Tim Tully of Menlo Ventures says people are buying up AI software left and right https://trib.al/hX8mnzg
Menlo Ventures
风险投资与私募股权管理人
Menlo Park，California 47,936 位关注者
We are a venture capital firm that strives to have a positive impact on everything we do. When we’re in, we’re all in.
关于我们
Menlo Ventures?is a venture capital firm that strives to have a positive impact on everything we do. That’s why we support businesses—including Abnormal Security, Benchling, Carta, Chime, Harness, Pinecone, Roku, Rover, Siri, and Uber—that are reimagining life and work for the better. Over 47 years, we’ve grown a portfolio that includes more than 80 public companies, over 165 mergers and acquisitions, and $5.8 billion under management. We invest at every stage and in every sector, with expertise in consumer, enterprise, and healthcare. From developing market strategies to creating communities, we provide real impact where entrepreneurs need it most. When we’re in, we’re ALL IN.
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https://www.menlovc.com
Menlo Ventures的外部链接
- 所属行业
- 风险投资与私募股权管理人
- 规模
- 11-50 人
- 总部
- Menlo Park，California
- 类型
- 私人持股
- 创立
- 1976
- 领域
- Adtech、Ecommerce、Mobile、Gaming、New Media、Social & Web、Cloud & SaaS、Comms & Hardware、Security、Storage、Big Data、FinTech、SaaS、DevOps、Infrastructure、Marketplaces和Life Sciences
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主要
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1300 El Camino Real
suite 150
US，California，Menlo Park，94025
Menlo Ventures员工
动态
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Check out Menlo’s partner, Tim Tully, on Bloomberg discussing our latest market research, “State of Generative AI in the Enterprise.” Watch the full segment here: https://lnkd.in/g_MTaEtC
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Earlier this week, we released our 2nd annual State of Generative AI in the Enterprise report, featuring 3 key predictions for the future. Enterprise AI Prediction #3: The AI talent drought is about to deepen. To understand where AI comp is now and how high it might climb, read this post on the cost of AI tech talent from our Talent Ninja Kandace Elam with JORDAN ORMONT: https://lnkd.in/ewHb3gVB
AI Compensation Trends: The Real Cost of Top 1% AI Technical Talent - Menlo Ventures
https://menlovc.com
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Enterprise AI Prediction #2: David beats Goliath. More AI incumbents will fall. “What we’re seeing goes beyond implementation—it’s disruption. In 2023, we reported that incumbents were keeping startups at bay, but in 2024, we saw Chegg lose 85% of its market cap to ChatGPT and StackOverflow lose half its traffic to GitHub Copilot. These aren’t isolated incidents—they’re early indicators that established leaders are vulnerable.” said Derek Xiao, an investor at Menlo Ventures. Full report here: https://lnkd.in/gQFpg5aZ
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Enterprise AI Prediction #1: Agents will drive the next wave of AI transformation. Agentic automation is poised to tackle complex, multi-step tasks, far surpassing today's systems focused on content generation and knowledge retrieval. Platforms like Clay and Forge hint at how advanced agents could disrupt the $400 billion software market—and reshape the $10 trillion U.S. services economy. This evolution will require new infrastructure: agent authentication, tool integration platforms, AI browser frameworks, and specialized runtimes for AI-generated code. Explore more on agentic automation: https://lnkd.in/g2Si99ur https://lnkd.in/gUdaKD5j Access the full report: https://lnkd.in/gQFpg5aZ
2024: The State of Generative AI in the Enterprise - Menlo Ventures
https://menlovc.com
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LIVE in 30min: Tim Tully joins Caroline Hyde on Bloomberg at 8:40am PT Tim will be discussing Menlo’s latest market research, captured in the “State of Generative AI in the Enterprise” released this week. ? He will touch on: - The trends that drove businesses to spend almost $14B in Generative AI this year–up 6X from 1012 - Which sectors are leading AI adoption - How AI is being used in business, and which use cases are most valued?? - Where incumbents are advantaged and where startups are starting to break through Watch live on Bloomberg TV or stream on https://lnkd.in/d5eC7wV Full report: https://lnkd.in/grj-_bYD
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Congratulations to the 50 pioneering AI companies recognized by Fortune! We're proud to see so many Menlo Ventures portfolio companies among those named to the #AI50 Innovators list! Anthropic leads the way in AI innovation with groundbreaking work on safer, more aligned systems and a strong focus on human-centered progress. Pinecone is a leading provider of vector databases, enabling vector databases, powering smarter, faster, and more accurate AI outputs by enabling efficient search and retrieval of high-dimensional data Xaira Therapeutics is revolutionizing healthcare with advanced AI, accelerating drug discovery, and transforming patient care through groundbreaking innovation We’re proud to partner with these exceptional teams as they shape the future of AI. Full list here: https://lnkd.in/esMTkmWT
