When 80% of the world’s cloud computing power is controlled by the five tech giants, Gonka AI attempts to draw inspiration from Bitcoin infrastructure. Through decentralized networks and financial incentive mechanisms, it aims to break the monopoly on computing power and make AI truly owned by the people. This article is adapted from an original piece by The Round Trip, compiled, translated, and written by PANews.
(Previous context: Elon Musk’s latest interview warns that “Old-fashioned humans” should prepare to be driven out: offices turning into blue-collar classes, energy being far more important than AI)
(Additional background: Huang Renxun at CES2026 promotes NVIDIA’s self-driving Alpamayo, Rubin’s new platform: Generative AI on screens has reached its limit, next comes physical AI)

Table of Contents

• From Gaming and AR to Decentralized AI
• Gonka AI’s “Bitcoin”-style Concept
• How Decentralized Networks Reshape the Computing Power Market?
• The AI Bubble Debate: Wave of the Era or the Collapse of a Specific Bet?

As the wave of AI sweeps the globe at unprecedented speed, an “arms race” over computing power has already begun. When NVIDIA’s market value surpasses a trillion dollars, and giants like AWS and Google Cloud nearly monopolize cloud computing, a profound challenge faces all AI innovators: Will the high concentration of computing power stifle open innovation and lock AI’s future within the “walled gardens” of a few companies?

With a successful track record of selling a company for $60 million to Snapchat and founding Product Science, which provides AI code optimization services to top-tier enterprises, Gonka AI’s co-founders David and Daniel Laborman bring continuous entrepreneurial experience from parallel computing to AR. They offer a unique perspective to break this deadlock: building a fully community-driven decentralized AI computing network.

In the new series Founder’s Talk of The Round Trip, produced jointly by PANews and Web3.com Ventures, David and Daniel elaborate on why they drew inspiration from Bitcoin’s development history, attempting to replicate the “ASIC revolution” in AI through an open financial incentive framework, to thoroughly break the shackles of computing power costs. They share how Gonka AI attracted $50 million in funding from industry giants like Bitfury and offer their insights on the current “AI bubble theory.”

From Gaming and AR to Decentralized AI

PANews: Welcome David and Daniel! Glad to have you here. I know you both have a very deep technical background and have been deeply involved in this field for many years. Could you start by sharing your background story with our audience?

Gonka AI: Hello everyone. First, we are brothers, and our lives and careers have always been closely linked. Our story begins in 2003, and since then, we have developed a strong interest in parallel computing and decentralized networks.

Later, we entered the online gaming industry, which is essentially a form of large-scale parallel computing—thousands of players interacting in real-time over the internet. To improve the efficiency and reduce the costs of game animation production, we then delved into the field of Computer Vision (Computer Vision).

Computer Vision led us toward a new direction: developing AR virtual avatars for Snapchat. This experience was very successful, and eventually Snapchat acquired our company for $60 million, marking an important transformation in our careers.

Throughout various projects and companies, we have always harbored a desire: to create something that can have a significant social impact, especially in social interaction. When AI entered our lives in a new form—the large language model (LLM)—everything changed. It was no longer just machine learning as we knew it; it became a powerful tool capable of real conversations and genuinely helping us solve problems. We saw that the new generation of AI based on the Transformer architecture is not just language models. Whether it’s image generation, video creation, breakthroughs in biology, chemistry, physics, or even more efficient nuclear reactor designs, this wave of AI is affecting everything.

Next, we will see rapid developments in robot software and autonomous vehicles, and these changes are happening very quickly, right now.

But accompanying this is a concern—not a sci-fi fear like “The Terminator,” but a worry about the current power structure. Currently, about 65% of global cloud computing power is controlled by three US companies (AWS, Google Cloud, etc.). If we include China’s Alibaba and Tencent, these five giants control up to 80% of global cloud computing. AI’s core is computing power, and at present, AI is almost equivalent to cloud computing power. These companies are fiercely competing to control 100% of AI compute resources. If this continues, we will enter a very strange world:

• Replacing large numbers of jobs
• Reshaping the entire economic structure
• Changing how society functions

Therefore, we believe that decentralized AI is a crucial and unavoidable issue.

This is why we ultimately decided to create Gonka AI.

PANews: Indeed, you are not newcomers to the AI field. Before founding Gonka AI, you also founded Product Science, a company backed by notable investors like Coatue, K5, and Slow Ventures. Can you talk about this experience and how it led you to Gonka?

Gonka AI: Of course. The computer vision work we did before is fundamentally about AI and machine learning. The earliest practical applications of AI largely occurred in image generation and animation, which helped us establish a reputation in the machine learning industry.

