Viral Bajaria

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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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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Hot Take… RIP #PLG, Long Live #SLG But before you throw shade, let me explain… 🔹 There are undoubtedly exceptions, I’m sure many will highlight monday.com, Freshworks, etc. 🔹 PLG works well for low ACV deals with low CAC… think SMB and mid-market customers. Target CAC payback <12 months 🔹 PLG is a fantastic top of funnel marketing channel for enterprise sales ⭐ But for 6-figure enterprise sales, you need enterprise salespeople pressing the flesh with customers

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Converting PoCs to Contracts - A Lesson in Difficult Conversations Like going from dating to co-habitating, moving your first customer from pilot to contract is a Big Leap moment for early stage startups. Waiting too long to have the conversation is the most common mistake. You are afraid of rejection, so you never learn if your product is making life easier for your customer, let alone how much they will pay. The truth is, once your pilot is launched, you can't be too early in discussing what it will take to move to a contract. I just got off the phone with a startup CEO who told of countless meetings, waiving integration fees, hiring a dedicated employee and more all to get their first customer live. After several revised timelines and a reorg, the engagement was going sideways until he said "We can't do this for free anymore. We need to move to contract or we are done here." At first, the customer balked. But once they assessed the situation and realized how crucial the software was, they changed their mind. The startup is now on track to sign their first big deal. That wouldn't have happened without a difficult conversation. Early stage startups are fragile. Moving from pilot to contract is the first real test of so many things - your product value, your customer success, and the real competition. Unless you test your relationship by taking the leap, you'll never figure out if you've got something real.

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Once you get to ~20M in revenue (slightly earlier for SMB, later for ent companies), the game is less about hustle and product features 🔑 Superior execution esp on GTM matters a LOT more in this phase 💪 Unfortunately a lot of founders miss this transition and find themselves doing what’s worked in the first phase and getting frustrated at the lack of results 😢 So what matters in the scale up phase? 1️⃣ stage specific people in the team — the team you build is the company you build. I am all for promoting from within but it’s important to couple that with external resources (advisors, mentors, full time hires) to make sure your team is seeing around corners vs reinventing the wheel. 👊 2️⃣ graduating from adhoc comms to an intentional approach — a lot of work discussions / key decisions in the early days happen in slack, 1:1 or in the office kitchen. Important to transition to better norms on comms across teams to lay the right foundation 🔥 3️⃣ prioritization is the name of the game — as you get more resources it’s easy to want to do everything. The best leaders force difficult choices and say no way more often than in the past 4️⃣ cross functional alignment matters more than strategy — this is the phase your teams become siloed and start to make up their own narrative and sometimes even their own data 😂 5️⃣ run amazing meetings — the necessary reality of this phase is having more meetings to keep things moving in the right direction. Running productive and FUN meetings is an undervalued skill most don’t invest 🙌 Embrace change 🔑 Find your tribe 🤗 Experiment to run your own race 🏃‍♀️ #scaleupphase #startups #cro #builder #leadershipinsights #startup

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PE - 3 ideas for the next 3 years - just like every traditional company needs to be a SaaS company, every SaaS company needs to ultimately be an AI company - ramp up revenue growth but always protect the profits - today is the time to innovate (i.e. not re-invent) your business model & strategy - importantly, this applies not only to PE portfolio companies but also to PE firms to get the best IRR --- #pe #privateequity #business #strategy #saas

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Today, GTMfund’s is excited to announce to launch of Operator, its first incubated company. Max Altschuler (founder of GTMfund) teamed up with co-founder Adam M. (design engineer and 0-1 builder) and co-founder Mark Kosoglow (Outreach employee #1 and former SVP of Global Sales) to create Operator.ai alongside an insanely talented team Pleasant Middelhof, Jeremy Jonas, Carl Gunderson. We've entered a period called “The Great Ignore”. Spam and irrelevant, poorly-automated outbound campaigns are running rampant.   Operator gives you an easy-to-use Growth Engineer that allows you to build and enrich lists with accurate data, and run experiments and queries using AI to generate highly targeted outreach.    Check out their website to get updates, early access, and see how they are taking the world back to 2 touches meant to attract buyers into conversations that convert, instead of 20 touches meant to make sure you hit your activity number: https://www.operator.ai/

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🧙‍♂️ My friend Gandalf dropping some real SaaS wisdom in his latest podcast episode: Took the new Runway ACT-1 for a little test drive yesterday. What do you think? 😃 Steps: 1. Open Runway Act-1 2. Record or upload a reference video of yourself 3. Create or upload a reference image (used Midjourney) 4. Click "Generate" (no prompt needed) 5. Final video edits with VEED.IO

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Goldcast's startup journey has perfectly mapped the last 4 crazy years of SaaS. Palash Soni and his cofounders first started the company in 2021 to build great, customizable virtual event solutions for enterprise marketers. While their business kept growing past the pandemic, it was clear that overall spend was shifting back to IRL events fast. However, video consumption continued to rise and quickly became the best way to build your brand and an audience in software - if you knew how to operationalize video marketing. Goldcast's product evolution from a virtual events platform to an AI-native video marketing solution has been incredible to watch. The company had its best quarter yet, adding customers like OpenAI and Coupa Software and I couldn't be more proud. Here's Palash talking about their product evolution and the future of video marketing in an AI-native world.

