Who Will Dominate AI in 2026? Nvidia, Google, or the Ambitious Anthropic?

AI Industry Leaders in 2026: Who Holds the Power Today and Where It's Heading

It is February 13, 2026, and artificial intelligence is no longer just a product. It has become a complex industrial sphere ranging from memory modules and advanced chip packaging to cloud AI factories and models capable of long agentic tasks and autonomous computer work. It is within this technological chain that it is now being decided who will generate profits in the coming decade, who will remain a mere supplier, and who will be pushed out of the market.

We present the current power map: see who makes up the AI elite today, where we stand, and which trends will be key for the near future.

1. Computing Power as the New Oil: NVIDIA and Co. Control the AI Electricity

NVIDIA: The Infrastructure King

NVIDIA uncompromisingly maintains its position as the toughest player on the market. This January, it launched the new generation Rubin platform, which brings more chips, better scaling, and a brutal increase in performance for both training and inference. Discussions about deployment in AI clouds for the second half of 2026 are already in full swing.

Why it's crucial: NVIDIA doesn't just win because of GPUs. Its dominance lies in selling entire racks and architectures where computing power, networking, software, and the ecosystem are integrated into a single whole.

TSMC, ASML, and Applied Materials: The Gatekeepers

Whether NVIDIA or anyone else wants to build the AI future, they must pass through TSMC and the subsequent manufacturing chain, which is currently hitting its physical limits. TSMC reports record results and expects robust growth driven specifically by AI demand.

Equipment manufacturers like Applied Materials confirm a clear trend: AI equals a memory boom. Massive investments are being made into HBM and advanced chip packaging technologies (3D stacking), without which further progress is impossible.

2. HBM Memory: The Bottleneck and the War for Stock

Today, it's no longer enough to just own top-tier GPUs. Without High Bandwidth Memory (HBM), performance and economic efficiency are drastically throttled. The market situation resembles a war over resources.

Samsung has begun delivering HBM4 to key customers in an attempt to catch up with market leader SK hynix, while Micron is hot on both their heels. The shortage of chips and memory continues to fuel the AI fever.

Prediction: Whoever masters the yield and volume production of HBM4/HBM4E in 2026-2027 will become the silent winner of the entire race. Without this memory, AI expansion will physically grind to a halt.

3. Frontier Models: Three Distinct Paths to Dominance

OpenAI: From Model to Agent

OpenAI has moved its Codex from being a code model to a full-fledged agent capable of professional computer work. Simultaneously, it introduced an ultra-fast variant, Codex-Spark. A key signal is that Codex-Spark relies on Cerebras hardware in preview—proof that even OpenAI is actively diversifying its hardware sources.

Wow moment: This is a direct path toward AI stopping just giving advice and starting to actually work—clicking, creating workflows, controlling tools, and generating final outputs.

Anthropic: Long Tasks and Massive Context

Anthropic released Claude Opus 4.6, which brings better planning, longer agentic runs, and a 1-million-token context window in beta. The company has become a magnet for investors, announcing a $30 billion fundraising round at a $380 billion valuation.

The goal is clear: Agentic teams and extreme context windows are not mere demos. It is an effort to dominate the market for office work, analysis, and development in large organizations.

Google DeepMind: Gemini 3 and the Power of Distribution

Google released Gemini 3 and subsequently the fast Gemini 3 Flash variant, aimed at low latency and mass use. Google has an ace up its sleeve that is hard for others to match—distribution channels (Search, Android, Workspace, YouTube) backed by its own TPU accelerators.

4. The Cloud Wars: The Push for Independence and Custom Chips

Microsoft: Reducing Dependency

According to the Financial Times, Microsoft is moving toward greater self-sufficiency. It is building its own foundation models to reduce its dependence on OpenAI, although the strategic partnership continues.

AWS: The Counterstrike Named Trainium

Amazon is heavily pushing its Trainium3 accelerators, selling a narrative of faster and cheaper training. Large labs are indeed combining multiple sources of computing power today because demand still outstrips supply.

Google Cloud: The Era of Inference

Google Cloud announced the general availability of TPU Ironwood as infrastructure for the age of inference. Simultaneously, Anthropic confirmed an expansion on Google Cloud worth tens of billions of dollars, with plans to utilize a capacity of up to one million TPUs by 2026.

Where it's heading: 2026 is a turning point. The market is shifting from "who can train the biggest model" to "who can handle inference cheaper and faster." This opens space for custom chips (TPU/Trainium) alongside the dominant NVIDIA.

5. Open-source and the Second Tier on the Rise

Meta and Llama 4

In 2025, Meta opened the Llama 4 family of multimodal models. This step was vital for an ecosystem that desperately needs an alternative to closed labs.

China: Growth Despite Sanctions

The Chinese open-source model GLM-5 (Zhipu) shows that local developers can respond to export restrictions. The model targets agentic and coding capabilities and is built on domestic hardware.

The Reality of 2026: The AI race is no longer Silicon Valley vs. the rest of the world. Two parallel ecosystems are emerging (USA/EU and China), often running on different hardware and proprietary models.

6. Regulation and Safety: The End of PR Talk

The EU AI Act has a clear schedule, and by 2027, all obligations will be in effect. Crucially for companies, rules for general-purpose AI are already active. The safety debate is intensifying—even Anthropic openly warns against the potential misuse of highly capable models.

Safety and compliance are ceasing to be chapters for the PR department. They are becoming hard requirements in tenders for the corporate sphere, government administration, and regulated industries.

What to Watch in 2026 (If You Want to Stay Ahead)

• Rubin and Rack-Scale AI: Whoever masters large-scale deployment will gain an edge in inference costs.
• HBM4 and Advanced Packaging: Technologies without which AI scaling is mere theory on paper.
• Agentic Work: The transition to models (like Codex) that actually perform tasks rather than just discussing them.
• Hardware Diversification: TPU, Trainium, and Cerebras as a path to cost efficiency and sovereignty.
• Geopolitical Splitting: Further decoupling of models and supply chains between the West and China.

Conclusion: Who is the Real Leader?

In 2026, the title of AI leader doesn't mean having the best chatbot. It means controlling five key pillars: compute, memory, distribution, products, and compliance.

NVIDIA, TSMC, and HBM manufacturers hold the physical limits of how far AI can go. OpenAI, Anthropic, and Google define what AI can actually do (agentic work, context, multimodality). Microsoft, AWS, and Google Cloud then decide who will have access to these technologies and at what price.

The most interesting part? AI is now behaving like the internet during the construction of the first data highways. The difference is that current bottlenecks are physical (memory, energy, packaging) and fundamental changes happen not over years, but weeks.