After leaving Snapchat, we founded Product Science. This company used AI to provide code optimization services for top global companies like Walmart, J.P. Morgan, Airbnb. Today, everyone is familiar with AI helping to write code, but equally important is ensuring that the code runs efficiently. Before shifting our full focus to Gonka and decentralized AI infrastructure, improving code performance was our core business.

Gonka AI’s “Bitcoin”-style Concept

PANews: You mentioned the problem of concentrated computing power, which is indeed concerning. Recently, a large-scale outage at Cloudflare caused half the crypto world to go down, and AWS often experiences failures, impacting many applications. How will Gonka AI address this? It seems not a general decentralized cloud, but more focused on AI.

Gonka AI: Yes, facing the current dilemma of highly concentrated computing power, we see the only solution as decentralization.

At the model level, we see independent labs like DeepSeek proving they are fully capable of training high-quality models comparable to tech giants, but the core bottleneck remains compute power. Currently, many cutting-edge labs rely on infrastructure built by large cloud service providers, and in the decentralized space, no comparable scale solutions have emerged. Even the largest decentralized AI compute network today, Bittensor, has only about 5,000 data center-grade GPUs. Meanwhile, companies like OpenAI and xAI are building massive clusters with millions of top-tier GPUs. The scale gap is huge.

We realize that, to truly make AI belong to the people and avoid single points of failure, the only way is to build a decentralized compute network of comparable scale. This is where we drew great inspiration from Bitcoin. We don’t just see it as “digital gold,” but as one of the greatest frameworks for building large-scale infrastructure.

Over the past 15 years, the Bitcoin community has built an incredible infrastructure through decentralization. Today, the Bitcoin network has about 26 GW of data center capacity, surpassing the combined total of Google, Amazon, Microsoft, OpenAI, and xAI. It’s a massive project built by countless independent participants worldwide, aiming to escape centralized systems.

What’s equally astonishing is the speed of hardware innovation. In 15 years, the energy consumption for 1 TH/s of Bitcoin mining power has dropped from 50 million joules to just 15 joules—a 300,000-fold efficiency improvement! We believe that, if we can bring the same revolution to AI compute, true “computing abundance” will become possible, and AI will be accessible to everyone on Earth.

Host: I noticed that Bitfury, an early Bitcoin infrastructure giant, just announced a $50 million investment in you. Does this suggest the market sees a similar pattern? Bitcoin made energy “interchangeable,” because whether energy is in Siberia or Silicon Valley, it can be converted into homogeneous compute value. Are you also making compute “interchangeable”? Considering AI’s sensitivity to latency, would that be a challenge?

Gonka AI: We believe the same story will happen in the compute field. Currently, NVIDIA chips are extremely expensive, and most of the costs in building data centers for companies like OpenAI go to NVIDIA. But if we can replicate the innovation of ASIC (Application-Specific Integrated Circuits) for AI, the world will be very different.

When the hardware cost per compute unit drops significantly, energy costs will again become a key variable. Early mining companies and hardware manufacturers like Bitfury investing in this ecosystem send a strong signal: they recognize a pattern similar to Bitcoin’s early development.

Recall 2012, when GPUs were the main mining hardware, but within a few years, ASICs with tens of times the efficiency of general-purpose chips became the only feasible mining path. The companies that made these ASICs weren’t big tech giants but small startups. This was entirely driven by Bitcoin’s financial incentive framework:

• Open competition: Whoever provides the most effective compute power to the network gets the largest share of tokens.
• Positive feedback loop: As token prices rise, rewards become more attractive, encouraging more participants to join the race to increase total network compute.
• Lowering innovation barriers: A small company in Korea or San Francisco, just by designing more efficient chips, can start earning immediately without large sales teams, without dealing with giants, or even traditional investors.

This framework greatly lowers the barrier and complexity of “producing compute power.” We believe this scene will repeat in AI chips. When protocols are established, people can earn by connecting their computing devices—whether their own computers, purchased NVIDIA GPUs, or rented data center resources—and contribute to the network for rewards. We expect that within the next one or two years, this financial-incentive-driven innovation will bring hundreds or thousands of times more compute capacity to AI networks, breaking today’s bottleneck.

How Decentralized Networks Reshape the Computing Power Market?

PANews: This model is fascinating, reminiscent of early crypto miners using idle GPUs in schools. Now many companies buy expensive H100 GPUs, but most are idle because they don’t know how to utilize them fully. Does your network also attract such users?

Gonka AI: We’ve encountered many similar and even more exciting cases. Some very successful AI startups bought hundreds of H200 GPUs early on with investor money, but only half are effectively used.

