“I have worked with Tim for many years and have watched him progress from technologist to company manager to venture capitalist to company founder of a successful Internet company. Tim has great vision, the ability to work with people at all levels, understands what it takes to win in the highly competitive Internet space, and always endeavors to do the right thing. I am confident that Tim can conquer whatever challenges may come his way. He must have been a "rocker" in a previous life.”
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Cincinnati, Ohio, United States
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Refinery Ventures
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Jeffrey Paine
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
665 Comments -
Jeremy Utley
What do you do when a radical new technology puts your main product right in the crosshairs of disruption? Listen to David Okuniev — co-founder of Typeform | Ask awesomely — discuss the challenges of innovation within existing structures. David shared a game-changing insight: Radical innovation is really, really difficult to do inside your own product. He emphasized the need to break free from the constraints of familiarity and embrace change from outside the box. Henrik Werdelin and I have both seen our fair share of this in our respective careers. What struck us most was how David leveraged structure to overcome the innovator’s dilemma. By creating a culture of experimentation and providing space for bold ideas, he propelled Typeform beyond incremental improvements. What other hacks have you seen or employed to help your organization overcome the innovator’s dilemma? Share your stories below! 👇 And if you want to dive deeper into our conversation, click the link in the comments to catch the full podcast episode!
42 Comments -
Mika Romanoff
Let's finally get rid off the "VC alphabet song" and redo the system to become more founder friendly. "The benchmarks set by funding stages are often arbitrary and not necessarily aligned with the actual development needs of individual startups." #vc #venturecapital #founder #fundraising #startups #fundingstages #funding #fundingrounds #pitchbook
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Amar Dhaliwal
I wrote this guide to AI for Chief Learning Officer Magazine (chieflearningofficer.com) in February of 2017. I concluded by saying ".... the pace of innovation and development is faster, and the implications more far-reaching, than most learning leaders realize." I think that's still true today. https://lnkd.in/euJxVQ2Y #clo #AI #ChiefLearningOfficer #l&D #enterpriselearning #learningmanagement
312 Comments -
Nakul Mandan
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.
9124 Comments -
Daniel Fetner
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.
393 Comments -
Caitlin Panasci
As we dive into 2024, the Series A fundraising landscape is becoming increasingly daunting. Investment standards are soaring, and fewer startups are making the cut. Series A investors are finding many seed stage companies too premature. The median valuation jump from seed to Series A skyrocketed from $19.5 million in Q1 2022 to $28.7 million in Q1 2024. Investors now demand stronger revenue performance, targeting $2 million to $3 million in ARR, up from $1 million to $2 million. Alarmingly, only 12% of Q1 2022 seed startups secured Series A funding within two years, down from 31.8% in Q1 2020. #venture #earlystage #seedtoseriesa #seed #seriesa #inspireglobalventures https://lnkd.in/gW4bkcNy
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Suresh Madhuvarsu
A great piece by NFX's partner Omri Amirav-Drory on why VC communication becomes suboptimal in time. There are great value-add VCs and others who are "late for meetings, always looking at their phone instead of listening, ghosting founders, stressing founders out for no reason, giving unthoughtful, obvious advice." I have always enjoyed GPs/Partners who's "been there and done that", they bring in empathy, deeper strategic insights and operational tactics. Of course, not all VCs need to "add-value" and it's about expectation setting. At the very least, we don't want VCs that hinder or slow the growth but enable the growth. Great insights into a day of VC on why they do what they do.
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Lucas Dickey
I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment
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Joanne Chen
The best ideas are often timeless. Insights from AI pioneers, like Allen Newell and Herbert Simon, have made a lasting impact and remain relevant even today. Joon Sung Park spoke about how important it is to balance the novel capabilities of cutting-edge technology with the time-tested principles of effective product development. Here's our full conversation: https://lnkd.in/gs4vAD2p
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Jim Forster
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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Jake Saper
We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.
