BBG Ventures’ Post

With the ever-present question of “should every start-up be an AI-start-up?” lurking in the background, we at BBG Ventures wanted to explore the question of what AI-first product design looks like in practice. We teamed up with Matthew Hartman and Becca Harris Lewy at Factorial Capital to discuss: and who better to kickstart the conversation than ML and data OG, Hilary Mason from Hidden Door? Check out the highlights from our conversation with Hilary, which covered everything from AI-first product design, why “the AI” is not really a thing, the uses of chat as an interface (and what’s next), plus how to think about building an AI team. Read it here: https://lnkd.in/geqyQtvG

I've found Janelle Teng's "Next Big Teng" substack to be one of the more useful VC blogs to read regularly, especially if you're trying to track real-world implications of AI on enterprise. This take on impacts of AI Coding on businesses is very interesting. Huge productivity gains will fall directly to the bottom line in the short term for big companies. With 'co-piloted' engineers, it also means startups going forward won't need as much capital to build meaningful value - and may get to profitability much faster. Maybe skipping the 'growth round' along the way. Big implications for the traditional VC model. https://lnkd.in/eawp7Jtd

If you're curious how AI will impact SMB and mid-market firms, check out Zach McCormick's book, The AI Manual! You can download this ebook for free from the SP Labs site: https://lnkd.in/eAVHPba8

I’ve been thinking a lot about how generative AI is going to impact businesses, specifically SMB and mid-market firms. These firms are more resource-constrained than large enterprises, which means they have more to gain from the productivity improvements afforded by AI than those larger firms. The availability of pretrained models means this technology is actually within reach of these smaller firms too, in contrast to past “AI” and “Big Data” waves. I thought it would be fun to write a book (with the help of AI of course) laying out my thoughts on how SMB and mid-market firms can leverage AI to drive productivity improvements in their businesses. The book is called The AI Manual, and it's currently 5 chapters and about 70 pages long. 1. AI Basics 2. Models and Systems 3. Deploying to Production 4. Customer-Facing Use Cases 5. Internal Use Cases I’ll be adding to this and editing it over time, so think of this as a first draft. If you get a chance to read through it, I’d love to hear your feedback! Also, I love talking about this kind of stuff, so feel free to reach out if you want to chat about the book or anything AI-related. https://lnkd.in/gtFSze5j

I'm fortunate in my role to collaborate with groundbreaking startups, LANDING AI is one that stands out for its work with unstructured data. Led by COO Dan Maloney, Landing AI is not just addressing AI complexities; it's pioneering new directions. Their Computer Vision Native Application, which they are building on Snowflake, epitomizes their drive to redefine AI's possibilities. I'm particularly impressed with their use of Snowpark Container Services and Native App, enabling the creation of AI applications that are not only flexible and robust but also straightforward and secure for our customers. This collaboration with Landing AI signifies a step towards a future where AI and Snowflake's platform work together to open new opportunities. I'm proud to support Landing AI's journey and work with their dedicated team. For a glimpse into how Landing AI is utilizing Snowflake to foster real AI innovation, check out the latest YouTube video. It's a testament to their commitment to technological advancement. Big shoutout to the team at Landing AI: Daniel Bibireata, Kai Yang, Junjie Guan, Yong (David) Park, Thiago F. Pappacena, Francisco Matias Cuenca-Acuna, Ankur Rawat And the Snowflake team working hard in the background: Moselle Freitas, Naveen Alan Deepak Thomas Gnanasekaran, Nagesh Cherukuri, Jon Alkan, Matthew Loewel, Dinesh Kulkarni, Eduardo Laureano, Prash Medirattaa, Brian Hess, Peter Mebane #LandingAI #Snowflake #AIInnovation #TechLeadership #Snowpark #NativeApp #Snowparkcontainerservices https://lnkd.in/gXDpnn3s

