Jukka Alanen

Key takeaways from VC Goat - Vinod Khosla podcast. - The firm cares more about working on interesting problems with large technical solutions rather than just maximizing IRR. The team is there because they believe in the mission. - Khosla assumes they've lost the money the day they invest, and then maximizes for the upside opportunity. He calls it "option value investing" rather than IRR investing. - Khosla has contrarian bets in AI (neuro-symbolic computing, probabilistic approaches), biotech, robotics, crypto, and more. - He believes aviation fuels, fusion energy, new transit systems, and other contrarian areas ignored by most investors will be huge opportunities. - Most large innovations come from outsiders, not industry insiders. Khosla looks for founders who can learn a business rapidly rather than have deep domain expertise. The Future Impact of AI - AI will be deflationary and increase productivity growth to 4% annually vs the typical 2% forecast. This will cause great abundance but also increasing income inequality. - Bipedal robots will take over most manufacturing and manual labor jobs within 20-25 years. This will free humans to be more creative and pursue their passions. - Education will shift from job training to creativity. Uniquely human elements like taste and curation will be most valued in an AI-enabled world. https://lnkd.in/gqBsmbks

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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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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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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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Here’s a question investors are often asked: When evaluating early stage companies, how much time do you spend on due diligence around future exits? It’s not surprising we hear this question a lot. Also not surprising: it’s got a wide range of answers depending on the firm. Some don’t spend much time here at all. Others make it a point to put meaningful time in as part of their process. Our current thinking: take the time to do the work on public market comps. At Alpaca VC, we spend significant time understanding how public market investors will  realistically value a business based on margin profile, product, business model & TAM. In short, we want to know: how will this company be valued at scale when we get taken out? Yes, we can acknowledge that the journey toward exit is a windy road and that there may be pivots along the way, but there are still public market companies that have a business model similar to the early stage company you're evaluating. And you can always look at gross profit multiples if you think the margin profile will change over time. So we still do the work on the comps. Quantitative metrics we look at when making the comparison to public market comps include EBITDA multiple, revenue multiple, Gross Profit multiple or all of the above. As part of this process, it’s also important to factor in the public market company’s year-over-year revenue growth as this will also significantly impact the multiple it trades at. Simple example: if you have two public market companies with similar business models and similar margin profiles, but one's growing 100% year over year, and one's growing 50% year over year, then obviously the DCF (discounted cash flow) analysis is going to spit out a very different valuation for the one that's growing faster. Why this matters: When you take all of that information into account as you evaluate an early stage business, you can begin to create a realistic picture of how this company will be valued in the public markets at exit - or how an acquirer will value the company for an acquisition. Strategic acquirers may, of course, pay a premium, but we won’t underwrite for that. This allows us, for example, to form conviction around valuation based on revenue and gross profit predictions. If we think they can do $100M of revenue five years from now, we use this diligence process to form a thesis about whether the characteristics above (product, margin, business model, etc.) will cause the company to be valued at $200M vs. $500M vs. $1B at exit. Curious how other early stage investors think about underwriting an exit and how much time they’re spending on public market comps even though these companies are in their infancy.

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Open-source models can be a strong lever for increasing gross margins at AI companies, especially those that have hefty cost structures (e.g., enterprise sales orgs that require significant customer success or implementation resources, as well forward-deployed AI services models common in healthcare and life sciences). One of many reasons to maintain a modular and flexible stack with minimal dependencies when building an AI company. More on the importance of flexibility in the AI stack: https://lnkd.in/eA2XATRk h/t Delip Rao #ai #artificialintelligence #generativeai #healthcare

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-Founders should avoid overuse of revenue multiple: what it really means in startups’ valuation- Traditional valuation methods rely on a company's financial statements. If founders use revenue multiples in dealing with potential investors, they should understand the basics of valuation. Otherwise, it might appear as a lack of common sense. Here's a breakdown of the basic approaches and why revenue multiple was invented. https://lnkd.in/gbYqTpAC #Hypergrowth #Valuation #HITSERIESCAPITAL #TANAAKK

