reasoning and systems that build ever deeper abstractions, it is clear that how we scale compute will shape who guides the most important ideas of tomorrow.

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