🔗GenAI paradox: exploring AI use cases | McKinsey
The “Gen AI paradox”: nearly 80 % of firms use generative AI, yet most report little impact . McKinsey argues for shifting from reactive gen AI to proactive, agentic AI—equipped with autonomy, planning, memory, and integration. They introduce the “agentic AI mesh” architecture and stress that success requires addressing both technical and human challenges: trust, adoption, and governance. Organizational transformation must be driven by the CEO, with a move from siloed pilots to strategic programs, workforce upskilling, infrastructure adaptation, data productization, and agent-specific regulation.
The “Gen AI paradox”: the widespread adoption of generative AI has yielded minimal financial impact. This is because organizations deploy horizontal solutions—chatbots, copilots—without reimagining core processes . AI remains reactive; to unlock value, it must evolve to proactive, goal-driven systems.
“Agentic AI mesh" implies systems that plan, act autonomously, remember, and integrate across workflows . It distinguishes single-agent from…
Helcio thank you for sharing this interesting article. My view is that Alibaba’s move to develop a new AI chip signals more than just another hardware milestone—it reflects a deep strategic shift. China has long been heavily dependent on foreign (especially U.S.) AI chip makers like Nvidia, but export restrictions and regulatory pressures have forced local companies to accelerate self‑reliance. Alibaba’s chip, designed for a broader range of inference tasks and built by a Chinese manufacturer, shows how necessity is driving innovation at pace.
What’s interesting is the dual tension this creates. On one hand, Alibaba wants compatibility with Nvidia’s platform so that engineers can reuse existing software, tools, and workflows. On the other hand, there are significant technical and capacity limitations: China’s domestic chip manufacturing remains less advanced in many respects, constrained by access to cutting‑edge semiconductor tech and production equipment. The new chip may not rival top U.S. chips in all respects, but its design is pragmatic—it covers inference, which is a huge part of existing AI workloads.
Strategically, this move strengthens China’s push toward an AI supply chain that is more autonomous. By filling the “void” left when Nvidia’s most powerful chips are blocked, Alibaba helps reduce vulnerability to external policy changes. The broader ecosystem—other chip designers, startups, and national funding—reinforces this trend: more investment, more local alternatives, more pressure to figure out how to do things at scale domestically.
But there are risks. Manufacturing capacity, heat dissipation, efficiency, and software/firmware integration remain challenges. Also, doing inference is one thing; training massive models (which require far more compute and hardware sophistication) is a different order. Alibaba’s chip seems focused on inference, which is important, but advancing local capability in training will likely be the tougher hurdle. Still, this is a meaningful step—it’s about reducing dependency, improving resilience, and shaping a future where geopolitical constraints force supply chain innovation.