Igor Taber 🇺🇦

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 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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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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PE - PE Portfolio & the Value in AI BCG on "Where’s the Value in AI?": > AI Adoption Challenges: 4% of companies are at the forefront, consistently driving substantial value with AI, scaling and realizing returns on AI investments - these are the market leaders in using AI > Value in Core Processes: market leaders achieve 62% of AI-generated value in core functions, including operations (23%), sales/marketing (20%), and R&D (13%). This focus yields a 50% revenue growth advantage and 60% higher shareholder returns over less mature companies. > Strategic Differences Among Leaders: Leading companies focus on: 1. Prioritizing AI in core functions and support 2. Driving AI initiatives aimed at revenue growth and cost reduction 3. Investing heavily in a few, high-impact AI projects with the potential to scale > Important - GTM Benefits: sales/marketing gains from AI are significant ins sectors like software, media, and telecom > AI Playbook for Success: 1. Set a bold strategic commitment from the top 2. Maximize the potential value of AI with initiatives that include streamlining everyday business processes, transforming entire business functions, and developing new offerings 3. Implement one to three high-value, easy-to-implement initiatives to fund the journey 4. Ensure that the minimal viable infrastructure required for these initiatives exists 5. Perform an AI maturity assessment to baseline current critical capability gaps versus peers 6. Ensure that implementation governance focuses on people and processes over technology and algorithms 7. Set up guardrails to deploy AI responsibly --- Source: BCG, "Where is the Value in AI?", https://buff.ly/4fifzNC ------------------ #pe #privateequity #ai #ceo #cro #saas #gtm #growth #middlemarket #boardofdirectors #board #revenue #revenuegrowth #investing #investment #finance #business

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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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PE - How to Use AI to Unlock Growth in Portfolio Companies All PE firms and portfolio companies should be thinking about unlocking GTM and growth via AI (i.e. not merely ChatGPT or other LLMs but actually implementing apps that are "AI Agents"). 1. What are "AI Agents"? - AI agents are autonomous systems powered by ML and natural language processing, designed to perform tasks with minimal human intervention - They can interpret data, determine the workflow & direction (i.e. make decisions), and execute actions across various functions, continuously improving through self-learning 2. Examples of using AI Agents in PE portfolio companies: - For Sales: AI agents handle lead qualification, respond to inquiries, book meetings, and provide personalized product recommendations, ensuring no opportunities are missed - For Marketing: Generate campaign briefs, segment audiences, create tailored content, and analyze performance to optimize outreach efforts - For Customer Success & Support: Provide 24/7 support, resolve customer issues autonomously, escalate complex cases to human agents with full context, and ensure consistent, brand-aligned responses 3. How PE portfolio companies benefit: - Efficiency: Automate repetitive tasks, enabling teams to focus on high-value activities - Scalability: Handle growing customer volumes without increasing headcount - Personalization: Deliver tailored experiences using real-time data insights - $ Cost Savings: Reduce operational costs by streamlining workflows 4. Business Impact for PE Portfolio Companies: - Accelerate GTM Execution: Faster lead conversion, better customer retention, and enhanced marketing ROI - Improve Metrics: Boost Customer NPS, CSAT, and efficiency - Enable Growth: Scale operations seamlessly while improving team productivity. 5. How PE Firms Must Think About Adoption - Define clear portfolio objectives to use AI agents (not just LLMs) - Ensure integration with existing CRM and data systems - Prioritize high-quality data and ongoing performance monitoring - Use AI agents to complement existing GTM teams, focusing them on automation ------------------ #pe #privateequity #investing #ai

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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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In vertical SaaS, the very best companies landed with a product solving a specific, burning pain point that horizontal solutions solved badly and then became broad product suites over time (ServiceTitan, Veeva Systems, Doximity, etc). My partner Gordon Ritter calls this “layering the cake”. In vertical AI, the landing wedges are much more brittle. While the painpoints they solve are very real (and often unaddressable by SaaS alone), they’re also much more likely to commodotize in the near term as the tech advances. 👀 Therefore, the need to layer the cake in vertical AI is much more urgent than in vertical SaaS. 👀 The best vertical AI CEOs not only recognize the dynamic, but talk about it openly. Helps those around them keep up the speed of innovation and their eyes on the bigger prize.

