Yuying Chen-Wynn

🌟Innovative VC Firm Overcomes Exits Drought with Secondary Sales🌟 ⛵Navigating a challenging landscape where exits are scarce, Santa Barbara Venture Partners (SBVP) has pioneered a novel approach to sustain its growth and attract investors for its second fund: secondary sales. Instead of waiting for traditional exits like IPOs or acquisitions, SBVP opted to sell shares of its portfolio companies, demonstrating its ability to generate returns for investors and stand out in a competitive market. 🎤According to Dan Engel, founder and managing partner of SBVP, these secondary transactions have been a game-changer, sparking investor interest and bolstering the firm's credibility. By leveraging its recent successes, including a lucrative stake in sports-betting company DraftKings Inc.' acquisition of digital lottery app Jackpocket, SBVP seized the opportunity to return profits to its limited partners (LPs) and pave the way for its second fund. 💡Engel highlighted the challenges faced by young VC firms in raising subsequent funds, particularly amid a downturn in exit activity and heightened investor scrutiny. With traditional exit routes becoming increasingly elusive, the pressure is on for firms to demonstrate tangible returns and establish a track record of success. ✨"For us, secondary sales have been a game-changer. They've helped us return profits to our LPs and attract investors for our second fund," said Dan Engel. 💰For SBVP, the decision to pursue secondary sales was driven by the need to provide liquidity to LPs and validate its investment thesis in the eyes of prospective investors. By strategically offloading portions of its holdings in high-performing portfolio companies like Bark Technologies and Rad AI, SBVP not only generated substantial returns but also bolstered investor confidence in its ability to deliver results. ⚠Despite the complexities and potential stigma associated with early share sales, Engel emphasized the importance of prioritizing investor returns and seizing opportunities to unlock value for stakeholders. With a focus on profitability and transparency, SBVP remains committed to its mission of delivering sustainable growth and maximizing returns for its LPs. 🔍 "Returning profits to our investors is our top priority. By strategically selling shares, we're proving our commitment to delivering results and driving value for our stakeholders," added Engel. As SBVP continues to explore secondary transactions and expand its investor base, the firm stands as a testament to innovation and resilience in the face of market challenges. 🚀 ✅ Looking to raise capital for your #fund and increase the international pool of your LP #investors?   🤝 Need warm #LP introductions?   📝 Selling #secondaries to increase liquidity?   🧐 Looking for co-investments?   ▶ G+QUANT's link for inquiries and fund decks: https://lnkd.in/gjC_EuTE #VCInnovation #SecondarySalesSuccess #InvestorReturns #ValueCreation 

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One of the things we see quite often at Grit Capital Partners, is a newly minted CEO. We see a big difference in the transition to CEO based upon the historical lane the CEO is coming from. There are different new muscles if you are coming from Product, Engineering, Finance, etc. In this article, we go deep on the CRO to CEO transition, which includes some thoughts from Rosie O'Meara, a particularly fine example of someone who has made the transition quite well. What were your biggest transition moments? https://lnkd.in/eEB-K2ZU

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We are just at the beginning of the AI led digital transformation! Lots more questions than we answers on adoption, evolution in Enterprise AI but that’s exactly what creates white spaces, opportunities for innovation. Let’s play!

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Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

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University of California, San Francisco Chancellor, Sam Hawgood, in an interview with Matthew Lungren MD MPH at #ConvergeSummit has outlined the use of #AI scribes to create clinical notes as well as other #AI-driven technologies. Key to their success: a group of practicing physicians with knowledge of AI ensuring that the technology fits into the physicians’ existing workflow, which improves physician adoption of the technology. Another key: measure success through reduction in physician burnout and improvement in patient satisfaction, not increases in physician productivity. Final key: have a real-time, frequent review of implemented technologies to ensure that they still support and are relevant to changing clinical care standards - and shut the technologies down if they are not supporting their purpose.

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From where I sit in the SF Bay Area, cradle of innovation for at least the last 30 years, I'm a true believer in the promise of the #AI revolution. That said, I find it fascinating to consider views on the opposing side. I'm sharing below two opinion pieces from the last two weeks suggesting that the AI revolution is fundamentally overhyped and a bubble ripe for bursting, and that AI tools are actually not useful or transformative. Contrast this with venture capitalist Kai-Fu Lee's prognostication that 50% of white collar jobs will be displaced by #AI within three years, and that OpenAI and the other cutting edge movers in the space are headed to a trillion+ dollar valuation. Which side will win out? Where will we be by 2030 in the AI race? I'd love to hear your thoughts in the comments.