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This Sequoia Capital article is easily the best article I have read on product-market fit. (link below) Its very relevant to (disruptive) deep tech startups, a category I often feel is ignored in these articles. 3️⃣ There's three methods to get to PMF, and the largely depend on the nature of the product and the perception of the market: 🔥Hair on Fire: Customers are acutely aware of the problem and actively looking for solutions. Success hinges on delivering the best-in-class solution through differentiation. [Smartphone market, after the iPhone] 🪨Hard Fact: Customers acknowledge the problem as an inevitable fact of life and haven't actively sought solutions. The challenge lies in convincing them that there's a better way and overcoming inertia. [Uber/ AirBnB] 👀Future Vision: Customers are either unaware of the problem or believe a solution is unrealistic. The entrepreneur needs to create a new paradigm and win over customers to this vision. [Apple iPhone/ OpenAI] Each of these have different approaches, as the nature of problem to be solved is different. 🔥Hair on Fire: Act fast and prioritize a great product with a strong go-to-market strategy, usually with a unique insight, because if its a hair-on-fire problem, there are likely existing competitors. [Rippling, the HR software did it unified HR Database for businesses] 🪨Hard Fact: Educate the market about the problem and how your solution offers a better approach. [Square had to do this to overcome the cash-only mentality] 👀Future Vision: Be prepared for a long journey, focus on attracting top talent, and find strategic pit stops for commercial traction along the way. [Nvidia did this with gaming chipsets] So which type of problem is your startup solving?

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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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I had a lot of fun recording a podcast episode with Shiva Singh Sangwan. We discussed the evolution of Humba Ventures & Susa Ventures; tips for raising and building a fund; how to improve investment sourcing & picking; the role of luck in investing, and so on. https://lnkd.in/eiqsStej

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Great panel at CAASA Start-Up Round-Up 2024 with Prathna Ramesh, Michael Branco and Ricky Mehra on the Gold Rush in #AI! Here's a summary of their talk: 🎲 Role of VCs in AI: Mike and Rick agreed that VCs are critical in sifting through the noise to find valuable investments. This involves rigorous due diligence, understanding the customer problems, and focusing on practical use cases of AI. VCs then derisk the ventures, for example, by introducing tier 1 customers, supporting the back office that the founders might not have cared about, and figuring out the right team to scale. Mike pointed out that founders might overlook business operations, particularly those from technical backgrounds. ⏳ Time Horizon and Growth Rate:  The panellists discussed the speeding up of investment horizons in recent years, even for traditional long-tail industries like telecommunications. They also emphasized the need for investors to stay ahead of emerging trends, focusing on what's coming in the next 12 to 18 months and how the future will roll out in 3 to 5 years. 📊 Data & Distribution: Prathna emphasized that data and distribution advantages are key to getting to market better, faster, and cheaper than competitors. It’s about being really good at execution and leveraging these strengths effectively. Rick discussed the challenge of proprietary data in healthcare, highlighting the potential of having a single dataset of longitudinal patient data to revolutionize the industry. The challenge is the outdated HIPAA and GDPR regulations, which must be updated to keep up with digital health advancements. 🏗️ Build vs. Use Existing Models: Mike discussed the efficiency of using existing models, such as Meta's published model with base datasets, to solve previously too challenging problems. He gave an example of using AI for weather forecasting for big events and how the amount of data needed to process was too big to handle before AI. ⚠️ Risks and Concerns:  Rick raised concerns about the potential existential risks posed by AI and the need to carefully consider its impact on humanity, especially in his space in healthcare. For example, misdiagnosing due to AI's black-box nature can be more problematic and cause more deaths than human error. It is crucial to evaluate if the AI system you're building is the right solution for the problem and at what risk. AI is the first time something more intelligent than humans might be created, so linear projections from past technologies like phones or the internet are inadequate. Rick stressed the importance of slowing down and spending time thinking about how AI will impact humanity. Very insightful panel! Thanks CAASA - Canadian Association of Alternative Strategies & Assets for the event! #investing #startup #tech

