Pradnesh Patil

There are two interesting and relatively new/emerging revenue models in hardware space. (1) Software Enabled Hardware Hardware is sold at lower but slowly improving gross margins (eg 20-40%), and software recurring revenues layered on top. Software sells at higher margins, enabling blended margins to appear in 50%+ category. These are applied in situations where hardware is not expected to be replaced or greatly improved upon in deployment for a significant number of years to come. Consider your car, or military equipment, or even infrastructure equipment. As always, there are often multiple business/revenue models utilized by different players in the same industry. (2) Hardware As A Service Hardware and software are bundled and sold with annually recurring revenues. Often multi-year contracts are signed to be able to amortize hardware over time, and enable blended gross margins over the length of the contract to be high (approaching 60-75%). This is more common in situations where vendor takes responsibility to upgrade hardware as needed along the way to provide maximum and highest functionality to the customer. Hardware inventory costs are, over time, passed on to suppliers or third party financing parties in lieu of few gross margin points. Consider robots as a service in factories (ala Formic), or AI-enabled equipment (eg Evolv, Lumafield etc). Hardware innovation will require deeper investigation, experimentation, and understanding of business/revenue models to fit customer needs as well as to maximize value for the innovating company. Strong and experienced management teams work hard to minimize sales friction, inventory costs etc, but also maximize gross margins, customer stickiness, and ability to upsell during the life of a contract.

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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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Task-specific models and routing architectures are the future. Robert Nishihara, co-founder and CEO of Anyscale, is seeing hybrid strategies emerge, where businesses are evaluating different types of models for different use cases. With the rapid advancement of AI tech, it won't be long before open-source models are "good enough" for the majority of business tasks. The natural progression from here is to develop smaller, task-focused models using proprietary company data. As Robert puts it: "You're going to need small, fast models. And task-specific models have a huge advantage when it comes to cost and speed because you can achieve the same quality with a smaller model if it's specialized." Another interesting development: the growing use of routing architectures. Imagine a classifier as a kind of traffic controller for your AI queries. It assesses the complexity of each query and directs it to the most suitable model: simpler requests are handled by smaller models, while complex ones are passed on to larger, more sophisticated models. “If you do it right,” Robert says, “you can achieve something like the cost of the smaller model with the quality of the more advanced model.” Dive deeper into AI architecture trends in my full discussion with Robert on the B2BaCEO podcast here: https://lnkd.in/ghigvERn

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Solid advice to founders from the co-founder and CEO of Databricks, Ali Ghodsi 👇 Target other startups first. Ali draws on Geoffrey Moore’s book Crossing the Chasm, which identifies five key groups in the tech adoption cycle: 1️⃣ Innovators 2️⃣ Early adopters 3️⃣ Early majority 4️⃣ Late majority 5️⃣ Laggards Ali’s advice is especially relevant for AI-focused founders, given the myriad of risks (security, privacy, explainability, and so on) that come with putting generative AI models into production. It is difficult to target established, traditional companies—the laggards—as an initial market. These large enterprises come with a labyrinth of security requirements, bureaucratic red tape, and drawn-out sales cycles. Beginning with other startups increases the likelihood that a product will be adopted and provides a powerful way to collect early user feedback. Establishing this data flywheel can help an AI startup differentiate their model and iterate their way to product-market fit. Salesforce and Dropbox used this particular playbook. Once you've proven your model with startups, then you can set your sights on enterprise 🚀

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Look forward to sharing ideas on how to power agentic workflows to make GTM teams become more efficient with K. Matthew Dupree and Amrutha Gujjar and others. You should book a spot if you are curious to learn about: 1. Strategies for transitioning users from a configuration-focused mindset to an interaction-focused mindset. 2. Techniques for designing products that foster trust, while carefully balancing the level of detail exposed to users to avoid overwhelming them. 3. An overview of emerging UI/UX design frameworks that are well-suited for agentic systems and environments. #plg #productledgrowth #agenticdesign #

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As we look ahead to 2025, it's becoming clear that the future of enterprise AI lies in compound systems, not monolithic models. Compound AI systems combine multiple models that work together iteratively, coordinating and leveraging external tools to evaluate, refine, and improve their own results. 🔄 The benefits are clear: ☑ Better performance than individual models ☑ Faster and less expensive iteration  ☑ Granular access controls on sensitive data  ☑ More reliably ensure that models behave as intended ☑ Flexible performance-cost optimization Early examples like AlphaCode 2, LLM Debate, and CoT-SC are already showing impressive results. 📈 As we move towards 2025, compound AI systems will become increasingly critical for enterprises looking to deploy AI at scale reliably, securely, and cost-effectively. 🎯 Check out my latest B2BaCEO newsletter for a detailed look at where AI is headed and what it means for founders: https://lnkd.in/gRrzX9nq h/t to Berkeley Artificial Intelligence Research and my colleagues Jaya Gupta Andrew Han Courtney Fiske for their research help

