Amir Rustamzadeh

Breaking down our latest newsletter section by section, starting with our expanded focus in Deep Tech: --- We have built expertise in the robotics sector, and will continue to double (and triple) down on investing in this category, but we have been increasingly finding that robotics is naturally steering us into two exploding adjacent areas: Aerospace, and Advanced Manufacturing (which we define as industrials, semiconductors, materials science, and energy production). Stepping back, this expansion makes perfect sense. Robotics is an inherently horizontal platform - meaning it can be applied productively across multiple industries. This quality, paired with the fact that robotics has long shown the highest TRLs of any deep tech technology, makes it a kind of “gateway” to other sectors in the hard sciences. Now with the recent breakthroughs in AI for robotics, growing semiconductor demand, and the resurgence of aerospace and defense tech, we are seeing a dramatic uptake of robotics and relevant technologies in both advanced manufacturing and aerospace. As an added bonus of the expansion, many of the founder (and funder) networks across these landscapes have natural overlaps and linkages. For AlleyCorp Deep Tech this means that we can be extremely helpful, knowledgeable, and engaged across all three sectors right away, and continue to be the best-in-class investing team across these categories.  --- More in our newsletter: https://lnkd.in/ewiTuYq6

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🤖 Mindtech New Sensor Features 🧠 Mindtech, one of Edge’s new portfolio companies and the developer of its end-to-end DataOps platform for synthetic data, has announced the launch of support for two new sensor types, LiDAR (Light Detection and Ranging) and NIR (Near-infrared) for its “Chameleon” platform. Mindtech Chameleon Editor instantiating a Lidar sensor Mindtech’s “Chameleon”, a platform enabling rapid creation of synthetic data matched to the real world, has added support for these two additional sensor types, allowing AI system developers to create Lidar and NIR training and test data. Today’s systems often require to see beyond the visual, to help with accurate depth perception (LiDAR) and low light monitoring applications (NiR) for example. Combining these non-human visible sensors with traditional human-vision (RGB-type) sensors, allows Chameleon users to create precise, synchronised data, with full annotations.

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There is so much that is so damn impressive about Nominal's execution. The team identified a truly needed product in the world. More than that... They serve their customers. Sometimes Nominal is saving days of work for an engineer detangling the performance of new hardware in an autonomous fixed wing drone. Other times, helping implement a transformational change for engineering orgs, delivering iteration speed from simulation to range to unlock an RFP goal and contract win. They are building dual use -- powering the best of commercial and public sector teams. Whether a government contractor delivering on a $12b contract. A scaled public company, integrating AI into your roadmap on to your existing HW platform. A startup which is willing a first product into existence. Nominal is there for all the dual use hardware builders who live real time, live in physical world time. The real world runs real time. The real world runs Nominal and so excited to be on the journey. Also, hiring, https://www.nominal.io/, kind of a lot. If you are awesome, they want to talk to you.

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The most impactful technology augments human capability. Take assembly line work as an example: Robots enhance speed and precision but still require human oversight. This dynamic mirrors our evolving relationship with AI, especially within the service-as-software paradigm shift. As Jaya and I wrote in our recent blog post, AI's evolution from a basic tool into an autonomous agent isn't about replacing jobs. It's about reshaping them. This shift is part of a $4.6 trillion opportunity where AI not only boosts efficiency but also creates new roles, blending tech with human insights. AI doesn’t just replace—it reinvents how we work, creating more value than ever.

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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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💡 Why do we need so many evaluation tools for LLMs? As engineers, we build 'production-ready' LLM products using these metrics. But what happens next? How do we maintain control and ensure reliability? At Feedback Intelligence, we’ve crafted a cookbook to keep your LLMs reliable and aligned with user expectations. 🍲 📖 Give it a read and let’s chat!

