Manuel Hernández Giuliani

  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 .

  Original Post in LinkedIn Enterprise Data and AI leadership in 2026 and beyond is defined by the ability to translate business intent into scalable, governed, and AI-ready data platforms. It is no longer about managing technologies, but about orchestrating data, architecture, and decision systems as a unified capability. Leaders must balance innovation with control, embedding governance, metadata, and quality into every layer while enabling real-time, autonomous intelligence through AI and agents. The focus shifts from delivering data to delivering outcomes, where platforms become products, teams operate with ownership, and every data asset is designed to drive measurable business value.

  Original Post in LinkedIn I remember when I started working in IT back in 1995 reporting meant green-bar paper coming out of big machines, and big noise, in cold data centers. If you wanted a new report, it could take weeks. Sometimes months. Then came Data Warehousing; we structured everything. ETL before loading. Star schemas (thanks Ralph Kimball and your Bus Matrix). Governance and Methodology first. It was powerful, clean, reliable, controlled. But rigid. Then the world exploded with data; we enter to the age of Data Lakes (Big Data here and there). We moved from ETL to ELT. From structured-only to everything: logs, sensors, clicks, documents, images, videos. It felt like freedom. But without governance, many lakes became swamps. The next natural step was inevitable: Data Lakehouse. We wanted scale and control. Flexibility and trust. Governance embedded into the platform. One foundation for BI, Data Science, and now AI. And today, we are living another evolution emerging. No...

  Original Post in LinkedIn Really valuable initiative. AI conversations must move beyond hype and into execution, scale, governance, architecture, and domain expertise. Sustainable AI is not about models alone. It is about integrating AI into real enterprise data platforms and operational systems. Looking forward to listening to these episodes, especially the intersection between AI and industrial transformation. AI at scale requires engineering discipline. hashtag # AI hashtag # DataArchitecture hashtag # EnterpriseAI https://lnkd.in/egTAgNrR