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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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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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Excited to publicly announce today that Rebellion Ventures emerges to build the future of AI super capability, generating 10-100x productivity gains. We've been partnering with entrepreneurs for some time and oversubscribed and closed our debut fund but haven't publicly disclosed it until now. We announced today our inaugural fund, which is dedicated to partnering with entrepreneurs to build what we coined the era of artificial super capability (ASC) through autonomous operations and AI agents that have the power to revolutionize business models and unleash the greatest productivity boost in human history. We foresee the shift from passive, intelligence-focused AI to action-oriented artificial super capability that drives business outcomes autonomously. In the real world, it is the context-specific ability to act that delivers results, rather than general intelligence. AI capabilities will evolve to be superior to humans in areas such as speed, cost, and accuracy. This shift will create an era of artificial super capability (ASC), with the potential to increase productivity by 10-100x. We see the emergence of a diverse AI workforce across industries, including AI workers, digital humans, autopilots, and autonomous agents. These will continuously learn, observe, think, and act 24/7 to proactively achieve business goals and elevate human work. Through the fund's companies, we target to collectively create at least $100 billion in annual productivity value for customers by 2030 based on initial ROI metrics. Our fund brings an operating track record of building companies to multi-billion dollar IPO and M&A exits and an advisor network of over 70 unicorn founders, operating executives, and industry experts in AI and automation who are LPs in the fund. We'll provide access to industry relationships, operating playbooks, and critical expertise that entrepreneurs need to build and scale disruptive startups. Our fund has thus far partnered with 12 pre-seed/seed startups creating or enabling autonomous operations and AI agents in areas such as engineering, sales, financial services, insurance, and healthcare (over half of these have already raised more capital at higher valuations). We'll partner with 25+ additional startups over the next 18 months. Recognizing our responsibility in this new era, we seek to be a force for good by elevating human work, committing to ethical practices in AI and automation, and contributing positively to societal and economic development (including pledging 1% of our profits to give back to the community). More details are in the announcement below. https://lnkd.in/gBbnAr3p

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If you enjoyed our first blog on the metrics that matter for Verticalized SaaS then don't miss on the Part II of the series where my colleagues Devraj Hom Roy and Matt Schaar discuss the strategic and operational choices to improve these operational indicators. https://lnkd.in/g5QkCrcH

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Key takeaways from VC Goat - Vinod Khosla podcast. - The firm cares more about working on interesting problems with large technical solutions rather than just maximizing IRR. The team is there because they believe in the mission. - Khosla assumes they've lost the money the day they invest, and then maximizes for the upside opportunity. He calls it "option value investing" rather than IRR investing. - Khosla has contrarian bets in AI (neuro-symbolic computing, probabilistic approaches), biotech, robotics, crypto, and more. - He believes aviation fuels, fusion energy, new transit systems, and other contrarian areas ignored by most investors will be huge opportunities. - Most large innovations come from outsiders, not industry insiders. Khosla looks for founders who can learn a business rapidly rather than have deep domain expertise. The Future Impact of AI - AI will be deflationary and increase productivity growth to 4% annually vs the typical 2% forecast. This will cause great abundance but also increasing income inequality. - Bipedal robots will take over most manufacturing and manual labor jobs within 20-25 years. This will free humans to be more creative and pursue their passions. - Education will shift from job training to creativity. Uniquely human elements like taste and curation will be most valued in an AI-enabled world. https://lnkd.in/gqBsmbks

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Thrilled to announce the launch of our inaugural edition of Beyond Benchmarks at Emergence Capital. This comprehensive report dives deep into the metrics and trends shaping the early-stage enterprise cloud market. A huge thank you to our VC partners and contributors for making this possible! Here's a sneak peek of our findings: --> 60% of companies have already integrated GenAI into their service offerings, with another 20% planning to do so this year. --> While most companies use OpenAI as their primary LLM, many are experimenting with multiple models. We’re seeing a trend toward intelligently routing GenAI inference requests based on cost, performance, and security. --> Companies that have implemented GenAI are showing promising results, with a 7% higher NDR compared to those that haven’t. Beyond Benchmarks goes further with more GenAI trends, insights on the current fundraising environment, and key performance metrics. Our goal is to provide founders and their teams with valuable benchmarks to help them make better-informed decisions. At Emergence Capital, we're committed to helping founders build iconic companies. Dive into the full report here: https://lnkd.in/g6bnvAZM

