About
Jay Haynes is the Founder & CEO of thrv, LLC (pronounced "thrive"). thrv is an…
Articles by Jay
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How to Resolve Facebook's Conflict Between Its Users and Customers
How to Resolve Facebook's Conflict Between Its Users and Customers
Facebook has been in the news for allowing a Donald Trump ad that makes untrue statements: essentially lies. They have…
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5 More Tips to Using Clay Christensen’s Competing Against Luck and Jobs Theory to Launch Great Products, Part 2: Answering The Right Question
5 More Tips to Using Clay Christensen’s Competing Against Luck and Jobs Theory to Launch Great Products, Part 2: Answering The Right Question
This is Part 2 of two-part series explaining thrv's process for executing Jobs Theory. Part 1 is about customer…
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A Step-By-Step Guide to Using Clay Christensen’s Competing Against Luck and Jobs Theory to Launch Great Products, Pt. 1: How to Ask the Right Question
A Step-By-Step Guide to Using Clay Christensen’s Competing Against Luck and Jobs Theory to Launch Great Products, Pt. 1: How to Ask the Right Question
This is a two-part series explaining thrv's process for executing Jobs Theory. Part 1: How to Ask The Right Question is…
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How Jobs-to-be-Done Completes Your Google Ventures Design Sprint
How Jobs-to-be-Done Completes Your Google Ventures Design Sprint
In 2010, Google Ventures design partner Jake Knapp developed a process that would help product teams go from abstract…
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11 Ways to Think Different about Product Management Using Jobs-to-be-Done
11 Ways to Think Different about Product Management Using Jobs-to-be-Done
Jobs-to-be-Done is often presented from a theoretical point-of-view. To help you apply it to your everyday life in…
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Activity
8K followers
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Jay Haynes shared thisCorporate venture capital is broken for AI. Here's the problem: traditional CVC operates like a second-class venture firm. You see deals later. You pay higher valuations. You can't move fast enough. Meanwhile, the best independent VCs are built for one thing — sourcing and closing deals. Some make investment decisions in hours. Corporate venturing? Weeks of internal approvals. Committees. Strategic alignment reviews. By the time you get to "yes," the round is closed and the cap table is full. If you want to play in AI venture, you can't run the old playbook. The deals move too fast, the valuations shift overnight, and the best founders have options. You have to innovate corporate venturing itself. That means: Decision speed that matches the market. Not months — days. Deal flow that comes from relationships, not brand reputation alone. Autonomy for your venture team to act like investors, not employees requesting budget approval. The companies winning in AI venture right now aren't the ones with the biggest balance sheets. They're the ones that rebuilt how they invest from the ground up.
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Jay Haynes shared thisSaaS companies are in serious trouble. And most of them don't even realize it yet. For two decades, SaaS has been built on a simple model: give users an interface to create, read, update, and delete data. The industry even has a name for it - CRUD. But think about what that actually means. We built billion-dollar companies around making customers do the work themselves… just in a browser instead of a spreadsheet. AI changes the equation entirely. AI doesn't need a UI. It doesn't need a user to click buttons and fill fields. It just needs to know what job the customer wants done - and then it does it. No forms. No dashboards. No manual updates. This is why SaaS valuations are compressing even as revenue grows. The market sees what's coming: the entire CRUD layer is being automated away. The question for every SaaS founder and executive right now: If AI can do the job directly for your customer… why would they need your application at all?
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Jay Haynes shared thisWall Street just flipped the script on corporate success. Profitable companies are being punished. Not for bad results — for not having a convincing AI strategy. Think about that. You can beat earnings, grow revenue, and still watch your stock drop because analysts don't believe you can compete with the wave of foundation models and the massive capital flowing into AI startups. This isn't a trend. It's a permanent shift in how businesses are valued. The playbook has changed: → Profitability gets you a seat at the table → Your AI strategy determines whether you stay The smartest leaders aren't scrambling to "add AI" to their pitch deck. They're rethinking their entire value chain — where AI creates leverage, where it creates risk, and where it opens doors that didn't exist 12 months ago. The market is no longer asking "are you profitable?" It's asking "are you relevant?"