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Excited to share our 0-1 #DevGTM Playbook for early-stage technical founders building developer tools and infrastructure! We at Speciale Invest had the privilege of working closely with many brilliant technical founders and evaluating many Devtools/Infra Cos. During our in-depth discussions, we discovered a recurring obstacle that many of these technical founders face: despite identifying problem statements and developing cutting-edge technology but crafting a clear and effective GTM strategy often proves to be a significant challenge. Recognizing this gap, we set out to create a comprehensive playbook tailored for early-stage technical builder/founders in their 0-1 journey. My colleague Aryan Pareek and I have spoken to over 50+ #DevTool founders, GTM experts, and #developers from companies like Amnic, Appsmith, DhiWise, Dyte, Esper,Hashnode,Hivel,Keploy 🐰, LambdaTest, Neverinstall, Portkey, Reo.Dev, Requestly (YC W22), ToolJet, Tailcall, Typo, Unstract , videosdk.live, Zenduty and many more. And we consolidated the best examples and learnings into our playbook. We dive deep into various aspects of #developer GTM, including defining your ICP, buyer journeys, messaging for developers, #SEO, content strategy, and more - all tailored for dev founders. Check out the playbook here: https://lnkd.in/g425vuqy Huge thanks to all the #devfounders and #GTMExperts who shared their invaluable insights and experiences with us. Special shoutout to Dev founders and all the amazing developers and GTM experts who gave us their valuable time. 🙏 📣 Special Creds to the devfounders who helped us with this: Sachin Jain Achintya Gupta Navaneeth Padanna Kalathil Vishal Virani Anirudh Murali Shiv Sundar Kshitij Mohan Sudheer Bandaru Venkatesh Radhakrishnan Harini Janakiraman Arjun Kava Narendran Hariparanthaman Sidhant Goyal Madhukar Kumar and many more.... (lol - I've exceeded the maximum mention limit) Special thanks to Freshboost for assisting us with SEO strategies. *Please tag technical founders/builders who would find this useful, and feel free to share your feedback with us over DM* P.S.: It's still a work in progress, and we'd love to hear from more technical founders about your experiences and challenges about your GTM journey. Your feedback will be invaluable in making this resource even better for the broader dev ecosystem. Arjun Rao Vishesh Rajaram #TheDeepTechVC — Build from India for the World

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I find it hard to believe that with millions of EVs entering the market and the proliferation of AI compute that PGE is actually going to have excess capacity in 2040 without any additional generation from 3rd parties. I am also curious if there are enough transmission lines and if PGE views that as someone else’s problem. Maybe they are planning on AI and ACTUAL figuring it all out! https://lnkd.in/gybsMmaw

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Excited to share Marathon Venture Capital's investment in Eva Magnisali's DataForm Lab! Construction is large enough to be measured in GDP terms, yet remains a laggard in the adoption of automation. Our buildings are still constructed manually! Offsite manufacturing, i.e. assembling buildings in factories, provides the perfect canvas for putting automation to work – still, however, robots are nowhere to be seen... Eva studied architecture and robotics, and worked with design firms, developers and industry vendors, realising first hand what's keeping the industry stuck in the past. Then she got to work... Dataform Lab's software platform translates designs to production drawings and machine code, along with optimising processes and scheduling, essentially offering a modern operating system for construction factories. The company already works with some of the most prominent offsite manufacturers worldwide. Today it announces the completion of a GBP 1.1m Seed round led by Marathon. Eva and her team are on a mission to drive automation in offsite manufacturing. If you are excited about working at the intersection of technology and construction, get in touch! Building construction has been slow and expensive for far too long. Our next homes and schools may come from fully-automated factories, delivered at a fraction of today's time and cost. Keep an eye on Dataform Lab! https://lnkd.in/dVVfnudS

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Questions we ponder on when evaluating an application layer B2B idea (AI or otherwise): - Is this a daily/weekly problem for the user/buyer? - Is this an urgent/impact problem for the buyer to solve? - Is there going to be budget to solve this problem? - Bottom-up market size? - Why now? - Is the GTM engine going to be scalable? - Is there near term term differentiation to get ahead early? - Are there going to be some accumulating advantages over time? (We invest at seed. So we don't expect all these questions to have perfect answers here and now, but these are the questions we think about.)

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To build a multi-agent system, start simple, validate your design, then gradually scale. When I spoke to Chi Wang, the creator of AutoGen, he explained why. Deploying one or two agents at first allows builders to evaluate and refine the core design and interaction patterns before introducing additional complexity. This method also streamlines debugging and optimization because it makes it easier to trace issues back to specific agents. Our full conversation here: https://lnkd.in/gEKmExu4

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Since 2016, SpaceX has launched ~300 Falcon 9 rockets into space. Every single one has been successful. The consistency is remarkable, but how did they achieve this across different weather conditions, payloads, and rocket designs? The answer is testing… and lots of it. On the surface, testing hardware is relatively simple. Build it, test it, see what breaks. Rinse and repeat until you have a robust product. This works great in theory, but it’s much harder when software is antiquated and the data volumes are overwhelming. As the number of variables increases, the number of trials needed increases exponentially. Let's do some quick math assuming we want to isolate different numbers of variables… 5 variables → 120 trials 10 variables → 3,628,800 trials 20 variables → 2,432,902,008,176,640,000 trials (2.4 quintillion) You get the idea. For complex hardware (like Falcon 9), it’s incredibly difficult and expensive to test all aspects of a system. Yet there are significant real-world consequences when mission-critical hardware fails. This is why Nominal is so critical. Nominal’s software platform streamlines testing and deployment of complex hardware so engineering teams can speed up innovation cycles and lower costs (and angst) in the process. It’s a godsend for industrial companies and is going to play a key role in enabling America’s manufacturing future. Overmatch is excited to be part of Nominal’s Series A and support Cameron McCord, Bryce Strauss, and Jason Hoch as they build the modern industrial data stack. We invested alongside Paul Kwan and Alexa Liautaud at General Catalyst; Bilal Zuberi and Josh Wolfe at Lux Capital; Delian Asparouhov and Trae Stephens at Founders Fund; Ross Fubini at XYZ Venture Capital; Semil Shah and Aashay Sanghvi at Haystack; and Matthew Colford and Baris Akis at HUMAN CAPITAL.

