Dylan Fox

Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

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🧩 One thing we've noticed with #VentureIQ technology is how well it can distill and communicate complex technical material 🧩 Whether it's a #deeptech venture, a novel #AI application, or even a patent submission, we can quickly distill the business and strategic implications for investors...and sometimes even the founders/PIs themselves! 🤯 Based on our work so far, the tech can handle a wide array of research areas and technological sectors -- anything from blockchain algorithms to vaccine development. It handles the nuance of the field, the specific terminology, and extracts the key concepts which matter for venture-building. 🤓 To demonstrate, I took a patent I helped write with Eric Laber on reinforcement learning. I ran it through #Ventureiq to build a commercial assessment (key differentiators, target markets). Then for fun I had #NotebookLM make a podcast out of it. I've uploaded the result it here (DM me if you'd like the actual report): https://lnkd.in/eiTejsny This is more than just automation or streamlining operations - it's about unlocking clarity and insight when it was previously unattainable, and the ability to do it over and over again no matter the sector or vertical. Interested in more info? DM me so we can chat.

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Exciting to see startups aiming to use AI to discover chemicals, materials, drugs, etc. Semi companies have taken notice: AMD is investing in and Cadence is acquiring them. Semi/EDA companies have broken out by becoming critical path, whether it’s through critical software, packaging, IP, design kits from foundries, or user communities. Aside from just making it easy to make new materials, these “AI for making X” companies also need to articulate a vision as to how they can establish monopolistic positions in their industries.

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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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Can AI Scaling Continue Through 2030? As AI advances at an unprecedented pace, one pressing question remains: can this growth trajectory be sustained through 2030? Recent analysis from Epoch AI outlines four potential constraint: 1. Power Availability. Mega data centers (1-5 GW) and distributed energy systems leveraging global grids make power scalability feasible. Projections suggest we could support 2-45 GW of AI infrastructure by 2030. 2. Chip Manufacturing Capacity. Despite challenges in packaging and high-bandwidth memory, manufacturers are scaling production. By 2030, capacity for up to 100M H100-equivalent GPUs is within reach. 3. Data Scarcity. AI depends on vast datasets. While human-generated data has limits, web data offers a 30x expansion. Multimodal learning (combining text, images, audio, and video) and synthetic data could further bridge the gap, though with added compute costs. 4. Latency Wall As models grow larger, processing times increase. However, this constraint is unlikely to become a critical issue before 2030. 🤔The Big Decision: To Scale or Not to Scale? ✔️ Scale: Scaling has consistently improved AI capabilities. With the potential to automate industries and generate trillions in economic value, the investment case remains strong. ❌ Not Scale: The cost of scaling—potentially hundreds of billions—is steep, and the ROI isn’t guaranteed. The Verdict: Whether AI continues scaling will depend on one key factor: ROI. If AI delivers breakthroughs and significant economic value, it’s likely that labs, corporations, and governments will continue funding the next phase of innovation. #ArtificialIntelligence, #Ai, #Aitech, #Startups https://lnkd.in/eCdNhtDt

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Sure, the opportunity to build AI Agents is there, but existing SaaS companies have a major advantage: their distribution channel. It’s easier to experiment providing agentic solutions (under their tailored business model) if you have an existing customer base and deep domain expertise, otherwise brace for 360° disruptive competition.

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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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Acquisitions reveal a lot about ambitions. NVIDIA is actively acquiring AI startups. 2023 saw a surge in Nvidia’s dealmaking activities. The trend continues in 2024. This year, they bought Run: ai and Deci AI. These companies specialize in optimizing AI workloads. Nvidia’s focus on AI performance and optimization is no news. This acquisition spree enhances its AI capabilities. Major corporations acquire startups to: - Access latest technology - Innovate faster - Gain new talent - Strengthen market position PayPal started acquiring BNPL startups before offering the Buy-Now-Pay-Later option on its main platform. Acquisitions are done to drive growth faster. You don’t buy companies; you buy growth. 𝙄𝙣𝙩𝙚𝙧𝙚𝙨𝙩𝙚𝙙 𝙞𝙣 𝙢𝙤𝙧𝙚 𝙈&𝘼-𝙧𝙚𝙡𝙖𝙩𝙚𝙙 𝙘𝙤𝙣𝙩𝙚𝙣𝙩? 𝙈𝙖𝙠𝙚 𝙨𝙪𝙧𝙚 𝙩𝙤 𝙛𝙤𝙡𝙡𝙤𝙬 𝙢𝙚. #entrepreneurship #venturecapital #startup #mergers #acquisitions Serial Entrepreneur & Investor Helping Startups Become Unstoppable – David Hauser

