John Sears

Attached is NVDA's price chart for the last year - up c.175% (up 2500%+ in L5Y period!!) 🚀 I nonetheless have conviction that it currently remains undervalued... Among other things, due to strong competitive positioning as a GPU-maker, AI-driven demand fuelled by investor and consumer interest, and moats it has built. This is mirrored by equity analysts – GS’ latest price target was $800, BofA’s $1,500. I do believe the growth is capped, and driven in equal measure by under-supply and bubble-like demand as it is by fundamentals. Even so, Nvidia remains an attractive medium term investment. ⚖️ Demand – Supply Dynamics: AI is a generational innovation, and agents /apps are already upending SaaS, or at least so think VCs who deployed $64.6B in AI start-ups in 2023 alone. As early movers and now the industry standard (c.80% market share), Nvidia's A100 and H100 chips are not only the most efficient option, but also crucial for large scale AI training and inference (chips are the biggest cost head for LLM companies). With unprecedented interest in AI (ChatGPT acquired 1M users in 5 days of launch, OpenAI reported $3.4B ARR in Apr’24, AI agents are widely recognised as the tech of the future), Nvidia stands to capture a large portion of the $250-$500B data centre conversion market over the next 5 years​. This is proven by the $22.6B in data centre revenue Nvidia brought in during Q2’24, up 23% from the previous quarter and up 427% from a year ago 🥇Competitive positioning: While Intel, AMD, Arm have been active, the only credible competitor, AMD, has a relatively smaller market share in the high-performance AI GPU segment, and Nvidia’s consistent technological advancements keep it further ahead, e.g., Nvidia’s Rubin which will succeed Blackwell in 2026 is orders of magnitude better than anything on-market. 🤝Moats via ecosystem building: Collaborations with hyperscaler cloud providers like AWS and Microsoft Azure have expanded Nvidia’s reach. Moreover, acquisitions like Mellanox have strengthened its position in high-performance networking, critical to AI infrastructure. Coreweave, a prominent cloud services partner / distribution channel for chip capacity is an NVIDIA investee and is built almost entirely on Nvidia chips. Finally, they continue investing in networking, software tools, data centre solutions but also purpose-built AI PC and smartphones to create an ecosystem that not only locks in customers and enhances switching costs, but pivots more of their revenue to more predictable recurring contracts. 📈 Financial underpinnings: Nvidia’s expected FCF for next 2 years stands at $100B, providing huge ammo for R&D, strategic investments and M&A (only $30 to $35B earmarked for buybacks) and in turn, growth. Even so, their shares trade at a P/E ratio of c.20x  NTM earnings, far below historical median of 35x+, indicating the market expects them to grow in the medium term. KEEN TO HEAR THOUGHTS! #Nvidia #AI #Compute #Chips #Tech #Growth

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This is an interesting analysis of Temu and its semi-managed model. So what is it, and is it really innovative? First, it is NOT like Amazon #FBA or Walmart #WFS. It IS more like self-fulfillment with Amazon #FBM. The semi-managed model allows merchants to control their #inventory, #warehousing, and #logistics. But unlike the traditional FBM model, where merchants are largely independent, Temu still provides significant support in terms of store operation promotion, customer service, and marketing. So, this is more like a hybrid approach that combines elements of both managed and independent fulfillment models. #Temu also maintains some control over pricing to ensure consistency and competitiveness across the platform. However, it is not that innovative. Temu’s semi-managed model can be more closely compared to models like Walmart's Drop Ship Vendor (#DSV) program or Amazon #Vendor Central (#1P). In these models, pricing, support, and promotions are handled by the marketplaces, while warehousing and logistics are managed by merchants. One key difference is that Amazon 1P and Walmart DSV cover shipping costs, while in Temu's semi-managed model, the responsibility for shipping costs generally falls on the sellers, although this can vary depending on the agreement between Temu and the merchant. One nice thing that Temu does is help sellers find #3PL partners, allowing for easy integration with multiple logistics providers. It is a very interesting model that is proving to work well, especially when dealing with cross-border #ecommerce, and might be used more often by others. https://lnkd.in/gY35bzMq