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Jay Haynes posted thisMost go-to-market (GTM) strategies start with product features and work backward to find customers who might want them. The result is messaging that emphasizes capabilities over context, and sales motions that struggle to connect with what buyers actually care about. A more effective approach starts with understanding the progress customers are trying to make, and then building GTM strategies that support that progress. This shift in starting point changes how you segment markets, craft messaging, train sales teams, and define success. When customers "hire" a solution, they aren't buying features. They're choosing something that helps them move from a current struggle to a desired outcome. That choice involves three dimensions of work: → Functional Jobs: What customers need to accomplish ("aggregate customer data") → Emotional Jobs: How they want to feel while doing it ("confident presenting insights to leadership") → Social Jobs: How they want to be perceived ("data-driven and informed") Most GTM strategies address only the functional dimension, and in doing so, miss a significant part of why customers make the choices they do. Emotional and social jobs often carry just as much weight in a buying decision. A buyer might recognize that two solutions are functionally equivalent, yet still choose one because it makes them feel more in control, or because it signals something important to their peers and leadership. Recognizing all three dimensions allows GTM teams to address the full context of the customer's decision, not just the task at hand. Messaging becomes more resonant, positioning becomes more differentiated, and the entire customer journey becomes easier when it reflects the real progress customers are trying to make.
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Jay Haynes shared thisSteve Jobs isolated the iPhone team from the iPod team. They didn't even know it existed. Why? Cultural contamination kills innovation. The iPhone destroyed the iPod — 50% of Apple's revenue — and replaced it with something far bigger. That's corporate venturing.
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Jay Haynes shared thisImprovement doesn’t stop at launch. Customer job success depends on ongoing cycles that reduce effort and enhance satisfaction. Customer Job Data-Driven Optimization Cycles → Regular analysis to uncover opportunities for effort reduction without sacrificing completion quality. Customer Job A/B Testing for Process Improvement → Systematic testing to find the most effective approaches for both experience and efficiency. Customer Job Feedback Integration → Actively collecting and analyzing feedback to identify where automation creates frustration—or where new value can be added. Continuous improvement ensures customer jobs keep getting easier, faster, and more reliable.
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Jay Haynes posted thisMost teams stop at identifying pain points. The real competitive advantage comes from applying CES insights systematically across your customer base. Here’s how teams can scale CES insights: 1) Segment-Specific Analysis Different customer segments may experience effort differently at the same job steps. - Enterprise customers might struggle with integration complexity. - Small businesses might struggle with setup simplicity. 2) Competitive Positioning Understanding where competitors create high effort for customers reveals positioning opportunities. If every competitor focuses on one job step, there may be an underserved step where you can dominate. 3) Content Strategy Alignment High-CES job steps become content topics that generate immediate engagement because they address genuine, widespread customer struggles. 4) Product-Market Fit Validation When your solution directly reduces effort at the highest-CES job steps, you achieve stronger product-market fit and sustainable competitive advantage. Scaling CES insights ensures every team (from product to marketing to sales) aligns around reducing customer effort where it matters most.
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Jay Haynes shared thisInternal teams are trapped. Investors want growth. But existing people, processes, and tech are set up for a totally different thing. Now you need change management — while keeping the business running. That's why most corporate innovation fails.
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Jay Haynes posted thisCES correlation analysis helps separate signal from noise. It reveals which roadmap changes truly reduce customer effort, and which ones simply add more features without making the job easier. Without this kind of analysis, it's easy to stay busy shipping improvements that feel meaningful internally but barely move the needle for customers. This distinction is critical. Too often, teams confuse more functionality with better outcomes. A longer feature list can actually make a product harder to use, adding cognitive load and complexity at exactly the moments where customers need clarity. But customers don't measure value in features shipped. They measure it in how much faster, easier, and more accurately they can complete their jobs. The practical implication is significant. If a roadmap item doesn't correspond to a reduction in effort on a high-CES job step, it's worth asking whether it belongs on the roadmap at all. Not every improvement is created equal, and not every customer request points to the highest-leverage opportunity. When CES is connected to specific job steps, it becomes a powerful guide for roadmap prioritization. Teams can move from debating opinions about what to build next to having a clear, evidence-based view of where customer effort is highest and what changes are most likely to reduce it. The result is progress defined by customer outcomes, not product activity. And that shift, from measuring output to measuring impact, is what separates teams that build what customers need from teams that simply build.
