Chris Chang

AI is reshaping how enterprise software delivers—and prices—value. Companies like Zendesk, Intercom, and Forethought are shifting to outcome-based pricing, charging only when AI autonomously resolves issues. I've been investing in AI for over a decade. The shift to outcome-based pricing in enterprise software is a seismic change. As I told Jon Victor at The Information, software companies now face a critical decision: Does a company decide to change business models at the expense of losing revenue? It's a necessary risk. With cheap access to LLMs, businesses can build their own enterprise apps. Adapt or die. Why this pricing shift matters: → Vendor success ties directly to customer outcomes → Short-term revenue risks enable long-term market expansion → Industry faces widespread pressure to demonstrate measurable value Pitching AI-powered automation software as a clear cost-reduction tool could ultimately lead to greater revenue for vendors. This is because customers may view AI solutions as fundamentally different from traditional enterprise apps, whose business value is often harder to quantify directly. It's a huge market expansion opportunity. Vendors that nail outcome-based pricing could see massive growth as AI transforms enterprise operations. Those that don't? 😬🫣 How do you see this impacting sectors beyond customer support? Leave a comment. And if you're building in this space, send me a message.

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Many assumed that the industry would be well on its way to AGI or superintelligence before AI model builders’ efforts to take advantage of scaling laws might hit a wall. Recent headlines are chipping away at that assumption. One question I've been getting in my inbox: is this the era of diminishing returns for AI? We don't think so. Even if an avalanche of new clusters and synthetic data fail to deliver significantly better model performance, there are good reasons to believe that AI is still going to become orders of magnitude more powerful in the coming years — powering a new generation of startups and ultimately productivity growth and positive benefits for mankind. In fact, with agents and reasoning just beginning to scale and thousands of industry-specific models coming online, it's about to get really exciting. Read our latest blog post on the topic: https://lnkd.in/gFhniNX8

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AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

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In case you missed it last week, one of my favorite SaaS benchmark reports was released. Thanks to High Alpha and OpenView for the work and having us as a partner! This thing is stuffed full of insights on everything from growth rates, net revenue retention, ARR per FTE, and more. You can't build a great SaaS business without it! https://lnkd.in/ghKT_tBY

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I love YC Demo Day season. It’s an insane concentration of driven founders building something they deeply believe in at the frontier of tech. I know it's more trendy for VCs to complain about YC. But I believe Garry Tan when he says 5-10% of companies become worth a billion dollars. Maven’s hit rate from YC is even higher: 16% have had $B+ exits, and we’re trending toward a 40% graduation rate from Seed to Unicorn with investments we’ve made alongside YC. Since Maven’s founding in 2013, we’ve funded 12 YC startups out of the 66 companies we’ve invested in. Here are a few: Cruise: acquired by GM for $1B+ Embark: $4B SPAC, acquired by Applied Intuition Chariot: acquired by Ford before Series A May Mobility: raised $300M+ in follow on funding, including from Toyota Carrot Fertility: raised $100M+ in follow on funding, including from CRV and USVP Daybreak Health: Series A from Lightspeed, Series B from USV Moment Robotics: acquired by Cruise founder Kyle Vogt’s new company, The Bot Co (Maven is an investor) Our strategy differs from other YC investors. Many funds build a YC “index fund”, investing in many startups per batch. It’s not a bad move. But we focus on concentration, meeting all the companies in our thesis area and investing in one or two. Gratefully, I think every company from the list above can point to Maven as one of their most trusted partners that made a difference: intros to customers, closing follow-on funding from top VCs, connecting key hires, and offering genuine care and advice in both the best and toughest of moments. What are we looking for in a YC investment? 1) Team - a technical product team with startup DNA and hustle that can achieve a bold vision. Brilliant, determined founders no one would bet against who are ambitious enough to build an iconic company. 2) Vision worth fighting for - what are you bringing into the world that is worth all the time, effort and money required for success? Why aren’t existing solutions good enough? Are we proud to help bring this idea to the world? 3) Massive market - because of the valuation premium YC demands, even a $B outcome may not return today’s average Seed fund. Honestly, that math sucks. In order to pay the premium, we have to believe that if we’re right, we’ll be wildly successful. 4) Consumer trend - Maven invests in tech companies addressing emerging consumer trends. Even if some investments are B2B or B2B2C, they are always grounded in a consumer insight: autonomous vehicles in 2014, fertility care in 2017, mental health in 2020. Today, we’re looking at consumer applications of AI and frontier consumer health. Many successful companies we fund are outside of YC as well – like Zoom, Hello Heart, Epic!, Class, Wildtype. So we match YC’s Unicorn % across the rest of our portfolio, too 💪 YC founders in consumer tech and digital health: if you want to build an iconic company that delivers a positive impact for millions of consumers - I can’t wait to talk to you!

