Emergent Ventures

AI is finally making services businesses scalable—and—exciting to VCs. The global services market is in the trillions of💰s, far larger than today’s software market. Yet, services businesses haven’t been the darlings of venture capital, as they were perceived to lack rapid scaling potential. 𝗔𝗜 𝗶𝘀 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗮𝘁. By blending AI seamlessly with human expertise, there is an opportunity to get into much larger markets with models that have the potential to scale in ways services - or even SaaS businesses - can't. For example, instead of offering a marketing SaaS, an AI-powered Service-as-Software business can deliver what the customer really wants: high-quality leads or compelling content. We’ve seen this potential firsthand through Emergent Ventures’ investments in multiple AI-powered companies that leverage humans-in-the-loop. These models resonate with B2B customers because they offer faster, clearer paths to value—reliable outcomes delivered with greater efficiency. For many customers, it’s a significant upgrade over traditional agency or service-provider relationships. While the potential is huge, only a fraction of AI-powered services startups will scale. 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗲𝗮𝗿𝗹𝘆 𝗰𝗵𝗼𝗶𝗰𝗲𝘀 𝗮𝗻𝗱 𝗲𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝗮𝗹 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. Here’s what we have learned works well: 𝟭. 𝗔𝗜-𝗛𝘂𝗺𝗮𝗻 𝗦𝘆𝗻𝗲𝗿𝗴𝘆: AI and software should do the heavy lifting, with humans involved strategically— e.g. for validating AI output, edge cases, enabling adoption, or acting on AI insights. Over time, reduce human input as the AI learns, and models improve. Target 60%+ initial gross margins, with a path to SaaS-like 75%+ margins over time. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗛𝘂𝗺𝗮𝗻 𝗜𝗻𝘃𝗼𝗹𝘃𝗲𝗺𝗲𝗻𝘁: The dependency on hiring & training humans should not constrain scale and economics. Have a path to tapping into freelancers or agency partners. Leverage human experts in a high-talent location such as India. 𝟯. 𝗥𝗲𝗰𝘂𝗿𝗿𝗶𝗻𝗴 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: Focus on high-value, recurring use-cases to ensure subscription-based revenue with strong net revenue retention (NRR). 𝟰. 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗣𝗼𝘄𝗲𝗿: Iterate to a solution that can command higher pricing, and a model that aligns incentives with customers, e.g. based on outcomes. 𝟱. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗮𝘁𝘀: Build solutions that improve with use, creating compounding competitive advantages over time. 𝟲. 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗧𝗲𝗰𝗵: Architect a stack that can evolve with AI advancements. 𝟳. 𝗙𝘂𝗹𝗹-𝗦𝘁𝗮𝗰𝗸 𝗧𝗲𝗮𝗺: A founding team that has the technical expertise to build and rapidly improve complex AI-powered solutions, and deep operational acumen. A rare combination. These are complex businesses to build, and the right playbooks are yet to be perfected. But where this works, 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀-𝗮𝘀-𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗔𝗜 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗺𝗮𝗻𝘆 𝗕𝟮𝗕 𝗰𝗮𝘁𝗲𝗴𝗼𝗿𝗶𝗲𝘀 📈 #EnterpriseAI #startups #vc #SaaS

Emergent Ventures is excited to host a mixer for early-stage enterprise AI founders and GTM experts during #SFTechWeek! On October 8th, join us for an evening dedicated to connecting founders with experienced professionals who know what it takes to scale early-stage companies. This is a unique opportunity to gain insights, share challenges, and build relationships with those who have successfully navigated the path from startup to scale. Apply to join us here: https://lnkd.in/geCgasJY. For the full #SFTechWeek calendar, visit tech-week.com. TECH WEEK by a16z

Congratulations Ankur Jain for being featured in Business Insider's Top #Seed Investor list for 3 years in a row! https://lnkd.in/gcCnTM5P

Congratulations Ankur Jain for being featured in Business Insider's Top #Seed Investor list for 3 years in a row! https://lnkd.in/gcCnTM5P

All of these statements about enterprise AI could simultaneously be true, even if they seem somewhat contradictory: 🌟 Hype will come and go, but GenAI will be a game changer for automating mundane parts of numerous enterprise workflows 🏗️ Making GenAI work within core workflows in the enterprise takes tremendous hard work beyond getting the tech to work - meticulous data pipelines, ensuring accuracy across real-life scenarios, safeguards against hallucination, robust guardrails, on-prem deployment capabilities where needed, change management and more, all while ensuring predictable and acceptable costs. Once done successfully, all this effort creates moats ⚙️ Large models may work out-of-the-box for some basic horizontal business tasks. Applications built around domain-specific models will work much better for many other tasks. Agentic workflows are one likely path to broader automation 👥🤖 Human Augmentation and Human-in-the-loop automation is what is here-and-now. Full automation solutions - outside of simple, low consequence tasks - will take time to perfect and be sufficiently trusted in the enterprise 🚫🎯 Enterprises will not buy “GenAI”. They will buy solutions and outcomes. If a GenAI-powered solution is the optimal one for their needs, then that will be bought 🔄 GenAI has opened tremendous opportunities for enterprise software startups that do other adjacent things to make their products more relevant, usable or actionable, and ‘close the loop’ ⚠️ Many “GenAI for X” startups will fail to deliver value in the enterprise. Many GenAI-native startups with the right solution and approach will be massive home runs. Success comes from focusing on the entire solution you are delivering, not just the technology that enabled it 🛠️ Some Enterprise AI startups will need to become services companies in order to deliver value 🕶️ Some Enterprise AI startups will succeed without becoming services companies 🤹 Both open source and proprietary foundation models will be used in enterprise AI deployments - several models may get used within the same application. The best model will win - on a per API call basis For many things being talked about enterprise AI, the claim and counterclaim could both be simultaneously true. Augmentation and automation of enterprise workflows is a massive undertaking that is broad, wide, deep and extremely bespoke. AI-fication of work and workflows is one of the biggest opportunities in our lifetimes. Things are just getting started!

