Berber J.

Impactful Biz, Ops & Investment Roles at Early Startups & VCs: • Chief of Staff to Ex. Head of Product at Google • Founding GTM at the most YC product used by other YC companies in the past four years • Chief of Staff to a Partner at a Top-Tier VC Fund • Head of Platform of a NY State Backed Accelerator • Investment Associate at a Seed Stage Fund Investing in Category-Defining Companies • Partner at a Defense-Focused VC Fund • Chief Revenue Officer of a New Space Company in El Segundo • Venture Capital Analyst at a fund investing in overlooked geographies • Investment Associate at a Spirit-Focused Fund • Strategy & Ops Lead at a Company Focused on Helping Improve Autism • Senior Product Manager at a Company Focused on Building Shopify for Content Creators • Program Manager for a Space Security Company • Open-ended Investment Position at a Top-Tier Accelerator • Biz & Ops Lead for One of the Most Loved Digital Consumer Products 💸 All companies are well-funded and backed by top-tier investors. ⬇ You can find links to the roles above, plus many more, in the comments.

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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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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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How do investors price pre-seed rounds?  This is perhaps one of the most misunderstood aspects of fundraising.  Oftentimes, founders want some framework to better understand what market value is when raising capital into their business.  Public equities and later stage privates have a well defined matrix of revenue/margin profiles that dictate valuation, surely there must be some framework for arriving at a pre-seed valuation. So how do investors price startups that are pre-revenue, or have maybe one of two early customers? In reality, pricing is more a byproduct of a VC fund’s portfolio construction model than any metric of the underlying business.  VCs model out portfolios to include a certain number of companies with an ownership target in each startup.  There are variations to portfolio construction, and the chosen strategy will have a direct impact on the types of rounds a fund will participate in. For example, an early stage fund might raise a $60M fund to lead investments in 30 companies and target 12.5% ownership.   After management fees, there is ~$48M in investable capital.  Assuming a fund holds back ~30% for reserves(this number varies across funds) that leaves ~$30M to invest in initial checks.   In this case a fund must average $1M checks at $8M valuations to reach their intended ownership target of 12.5%.   When you are in diligence with a fund, they are first and foremost trying to get to a yes on the investment opportunity.  When arriving at a valuation though, the math outlined above is the determining factor, more so than any multiple of revenue on the business, or the stated desires of where a founder wants to raise capital.  At the end of the day, a VC is trying to arrive at a valuation that works within their portfolio strategy. This means they are very unlikely to price a deal at $15M cap given the aforementioned portfolio construction, as they would either not own enough($1M = 6.67% ownership) or have to cut too large a check( $1.875M = 12.5%). The thing that is most likely to move an investor off their target valuation is another investor in the picture that is driving the price higher, but even then there are upward bound limits of how high an investor is willing to price a deal. I always advise our port cos to ask an investor early in conversations what their ideal check size/valuation is to make sure there is alignment with the founder's objectives.    

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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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The Insane Energy Costs of AI: A Growing Crisis Alex Wilhelm and Exowatt's Hannan P. discuss the staggering energy demands driven by AI adoption—and the lengths companies are willing to go to stay competitive. • Sky-High Premiums: Companies are paying up to 500% more for reliable, behind-the-meter power to keep their data centers running—highlighting the relentless drive to dominate AI. AI Search is a Power Hog: AI-driven searches consume 10-25x more energy than traditional searches, creating unprecedented strain on energy resources. So next time you use OpenAI for a simple query, remember the cost—in energy and environmental impact. Underestimated Growth: Energy forecasts may be way off, with annual growth likely closer to 5-6%, pushing energy infrastructure to its limits. The AI arms race is rewriting the rules of energy consumption.