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The Prompt: Machine Learning Paradise Plus: Scathing reviews of Humane’s AI Pin, and a $400 AI toothbrush The Prompt is a weekly rundown of AI’s buzziest startups, biggest breakthroughs, and business deals. Welcome to The Prompt. I’m Rashi Shrivastava, a tech reporter at Forbes, and I’ll be walking you through the week’s most important developments in artificial intelligence. Top of mind this week is OpenAI’s announcement of a new office in Japan. It will be the ChatGPT-maker’s first outpost in Asia, where it’s also rolling out a custom GPT-4 model optimized for translating and summarizing text in the Japanese language. Tadao Nagasaki, a former Amazon Web Services executive, will head the operations. It’s a noteworthy move for OpenAI, as Japan’s copyright laws and regulations for AI developers have historically been more lax than other countries like the U.S. and U.K. Japan, which has been called a “machine learning paradise,” passed a law that broadly allowed some copyrighted materials to be used for training AI models without consent — even for commercial purposes. “The idea was to make Japan very attractive to AI businesses so that they would develop their models in Japan,” said Joseph Grasser, a partner at law firm Squire Patton Boggs. “This was seen as a way to get around the perception that Japan is ‘behind’ in the AI race.” Now, let’s get into the headlines. https://lnkd.in/dJjf6Wp6 By Rashi Shrivastava

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Summary: Lacey Hunter, founder of TechAid, went through the Newchip accelerator in 2022 and was blindsided when the organization filed for bankruptcy in May 2023. The bankruptcy court is auctioning off the warrants held by Newchip in over 1,000 startups, causing outrage and forcing some companies to shut down. Newchip had a tumultuous history, facing criticism for its CEO's leadership style and ultimately filing for Chapter 11 and 7. Key takeaways: Many startups paid thousands of dollars to go through the Newchip accelerator, giving the organization the right to buy shares in their company at a future date. Now, the bankruptcy court is auctioning off these warrants, causing distress and in some cases, leading to the closure of startups. Newchip's founder and CEO, Andrew Ryan, faced criticism for his leadership style and reportedly fired a manager in front of the entire company as a "test" of loyalty. The bankruptcy revealed that Newchip had missed keeping track of some companies that had exited or raised money, potentially causing the organization to miss out on millions of dollars in potential upside. Counter arguments: Newchip may argue that their bankruptcy was due to unforeseen circumstances and was not a #vc #startups #accelerators

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"Advanced computing is starting to serve utilities..." As the rise of AI is contributing to a near-doubling of five-year energy load forecasts (from 2.6% to 4.7% growth), AI is also at the core of some of the innovation to solve the biggest problems in climate and energy. A couple of weeks ago I was in a room of investors who were asked if it's "too early" to be investing at the nexus of AI and climate. I was surprised to even hear the question, because we've been doing it for years. This article from Utility Dive highlights three companies in which MUUS Climate Partners was an early investor: - BrightNight, which created an advanced simulation tool to optimize the design and operations of clean energy projects; - Amperon, which has built the world's most accurate energy demand forecasting system; - Utilidata, which has partnered with NVIDIA to deliver the first distribution system AI platform. If you're interested in the AI-climate nexus (and not just the buzzwords, but the actual solutions being built and deployed), this article might be of interest. https://lnkd.in/evkkY4nX

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On AI landscape, my current working hypothesis is: Model layer: commoditized other than specialized verticals like bio. Infra layer including compute capacity: hyperscalers like nvidia, Amazon will win. Groq likely gets acquired by Google, Amazon or Microsoft. Dev tools: Most startups will not monetize but some will emerge as large winners. App layer: This is where there could be 1K+ winners. Would love to hear where I’m wrong or missing the mark completely.