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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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LinkedIn team (Juan Pablo Bottaro Karthik Ramgopal) shared a helpful deep dive into their deployment of an LLM-powered job search assistant. Highly recommend this read! Few highlights:  💡 Relatively easy to setup RAG-based pipeline. Achieved 80% of the basic experience in 1 month, then 4 more months to reach 95% mark. 💡 Evaluating the quality of application outputs is hard. I hear this from many enterprises developing LLM applications. (check out Okareo by Matthew Wyman Boris Selitser) 💡 Balancing latency with quality is important. While certain techniques like Chain of Thought could improve accuracy and reduce hallucinations, it will increase response times, which can affect user experience. https://lnkd.in/gtGGwf5P #largelanguagemodels #RAG #EBR #LLM #ml #ai #hallucinations #evaluations #

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In an era where innovation outpaces regulation, how can entrepreneurs embed social responsibility into the DNA of their startups? 🌐💡 Recent conversations and insightful pieces, particularly one by Lyel Resner and Dr. Wilneida Negrón, have sparked a profound consideration in my thinking: the imperative for responsible innovation in tech ventures. They posit a tantalizing question - what if the tech giants of tomorrow were built on the foundations of public interest and social responsibility today? Diving into this thought experiment reveals more than just an ethical proposition; it opens up a paradigm where businesses can be both profitable and purposeful. Embedding social responsibility from the outset could guide ventures to prioritize long-term societal well-being over short-term gains. Imagine a future where companies are not just evaluated by their financial performance but also by their contribution to solving societal issues. The notion is ambitious but certainly not unattainable. This approach challenges us as entrepreneurs, investors, and innovators to rethink our roles in shaping the future of technology. It's a call to action to pioneer a new wave of responsible businesses that are equipped to address, rather than exacerbate, global challenges. Let’s discuss: How can we as part of the startup ecosystem integrate these principles into our work from day zero? Your thoughts could light up paths not yet traveled. 🚀🌍

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There's a new breed of CEO in town. The AI-Native CEO. These people are either rebuilding archaic companies or building in "adjacent possibilities," they're creating truly durable businesses with real customer value, and importantly, they're building their companies in an AI-native way; more efficient than ever, stitching together technologies that once required high-overhead, and focusing their hiring on people who are autodidacts, the ones who are inhaling knowledge on how to improve their own functions leveraging the newest tooling for the future of their jobs. A few of my favorite examples... Valley, backed by General Catalyst, getting 30-50% response rates for salespeople using conversational selling instead of archaic and annoying drip campaigns. AminoChain, backed by a16z, turning biobanks into to shoppable specimen centers so that healthcare institutions and researchers can purchase biomaterial like blood an stem cells and so donors can earn and track their participation in science. HIFI, backed by Blockchain Founders Fund, using stablecoin for B2B payments, creating instant settlement times and eliminating fee extortion. Dune Security, backed by Craft, using AI to test, identify, and train employees on their own risky actions, automatically reducing the biggest risk any business has--the behavior of its own employees. https://lnkd.in/eJUnuM4z #venturecapital #ai #artificialintelligence Antler

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DePIN & RWAs dominance isn't as far off as most people think. Across every innovative cycle, three fundamental pillars emerge: foundational technology development, proliferation of infrastructure, and a surge in application development. As we venture into the third cycle, DePIN and RWA protocols are poised to become market leaders paralleling the dominance witnessed by infrastructure projects from 2015 to 2024.