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🌐 As we enter 2025, the AI landscape is evolving. The spotlight is shifting from chipmakers to businesses adopting AI. #AI #TechTrends 🚀 Early stages saw companies like Nvidia reaching a $3 trillion market cap due to AI infrastructure investments. Now, the real growth is with AI adopters. #Innovation 💼 Experts highlight that companies integrating AI into their operations will drive the next wave of growth. Efficiency and new revenue streams are on the horizon. #BusinessGrowth 🏦 In banking, AI is revolutionizing risk management, fraud detection, marketing, and customer relations. #FinTech 🏥 Healthcare is seeing AI-driven diagnostics, personalized medicine, and automation, leading to significant advancements. #HealthTech 🛒 Retail benefits from enhanced customer experiences, supply chain optimization, and predictive analytics. #RetailTech 🤝 The AI market is consolidating, with major players acquiring smaller firms to expand capabilities. #MergersAndAcquisitions 📉 Decreasing costs and technological maturity make AI accessible to smaller businesses, driving widespread adoption. #AffordableAI 🚚 AI optimizes manufacturing, logistics, and customer service, enhancing productivity and engagement. #SmartTech 🌟 The focus is now on leveraging AI for value creation and competitiveness, promising substantial growth in the coming year. #FutureOfAI #BusinessInnovation Link to the full article in first comment:)

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Founderville believes one of the most significant opportunities is emerging in legacy system applications. We look for manual tasks to leverage AI for efficient automation. Rather than chasing massive TAM, which will be addressed by larger VCs, we focus on smaller market domains that won't be chased as hard. Think of "boring" processes that require human intervention or manualized tasks. Those are opportunities. Keep your eyes open if you have deep domain expertise in a non-automated and fragmented industry. #entrepreneurs #venturecapital #startups #saas

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Today's a very special day as we announce the launch of Resolve AI and share that we at Greylock led their $35M Seed round. Reimagining software development may be the biggest opportunity in the AI era. Resolve is unveiling the world's first AI Production Engineer -- an AI engineer that understands source code, handles alerts, performs root cause analysis, and resolves incidents. In less than six months since starting, Resolve is already in production across enterprises, dramatically reducing Mean Time to Resolve (MTTR), increasing uptime and improving reliability. Most importantly, companies that use Resolve free up their teams to focus on what they love and not the operational toil of keeping systems running in production. We are tremendously proud to be backing Spiros Xanthos, Mayank Agarwal and team. Spiros and Mayank are proven entrepreneurs who have built multiple successful companies, most recently leading observability at Splunk following the acquisition of Omnition. They also are the co-creators of OpenTelemetry, one of the most widely adopted OSS projects. Running production with AI is a tall order and the Resolve team has the rare intersection of deep observability, developer and AI expertise to make it happen. For the first time, machines are on-call for humans, and not the other way around!

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💥 𝐃𝐚𝐲 𝟏 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬 𝐟𝐫𝐨𝐦 TechCrunch 𝐃𝐢𝐬𝐫𝐮𝐩𝐭 💥 🌍 Vinod Khosla’𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐬𝐭𝐢𝐜 𝐕𝐢𝐬𝐢𝐨𝐧 𝐟𝐨𝐫 𝐀𝐈  ➡️ AI has the potential to create great abundance, drive GDP growth, boost productivity, and even increase income equality. ➡️ The vision is ambitious but clear: AI can lead to an economic surplus that supports those impacted by technological change. 💡 𝐏𝐨𝐥𝐢𝐜𝐲 𝐚𝐧𝐝 𝐆𝐥𝐨𝐛𝐚𝐥 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 ➡️ There is a need for smart policy to manage AI’s impact, different countries will make choices on resource redistribution. ➡️ There is risks in geopolitics, specifically China as a critical player in the AI race. 🚀 𝐓𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫𝐬 ➡️ “𝘌𝘹𝘱𝘦𝘳𝘵𝘴 𝘦𝘹𝘵𝘳𝘢𝘱𝘰𝘭𝘢𝘵𝘦 𝘵𝘩𝘦 𝘱𝘢𝘴𝘵, 𝘣𝘶𝘵 𝘦𝘯𝘵𝘳𝘦𝘱𝘳𝘦𝘯𝘦𝘶𝘳𝘴 𝘪𝘯𝘷𝘦𝘯𝘵 𝘵𝘩𝘦 𝘧𝘶𝘵𝘶𝘳𝘦.” The best entrepreneurs build from 0 to 1. ➡️ Big innovations rarely come from large enterprises; instead, they’re driven by entrepreneurs with first-principles thinking, persistence, and a big vision. ➡️ The bigger the problem startups solve, the bigger the opportunities and impact they befriend. Those insights remind us that while AI’s path forward involves challenges, it’s also a time of unprecedented opportunity for those willing to build boldly. 💪 📖 𝐒𝐮𝐠𝐠𝐞𝐬𝐭𝐞𝐝 𝐫𝐞𝐚𝐝𝐢𝐧𝐠𝐬 𝐟𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞: ➡️ '𝘙𝘦𝘪𝘯𝘷𝘦𝘯𝘵𝘪𝘯𝘨 𝘚𝘰𝘤𝘪𝘦𝘵𝘢𝘭 𝘐𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 𝘸𝘪𝘵𝘩 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺' (2018) by Vinod Khosla (https://lnkd.in/g8mdExgH) ➡️ '𝘌𝘹𝘱𝘦𝘳𝘵 𝘗𝘰𝘭𝘪𝘵𝘪𝘤𝘢𝘭 𝘑𝘶𝘥𝘨𝘮𝘦𝘯𝘵: 𝘏𝘰𝘸 𝘎𝘰𝘰𝘥 𝘐𝘴 𝘐𝘵? 𝘏𝘰𝘸 𝘊𝘢𝘯 𝘞𝘦 𝘒𝘯𝘰𝘸?' by Philip E. Tetlock Thank you for very inspiring talk, Connie Loizos. #greatabundance #venturecapital #techcrunch #AI

