Sridhar Ramaswamy

The Forbes 2024 Cloud 100 List is live ☁ Congrats to all the honorees – the world's top 100 private cloud companies –including the 3 healthcare and life sciences companies featured this year: • Benchling - biotech R&D platform • Komodo Health - healthcare data analytics • Abridge - AI for clinical documentation For more on how AI is colliding with cloud computing, check out Bessemer Venture Partners's 2024 Cloud 100 report. Personally I'm focused on advancements in multi-modal AI, as applied to biomedical applications (Trend 3!): https://lnkd.in/euzurvQV

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We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

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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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Open-source models can be a strong lever for increasing gross margins at AI companies, especially those that have hefty cost structures (e.g., enterprise sales orgs that require significant customer success or implementation resources, as well forward-deployed AI services models common in healthcare and life sciences). One of many reasons to maintain a modular and flexible stack with minimal dependencies when building an AI company. More on the importance of flexibility in the AI stack: https://lnkd.in/eA2XATRk h/t Delip Rao #ai #artificialintelligence #generativeai #healthcare

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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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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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$4 trillion. As we announced last week, that’s the amount of closed-won revenue flowing through the Clari Revenue Platform. For perspective, that’s the approximate market cap of Alphabet (Google) + Amazon. It’s more than the UK’s GDP. And thanks to our amazing customers, we know more about running revenue than any company on earth. Why? We’re constantly collecting terabytes and terabytes of revenue data and analyzing how companies manage their enterprise revenue process. All day. Every day. As the first RevTech company to cross $4T of revenue under management... - We have the largest revenue database in the world, which fuels our best-in-class Rev AI. - We have the widest and deepest product functionality on the market. - We have the most expertise for running revenue of any player in the space. - We have the largest footprint of massive multi-billion publicly traded companies leveraging our Revenue Platform.   And at Clari we’re committed to using all our data, expertise, and technology to help you stop revenue leak and capture more revenue predictably and repeatedly. Which is why we put together our biggest learnings on how to run revenue from managing $4T. Check it out in the comments.

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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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This is what I mean when I say AI x DEEPTECH A revolution is coming. Upending entire swaths of our economy that have been previously ignored. Nearly $10 Trillion of our GDP rebuilt with AI and software.

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Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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The AI Demo isn’t easy. Many of the major AI companies have demoed their AI systems, first starting with pre-recorded, & now pushing into live demos. They don’t always work. Multiply Murphy’s Law by a non-deterministic system & it’s not unreasonable to expect AI demos to nearly always hiccup. Demo disruptions aren’t disaster. These systems are early & changing rapidly. They might suggest the system requires work & tuning, not a fundamental challenge. But, they can be problematic in proofs-of concept. Proofs of concept are extended demonstrations of the software. Well-structured PoCs align on success criteria at the outset. These criteria enable vendors & customers to agree on what success looks like. Worflow proofs-of-concept are relatively straightforward. They are deterministic. Can I process a loan application in 5 minutes? Yes or no. But as AI applications shift to selling outcomes implicitly or explicitly, the PoC becomes a testing ground of those outcomes. Non-determinism means sometimes the PoC won’t produce the required wow moment. This also means the PoC criteria must be more flexible. How does a buyer evaluate a probabilistic system? Do we compare it to human performance? Speaking to some practitioners, they’ve shared with us human labelers typically agree on 60-70% of the time. Does a AI robot need to be as accurate as a human assuming it will be much less expensive? Or will we expect more as we do in self-driving cars? If AI systems require human assistance, then the ROI of the system must include some human operating expense - whether explicit or implicit. Some teams will want to benchmark systems in parallel to determine the relative performance. With most startups building atop existing models & setting aside differences in fine-tuning, the ultimate performance should be relatively comparable, provided they use the same data sets. Will startups compete on access to different data sets? Today, there are more questions than answers about how to sell AI agent systems. We’re hosting an event on the evening of Sep 10th in San Francisco to interview leaders in the space moderated by Dave Morse, former CRO at Hebbia & VPS/VPCS at ScaleAI to talk about some of these questions. If you’re interested to attend, see the details here : https://lnkd.in/dA8tJEcz

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We are just at the beginning of the AI led digital transformation! Lots more questions than we answers on adoption, evolution in Enterprise AI but that’s exactly what creates white spaces, opportunities for innovation. Let’s play!

