Jason Maynard

Sharing the full podcast on the key trends in the data world going from spreadsheets to BI to AI and how Zetta is helping shape the future of #data #analytics to help companies grow using #data and #AI. Important vs. Urgent: The Data Decisions Startups Get Wrong https://lnkd.in/gcz_xxFg Summary from the amazing podcast host Jay Singh: In this episode, I chat with Harini Karthik, co-founder and CEO of Zetta, a platform dedicated to helping companies accelerate growth through data and AI. Harini shares her perspective on the critical value of early investment in data teams, emphasizing the need for companies to allocate 10% of their workforce to data roles. We discuss the evolution from spreadsheets to BI to AI in data analytics, and how Zetta is helping companies make this transition quickly and efficiently. Throughout our conversation, Harini emphasizes the importance of measuring key metrics like LTV to CAC ratio, activation rates, and revenue per employee. She shares her perspective on bootstrapping versus venture funding, highlighting that over 50% of bootstrapped companies succeed compared to only 10% of funded startups. We also explore the challenges and benefits of bootstrapping, including the creative freedom it provides and the long-term sustainability it can offer.  Chapters: (02:58) - Importance of Data in Startup Success (10:13) - Balancing Intuition and Data in Decision Making (15:21) - Key Metrics for Early-Stage Consumer Products (25:47) - Transition from Spreadsheets to BI to AI (38:08) - Bootstrapping vs. Raising Capital

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MarkovML was founded on the belief that your data is the ultimate competitive advantage. As Peter Norvig famously stated, "𝙈𝙤𝙧𝙚 𝙙𝙖𝙩𝙖 𝙗𝙚𝙖𝙩𝙨 𝙘𝙡𝙚𝙫𝙚𝙧 𝙖𝙡𝙜𝙤𝙧𝙞𝙩𝙝𝙢𝙨 𝙗𝙪𝙩 𝙗𝙚𝙩𝙩𝙚𝙧 𝙙𝙖𝙩𝙖 𝙗𝙚𝙖𝙩𝙨 𝙢𝙤𝙧𝙚 𝙙𝙖𝙩𝙖." This adage is particularly relevant in today's landscape, where Large Language Models (LLMs) are becoming commoditized. This has again been proven by the LLama3.1 paper (links in the comments). The difference between success and failure often hinges on having right data and how effectively it's managed and utilized by business. Here are some of the best practices to maximize value for your enteprise. 👉 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 ✨ Regular Audits: Ensure data accuracy and integrity through consistent and thorough audits. ✨Validation and Updates: Ensure quality metrics are in place to ensure consistency and relevancy. 👉 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗗𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 ✨ Inclusive Data Collection: Diversify your data inputs to ensure your datasets genuinely represent global perspectives and scenarios, enhancing the applicability and relevance of your models. ✨ Broad Representation: Strive for datasets that encapsulate a variety of demographics, industries, and use cases. This diversity helps improve the generalization of your models across different contexts and applications. 👉 𝗘𝗻𝘀𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗶𝘀 𝗨𝗻𝗯𝗶𝗮𝘀𝗲𝗱 ✨Bias Detection: Models are biased because the data used to train them is. Identify & fix the root cause. ✨Fairness Metrics: Establish fairness metrics and ensure your models deliver equitable outcomes across all user groups. 👉 𝗔𝗱𝗼𝗽𝘁 𝗮 𝗣𝘂𝗿𝗽𝗼𝘀𝗲-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗼 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 ✨Clear Objectives: Ensure you establish your expectations from the model and understand the trade-offs. ✨Alignment with Business Goals: Models scale efforts constrained by time and resources. Have clear business metrics and measure model performance against these to maximize value. 👉 𝗘𝗾𝘂𝗶𝗽 𝗬𝗼𝘂𝗿 𝗧𝗲𝗮𝗺𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗧𝗼𝗼𝗹𝘀 Give your knowledge workers the right tools to automate routine data tasks and efficiently transform raw data into strategic insights so the business can make informed decisions faster. Superior data and the right tools aren't just assets; they're your competitive edge—invest in them for long-term success. #DataQuality #DataDiversity #AI #MachineLearning #BusinessStrategy #MarkovML #GenerativeAI #GenAI #Llama3.1

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You MUST see this. TL;DR We've reached a true breakthrough in clinical AI, developing CARE1 - the world's first clinical foundation model with an application submitted to FDA review. Truthfully this is just the tip of the iceberg, we've used a fraction of our data and two years from now we'll see these as the first baby steps. You can read more about it here: https://lnkd.in/drhPYTtX Shameless plug - this recent breakthrough is just one example of the impactful work our teams do every day. We're looking for highly experienced algorithmic experts, both ICs and tech leads, to join us and take a pivotal role in this and other high impact projects. Feel free to reach out to me directly. This is first and foremost thanks to the amazing team working on this effort - Itay Katzir the mastermind who's been planning this for years building paradigm after paradigm to enable this. From the algorithmic engine, through NLP, through scalable training pipelines and more. Ariel Persiko - truly a one man algo team, the exact person you want on this type of breakthrough project. Dor Alon - AI Infrastructure expert, built components that were critical to this endavour. And of course some of our amazing leaders who were deeply involved in invaluable strategic guidance - Uri Goren, Ronny Levanda, Tamar Lin. Not less than that - I think this is a true lesson of how good founders really move the needle even at a 450 person company. Elad Walach our CEO was maybe the first person who started pushing strongly for us to invest in this, and Michael B. Braginsky our legendary CTO who continues to amaze me even after 8 years with his ability to inspire us with unachievable goals which we are then able to achieve.