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"So getting back to NVIDIA, I don’t see any force stopping the juggernaut in the next few years, other than a huge decline in the need for AI compute, and that seems unlikely. Any startup competitor could easily be acquired by NVIDIA for tens of billions of dollars and it would barely be material given NVIDIAs market cap. And while the other major chip companies are mounting a challenge, particularly AMD, the demand for GPUs is so great, and will be for the next few years, there is room for massive growth for multiple players. When NVIDIA eventually slows down, I suspect it will come from one of two areas. The first is a surprising shift in market use cases. Given the long development cycles of computer chips, it’s hard to be as responsive to the market demands as software companies can be. And product designers have to choose some level of predictability and reliability in their components and their partners, which is why they favor proven tech over innovative new designs most of the time. But the way NVIDIA benefited because their chips for graphics cards happened to be a good match for new AI workloads - I think something similar could happen to slow them down. Some new popular AI workload will be a better fit for something other than a GPU, but a chip that is already established in the market in other ways. Or secondly, it comes from a company that is building their own chips and decides to get into the broader semiconductor business. This means Apple, Amazon, Facebook, or most likely - Google. But all those companies, even if they can compete technically with NVIDIA, lack the infrastructure to sell and support chips and those who develop them. Building those capabilities will be slow." https://lnkd.in/dFpj5hqv

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After a multi-year period when it seems that venture capital funds would only get larger, CRV is moving in the opposite direction. Instead of raising a $500 million growth fund and then a $1 billion vehicle to follow it, the firm is returning just over half of its 2022-era growth fund. Why release $275 million worth of LP commits? "Market conditions have changed for the worse. And valuations for startups are too high relative to their potential payoff." Will other firms follow CRV? If exits do not recover in the next few quarters, the answer may be yes. The nuances of capital calls can play a larger role in eventual IRR than you might have thought.👇

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✨ EXCLUSIVE ✨ OpenAI is in talks to remove a key clause from its corporate structure—one that currently prevents Microsoft from accessing its most advanced models once AGI (artificial general intelligence) is achieved. In its structure documents, OpenAI describes AGI as a “highly autonomous system that outperforms humans at most economically valuable work" and one which the board will determine when it has been reached. Yet, OpenAI has been increasingly stepping back from defining AGI as a singular moment, with co-founder and CEO Sam Altman resisting any firm timetable. Speaking at a New York Times conference on Wednesday, Altman said: “My guess is we will hit AGI sooner than most people in the world think, and it will matter a lot less.” The clause was included to protect the potentially powerful technology from being misused for commercial purposes, giving ownership of the technology to its non-profit board. According to OpenAI’s website: “AGI is explicitly carved out of all commercial and IP licensing agreements.” If it is removed under the new for-profit model, Microsoft, its largest shareholder will have access to all new technologies from OpenAI. What do you think the implications of such a change are? (Scoop written with George Hammond) 👇 #ArtificialIntelligence #AGI #OpenAI #AInews #Microsoft https://lnkd.in/gEj4AVvX

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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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Foundation models are all the rage in robotics right now. See the deal between Amazon and Covariant for proof. Grateful to have one of the best in this area joining us at RoboBusiness, which runs Oct. 16-17 in Santa Clara. Sergey Levine co-founded Physical Intelligence in early 2024 to help bring general-purpose AI into the physical world. During RoboBusiness, Sergey will talk about how foundation models for end-to-end control of robotic systems can be built, how developers can gather large and diverse datasets for such systems, and how they can enable new and exciting robotic capabilities. #robotics #robots #ai #foundationmodels #rfms #technology #artificialintelligence #robobusiness 🤖

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Scoop w/Riddhi Kanetkar: Holistic, a new AI startup founded by ex-Deepmind scientists, is set to raise $200 million in initial funding, sources told Business Insider. The Paris-based company is bringing funding in across a large equity financing and a convertible note, with a bevy of leading investors taking part. It’s understood that Accel are leading the round, with participation from AI software giant UiPath and BPI France. Following on from the success of Mistral, France is now a hotbed for businesses building foundational models and LLMs in AI. You can read more about the deal’s structure and valuation here: https://lnkd.in/ernn5ze4 #vc #ai #startups #tech #LLMs #funding #capital