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Jay Haynes liked thisJay Haynes liked thisI am excited to announce that I'll be joining Composition Wealth this summer as an intern on their growth team. I'm deeply grateful for this opportunity to learn the ins and outs of high-end wealth management alongside such a talented team. I can't wait to get started! Thank you to my mentors, family, Jake Hungarter, Jay Rudwall, and Brock Moseley for guiding me through this process and this phenomenal opportunity.
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Jay Haynes liked thisHarvey just raised at $11B. Then killed its own AI model. Harvey just closed an $11B‑valued round—then quietly dismantled the very thing everyone thought was its moat: its proprietary AI model. For 18 months, Harvey’s pitch to every law firm was: “We trained a custom model on legal data.” That was the moat. That’s how you justify $11B. Then GPT‑5, Claude 4.5, and Gemini 3 Pro beat Harvey’s proprietary model on BigLaw Bench—their own benchmark, built to show off their own training. So they scrapped it. Now Harvey is a model selector. A routing layer that picks between OpenAI, Anthropic, and Google based on the query. 400,000 agentic queries a day. 25,000 custom workflows built by lawyers inside the product. Full RAG with per‑claim citations. Zero proprietary model. Every AI startup deck I’ve seen this month still pitches a “custom model” as the moat. Every founder talks about proprietary training data. Here’s what the $11B company just proved: 1. Frontier models eat vertical training for lunch. Six to twelve months after you ship a domain‑tuned model, GPT or Claude closes the gap. The cycle keeps repeating—and the same pattern is now playing out in finance, compliance, and other regulated verticals. 2. The moat isn’t the model. It’s the 25,000 workflows. Those are not replicable by the next Claude release. In regulated environments, those workflows are where policy, risk controls, and audit trails actually live. 3. If your pitch starts with “we trained our own model,” you’re fighting a war you can’t win. The layer that matters is orchestration, context, workflow, and governance—the stuff frontier labs won’t build for your vertical or your compliance stack. Harvey didn’t raise at $11B because of its model. They raised because every BigLaw firm is locked into their workflows. The model was a line item. Replaceable. That’s the play: Build the workflow. Rent the intelligence. I write about AI shifts like this, focusing on non-obvious dynamics and real strategic implications. Follow if you want signal, not recycled headlines.
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Jay Haynes liked thisJay Haynes liked thisIs Anthropic on a collision course with Microsoft? Adam makes the case on last week's AI First with Adam and Andy. AI labs are shipping faster than big tech can keep up, and the implications for enterprise AI decisions are real. Catch the full episode: https://lnkd.in/gKbZTtbG
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Jay Haynes liked thisJay Haynes liked thisWas John Ternus the right selection for Apple's new CEO? David Pogue, author of Apple: The First 50 Years, thinks so. David, Caleb and I had a rollicking conversation about the history and future of Apple. Listen on Apple or Spotify and let us know what you think in the comments below.| https://lnkd.in/efZUK8HB
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Jay Haynes liked thisThe way customers learn has fundamentally changed. And it’s forcing a bigger question: Where does customer learning go in an AI-powered, omnichannel world? One theme is clear from conversations with leaders across our community: ⚡ Customers expect learning to be immediate, contextual, and personalized 📈 Business leaders expect it to scale efficiently and drive measurable outcomes Bridging that gap isn’t incremental. It requires a step-change. Today, we’re excited to introduce AI Wave—a new series of launches that marks the beginning of the Learning & Intelligence℠ era. Your academy remains the trusted foundation—deep, accurate, and essential. What’s changing is where and how that knowledge shows up. Customers are no longer “going to learn.” They’re searching, asking, exploring—across AI tools, in-product experiences, communities, and help centers. Learning now happens in the moment of need. AI Wave extends your trusted content into those moments—so every customer can get the right answer faster, moving closer to value. And for your business, every interaction becomes a signal - helping you understand customer intent, reduce friction, and drive adoption, retention, and growth. We’re starting with two foundational capabilities: * Conversational AI Learning * Omnichannel Learning Together, they transform customer learning into a dynamic, AI-first experience—delivered wherever your customers are. We’re incredibly grateful for the partnership, feedback, and trust that got us here—and even more excited for what we’re building next, together. 