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Great post by Andrew Petcash on the rise of wearable tech in sports. From performance tracking to data-driven decision-making during games and practices, there is now - literally - too much information available for any one person to act on in real time. And that’s where AI has been transforming the playing field (pun intended) by turning a flood of data into guidance and insights. Not to mention leveling the landscape: this kind of individualized insights and bespoke planning isn’t just for elite athletes anymore, it’s there for anyone who can strap on a wearable. And this $120bn market is wide open for future innovation. It’s an exciting time to be part of it, with BlueSkyAI helping Fieldhouse Holdings integrate all the wearable tech. #ai #wearables #blueskyai #genai https://lnkd.in/eafhpbmC

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LLMs are trained on internet-scale data. But to make AI work for work, models need to be grounded in your enterprise data -- a much harder problem than meets the eye! How do you connect to all your data sources and ingest them frequently enough while managing compute/storage costs? How do you make sure that you only show users results from documents they have access to? How do you rank results without internet-scale click and link data? How do you process the content of documents that are often hidden in PDFs and presentations? These are tricky problems, and it's why enterprise search didn't work for nearly a decade, until Glean came along. Turns out it's the exact same set of challenges needed to give LLMs the right context to work their magic on your enterprise content. Glean spent 5 years building the ingestion, search, and ranking infra needed to solve this problem well. This has given them the right to run the "Work AI" race and delight users at work. CEO Arvind Jain shares more on the latest episode of Training Data. Listen here, or wherever you get your podcasts: YouTube: https://seq.vc/ytd Apple: https://seq.vc/pit Spotify: https://seq.vc/jnz 

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Interesting answer from AlphaSense CEO Jaakko (Jack) Kokko on why they acquired Tegus - all about access to proprietary data, in this case the transcript library on private companies. You recently acquired a company called Tegus. Was this about having more proprietary data to feed into your models? First and foremost, it was about providing our customers with the best possible intelligence on the market, especially on private companies. A couple of years ago, we acquired a company called Stream, the No. 2 player in what you might think of as a library of interview transcripts between investors and experts on a company — their former executives, competitors, customers, partners. It allowed us to produce information that you can’t access anywhere else, and our customers loved it. Tegus is the market leader in that sector and was the natural next step for us. They have the world’s best library of private company content, and we immediately wrapped our search capabilities, our generative AI, our monitoring capabilities around their content and made it much more valuable. We now have the broadest coverage of insights on both public and private companies, allowing our AI to provide even better answers to our customers.

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LLMs are not vast, infallible databases. As models grow larger so does their tendency to confidently state falsehoods (hallucinations). Matei Zaharia, co-founder & CTO of Databricks, warns against treating LLMs as internet-scale encyclopedias and proposes we focus on what they excel at:  → Reasoning → Interpreting language → Understanding context For founders building AI applications, treat LLMs as powerful reasoning engines and not fact repositories. Build your knowledge base separately, feeding the LLM with trusted up-to-date information through retrieval mechanisms and tool-calling. This approach lets you harness the LLM's strengths in language analysis and generation while sidestepping the risks of its unreliable memory. Building with AI? Share your approach to balancing LLM reasoning power with factual accuracy in the comments. Let's hear it!

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ANNOUNCEMENT- I’m delighted to share that Asset Class has partnered with Palantir Technologies to bring AI to the Alternatives and Private Capital Markets. At Asset Class we will integrate the AIP platform and Foundry capabilities of Palantir with our Fund Operating System (Fund OS). We will work with the AIP Platform and Palantir's Foundry Ontology to support highly complex tasks / co-pilot features that will allow us to leverage the FULL dataset that comes with the Asset Class Platform. By linking EVERY step of the Investor and Fund journey on a SINGLE platform, we have a uniquely complete dataset that can inform our AI actions. Over time we will integrate this as part of our Asset Class Network. This will continue to ensure Asset Class is a far more powerful system than any other in the market. We are a platform - not just an app. In the near future, we will be an AI Enabled, Networked Platform I cannot wait to bring the power of AI to our platform to enable our clients to super-charge their investment in our platform. See the announcement here: https://lnkd.in/eCSy5Tnu

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$2 H100s: How the GPU Bubble Burst Last year, H100s were $8/hr if you could get them. Today, there's 7 different resale markets selling them under $2. What happened? The market has flipped from shortage ($8/hr) to oversupplied ($2/hr), because of reserved compute resales, open model finetuning, and decline in new foundation model co’s. Rent instead. https://lnkd.in/gpQEidmC

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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"Data democratization" (an iconic buzzphrase) has been around for a long time. Tons of companies have promised to deliver it... with mixed results. Lots of dashboards, heavy implementation, data siloes, the list goes on. But advancements in AI have made "simple data access for all" a reality. Check out Bessemer Venture Partners's Data Shift Right roadmap. Thanks for the shout out Lindsey Li, Yael Schiff, and Amit Karp.