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🧠 So many pearls of wisdom in this Vinod Khosla interview, for both entrepreneurs and VCs. I've pulled out some of my favourites for you below. 1️⃣ On making contrarian bets "Here's a philosophy, whether you're a venture firm or an entrepreneur. You have to bet on something that you believe in - that the market doesn't believe in - and then stick with it through the ups and downs. You can't predict the timing of most innovations." 2️⃣ On new innovations "I can't think of a big new innovation that came from a person who was in the area they disrupted. Most board members advise founders to 'get somebody who understands the domain' - that's really bad advice generally. You want someone who can think from first principles, and can learn the business rapidly in 6 months. If Elon Musk had been in the automotive industry, I guarantee you we wouldn't have Tesla. When people who came from healthcare tried to do digital health, they generally failed at it." 3️⃣ On risk and failure "There's no one in the venture business who has failed more often than I have failed. I've just tried many more things and screwed up more often than most people. Then a tiny percentage have become the big visible successes. People need to understand that venture is really asymmetric, so you don't want to take low risk. You want to take very high risk - it's not IRR investing. We are buying an option in a great team, and then helping them succeed." 4️⃣ On taking advice as founders "Ask yourself: how many billion dollar companies has the person built? From scratch - not joined Google or Facebook and were given an internal business. When you worry about your pay check, or you struggle to hire someone because you can't offer them a large enough pay check... that's pain. That's when you learn empathy for entrepreneurs." 5️⃣ On thinking long term "Ukraine, Covid and AI are massive turbo-chargers for venture. This is not how the world looks at it. They're looking at short term interest rates, or what's happening with GDP growth. I'm looking at it in terms of what companies will be built over the next 20 years given the changes in the world. I'm not investing in crypto one year, then AI the next. It's a long term approach to venture." 6️⃣ On future predictions "Today's 30-year-olds, take note. In twenty years, jobs won't be the key criteria for education. In twenty years, the world's 50-year-olds will have a hard time adjusting to the fact that they generally won't have to work. As robots and AI do much of the labour-intensive work, they'll be able to lean into their passions and be more creative. Human selection, human preference and taste will become highly valuable. Human's will always prefer human uniqueness."

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⚡ Kicking off 🌎 Earth Day with exciting news as we unveil 8090 Industries' latest investment in Exowatt led by Hannan P. along with Sam Altman Andreessen Horowitz, Atomic, Overmatch Ventures, Protagonist, and many more. As the boom in AI heightens the energy demand for data centers, Exowatt is pioneering a fundamental breakthrough in baseload renewable energy that helps makes $0.01 per kilowatt-hour, 24 hours a day a reality for commercial and industrial use cases. 🏗 How does it work? The Exowatt P3, represents a breakthrough 3-in-1 modular system, a heat collector, a heat battery, and a heat engine capable of providing dispatchable power and heat 24 hours a day. Unlike traditional solar panels that convert sunlight into electricity directly, Exowatt uses a unique approach by storing solar energy in a thermal battery, which can retain this energy for up to 24 hours per day. The Exowatt P3 modules are designed to fit the space of a standard 40-foot shipping container. They can be deployed on small and large commercial and industrial projects, linearly scaling with workload size and infrastructure requirements. Best captured by Founder and CEO Hannan P. himself, an industrialist you're going to want to pay close attention to: “You don’t have to go back to fossil fuels to solve the data-center energy problem…That’s counterproductive and unlike traditional renewable solutions that require significant upfront costs and extended setup times, Exowatt's modular system can be deployed rapidly and cost-effectively – and it's available THIS YEAR. Exowatt is built to respond quickly to the escalating energy demands of the modern world, especially those spurred by the rapid growth of AI." 👀 Who Cares? Exowatt has a backlog of demand for OVER 500 megawatts for data centers across the U.S. and plans to begin deployments later this year. With this $20 million seed funding, Exowatt intends to expand its team and deploy the Exowatt P3 with its first set of data center customers. Stay tuned! Lots to come! cc Kerem Ozmen Wes Mendenhall Filip Vurdelja Haluk Sabanci Dincer Grant Brown Ishan Meswani Garuth A. Andrew Irwin Charles Oppenheimer Fatimah Bello #energyabundance #decarbonization #climatetech

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