Microsoft CEO Satya Nadella on the Future of AI

Okay, here's a summary of the chat with Satya Nadella about the future of AI, based on the transcript.

Microsoft is going all-in on AI, fundamentally reimagining its entire tech stack, from infrastructure and data to the application layer. This transition involves integrating powerful AI agents and copilots into familiar products like Office and Teams, while also exploring new computing paradigms and addressing the significant economic and environmental implications of this technological shift.

Here are the key points discussed:

• Embracing the AI Era: The shift to the "AI era" and "agenting web era" requires embracing what's new. Patterns for building agents and apps are becoming clearer.
• Reimagining the Tech Stack: Every layer of the tech stack needs to be rethought from scratch for new AI workloads, but Microsoft can leverage its past 15 years of work to benefit developers.
• Infrastructure Layer (Azure): The focus is on transforming existing Azure regions (70 globally) into "AI factories." While GPUs are crucial, AI workloads, especially agents, require a lot more – tons of storage (for training and inference) and regular compute for agent environments. What Microsoft built over 15 years seems even more relevant now due to agents needing so much infrastructure, just at a different scale.
• Data Layer: Intelligence and reasoning engines can now be brought directly to the data layer. A cool example is mixing LLM responses directly into a SQL query in Postgres, which can generate interesting query plans.
• Changes to Microsoft Products (Microsoft 365): Microsoft 365 is evolving in three ways:
  • Brand-New UI for AI: This includes a new scaffolding with chat, search, notebooks (for collecting heterogeneous data and doing things like audio overviews), and dedicated agents (like researchers or analysts) for delegating tasks. There's also a Copilot studio to build custom agents. This feels like a new "UI for AI" and agents.
  • Multiplayer Mode (Teams): Teams integrates all the AI capabilities, making agents and copilots available in channels and meetings, allowing users to work with AI collaboratively. Teams acts as the scaffolding for AI in a multiplayer context.
  • Heads-Down Productivity: AI is integrated directly into existing applications like Excel and Word. Just like having a GitHub Copilot in VS Code, you can have a data scientist next to you in Excel or a researcher next to you in Word via a copilot chat interface. The idea is turning every Office canvas into an "IDE with chat."
  • Satya feels the value of the M365 system is significantly compounded by building intelligence into all these layers.
• SaaS is Dead? (Collapse of the Application Layer): The idea is that the application layer will "collapse" into agents orchestrated above grounded databases.
  • This means SaaS companies need to radically change. If a SaaS company's scope is just being a system of record or engagement with workflows on top of their own data, that won't likely persist.
  • Instead, SaaS applications must participate in the "new orchestration layer in the agentic web." They need to become "backends" for agents and support standards like MCP (Microsoft Copilot Protocol, implied) to be part of this agentic web.
  • Something like NL web (Natural Language web, implied) could potentially reduce friction caused by traditional connectors between systems.
  • Satya believes it's "clear as day" that composing complex business processes will require orchestrating multiple backends (including SaaS apps).
• Who Owns the Agents? (Personal vs. Corporate): When you "hire" an employee, you're hiring them and their "basket of agents."
  • Companies will likely own the IP of agents used for work, just like they own other work products.
  • Microsoft is extending existing IT management rails (identity, security, data protection) to agents.
  • Agents will have an Entra ID (Microsoft's identity service), allowing for conditional access management like with people.
  • Purview (Microsoft's data governance service) will ensure agents accessing data are subject to the same data protection and rights.
  • Defender (Microsoft's security service) will manage the agent environment like an endpoint to prevent issues like credential theft. It's crucial to apply all the identity, management, and security frameworks built for people and IT infrastructure to agents and their infrastructure.
  • Bringing personal agents into work requires keeping the two worlds separate to prevent data leakage (privacy and IP reasons). Using separate identities (Microsoft account for personal, Entra for corporate) and models like having different profiles in Edge for different accounts is helpful to maintain a simple mental model and avoid tangling personal and corporate data/agents.
• Zero-Cost Intelligence & Economic Impact: Satya is excited about the prospect of the cost of intelligence dropping rapidly, ideally approaching zero.
  • He believes abundance of technology and intelligence is needed to drive productivity and economic growth, especially to help tame inflation or improve growth rates.
  • A key example is applying AI (multi-agent frameworks, orchestrating disparate data sources like pathology, clinical trials, PubMed) to high-stakes areas like healthcare (tumor board meetings, cancer care). This could profoundly impact society by improving patient care, outcomes, and reducing costs in a sector that's a significant portion of GDP. This is what he's "really looking forward to."
  • Other areas like material science (e.g., using AI to discover new materials like immersion cooling fluids, shown in a demo) also show immense potential.
• Environmental Impact of AI: The younger generation's concern about AI's energy usage is inspiring and provides a crucial push for the industry to focus on sustainable outcomes.
  • AI creation must fundamentally help solve societal challenges in areas like healthcare, education, and financial services to achieve economic prosperity and abundance, not just be a "tech accomplishment."
  • Doing this sustainably is essential ("sustainable abundance").
  • The key metric becomes "tokens per dollar per watt" – using software efficiently to generate the most output (abundance) for the least energy, which then improves real-world outcomes.
  • While tech is currently a small percentage of total power consumption (2-3%), it will grow. To have the "social permission" to double energy usage, AI must create significantly more value in the real world across many domains (healthcare, material science, small business productivity), not just for "fun things."
  • Microsoft is a large buyer of alternative energy and works to subsidize new clean energy projects, pushing for sustainable practices while delivering value efficiently.
• Future Operating System & Blurring Determinism: There's a major shift in computing architecture, blurring the lines between deterministic (traditional code) and non-deterministic (AI models). Examples include generative models creating game frames or controlling virtual worlds.
  • The idea of an operating system with little to no traditional code is intriguing.
  • Satya notes that even supposedly deterministic software programs have fundamental challenges in proving correctness. While AI systems are stochastic, they need to operate in ways that allow inspection.
  • Understanding the "physics of intelligence" (as Elon Musk suggested) is needed to understand how complex AI systems work and how to "bound" or sandbox them.
  • Blending deterministic systems (imperative code, like virtual machines) with AI agents is happening now. Coding agents, for instance, run in controlled virtual machine environments with defined boundaries (internet access, tool access) and full audit logs.
  • This blending of different software types is part of the "middle innings" of this transition. Learning how to monitor and control the interaction between deterministic systems and agents is key for the future of computing, including operating systems.