YAGNI for hardware

Scale Crisis and Scarcity as an Engine of Progress

Artificial intelligence has outgrown the capabilities of traditional centralized infrastructure. What began in 2025 as cautious experiments evolved into an infrastructure famine by 2026, turning graphics accelerators into the most scarce resource on the market and making decentralized computing the only salvation for small, medium, and large enterprises.

Price Shock and Market Maturation

Figures from the October report demonstrate the industry’s overheating. In the first five months of 2026, the cost of renting GPU servers for machine learning skyrocketed by 64%, settling at 2.3 million rubles per month (compared to 1.4 million the year prior).

This jump was not caused by market manipulation but by a qualitative shift in the corporate sector. Finance, heavy industry, retail, and telecom are massively deploying AI into mission-critical business processes. Experts note that companies are no longer looking for just a cloud with GPUs. They need highly specialized nodes with colossal video memory, ultra-fast interconnects, and engineering infrastructure (power and liquid cooling) capable of sustaining 24/7 peak loads.

Anatomy of the «Bottleneck»

Corporations have hit the ceiling of network bandwidth and data center power supply limits.

If IT directors previously debated the feasibility of implementing neural networks, today their main question is where to physically get accelerators and how long they will have to wait.

Logistics Deadlock: The internal procurement approval cycle in a large company now lasts longer than the current GPU price’s validity. While management approves the budget, the configuration cost grows, and delivery times shift by months.
Utilization Problem: Cloud hyperscalers, despite their Tier III status, are operating at their limits. Queues for cluster deployment have become the norm, and the cost of stable ownership of proprietary power has turned into an unbearable burden for operational budgets. Furthermore, for companies handling critical data (CII) or personal information, a full transition to the global cloud is often impossible due to regulatory requirements.

Cocoon and the Economics of Distributed Survival

Against the backdrop of this paralysis in centralized systems, the concept of Confidential Compute Open Network (Cocoon), presented by Pavel Durov, transitioned from a crypto-utopia to a pragmatic business tool. A decentralized pool of GPU resources proved to be an ideal answer to these challenges for several reasons:

Bypassing Physical Scarcity: Networks like Cocoon aggregate the «long tail» of the market—thousands of semi-professional and gaming GPUs (from RTX 4090 to professional A4000/A5000) sitting idle in offices and homes. For model inference tasks, this power is sufficient, allowing AI to be launched without months-long waits for NVIDIA server shipments.
Flexibility vs. Rigid Contracts: Instead of capital expenditures (CAPEX) on hardware procurement and waiting for installation, businesses gain access to power via API. This allows for instant scaling of computations to the current task.
Incentive Mechanics: The miner reward model in Toncoin creates a self-regulating ecosystem. Growth in demand for AI inference automatically increases profitability for card owners, forcing them to connect to the network and thereby eliminating the supply deficit.

Decentralization ceased to be an ideological choice in favor of Web3. It became a production necessity. When centralized data center racks can no longer handle the flow of requests, a distributed network becomes not just an alternative but the sole guarantor of business continuity. The market proved a simple truth: if computing power cannot be bought in one place, the industry will create a network where it is available everywhere.

Architectural Shift: From CUDA to Optics and the Edge

The first and most radical response to the energy crisis was the rejection of electrons in favor of photons. Traditional silicon chips hit the ceiling of copper’s bandwidth and heat dissipation limits. Photonic Integrated Circuits (PIC) entered the commercial stage. Companies working in this paradigm proved that using light for matrix multiplications—the foundation of any neural network—allows for a manifold reduction in energy costs and an order-of-magnitude increase in data transfer speed between cores.

This technological solution became a salvation for Edge AI. Infrastructure ceased to be tied to server rooms. Optical co-processors allow industrial robots and computer vision systems in smart factories to analyze video streams in real-time on-site. The need to shove terabytes of data into the central cloud for a millisecond response disappeared: light processes information faster than an electrical signal can leave the device, taking a colossal load off global communication networks.

Pocket Sovereignty

The second development vector is dictated by economics and geopolitics. The scarcity of NVIDIA server accelerators and strict legislation made storing and processing all data in global clouds impossible. AI began to «slim down» and distribute rapidly, moving from monstrous data centers into microcomputers.

Today, neural networks live inside industrial camera housings, drone flight controllers, and Mini-PC format local servers. This decentralization perfectly aligns with the philosophy of networks like Cocoon. Instead of renting scarce power in a Tier III data center, businesses deploy trusted inference on their own turf. Data physically does not cross the enterprise border, removing questions of cybersecurity and compliance. New generation compact chips (from RISC-V boards to specialized mobile accelerators) have proven that for most applied tasks—from quality control on a conveyor to autonomous driving—racks with water cooling are no longer needed.

Algorithmic Engineering

The third path turned out to be the most massive. When hardware began to cost its weight in gold, engineers started saving bits. Software optimization transformed from an academic exercise into a harsh production necessity. To run modern models on available hardware, the industry implemented an aggressive «slimming» pipeline:

Radical Quantization: Transition to FP4 and INT4 computations. Reducing mathematical precision allowed for a several-fold decrease in video memory usage and accelerated model response.
Industrial Distillation: Creating compact «student» models that adopt the logic and knowledge of giant «teachers.» This method allowed achieving flagship algorithm performance on mid-range hardware.
Structural Pruning: Surgical removal of redundant neural connections that consume resources but do not affect the result.