1046 Comments -
M. Darius Gant, CPA
New Episode: Pressure can can burst pipes or it can create diamond. Justis Mendez (Managing Partner, OneSixOne Ventures) shares his story, moving from the streets of Cleveland to a high impact VC investor. (Link below) We discuss topics such as: - Building a tech ecosystem: The genesis of OneSixOne Ventures - Launching a virtual accelerator during the pandemic - Launching a fund: Realizing the need for monetization - Focus on enterprise software and AI - Investing in pre-seed and seed stages with small checks - Current state of the VC space and capital deployment - Advice for founders: Authenticity and technical expertise - Considerations when outsourcing technical development for AI Spotify: https://lnkd.in/eXwCFfgE Apple: https://lnkd.in/ejRF5KBi
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Prasanna Krishnamoorthy
A common and obvious assumption about generative models (incl LLMs) is that they can only be as good as the data they're trained on. They can't "do better" than that. Can they? This interesting new paper "Transcendence: Generative Models Can Outperform The Experts That Train Them" shows that these models can sometime perform better than any of the experts who trained them! While this was done in the context of chess, there is no reason to believe that it might not be the case in other domains as well. Personally, I especially find this useful when I munge two frameworks from different areas to apply to a problem I have. Doing that myself would be challenging, but having the LLM transcend my abilities to do it gives me a great starting point! I like the term "Transcendence" as well - good marketing :)
219 Comments -
Eli Blee-Goldman
Character Labs is back! And we want to work with you if you are an early-stage founder! We are living in the most interesting time ever for building software. But it's also wild out there: 1️⃣ It's easier to build software than ever, so how do you build a durable competitive advantage? 2️⃣ The landscape is changing at every moment. How do you adjust to changes (or should you?) 3️⃣ How do you find product-market fit?? ➡ At Character, we think the best way to address these questions, and many more, is through Sprints! Jake Knapp, John Zeratsky, and I want to work with you at Character during the earliest part of your journey. Character Labs is 100% focused on product-market fit. Across 4 weeks we work with 5 companies to help them gain clarity on product market fit through Sprints. Apply by July 14, the program starts on August 14th
125 Comments -
Akihide Narita
Some midweek wisdom from Y Combinator founder, Paul Graham, on why startups shouldn't stress over competitors. According to Graham, based on Y Combinator's extensive data set, few of their startups are actually killed by competitors. He compares the protection a startup has against competitors to the vastness of space, emphasising that the market is large enough for multiple players to thrive. Graham's analogy likens encountering a competitor to suddenly finding another runner in your race lane - the best course of action? “It’s sort of like if you’re running the hundred meters and suddenly a lane appears with another runner in it. What should you do? Run as fast as you can, just like you presumably were.” #Startups #Competition #Entrepreneurship #wearehiring
51 Comment -
Kennet Partners
🚀 Beyond the hype: Generative AI Insights from Eric Barroca 🚀 Kennet’s investment team had an interesting conversation with Eric Barroca, former CEO of Kennet portfolio company Nuxeo and now at the helm of Composable Prompts, a trailblazing Generative AI start-up. Here are some of the most compelling insights: 🔍 Understanding AI’s True Value: ▪ If you switch off the model in your product, what proportion of the value you provide to a customer is removed? This question is essential to determine if AI is integral to your offering or merely a gimmick. ⚙️ Current State of AI Deployment: ▪ Many AI teams are still in the experimental phase, playing with scripts and deploying small-scale use cases. ▪ This approach won’t suffice for enterprise-level deployment, highlighting a significant gap between the promise of AI and its current implementation. ❌ Challenges and Successes: ▪ Long-form outputs like generating entire contracts are not feasible. ▪ Complex tasks such as travel planning are too intricate for LLMs alone. ▪ AI excels in guided summaries, contextual information generation, and document review when provided with clear guidelines. ☁️ The Cloud Analogy: ▪ AI needs robust applications built around LLMs, similar to how cloud computing required surrounding software to realise its full potential. ▪ Deploying AI without this infrastructure is like using the cloud without proper cloud services. 📚 The Reality of Source Citations: ▪ An LLM is a massive matrix of probabilities, making it impossible to directly trace the origin of specific information. ▪ Current source citations use Retrieval-Augmented Generation (RAG), where the model queries data and adds sources to prompts. This retrieval happens outside the model. After the LLM generates content, we link it to relevant sources. 🏢 Business Applications vs. Generic Software: ▪ Success in AI-driven business applications requires dynamic prompts tailored to specific needs. ▪ Generic prompts offered by software vendors often lead to subpar results, emphasising the need for customised solutions. 💻 AI in Coding: ▪ AI shows promise in high-context, easily trainable environments but lacks human-like memory. ▪ An LLM can't recall previous bugs to make connections like a human developer can, making it a useful tool but not a standalone solution. 🚀 Driving the AI Industry Forward: ▪ The industry is in a nascent stage with a chicken-and-egg dilemma between deployment and development. ▪ AI teams within companies are still small, but the pressure to deploy developed tools is growing, hinting at an imminent leap forward. Eric's insights underscore the transformative potential of AI, akin to the early days of cloud computing. It’s not just about the technology itself but the ecosystem built around it that will drive real innovation! 🌐💡 #AI #GenerativeAI #BusinessInnovation #TechLeadership #FutureOfWork
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Simon Lancaster 🇺🇸🇨🇦🇵🇹
Join me, Winter Mead and 500+ GPs, LPs, Family Offices, and all VC/PE stakeholders at the Fund of Funds and Family Office Online Conference on June 20th – Online on Zoom. Register for free: https://lnkd.in/gQ7e3q9X All attendees will be invited to contribute their fundraising and capital allocation hacks and insights. What will be your small piece of advice for GPs or LPs? Comment, and the host, Max Pog will include your answer in the database. Some participants will be gifted books. My hack will be… to always be transparent with what you need help with and genuinely try to help other GPs and LPs. VC is a multi-player game after all!
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