How AI is Evolving Beyond Consumer Applications

Physical AI is becoming real—and fast. 🦾 At GTC 2025, NVIDIA didn’t just launch new chips or tools. They showed us how AI is evolving beyond language and vision—into machines that can act in the real world. Here’s what you need to know about physical AI: 1️⃣ It’s not about one robot. It’s about transferable intelligence. The big leap isn’t hardware—it’s the idea that a single model can power many robots. Trained on both real and synthetic data, foundation models like GR00T can learn general skills—like grasping, walking, or organizing—and adapt to new environments. It’s the same shift we saw in NLP: one model, many use cases. 2️⃣ Simulation is more than a test environment—it’s a learning engine. With realistic physics, sensors, lighting, and even human avatars, today’s simulators are rich enough to train robots from scratch. This dramatically reduces the cost of failure, accelerates iteration, and unlocks edge-case training you’d never risk in real life. 3️⃣ The AI stack is converging—from perception to motion. Historically, vision, planning, and control lived in silos. Now, we’re seeing unified models that combine them—so robots can see, understand, and act in milliseconds. That unlocks autonomy that’s adaptive, not brittle. 4️⃣ Edge deployment isn’t optional—it’s foundational. Robots don’t have time to wait for cloud inference. Running large models locally—with fast, efficient chips—means faster reactions, safer systems, and more robust performance. This is especially critical in healthcare, manufacturing, and logistics. 5️⃣ Physical AI is becoming infrastructure. From humanoids in factories to autonomous X-rays in hospitals, the same core ingredients are emerging: • Generalist models • Simulation pipelines • Edge AI hardware • Domain-specific fine-tuning The implication? We’re not just building robots. We’re building a new interface between AI and the real world. — Why it matters: Most people still think of AI as something that writes text or generates images. But the next wave is embodied. AI that moves. That helps. That does. Physical AI isn’t a product category. It’s a shift in what AI can be. #PhysicalAI #GTC2025 #EmbodiedAI #Simulation #EdgeAI #Robotics #AIInfrastructure #Autonomy #DigitalTwins #AIforRealWorld

The next wave of AI transformation is here – and it’s not just about language-based models anymore. The real breakthroughs are happening now with Large Quantitative Models (LQMs) and cutting-edge quantum technologies. This seismic shift is already unlocking game-changing capabilities that will define the future: Materials & Drug Discovery – LQMs trained on physics and chemistry are accelerating breakthroughs in biopharma, energy storage, and advanced materials. Quantitative AI models are pushing the boundaries of molecular simulations, enabling scientists to model atomic-level interactions like never before. Cybersecurity & Post-Quantum Cryptography – AI is identifying vulnerabilities in cryptographic systems before threats arise. As organizations adopt quantum-safe encryption, they’re securing sensitive data against both current AI-powered attacks and future quantum threats. The time to act is now. Medical Imaging & Diagnostics – AI combined with quantum sensors is revolutionizing medical diagnostics. Magnetocardiography (MCG) devices are providing more accurate cardiovascular disease detection, with potential applications in neurology and oncology. This is a breakthrough that could save lives. LQMs and quantum technologies are no longer distant possibilities—they’re here, and they’re already reshaping industries. The real question isn’t whether these innovations will transform the competitive landscape—it’s how quickly your organization will adapt.

7 Enterprise AI Trends I’ve Been Seeing Over the past 30 days, I’ve had a bunch of conversations with teams across enterprises, MSPs, SIs, and ISVs. These aren’t headlines — they’re the themes that keep surfacing when people talk honestly about where AI is heading. Here’s what I’m hearing again and again: 1. Shadow AI is showing up Consumer employees are bringing tools like ChatGPT to work without looping in IT. Just like with Shadow IT, it’ is not-stoppable… but also risky — especially when it comes to data and compliance. 2. Big consulting firms are getting in early Top-tier consultants are already leading many enterprise AI initiatives. Everyone else is now trying to catch up and get in on the action. 3. Internal AI projects are getting stuck Lots of pilots aren’t making it past the experiment stage. Lack of security, observability, and governance is slowing things down. AIOps and trust layers are becoming essential. 4. AI budgets are starting to show up Many forward looking companies are budgeting for security in 2025, not just models. I’m also seeing more interest (and noise) for AI agents and MCP servers. 5. Under-performing Public companies want the AI premium Companies not viewed as “AI plays” are waking up — buying AI startups, hiring AI-native execs, partnering with AI startups and doing whatever they can to reframe their story. 6. Adoption is uneven across industries Tech, financial services, healthcare, and legal are way ahead of others. It’s a mix of urgency, regulation, and clearer ROI. Some verticals just have more at stake. 7. AI needs to fit how people work GenAI only works when it actually fits into real workflows. If it doesn’t connect to existing systems and roles, it becomes shelfware pretty fast. — My takeaway: We’re past the phase of just “playing with LLMs.” What matters now is execution — trust, scale, architecture, and making AI work where the business actually happens. What are you seeing in your world? Curious how this compares. P.S. Not one person I spoke with mentioned “machine learning.” That shift says a lot. — #EnterpriseAI #GenAI #AITrends #DigitalTransformation #MSP #SystemIntegrators #ISV #AIinBusiness #AIstrategy #AITrustScore Tumeryk

