Usamah Sedqi Al-Baik أسامة صدقي البيك

Twenty-one months ago, I wrote about how OpenAI’s Operator could transform traditional automation. Today, with the arrival of Claude Duet, ChatGPT Dots, Meta Muse, and Grok Bot, that prediction is evolving into something much bigger: AI systems that don’t just execute tasks, but proactively manage work. When AI Learned to Use a Computer On January 24, 2025, I published an article titled How OpenAI’s Operator Outpaces Traditional RPA . It was just one day after OpenAI introduced Operator, an experimental AI agent capable of interacting with websites much like a human being. At the time, I argued that Operator represented an important shift in automation. Traditional Robotic Process Automation (RPA) relied heavily on predefined rules, scripted interactions, and structured workflows. These technologies were highly effective at repetitive tasks, but often required adjustments whenever an application changed or an unexpected situation appeared. Operator in...

The third chapter in a developing story about agentic AI, cybersecurity, and digital trust Imagine waking up to discover that the locks protecting millions of digital doors may not be as secure as we believed. Some doors lead to ordinary websites. Others lead to government services, customer records, business platforms, and financial information. That is the uncomfortable picture emerging from several cybersecurity developments this week. The internet has not collapsed, and there is no reason for everyone to abandon it. But the events of October 2026 deserve our attention for a much more important reason: artificial intelligence is changing the speed at which weaknesses in our digital world can be discovered, exploited, and repaired. From an early warning to a real-world test For me, this story did not begin with this week's security alerts. In June 2025, I published “From Chatbots to Cyber-Worms: Why the Age of Agentic AI Demands Radical Rethinking of ...

In my previous article , I argued that the next major bottleneck for artificial intelligence may not be model intelligence, chip supply, or investor enthusiasm. It may be something far more physical: electricity . AI is becoming a power-hungry industrial system. Data centres are expanding rapidly, and the International Energy Agency expects global data-centre electricity consumption to roughly double by 2030, reaching about 945 terawatt-hours . That is not just a technology trend; it is an energy-infrastructure challenge . The future may be intelligent, but as I wrote before, we still need somewhere to plug it in. This raises the next question: if AI needs enormous amounts of electricity, where should that electricity come from? The Search for Firm Clean Power Solar and wind are essential, and their growth has been extraordinary. In 2025, global renewable power capacity reached a record 5,149 GW , with solar alone adding 511 GW and reaching 2,392 GW worldw...

The future of SaaS, the rise of vibe coding, Adobe’s market dominance, and the strategic threat to established software companies Introduction: The threat is no longer theoretical For decades, the software industry has operated on a relatively stable assumption: developing sophisticated applications requires substantial investment, specialized engineering teams, years of accumulated intellectual property, and continuous maintenance. These barriers have allowed established software companies to build powerful market positions, sustain premium pricing, and maintain considerable control over their customers. Artificial intelligence is changing not only how software is used but, more fundamentally, who can build it, how quickly it can be built, and what customers should reasonably be expected to pay for it. The rise of vibe coding, AI-assisted professional software engineering, and increasingly autonomous coding agents introduces a competitive threat that many estab...

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...