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“AI-First” Design is the Future of Application Development It’s becoming clear that simply tacking on AI features to existing workflows is not enough to unlock their full potential. Just as mobile applications were transformed by the shift to a “mobile-first” design philosophy, today’s applications need to be reimagined from the ground up with an “AI-first” mentality. A decade ago, as mobile devices became ubiquitous, companies found that they couldn’t just retrofit desktop apps for smaller screens; they had to rethink the user experience for mobile. I see this same shift happening with AI. Companies that bake AI into the DNA of their products stand out, while those that merely bolt on chat and LLM features may find themselves falling behind. Take a classic example: forms. These are everywhere in application design, and most are terrible because they force the user to conform to rigid questions and formats. Consider e-commerce. If I want “a blue bicycle for a 12-year-old girl that’s really safe and has great ratings,” it takes me 10 seconds to type, but navigating through filters, clicking options, and reading reviews could take much longer. An AI-first approach would act as an agent on user needs and streamline that journey—transforming an exhausting task into a seamless experience. However, shifting to an AI-first design isn’t without its challenges. Existing workflows are often deeply ingrained in technical debt, making it difficult for companies to rethink processes at a foundational level. But those willing to disrupt the status quo will be the ones driving meaningful innovation. How to Get Started: Embracing an AI-first mindset doesn’t mean overhauling everything at once. Start small, but think big. Identify key areas where AI can have the most impact—whether it’s personalizing experiences, automating tasks, or improving decision-making. From there, build AI-driven solutions that elevate the entire user journey, rather than just tweaking existing features. A New Standard: AI-first design isn’t just an advantage—it’s the new standard. Companies that fully adopt AI into their product experience will create smarter, faster, and more intuitive products that resonate with users and set them apart in the market. This is more than a trend; it’s a shift in how we think about and build for the future.

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I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

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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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The starting gun for Agentic pricing is off! How will your SaaS company communicate value and cost of solutions to increase NRR, and mitigate churn from legacy ARR useage in 2025?

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Service-as-software is a $4.6T opportunity. But no one knows how to actually grab that opportunity yet. Enterprise software has gone from assisting human workflows to now, LLMs giving software the ability to actually perform parts of the workflow. Instead of relying on timely and sometimes inaccurate human inputs of structured data, today's systems actively capture and process both structured and unstructured data. The service-as-software shift was huge—but we're on the edge of one that's even bigger. My Foundation Partners Joanne Chen and Jaya Gupta have now outlined how to put the service-as-software paradigm shift into action: through a System of Agents, which doesn’t just streamline a workflow but actually completes whole tasks, becoming the worker. A System of Agents mirrors a human team, with agents taking on collaborative, specialized tasks and continuously learning from each other, just as human teams do. Advice for founders building with Systems of Agents: → Position at the data source, own the path. Traditional software waits for humans to input data, whereas Systems of Agents capture it at the source. When an agent owns the interface where data is born, the system can then orchestrate every downstream action. → Move beyond software budgets, tap workforce spend. When a System of Agents completes a whole job task, it should be categorized as a personnel cost, not a software expense. → Use AI’s 24/7 nature and scalability to create new workstreams. AI agents working in areas like healthcare can operate 24/7, providing reliable support and an around-the-clock presence. → Stay ahead of pricing and business model shifts. Service-as-software shifts the core business model from seat-based pricing to outcome-based pricing; value is placed on the tangible improvements that AI delivers. In the past 6 months, we’ve heard from hundreds of startups working in this space. After those conversations, we’re announcing the System of Agents 50, which includes the companies you see here. 👇 Full blog in the comments. 

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Hosted a panel discussion Pear VC SF with four brilliant and prolific startup founders, Harrison Chase (LangChain), Shreya Rajpal (Guardrails AI), Rayan K. (vals.ai) and Simon Suo (LlamaIndex); between them they're teaching the world how to build robust #GenAI apps! We discussed 1. What GenAI problem each of them solves, namely: RAG+agents, Reliability, Eval and Data ingestion. 2. What future areas are most important, namely: multi modality, new model architectures, and agents. 3. Our panelists emphasized the importance of UX for agents. We discussed the balance between human directed agents vs machine orchestration. We concluded with each panelist sharing how they manage their time, how they think about long term differentiation and what they hope the future holds.

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Success with generative AI can only be unlocked by strategically using an organization’s unstructured data. This is giving rise to an emerging unstructured data stack, which consists of three crucial components: 1️⃣ data extraction and ingestion 2️⃣ data processing 3️⃣ data management In this post, I share more context about the solutions in these spaces and how they are used: LlamaIndex Reducto Tensorlake unstructured.io Vectorize Eventual Databricks Polars Anyscale Pandas Onehouse Tabular (now part of Databricks) Chroma Pinecone Supabase Spiral LanceDB MongoDB Stately Neo4j FalkorDB TigerGraph https://lnkd.in/dT74Yj3C

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