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We need to talk about Agents! Inspired by LlamaIndex's recent tutorials on Agents (Ravi Theja Desetty) and Jerry Liu's talk on Advanced Context-Augmented LLM Applications, this post looks at the Planning component of an agentic workflow. We go through Parallelizable sub-queries, Task Decomposition and Planning with Feedback in detail to help LLMs "reason" vs "generate". Happy Reading! #ai #planning #agents #workflowautomation #tech

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Came across this great resource in the smashmag newsletter this morning and it spawned some thoughts about Zen and the art of Data Viz… Data visualizations are often the first thing users encounter when they log into software solutions, especially those designed to support their daily professional workflows. However, many platforms fall short by not offering appropriate visualizations for their specific data types. The Financial Times Visual Vocabulary is an excellent starting resource for teams operating in data-intensive environments. Selecting the right visualization is only the beginning—and arguably the easier part. The real challenge lies in presenting insights that enable users to draw meaningful conclusions and take informed actions. This requires a blend of domain expertise, clear business objectives, and iterative learning. By focusing on the relationship between accurate data representation and actionable insights, we can enhance user experience and drive better decision-making. Improvements don't always necessitate a full redesign. Often, small tweaks can unlock significant outcomes. It all starts with the seemingly simple question, "What are your users trying to accomplish?" I’m 2 months into mat leave so hopefully sleep deprivation has not eroded my ability to talk shop and that these are coherent musings. Shout out to CGPT for at least supporting sentence structure and grammar #UX #datavisualization #dashboards #SaaS #usability #maternityleave

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Hosting the 2024 Paris Olympics has cost an estimated $9.7B to-date, a whopping figure fit for a global event showcasing the best of human athletic capability and the unifying nature of sport. I only mention this to provide timely context on the magnitude of Walmart's financial performance. Walmart generates over $10B of revenue a week on average... let that sink in. For context, Walmart's 2024 fiscal year revenue of $648B would place it among the 25 richest economies of the world ranked by GDP. While there are many factors that have played into Walmart's success, from its decentralized logistics strategy to its bargaining power over suppliers, arguably the most important has been the company's extraordinary ability to never lose sight of its overarching mission. Even from Walmart's origins in 1962, Sam Walton had the vision for what a true customer-focused retail experience looked like, one where low prices did not mean compromising on great experience. In the world of VC, I think it's important to always remember that product and purpose go hand-in-hand, and that one without the other will always limit greatness.

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I'm really intrigued by this idea (not mine) that some bitcoin mining companies (Iris, Cleanspark, etc) have successfully built low-cost power shells that can now be leveraged by AI compute needs -- where power itself is becoming the gating factor in the continuing growth of AI. I think this will become a real 2025 story -- as the gating factor of "chip shortage" lessens, and the gating factor of "cheap power" rises in significance. There's not really a venture capital angle here, but there is a public market one. Perhaps more importantly, it becomes a touchpoint between "crypto" and AI. Something to ponder and keep an eye on (full disc: I own a bunch of bitcoin miner stocks, including those mentioned above).

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Tembo is leading in the 'Postgres as a Platform' movement. Their most recent blog post(s) below do a pretty good job of explaining why 'Postgres as a Platform' is rapidly becoming an anecdote for database sprawl.

