Algaurizin

𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗜 𝗘𝗿𝗮 As AI continues to transform the data landscape, Data Product Managers are stepping into a more critical role. It's not just about delivering quality data; it's about harnessing AI to drive decisions, power real-time experiences, and build trust through responsible AI practices. 𝟯 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆: Bridge the gap between business goals and technical execution by understanding AI architecture patterns and staying updated on practical insights. 𝗚𝗲𝘁 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻 𝘄𝗶𝘁𝗵 𝗥𝗔𝗚: Build a simple Q&A interface over an existing data product to gain hands-on experience with Retrieval-Augmented Generation (RAG) systems. 𝗠𝗮𝗸𝗲 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝗮 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆: Embed "Responsible AI" into your product reviews, focusing on bias, transparency, and security to build trust with users. What's your biggest challenge integrating AI into your data products? Let's discuss! #DataProductManagement #AI #GenerativeAI #ProductManagement #DataScience #MachineLearningLearning #LLM #RAG #ResponsibleAI #TechLeadership

𝗣𝘀𝘆𝗰𝗵𝗲𝗱𝗲𝗹𝗶𝗰 𝗠𝗲𝗱𝗶𝗰𝗶𝗻𝗲 𝗖𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗵𝗲 $𝟳.𝟳𝟱 𝗕𝗶𝗹𝗹𝗶𝗼𝗻 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗧𝗵𝗮𝘁 𝗗𝗼𝗲𝘀𝗻'𝘁 𝗘𝘅𝗶𝘀𝘁 𝗬𝗲𝘁 Psychedelic-assisted therapy is approaching FDA approval. The market is projected to grow from $4.08B (2025) to $7.75B by 2030. But here's the problem: When these therapies are approved, where will patients actually receive them? 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸: - COMPASS Pathways preparing FDA submission for psilocybin therapy - FDA's 2024 MDMA rejection reset industry expectations - Treatment requires 6-8 hour supervised sessions with trained therapist pairs - Cost: $10,000-15,000 per patient per treatment course 𝗧𝗵𝗲 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗚𝗮𝗽: We need 12,500 certified treatment centers globally by 2030. Current capacity: fewer than 100 specialized centers worldwide. That's a 125x scale-up requirement in just 5 years. 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: → No existing billing codes for 8-hour therapy sessions → Therapist training programs still developing standards → DEA-compliant facilities for Schedule I/II substances → Payer coverage remains undefined → Patient affordability crisis looming 𝗪𝗵𝗮𝘁'𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝘁𝗵𝗶𝘀 𝘁𝗶𝗺𝗲: Unlike traditional pharmaceuticals, these aren't take-home medications. Treatment cannot be separated from delivery context. This requires entirely new commercial infrastructure—specialized facilities, certification programs, reimbursement models. Australia became the first country with regulated access in 2023. Their real-world data will inform global strategies and address FDA's long-term safety concerns. The science is advancing. The regulatory pathway is clarifying. But the commercial infrastructure? That's the critical path nobody's fully solved. Full analysis in our latest issue exploring treatment center economics, payer strategies, and what comes after approval ↓ https://lnkd.in/dUXTXmpR #PsychedelicMedicine #MentalHealth #HealthcareInnovation #Biotech #ClinicalResearc

𝗧𝗵𝗲 𝗔𝗠𝗗-𝗢𝗽𝗲𝗻𝗔𝗜 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 𝗮 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗦𝗵𝗶𝗳𝘁 𝗶𝗻 𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 The 6-gigawatt agreement between AMD and OpenAI, with the first gigawatt deployment of MI450 GPUs starting in late 2026, represents more than just a massive procurement deal—it's a strategic recalibration of the entire AI compute landscape. Three aspects stand out: 𝗦𝗰𝗮𝗹𝗲 𝗺𝗲𝗲𝘁𝘀 𝗽𝗿𝗮𝗴𝗺𝗮𝘁𝗶𝘀𝗺. OpenAI is committing to a multi-year, multi-generation partnership that extends beyond the MI450 series, signaling confidence not just in current hardware but in AMD's roadmap execution. This isn't just about diversifying away from a single vendor—it's about betting on sustained innovation across generations. 𝗔𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗲𝗾𝘂𝗶𝘁𝘆. AMD issued OpenAI a warrant for up to 160 million shares, with vesting tied to deployment milestones, share-price targets, and technical achievements. This structure creates a true partnership where both companies' success is interdependent—a refreshing departure from traditional vendor-customer dynamics. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝘀 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗺𝗼𝗮𝘁. While others debate AI model architectures, OpenAI is securing the computational foundation needed for the next decade. AMD expects this partnership to deliver tens of billions in revenue and be highly accretive to earnings, while OpenAI locks in the capacity to train increasingly capable systems. The real winner here? The broader AI ecosystem. Competition in compute infrastructure accelerates innovation, improves economics, and ultimately makes advanced AI more accessible. What's your take on this partnership's implications for the AI industry? #AI #TechStrategy #Semiconductors #Innovation https://lnkd.in/gxyfK_4E

