Reevaluating AI performance: From speed to contemplation

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

AI’s Clock Speed is Accelerating This chart from METR tells a profound story: The length of tasks AI can complete at a 50% success rate is doubling every 7 months. What started with GPT-2 answering questions in seconds has scaled to today’s frontier models (GPT-4, GPT-4o, Sonnet 3.7) handling tasks like training classifiers or building robust image models—things that once took humans hours. The implications are staggering: Productivity Compression → Work that took months to execute may soon take minutes. Capability Compounding → Each model builds on the last, accelerating discovery and application. Strategic Urgency → Enterprises and governments don’t just need AI roadmaps—they need adaptive AI operating systems that evolve with this pace. But speed also sharpens risks. As models race forward, governance, safety, and resilience must scale at the same rate—or faster. This is the defining paradox of our age: AI is compounding like Moore’s Law on steroids, but our institutions move at human speed. The question isn’t whether AI can keep doubling—it's whether leadership, governance, and society can keep up. 👉 What do you think? Are we ready for this pace, or is the gap widening too fast?

A funny thing happens when we lean too much on AI… It starts thinking for us instead of with us. But AI isn’t meant to be a replacement. It’s more like a sharp mirror. It tests your ideas, clarifies your blind spots, and pushes your thinking higher. And here’s the kicker: The secret isn’t just in writing better prompts. It’s in shaping the right context. Because without context, even the smartest AI will give you surface-level answers. The real game isn’t prompt engineering. It’s context engineering. So here’s my question for you: Do you see AI as your co-pilot to think better, or a shortcut that risks dulling your own thinking? #Contextengineering #promptengineering #AI #Tech #AIAgents

Forget everything you thought you knew about AI. The "AI" we're all using—the statistical parrots rearranging old data—is already dead. It's a tool of imitation, not a catalyst for true creation. We're at a critical turning point. A new class of intelligence is emerging behind closed doors, a breakthrough known as Synthetic Intelligence. This isn't just a smarter algorithm; it's a completely different paradigm. While today's AI requires constant human babysitting and static instructions, Synthetic Intelligence is an autonomous force. It designs, builds, and evolves solutions in real time, anticipating needs before they're fully defined. While your current tools are still writing drafts, Synthetic Intelligence is delivering finished, operational realities. This is the difference between imitating and creating. The few who have access to this new intelligence are already outpacing entire markets. Don't get left behind. 🚀 #SyntheticIntelligence #FutureofAI #TechBreakthrough #Innovation #AI #Automation #FutureofWork #EmergingTech #SelfEvolvingSystems #BeyondAI

🔑 Redefining Machine Learning My research demonstrates that by combining mathematical and software concepts it is possible to achieve 99.99966% accuracy in code generation while reducing energy consumption by 98–99% across the majority of AI workflows (approximately 65–70% of current systems). The energy results are impressive — but the deeper impact is that in the process, I effectively reinvented what an ML model means. I now have mathematical proof that these new models learn correctly. This also means that existing approaches to ML will likely become obsolete. To help the ecosystem transition, we will be providing a migration path for vendors and customers to move into this new paradigm. The foundation of AI is changing — and it’s changing fast. #ArtificialIntelligence #AI #MachineLearning #GenerativeAI #Sustainability #EnergyEfficiency #ClimateTech #EnterpriseAI #Compliance #Accuracy #SixSigma #Innovation #FutureOfAI #ResponsibleAI

Wide AI: Still Automation, But a Real Step Forward When we look at the evolution of AI, it’s tempting to see “intelligence” everywhere. But if we strip away the hype, Wide AI isn’t a mind—it’s still automation, just on a broader scale. The core idea remains the same: Represent the world in a way machines can process. Define computational steps to act on that representation. That’s why Deep Blue mastered chess, AlphaGo conquered Go, and today’s LLMs simulate conversation. Each breakthrough is powerful—yet still bound by representation and specification. What’s changed? Scale. Moore’s Law and the scaling hypothesis have given us models trained on massive datasets with staggering compute power. The results feel wide, but they remain constrained. The hallucinations, brittle reasoning, and lack of true understanding remind us: imitation ≠ intelligence. This raises important questions: How do we use Wide AI responsibly as automation rather than mistaking it for cognition? How do industries adapt to the deskilling and disruption it accelerates? And what comes next, if scaling alone cannot unlock general intelligence? The promise of AI lies not in pretending machines are minds, but in seeing clearly what they are—and then deciding wisely how to apply them. The more things change, the more they remind us of what has not. For further reading: https://lnkd.in/enAU93ec Hashtags #AI #Automation #WideAI #DigitalTransformation #FutureOfWork #ProjectManagement #Innovation #TechnologyTrends #Industry40 #Leadership

AI skepticism is in the air. This fall, businesses are hesitant about AI because of a flawed MIT study and market narratives. But the skepticism masks an important shift: the real challenge is implementing AI effectively, not its inherent worth. Companies that focus on AI infrastructure are likely to reap benefits first. This means improving how tools access and utilize data—from context engineering to agent collaboration. This week, consider mapping one bottleneck in your operations where AI could make a meaningful impact. Start a small pilot focused on effective implementation. What challenges do you face with AI in your business? Let’s talk in the comments. #smb #ai #operations

Why Prompt Engineering Isn’t the Future of AI It’s a symptom of a deeper problem. When machines became “intelligent,” we imagined a future where AI would finally adapt to us. Instead, we’re being told to adapt to it. Prompt engineering is marketed as a skill, but it’s really a survival tactic. Learn to speak the machine’s language — or be left behind. Let’s be honest: • If AI were truly intelligent, it would already understand us. • If it were relevant, it wouldn’t need 27 lines of prompt logic to produce a usable result. • If it were empowering, it wouldn’t make you dependent on clever phrasing and memorized syntax. So what is prompt engineering really? It’s a spotlight on the current limits of AI — and a warning sign that the so-called intelligence revolution is turning inward. • Not toward you. • Not toward context. • Not toward real understanding. • But toward systems that reward prompt manipulation over human intent. At iCRUZR, we’re designing something different. Not just another LLM. Not another wrapper. A relevance engine that learns you — not the other way around. Because the real future of intelligence? • Is defined by what adapts...and measured by what matters. #iCRUZR #FutureOfAI #AIethics #RelevanceByDesign #ContextualIntelligence #ArtificialIntelligence #HumanCenteredAI

Why 95% of AI projects fail? (According to recent MIT research) I think it’s not because the technology is weak. They failed because of the basics are skipped. - No clear problem to solve - No measurable success - No Problem-solving or Design Thinking Just because it’s AI people expect magic. But AI isn’t magic. It’s all Engineering. #AI #GenAI #Innovation #ProblemSolving #DesignThinking