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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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Just finished reading "The Next Giant Leap" by Space Capital - a pioneering VC investing in the emerging Space Economy. The report speaks to how SpaceX’s Starship is more than just the most powerful rocket ever built—it is poised to reshape the global space economy in profound ways. With its immense payload capacity, reusable design, and lower cost per launch, Starship will unlock entirely new industries and transform existing business models. But how should industries and businesses evolve to align with this paradigm shift? 📈 Rethinking Business Strategies & Supply Chains - For businesses, Starship represents an era of mass-production over precision engineering. This means moving away from the focus on mass efficiency toward cost-effective, redundant, and scalable production. - In terms of supply chains, this opens opportunities for new players in manufacturing, materials, and space components. The demand for off-the-shelf parts and non-aerospace suppliers will rise dramatically. 💡 New Use Cases & Opportunities   The opportunities Starship unlocks go beyond launching satellites. - In-space manufacturing of high-value products like pharmaceuticals and advanced materials that cannot be created on Earth. - Mega-constellations of satellites for global broadband, expanding on what Starlink has already begun. - In-orbit assembly and servicing, allowing for larger space structures like telescopes or even habitats to be built in orbit, radically reducing costs. - Lunar infrastructure and resource extraction will become more viable, paving the way for sustainable human presence on the Moon and, eventually, Mars. 📊 What Should Businesses Do Now?   1. Embrace Redundancy & Scale – Instead of focusing on one-off, high-cost solutions, businesses should embrace redundancy and mass production.    2. Simplify Design – With Starship’s payload capacity, there is less pressure to optimize for mass efficiency. Simplified, scalable designs will allow for faster production and iteration cycles, akin to how SpaceX rapidly tests and evolves Starship itself. 3. Develop Strategic Partnerships – Collaborating with forward-thinking space startups, suppliers, and even non-aerospace sectors will be essential to capitalize on the opportunities Starship provides. 🌌 A New Era of Innovation   Starship will democratize access to space, lowering the barriers for businesses to create and scale. The next decade will witness breakthroughs in scientific exploration, energy production, and in-space transportation that we can only begin to imagine today. #SpaceEconomy #Starship #Innovation #SupplyChain #Manufacturing #SpaceTech #Entrepreneurship #FutureOfSpace https://lnkd.in/gNdbwXdq

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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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During yesterday's LibreQoS APNIC webinar, I posed the question: won't more bandwidth solve these problems? As Herbert Wolverson said, yes, more bandwidth is good, but, still these problems remain if queueing is done incorrectly. Here's my take on why bandwidth alone is not as good as bandwidth + good queueing policies: Generally it was believed that 'data is important, so don't throw it away; hang on to it and send it later'. In practice, this has proven to be suboptimal as two issues may emerge: 1) latency increases for some flows due to heavy demand from other flows using the same bottleneck link, 2) even a single flow can have excessive latency due to aspects of typical TCP behavior (referred to bufferbloat) when the buffers grew large enough that the data being buffered was retransmitted anyway. It turns out that not all data is equally important. Active Queue Management is the art of deciding priorities, both in deciding what data to throw away, but also in allowing some later arriving data to be transmitted ahead of data in another connection that arrived before it. These problems have been studied, and good solutions have been found by using certain queueing policies in routers and switches, referred to as “fq_codel’ and “cake”. These track the different flows not by classifying the data, but by watching the behavior. Flows that send relatively little data (DNS lookups), or at a measured pace (video chat) have priority over flows that send a lot of data as quickly as possible (App and System updates, Video and ISO downloads).

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In a recent Office Hours, Baris Gultekin, Head of AI at Snowflake & a friend from Google, shared his insights into Snowflake’s approach to AI. It was a great conversation to understand how one of the leading data companies is pushing AI forward. The video for the session is here : https://lnkd.in/gfBesq35 & the audio is here : https://lnkd.in/gUK8hR66. There are four major pushes for Snowflake within AI. 1. Bringing AI to Data with Cortex. Cortex is a suite of AI building blocks that enable customers to leverage large language models (LLMs) & build applications. Both structured data & unstructured data (extracting information from presentations & pdfs) can be built using Cortex. 2. Developing open source initiatives including a data catalog, Polaris, & an open LLM Model Arctic which focuses on SQL performance. Open source is a new motion for Snowflake. 3. Securing data sharing to bring data into customers’ accounts for AI. Data sharing across customers enables a marketing organization to use data from a credit card company to build a model for customer marketing. 4. Developing a platform atop Snowflake with native apps to enable a retailer to provide a search experience to their wholesalers, for example. We also chatted about the importance of data access security for AI applications. For many data leaders, this is one of the top priorities to understand before deploying AI. Thanks, Baris, for sharing your perspectives!

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