$CBRS The Valuation Hawks Are Running Out of Excuses At roughly $35B in market cap today, I see an S-tier chipmaker with exceptional wafer-scale IP, a valuable ultrafast-inference niche and enormous demand. For my 2029–2030 thesis, this looks cheap. The commercial evidence is substantial: Q2 core revenue grew 103%, cloud revenue nearly quadrupled, and remaining performance obligations reached $25.4B. Management plans to more than triple revenue in 2027. OpenAI, AWS $AMZN, AMD $AMD and CrowdStrike $CRWD are already part of the customer and partner story. On September’s Latent Space podcast, CTO Sean Lie said Cerebras was “sold out of everything that we’re building.” He described OpenAI allocating capacity to critical research and incident response. That illustrates how valuable speed becomes when every second matters. Reuters reported that Jensen Huang’s $NVDA approached Cerebras about a potential acquisition and Cerebras declined. Asked about acquisition approaches, Feldman’s answer was classic: “We don’t kiss and tell. We’re gentlemen.” $NVDA’s roughly $20B Groq licensing-and-talent deal makes $CBRS at approximately $35B in its first year as a public company look like a serious bargain to me. For less than twice that strategic price tag, investors get exposure to exceptional wafer-scale IP, established OpenAI and AWS $AMZN relationships, and rapidly growing inference demand. I see an opportunity to own a future powerhouse early in its public-market life. Further swings lower could make that entry price even more compelling. The model-specific output-speed comparisons have real punch: -Llama 4 Scout: 2025 benchmark figures reproduced by Cerebras showed approximately 2,014 tokens/sec versus Groq’s 275, about 7.3×. -Llama 3.3 70B: Cerebras’s September 2025 comparison reported over 2,500 versus approximately 403, over 6×. -GPT-OSS-120B: today’s Artificial Analysis public-API benchmark shows approximately 1,681 versus 475, about 3.5×. Lie’s architectural argument is that Groq’s smaller SRAM pools make frontier-model scaling harder. Cerebras packs 44GB of SRAM per wafer, approximately 88× the 500MB per Groq 3 LPU. More local memory can reduce the number of devices needed to hold weights and the communication between them. Cerebras’s dedicated inference catalog includes Kimi, DeepSeek and GLM, alongside its wafer-scale training capability. The roadmap adds another reason for conviction: CS-4 demonstrated over 4,400 tokens/sec per user on GPT-OSS-120B. CS-5 targets up to 10,000 on medium-sized models and 5,000 on frontier models in 2027. Faster generation can support more reasoning and agent iterations within the same time budget. The market has been brutal across semiconductors: 22 of the 30 PHLX Semiconductor Index stocks sit more than 20% below their record highs. Another leg lower is possible. Timing the bottom is anyone’s guess. I view this as an opportunity to own a future powerhouse at an attractive price. The IP, demonstrated growth and established relationships give me conviction that this business can become massive. I’m more bullish than ever, volatility included. Ultrafast inference is the future.

$TEM Tempus AI Valuation Hypothesis: $1,963 per share by 2030? Great article by Bhavneesh Sharma on Seeking Alpha about Tempus AI, check it out! Looking at Tempus AI's potential based on their healthcare AI positioning and their massive data advantage of over 200 petabytes from 2,500+ institutions, we can analyze a compelling scenario where they capture $50B in revenue (far less than 148B per year) by 2029-30 from the projected $148.4B healthcare AI market. Their current enterprise value of $11B and market cap of $5.52B serve as starting points, but the real value driver is their unmatched clinical and molecular data library that powers their AI platform. This extensive data moat, combined with their established institutional relationships, positions them uniquely in the healthcare AI space where data access and quality are crucial competitive advantages. Applying a standard AI industry multiple of 7.5x sales to $50B revenue (if they can capture a third of the market) suggests a future enterprise value of $375B by 2029. Factoring in 4% annual share dilution from the current 157M shares would result in approximately 191M shares by 2029-2030, pointing to a potential share price of around $1,963 ($375B ÷ 191M shares) - representing roughly a 56x return from current levels of $35.15. This valuation would be strongly supported by Tempus's established data moat in healthcare AI, their extensive network of institutional partnerships, and the high barriers to entry in healthcare AI due to regulatory requirements and the complexity of medical data integration. The company's strategy of leveraging their vast data library for AI-driven insights creates a powerful network effect: as more institutions contribute data, their AI models become more sophisticated, attracting more partners and further widening their competitive moat. Their potential to capture a third of the healthcare AI market by 2029 seems achievable given their technological leadership, established healthcare relationships, and the valuable head start they've built with their comprehensive data library that would be extremely difficult for competitors to replicate. Obviously there is risk involved and this is a highly optimistic projection (could take longer and there could be competition), but I believe the potential is there as AI is revolutionizing healthcare. Industry experts highlight how AI is transforming personalized medicine through multiple breakthrough capabilities, and Tempus is already demonstrating this across their product lines. Their platform integrates genomics, data, and applications, offering solutions like NOW for therapy matching, COHORTS for processing billions of text pages using LLMs, and LENS for complex biopharma analytics. The company has also secured significant commercial traction, working with 19 of the 20 largest pharmaceutical companies and holding contracts worth over $900 million. With their AI platform connected to over 2,000 U.S. hospitals and tools spanning oncology, cardiology, pathology, and radiology, Tempus has built an impressive ecosystem for delivering AI-powered insights to physicians and researchers. While challenges around governance, privacy controls, and economic accessibility need to be addressed, Tempus's comprehensive platform approach and established market position make them well-positioned to be a leader in this healthcare AI revolution that is rapidly becoming reality rather than science fiction. The compelling case for widespread adoption of Tempus's platform extends across the entire healthcare ecosystem. Every insurance company will be driven to adopt it to reduce costs and improve patient outcomes, as the platform's predictive capabilities can help prevent expensive complications and optimize treatment plans. Medical practices will need it to stay competitive and provide the best possible care, as the AI-powered insights can help doctors make more informed decisions and catch potential issues earlier. Research organizations will find it indispensable for accelerating drug development and clinical trials through its vast data repository and advanced analytics capabilities. The economic argument is clear: the efficiency gains in resource allocation, reduced treatment failures, and improved patient outcomes will make Tempus's platform a must-have tool that pays for itself many times over. This network effect of universal adoption could actually make our revenue projections look conservative, as the platform becomes the de facto standard in AI-powered healthcare analytics.