🎥 Watch the launch: https://lnkd.in/gUCyVnCJ The Learning & Intelligence era is here. #AIWave #LearningAndIntelligence #CustomerEducation #CustomerLearning #CustomerSuccess #ProductAdoptionJay Haynes liked thisToday, Thought Industries unveiled AI Wave, a new launch series bringing the Learning & Intelligence℠ era to customer education. Thought Industries is defining a new model for customer education, one built for how customers now learn, search, adopt, and realize value. The Learning & Intelligence era represents a shift from static course delivery to intelligent customer guidance. Customers get the right answer, the right next step, in the right moment, wherever they are. And every search, question, click, and interaction becomes a signal businesses can use to improve what happens next. The result is a new era of customer education, one built to drive adoption, retention, and growth. And companies including MongoDB, SkillPath, SOCi.ai, VetFolio, and Institute of Management Accountants (IMA) are already using the new capabilities to accelerate product adoption, retention, and growth. The first AI Wave launches introduce two new capabilities available now: 🌐 Omnichannel Learning, extending trusted education content beyond the academy and into the places customers already look for answers, including product experiences, Slack, LinkedIn, search, and AI answer engines like ChatGPT, Google Gemini, and Claude. 💬 Conversational AI Learning, helping customers ask questions in natural language, get instant answers grounded in approved learning content, and take the next step faster. Your academy remains the trusted foundation. AI Wave helps it go further. https://lnkd.in/e4CK8Puk #AIWave #LearningAndIntelligence #CustomerEducation #CustomerLearning #CustomerSuccess #ProductAdoption
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Jay Haynes liked thisJay Haynes liked thisWe are so excited for Living Future! ecomedes is proud to sponsor this event and we will be hosting panels, workshops, and exhibiting at a booth. And we want to meet you there! If you want to make product sustainability data an ADVANTAGE instead of an OBSTACLE, lets talk: https://lnkd.in/g-CpSeim Paul Shahriari Layne Hayward Abid Saifee Kate Bachner Mike Johnson, LFA Lindsay Baker
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Jay Haynes liked thisExcited to share some big news: Braided Health is joining Jukebox Health. We built Braided because we believe that care managers serving dual-eligible populations can drive greater impact with better tools. We built CareAssist™ to work alongside a plan's existing software, helping care managers focus their time on the right members, see problems before they escalate, and spend less time doing administrative work behind a screen. Jukebox takes this concept further. They deploy clinicians into the home to understand members' needs and limitations firsthand, surfacing insights that traditional solutions miss and putting them directly into the care manager's hands. From there, they recommend and facilitate solutions that address these findings. That's what drew us to them: the ability to act on insight, not just generate it. As I got to know Romanos Fessas and Oren Shatken, I was incredibly impressed with their model, their entrepreneurial instincts and the Jukebox team. More importantly, Rom & Oren demonstrate the integrity and passion to lead us in delivering meaningful innovation. Together, we're building a platform that connects care management workflows to in-home clinical data and translates it into focused, home-based interventions that drive measurable ROI for health plan partners. Thrilled to have Emily Olson, RN joining as well, bringing deep clinical leadership across behavioral health and care team operations. I'd like to recognize Joel Rabasco for his many contributions and AlleyCorp for their invaluable support on this journey. Privileged to be part of Jukebox! We're just getting started.Jay Haynes liked thisJukebox Health is proud to announce our acquisition of Braided Health, expanding our home safety and independence ecosystem to help #ManagedCareOrganizations modernize legacy care management systems and lower total cost of care. Braided Health addresses care management gaps with its AI-powered "sidecar," which works alongside existing platforms to surface actionable insights without requiring plans to replace their current software. Jukebox acts on this intelligence by deploying our interdisciplinary network of clinicians to the home to validate member needs, supported by a service network capable of immediate action. Together, these capabilities give care managers the insights, tools, and structure to focus on high-impact member engagement. To learn more about this acquisition visit: https://lnkd.in/g4jreTeU
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Jay Haynes liked thisJay Haynes liked thisAI was everywhere at CEdMA 2026 - and for good reason. A standout session was Julie Cochrane, M.Ed. from Charles River Development, who led an interactive workshop focusing on a practical framework to accelerate content development. Julie walked through how to identify the best places in your workstreams to adopt AI and scale - reinforcing the value of strong collaboration across teams, SMEs, and stakeholders, and why building early buy-in is what drives alignment and shared success. Thank you Julie - for delivering a session we're still talking about. Hear more from Julie in our recent blog, Advice for Customer Education Leaders in 2026 👇 https://lnkd.in/exyAxVen
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Education
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Harvard Business School
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Activities and Societies: MBA with Distinction, Honor Committee
Jay studied with Clay Christensen, who pioneered the concept of disruption (in his book The Innovator’s Dilemma) and popularized the jobs-to-be-done approach to innovation (in his book The Innovator’s Solution). At HBS, Jay focused on innovation, product management, and equity investing.