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Calling all AI founders and innovators! Join the AI Founders & Innovators Networking Reception at #AWSreInvent on Dec 3, hosted by Menlo Ventures, Anthropic, and Amazon Web Services (AWS). - Fireside chat w/ Mike Krieger and Matt Murphy - Limited to 100 attendees - Special AWS credits bundle Apply now! https://lnkd.in/gb9fDCZE
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At Menlo Ventures, we’ve witnessed the enterprise AI landscape transform at breakneck speed. Generative AI, once a frontier technology, is now a foundational business tool driving real-world impact across industries. Our latest report, The State of Generative AI in the Enterprise, highlights key insights and trends: ?? AI Spending Takes Off Enterprise AI spending surged to $13.8 billion in 2024, up from $2.3 billion in 2023—a testament to the shift from pilot projects to production-grade implementations. ?? Top Use Cases Driving ROI From code copilots and chatbots to enterprise search and meeting summarization, generative AI is transforming workflows and boosting productivity. ?? Applications and Infrastructure on the Rise The application layer saw $4.6 billion in investment this year, while the modern AI stack continues to evolve with multi-model strategies, RAG, and agentic architectures. These shifts aren’t just theoretical—they’re tangible, transformative, and happening now. Across our portfolio—companies like Anthropic, Benchling, Eleos Health, OpenSpace, Harness, Pinecone, Typeface, Vilya, and Xaira Therapeutics —we’re seeing the impact of AI on industries from healthcare to manufacturing, security, and beyond. At Menlo, we backed the infrastructure that made this possible. Now we're doubling down on applications that will reshape entire industries. The opportunity to build transformative AI companies has never been better. For more details on what is happening in AI, where the industry is headed, and three predictions for what happens next, read: https://lnkd.in/grj-_bYD Authored by Tim Tully, Joff Redfern, and Derek Xiao with a little bit of help from Claude Sonnet 3.5 #AI #GenerativeAI #EnterpriseAI #VentureCapital #MenloVentures
2024: The State of Generative AI in the Enterprise - Menlo Ventures
https://menlovc.com
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Sharing a new ressarch paper from Anthropic on adding error bars to evals to ensusure statistical rigor and consistency in model evaluations.
Our new research paper: Adding Error Bars to Evals. AI model evaluations don’t usually include statistics or uncertainty. We think they should. Read the blog post: https://lnkd.in/d2jKfpyT When a new AI model is released, the accompanying model card typically reports a matrix of evaluation scores on a variety of standard evaluations, such as MMLU, GPQA, or the LSAT. But it’s unusual for these scores to include any indication of the uncertainty, or randomness, surrounding them. This omission makes it difficult to compare the evaluation scores of two models in a rigorous way. “Randomness” in language model evaluations may take a couple of forms. Any stream of output tokens from a model may be nondeterministic, and so re-evaluating the same model on the same evaluation may produce slightly different results each time. This randomness is known as measurement error. But there’s another form of randomness that’s not visible by the time an evaluation is performed. This is the sampling error; of all possible questions one could ask about a topic, we decide to include some questions in the evaluation, but not others. In our research paper, we recommend techniques for reducing measurement error and properly quantifying sampling error in model evaluations. With a simple assumption in place—that evaluation questions were randomly drawn from some underlying distribution—we develop an analytic framework for model evaluations using statistical theory. Drawing on the science of experimental design, we make a series of recommendations for performing evaluations and reporting the results in a way that maximizes the amount of information conveyed. Our paper makes five core recommendations. These recommendations will likely not surprise readers with a background in statistics or experimentation, but they are not standard in the world of model evaluations. Specifically, our paper recommends: 1. Computing standard errors using the Central Limit Theorem 2. Using clustered standard errors when questions are drawn in related groups 3. Reducing variance by resampling answers and by analyzing next-token probabilities 4. Using paired analysis when two models are tested on the same questions 5. Conducting power analysis to determine whether an evaluation can answer a specific hypothesis. For mathematical details on the theory behind each recommendation, read the full research paper here: https://lnkd.in/dBrr9zFi.
A statistical approach to model evaluations
anthropic.com