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Berkeley, California, United States
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Articles by Jeremy
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The House Fund Welcomes New Part-Time Partners
The House Fund Welcomes New Part-Time Partners
We are proud to welcome Brett Wilson, Co-founder and CEO of TubeMogul, and Jason Wang, Co-founder and founding CEO of…
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Jeremy Fiance shared thisIt would be hard to do a weekly series on Berkeley's impact on technology without getting to Matei Zaharia. His Berkeley story starts with Apache Spark, which began as a research project on campus in 2009, when Matei was a PhD student in the lab that became AMPLab, working on ways to make large-scale data processing faster and more flexible. Spark eventually became one of the most widely used open-source systems for processing enormous datasets. Then the researchers behind it set the foundations for Databricks. Matei and a group of fellow Berkeley researchers turned the technology they had been developing on campus into a company that has since become one of the defining data and AI platforms of its generation. It's now valued at $190B! The story didn't end with Spark, either. Matei has continued working on widely used open-source data and AI projects including MLflow and Delta Lake, while Databricks has grown from a Berkeley research spinout into a company serving organizations around the world. And now there's another Berkeley chapter. Matei is still Databricks' Co-Founder/CTO, but he's also back at Berkeley as an Associate Professor in EECS, conducting research and working alongside another generation of students who may eventually spark pioneering research and/or companies of their own. Today, his research focuses on one of the biggest open questions in AI: how to build and scale reliable agents. He's a co-author on recent open-source projects including DSPy and GEPA, which automatically optimize prompts and models so agents get better at specific tasks. This April, ACM awarded him the 2025 ACM Prize in Computing for the distributed data systems that made large-scale analytics, machine learning, and AI possible. A fitting bookend: his Spark dissertation won ACM's Doctoral Dissertation Award back in 2014. I talk a lot about Berkeley's startup flywheel because Matei's career is a pretty clean example of how it works. A PhD student invents important technology on campus. The work is released openly and gets adopted around the world. A company grows out of it and becomes a category leader. The people responsible stay connected to Berkeley, and the next generation gets to learn and build alongside them. Nearly two decades after Spark got its start on campus, that cycle is still going.
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Jeremy Fiance shared thisLargest individual gift in higher education history - inspiring!
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Jeremy Fiance reposted thisJeremy Fiance reposted thisA decade ago, I started dreaming about creating enough customer love to host a Customer conference. Last week, the dream came true. More than 100 leaders across professional services, financial services, and tech came to New York for Proof, our first customer summit. The customers in that room have entrusted us with $13.4B of revenues. Much more importantly, our partnership has returned 1.56 million hours to over 45,000 professionals across 70 countries. That is 736 years of human time. Returning time is personal for us. Once life teaches you that time is finite, you understand it is all that matters. Proof was filled with people who understand that. Meg Whitman reminded us that AI will change everything, and yet timeless principles, like working hard, being passionate, and delivering customer value, will always matter most. Arianna Huffington reminded us that AI makes "busy" the new stupid. We haven't stopped repeating it. Charles Pickett of PwC showed how his team measures both the inputs and outputs of AI (quality, efficiency, speed to insight) to find its real ROI. There is a lot of noise in AI right now. A lot of demos that don't work in production. A lot of AI spend disconnected from customer value. A lot of fundraises with splashy numbers. Proof is the signal in AI. The most incredible people in the world, connecting in real life, to build the future of work together. That is being human in the age of AI.