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✨ Breaking: Magnetar Announces New Fund.- Magnetar Magnetar AI Ventures Fund LP, launched on August 26, 2024, is a $235 million venture capital fund focused on early-to-growth stage AI startups. The fund uniquely addresses challenges faced by AI startups in accessing compute resources by offering compute in exchange for equity. The strategy is designed to accelerate the development of generative AI technologies and enable startups to secure critical contracts. Magnetar aims to leverage its resources to provide the compute power necessary to support innovation in the AI sector. https://lnkd.in/dRwmTnCy Please share to let other #Founders know For the ❤️ of Startups #Fusion42 #Startups #Venturecapital

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Despite its 671B parameters, DeepSeek-V3 achieves energy efficiency via its Mixture-of-Experts (MoE) architecture, activating only 37B parameters per token. Training required 2.788M H800 GPU hours, costing $5.57M, setting a new standard for cost-effective, scalable AI. Benchmarks on MMLU, MATH, and Chinese SimpleQA show DeepSeek-V3 outperforming GPT-4 and Claude 3.5 in coding and math tasks, with a 90.2% accuracy on MATH-500. DeepSeek-V3 leverages FP8 mixed precision with fine-grained quantization, reducing memory usage by 50% while maintaining accuracy through increased accumulation precision. Multi-token prediction accelerates inference by generating multiple tokens sequentially, supported by speculative decoding that handles error propagation through parallel verification. https://lnkd.in/dWR32WUp

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Cost-aware inference is important, but currently LLM prompts are not interoperable. Prompts that work good on one LLM may show worse performance on the other, even more advanced LLM or even on the different versions of the same LLM. Once interoperability problem is solved it will also be possible to implement cost-aware inference that strategically manages computational resources for prompt processing, ensuring sustainable costs. This approach includes model routing https://www.notdiamond.ai/ - automatically selecting the most appropriate model for each task. Startups in this area are actively supported by the industry veterans.

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December 21 2024 – 5 Notable Articles To Read 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: AI Startup SandboxAQ Raises Funds at Over $5.6B Valuation 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Secured over $300M in funding, this investment will enable the startup to hire additional engineers to advance its efforts in integrating quantum technology with AI development. 𝗦𝗼𝘂𝗿𝗰𝗲: Bloomberg 𝗟𝗶𝗻𝗸: https://lnkd.in/dCJdmU8f 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: Insight Partners Considers Possible $5B Sale of Dotmatics 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Scientists leverage the company's software for drug research, while a technology-focused private equity and venture capital firm has engaged advisers to assess potential acquisition interest in the company, which specializes in providing software for the life sciences industry. 𝗦𝗼𝘂𝗿𝗰𝗲: Bloomberg News 𝗟𝗶𝗻𝗸: https://lnkd.in/d_679bKc 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: The $115T World Economy in One Chart 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The United States has held the title of the world's largest economy for over a century and is projected to maintain its lead in 2025 with a $30.3T GDP. 𝗦𝗼𝘂𝗿𝗰𝗲: Visual Capitalist 𝗟𝗶𝗻𝗸: https://lnkd.in/d6JSd37r 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: XGEN Venture closes $190M fund with plans to invest in early-stage biotech, medtech firms 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The European VC firm has begun investing in six startups, including a $52M Series B for the Italian gene therapy company Genespire and iOnctura. The firm plans to make nine more investments within the next three years to round out the fund’s portfolio. 𝗦𝗼𝘂𝗿𝗰𝗲: Fierce Biotech 𝗟𝗶𝗻𝗸: https://lnkd.in/dq_RdEbg 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: VÆRIDION raises €14M for zero-emission regional flights 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The patented Microliner prototype will initially operate as a nine-seater aircraft, with plans to scale up for pan-European flights. 𝗦𝗼𝘂𝗿𝗰𝗲: Tech.eu 𝗟𝗶𝗻𝗸: https://lnkd.in/dd5GS8fh #ai #drug #economy #biotech #funding

