$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 forecast that 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, revenues are growing impressively: billions of dollars annually from ChatGPT subscriptions and APIs. On the other hand, costs are growing even faster. Each new model generation requires exponentially more resources:

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

This model works only with an endless influx of venture capital. OpenAI has raised tens of billions in investment, including rounds from Microsoft and SoftBank. But investors are not philanthropists. 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 1,216 GPUs. The cost of inference is $0.0069/1M 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 unites existing GPUs around the world. 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 expenditures: the Gonka network does not raise hundreds of billions for construction. The protocol, blockchain, and software are all created by the team. GPUs are provided by independent hosts around the world — each at their own expense.

No depreciation spread over 6 years: when an H100 becomes obsolete, the host simply replaces it with an H200 or the next generation. The decision is made by the equipment 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 a protocol audited by CertiK. There are no "hidden" costs that will be discovered in 5 years during asset revaluation.

Distributed risk: each host carries its own risk. If one host goes bust on a bad GPU investment, it is their problem, not the network's. In a centralized model, one $10 billion mistake can bring down the entire company. In Gonka, such an error is impossible by definition — because there is no single participant capable of making a $10 billion decision.

Result: the cost of inference via Gonka is $0.0069 per million tokens. This is ~720 times cheaper than OpenAI GPT-5.5. And this price is stable — because it is not backed by trillion-dollar infrastructure that needs to be recouped.

Let's compare two AI infrastructure models:

Gonka runs 1,216 GPUs (participants in the epoch: 24, MLNodes: 222). The project raised $80M — this is thousands of times less than what one Stargate spends. Yet the network does the same thing: it processes AI requests via open models like MiniMax M2.7, DeepSeek V4 Flash, and GLM-5.3 Flash, accessible via an OpenAI-compatible API.

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