The world is increasingly data-driven, but if your business data is unreliable, your AI investment isn't worth much. That's why Menlo's Matt Murphy is betting on the rockstar team at Cleanlab. Curtis Northcutt, Anish Athalye, and Jonas Mueller, founded Cleanlab in 2021. Today, more than 10% of Fortune 500 companies (including AWS, JPMorgan Chase, Google, Oracle, and Walmart) and a roster of innovative startups (like ByteDance, HuggingFace, and Databricks) use Cleanlab to find and fix problems in sizable structured and unstructured visual, text, and tabular datasets. Read more in Forbes from Alex Konrad https://lnkd.in/gxPJBG65 Menlo Ventures and TQ Ventures co-led the Series A, with cloud heavyweight Databricks Ventures joining the round alongside earlier investor Bain Capital Ventures (BCV). #AI #Data #EnterpriseData #LLM

Today marks a milestone day for Patronus AI as we announce our $17M Series A funding round led by Notable Capital with participation from Lightspeed and Datadog 🎉✨ A year ago, after designing and launching Airbnb's first chatbot, I left Airbnb to join Anand Kannappan and Rebecca Qian as the first employee to tackle the problem around LLM Evaluations. Reflecting on this journey, I feel an immense sense of gratitude for everything that has led us to where we are today. In just 8 months, our world-class team has taken Patronus AI from an idea to a product and brand that is now trusted by Fortune 500 enterprises and leading AI companies worldwide. Patronus AI has been a place where I've grown in so many ways, beyond just as a designer, but as a professional and as an individual. It's where I'm learning to design "trust" at the forefront of everything—for our users, our product, and every touchpoint. In the rapidly changing world of AI development, it's crucial that we design experiences that are reliable, transparent, and user-centric. This is important because trust is the foundation of any technology's success; it ensures our solutions are adopted, valued, and sustained over time. Witnessing our company's evolution has been thrilling, and even more rewarding with my own journey of growth and discovery. Here’s to many more milestones and growth together! 🚀✨ - Read our story: https://lnkd.in/eSm3kCwm Read our press release: https://lnkd.in/evBeXvet Read our VentureBeat coverage: https://lnkd.in/exSZYUn7 Read our TechCrunch coverage: https://lnkd.in/eQniJnwx - 🚀 Want to work with our world-class team? View our open roles here: https://lnkd.in/gjHrDvKN - 🎬 Video motion graphic credits Erica Kim

Congratulations to Lightspeed-backed Glean for raising a $260 million Series E round. Arvind Jain, Co-Founder and CEO, and the team are transforming how businesses integrate AI into their daily operations. Glean’s mission is to bring Work AI to everyone. With the launch of Glean Work AI, businesses can now deploy AI at scale, building custom AI agents and apps that automate tasks. Their latest product update, next-generation prompting, introduces multi-step prompts, a prompt builder, and a prompt library, making AI more intuitive and collaborative for all teams. This funding will support Glean’s growth, helping enterprises adopt AI at scale. https://lnkd.in/e-k7FMQz

Thanks to Oliver Molander for some examples of articles on how organizations have started to implement LLMs tuned for their business need. Very useful!!

Special thanks to Cowboy Ventures for featuring us in their vertical AI market map! 🔥 In today’s landscape, AI is opening up new possibilities in a new generation of vertical AI software companies. These tailored AI solutions for specific industries are gaining popularity, transforming historically challenging markets—like smaller markets, lengthy sales cycles, and lower price points compared to general-purpose software. At Truewind, we’re proud to be a part of this exciting trend. Much like the vertical AI approach, we’re leveraging focused models trained on industry-specific data to create solutions that prioritize precision, efficiency, and client-centricity. As the AI landscape becomes increasingly crowded, how should startups prepare? How do you navigate this competitive terrain? Which direction should you choose? These are the challenges that lie ahead, and we're here to explore the answers together. 📕 Read the full article here https://lnkd.in/ePVJ9ZUH