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Jeremy Fiance reposted thisJeremy Fiance reposted thisToday, we’re proud to announce Databricks as the new sponsor of the Databricks Field at California Memorial Stadium! 🐻 All seven of our co-founders are Cal alumni. The earliest lines of what would become Databricks were written at Berkeley’s Soda Hall, fueled by late nights and a shared belief that data and AI could accelerate human progress. This sponsorship is our way of investing in the next generation of researchers and builders who’ll have their own breakthroughs there. Our mission has always been to democratize data and AI. It started at Cal. We’re glad to help it continue there. Go Bears. 💙 https://lnkd.in/giTRx-xp
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Jeremy Fiance shared thisAli Ghodsi none of us can even begin to imagine how massively impactful you & Databricks investing in the next gen of Berkeley students, builders, and athletes is for us... It's changing the game for Berkeley. And will shore up / scale the next 150 years of Berkeley's impact on society & the world. Thank you & Go Bears!Jeremy Fiance shared thisAll seven Databricks co-founders began our journey together at UC Berkeley. We started the company in a room in Soda Hall, moved to a small office on Addison Street, and spent our first few years in Berkeley before eventually heading to SF. Two of our co-founders are still on the faculty. Today we announced Databricks Field at California Memorial Stadium, our first collegiate athletics sponsorship. Berkeley shaped everything about how this company thinks, and this is our way of investing in the next generation of students and builders who'll do their best work on that campus. Go Bears! https://lnkd.in/g9McUUVYFrom Soda Hall to the Football Field: Databricks Returns to Berkeley Roots with Cal Athletics PartnershipFrom Soda Hall to the Football Field: Databricks Returns to Berkeley Roots with Cal Athletics Partnership
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Jeremy Fiance shared thisDatabricks Field was just announced ~ BIG NEWS for the Cal Bears!!! All seven Databricks co-founders started at Berkeley. The company began in a room in Soda Hall, and its co-founders still teach on campus. Now their name is on the 50-yard line, in the largest partnership in Cal Athletics history and Databricks' first ever in college sports. My favorite detail: the deal is funded by UC Berkeley becoming a Databricks shareholder. Ali Ghodsi said it best. This is about "investing in the next generation of students and builders who'll do their best work on that campus." That's the Berkeley flywheel in one sentence. Research becomes a company, the company becomes a giant, and the giant pays it forward. Grateful to have been in some of the rooms that helped bring this together. More on the big news in the comments. Thank you to the founders, execs, and Databricks team. See you tomorrow night against Clemson. Go Bears!
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Jeremy Fiance shared thisThe last time a portco hosted at One Liberty Plaza in NYC, they were acquired for ~$1.7B soon after. Complete coincidence, of course. It's a big city. (And to be clear, Laurel is not for sale) Today Laurel is hosting its first conference, called Proof. Laurel captures professional services work automatically at the source, so lawyers, accountants, and consultants stop reconstructing their week from memory. Firm leaders finally see where their time goes and where each matter earns or loses money. The premise of Proof is simple: less theory, more receipts. Leaders from PwC, EY, Freshfields, Reed Smith, Cozen O'Connor, and more are sharing how their AI rollouts actually went and what it means for how firms staff and bill. Meg Whitman and Arianna Huffington close out the day with a fireside chat. Congrats to Ryan Alshak & the Laurel team on this epic first one. I've already learned a ton.
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Jeremy Fiance reposted thisJeremy Fiance reposted thisToday, we’re launching an ambitious new school called The Horowitz Andreessen Academy. Based in San Francisco, The Academy serves the most promising young high school graduates. We think this can be an elite institution that attracts top tier talent. One that prepares students for the future rather than remaining stuck in the past. The #1 goal is to help students learn to build, which is the most important skill in the AI era. They'll learn primarily by pursuing their own projects, either individually or in groups. There are classes and guest lectures, too, from some truly amazing people who have built modern-day Silicon Valley. The Academy is designed as a network, since that’s the reason students go to school in the first place. Core to that network are our 10 Founding Partners: Anduril, Anthropic, Coinbase, Google, Meta, NVIDIA, OpenAI, Palantir, Replit, and Stripe. The network includes over 50 hiring partners and over 200 speakers and mentors. To join as a hiring partner or faculty member, you can apply on our website. We raised $42M in funding led by Andreessen Horowitz. I'll be CEO and Marc Andreessen and Erik Torenberg will join me on the board. Applications are open for our Founding Class Fellowship, which will be one year and tuition-free. Eventually, pending regulatory approval, we plan to offer a two-year program that charges tuition, similar in cost to an elite private university. We're looking for the most unusually ambitious young builders on the planet. Come join us in San Francisco: theacademysf.com
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Jeremy Fiance shared thisA fun piece of House Fund history: the first company we ever invested in was tbh, founded by the now-legendary Nikita Bier, where we were his first investor when he started building social products. That was back in the earliest days of the fund, when we were doing more consumer investing and still figuring out what The House Fund itself would become. tbh became #1 in the US App Store and Facebook acquired it soon after, Nikita went on to become one of the best-known consumer product builders in the world, and over time our own portfolio shifted much more heavily toward areas like enterprise software, infrastructure and AI. For a few years, we made relatively few consumer investments. This year, that has started to change. We’ve made more consumer investments in 2026 than we did in roughly the previous four years combined, largely because AI is opening up a new set of opportunities to rethink products, interfaces and behaviors - that either didn’t make sense before or would have required a much larger team to build. There are categories that have felt relatively settled for years where suddenly the assumptions underneath them are changing. A small team can build much more capable products, software can adapt much more deeply to an individual user, and entirely new interfaces are becoming possible as people get more comfortable interacting with AI in different ways. None of that makes consumer investing easy. Human behavior is still incredibly difficult to predict. You can have a market that looks enormous, a product that works beautifully, and a thesis that makes perfect sense on paper, and people may simply decide they don’t care. Then occasionally something that looks almost too simple catches on because it hits exactly the right behavior at exactly the right moment. tbh was a pretty good early lesson in that. Nearly ten years after our first investment was a consumer company, it feels fitting that we’re spending more time in the category again.