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As LLM's get increasingly more sophisticated, with OpenAI's o1 model now including reflection before answering, I am increasingly investigating the emergence of autonomous AI Agents. My path here was somewhat circuitous in that I was going through Y Combinator's Summer 24 cohort which had a plethora of AI use cases that were very industry specific. I found that curious in that earlier SaaS offerings should have already automated and/or digitized most of these process, and I couldn't figure out why AI startups would focus on basic workflows such as inventory management and book keeping. As I dove into the matter, with the help of my now indispensable Perplexity, I landed on a very insightful post by Bessemer Venture Partners (https://lnkd.in/gHtQQRha). The thesis is that Vertical AI companies are going to be magnitudes bigger than general solutions, especially as they focus on industries that were earlier inaccessible to SaaS given very high language based workflows. Within that thesis, there was also a mention of AI Agents and how these would come to dominate workflow automation going forward. This neatly tied in with a great conversation between Peter H. Diamandis and Emad Mostaque (https://lnkd.in/g-ccxbma) that talked about 10 billion agents!! I have spent the last few weeks diving into AI Agents with the help of content from interesting voices such as Adam Silverman (Hiring) and David Ondrej. Over the next few months, I will try to create and deploy AI Agents on some basic use cases to get hands on experience on these systems. In the meantime, I will close this post with a link to a video from Andrew Ng, a pioneer in AI systems that provides a short overview of AI Agents (https://lnkd.in/g8v4pUQu). I hope you enjoy the content and wish everyone a productive week ahead.

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This is just incredible. Neal Ghosh's demonstration of how #VentureIQ takes deeply technical and complicated content across a range of industrial use cases and ages/stages through an AI-generated conversational podcast (a la #notebookLM) is really mind-blowing. Check out his post, below, and if you haven't seen VentureIQ, yet, let's get you set up to learn more. You can visit VentureIQ.ai or DM any of us over here to book a demo!

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The ‘help’ VCs offer that founders wish they’d skip : From unsolicited intros to random conference invites 😐! -Making random introductions that weren’t requested -Inviting founders to conferences where the VC is speaking -Providing strategic advice without understanding the startup’s specific context -Conducting frequent check-ins without offering meaningful support -Forwarding articles about competitors -Recommending broad networking connections that aren’t relevant -Offering unsolicited product UX suggestions -Imposing rigid meeting schedules at inconvenient times -Sharing generic industry trends without actionable insights -Sending irrelevant templates or tools that don’t fit the startup’s stage or needs -Offering feedback that feels more critical than constructive -Making broad claims about experience without engaging with the startup’s unique situation For VCs, adding real value means aligning support with the founder’s specific needs and goals. Any other pointers you like to add? Cc. Ed suh #startups #venturecapital #frustrations