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Instacart is a market leader when it comes to leveraging AI tools to boost productivity at work. I had an amazing conversation with JJ Zhuang, Instacart’s Chief Architect, who has shepherded over 50 AI-driven initiatives to market across the org. We explored what catapulted Instacart to the forefront of AI integration, supporting radically diverse use cases across various functional areas. One striking insight (echoed by Ethan Mollick and others elsewhere) is that you often don't need specialized tools for specialized work. Frontier models like GPT-4 can seamlessly adapt to a wide range of functions and use cases. They're not just for engineers; they're incredibly versatile tools used by marketing, comms, and legal teams. From generating code to proofreading and ideation, GPT-4 empowers every department to leverage AI for enhanced productivity and innovation. By integrating LLM’s like GPT-4 into their workflows, teams can streamline processes, automate repetitive tasks, and unlock bandwidth for new creative possibilities. How can you harness the power of AI in your own work to boost productivity? Share your thoughts below 👇 Want to hear more insights from JJ? Check out the full podcast episode by clicking the link in the comments.

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Old school and probably not popular but I still believe products should be customer-satisfaction focused/led rather than technology-led. Use cases for AI should be byproducts of the product improvement process primarily. The thing though is that founders need to fundraise so they're likely to sprinkle some AI on their decks to even look serious. Not sure that's how the most enduring companies have been built. Of course, if sources of capital become AI-biased, founders have little choice. Happy to keep an open mind and dive in when a use-case that can 10x customer experience for Good Tenants customers and users shows up.

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Insightful look into bioplatform strategy, and what it takes to raise a Series A from my partner, Omri Amirav-Drory: https://lnkd.in/dFv7GbF6

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What a week this has been for innovative companies in Seattle (first Smartsheet and now OctoAI). Today NVIDIA made public the news that they have acquired Madrona portfolio company OctoAI. Partnering with the founders since Day 1 (ok Day -1) has been one of the greatest experiences of my life. Luis Ceze, Jared Roesch, Tianqi Chen, Jason Knight, Thierry Moreau and Carlos Guestrin were all involved in the research and insights that helped create the TVM open source technology at the University of Washington computer science school and then the creation of OctoAI back in 2019 to drive commercial impact with this technology. From sorting out the terms of the original "pre-seed" round over a bottle of Caymus Vineyards wine to helping Amazon Web Services (AWS) understand the dynamics around the open source movement in Applied AI to learning our way in to product-market fit with the BEST inference optimization service available (OctoAI can help customers find their optimal fit of performance, customization, and cost), OctoAI has been the best! The company has helped set the table for AI-driven innovation for the years ahead and they will be a perfect compliment to the many capabilities at Nvidia. Luis, his leadership team, the board (including Carlos Guestrin Lip-Bu Tan and Mike Dauber) and so many "friends of OctoAI" know what a motivated, talented and authentic group of people are associated with this company. It is inspiring to see an acquisition at a total deal value of over $250 million in this highly dynamic and fast-moving phase of AI disruption. It is even more exciting to anticipate what will be possible with innovations powered by AI models and related technologies in the years ahead. Madrona has been investing in Applied AI for over a decade and we truly believe that we are just getting started! One of the hardest judgement calls as an investor and board member is to assess the benefits and risks of selling a company at a key inflection point in the journey. Nvidia made a compelling case that the impact for the OctoAI team and technology would be maximized within the market leader for AI infrastructure. I will be rooting every day that they all maximize this impact in the months and years ahead!

