Adrian Brown’s Post

The AI revolution won’t be software. It’ll be silicon. For years, AI progress was driven by code. Smarter algorithms, better frameworks, larger datasets. But now we’ve hit a wall, not of imagination, but of physics. The next generation of AI models isn’t limited by ideas. It’s limited by energy, memory, and heat. The real breakthroughs won’t come from optimizing software. They’ll come from rethinking the hardware that runs it from how memory interacts with compute, to how data actually moves through silicon. The future of AI will be defined not by how much we can code, but by how efficiently we can compute. What do you think? Will the next AI giant be a software company or a chip company? #AI #Hardware #Semiconductors #Innovation #FutureTech

𝗧𝗵𝗲 𝗧𝗿𝗶𝗽𝗮𝗿𝘁𝗶𝘁𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗳𝗼𝗿 𝗔𝗚𝗜: 𝗪𝗵𝘆 𝗪𝗶𝗹𝗹𝗼𝘄, 𝗦𝗽𝗶𝗸𝗶𝗻𝗴𝗕𝗿𝗮𝗶𝗻 𝗔𝗜, 𝗮𝗻𝗱 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹-𝗥𝟭 𝗔𝗿𝗲 𝘁𝗵𝗲 𝗡𝗲𝘅𝘁 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿. Current Artificial Narrow Intelligence (ANI) is hitting the Computational and Energy Walls. The pursuit of true Artificial General Intelligence (AGI) and Superintelligence (ASI) demands a radical new design—a multi-paradigmatic system that fuses three state-of-the-art technologies to solve the grand challenges of AI scaling: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗪𝗶𝗹𝗹𝗼𝘄 (𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴): 𝗧𝗵𝗲 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗼𝗿. Provides quantum speedup for massive optimization tasks, enabling the system to rapidly converge on complex solutions and accelerate policy exploration in learning. 𝗦𝗽𝗶𝗸𝗶𝗻𝗴𝗕𝗿𝗮𝗶𝗻 𝗔𝗜 (𝗡𝗲𝘂𝗿𝗼𝗺𝗼𝗿𝗽𝗵𝗶𝗰 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴): 𝗧𝗵𝗲 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗖𝗼𝗿𝗲. Solves the energy crisis. It uses biologically plausible, event-driven SNNs to achieve up to two orders of magnitude in energy efficiency, making lifelong, real-time AGI deployment sustainable and feasible. 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹-𝗥𝟭 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗟): 𝗧𝗵𝗲 𝗩𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲. The cognitive director. It implements "parallel thinking" and rigorous multi-perspective verification, overcoming the reasoning deficit and structuring the system for continuous learning and robust generalization. 𝗧𝗵𝗲 𝗦𝘆𝗻𝗲𝗿𝗴𝘆: This Quantum-Neuromorphic-Reinforcement Learning (Q-N-RL) loop transforms deliberate System 2 thinking into a near-real-time capability. By combining speed, efficiency, and verifiable reasoning, this architecture drastically compresses the timeline for achieving self-adapting Superintelligence. 𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: This rapid acceleration into ASI capabilities makes system Alignment and Control the most immediate and critical engineering challenge. Safety must be systematically designed into the core architecture, not patched on later. What single bottleneck do you think this hybrid approach solves first? #AGI #ASI #QuantumComputing #NeuromorphicComputing #ReinforcementLearning #FutureofAI Google Artificial Superintelligence Alliance Alphabet Inc.

The convergence of AI and edge computing is reshaping how we think about real-time data processing. As we move into 2025, I'm seeing three game-changing trends: 🚀 Edge AI is bringing intelligence closer to data sources, reducing latency from milliseconds to microseconds 🔗 Federated learning is enabling AI models to learn across distributed networks without compromising privacy ⚡ Neuromorphic chips are mimicking brain architecture, promising 1000x energy efficiency improvements What excites me most? The democratization of AI capabilities. Small businesses can now deploy sophisticated AI solutions that were once exclusive to tech giants. The question isn't whether AI will transform every industry - it's how quickly organizations can adapt to stay competitive. What tech trend are you most excited about this year? #AI #EdgeComputing #TechTrends #Innovation #DigitalTransformation #MachineLearning #TechLeadership #FutureOfWork #Automation #TechInnovation

