$112B Hole — Big Tech's Hidden Bankruptcy

Project Stargate — hundreds of billions of dollars to build giant data centers. This is not a typo: we are talking about amounts comparable to the GDP of small countries. Microsoft, Google, and Meta annually spend tens of billions on GPU infrastructure: Microsoft alone invested over $50 billion in capital expenditures in 2025, most of it for AI.

The problem is hidden in accounting. H100 generation GPUs become obsolete in 2 years with the release of H200, B100, B200 — each subsequent generation is 50-100% faster than the previous one. But corporations record depreciation over 5-6 years, creating an accounting illusion. Example: a company bought GPUs for $20 billion. In the accounting books, after 2 years, they still “cost” $13 billion (with linear depreciation over 6 years). In reality — they cost ~$5 billion, because the new generation does the same work twice as fast and cheaper.

This creates a hidden deficit: the difference between the book value of assets and their real market value — trillions of dollars across the industry. When (not “if,” but “when”) auditors demand a revaluation — this could lead to massive write-offs, collapse AI company stocks, and provoke a crisis of confidence in the entire industry.

Analysts predict OpenAI will accumulate about $112 billion in losses by 2030. This figure is not pulled from thin air: it reflects a fundamental problem with the centralized AI business model.

On one hand, revenue is growing impressively: billions of dollars annually from ChatGPT subscriptions and API. On the other, expenses are growing even faster. Each new generation of models requires exponentially more resources:

• GPT-3 → GPT-4: training costs increased by approximately 10x
• GPT-4 → GPT-5: another exponential increase—a steep curve
• Inference: millions of users = billions of tokens per day = billions of dollars per year in GPU power

This model only works with an endless influx of venture capital. OpenAI has raised tens of billions in investments, including rounds from Microsoft and SoftBank. But investors are not charities. Sooner or later, they will demand profit. The question is not "if," but "when"—and what will happen at that moment to the millions of businesses built on the OpenAI API?

For comparison: Gonka has raised $80M and is already processing real AI requests through a network of ~4,648 GPUs. The cost of inference is $0.003/1M tokens. This is possible because, in a decentralized model, there is no need to recoup trillion-dollar investments in data centers.

Gonka does not build data centers—it pools existing GPUs worldwide. This is not just an alternative business model; it is a fundamentally different economic architecture that eliminates the root cause of the bubble.

No Capital Expenditure: The Gonka network does not raise hundreds of billions for construction. The protocol, blockchain, and software are all the team creates. GPUs are provided by independent hosts around the world, each at their own expense.

No 6-year amortization: When an H100 becomes obsolete, the host simply replaces it with an H200 or the next generation. The decision is made by the hardware owner based on market conditions, not by a corporate CFO trying to hide write-offs.

No accounting tricks: All transactions on the Gonka blockchain are transparent. Rewards are distributed according to the protocol audited by CertiK. There are no "hidden" expenses that will be discovered in 5 years during asset revaluation.

Distributed risk: Each host bears their own risk. If one host fails due to a bad GPU investment, that is their problem, not the network's. In a centralized model, one $10B mistake can bring down the entire company. In Gonka, such a mistake is impossible by definition—because there is no single participant capable of making a $10B decision.

Result: The cost of inference via Gonka is $0.003 per million tokens. This is ~830 times cheaper than OpenAI. And this price is sustainable because it is not backed by a trillion-dollar infrastructure that needs to be paid off.

Let's compare two models of AI infrastructure:

Gonka currently has about 4,648 GPUs operating across ~113 participants (~582 MLNodes). The project has raised $80M—thousands of times less than what a single Stargate spends. Yet the network does the same thing: processes AI requests via the Kimi K2.6 neural network, available via an OpenAI-compatible API.

Analogy: Imagine back in the 2000s, someone suggested, "Instead of building giant servers for the internet, let every homeowner set up a mini-server and get rewarded for participating." It sounds utopian—but that is exactly how Airbnb works for housing, Uber for transportation, and exactly how Gonka works for AI computing. Decentralization is not a utopia; it is the next stage of infrastructure evolution.