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I first met Mike Maples, Jr in 2010, when Founders Network was just an idea. Over the years, Mike and his team at Floodgate (special shoutout to Arjun Chopra) have been great mentors, sharing their insights and guiding our founders. Most recently, their advice played a pivotal role in helping us launch Founders Network Fund. Today, Mike shared the key ideas with FN'ers from his new book Pattern Breakers (https://a.co/d/buKBd5O ), which explores the insights he’s gained from backing companies like Twitter, Twitch, and Okta -- and which put him on the Forbes Midas List 8x. This is the most important book on startups in the last ten years, according to Steve Blank. My notes on the key concepts of his talk: 1) Inflections – "not a trend but a change event that represents a turning point in our ability to provide empowerment." e.g. The GPS chip in iPhone 4s 2) Insights – "a non-consensus and right insight about the future that's powerful and non obvious and leverages inflections." e.g. Lyft = GPS + strangers driving you 3) Founder future fit – "Living in the future before other people live in the future and then try to build what's missing in the future." If you're a founder, check out the book. Grateful for the impact Mike and Arjun continue to have on both me and the Founders Network community!

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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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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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"AI-powered company may challenge or displace a SaaS incumbent" Joanne Chen AI as SaaS is probably the best use case. 1. Lower cost of starting 2. Faster to test 3. Mostly no increase in headcount 4. More tangible ROI as you focus on problems that currently exist. My first use case for using AI was creating a customer cohort from a large amount of customer data. The second use case was matching prospect behavior to existing customer behavior. I could not have done any of these without using AI Ops as a Service or AI as SaaS. Read Joanne Chen's article here. https://lnkd.in/ebPj9QJk Access my article on AI in Sales and Marketing here https://lnkd.in/e9ry76mj Happy to storm with you on your AI strategy.

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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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Rolling into another day of the Primary Summit. Hit me up if we should connect onsite. Kudos to the Primary Ventures team so far, as yesterday’s Pre-Seed Summit was on point. Mar Hershenson, Benjamin Sun, Chris Douvos, Zach Weinberg, and Graham Pingree all dropping helpful nuggets for fund managers. Some paraphrased highlights: - Chris Douvos (AHOY Capital) - Most fund decks are terrible. Start with your people (not your strategy) and know you have about 6 slides of his attention. - Zach Weinberg (Curie.Bio ) - beleives SF’s AI talent is a significant advantage over NY, wouldn’t build AI in NY. Also questions if VC exits can keep up with VC expansion (generally hilarious and smart Twitter follow) - Mar Hershenson (Pear VC) - VC is so much more competitive now. It’s not enough to be an ex-founder with a great network and domain expertise. Have a better “why you are different” or do something else! - Benjamin Sun (Primary Venture Partners)- still sees arbitrage in going long NY venture (vs. SF). Do the work to keep various influential networking nodes aware of what you’re doing and how you think.

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Should every partner be a special snowflake? There's a delicate balance between building the meaningful relationships that make partnerships a unique GTM rhythm and building the operational efficiency to scale them. This is particularly difficult for partner enablement functions. Aaron Howerton and Adam Pasch and I got right into it on this episode of Literal Scratch. Also a little shout to Chris Lavoie, PhD... can't stop thinking about your post on leading indicators.

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I’ve looked around and haven’t found a casual regular zoom meetup with product/growth/product marketing folks working in B2C2B or PLG+Sales-led companies to trade ideas and talk shop about monetization, freemium upgrade paths, success metrics, and retention. Drop me a note here in the comments if you’d be interested in such a thing. Maybe we should make it happen? #plg #productmarketing #product #growth

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Most emerging AI-native GTM tech is downstream from data accuracy. (enterprise seller account planning, AI SDR, signal-based outbound platforms, etc.) It used to be fine for reps to fix stuff at the last mile or to have an ops team that did some pre-cleaning, but if you want to do things with AI at scale it’s hard to rely on that. If any of these ones go wrong, you’re exposed to errors: 1) Inbounds usual suspects: Accounts are linked to Gmail, Yelp, Baidu, etc. 2) Account name <> URL mismatch 3) URL is not working 4) Data is missing (particularly hard for SMB) 5) Incorrect firmographics (it says retail, but is it really retail?) 6) The domain and the LinkedIn URL don’t refer to the same company 7) Duplicates 8) Corporate hierarchies None of these are new problems, but their impact compounds when you rely on your data to do LLM-based automations. The irony is that few buyers are willing to pay a lot of money for ‘cleaning’. For obvious reasons: it’s hard to link to next quarter’s targets. We used to go back and forth on whether to ‘work around it’ or ‘fix it’. But for LLMs to work for wall-to-wall implementations in the enterprise that is no longer an option.

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