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Jeremy Fiance liked thisJeremy Fiance liked thisDatabricks Field at California Memorial Stadium makes its debut tonight. Cal vs. Clemson on ESPN. Go Bears. 💙 🐻
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Jeremy Fiance reacted on thisJeremy Fiance reacted on thisarxiv is tapping the brakes. Announced today, the new cap of two submissions a month looks like a small policy change. For many of us in science, it's a sea change.
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Jeremy Fiance liked thisJeremy Fiance liked thisExcited to announce our new partnership with Decagon to bring richer enterprise data intelligence directly into customer conversations. AI agents are only as useful as the context they can access. Enterprises already have an incredibly rich record of their customers in Databricks. Orders, payments, product usage, preferences, and countless other signals all help explain who a customer is and what they need. Through this partnership, Decagon agents can access governed data from the Databricks when they need it, while insights from those conversations can flow back into Databricks and become useful across the rest of the business. We’re excited to work with the Decagon team to make enterprise customer agents more context aware while keeping data governance at the center. https://lnkd.in/gpTT8gWXDecagon partners with Databricks to bring enterprise context into every customer conversation | DecagonDecagon partners with Databricks to bring enterprise context into every customer conversation | Decagon
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Projects
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Startup Weekend UC Berkeley
Startup Weekend Berkeley is a 54-hour weekend competition during which groups of developers, business managers, startup enthusiasts, marketing gurus, graphic artists, and more pitch ideas for new startup companies, form teams around those ideas, and work to develop a working prototype, demo, or presentation by Sunday evening. The teams pitch these ideas to a panel of Venture Capitalists, who then select a winning team. Winning team receives seed funding, incubator space, and various other…
Startup Weekend Berkeley is a 54-hour weekend competition during which groups of developers, business managers, startup enthusiasts, marketing gurus, graphic artists, and more pitch ideas for new startup companies, form teams around those ideas, and work to develop a working prototype, demo, or presentation by Sunday evening. The teams pitch these ideas to a panel of Venture Capitalists, who then select a winning team. Winning team receives seed funding, incubator space, and various other prizes.
Keynote Speakers:
Adam Cheyer: Co-founder & VP Engineering at Siri, Inc.
Craig Walker: CEO/Founder of Firespotter Labs & former CEO at GrandCentral (now GoogleVoice)
Sponsors/Partners:
Microsoft, Uber, Skydeck Incubator, Foundry Accelerator, Lowenstein Sandler LLP, EQ Network, Draper University, Berkeley Startup Cluster, City of Berkeley, Big Ideas @ Berkeley, Runway SF, Keiretsu Forum, ReadyForce, Dorm Room Fund, Strikingly, Highland Capital, General Catalyst Partners, Draper Fisher Jurvetson, Blackbox.vc, Blue Fog Capital, Matrix Partners, Accel Partners, Firelake Capital, and AlumniFunderOther creatorsSee project -
Givair
Givair is a social gifting platform for spontaneous and occasion gifting with apps developed for iOS and Android incubated at UC Berkeley's Skydeck.