Green AI: Building Intelligence Without Megawatts Artificial intelligence is transforming industries — but it’s also burning through megawatts of energy. Training today’s largest models can consume as much electricity as some small towns. At Brain-CA Technologies, we’re asking a different question: 💡 What if AI had been designed for efficiency from the start? Our Teleomorphic Computing architecture reimagines intelligence as simple, binary interactions — not endless matrix math. The result: AI that’s scalable, sustainable, and ready to run anywhere — from data centers to the edge. Because true intelligence shouldn’t come at an environmental cost. 🔗 Read the full post: https://lnkd.in/gJxbjDZV #GreenAI #SustainableTech #AIInnovation #BrainCA #TeleomorphicComputing

𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗛𝘆𝗽𝗲: 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗟𝗶𝗺𝗶𝘁𝘀 𝗼𝗳 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗔𝗜 At last week’s AI+ Multimodal Day in San Francisco during SF Tech Week, founders and researchers shared bold visions of AI that can see, hear, and act. The demos were impressive, but one question stayed with me: Can multimodal AI really scale within the limits of memory, data, and trust? The next phase of AI will not be limited by creativity. It will be limited by physics and governance. 1. Memory Bandwidth People talk about compute power as if it measures progress, but modern inference is memory bound. Chips wait for data to move. HBM helps, but physics wins. The next breakthroughs will come from architectures that minimize memory motion and reuse results efficiently. 2. Data Quality and Contamination We are running out of clean, diverse training data. Models that learn from their own synthetic outputs lose meaning over time. Scaling forward means careful curation, continual learning, and human oversight. Compute can scale almost infinitely. Clean data cannot. 3. Governance and Reliability As models become modular and agentic, governance is not a formality. It is infrastructure. Provenance, audit trails, version control, and kill switches are essential. Without them, orchestration breaks down and reliability fails. 4. Socio-Political Limits Every watt, liter, and square foot comes from shared public systems. Local resistance to data centers is growing. The companies that last will be those that align locally, measure impact transparently, and build with accountability. What Still Does Not Work Mixture of Experts often collapses under real workloads. Interruptible training remains unreliable at frontier scale. Optical compute has promise but is not yet proven in production. Centralized cloud inference cannot meet real-time robotics or AR latency needs. What Comes Next: Multimodal AI will not emerge from one massive model. It will come from distributed intelligence: smaller, faster systems working together across hybrid networks, close to where data lives. The next frontier is not smarter models. It is smarter infrastructure that is sustainable, composable, and trustworthy. Which of these constraints will break first: memory, data, or trust? #AIInfrastructure #MultimodalAI #SystemsThinking #AI

Nature solved AI 3.8 billion years ago. We're just catching up. Here's what biomimetic computing teaches us. Say hello to nature's operating system. ☑ Features (what it does) -Mimics biological intelligence in machines -Processes like brains, not data centers -Runs on watts, not megawatts ☑ Advantages (why it's better) -1,000x more energy efficient -Works at the edge without cloud -Learns in real-time, no retraining ☑ Benefits (what you gain) -Cut AI energy costs by 99% -Deploy intelligence anywhere -Build sustainable, scalable systems When biomimetic computing wins: -Edge devices need local intelligence -Power budgets are tight or non-existent -Real-time adaptation matters Why nature's approach works: -3.8 billion years of R&D -Optimized for efficiency, not scale -Proven across every environment How it's being applied (real examples): -Intel's Loihi chip → 1,000x less energy than conventional processors -IBM's TrueNorth → 1 million neurons on 70 milliwatts (hearing aid battery power) -DNA computing → 215 petabytes per gram of storage -Melbourne neurons → Learned Pong in 5 minutes (AI took weeks) -Swarm algorithms → Optimize global logistics without central control The shift happening now: -Old way: Bigger models, more data, massive compute -New way: Smarter architectures, biological efficiency, minimal power Your brain runs on 20 watts. ChatGPT's infrastructure? A small city's electricity. You're one biomimetic principle away from 1000x efficiency. What biological system should we study next? Drop it below. 👇 Useful? Repost ♻️ to your nature-inspired community.