Collectively, these software methods allowed shrinking neural network architectures by dozens of times while retaining the vast majority of accuracy. This finally shattered the monopoly of ultra-expensive clusters. The entry threshold announced within the Cocoon concept became a reality: launching quality AI today requires only an optimized local server or even a powerful workstation.

New Computation Topology

There is no longer a single «best» solution for AI. Architecture has become context-dependent. Training fundamental models remains the prerogative of giants with their supercomputers. However, exploitation (inference) has turned into a complex engineering assembly: photonic chips take tasks where every nanosecond is critical; microcomputers close security and local processing questions; and software optimization binds it all into a single profitable system. The era of the universal GPU is over. The time of an architectural mosaic has arrived, where the choice of tool is dictated not by the manufacturer’s brand, but by the physics of the cable and the cost per kilowatt-hour.

Hybrid Topology as a Survival Standard

The scarcity of computing power, tightening state control over data, and the physical impossibility of instantaneous gigabyte transfers made classic centralized architecture unworkable. It was replaced by hybrid multi-cloud topology—a complex but only effective mechanism where data remains stationary, and artificial intelligence moves to it in the form of lightweight code.

Anatomy of the Modern AI Pipeline

The lifecycle of a neural network now resembles a distributed production cluster, where each stage is moved to its own geographic and technological zone:

Foundation (Training Zone): The birth of intelligence still requires colossal costs. Training base models occurs in the closed ecosystems of hyperscalers or on experimental optical supercomputers. Only here can one find free GPU or photonic accelerator clusters capable of digesting petabytes of source information.
Exploitation (Action Zone): The most massive and expensive stage—inference—completely left data centers for the edge. Optimized models are deployed directly on Edge devices: from microcontrollers in drones to optical chips on assembly lines. This excludes communication delays. If an industrial manipulator needs to make a decision in milliseconds, it cannot afford the ping to a central server.
Adaptation (Trust Zone): Training on closed corporate data occurs within the security perimeter. Under strict compliance and leak risks, companies prefer to train models on local servers, where a «slimmed-down» and compiled base, received from the cloud, is delivered.

Cocoon Network and the Economy of Abundance

The problem of cloud queues was solved radically—through the legalization of distributed consumption. The decentralized Cocoon network on the TON blockchain turned thousands of idle gaming and semi-professional GPUs worldwide into a single compute pool. Businesses gained the ability to rent power directly from hardware owners, paying with tokens. This works on the «Uberization» principle of computing: if giants cannot handle the load, the request automatically goes to distributed nodes. Meanwhile, Confidential Computing architecture guarantees that data is encrypted on the fly, and no network participant sees unencrypted traffic, making the network a secure alternative to AWS or Google Cloud.

Heavy Data Stays, Light Code Travels

The main technological achievement is the rejection of dataset migration. Previously, analyzing a factory’s work required uploading video archives to the cloud. Today this process is flipped:

Raw data never leaves the local server or camera.
Only a request for model assembly or optimization is sent to the centralized cloud.
A compact file (artifact) weighing megabytes, containing only «knowledge,» flies back.
This file instantly deploys on the local Edge node or decentralized network.

Automation of Chaos

Managing such a distributed system manually is impossible. Modern Platform-as-a-Service (PaaS) solutions have taken on this task. They work as smart dispatchers: the algorithm analyzes cost, latency, and legal constraints, choosing whether to send the task to an expensive TPU for speed, to the Cocoon network for savings, or to a local server for compliance. Layered on top of all this is Zero Trust architecture. Since computations occur on someone else’s or border equipment, each model launches inside a protected hardware enclave (TEE). This guarantees that even a GPU renter in a decentralized network or a system administrator of an Edge node cannot extract confidential data from RAM during inference.

Conclusion

The 2026 crisis became not a catastrophe for the AI industry, but a painful yet necessary point of maturation. The era of cheap computing and boundless faith in vertical scaling has come to an end. Faced with the physical limit of centralized infrastructure, business did not stop technology development but made a qualitative leap, rethinking the very architecture of digital intelligence.

What today seems like a forced measure—distributed networks, optical co-processors at the edge, and radical model optimization—will become an industrial standard tomorrow. Decentralization, embodied in projects like Cocoon, proved its viability not as an ideological alternative to Big Tech, but as the only pragmatic answer to the logistics and energy paralysis of hyperscalers. Business learned the main lesson: in a world where every kilowatt and every transistor counts, intelligence must live where the data is.

There will no longer be a universal solution that fits everyone. The choice between training in a closed cloud, inference on an optical Edge node, or adaptation in a decentralized network will become as routine as today’s choice between SQL and NoSQL databases. Hybrid topology, where heavy data remains stationary and compiled models move to it, will become a basic requirement for any enterprise project.