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"Baking & Entering" an NYU Student Film
Assistant Director for short film called "Baking and Entering"
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Upstart Ambassador
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See projectUpstart lets you raise money in exchange for a small share of your income for 10 years. It's an investment in you, not your idea or your business. Pursue your dreams with guidance from backers who believe in your aspirations. You share some of your upside, but payments are capped regardless of your success.
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David Cohen
Techstars • 42K followers
Check out the latest funding news from across the Techstars portfolio. → Zipline (Techstars 2011), a drone delivery and logistics startup, raises more than $600M in new funding, boosting its valuation to $7.6B, as it expands commercial deployments. → Language learning startup Preply (Techstars 2015) raises $150M and becomes the 23rd known unicorn from Techstars accelerators. → Hydrosat (Techstars 2019) raises $60M in Series B funding → Tive (Techstars 2017) receives a $20M investment, valuing the shipment-tracking systems developer at slightly more than $500M. → Parambil (Techstars 2023) raises an additional $6M in seed funding ($8M to date). → Seasats (Techstars 2021) receives $24M in Department of War APFIT funding to accelerate the fielding of its autonomous surface vessels. → Hawaiian Airlines’ newly announced $600M Kahuʻewai Hawai‘i Investment Plan features a strategic investment in Ampaire (Techstars 2018) to support hybrid-electric aviation in the islands. → Fintalo (Techstars 2025) secures $500K+ in oversubscribed Pre-Seed funding. → PraxisPro (Techstars 2024) closes an oversubscribed $6M seed round. → Samara Aerospace (Techstars 2024) closes $10M seed round to help bring more stability to sats in orbit. → MyARC (Techstars 2022), a platform that enables fitness creators to train their communities at scale, secures £1.5M investment. Massive congratulations to these incredible portfolio companies! 🔗 Read more at https://lnkd.in/dxtCTvrg
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Jeffrey Berman
SERHANT. • 12K followers
The next big AI infrastructure opportunity may not be another model, it may be the layer that sits between the models and the enterprise... ...or so that can be gleaned from the conversation I had with Camber Creek portfolio company founder & CEO David Potts on the latest Catalyst podcast (link in comments). David’s story is particularly interesting because CloudQix wasn’t dreamed up as an AI startup. It emerged from a problem he was solving at SalesWarp (also a Camber Creek portfolio company) for years: getting disparate enterprise systems to actually talk to each other. Then something changed: as AI adoption accelerated, that integration layer became even more important. Enterprises now have more models, agents, data sources and applications than ever but they also have more questions around security, identity, governance, observability and compliance. And in regulated industries, “just connect it” isn’t good enough. One point David made really stuck with me: AI can generate the connection. It can’t necessarily deploy it securely, govern it, manage identity and secrets, provide observability, or create the audit trail. That distinction is going to matter a lot. CloudQix is starting with wealth management and RIAs, where the need is particularly acute. The goal isn't to replace the systems advisors already use. It's to connect them and put a governed AI layer across the workflow. A simple example: client onboarding can require pulling together documents, custodial data, CRM information and notes from multiple systems. CloudQix can bring that information together, use AI to process it, identify missing information and surface recommendations, all while keeping a human in the loop for approval. The result isn't just “AI productivity.” It's a new operating layer for how regulated businesses actually use AI. The models may be the headline. But the pipes, controls and governance underneath them could be where a lot of the enterprise value gets created. Great conversation with David; worth a listen if you're thinking about what the AI infrastructure stack looks like beyond the model layer. #podcast #venturecapital #AI #infrastructure
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Krishan Patel
Laplace Capital • 3K followers
The venture ecosystem has shifted and founders have been caught stuck between two worlds: 1. Narrative chase what's "hot" and build a story/thesis around something that is exciting enough to showcase power law gains, be extremely coherent and directly associated with enough validation to convince VCs it's plausible, be young (sub 25 yrs) willing to showcase your "ambition and hustle/grind" i.e. sacrafice your personal relationships, show you will move to SF and move in to an apartment with 5 other people, etc. Essentially be focused on being a soldier to the cause, avidly chasing the trends and topics that are constantly flowing through SV, perpetuated talking points and driving consensus until it lands just enough to raise a small round. 2. Build and validate, with the hope you can become VC backed at some point which ultimately leads you to build bootstrapped (or seed strapped) traction. Unfortunately this leads to a path where despite great traction and revenue generation, you actually work against the narrative that allows you to raise in the current environment. Power law story telling diminishes through unit economic diligence and every question smashes against an established business model. Essentially, you spend countless hours trying to find the balance between P&L, taking out loans to reach cash flow positive but are stuck between signing a contract that might solve your problems or a opeational efficiency you need to invest effort/capital into, to fix scaling costs. We've spoken with many founders in #2 and it's pretty crazy how many great near break even assets from $300k - $1m in ARR that could actually be fantastic businesses regardless of VC funding or not. The unfortunate part is, there is equally quite a bit of rhetoric around fame within the space and it's also convoluted the desires between building a great business and receiving the validation/glory for doing so. One thing for certain there will need to be a shift in mentality towards startups in bucket #2 as there will be a ton of these assets in the market stuck in a limbo not able to raise VC funding but stuck in this range that is also challenged in the M&A markets.