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Factors.ai uniquely solves for converting G2 buyer intent signals to revenue with LinkedIn Ads & importantly complete the feedback loop on which signals are working with Attribution and Lift analytics to iterate on the campaigns. Looking forward to scaling this partnership with G2 and drive more revenue for G2 Customers

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Join Us for an Exclusive Partnership Leaders Meetup in San Jose 🚀 Calling all partnership leaders! Let’s tackle two of the hottest topics on your mind: budgeting and headcount planning for 2025. This meetup is the perfect chance to: - Share strategies on securing budget approvals - Discuss headcount needs and justification in a lean economy - Connect with fellow leaders facing similar challenges See you tonight, bring a friend!

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Surprising Parallels Between Human Behavior and LLMs? Ever noticed how the opening and closing of a presentation can make or break your audience's engagement? It's not just human speakers who exhibit this pattern—Large Language Models (LLMs) show similar behaviors. As Sapphire is just one week away that’s at least what I observed when preparing pitches and working on generative AI use cases in parallel. Over the past week, I've been exploring how LLMs process and generate text. It's fascinating to see the parallels between human communication and how these models handle information. Human Focus: The First and Last Impressions As speakers, we know the importance of starting strong and closing with impact. These moments linger in the audience's memory, shaping their overall impression. This principle, known as the serial position effect, highlights that people recall the first and last items in a series best. LLM Behavior: Emphasis on Extremities Similarly, LLMs often give more weight to the beginning and end of a large context, sometimes overlooking the middle. This isn't by design but reflects how these models 'understand' and prioritize information. It mirrors our cognitive biases in processing and remembering information. What are your thoughts on these parallels? Share your insights in the comments!

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Recently, James Wood, Head of Product at m3ter wrote online, ”Usage-based pricing is here to stay, but not staying the same. The rise of generative AI and continued development of cloud infrastructure and SaaS pricing models will lead to even further innovation in hybrid and success-based pricing. ” James and I sat down last week to talk about inflections in B2B SaaS, pricing & packaging, hybrid billing, how AI is impacting pricing, and more. We’ll release the entire interview soon on youtube, but here's a great snippet where James discusses the maturation of usage-based billing. James is an expert on pricing & packaging, having consulted for Simon-Kucher, run pricing at Segment, and the led Insight Partners' center of excellence for pricing & packaging. Bit of backstory, Jasdeep & I first got to know James while working on pricing & packaging & #PLG at Automox, and he's been a great thought partner over the last year as we've launched Schematic. #pricing #packaging #b2bsaaS

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Join us on September 25 on Zoom if you believe software pricing should become more science than art. My Co-founder, dr. Anouar El Haji, PhD CEO at RevFixr, will cover and answer the following questions: ↳Why and how software pricing is broken ↳Why you shouldn’t neglect pricing ↳What are the biggest pricing opportunities and pitfalls Relevant for software founders, investors, and anyone with monetization responsibilities. Sign-up using the link below. Monetization is our specialty. Growing your revenue is our responsibility. #software #pricing #saas #monetization

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🚀 Landing a VP Role in AI/ML: Key Insights from Recent Executive Searches In my recent work with clients on executive roles, especially VP positions in AI/ML, I've observed clear differences between candidates who succeed and those who don't. Here are three simple yet critical insights: 1- Show Your Future Value and Potential: In AI, things change fast. Successful candidates don't just talk about past jobs; they demonstrate how they can be valuable in the future. Tip: Discuss projects where you've adopted new technologies with no prior experience, focusing on the challenges and your learning process. This approach has significantly helped candidates reach the offer stage. 2- Keep It Simple: Explaining complex AI ideas in easy-to-understand ways is essential. The most successful individuals can discuss their complex projects simply and engagingly, like telling a story that keeps everyone captivated. Tip: When describing complex projects, use straightforward comparisons or examples to make your points clear. 3- Impact Statements: I've often heard from hiring managers about candidates lacking impactful statements in their resumes and conversations. It’s crucial to know your impact and be able to quantify it. Tip: In your resume and interviews, focus on specific outcomes of your leadership, such as the adoption rate of a new system you implemented or the growth driven by an AI initiative you led. These insights are derived from feedback across multiple industries and from leaders we've supported. I've distilled them into three pivotal areas. Have you noticed trends or qualities that set apart successful executive candidates in AI/ML?