Mary Meeker just dropped her 2025 AI Report. 300 slides. Decades of foresight. And one clear takeaway: AI is scaling faster than any tech in history. And the machines aren’t just catching up — they’re racing ahead. Here are 10 unmissable insights — in “What → So What → What Next” format 👇 1. ChatGPT hit 800M users in 17 months. → What: AI adoption is 7x faster than the internet. → So what: We’re living through the fastest tech diffusion ever. → What next: Every user experience must become AI-native — or risk irrelevance. 2. Big Tech spent $212B on AI CapEx in 2024. → What: Infra spend is up 63% YoY. → So what: Chips, not code, are the new bottleneck. → What next: Control compute — or be controlled by those who do. 3. Training costs are skyrocketing, inference costs are crashing. → What: Model training = $1B+ | Inference = -99.7% in 2 years. → So what: Serving gets cheap, but building gets brutal. → What next: Expect a flood of narrow, cheap, fine-tuned models. 4. Model performance is converging. → What: Top LLMs (GPT-4o, Claude, Gemini, DeepSeek) score within 2% of each other. → So what: The edge is no longer model — it’s UX, speed, cost. → What next: Developers will pick based on use-case and cost, not brand. 5. Developer adoption is exploding. → What: NVIDIA and Google ecosystems each have 6–7M+ developers. → So what: AI building is going mainstream. → What next: Tooling, infra, and agents will follow the dev wave. 6. AI is entering the physical world. → What: Autonomous taxis now hold 27% market share in SF. → So what: AI is moving beyond screens into streets. → What next: Expect more AI-native hardware startups (health, mobility, robotics). 7. Work is evolving fast. → What: AI IT jobs +448% since 2018, non-AI jobs -9%. → So what: Talent is migrating to AI-centric roles. → What next: Career strategy must now be AI-literate or AI-augmented. 8. China and open source are rising. → What: Chinese LLMs gaining global share; 100+ open-source models launched. → So what: The west’s monopoly is breaking. → What next: Watch for price wars, localization, and regulatory tension. 9. Generative AI is the new printing press. → What: We’ve moved from static → digital → generative knowledge. → So what: AI is not just consuming data — it’s creating it. → What next: Every knowledge worker becomes a content node. 10. Data centers are the new oil rigs. → What: xAI deployed 200K GPUs in 7 months; global energy use is surging. → So what: AI’s hunger is now limited by electricity, not imagination. → What next: Sovereign infra, clean energy, and GPU nationalism are the next frontiers. If you’re not building with AI, you’re falling behind it. Save this. Share this. Stay ahead. _______________ I’m Amit Rawal, Chief AI Officer and Ex-Apple AI Product leader. I help ambitious thinkers and founders design their lives like systems: using AI to work smarter, live longer, and grow richer with clarity and calm.