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Who is NVIDIA going to acquire next? They tend to partner before acquiring which we break down here And we suggest their next target P.S. follow the data https://lnkd.in/gJuVzrzs

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Worth reading: Generative AI is not going to build your engineering team for you. So, so good - peppered with gem quotes and observations- chuckle. https://lnkd.in/gZBKtU4X Thanks to Eric Brooke for sharing.

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"AI is powerful, but without the right data foundation, it’s just noise." In the latest Tech Teams Today podcast episode, I had a great conversation with Yongsheng Wu, VP of Engineering at Granica, about why a solid data foundation is crucial for AI success. Here are the top insights from the episode: 1️⃣ Data Foundations Matter: Before diving into AI, ensure your data systems are robust. Solid pipelines & data integrity are critical for meaningful insights. 2️⃣ AI as an Enhancer: AI and ML should optimize existing processes, not solve undefined problems. 3️⃣ Data Quality is King: Curating and labeling data accurately is vital for effective AI models. The better the data, the better the outcomes. 4️⃣ Efficiency & Cost-Effectiveness: Successful AI solutions balance effectiveness with cost efficiency, especially as models scale. Catch the full episode on all major podcast platforms or watch it on YouTube: https://lnkd.in/dbkE__JC #AI #EngineeringLeadership #DataScience #MachineLearning #TechTeamsToday

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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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𝐇𝐮𝐦𝐚𝐧𝐬 𝐢𝐧 𝐀𝐈 𝐢𝐬 𝐁𝐚𝐜𝐤! 🎉 Welcome back to Humans in AI. This week, we are joined by Dheeraj Pandey, a successful serial entrepreneur, co-founder and CEO of DevRev and former co-founder and CEO of Nutanix. DevRev is a Mayfield Fund portfolio company building #AITeammates for Product & Support teams. In this video, Dheeraj Pandey will share his invaluable insights on: - AI Teammates and how they benefit humans. - Advice for fellow founders, drawing from his own impressive experience. Who would YOU like to see next? Let us know in the comments! 👇 #HumansInAI #AI #AITeammates #GenAI #DevRev

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When you sell into an existing category, there are known GTM channels and expectations you can play into. Anish Dhar points out that category creation isn't just about messaging or product but you have to discover and define all those GTM patterns anew.

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Why do paying customers have nearly 0 loyalty to SOTA AI chat products like ChatGPT, Claude, & Gemini? I think a major contributor is the lack of contextual memory recall. If information about your career, family, interests, preferences, etc isn't in the context window, these LLMs effectively don't know who you are. Provide them as much information as possible, even in the system prompt, and they struggle to reason over which are the relevant pieces of information to use to complete a given task. Nobody wants to write to their boss in the same style they write to a romantic partner (except maybe if they are one and the same 🤭). Congrats to the Letta founding team of Sarah Wooders and Charles Packer for their recent raise and coming out of stealth with a commercial product version of MemGPT to bring better contextual recall to LLMs. I would love to see a comparison against other memory techniques like SSM, xLSTM and hybrid archa to see where different approaches excel. Overall, I'm excited for the future memory capabilities this signals. #ai #generativeai #memgpt #research #berkeley

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How will Amazon's 980M parameter LLM influence the AI industry? Amazon's 980M parameter LLM is a colossal leap forward, setting a new benchmark in AI capabilities. This technology titan is poised to spark fierce competition and relentless innovation, driving the AI industry into a new era of rapid advancement and astonishing applications. We are starting the age where each lab will move on its own trajectory and velocity in AI development.

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