Emergence Capital

"𝗧𝗵𝗲𝘀𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗟𝗟𝗠𝘀 𝗹𝗶𝗸𝗲 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗮𝗻𝗱 𝗖𝗹𝗮𝘂𝗱𝗲 𝗮𝗿𝗲𝗻’𝘁." That's how Pitchbook describes Small Language Models (SLMs), and we couldn't agree more - from Day 1 at Arcee AI, we've been saying that the vast majority of #GenAI business use cases don't require a Large Language Model (LLM), but rather, the small, specialized, and secure models that we're offering. Read the full article here ⬇️ and let us know if you agree with the companies we hear from every day that tell us they're quickly realizing LLMs are NOT the business-friendly solution they had been promised. And to learn more about our SLMs, you can get started by trying out our flagship model, SuperNova, on our website (link in the comments). https://lnkd.in/e6FJMuvk #NLP #AI #enterpriseAI

10 lessons I learned from Anthony Kennada on the Revenue Mind podcast. - “We really don’t control anything, do we? There’s beauty in surrendering our need to control everything.” - “Anxiety is the shadow side of control, a fear of not managing the outcome.” - “Putting work in its rightful place in the hierarchy of my life is where I’m finding healthier boundaries.” - “My default mode is anxious, so my cross to bear in life is learning to intentionally let go of control.” - “Behind every vanity post on LinkedIn, there are real human beings doing the best they can with the information they have.” - “We need to create cultures where people can raise their hands and say, 'I can’t do this right now,' and that’s okay.” - “Real vulnerability in leadership isn’t about a teaching moment—it’s about the unplanned, raw moments when you can’t hold it together any longer.” - “There’s no manual for how to lead through personal trauma while trying to build a company—sometimes, just surviving is the greatest victory.” - “The truest mark of leadership is creating space for others to be vulnerable, knowing that everyone will face their own battles at some point.” - “The hardest lesson I’ve learned is that you can’t always be there for everyone, including yourself, in the way you want to be.”

Emergence Capital is going even deeper into the SaaS benchmarking arena. Note: the $100 gift card raffle. Their last report in the comments (h/t Joseph F., Andrew Crinnion, CPA Paul Smalera) Participate with your metrics here: https://lnkd.in/evCfTknk

Yesterday was a pretty epic day as Arcee.ai was asked to present NYSE Wired luncheon 📊! 🎤 Here are the top takeaways from my SLMs discussion: 🤯 Myth-busting alert: SLMs vs LLMs - let's set the record straight! 📝 🚀 The future is here: SLMs + Agentic Workflows = 🔥 (But let's be real, if the model isn't on point, adding workflows won't save the day 😅) 📈 SLMs are on the rise! More enterprise-focused and ready to take over 🚀 And the tea is... 🍵 RAG is DEAD 💀! RIP, it was nice knowing you 😜 Thanks to everyone who joined me at the NYSE Wired luncheon! 🎉 What's your take on SLMs and the future of AI? Share your thoughts in the comments! 💬 #SLMs #AI #FutureOfWork"

We just hit a HUGE milestone 🚀 We crossed $4,500,000 in pipeline generated by Unify-powered Automated Outbound. Yes, we're proud, but we've also hit so many speed bumps and learned many lessons along the way. Here's 7 lessons learned from using Unify to grow Unify: 1. Success doesn't happen overnight. Our first 3 months we saw close to zero results and felt like giving up. Then we started to have breakthroughs. Don't let early hiccups discourage you. 2. You need 10+ signals/plays to find real success. A few plays won't cut it if you want to make this a true channel. You need many signals. 3. Copywriting is hard but worth it. Hire the experts if you have to, this is one place you absolutely can't skim. Every word matters, bad copy won't convert. Copy needs to be specifically written for outbound. 4. Person-level signals perform. Conversion is insanely high because your messaging can be hyper-relevant. Squeeze as much juice as possible from these. 5. Automated outbound is our most CAC-efficient channel. We get SO much more value out of Unify than $10s of thousands per month of paid ads spend. 6. Someone needs to hold the bag. You can't hit $5M without clear pipeline ownership (this is Garrett at Unify). 7. Attribution also shows up in inbound. Several inbound meetings each month are driven by Unify automated outbound. People see our emails and then come inbound — crazy. I have to give a huge shoutout to Garrett and Rhea from our growth team for relentlessly pushing the bar in using Unify. If you want to see how we used our own product to accomplish this (and how you can, too) shoot me a DM! Image: all-time cumulative chart on pipeline creation by AOB