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Thank you Fortune for featuring our predictions for AI funding and adoption following $25B poured into the landscape last year (https://lnkd.in/gEKpBP7p) While we expect funding funneled into core infra companies building foundation models will decline, we believe there will be growing adoption and investment in a few buckets: * AI-native apps. We’re already observing increased enterprise adoption of SaaS AI solutions across verticals such as knowledge management, marketing, sales, legal, and customer service. * Picks and Shovels plays. Companies that help enterprises go from pilot to production in a scalable way, including prompt management, model security and routing, output evaluations, interference and cost optimization, and extract, load, and transform (ETL) processes on unstructured data. * Autonomous agents. Software agents that can simulate human behavior and plan, make decisions, and execute tasks in complex environments without human intervention or supervision.  * Regional players. Companies applying GenAI to local use cases across languages. Vibhor Rastogi Arvind Purushotham, Matt Carbonara ✅, Arti Kuthiala Jeff Gilliland, Dana Wohlfarth-Piper, MSPR

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AI - How LLMs & Transformers work Great explanation on how AI LLMs work - https://buff.ly/3ZvSIsP Some of the basic takeaways - at a basic level, Large Language Models (LLMs) are advanced AI systems designed to understand and generate human-like text. They rely on transformer architectures, which use "attention" mechanisms to process and understand the context of inputs. At the heart of LLMs is matrix math, which powers their ability to scale and learn effectively: - Understanding words: words are converted into numbers (embeddings) that capture their meaning, making them usable for the model - Focusing on context: the model uses a process called "attention" (which was the revolutionary concept several years ago that accelerated AI LLMs / ChatGPT as we know it today) to figure out which words in a sentence are most important by comparing them through sets of numbers (called key, query, and value matrices) - Making results clear: a method called softmax adjusts these importance scores so they’re easy to interpret - Parallel processing: matrix multiplication enables efficient handling of large datasets, allowing computations across sequences to happen simultaneously (of course, this is all possible when using powerful processors like Nvidia H100) - Positional encodings: Transformers add extra information to numbers as matrices to encode word order, crucial for understanding sentence structure and to know the sequence of words, like which comes first in a sentence. --------- #ai #artificialintelligence

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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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https://lnkd.in/e_D62QfR AI is making the entry level CS jobs scenario bleek. Though the opinion expressed in this article is debatable, the issue needs to be discussed. Share your thoughts.

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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

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Sharing a sanitized version of the mu ventures Q2 q'ly LP update. Included musings: 👉 SEOs shrinking impact, and the rise of LLMO (large language model optimization) 👉 examples of the Commerce AI application layer coming to life 👉 a modern fund strategy that accepts companies require less capital, time, and people to trigger the power law 👉 Mu's guide to investing in AI 👉 all 30 companies in the Mu-niverse (and several others we admire) Founders, VCs, LPs - if you like how we think and suspect we can partner, hit us up! https://lnkd.in/dTJ2zjav

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Connie Kwan 🎤 Story-led Leadership, Chief Product Officer at Nobody Studios, is joining Sejal Thakkar and I on tomorrow's Making of a Nobody podcast. Join us live at 10:00 am PST as we share how to take an idea to an early stage-product. Link is in the comments, see you there! #product #startup #tech #futureisnow #venturestudio

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

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Interesting read on "empathy engineering" and customer simulation for LLMs #llm #simulation #customersupport #ai https://lnkd.in/gEFjmubk

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Here is a much-discussed post from Sequoia Capital that may be missing a point: by investing in Gen AI capex, big tech vendors are playing defense as much as offense. What happens in the world where Google does not invest in Gen AI data center capacity? A portion of their search revenue would likely go to Perplexity, Bing, OpenAI, etc. Apparently, big tech vendors assessed their losses from gen AI inaction to be greater than the capex-revenue gap calculated by Sequoia.

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Generative AI is outpacing the internet and PCs in adoption speed. Nearly 40% of U.S. adults have embraced this technology in just two years, compared to 20% for the internet and PCs in their early days. This rapid uptake signals a seismic shift in how we work and interact with technology. Are you ready for the AI revolution? #GenerativeAI #TechAdoption #FutureOfWork #AIRevolution #DigitalTransformation

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