𝗧𝗵𝗲 $𝟱𝟬𝟬𝗕 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗔𝗿𝗲 𝗪𝗲 𝗪𝗶𝘁𝗻𝗲𝘀𝘀𝗶𝗻𝗴 𝗩𝗮𝗹𝘂𝗲 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝗼𝗿 𝗩𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗜𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻? OpenAI just completed a $6.6 billion secondary share sale at a $500 billion valuation, making it the most valuable private company in the world. To put this in perspective, that's a jump from $300 billion in March, a 67% increase in just seven months. This milestone forces us to ask some uncomfortable questions: 𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝘄𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘃𝗮𝗹𝘂𝗶𝗻𝗴? Unlike traditional tech giants that scaled to these valuations with proven revenue models and market dominance, AI companies are being valued on potential and promise. The fundamentals, sustainable unit economics, path to profitability, defensible moats, are still being figured out in real-time. 𝗜𝘀 𝘁𝗵𝗶𝘀 𝟮𝟬𝟮𝟭 𝗿𝗲𝗱𝘂𝘅? The broader AI ecosystem is seeing similar euphoria: Anthropic heading toward $170 billion, xAI chasing $200 billion, Perplexity at $18 billion. The pattern feels familiar, and history suggests that when everyone assumes something is inevitable, we should pause and pressure-test our assumptions. 𝗧𝗵𝗲 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘃𝘀. 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗱𝗶𝘃𝗶𝗱𝗲: These valuations are betting that foundation model creators will capture most of the value. But what if the real value accrues to the application layer, or to whoever figures out reliable enterprise deployment? We're pricing in certainty where none exists. I'm not bearish on AI, the technology is transformative. But I am cautious about conflating technological progress with guaranteed value capture. The companies building real solutions to real problems, even at more modest valuations, might be the ones we should be watching more closely. What's your take? Are these valuations justified, or are we setting ourselves up for another correction? #AI #Startups #TechValuations #OpenAI #VentureCapital https://lnkd.in/gmaT6qA4

𝗧𝗵𝗲 𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗚𝗼𝗹𝗱 𝗥𝘂𝘀𝗵: 𝗪𝗵𝘆 𝘁𝗵𝗲 "𝗣𝗶𝗰𝗸𝘀 𝗮𝗻𝗱 𝗦𝗵𝗼𝘃𝗲𝗹𝘀" 𝗣𝗹𝗮𝘆 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 𝗠𝗼𝗿𝗲 𝗧𝗵𝗮𝗻 𝗘𝘃𝗲𝗿 While headlines focus on which AI model will win, savvy investors are looking elsewhere—at the unglamorous but essential infrastructure that makes it all possible. Tech giants are deploying over $300 billion in 2025 alone to build the foundation for AI's future. Microsoft's $80 billion, Meta's $65 billion, and the ambitious $500 billion Stargate Project represent an unprecedented capital deployment cycle. But here's what fascinates me: this creates a classic Jevons Paradox situation—as AI becomes more efficient, adoption accelerates, and total infrastructure demand actually increases. The opportunities extend far beyond data centers: 𝗣𝗼𝘄𝗲𝗿 & 𝗚𝗿𝗶𝗱: McKinsey projects a 250% increase in U.S. electricity demand by 2030, requiring 47,300 miles of new transmission lines—a 57% increase from 2023. We're not just talking renewables; nuclear and natural gas will be essential to bridge the gap. 𝗪𝗮𝘁𝗲𝗿: AI workloads may require 2-3 billion cubic meters of water by 2027 for data center and power plant cooling—creating opportunities in water transport, recycling, and treatment infrastructure. 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Global cybersecurity spending is expected to grow 33% to $272 billion between 2025 and 2030, driven by AI-enabled threats requiring AI-powered defenses. The lesson from historical gold rushes holds: those who sold picks and shovels often outperformed those digging for gold. Infrastructure investments offer steady cash flows, natural monopolistic characteristics, and inflation hedges—without betting on which AI companies will dominate. This isn't just a technology revolution—it's a once-in-a-generation infrastructure buildout that will reshape our physical and economic landscape for decades to come. What infrastructure plays are you watching most closely? #AI #Infrastructure #Investment #DataCenters #Energy #CriticalInfrastructure https://lnkd.in/geqtdFFK