OpenAI CEO Sam Altman published a blog post revealing plans to build infra capable of producing one GW of AI capacity weekly, arguing that compute expansion will drive both revenue and humanity's ability to tackle major challenges. 🔑 Altman argued that limited compute forces choices between breakthroughs like curing cancer or universal education, making infrastructure expansion key. 🔑 He said OpenAI plans infrastructure announcements over the coming months, with new financing approaches also scheduled for discussion later this year. 🔑 Altman also highlighted global competition concerns, wanting to “help turn that tide” of other nations outpacing the U.S. in chip and energy infrastructure. By securing both the compute Altman calls essential and the capital to deploy it rapidly, OpenAI transforms philosophical questions about AI priorities into engineering challenges. With infrastructure this massive, OpenAI shifts from choosing compute-limited priorities to pursuing multiple AI moonshots simultaneously. #ai #technology https://lnkd.in/g_mfq3eT

𝓣𝓱𝓮 𝓜𝓸𝓵𝓮𝓬𝓾𝓵𝓮 𝓪𝓷𝓭 𝓣𝓱𝓮 𝓐𝓽𝓸𝓶 𝓦𝓱𝓮𝓻𝓮 𝓐𝓘 𝓕𝓲𝓷𝓭𝓼 𝓘𝓽𝓼 𝓔𝓽𝓮𝓻𝓷𝓪𝓵 𝓜𝓮𝓶𝓸𝓻𝔂 Reimagining Memory for an AI World We are running into a wall that compute alone cannot fix. AI is producing oceans of data while our storage tiers burn electricity to keep bits alive. Two technologies could change the conversation. DNA storage gives us molecular scale density and centuries of stability with zero power at rest. One gram can hold petabytes, and a vial on a shelf can quietly preserve our training corpora, model lineage, and compliance archives without fans, refresh cycles, or forklift migrations. Access is slower than disk, which is exactly why it belongs at the deepest tier of the AI stack. Write once, read when needed, and remove idle watts from the facility. That is how we cut energy, heat, and footprint while keeping the provenance of intelligence intact. At the other frontier, atomic scale storage is a physics lesson in the limits of matter. Bits written as individual atoms or single atom magnets prove what density looks like at the ultimate boundary. It is not ready for the data center, yet it points to a future where information is sculpted in lattices and surfaces with precision at the scale of nature. The lesson for leaders is simple. Build architectures that evolve. Keep hot data on HBM and NVMe. Keep warm on object and tape. Move the long tail to DNA where it draws no power. Track atomic progress for what it teaches about materials and the next class of solid state memory. AI is not only a race for bigger models. It is a race for responsible permanence. The systems that win will remember what they learned, prove how they learned it, and preserve it with integrity. DNA gives us an archival tier that lowers heat and electricity while expanding capacity beyond any warehouse. Atomic research shows the destination line for density and inspires the next steps in engineering. I am exploring that horizon now. If you are rethinking storage for AI, it is time to design with biology, learn from atoms, and retire the idea that every byte must stay powered to exist. Singularity Systems subsidiary of Cybersecurity Insiders is partnered with Brilliancy Deep Tech, we will be pushing the world to keep up! Because it is time to #changetheworld!

The $70 Billion Question: Why is AI Chip R&D Still Based on Intuition? I was deeply interested in the strategic bottlenecks of the AI chip race. Billions go into silicon, but software optimization delivers disproportionately higher performance gains at lower cost. I was motivated to solve the massive R&D efficiency gap in AI chips, where intuition beats data. Check out my latest Medium article on the quantitative framework I built to measure optimal budget splits between R&D split for AI chips based on Cobb-Douglas Framework. https://lnkd.in/g_kYTJMf (The data analysis starts in Part 2, Coming soon! 🙃 ) #ProductManagement #AI #Semiconductors #TechStrateg #RND #DataScience #EconomicModeling #FinTech #ChipDesign #AMD #HardwareSoftwareCoDesign #MooreLaw #Quantification

Agentic AI + Infrastructure Strain = The Next Frontier What if your AI assistant could think, plan, and act, and yet we don’t yet have the infrastructure to support millions of them globally? Over the coming years, two trends will define the AI frontier: 1. Agentic AI: AI that acts autonomously, not just replies. 2. Compute pressure: AI is demanding more compute, power, and infrastructure than ever before. The intersection is where the real battle will be: - Can our data centers, chips, and energy grids keep up? - Will intelligent agents be limited by infrastructure, not algorithms? To lead into the future, we must not only adopt smarter AI, but also invest in the infrastructure that enables it. Agents + architecture = tomorrow’s competitive edge. #AI #AgenticAI #TechTrends #FutureOfWork #Infrastructure #Leadership