December 18, 2025 · 24-Hour AI Briefing: Google and Meta Challenge NVIDIA’s CUDA Lock-In, Microsoft Redefines AI Databases, Apple Opens App Distribution
Over the past 24 hours, three developments across chips, enterprise software, and platform regulation revealed the same underlying shift:
big tech is not abandoning control—but redesigning how control works.
From Google and Meta quietly undermining CUDA lock-in, to Microsoft repositioning the database as an AI-native system, and Apple introducing a tightly managed opening of app distribution in Japan, the AI platform war is entering a more structural phase.
1. Google and Meta push PyTorch on TPU, directly challenging NVIDIA’s ecosystem lock-in
Google is working with Meta to enable PyTorch to run on TPU with minimal performance loss, significantly lowering the migration barrier for developers. At the same time, Meta is actively seeking alternatives to NVIDIA GPUs due to pricing pressure and supply constraints, and may rent—or eventually purchase—Google’s TPU chips.
Commentary:
NVIDIA’s sustained premium is not just about performance or supply. Its real moat is CUDA—the ecosystem that makes switching painful, risky, and expensive.
This Google–Meta collaboration is therefore not a simple “compatibility effort” or cost optimization story. It is a coordinated attempt to weaken NVIDIA’s ecosystem dominance at the system level.
Historically, TPUs were confined to Google Cloud, limiting customers’ control over data locality and deployment cadence. Allowing hyperscale buyers like Meta to rent—and potentially procure—TPUs turns them from a closed internal asset into shared infrastructure.
The real test is whether Google can deliver a first-class, PyTorch-native TPU experience.
If PyTorch can truly switch across GPU, TPU, and custom ASIC backends with near-zero friction, large buyers will start purchasing compute the way they buy electricity and rack space—capacity first, architecture second.
At that point, pressure shifts decisively toward NVIDIA.
It remains remarkable that two companies locked in fierce advertising competition are now aligned in the chip war—but whether this partnership matures beyond technical collaboration is still an open question.
2. Microsoft launches Azure HorizonDB, reframing databases for AI-native workloads
Microsoft has introduced Azure HorizonDB, a new enterprise-grade PostgreSQL service tightly integrated with Foundry AI models. The company claims up to 3× faster vector search performance compared with traditional approaches. The system uses decoupled storage and scalable compute, positioning it as a direct competitor to AWS Aurora for AI workloads.
Commentary:
HorizonDB is not “yet another managed Postgres.”
It represents Microsoft’s attempt to shift the enterprise database battleground from classical OLTP/OLAP toward AI-native data architectures.
Today, most enterprises follow one of three paths:
Postgres with pgvector, a standalone vector database such as Pinecone or Milvus, or cloud-native offerings bundled with Aurora, AlloyDB, or Cloud SQL.
By making vector retrieval performance a primary selling point, Microsoft is signaling a clear goal: enable enterprises to handle transactional data, embeddings, and retrieval inside a single database—reducing operational complexity and consistency risk.
If HorizonDB delivers on horizontal compute scaling, read-write separation, fast failover, smooth elasticity, and competitive pricing, it could materially influence migration decisions for mid-to-large enterprises.
Strategically, this move targets not only AWS Aurora, but also Oracle and MongoDB.
The remaining question is straightforward: will enterprises be willing to re-architect around HorizonDB, or will inertia prevail?
3. Apple opens alternative app stores in Japan, but keeps distribution firmly controlled
Apple announced that it will allow alternative app stores on iPhone in Japan to comply with new competition regulations. Japanese developers may launch their own marketplaces, with commissions as low as 5%. Developers can offer external in-app payment options, but must still pay commissions to Apple.
Commentary:
Apple is not abandoning closed distribution—it is converting it from an absolute rule into a compliance-driven commercial structure.
This is best understood as a carefully designed form of limited openness. Apple satisfies the requirements of Japan’s Mobile Software Competition Act (MSCA), while preserving ecosystem control through tiered commissions, mandatory security review, and parallel payment rules.
Even when developers enable third-party payments, Apple’s in-app purchase option must still be offered. Users continue to see Apple’s payment flow, and developers cannot fully escape commissions.
Compared with the previous zero-choice model, this introduces limited flexibility—but not structural decentralization.
The larger question now is whether Japan’s regulatory approach becomes a template for other markets, including the EU and South Korea.
Key AI Developments Worth Following Over the Past 72 Hours
For readers who want additional context behind these shifts, the following in-depth briefings are worth revisiting:
Conclusion
From PyTorch loosening compute lock-in, to Microsoft redefining AI-era databases, and Apple introducing tightly scoped openness, a consistent pattern emerges:
platforms are not giving up control—they are redesigning its boundaries.
In the AI era, competition is no longer about who trains the best model, but who defines the most advantageous system boundaries between openness and lock-in.
This phase of the platform war is only beginning.