Usamah Sedqi Al-Baik أسامة صدقي البيك

In my previous reflection on artificial intelligence , I argued two things that may seem contradictory at first but are not: we should not bet against AI, yet we should not assume that today’s large language models will automatically reach artificial general intelligence by simply becoming bigger, faster, and more expensive. The reason is simple. AI has already broken many of the limits that people once considered impossible. It writes, summarizes, translates, codes, reasons across documents, creates images, operates tools, and increasingly participates in workflows that used to require human experts. In that sense, betting against AI has become a dangerous habit. But there is another side to the argument. Large language models are still primarily language engines. They are astonishing at producing plausible text, code, and explanations, but they are not necessarily reliable decision-makers. They can hallucinate. They can be manipulated by prompts. They can struggle outsid...

In my previous articles, I explored two related questions. The first was about entropy, information theory, and the fundamental limits of computers . Information may appear abstract, but computation ultimately happens in the physical world. Bits require physical states. Processing requires energy. And even our most sophisticated machines remain subject to the laws of physics. The second article took the question further: if computers have limits, does intelligence itself have limits? I argued that humanity has always extended its intelligence through tools—language, writing, mathematics, scientific instruments, computers, and now artificial intelligence. Yet seeing farther does not mean seeing everything. The Qur'an captures that intellectual humility beautifully: (وَمَا أُوتِيتُم مِّنَ الْعِلْمِ إِلَّا قَلِيلًا) "And you have not been given of knowledge except a little." — Qur'an 17:85 But there is another question behind both arti...

Recently, I came across a reflection by Dr. Ahmed Omar Hawsawi that immediately connected with a series of articles I have been writing about AI, authorship and authenticity. He described a situation that I suspect many of us are beginning to encounter. Someone presents an impressive presentation. It is clear, beautifully structured and professionally designed. Then comes the comment: “Obviously this was made by AI.” And sometimes there is an unspoken second sentence: “Therefore, it is somehow less valuable.” That is where I think our conversation about artificial intelligence has taken a wrong turn. Because increasingly, “This is AI” is being used not as a description of a tool, but as a criticism of the person who used it. We Are Judging the Tool Instead of the Work Imagine that employee presenting the slides. Perhaps AI created the visual layout. Perhaps it reorganized the bullet points. Perhaps it summariz...

Can AI solve humanity’s hardest mathematical problems without quantum computers — and what happens to science if it can? In my previous reflections on quantum computing and artificial intelligence , I argued against a simple “AI versus quantum” narrative. I still believe that. Artificial intelligence and quantum computing are fundamentally different technologies with different strengths. AI is already extraordinarily capable at pattern recognition, language, reasoning, search, prediction and increasingly scientific discovery. Quantum computing offers something different: the ability to manipulate quantum states in ways that could eventually make certain simulations and mathematical operations tractable when they are prohibitively difficult for classical machines. My expectation was therefore a heterogeneous future: CPU + GPU + AI + QPU , with each type of processor handling the part of a problem for which it is best suited. IBM is already explicitly pur...

Why the most important relationship between artificial intelligence and quantum computing may be cooperation, not competition In my previous article on quantum computing , I argued that the biggest opportunity today is not necessarily to build quantum machines, but to build readiness : understand the technology, identify meaningful use cases, develop talent, prepare security, and learn where quantum may eventually create a genuine business advantage. Looking more closely at the intersection of artificial intelligence and quantum computing makes that conclusion even stronger — but also more nuanced. There is a tempting narrative that quantum computing will eventually supersede AI infrastructure, or that adding quantum processors to machine learning will suddenly produce exponentially more powerful artificial intelligence. I don’t believe either description captures where the technology is heading. The emerging picture is more interesting. AI and quantu...