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During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

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I’ve been fortunate enough to do AI shows with Sam Altman, Yann LeCun & Arthur Mensch. But this show today with David Cahn at Sequoia Capital is the best of all of them: 🧠 AI’s $600BN question 🏃 Why no one will never train a frontier model on the same data center twice 🏗️ Servers, steel & power: The 3 pillars of the future of AI Not one to be missed, my 8 key takeaways 👇 1. The Oligopoly That is Blowing Billions on AI - Microsoft, Azure, & Google now represent $7T market cap. - Of course they will spend billions to protect their oligopoly - What is most interesting is Meta is the only incumbent that is financing this not with cloud being their cash cow. 2. How Startups Could Win with the Cost of Compute Coming Down - Big tech companies are producers of compute & startups are consumers of compute. - Overproduction of compute = lower prices.  - This gives startups higher margins & makes them more valuable. 3. Is Compute the Currency of the Future or Do the Costs Come Down and It Is Commoditised? - We are forgetting “compute” is a euphemism for a $2BN data center in the middle of nowhere. - They have GPUs & liquid cooling systems that need to be constantly optimized & upgraded. - We cannot simply build 15 years worth of compute all at once.  No one's ever going to train a frontier model on the same data center twice. 4. Data Centre Teams and Model Teams Need to Be Coupled - You cannot have separate teams running data centers & building models. - Elon & Zuck control all their data centres. - It will not work as these models get better & better. 5. Why the Data Center Is the Most Important Asset - There is not much difference between these models today. - AI researchers are jumping from one lab to another. - Scaling laws will become more dominant as these models get bigger. 6. Servers Steel and Power: The Three Bottlenecks of AI Three things: 1. Servers: NVIDIA, AMD, Broadcom… 2. Steel: Construction & real estate developers for data centers. 3. Power: All the energy required for AI. - More factories will be built in 12 months for AI and we will see another industrial revolution. 7. You Have to Have a Cash Machine to Play the Big Model Game - Amazon has AWS - Meta has Instagram - Microsoft has Azure - You need a cash machine to compete and it CANNOT be the AI business. 8. There is One Definition of Success in Venture: Are you a Slugger? - If you are generating billion dollar gains, you are a slugger. - Doug Leone, Pat Grady, Andrew Reed are all sluggers. - You cannot be good at this business without being a slugger. (links in comments) #founder #funding #business #investing #vc #venturecapital #entrepreneur #startup #seed #funding #sequoia

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Thanks for sharing these insights. What I am seeing is that orgs test an idea using a proprietary #LLM like #OpenAI, then put it into production with an open source LLM using a development platform like #Llamaindex. That way, private data remains private.