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My paper on the Cruise robotaxi pedestrian dragging mishap has been updated and is now available in its final form. I'll be presenting this paper at Safecomp in September, but you can see the preprint right now. Thanks to the SafeComp reviewers for their review comments! If you haven't had a chance to look at this paper before, the short takeaway is that there is a lot buried in the external reports that is not obvious from a casual reading. My paper is the result of carefully assembling the pieces to tell a more direct story of the events that unfolded. Title: Anatomy of a Robotaxi Crash: Lessons from the Cruise Pedestrian Dragging Mishap (Safecomp 2024 practical experience report) Abstract: An October 2023 crash between a GM Cruise robotaxi and a pedestrian in San Francisco resulted not only in a severe injury, but also dramatic upheaval at that company that will likely have lasting effects throughout the industry. Is-sues stem not just from the loss events themselves, but also from how Cruise mishandled dealing with their robotaxi dragging a pedestrian under the vehicle after the initial post-crash stop. External investigation reports provide raw material describing the incident and critique the company's response from a regulatory point of view, but exclude safety engineering recommendations from scope. We highlight specific facts and relationships among events by tying together different pieces of the external report material. We then explore safety lessons that might be learned related to: recognizing and responding to nearby mishaps, building an accurate world model of a post-collision scenario, the in-adequacy of a so-called "minimal risk condition" strategy in complex situations, poor organizational discipline in responding to a mishap, overly aggressive post-collision automation choices that made a bad situation worse, and a reluctance to admit to a mishap causing much worse organizational harm down-stream. https://lnkd.in/e5h84-up

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🚨 How does it feel when your portfolio startup is acquired by NVIDIA? Awesome! 🚀 It can now be disclosed that NVIDIA is acquiring our portfolio artificial intelligence startup Deci AI! 👏 Congratulations to the founders Yonatan G., Jonathan Elial, and Ran El-Yaniv, as well as fellow investors Insight Partners, Square Peg, Emerge Ventures, Jibe Ventures, Vintage Investment Partners, ICON - Israel Collaboration Network etc. 👉 Read more on Calcalist: https://lnkd.in/diSPt4Jc A few reflection points: 1/ Nvidia is very active in Israel, having previously acquired Run:ai earlier in 2024, Excelero in 2022, and started with the $6.9B acquisition of Mellanox Technologies in 2019. Now there are more than 4,000 Nvidia engineers in Israel. 2/ Deci AI was founded in 2019 and has since raised $55M in financing. We participated in the seed round when Sharin Fisher first brought the deal, then doubled down during Series A with Sharin as the lead and Victor Orlovski's active involvement. We were honored to serve on Deci AI's BoD as well. 3/ Nvidia commented on the deal 🗣️: "NVIDIA's expertise in AI will be complemented by Deci's capabilities in NAS, which automates the design of deep learning models to improve performance. Deci's engineering team has knowledge in NAS, Foundational Models, Inference, and in developing complex algorithms." This is a great appreciation of Deci AI's mission to enable deep learning models to run efficiently on any hardware in real-time. 4/ I hope that soon AI models will be built, optimized, and operated in various other environments, such as cloud or even mobile, of course with the help of Deci AI as part of Nvidia. I'm really looking forward to it! 🙏 Congratulations to my fellow colleagues at R136 Ventures, especially Victor Orlovski and Evgeny Pestryakov as well as Tom Dennedy, Ratan M. and again, to the founders and Deci's team – it was a pleasure for us to support such great entrepreneurs, engineers, and operators!

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We are delighted to present the concluding installment based on our paper, "Vision 2035: The Emergent Singularity" (link in the comments). In this final section, we illuminate the three critical components that will prove instrumental for companies as they navigate the transformative landscape of machine intelligence: 1. The Role of Data: In the age of machine intelligence, data reigns supreme. As we navigate this data-rich landscape, challenges emerge in harnessing its full potential. The concept of "Big Data" evolves rapidly, demanding advancements in edge computing, natural language processing (NLP), and even quantum computing. Despite this growth, there's a growing reluctance to share data, hindering accessibility. Startups can serve as pivotal players, breaking down barriers and showcasing the value of innovative solutions. From technical to regulatory hurdles, the path to data accessibility is fraught with challenges, but also ripe with opportunities for venture-backed initiatives to lead the way. 2. Open-stack technologies: Rooted in openness across software, data, storage, and computation, open-stack technologies are analogous to open-source software in many ways. By embracing open-stack, companies transcend closed system limitations, fostering innovation and enabling agile, cost-effective development through collaborative ecosystems. Leveraging existing communities potentially reduces customer acquisition costs while cultivating dedicated user bases for lasting relationships. 3. Collective intelligence: Ecosystems built on shared, community, and aggregate knowledge offer prime opportunities for open-stack-centric machine intelligence startups. The emergent benefits from group knowledge can speed up product development and broaden customer reach. Collective intelligence approaches like federated learning can also streamline data processes, minimize data sharing while maintaining control and adaptability, enhance model accuracy, and mitigate technical and product risks across an ecosystem. We hope you have found our series of summary analysis on the realm of machine intelligence to be insightful. As highlighted in our longer vision paper, we are convinced that the value creation potential of winners in this space will amount to a remarkable two orders of magnitude (100x) compared to today's companies. Lastly, we may follow up with a little encore in two weeks. Keep your eyes peeled! Patrick & Sri Machine Ventures #singularity #machineintelligence #ai

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