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AI is now tuning in to 𝘩𝘰𝘸 executives deliver company updates—not just what they say. I recently spoke with Alex Kim, a pioneer in using AI to quantify vocal cues. Research shows 𝘁𝗵𝗮𝘁 𝘂𝗽 𝘁𝗼 𝟳𝟬% 𝗼𝗳 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗼𝗿𝗮𝗹 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 comes from voice rather than text. Kim’s work on integrating AI and LLMs into vocal analysis is already turning heads on Wall Street—catching the attention of some of the largest hedge funds. A few years ago, tools for real-time transcription and vocal analysis were scarce. Today, advancements like ChatGPT have made these insights much more accessible. “It’s still tougher than analyzing text,” Kim explained. “But the barriers have dropped significantly compared to three years ago—and they’ll keep falling.” Within a few years, 𝘃𝗼𝗰𝗮𝗹 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗰𝗼𝘂𝗹𝗱 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝘀𝘁𝗮𝗽𝗹𝗲 𝗶𝗻 𝗲𝘃𝗲𝗿𝘆 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿’𝘀 𝘁𝗼𝗼𝗹𝗯𝗼𝘅. 👉 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗶𝗻 𝘁𝗵𝗶𝘀 𝘄𝗲𝗲𝗸’𝘀 𝗔𝗜 𝗦𝘁𝗿𝗲𝗲𝘁 and subscribe to stay ahead of how AI is transforming investing. Link in the first comment. #AI #Finance #Earnings #AIinFinance

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NYC is undisputedly the best place to build in healthcare AI 🏥 , sitting at the intersection of exploding tech/AI momentum + matured healthcare/life sciences economy, with all-time-high venture + gov funding + R&D scientists Had a ton of fun hosting an intimate healthcare-centric dinner tonight with researchers, engineers, founders, health system operators, and physicians from Tandem, Tennr, Mount Sinai Health System, Google DeepMind, Counsel Health, EliseAI, Neo, Weill Cornell Graduate School of Medical Sciences, Inductive Bio, NYC Health + Hospitals with John Bryson (Banc of California) Lots of excitement all around, coupled with some frustration with the deeply brokenness of U.S. healthcare - some of the hot takes / promising areas we talked about: - If your healthcare AI startup’s product is on Epic’s roadmap, you have no chance of winning, as health systems would much rather adopt an okay solution from Epic ⏳ - AI scribing tools have not meaningfully evolved since v1 📑 - Healthcare will get much worse before it gets better - the U.S. society is totally unprepared for the silver tsunami / rapidly aging population 👵 (Visible Ventures portco Grayce is tackling this problem!) - Better retrieval of medical records with precision on what part of a patient’s records is relevant to diagnostics will unlock huge time savings for doctors (and correct their human errors) 🔬 - Improving scientific reasoning abilities of frontier models will lead to better reasoning in diagnostic workflows, which is currently under-explored due to difficulty of finding high-quality benchmarks 🧠 - Startup founders can’t build a durable, venture-scale company on the sole basis of prior auth 🛣 - Machine learning models are speeding up DFT calculations significantly, enabling improved analysis and understanding of biomarker systems 👩‍🔬 - We will live in a future where pharmacy benefit manager (PBM) is obsolete 💊

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Google parent Alphabet’s third quarter revenue grew 15% to $88.3 billion, with strong growth in its cloud computing business powered by AI computing demand. Google’s Gemini AI model has been squeezed into pretty much all of its products, resulting in more than 2 billion monthly users of Gemini models. Though not as sexy as the company’s many AI products, Google’s bread and butter search business was still a huge money maker, generating about 55% of Alphabet’s revenue, pulling in $49.4 billion, which grew 12% year-over-year. YouTube’s Q3 ad revenue grew 12% to $8.9 billion. Youtube Shorts are really dominating the product, with 70 billion YouTube shorts being watched every day, according to the company. https://lnkd.in/eFpuCPSQ