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PSA: Stop using the original OpenAI CLIP weights. I see a lot of people using CLIP for multi-modal retrieval. What many people may not know is that there are 100’s of CLIP models now since OpenAI’s original release. These newer models offer significantly better performance across many benchmarks. For product search related tasks we recommend the following. + Fastest inference: ViT-B-32 [laion2b_s34b_b79k] + Best balanced: ViT-L-14 [laion2b_s32b_b82k] + Best-all round: xlm-roberta-large-ViT-H-14 [frozen_laion5b_s13b_b90k] You might notice these are not the ones that perform the best on the benchmarks. Why? Because the benchmark only test for very specific tasks which are often quite different from search and recommendations. These are ones we have found to be the best for product related search and retrieval tasks and generally very good first choices if you are unsure where to start (even for non-product related search). Here is a hugging face space with all the data: https://lnkd.in/gxu5UYkt Read more: https://lnkd.in/g5_D4ccs

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Did you know that a high churn rate is often linked to poor customer support? DevRev can help you tackle this challenge head-on. By unifying product and customer data, and people and their work, DevRev empowers your team to: - Understand customer needs better - Respond to issues faster - Provide personalized support - Track customer health scores - Identify at-risk accounts proactively With DevRev, you can transform your customer support from reactive to proactive, reducing churn and boosting customer loyalty. Ready to revolutionize your customer support and reduce churn? Let's chat! Book a slot on my Calendly to learn more: https://lnkd.in/g3YBttbB Read more: https://lnkd.in/gTmhPFFe #DevRev #CustomerSuccess #CustomerSupport

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🎧🍌 New podcast with Nilam Ganenthiran, Founder of Beacon Software and ex-President of Instacart He takes us inside key moments from his 8+ years at Instacart, joining as the 12th employee and helping scale the company to the public markets. We talk tactics for customer research, building customer relationships, balancing strategy and execution, how to serve on and manage a board, what happened when Amazon bought Instacart’s biggest customer Whole Foods, inside its COVID response, surviving eight months of runway in 2015, his new company Beacon Software, and more. Search "The Peel" in your favorite podcast player, or link to listen / watch in the comments 👇

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Great data and a great way to frame this up Boris! We're just at the beginning of the AI revolution, so a perfect time to take stock of the externalities and plan accordingly. This also makes it clear why big tech and others are signing power purchase agreements for nuclear power.

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Want some cool general purpose AI tech that has nothing to do with LLMs? Check out The Forecasting Company They’ve built a “predict any time series” machine.  As an example - A big theme park chain can invest in detailed demand prediction ML, so they can predict how many visitors they will get and optimize staffing, maintenance, food production etc.  A small independent tourist attraction couldn’t, until now. To test the tech, I downloaded total hospital emergency admissions for England, excluded 2024 from the data I sent to the model and asked it to predict 2024 admissions. The result, not bad (see graph).  Significantly better than just using last year’s number as a proxy.  Could I have achieved the same or better with a few hours messing about with the data in excel, yes.  Could I achieve much better by modeling it all properly with a custom ML model and tons of clean custom features, definitely.  But it took 5 minutes, the model doesn’t require retraining (by me), and I got a decent response. My prediction, LTSMs (large time series models) are going to be a big deal, and this is just the first one I’ve seen. AI disclosure: No generative AI was used to create this post.

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If you're like me and really want to know how an LLM (ChatGPT) works under the hood, check this out! If you can work a spreadsheet you can understand an LLM.

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"Data democratization" (an iconic buzzphrase) has been around for a long time. Tons of companies have promised to deliver it... with mixed results. Lots of dashboards, heavy implementation, data siloes, the list goes on. But advancements in AI have made "simple data access for all" a reality. Check out Bessemer Venture Partners's Data Shift Right roadmap. Thanks for the shout out Lindsey Li, Yael Schiff, and Amit Karp.

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You don’t outgrow the need for design partners. This was my biggest takeaway from my conversation with Eilon Reshef on Gong's path to product-market fit. I was super surprised that even at Gong's current scale (Series E, $7B valuation), his team of 20+ PMs still use them, working with up to a dozen design partners at any given time. That tradition stretches back to building the initial product in 2015. The earliest signs of traction weren’t splashy — in fact Eilon didn’t even make much of them at the time. But in hindsight, he can trace an important milestone of product-market fit back to a quiet victory just after launching: 11 out of 12 of the first design partners converted to paying customers. Tons more learnings from Eilon on design partners, as well as the non-obvious signs of early PMF, and how to balance long-term product strategy with customer input in the First Round Capital Review article below.

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"I find that problem so interesting. There's this relationship between deal economics, sales incentives, and a company's bottom line. It's time to look at this from more of a growth marketing perspective and optimize it." Spyri Karasavva joins Fynn Glover to talk about what we're working on at Dealops! 🚀 give it a listen, we'd love to hear your thoughts on sales and pricing!

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We’re excited to share a new ICONIQ Growth report that dives deep into how early-stage SaaS companies can effectively scale from $1M to $20M ARR. In this research, we highlight the metrics that truly matter when measuring success through this phase of growth and offer guidance on how to prioritize them. We also include strategies for overcoming the Growth Plateau many companies experience after hitting $20M ARR. Whether you're part of a growing team or building something new, I think you'll find some valuable insights for long-term success in this report. You can download 📊 Scaling SaaS from $1M to $20M ARR here: https://bit.ly/4eJtNaA Claire Davis

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