Generative AI and Robotics – A tipping point? Robotics has been a promising field for many years, steadily adding value across various industries by improving processes and outcomes. Despite its progress and the substantive promise, the technology has remained in early stages of adoption. There are considerable opportunities for growth and innovation that could drive the impact of robotics to the next level - and help address labor shortages, do dangerous jobs safely, and ultimately make goods and services more accessible to users. GenAI has the potential to be particularly transformative, streamlining the programming of robots to an extent not possible before. It is enabling the development of intuitive, natural language programming interfaces and sophisticated algorithms that can learn and optimize tasks on their own. This can not only reduce the barrier to entry for utilizing robotics technology but also accelerate the pace of innovation within the sector. Supporting software advancements are hardware breakthroughs, particularly in GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units).  And the shift towards a Robotics-as-a-Service (RaaS) model represents a significant evolution in how robotics solutions are delivered and monetized. Link in first comment below to thoughts from our Emergent Ventures team on the opportunities (and challenges) we are seeing in this space. If you building something new in this space, please reach out to my colleague Niha Gottiparthy, who is driving our enquiry into the opportunities at the intersection of robotics and modern AI. #startups #robotics #venturecapital #genAI

We are excited to share our seed round of $2.6M led by Emergent Ventures along with participation from Y Combinator, Neon, First Cheque and executives from leading listed enterprise software companies. The entire business intelligence (BI) industry originated to help businesses make faster decisions but they end up making a lot of pretty dashboards which take a lot of time to build. The majority of intelligence in traditional BI tools still comes from the humans involved and not the machines. However, LLMs allow us to put intelligence into the software and help businesses make faster decisions. We have been very fortunate to find our early backers and investors who believe in us: Anupam Rastogi, Ankur Jain, Harj Taggar, Sahil Makkar, Siddhartha Ahluwalia, Dinesh, Anshul, Saurabh, Ishank, Brijesh, Nitin, Siddharth, Vaibhav Domkundwar, Aayush, Tej, Varun, Karthik, Shantanu, Krishna, Raghav, Ani, Shubham A, Piyush, Vikas, Mustaq Singh, Vaibhav and others Thanks for taking part in our journey Mithil Vakde, Rishabh Ranjan, Parth Pandey, Chandanpreet Kaur, Kundan Kumar Kushwaha, Anupam Anand Atharva Padhye Prabhat Singh Crux (YC W24)

Global enterprise SaaS/AI products built in India are taking off 🚀 ! But getting traction in competitive and complex markets like the US is not easy, especially from afar. At Emergent Ventures, we’ve been the first venture investors in several enterprise/SaaS/AI startups that started out of India and are building for the US and global markets. Some of these companies are now emerging leaders in their respective categories. Over the years, there have been numerous learnings on what works and what doesn’t. There are several common threads that we see over and over for enterprise/mid-market focused startups targeting the US, for example: ✅ If the US is your ultimate target market, then the earlier you focus on the US market, the better. US as the sole initial market focus works much better than straddling multiple markets ✅ Founder presence in the US significantly boosts momentum and likelihood of success ✅ The qualities to look for in the first sales and marketing hires could often be quite different from conventional wisdom ✅ You don’t necessarily need as much upfront capital to get started with US GTM as many imagine However, each journey is unique, so general ‘gyan’ may not always be relevant to your unique situation. We work closely with portfolio founders as they find and refine PMF in the US, and build out US Go-To-Market. Everything from iterating on ICP, use cases, positioning, messaging, initial hires in sales/marketing/customer success, lead-gen tactics, sales qualification, funnel management, connections to experts and advisors, and much more. I will be in India later this month and early Jan, and will be doing coffee chats ☕ on US GTM with a few founders in person, in Bangalore and Delhi NCR (Gurgaon). Format: Quick, focused one-on-one discussions on your US GTM. No investment pitch. You bring a couple of US GTM questions that you are wrestling with, and I share my perspective based on my experience working with dozens of founders at a similar stage/situation. Who you are: Early-stage SaaS/AI/Infra/Dev Tools founder. US market is currently your primary focus from a GTM perspective. You’ve done at least some customer discovery with US customers (and/or already have some US revenues). Drop me a DM if that is you. Please share along, and use the comments to tag other founders who may find this relevant. Looking forward to this! #gtm #saas #startups #ai