Manuel Hernández Giuliani

  What three years of working with AI taught me about building software without losing control A few days ago, I was talking with Rafa, a colleague and a friend who has shared many battles with me. I was trying to explain what I had learned after more than three years of hands-on research and work with artificial intelligence: months of development, experiments, broken and repaired code, prompts that seemed brilliant but were not, late nights, mistakes, and repeated trips back to square one. As we talked, I shared that everything is distilled into the idea far simpler than any collection of prompts: Architecture and governance before engineering. At the beginning, I thought the challenge was to make AI do more: write more code, understand instructions better, or solve a larger task with less intervention. Today, I believe that was the wrong question. The real challenge is to ensure that AI makes sound decisions within clear boundaries, without replacing human authority or causing u...

  DeepSeek V4 Pro vs. Kimi K3: What Months of Real-World AI Agent Work Taught Me This article is based entirely on my personal experience using both models extensively through Pi.dev and my ARAYA framework. It is an independent research exercise, not a sponsored comparison, a reproduction of marketing claims, or an analysis based on technology news. This Is Not a Benchmark Most comparisons between artificial intelligence models begin with public benchmarks, release announcements, pricing tables, or carefully prepared demonstrations. My comparison began somewhere very different: inside real repositories, with incomplete context, conflicting documents, failing gates, distributed agent responsibilities, Git history , runtime evidence, and requirements that could not be considered complete merely because the generated code looked correct. Over several months, weeks, and many hours of intensive use, I worked with DeepSeek V4 Pro and Kimi K3 through Pi.dev while developing and operat...

 The AI industry may be entering a strange phase where narrative engineering advances faster than the underlying engineering itself. That does not mean frontier AI is fake. It is not. The progress is real. Models are improving quickly in coding, search, reasoning assistance, content generation, and cybersecurity analysis. Anyone working seriously in enterprise technology can already see the productivity impact. AI is becoming operationally useful. But something else is happening in parallel. Every few months, another company announces that its newest model is so powerful, so dangerous, or so transformative that humanity must proceed carefully. The language changes slightly each cycle, but the structure remains almost identical: existential concern, dramatic warnings, selective access, media amplification, institutional reaction, investor excitement, and another jump in valuation. At some point, it becomes reasonable to ask whether we are witnessing purely a technological revolution...

    This started while I was preparing my Big Data labs for my students. I wanted something closer to reality. Not only pipelines and Spark jobs, but how AI agents would actually interact with a governed data platform. So I set the environment as it should be in a serious setup. A lakehouse governed with AWS Lake Formation, metadata centralized in AWS Glue Data Catalog, and Spark handling execution. Clean, controlled, auditable. Then I added the missing piece. An agent that needs to understand intent and retrieve context, not just run queries. And almost immediately, the same request appeared. “We need a graph database for ontology.” In AWS terms, that means Amazon Neptune. I see this pattern often, not only with students. Also in real projects. Someone comes with a solution already decided. I always give the same answer. What is the business problem you are trying to solve? Because “I need Neptune” is not a requirement. It is a conclusion. When you force the conversation back...

There’s something powerful about seeing architecture come to life outside of slides and tools. Today I want to recognize Ahra, one of my students, who took the time to translate her understanding of our Big Data labs into a hand-drawn reference architecture. Not only is it correct in structure, but it reflects clarity of thought and ownership of the problem. From ingestion to Bronze using a dual pipeline pattern (batch and streaming), through data quality and standardization in Silver, to consumption-ready Gold for BI and AI use cases, she captured the full journey. Even more interesting, she extended the architecture into Labs 11 and 12, incorporating natural language AI agents capable of querying the data platform using skills, RAG, and process knowledge. This is exactly the mindset we aim for: not just using tools, but designing systems that solve real problems and enable business interaction through AI. Kudos to you, Ahra. This is the kind of thinking that builds real data architec...

Over the past few years, artificial intelligence has moved from promise to pervasive investment. Across industries, organizations have committed significant resources to AI initiatives. New models, new platforms, new capabilities. The pace of innovation has been extraordinary. And yet, a new question is emerging. Not what can we build with AI, but what is actually delivering measurable value. This shift marks an inflection point. We are entering a new phase of AI maturity. One defined not by experimentation, but by accountability. The Illusion of Progress From the outside, the landscape looks impressive. More models. More pilots. More demonstrations of capability. But inside many organizations, a different reality is taking shape. AI initiatives often remain disconnected from measurable business outcomes. Value is assumed, but not always quantified. Success is described in terms of activity, not impact. This is not a failure of technology. It is a reflection of something de...

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 .

  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