Another common scenario is that many companies rent large data center compute to run open-source models. Later, they realize they can do something smarter: instead of running models inefficiently themselves, they use Gonka’s API to access the same services; meanwhile, they install Gonka nodes on their rented GPUs and contribute to the network. This way, they can use AI models and earn token rewards simultaneously, gaining much higher efficiency and returns.

To utilize GPUs efficiently, you need to handle thousands of requests simultaneously, which is very difficult for a single project. So, companies either tolerate low utilization of their own or rented hardware, or pay expensive API fees—neither is ideal. Connecting to the network and becoming part of the ecosystem is a better choice.

Many participants in our network are not just “idle” compute. For example, data centers like Gcore and Hyperfusion are highly efficient operators with little idle capacity. But in recent months, they found that connecting their GPUs to Gonka’s network can earn higher returns than directly renting to clients, because they gain exposure to network growth. So they are gradually shifting hundreds of GPUs from rental to our network.

This is the key reason why the network can expand from thousands to hundreds of thousands of GPUs. Although giants like OpenAI buy most GPUs on the market, hundreds of thousands remain scattered among independent participants. They can’t compete alone, but together they form a powerful force.

This logic also applies at the national level.

A year ago, when we communicated with some governments, their main idea was “building their own clusters to develop sovereign AI.”

A year later, when we meet with ministers from the UAE, Kazakhstan, and others, they all realize that as small independent players with few GPUs, they cannot compete with giants.

But if they join a large, trusted decentralized network, they can maintain sovereignty because everyone can trust a decentralized system.

( The AI Bubble Debate: Wave of the Era or Collapse of a Specific Bet?

PANews: Undeniably, the AI field is experiencing huge enthusiasm and rapid growth. But with high expectations from investors and users, are we heading toward an “AI bubble”? Many compare it to the dot-com bubble of 2000.

Gonka AI: That’s a very interesting question. Looking back at the 2000 dot-com bubble, although there was a “small burst,” what has the world become 25 years later? The internet is a real technological revolution, and the economic transformation it brought is real. Those companies have grown into trillion-dollar giants, fundamentally changing our lives.

Compared to the internet, the transformation AI will bring is even more radical and thorough. Imagine that in the next 30 to 50 years, everyone will have a personal robot capable of working in factories for them—that’s not science fiction, but an imminent reality. So, it’s not irrational for investors to pour hundreds of billions into this technology.

Of course, there will be failed investments, just like in venture capital over the past 30 years, with many losing money. But overall, the returns in this field are extremely substantial, and it is genuinely changing the world.

So, whether it’s a bubble depends on your perspective. Some companies will go bankrupt due to false assumptions. For example, Gonka’s judgment on the feasibility of decentralized AI might be wrong; conversely, all current bets on NVIDIA could also be a huge bubble.

History has seen similar scenes. In 2012, driven by cryptocurrency narratives, NVIDIA’s stock soared because the market thought it would dominate mining. But then the ASIC revolution happened, and NVIDIA almost completely lost that market. Now, AI is bringing even greater value growth to NVIDIA because the market expects a multi-trillion-dollar market. This expectation might be correct, but no one can guarantee NVIDIA will stay dominant forever. If the ASIC revolution occurs again in AI, what will happen?

Imagine rebuilding the entire Bitcoin network’s compute power, but not with ASIC miners—using NVIDIA’s latest Blackwell chips. You’d need to invest 500 trillion dollars! Obviously unsustainable.

Therefore, what we’re discussing might not be an “AI bubble,” but a “bubble formed by bets on specific companies and technologies.” If the market’s judgment on NVIDIA is wrong, 5 to 7 trillion-dollar companies could suffer heavy losses. But that doesn’t mean AI itself is a bubble. The technology will not disappear; the process of changing lives and business will continue. Only the companies carrying these values might change.

PANews: I totally agree. Just like now we don’t say “I use the internet,” but “I use an app,” and that app happens to use the internet. In the future, every application will use AI in some form, becoming ubiquitous and so integrated that we won’t even notice it.

Gonka AI: Exactly. If you look at the NASDAQ’s K-line chart from its inception, you’ll see that the 2000 “big crisis” is just a tiny wave in a decades-long growth curve. Back then, people thought all goods would be sold online within five years—that didn’t happen, but it did within 15 years.

The same applies to AI. The future where robots are everywhere might not happen in five years, but it’s almost certain to happen, and nothing can stop it. From this perspective, our future demand for compute power will grow by thousands of times. We need a long-term economic model, like Bitcoin’s, designed for the next few decades to support this vision.

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