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The AI-Driven Telegram Revolution: Why Semiconductors and Cloud Stocks Are Poised to Soar

Henry RiversThursday, May 15, 2025 3:25 pm ET

37min read

The Telegram ecosystem is undergoing a seismic shift. Oleksandr Savieliev’s AI-driven feed upgrades—now live across its TripleA mini-app and core platform—promise to redefine how 800 million users consume real-time data. But beneath the surface of this consumer-facing innovation lies a massive opportunity for investors: the infrastructure powering this transformation. From GPUs to cloud servers, the demand for advanced computing resources is about to explode—and those positioned to supply it will profit handsomely.

The AI Feed’s Appetite for Data Processing

Savieliev’s system isn’t just a “smarter” news feed—it’s a real-time AI engine. To deliver hyper-personalized crypto prices, stock trends, and cross-market analysis, Telegram’s AI must process terabytes of data every second. The technical specs reveal a system trained on 5 years of anonymized user interactions, running on a custom transformer neural network distributed across 12 global data centers. This architecture isn’t just computationally intensive—it’s a blueprint for infrastructure demand.

Consider the math:
- GPU Workload: Natural language processing (NLP) and real-time translation require massive parallel processing. NVIDIA’s A100 GPUs—capable of handling 19.8 teraflops—are likely powering these workloads, as AMD’s MI300X (with integrated CPU cores) could manage hybrid tasks.
- Cloud Scalability: To handle 200ms response times for 500,000 concurrent requests/second, cloud providers like Google Cloud (with its Vertex AI platform) and AWS (via EC2 GPU instances) must expand their compute capacity.

The trend is clear: as AI adoption accelerates, NVIDIA’s GPU sales have surged, up 47% in 2024. This isn’t just hype—it’s a structural shift.

The Semiconductor Play: NVIDIA and Beyond

The AI feed’s technical requirements create a “moat” for GPU specialists. Savieliev’s system relies on:
1. Transformer Neural Networks: These require the tensor cores in NVIDIA’s A-series GPUs, which are 10x faster at matrix multiplication than CPUs.
2. Edge Computing: Low-latency features like “LocalAI” (training models on user devices) favor companies like Qualcomm, whose Snapdragon chips integrate AI accelerators.
3. Memory Bandwidth: HBM (High Bandwidth Memory) from Samsung or SK Hynix is critical—each NVIDIA H100 chip uses 80GB of HBM3 to handle 350GB/s data streams.

Investors should target companies with exposure to AI chip architectures, not just GPUs. Intel’s Habana Gaudi2 (optimized for inference tasks) and Cerebras’ Wafer-Scale Engines (ideal for large language models) could also see demand spikes as Telegram’s AI scales globally.

The Cloud Infrastructure Gold Rush

Telegram’s 12-data-center architecture isn’t just about redundancy—it’s a play for low-latency global reach. Cloud providers with hyperscale infrastructure are the unsung heroes here:
- Google Cloud: Its Anthos platform for hybrid AI workloads aligns perfectly with Telegram’s distributed neural networks.
- AWS: EC2’s Inf2 instances (built for inference) will handle the 90% of AI workloads that don’t need top-tier GPUs.
- Microsoft Azure: Its OpenAI partnership gives it a leg up in NLP tools critical for Telegram’s multilingual support.

Google Cloud’s revenue has grown at a 23% CAGR since 2020—this AI-driven leap could push it into triple-digit growth.

Risks: Regulation and the AI Arms Race

The dark clouds on the horizon?
- Regulatory Overreach: The EU’s AI Act could classify Telegram’s behavioral prediction algorithms as “high-risk,” triggering compliance costs.
- Competitor Backlash: Meta’s Llama3 and TikTok’s AI Stories are direct competitors for attention—failure to innovate could derail demand.
- Supply Chain Bottlenecks: Taiwan’s chip fabs are already maxed out; a shortage of 3nm nodes (critical for advanced GPUs) could delay upgrades.

Why Act Now?

The rollout timeline is clear: Q2 2025 marks the global launch of Telegram’s AI feed. Investors who wait until Q4 will miss the initial infrastructure build-out.

The Investment Thesis

Buy: NVIDIA (CUDA’s dominance in AI training), AMD (hybrid CPU-GPU chips), and Google Cloud (global data center footprint).
Watch: Intel (Habana’s AI ASICs), Cerebras (custom chip design), and Micron (DRAM for GPU memory).

This isn’t just about Telegram—it’s about the future of AI-driven communication. Every scroll, click, and voice command in the next decade will demand faster chips, smarter clouds, and more bandwidth. The infrastructure to power it is the next trillion-dollar opportunity.

The question isn’t whether to invest—it’s whether you’ll act before the market does.

Joe Weisenthal is a pseudonym for a financial journalist specializing in tech and AI trends. The views expressed are the author’s own.

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