𝗧𝗵𝗲 𝗢𝘃𝗲𝗿𝗹𝗼𝗼𝗸𝗲𝗱 𝗔𝗜 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁: 𝗪𝗵𝘆 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗠𝗮𝘆 𝗢𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺 𝘁𝗵𝗲 𝗛𝘆𝗽𝗲 While the market fixates on which AI model will win, a more fundamental story is unfolding, one that's less about algorithms and more about physics. US data center power demand is projected to more than double by 2035, rising from 35 gigawatts in 2024 to 78 gigawatts. Goldman Sachs forecasts global power demand from data centers to rise 165% by 2030. These aren't speculative projections, they're infrastructure requirements already being locked into 20-year contracts. Consider Microsoft's agreement to take 100% of a revived Three Mile Island nuclear plant's 837MW output to fuel its AI data centers. When tech giants commit to reopening nuclear facilities, they're signaling something profound about the magnitude of this transformation. 𝗧𝗵𝗿𝗲𝗲 𝗱𝗲𝗿𝗶𝘃𝗮𝘁𝗶𝘃𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝘄𝗼𝗿𝘁𝗵 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴: 𝗣𝗼𝘄𝗲𝗿 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Utilities like Constellation Energy, NRG Energy, and Vistra rank among the top 30 S&P 500 performers year-to-date, with Constellation securing landmark 20-year power agreements with Microsoft and Meta. These aren't the defensive utility plays of yesterday, they're becoming essential AI enablers. 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿 𝗥𝗘𝗜𝗧𝘀: Companies like Equinix and Digital Realty provide exposure without betting on specific AI winners. The recurring revenue model through multi-year contracts offers stability, though interest rate sensitivity remains a real consideration. 𝗖𝗼𝗼𝗹𝗶𝗻𝗴 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Traditional air cooling cannot handle modern AI cluster power densities, driving demand for liquid cooling solutions. Companies like Vertiv are solving genuine bottlenecks, you simply can't deploy next-generation AI without addressing the heat problem. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘁𝗿𝗮𝗿𝗶𝗮𝗻 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲: While pure-play AI stocks trade on future promises at premium multiples, infrastructure players are capturing measurable demand today. They're solving physics constraints, not algorithm challenges. The risk profile is fundamentally different. The strategic question: If AI adoption slows, software valuations compress instantly. But infrastructure serves multiple technology generations with long replacement cycles. Google expects to spend $75 billion on AI infrastructure alone in 2025, that capital creates tangible assets with enduring value. What's your take? Are we underestimating the infrastructure layer while overvaluing the application layer? #AI #Infrastructure #Investing #EnergyTransition https://lnkd.in/d_Cxnf7d

