Manuel Hernández Giuliani

Value Capture The Third Layer of the AI Story: Value Capture A reflection on how the AI economy is splitting between model builders and infrastructure enablers, and why long-term value will shift toward applications, agents, and business outcomes. In my previous reflections, I explored two dimensions of the current AI wave. First, the responsibility that comes with systems that act. Then, the unprecedented scale of infrastructure investment and its financial implications. There is now a third layer that completes the picture. The structure of value itself. We are starting to see a clear split in the AI economy. On one side, pure-play model companies are scaling rapidly. Revenue is growing at extraordinary speed, but so are losses, driven by compute, training, and inference. On the other side, hyperscalers and enablers are generating strong profits. But they are also committing to massive capital expenditure, with projections of $650–700 billion in AI infrastructure in 2026 al...

  LinkedIn What we may be witnessing right now feels uncomfortably familiar. Not in the technology itself… but in the economics behind it. We are seeing hyperscalers committing extraordinary levels of capital, with projected AI-driven capex in 2026 approaching $650–700 billion annually, up from roughly $380–410 billion in 2025. In just one year, that is a 60–70%+ increase, with an estimated ~75% of that spend directly tied to AI infrastructure. Individually, the scale is even more striking: - Amazon alone is projected around $200B - Alphabet around $175–185B - Meta between $115–135B - Microsoft exceeding $100B+ run rate - Oracle adding another ~$50B This is no longer incremental investment. This is a structural capital shift at global scale. And this is where the parallel with the dot-com era becomes difficult to ignore. Back then, the belief was that the internet would transform everything. It did. But the timing of value creation and the scale of investment were completely misali...

  Original Post in LinkedIn I have been saying for a while that AI is no longer just about models… it is about systems that act. Now regulators are catching up. The Spanish Data Protection Agency (AEPD) has published guidance on agentic AI from a data protection perspective. And this is important. Because agentic AI is fundamentally different. These are not systems that just answer… they decide, interact, and execute autonomously to achieve goals. That changes everything. It changes how data is accessed, how decisions are made, and how responsibility is assigned. And one key message stands out: using AI does not remove accountability. Organizations remain fully responsible for data protection, transparency, and control. From an architecture perspective, this reinforces something we often underestimate. You cannot build agentic AI on top of weak data foundations. Governance, data minimization, traceability, and privacy-by-design are no longer compliance topics… they are cor...

     Original Post In LinkedIn      Today I had a conversation with my boss, Soumya, someone I genuinely consider a mentor, and it led to a reflection that stayed with me. I asked him: Do you know who Dennis Ritchie is… without googling it? Do you know who Steve Jobs is? Then I told him: “I am Dennis Ritchie… and you are Steve Jobs.” And I meant it. Because there is a quiet truth in how impact is perceived. Not everyone remembers the inventor of the wrench. But everyone remembers the brand that made it known, usable, and desirable. Some of us operate deep in the foundations; building systems, architectures, and platforms that power everything else. Work that is critical, but often invisible. Others have the ability to translate that complexity, connect it to real human value, and bring it into the world in a way that people understand, adopt, and remember. The world doesn’t always reward creation alone. It rewards connection, storytelling, and adoption. But...

Original Post in LinkedIn Too many data initiatives start with technology. New platforms. New tools. New architectures. But the most successful organizations work differently. They start with the business problem. From there, they translate the problem into signals that can be measured . Then they design the architecture capable of capturing and processing those signals. And finally they deliver decision-ready intelligence . Technology is not the objective. Better decisions are. Real data platforms are not built around tools. They are built around business outcomes . That is what separates technology projects from data-driven organizations .