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How 𝘾𝙖𝙪𝙨𝙖𝙡 𝘼𝙄 Can Help Reduce Hallucinations: New Q&A with Jayeeta Putatunda I recently spoke with Jayeeta, Lead Data Scientist and Director at Fitch Group Inc, about using causal AI to ground language models in real relationships, not just correlations: 𝘛𝘩𝘪𝘯𝘬 𝘰𝘧 𝘧𝘪𝘯𝘢𝘯𝘤𝘦 𝘰𝘳 𝘢𝘯𝘺 𝘥𝘰𝘮𝘢𝘪𝘯 𝘸𝘩𝘦𝘳𝘦 𝘺𝘰𝘶 𝘤𝘢𝘳𝘦 𝘢𝘣𝘰𝘶𝘵 𝘤𝘢𝘶𝘴𝘦 𝘢𝘯𝘥 𝘦𝘧𝘧𝘦𝘤𝘵 𝘳𝘢𝘵𝘩𝘦𝘳 𝘵𝘩𝘢𝘯 𝘫𝘶𝘴𝘵 𝘤𝘰𝘳𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯. 𝘌𝘤𝘰𝘯𝘰𝘮𝘦𝘵𝘳𝘪𝘤𝘴 𝘩𝘢𝘴 𝘢𝘭𝘸𝘢𝘺𝘴 𝘥𝘰𝘯𝘦 𝘵𝘩𝘢𝘵, 𝘵𝘳𝘺𝘪𝘯𝘨 𝘵𝘰 𝘴𝘦𝘦 𝘪𝘧 𝘢 5% 𝘤𝘩𝘢𝘯𝘨𝘦 𝘪𝘯 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳 𝘈 𝘭𝘦𝘢𝘥𝘴 𝘵𝘰 𝘴𝘰𝘮𝘦 𝘴𝘩𝘪𝘧𝘵 𝘪𝘯 𝘱𝘢𝘳𝘢𝘮𝘦𝘵𝘦𝘳𝘴 𝘉 𝘰𝘳 𝘊. 𝘠𝘰𝘶 𝘤𝘢𝘯 𝘳𝘶𝘯 𝘴𝘪𝘮𝘶𝘭𝘢𝘵𝘪𝘰𝘯𝘴 𝘵𝘰 𝘴𝘦𝘦 𝘪𝘧 𝘵𝘩𝘢𝘵 𝘭𝘪𝘯𝘬 𝘩𝘰𝘭𝘥𝘴, 𝘰𝘳 𝘪𝘧 𝘵𝘩𝘦𝘳𝘦’𝘴 𝘫𝘶𝘴𝘵 𝘢 𝘭𝘰𝘰𝘴𝘦 𝘤𝘰𝘳𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯. 𝘕𝘰𝘸 𝘤𝘰𝘮𝘣𝘪𝘯𝘦 𝘵𝘩𝘢𝘵 𝘸𝘪𝘵𝘩 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘈𝘐. 𝘐𝘧 𝘺𝘰𝘶 𝘧𝘦𝘦𝘥 𝘵𝘩𝘦 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘮𝘰𝘥𝘦𝘭 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘦𝘥 𝘤𝘢𝘶𝘴𝘢𝘭 𝘮𝘢𝘱𝘴 𝘰𝘳 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴, 𝘵𝘩𝘦𝘯 𝘸𝘩𝘦𝘯 𝘪𝘵 𝘱𝘳𝘰𝘥𝘶𝘤𝘦𝘴 𝘢 𝘳𝘦𝘱𝘰𝘳𝘵, 𝘪𝘵’𝘴 𝘭𝘦𝘴𝘴 𝘭𝘪𝘬𝘦𝘭𝘺 𝘵𝘰 𝘪𝘯𝘷𝘦𝘯𝘵 𝘧𝘢𝘭𝘴𝘦 𝘤𝘰𝘯𝘯𝘦𝘤𝘵𝘪𝘰𝘯𝘴. 𝘐𝘧 𝘺𝘰𝘶’𝘳𝘦 𝘴𝘶𝘮𝘮𝘢𝘳𝘪𝘻𝘪𝘯𝘨 𝘩𝘰𝘸 𝘈𝘱𝘱𝘭𝘦’𝘴 𝘨𝘳𝘦𝘦𝘯 𝘪𝘯𝘪𝘵𝘪𝘢𝘵𝘪𝘷𝘦𝘴 𝘭𝘦𝘥 𝘵𝘰 𝘢 𝘳𝘦𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘪𝘯 𝘤𝘢𝘳𝘣𝘰𝘯 𝘦𝘮𝘪𝘴𝘴𝘪𝘰𝘯𝘴, 𝘺𝘰𝘶 𝘤𝘢𝘯 𝘷𝘢𝘭𝘪𝘥𝘢𝘵𝘦 𝘵𝘩𝘢𝘵 5% 𝘰𝘳 10% 𝘧𝘪𝘨𝘶𝘳𝘦 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘺𝘰𝘶𝘳 𝘤𝘢𝘶𝘴𝘢𝘭 𝘮𝘰𝘥𝘦𝘭. 𝘛𝘩𝘦 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘪𝘷𝘦 𝘮𝘰𝘥𝘦𝘭 𝘤𝘢𝘯 𝘵𝘩𝘦𝘯 𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦 𝘵𝘦𝘹𝘵 𝘵𝘩𝘢𝘵 𝘳𝘦𝘧𝘭𝘦𝘤𝘵𝘴 𝘵𝘩𝘰𝘴𝘦 𝘳𝘦𝘭𝘢𝘵𝘪𝘰𝘯𝘴𝘩𝘪𝘱𝘴, 𝘳𝘢𝘵𝘩𝘦𝘳 𝘵𝘩𝘢𝘯 𝘨𝘶𝘦𝘴𝘴𝘪𝘯𝘨. Jayeeta also shares her path from econometrics to NLP innovations, her perspective on the rapid evolution of AI technologies, and the importance of bridging research with real-world industry challenges. Other takeaways from our discussion: • Finance needs specialized frameworks beyond generic AI metrics • Regulators require clear evidence chains, not black boxes • Traditional statistical models + new AI techniques are key to adoption The full interview is out on Thursday. Subscribe to AI Street to get in your inbox. Link in the first comment. Follow me (Matt Robinson) and AI Street for more on AI + Finance.  

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