𝗧𝗵𝗲 𝗔𝗜 𝗕𝗼𝗼𝗺'𝘀 𝗛𝗶𝗱𝗱𝗲𝗻 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: 𝗪𝗵𝘆 𝗦𝘁𝗲𝗲𝗹 𝗮𝗻𝗱 𝗖𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗠𝗮𝘁𝘁𝗲𝗿 𝗮𝘀 𝗠𝘂𝗰𝗵 𝗮𝘀 𝗦𝗶𝗹𝗶𝗰𝗼𝗻 While most investors chase semiconductor stocks and cloud software plays, there's a fascinating infrastructure story unfolding beneath the AI revolution, literally. Global AI infrastructure spending is projected to reach $375 billion in 2025 and $500 billion by 2026, but here's what caught my attention: AI data centers will drive copper consumption from 500,000 metric tons per year to 3 million by 2050. A single hyperscale facility requires up to 20,000 tons of steel. The investment narrative has been dominated by Nvidia, Microsoft, and Meta, and rightfully so. But the physical buildout of AI infrastructure represents a multi-trillion dollar opportunity that extends far beyond chips. We're seeing data center construction investment nearly equal office development spending in the U.S., a remarkable shift from just a few years ago. What makes this particularly interesting from an investment thesis perspective: → Materials companies represent the "invisible scaffolding" of AI growth with more predictable, long-cycle demand → ESG considerations are driving innovation in low-carbon concrete, hydrogen-based steel, and battery energy storage systems → Geographic diversification is expanding beyond traditional hubs, Texas, Arizona, and emerging Midwest locations are becoming AI infrastructure corridors The bottleneck risk is real though. Local resistance has delayed or blocked an estimated $64 billion in U.S. data center projects between May 2024 and March 2025, highlighting that permits and power availability matter as much as capital commitment. For those building infrastructure investment theses, it's worth considering: every breakthrough in AI model training creates downstream demand measured in millions of tons of physical materials. What's your take, are materials and construction the overlooked enabler of the AI revolution, or just cyclical beneficiaries? #AIInfrastructure #InvestmentStrategy #DataCenters #InfrastructureInvesting https://lnkd.in/dYAXvgtE

𝗧𝗵𝗲 𝗔𝗜 𝗔𝗿𝗺𝘀 𝗥𝗮𝗰𝗲 𝗶𝘀 𝗮𝗻 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗔𝗿𝗺𝘀 𝗥𝗮𝗰𝗲. 𝗧𝗵𝗲 𝗕𝗲𝘁 𝗶𝘀 𝗕𝗶𝗴𝗴𝗲𝗿 𝗧𝗵𝗮𝗻 𝗬𝗼𝘂 𝗧𝗵𝗶𝗻𝗸. We talk a lot about AI models, algorithms, and applications. But the real, multi-billion-dollar battleground is several layers beneath the code: in the physical infrastructure required to power this revolution. A new report highlighted by TechCrunch reveals the staggering scale. 𝗖𝗼𝗿𝗲𝗪𝗲𝗮𝘃𝗲'𝘀 $𝟯𝟬 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 𝗱𝗲𝗮𝗹 𝘄𝗶𝘁𝗵 𝗕𝗹𝗮𝗰𝗸𝗥𝗼𝗰𝗸 isn't an outlier; it's a sign of the new normal. This isn't just about buying more GPUs; it's about building the foundational utilities, the "power plants", for the AI economy. This massive capital influx signals a critical shift: 𝗧𝗵𝗲 "𝗔𝗜 𝗙𝗮𝗰𝘁𝗼𝗿𝘆" 𝗶𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗗𝗮𝘁𝗮 𝗖𝗲𝗻𝘁𝗲𝗿. These aren't your grandfather's server farms. We're talking about specialized, high-density compute clusters designed for one thing: relentless, parallel processing. The physical footprint and power requirements are on a different scale entirely. 𝗖𝗮𝗽𝗶𝘁𝗮𝗹 𝗶𝘀 𝘁𝗵𝗲 𝗡𝗲𝘄 𝗠𝗼𝗮𝘁. In the past, a tech moat was built on network effects or proprietary data. Today, a significant barrier to entry is the ability to secure and fund billions in infrastructure. The companies that control the compute will, to a large extent, control the pace of innovation. 𝗧𝗵𝗲 𝗜𝗺𝗽𝗹𝗶𝗰𝗶𝘁 𝗕𝗲𝘁: This level of investment is a massive bet on long-term demand. The infrastructure players are betting that the current AI wave isn't a bubble, but the foundation of the next 20 years of technology. They're building for a future where AI is as ubiquitous and essential as the internet itself. 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 𝗳𝗼𝗿 𝗹𝗲𝗮𝗱𝗲𝗿𝘀: While your data scientists experiment with models, your strategy needs to account for the compute layer. Availability, cost, and scalability of this foundational infrastructure will be a primary constraint, or catalyst, for your AI ambitions. The race isn't just to build the smartest AI; it's to build the engine it runs on. What's your take? Are we building the grid for the future, or is this a prelude to overcapacity? #AI #Infrastructure #CloudComputing #GPU #TechInvestment #Innovation #FutureofWork https://lnkd.in/eHydAFw9