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Eric Manges
ADONYX • 6K followers
In the lexicon of venture capital investing there exists a shorthand classification for founders that attempts to capture their deepest motivating forces, their operational DNA, and the ultimate drivers of their decision making. At one end of that spectrum resides the mercenary founder, a figure whose primary lodestar is growth, transaction value, outcome, and return on capital. At the other lies the missionary founder, a visionary driven by mission, purpose, systemic change, and impact beyond simple financial returns. Among investors, founders are often crudely described as one or the other, as if every entrepreneur is either a profit maximizer with a sharpened eye toward exit multiples or a purpose-driven idealist bent on rewriting the rules of industry or society. The reality however is far more nuanced, and it is precisely within the interplay of these archetypes that the rarest and most transformative founders arise. Read more...
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Saanya Ojha
Bain Capital Ventures • 87K followers
It feels almost poetic: the year that began with market-wide panic after DeepSeek R1’s surprise January drop is ending with the equally disruptive December launch of DeepSeek V3.2. In January, R1 cracked open the idea that aggressively scaled RL - not just larger and larger pre-training runs - can push a model into frontier-level cognitive behavior at a radically lower cost. Now, months later, V3.2 bookends the year with an even louder message: open-source is no longer trailing by quarters, it’s operating on a near-synchronous innovation clock. And in some benchmarks, it’s outright leading. We now have a publicly available model with gold-medal performance across IMO 2025, CMO 2025, IOI 2025, and ICPC-level tasks. No Western lab has open-sourced anything in that tier. It’s early. Independent benchmarking will come, along with the usual debates about framing, cherry-picking, and reproducibility. But you don’t need perfect clarity to see the shape of things. DeepSeek’s story has always been about discipline. While the frontier race spirals into billion-dollar training runs and million-token contexts, the team has stayed focused on a narrower, almost stubborn question: how far can you push intelligence per dollar. This model delivers frontier grade performance at a fraction of the cost (30x cheaper than Gemini 3 Pro, 50-75% cheaper than prior Deepseek models). Defending against a cost advantage is easy if you can point to a performance gap. But if a competitor matches your performance and undercuts your price, the defense collapses. That’s the corner V3.2 pushes frontier labs toward. Most of the world - nations, small enterprises, scrappy startups - will never train trillion-parameter models. And crucially, they don’t need to. They need models that are: - cheap to run - fine-tunable on commodity hardware - good enough to support agents, search augmentation, and code workflows - predictable on inference cost V3.2 sits precisely at that intersection: high-enough capability, low-enough cost. This is why a growing number of Silicon Valley startups are building on Chinese open-weight models. The logic is straightforward: they can download the weights, fine-tune locally, deploy on smaller hardware, avoid vendor lock-in and keep the price of inference predictable. For a startup with limited runway, this matters more than a marginal accuracy edge. DeepSeek’s trajectory transforms “Chinese open-source” from a curiosity into a default path for cost-sensitive builders. The innovation frontier is being pulled sideways, not upward. Today: ▪️ U.S. frontier labs chase maximal capability - climbing vertically up the y-axis. ▪️ Chinese labs chase maximal cost-performance - scaling horizontally across the x-axis. The model with the highest peak will win prestige. But the model with the widest base will win global adoption. DeepSeek V3.2 shows that efficiency is not a consolation prize, it is a competitive moat.
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