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Friends, Q2 was a quarter of progress and learning for us at HawkTower, where we invest in early-stage startups applying breakthrough computing technologies to maximize California's natural resource markets. Our focus is on technology-enabled interventions (e.g., AI-enabled geospatial radars for lithium-ion batteries in the waste stream) for critical, market-level imperatives (e.g., disaster prevention) that can drive rapid commercial adoption (e.g., placed on every recycling truck in the world). Our current investment priorities are: - Agricultural productivity and efficiency - Biomanufacturing - Labor and mobility automation - Waste stream optimization and decarbonization - Disaster prevention and management Here’s a snapshot of what we’ve been up to: Milestones and Investments: - In late April, we officially launched our fund, filing with the SEC. Yay. - By June, we completed our first fund close ahead of schedule, with backing from family offices, foundations, high-net-worth individuals, and organizations aligned with California's future. - Our portfolio now includes three early-stage investments (Farm-ng, Agriful Software, and Nexstera Tech). Strategic Partnerships: - We believe that startups operating closer to their customers and resources, especially in agriculture and biomanufacturing, have a strategic advantage. For instance, the Salinas Valley, a major agricultural hub, is less than an hour from Silicon Valley and serves as a center of thought leadership in the convergence of agriculture and AI (from Western Growers, Taylor Farms, and others). - Similarly, the North San Joaquin Valley is emerging as a biotech and agtech innovation hub, and we are proud to support BEAM Circular, a startup hub for the circular bioeconomy. Key Relationships: - Karen Warner, CEO of BEAM Circular, joined HawkTower as a strategic advisor. - Jackie Cruz, Ed. D. Cruz, Hartnell College’s VP of Advancement, advises on Salinas Valley and workforce development. - Ben Palone from Western Growers, with his technical background in agriculture, became a trusted advisor. - Matthew Hoffman from Driscoll's Global R&D team provides insights on global agriculture, product positioning, and investment strategy. - We collaborated with Monterey Bay DART for workforce development and automation in agtech. - Dominic Milano and the Milano Technical Group from Merced collaborated on key initiatives and advised us on investments. - HawkTower is participating in Santa Cruz Works's accelerator cohort as a mentor and advisor. Thank you Doug Erickson. - We published an overview of entrepreneurship across the UC system with input from across the University of California system. Moving into the fall, we remain deeply committed to supporting founders shaping California's future natural resource markets. Stay tuned and be in touch! : ) Db

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AI investments are nosediving faster than a clumsy bird, folks. Stanford's HAI reports a 20% drop in funding from last year. But don't worry—those big ol' companies are still hoarding cash like it's going out of fashion while the little guys fight for crumbs! The age-old tale of survival of the fittest continues. Meanwhile, Airchat is here to revolutionize social media with its groundbreaking concept - wait for it - people talking to each other! Who'd have thought? It's climbing up the App Store charts, but remember, folks, you need an invite because, apparently, listening to strangers ramble is now exclusive club material. In other news, Noname lost its unicorn status and Humane is pitching a $699 wearable that might just replace your smartphone (but don't hold your breath). Also, Betaworks has launched nine AI agents designed to tackle mundane tasks because who wants to sort emails when robots can do it? All this and more in my TechCrunch Startups Weekly recap: https://lnkd.in/gU6nKFfh

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Scoop with Kenrick Cai: OpenAI is formally resurrecting its robotics team after abandoning efforts to build general purpose robots in 2020. Its new team has been around for just a couple of months and is currently hiring research engineers to develop models for just partners right now. The team's narrower focus may nevertheless have some overlap with companies that OpenAI hopes to engage in business. Companies like Covariant, started by former OpenAI robotics team members, are also attempting to train their own robotics models. And, two sources said, OpenAI has already gone head to head with companies in this space for a limited pool of talent. https://lnkd.in/g8Vs4nP3

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This summer, 11 Harvard-founded tech startups (Series A+) raised ~$520M! Series A: 7 rounds, $349.6M raised Series B: 3 rounds, $153M raised Series C: 1 round, $25M raised Sector count: 5 Health, 3 AI, 2 FinTech, 1 SaaS 3 of the 11 rounds were led by Andreessen Horowitz Select rounds include: Function Health raised a $53M Series A led by Andreessen Horowitz. Founded by Pranitha Patil (Masters, 2020) Pomelo Care raised a $46M Series B led by First Round Capital and Andreessen Horowitz. Founded by Marta Bralic Kerns (Harvard College) Pika raised a $80M Series B led by Spark Capital. Founded by Demi G.(Harvard College) Decagon raised a $30M Series A led by Accel and Andreessen Horowitz. Founded by Jesse Zhang (Harvard College) OnRamp raised a $14.2M Series A led by Javelin Venture Partners and Contour Venture Partners. Founded by Ross Lerner (HBS, 2020) Exa raised $17M in Series A funding led by Lightspeed. Founded by Jeffrey Wang and Will Bryk (Harvard College) Full post below. Subscribe if you’re interested in Harvard’s startup ecosystem Know of a Harvard student or alumni building a startup? Please connect — Phoenix Fund is a pre-seed and seed-stage venture capital firm investing in Harvard-founded startups. Please feel free to share a note https://lnkd.in/ey9bqN23

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