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Venture Capital Contact Compilation. Explore a detailed list of 1,250+ VC firms, 7,400+ professionals in the VC space (including partners and principals), and 6,400 email contacts. https://payhip.com/b/cs7UQ #startup #startups #vc #venturecapital #investmentbanking #privateequity #managementconsulting #b2b #founders #entrepreneurs

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Let the wave begin! Pioneer Fund company Recall.ai *grew their revenue 10x* in the past year, has 300 enterprise customers, and just raised a $10M Series A led by Ridge Ventures. David Gu and Amanda Zhu dropped out of college at age 19 to start the company. They've grown it exponentially with a tiny team. They make it easy to build meeting bots that are cross-platform. They're aggregating all these different APIs into one unified API, the way Twilio did with cell phones. At first it "sounds" easy to build a meeting bot -- just integrate with the Zoom API. But in fact there's a huge long tail of things you need to build, security and privacy pitfalls, automations, transcription complexities, integrations, the calendar invites where someone is using Google Meet or Teams, staying up-to-date with the libraries, etc etc. Recall takes care of all of that. It's WAY easier to build on top of Recall.ai and focus on the features that make you unique. Congratulations to the Recall team!

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As AI-powered software takes flight we're seeing stress emerge on SaaS business models. Outcomes > seats and the shift to services-as-software means we need more nimble ways to price, bill, and take payments. A few threads we're pulling on: 🔹 Software builders need more flexibility - it's a shame to build incredible software or have new tools at your disposal only to have pricing / business models restrict adoption and innovation. 🔹 As companies add AI features (often on top of OpenAI or other models) they need to fundamentally rethink pricing to ensure they maintain margins and don't have flat fee users scaling usage disproportionally. Unit economics are under threat. 🔹 This becomes even more crucial as we introduce AI agents into workflows as the volumes of automations will skyrocket. And, the multi-agent teaming economy will be fascinating (forget your smart fridge paying for groceries). More here: https://lnkd.in/ezYq-XPx 🔹 AI is making software more effective and employees more productive. Seat based pricing isn't set up to share in value creation, especially when companies can avoid having to hire new folks / can reduce headcount. 🔹 Modern database, data streaming, and unstructured data infrastructure is making it possible to power new models, make use of existing data, and for vendors to often know a company's data better than the company does. More here: https://lnkd.in/e5JpJiJH 🔹 Modern FP&A tooling is now capable of ingesting data at scale and reducing the headache on finance. More here: https://lnkd.in/ejrQGcun We're actively working on an Activant Capital Research report on usage based billing and would love to share learnings with anyone actively using or considering a shift to UBB. Please reach out to Richard and I if you want to chat --

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20 meetings w/ investors and startups in 3 days around SF/Bay Area. Here's what’s happening in hiring right now: 1) Elite engineers demand elite offers. Early-stage startups are winning top talent by paying up (sometimes as high as $250k-$300k + equity). 2) Startups are prioritizing candidates with brand names, interest in startups, history of solving complex problems, ambition, higher ego (not too high). 3) The best candidates have clarity on career direction. Some good questions I heard founders ask, “Where are you going?” and “How can we help you get there?” 4) Despite early funding surges in Q1/Q2, investors are becoming more selective - especially in AI.  This creates an opportunity for startups to hire from struggling shops with top talent. Invest now before the market shifts again.

