Our latest episode is on Spotify too: open.spotify.com/episode/3HnP...

The next AI advantage won’t come from smarter models alone. It will come from organizations designed to learn, adapt, and delegate faster.

3. Responsible AI must live in the platform, not policy decks: Security, identity, observability, and red-teaming only scale when embedded directly into developer workflows. Governance that relies on after-the-fact review will not survive autonomous agents.

2. Model choice is a portfolio decision, not a belief system: Open vs closed isn’t ideology — it’s architecture. Different agents, workflows, and cost constraints require different models. Future-proof products assume multi-model orchestration from day one.

1. Software development is becoming parallel by default: AI agents collapse timelines by running multiple tasks simultaneously — coding, testing, refactoring, research. This breaks traditional planning models built around linear human throughput.

Here are our three strategic takeaways for leaders and builders for those who want to raise their AI ambitions

Speaking to our host, Bernard Leong @bernardleong.com, he shared how Microsoft enable and challenges startup founders and developers to think bigger in building with AI.

Watch it on Spotify too! open.spotify.com/episode/3QNi...

🎙️ Watch or listen to the full episode on our channels:

YouTube: youtu.be/WPwUvc29j2Q

At Analyse Podcast, our goal is simple: Bring clarity to the forces shaping the future of technology globally. This conversation with Karen is a must-listen for anyone trying to understand the real power dynamics beneath the surface of AI’s rapid advancement.

3️⃣ Fragmentation at the top doesn’t mean democratization: Yes, more labs exist today — but they remain concentrated among a small group of billionaires and nation-states. More competitors does not translate into more public control or transparency.

2️⃣ The global supply chain behind AI is invisible but immense: Data annotators, content moderators, data-center communities — the infrastructure behind AI spans continents, yet these stakeholders are rarely acknowledged in mainstream narratives about “progress.”

1️⃣ Knowledge monopolies are reshaping the scientific landscape: Most AI researchers — even in universities — are now funded by private labs. This creates a structural blind spot: policymakers and the public struggle to get truly independent assessments of AI’s capabilities and risks.

As Karen shows in Empire of AI, the industry is not simply a collection of innovative labs. It is an emerging geopolitical power bloc with growing influence over society, labour, and governance globally.

Here are three takeaways that stood out:

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Watch the full conversation to understand how enterprise AI is actually being built across Asia Pacific — far beyond the hype.

3️⃣ Real use cases are accelerating across APAC.
Fraud detection in finance, precision agriculture using IoT+satellite imagery, healthcare documentation reduction, energy-grid optimization — these are not prototypes; many are already in pilot or production.

2️⃣ Enterprise AI needs multicloud, openness and interoperability — not lock-in.
Oracle’s ability to run consistently across hyperscalers, on-prem and hybrid environments is becoming a major differentiator for regulated industries.

Here are 3 key takeaways for the Analyse Asia community:

1️⃣ Oracle’s “Four-Tier AI Stack” is now the company’s strategic backbone.
Infrastructure → Data Platform → Applications → Agentic Orchestration. Few players are delivering all four layers end-to-end with this level of integration.