Jan 28, 2025 · 24-Hour AI Briefing: Nvidia’s “Non-Core” Intel Play for Feynman, DeepSeek-OCR 2 Targets Agent Workflows, Qwen3-Max-Thinking Hits 1T Params, and Cloud Pricing Enters the Value Era
Today’s signals all rhyme: AI is shifting from “bigger model headlines” to the hard parts—supply-chain resilience, enterprise-grade reliability, and sustainable unit economics. Chips diversify risk, OCR must ship into automation, foundation models must prove reusable value loops, and cloud vendors are re-pricing AI-era infrastructure.
1) Nvidia’s 2028 Feynman platform may partner with Intel—“limited, lower-tier, non-core”
Commentary:
This looks like resilience and leverage, not a foundry migration. The core GPU die stays on TSMC, while some I/O dies may use Intel 18A/14A. Nvidia gets diversification and compliance optics while keeping the competitive core tightly controlled.
For Intel Foundry, even small non-core volume matters as validation from a top-tier customer. Scaling beyond a “proof point,” however, depends on sustained yield, delivery stability, cost, and ecosystem readiness—areas where Intel still trails TSMC. Can Intel turn a pilot-style win into repeatable high-end orders?
2) DeepSeek releases DeepSeek-OCR 2 with a new visual encoder + “Visual Causal Flow”
Commentary:
The headline isn’t just OCR accuracy—it’s the attempt to shift from pixel scanning toward semantic, reasoning-like reading via DeepEncoder V2 and a Visual Causal Flow mechanism.
If OCR 2 can deliver robust structured outputs and citation/position grounding, it reduces human verification cost and immediately improves RPA/Agent workflow viability for real-world documents and tables.
But “breakthrough” status depends on three stress tests: messy layouts, low-quality inputs, and reliable long-form structured extraction. What matters more to you—raw accuracy, or production-grade structure + grounding?
3) Alibaba launches Qwen3-Max-Thinking: 1T total params + explicit Thinking Mode
Commentary:
Qwen3-Max-Thinking pushes scale (1T params, 36T tokens) and emphasizes explicit multi-step reasoning with self-checking before final answers.
The real question is not launch-day claims, but run-time reality: activation cost, reasoning stability, and whether it can produce reusable closed-loop value inside Alibaba’s ecosystem and enterprise deployments.
At this size tier, the market will re-price via reproducible evals and unit economics. Would you pay materially more for stronger reasoning if the costs scale up?
4) Google Cloud raises prices, following AWS—price wars end, but “by layer”
Commentary:
Google Cloud will raise global data transfer pricing starting May 1, 2026: North America doubles ($0.04→$0.08/GiB), with Europe and Asia up ~60% and ~42%. With cross-region traffic surging from training/inference, HBM costs rising sharply, GPU supply tight, and power + liquid cooling capex climbing, cloud vendors are moving away from the old price-war playbook.
This is more than pass-through cost. It’s a structural shift: AI-related resources move into value-based pricing and supply management, while general cloud competition continues in more contract-driven, less visible ways. Who do you think raises next—Azure?
Closing:
Taken together, these stories point to a single pivot: AI winners will be defined by deliverability—resilient supply chains, lower unit costs, and enterprise-grade reliability—not just model size. The next phase is about turning capabilities into scalable, profitable, sustainable supply.
Further reading (top AI events in the last 72 hours):