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After working with over 200 employees, 10 joint-ventures and 5 exits, here’s my simple framework to set-up any collaboration for success: 1. Why: Clarify the reason why we’re working together. What is the project? And why are we doing it? 2. Value: What is each party contributing to the project? What are each party’s responsibilities? 3. Success: Clarify how success looks like in 3, 6, 12, 24, 48 months from now for the overall project and how we measure success of each party involved. I like the traffic light system (green for great, yellow for ok, and red for bad outcomes) 4. Compensation: How is each party winning based on the project and our contribution? 5. Termination: Agree ahead of time how lack of success looks like in each scenario (green we’re all happy, yellow we explore / analyse how to get to green, red we stop) and how it works when we stop working together. These are not exchaustive but just a list with the essentials in my opinion. This works in a lot of different types of relationships, could be an employee, a contractor, or even other partnerships and even M&A contexts. Wondering about the key elements you’ve seen are important to set-up any collaboration for success?

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We've mapped the 250 most relevant early-stage startups within the European data ecosystem! 🚀 Our coverage spans categories like Data Collection, Storage, Integration, Security & Privacy, and Platform. Discover how these startups are leveraging AI 🧠, the most active sectors 👩‍💻👨‍💻, and key funding trends 🏦. Don't miss the simple table listing all the companies! 😀 #AI #Europeanstartups #MMCresearch #vcfunding Advika Jalan

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ex Google CEO Eric Schmidt spoke at Stanford this week. Lots of nuggets here, but most interesting for the near term, Schmidt believes in the next year AI is set to make a big leap forward. He predicts AI will combine three important features: 1. Vast knowledge retention (1 million token models) 2. Text-to-action capability 3. Personal AI agent fleets While the full impact is unknown, everyone may soon command their own AI teams. Here's an example: Imagine you're developing new quantum computing algorithms. Your AI assistants could: - Continuously scan and synthesize global quantum research papers - Generate novel algorithm ideas based on this knowledge - Simulate and test these algorithms across various quantum architectures - Automatically document findings and prepare draft papers - Identify potential collaborators and draft outreach emails All initiated by a command like "Advance our quantum algorithm research." This could dramatically accelerate R&D cycles and scientific breakthroughs.. https://lnkd.in/eJbg6cnD #frontiertech #AI #futurefrontier

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Thrilled to announce our team at Andreessen Horowitz is leading the Series A in HappyRobot! (CC Olivia Moore, Seema Amble) Happyrobot is reinventing communication for logistics - starting with AI phone agents that can handle load updates, check calls, negotiations, and more. Freight is a massive market, which today runs on calls, faxes, and emails. AI can handle this back-and-forth at much lower cost than humans and with 24/7 uptime, enabling brokers and carriers to pick up every call. This unlocks better margins, and frees up employee time to build deeper relationships. Over the last year, Happyrobot has completed more than 1M calls for some of the largest players in freight. Customers use Happyrobot to trigger custom workflows during and after each call, connected to their data systems...and, even transfer to a live human if needed. The last gen of freight software cos got it wrong by trying to disrupt the broker. We (+ the HappyRobot team!) believe brokers are the experts, and the right approach is empowering them. We're excited to partner with logistics leaders on this round - including Ryder System, Inc. Ventures, Andrew Berberick, Tobenna Arodiogbu, Jin Shieh, Jett McCandless, Dan Lewis, and George Ezenna - and work with angel investors Carles Reina, Bram Sugarman, Christopher Coleman, Nicholas Hinrichsen, Andrew Berman, and Gokul Rajaram. We're joining Y Combinator and @Array Ventures who led Happyrobot's seed round. We couldn't be more excited to work with co-founders Pablo Palafox, Javi Palafox, and Luis Paarup as they build software to solve big problems for freight. They're growing quickly - and hiring engineers! Read more about the company and our investment here: https://lnkd.in/g4ZdHnGb

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