𝗧𝗵𝗲 𝗛𝗶𝗱𝗱𝗲𝗻 𝗦𝘁𝗼𝗿𝘆 𝗕𝗲𝗵𝗶𝗻𝗱 𝗔𝗜'𝘀 𝗧𝗿𝗶𝗹𝗹𝗶𝗼𝗻-𝗗𝗼𝗹𝗹𝗮𝗿 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗥𝗮𝗰𝗲 We're witnessing something remarkable: Jensen Huang estimates that $3-4 trillion will be spent on AI infrastructure by the end of the decade. But the numbers tell only part of the story. What strikes me most isn't the size of these deals, Oracle's $300 billion agreement with OpenAI or Meta's $600 billion U.S. infrastructure commitment through 2028, it's what they reveal about the fundamental shift happening in enterprise strategy. 𝗧𝗵𝗿𝗲𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘄𝗼𝗿𝘁𝗵 𝗰𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗶𝗻𝗴: 𝟭. 𝗘𝗻𝗲𝗿𝗴𝘆 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸. The IEA projects that electricity demand from AI-optimized data centers will more than quadruple by 2030. Meta's new Louisiana facility requires arrangements with a local nuclear power plant just to handle the energy load. We're not just building compute capacity, we're rebuilding energy infrastructure around AI. 𝟮. 𝗖𝗹𝗼𝘂𝗱 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽𝘀 𝗵𝗮𝘃𝗲 𝗯𝗲𝗰𝗼𝗺𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗺𝗼𝗮𝘁𝘀. The Microsoft-OpenAI playbook, where cloud credits became investment currency, has fundamentally changed how AI companies capitalize growth. It's a brilliant alignment of incentives, but it also creates dependencies that shape the entire competitive landscape. 𝟯. 𝗧𝗵𝗲 "𝗯𝘂𝗶𝗹𝗱 𝘃𝘀. 𝗯𝘂𝘆" 𝗰𝗮𝗹𝗰𝘂𝗹𝘂𝘀 𝗵𝗮𝘀 𝗳𝗹𝗶𝗽𝗽𝗲𝗱. When your infrastructure needs are measured in gigawatts, traditional procurement models break down. These aren't vendor relationships, they're decade-long strategic partnerships that blur the lines between customer, investor, and co-builder. The real question isn't whether these investments will pay off. It's whether our energy grids, regulatory frameworks, and environmental commitments can evolve fast enough to support this transformation. What's your take? Are we building the foundation for the next technological era, or are we moving too fast for our infrastructure to keep up? #AI #Infrastructure #Technology #EnergyTransition #CloudComputing

𝗧𝗵𝗲 $𝟮𝟮𝟯𝗕 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: 𝗔𝗿𝗲 𝗪𝗲 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗙𝗮𝘀𝘁𝗲𝗿 𝗧𝗵𝗮𝗻 𝗪𝗲 𝗖𝗮𝗻 𝗣𝗼𝘄𝗲𝗿 𝗜𝘁? IDC's latest data reveals a staggering reality: AI infrastructure spending grew 97% year-over-year in the first half of 2024, reaching $47.4 billion, and is projected to surpass $223 billion by 2028. But here's what keeps me up at night: accelerated servers—the GPU-powered workhorses of AI—now account for 70% of AI infrastructure spending and are growing at 178% annually. The math is uncomfortable. Data center power demand is expected to increase 165% by 2030, while data centers already consumed around 1.5% of global electricity in 2024. We're in an infrastructure arms race where 72% of AI server spending goes to cloud environments, concentrating energy demands in ways our grids weren't designed to handle. The good news? Google reports that the median AI prompt now uses 33 times less energy than it did a year ago, proving efficiency gains are possible. But optimization alone won't solve this. As IDC's Lidice Fernandez notes, energy consumption concerns are becoming a critical factor for datacenters seeking to optimize architectures and minimize energy use. The question isn't whether AI infrastructure will hit $223B—it will. The question is: 𝗪𝗶𝗹𝗹 𝘄𝗲 𝗵𝗮𝘃𝗲 𝘁𝗵𝗲 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗲𝗻𝗲𝗿𝗴𝘆 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘁𝗼 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗶𝘁? What innovations do you think will bridge this gap? Nuclear? Advanced cooling? More efficient architectures? #AI #DataCenters #Sustainability #Technology #EnergyEfficiency https://lnkd.in/diWh4DXP