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Here are the Top 10 SaaS funding rounds of the past week (Seed to A) Starting at number 1... 🥇 Emergence, $97.2mm Seed 🤯 Emergence is developing an AI "agent-based" system that automates tasks typically performed by knowledge workers. Founded by Satya Nitta. 💸 Investors: Learn 🔗 https://bit.ly/3XSitTA 🟠🟠🟠 🥈 Formic, $27.4mm Series A Formic enables manufacturers to scale faster by making it easy to deploy robotic automations, like packing and palletizing. Founded by Misa Ilkhechi and Saman Farid. 💸 Investors: Blackhorn Ventures, FJ Labs, and more 🔗 https://bit.ly/3XGzEYr 🟠🟠🟠 🥉 normai, $27mm Series A Norm Ai is automating compliance processes, making them more efficient, cost-effective, and accurate via (you guessed it) AI. Founded by John Nay. 💸 Investors: Coatue, Bain Capital Ventures, and more 🔗 https://bit.ly/4ckpNft 🟠🟠🟠 🎖️ 4. ORA, $20mm Series A Ora is a blockchain project incorporating AI into dapps. Founded by Kartin Wong. 💸 Investors: Polychain Labs, HF0, and more 🔗 https://bit.ly/3zpW7Ph 🟠🟠🟠 🎖️ 5. Payabli, $20mm Series A Payabli builds the infrastructure that allows companies, specifically software companies, to embed and facilitate payments through APIs. Founded by Joseph Elias Phillips and William Corbera. 💸 Investors: QED Investors, Bling Capital, and more 🔗 https://bit.ly/4bwkGHE 🟠🟠🟠 🎖️ 6. Sift, $17.5mm Series A Sift develops tools to accelerate the development and ensure the reliability of complex machines (think Spacecrafts). Founded by Austin Spiegel and Karthik Gollapudi. 💸 Investors: GV (Google Ventures) 🔗 https://bit.ly/3L7zlyk 🟠🟠🟠 🎖️ 7. Ario, $16mm Seed Ario is an AI-powered assistant for parents that helps with daily tasks, from meal planning to scheduling playdates. Founded by Sumit Agarwal. 💸 Investors: Wing Venture Capital, Floodgate, and more 🔗 https://bit.ly/4eEjeG9 🟠🟠🟠 🎖️ 8. MagicSchool AI, $15mm Series A MagicSchool provides lesson-planning solutions for teachers to fight burnout. Founded by Adeel Khan. 💸 Investors: Bain Capital Ventures, Adobe Ventures and more 🔗 https://bit.ly/3RQt5Pn 🟠🟠🟠 🎖️ 9. AuthZed, $12mm Series A Authzed makes it easier for developers to build permissions in their applications. Founded by Jacob Moshenko and Jimmy Zelinskie. 💸 Investors: General Catalyst, Work-Bench, and more 🔗 https://bit.ly/4bqkh9B 🟠🟠🟠 🎖️ 10. SLUSHY.com, $10.2mm Seed SLUSHY is a creator monetization platform. Founded by David Gross and Fred Sprivock. 💸 Investors: Mantis Venture Capital, Jon Oringer, and more 🔗 https://bit.ly/4eN9Boi ⚫⚫⚫ I'm Gyan, Head of Community at Venture House. I create tools, guides, and other free resources for Junior VCs and founders. If you found this helpful, check out the Venture House website for more 👇 https://venturehouse.xyz/ #startups #venturecapital #funding #saas

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Successfully navigating the current investment landscape is no small feat especially for innovative deep tech. We ran across this article and wanted to post it with our comments: https://tcrn.ch/3zKkNSA I'd love to share some of the key techniques that have helped recent startups: 1. You need both business acumen and ‘the science’ on your side. Solving for a combination of entrepreneurial talent with technical expertise is proven to be an essential early test of your long term venture success. Ideally you are working towards having co-founders with complementary skills and significant stakes in the business. 2. Deep tech opportunities have more complicated journeys to get to market than the average startup does. These journeys are more complicated due to everything from regulatory hurdles, buyer concentration, competing industry visions from competing technology approaches, and much much more. Whether the founding team is going through a pilot study or working through a detailed roadmap, demonstrating tangible progress will always be paramount. Breaking down and executing a plan with manageable steps and milestones shows commitment and foresight, on top of being a necessary exercise for preserving energy and resources. This can help build investor confidence in your ability to deliver on promises. 3. Emphasizing the value of your product is absolutely crucial, not just its technological prowess. By vividly describing how your innovation could transform the world, you can tap into the motivations of investors who are eager to be part of a monumental story. Which leads us to… 4. Be a storyteller for your audience. Go beyond the standard pitch deck and introduce new materials that can go deeper into the details of your product. You should be able to easily articulate the problem that your product solves and its unique value proposition. This in-depth approach can help potential investors understand your vision and the market dynamics in a compelling way and make it easy for them to get started on due diligence when they begin to seriously evaluate investing in you. #DeepTech #StartupSuccess #Innovation #Investment #TechFunding #Entrepreneurship #FutureTech

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