Great to be back at Build today.

I think the IQ layer concept is what stands out most to me. unifying Foundry, Fabric, and M365 into a continuously updated organizational understanding is essentially solving the data fragmentation problem that makes enterprise AI so hard to trust.

OPAS Authority OS is now officially discoverable, deployable, and purchasable through the Microsoft Commercial Marketplace — a major unlock for enterprise adoption. This is just the beginning. Over the next 90 days, we’ll be rolling out: • The OPAS Obsidian‑Jarvis Agent System • Deterministic governance pipelines • Enterprise‑grade compliance automation • Full cinematic‑hybrid product visuals • Partner integrations across Azure + M365 If you’re building, scaling, or governing at enterprise altitude — OPAS is now one click away inside your Microsoft ecosystem. Let’s go. #OPASAuthorityOS #MicrosoftMarketplace #Governance #Automation #EnterpriseOS #Azure #M365

Satya, building a frontier intelligence ecosystem makes complete sense for orchestration and prediction. Crucially, when it comes to the final physical release of high-value assets — including the silicon and infrastructure powering that ecosystem — probabilistic systems reach their limit. Courts and insurers are increasingly treating unauthorized physical handover based on visual checks or compromised credentials as a failure of Due Diligence. This creates direct exposure for the companies responsible for moving these assets. LTP V7 was built exactly for this gap. It adds a deterministic cryptographic layer at the physical gate, generating a court-admissible record that the handover occurred strictly in accordance with the shipper’s original data. AI can optimize the entire journey. At the point of physical release, mathematics removes the ambiguity.

And how i build is Build AI agents which are not glued to any plarform. Then seltup Qwen 8B for training on the AI agents either on runpod or local MAC (enough power). Then setup a learning shedule. and Lo we ahve a Fully autonomous AI agents in under 2 Months self learning and using $2 on Brand LLMS

Let me stop you before you say: "OpenAI just killed Lovable"

Look, moving the entire control plane from traditional applications to autonomous systems of agents introduces massive strategic variables. The technology is impressive, but handing off execution intent without an airtight operational governance framework is an immediate operational risk. A chatbot hallucination costs reputation—an agent execution error without proper guardrails directly impacts the P&L through broken workflows, unauthorized data calls, and misaligned pipeline metrics. Deploying agentic AI requires human-in-the-loop validation built into the core business architecture. C'est le problème with treating advanced systems as a magical shortcut rather than infrastructure that demands strict oversight.

One thing that stands out to me is that we're moving beyond building tools. The bigger challenge now is building ecosystems where intelligence can be trusted, discovered, and applied at scale. That's a very different problem than just making models smarter.

Letting companies participate in the frontier ecosystem empowers countries, companies, and teams. When intelligence becomes infrastructure, then only consuming it is weak position. You need models, tuning, data layer, agents, devices, and governance close to your own workflows. Otherwise you are renting the future from someone else.

Quantum‑Anchored Inference Is a Hyperscaler Fantasy Microsoft’s internal assumption: “Inference will move to quantum accelerators, and enterprises will run MAI‑shaped models in Azure.” Reality: 99% of enterprises will never touch quantum inference 95% cannot afford persistent cloud agentics 90% cannot tolerate unpredictable token burn 80% operate in bandwidth‑constrained environments 70% have strict data‑sovereignty requirements 60% have fixed IT budgets that cannot absorb cloud volatility Quantum inference is a hyperscaler luxury, not an enterprise baseline.