The future of AI isn't just on screens anymore. It's in the real world, and it's called Physical AI. Jensen Huang's NVIDIA GTC keynote revealed a game-changing shift: His message was clear: AI is moving beyond computers. It's entering factories, hospitals, and streets. It's controlling robots and machines. It's transforming how we work. 5 𝐤𝐞𝐲 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐟𝐨𝐫 𝐟𝐨𝐫𝐰𝐚𝐫𝐝-𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐥𝐞𝐚𝐝𝐞𝐫𝐬: 1. 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐥𝐥 𝐜𝐡𝐚𝐧𝐠𝐞 • Physical AI means smart machines everywhere • Not just automation, true intelligence • Example: GM using NVIDIA's platforms for next-gen vehicles and factories 2. 𝐘𝐨𝐮𝐫 𝐭𝐞𝐚𝐦 𝐧𝐞𝐞𝐝𝐬 𝐧𝐞𝐰 𝐬𝐤𝐢𝐥𝐥𝐬 • Fusion of robotics experts and AI engineers • Cross-disciplinary is the new normal • Example: Gatik integrating DRIVE AGX for autonomous trucks shows the need 3. 𝐒𝐚𝐟𝐞𝐭𝐲 𝐢𝐬 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 • Real-world risks need real solutions • No compromises on safety protocols • Example: Volvo Cars using NVIDIA DGX to enhance vehicle safety 4. 𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞 𝐢𝐬 𝐚𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐧𝐠 • NVIDIA's Blackwell Ultra platform just announced • 1.5x more AI performance than previous generation • Adoption could be faster than expected 5. 𝐏𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩𝐬 𝐚𝐫𝐞 𝐜𝐫𝐮𝐜𝐢𝐚𝐥 • Google Cloud and NVIDIA expanding collaboration • Ecosystem approach accelerates innovation • Strategic alliances will define winners The future isn't digital-only anymore. It's physical. And it's coming faster than we imagined. → What's your organization doing to prepare? Drop your thoughts below or 𝐃𝐌 𝐦𝐞 𝐢𝐟 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐡𝐞𝐥𝐩 𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐀𝐈 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲. ♻️ Repost to help others navigate AI transformation ✚ Follow for insights on human-centered AI, digital transformation & innovation #PhysicalAI #DigitalTransformation #NVIDIAGTC #FutureOfWork

Reimagining AI: Moving Beyond the Human Form AI doesn’t need to look human to be transformative. While popular culture often depicts AI as humanoid robots, like Data from Star Trek or the quirky machine in WALL-E, this vision limits what AI can truly achieve. The future of AI isn’t about mimicking human behavior or appearance—it’s about seamless integration into the systems that power our world. AI agents will operate invisibly within machines, interacting with data, optimizing processes, and solving problems at scales and speeds humans could never achieve. This isn’t speculation; it’s already happening. AI systems are embedded in devices, infrastructure, and industries, where their purpose is efficiency, precision, and innovation—not emulation of human form or behavior. Why does this matter? Because it changes how we think about AI’s role. Instead of seeing AI as a human-like assistant, we should embrace it as an omnipresent force driving transformation. Its ability to process massive amounts of data, adapt to complex environments, and evolve autonomously will reshape industries and solve challenges we’ve struggled with for decades. The future isn’t about AI walking or talking—it’s about AI being everywhere, solving problems we didn’t even know existed. This shift in perspective will define how we design, deploy, and embrace the AI systems of tomorrow. The question isn’t whether AI will replace humans—it’s how we can maximize its potential to complement and enhance human capability, without being constrained by outdated, human-centric expectations. AI isn’t here to look like us. It’s here to transform the world around us.

Innovation has always been our growth engine: it gave us the steam engine, vaccines, the integrated circuit—but what if that engine begins to sputter? Our research shows a sobering truth: across industries, R&D productivity has been declining for decades. In semiconductors, it now takes 18 times the R&D spend to sustain Moore’s Law. In pharma, “Eroom’s Law” (yes, that’s “Moore” spelled backwards) tells us the cost of bringing a new drug to market has skyrocketed. And this pattern shows up in agriculture, automotive and beyond. However, there’s hope. Innovation is getting harder and costlier, but AI may be the spark that reignites it. Not just by making existing processes faster, but by fundamentally reshaping how we innovate. Think: 1. Increasing the velocity, volume, and variety of design candidate generation. 2. Rapidly testing ideas using AI proxy models. 3. Streamlining research operations to get from idea to insight faster. AI can bend the curve of R&D productivity—and with it, unlock new frontiers of economic and human progress. It’s not just about cost savings or margin expression; it’s about fueling the next era of discovery. Take a look at our latest article (link in comments) to learn more. #AIbyMcKinsey #Innovation #Technology

🤖 The Future of AI is Beyond Language: Introducing "World Models" Top AI researchers like Fei-Fei Li and Yann LeCun are revolutionizing artificial intelligence by moving beyond traditional language models. Here's what makes their approach groundbreaking: 🌐 World Models: Not just processing words, but understanding spatial intelligence 📐 3D Reasoning: Training AI to comprehend and interact with complex environments 🧠 Mental Constructs: Mimicking how humans actually perceive and predict the world Key Insights: - Language is limited - the world is fundamentally three-dimensional - AI needs to understand context, not just statistical word relationships - Spatial intelligence is the next frontier of machine learning Li's World Labs has already raised $230M to develop these advanced models, focusing on creating AI that can: - Generate infinite virtual worlds - Enhance robotics - Improve perception in complex scenarios The challenge? Gathering sophisticated spatial data is incredibly complex. But the potential is transformative. What do you think? Are we witnessing the next quantum leap in AI technology? #ArtificialIntelligence #FutureOfTech #WorldModels #AIInnovation