Did a pull of the top theses from SandHill in 2024 based on reads (of 900+ posts): 1. The Emergence of AI-Powered Services Firms (Alex Brussell & Brian O'Malley, Forerunner) 2. AI in 2024: From Big Bang to Primordial Soup (David Cahn, Sequoia Capital) 3. The End of Software (Chris Paik, Pace Capital) 4. AI-first Service Businesses (Louis Coppey, Point Nine ) 5. The Death of the Big 4: AI-Enabled Services Are Opening a Whole New Market (Jake Saper & Jessica Cohen, Emergence Capital) 6. AI unlocks the small businesses legacy SMB tech can't acquire (Jason Shuman, Primary Venture Partners) 7. The Fastest Growing Software Sectors in 2024 (Tomasz Tunguz, Theory Ventures) 8. Modern Vertical SaaS — Show Me the Money (David G. Cheng, DCM Ventures) 9. Service is the new SaaS (Desmond Fleming, FirstMark) 10. Crossbeam's Request for Startups (Ryan Morgan, Sakib Jamal, Mili Raina, & Moonlan Zhang, Crossbeam Venture Partners) Direct links to each: https://lnkd.in/d8afqrCw h/t to Riley Rodgers for the original idea to pull the data

Accessing Corporate Data: The Key to Unlocking AI’s Next Innovation Cycle The Emergence Capital team has made a compelling argument in their recent piece on applying innovation cycles to the latest AI platform breakthroughs. They contend that accessing corporate data is crucial for unlocking AI’s next innovation cycle. Historically, technological advancements often follow an S-Curve pattern, characterized by rapid innovation followed by stabilization. This pattern is evident in the development of networking protocols in the 60s-80s, browser technology in the 90s, and more recently, mobile application development. Applying the S-Curve concept to Large Language Models (LLMs), we have witnessed multiple breakthroughs with the releases of ChatGPT in 2022 and GPT-4 in March last year. However, recent advancements have been incremental in nature. Two primary reasons contribute to this AI ‘plateau’: 1. Exhaustion of Public Data Sources: The latest versions of foundation models were trained on the entire corpus of the internet, leaving public data sources depleted. 2. Suspect Quality of Public Data: Public data sources are often of questionable quality, particularly when large training runs indiscriminately ingest niche subreddits and forums. In contrast, data produced in a work context is more valuable and abundant than public data. Imagine foundational model companies gaining unfettered access to the data trapped in work deliverables, corporate databases, and enterprise software solutions. So, what are the opportunistic use cases to pursue? According to the Emergence team, four key areas are emerging: 1. Engaging Experts: Source high-quality data from experts in each field using novel incentive structures and community engagement. Examples include Centaur Labs and Datacurve (YC W24). 2. Leveraging Latent Data: Help enterprises prepare and utilize data from business apps for AI training. Examples include unstructured.io and Shelf. 3. Capturing in Context: Develop methods to capture new data without disrupting workflows, including multimodal content. Examples include Textio and Superlinked. 4. Securing the Secret Sauce: Assist enterprises in creating and deploying their own custom models to protect proprietary IP. Examples include Together AI and Holistic AI. ----- 🔗 For the full investment thesis, check out ‘The AI Plateau Is Real — How We Jump To The Next Breakthrough’ from Emergence Capital. 🔗 Find "Read Capital Chronicles 24" on my profile for original materials, additional takeaways, and more. #Technology #Entrepreneurship #VentureCapital