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Patents
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Systems and methods for job segmentation and corresponding need identification
Issued US20230410003A1
See patentSystems and methods for job segmentation and identification of unmet needs are provided. An example method includes receiving, from a user, identification of a job to be performed. The method also includes obtaining a set of job steps corresponding to segmentation of the job to be performed and obtaining a set of product features for a product designed to assist with completion of the job to be performed. The method further includes assigning each product feature of the set of product features…
Systems and methods for job segmentation and identification of unmet needs are provided. An example method includes receiving, from a user, identification of a job to be performed. The method also includes obtaining a set of job steps corresponding to segmentation of the job to be performed and obtaining a set of product features for a product designed to assist with completion of the job to be performed. The method further includes assigning each product feature of the set of product features to a corresponding job step of the set of job steps, assigning respective effort scores to the set of job steps, assigning respective measures of importance to the set of product features based on the respective effort scores, and causing the set of product features to be presented to the user with the respective measures of importance.
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Systems and Methods for Determining Product Roadmap Investment Decisions and Risk Assessment
Issued US11720967B2
See patentSystems and methods for determining a product investment decision and risk assessment are provided in which a plurality of data is obtained in electronic form. Based on a subset of this data, a taxonomy of data is formed using a plurality of classification models. The taxonomy includes a plurality of classifications determined by the classification models. At least one classification includes a corresponding plurality of input variables in a plurality of variables. Based on the forming, a…
Systems and methods for determining a product investment decision and risk assessment are provided in which a plurality of data is obtained in electronic form. Based on a subset of this data, a taxonomy of data is formed using a plurality of classification models. The taxonomy includes a plurality of classifications determined by the classification models. At least one classification includes a corresponding plurality of input variables in a plurality of variables. Based on the forming, a respective output is determined in accordance with a selection of a set of variables. The respective output is evaluated against a plurality of threshold criteria. If the respective output satisfies each threshold criterion is determined. Based on this determining, a product investment decision and risk assessment for the candidate subject is determined in accordance with the satisfaction of each threshold criterion.
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Facilitating Growth Investment Decisions
Issued US 8,583,469
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System and method for ensuring a person reaches a destination on time
Issued US 8,521,407
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Commercial Investment Analysis
Issued US 8,214,244
Honors & Awards
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Aberdeen Streaming Award
Aberdeen Group
What works in streaming media technology award for our distributing networking software.
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MBA with Distinction
Harvard Business School
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Phi Beta Kappa
Brown University
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William Gaston Fellowship
Brown University
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Paul Hsu
Working with NRF to pursue our patent strategy is an important step in recognizing that proprietary AI systems and workflows are becoming a new form of intellectual property for modern firms. Great to partner with George, John McBride, and the team at Norton Rose Fulbright as we work at the frontier of how AI can transform investment decision-making in venture capital. At Decasonic, we believe the next generation of companies will reinvent with AI workflows and systems how organizations operate, how leaders make decisions, and how teams collaborate with intelligent systems. In going through this streamlined process, it's clear to me that many more startups should clarify their IP strategy. Our internally developed AI platform, RLEN, reflects that philosophy. We are building AI not to automate tasks and flourish outcomes, in collaborative ways for human and AI teammates. This enables our investors to test ideas, explore alternative perspectives, and reach deeper insights faster when navigating uncertainty. More broadly, we are seeing trailblazing work across industries and cities as leaders experiment with how AI can reshape communities, companies, and countries. Leadership with AI forever transforms. We are still in the early innings. Appreciate the partnership and collaboration from the Norton Rose Fulbright team as we continue inventing and innovating. Special shoutout to my colleague Abdul Al Ali who's listed as a co-inventor.
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Jason Shuman
I’ve spoken to over 2 dozen MDs at PE firms I can confidently say that the arb of figuring out how to implement Vertical AI at portfolio companies is very real right now It will fundamentally change underwriting for those who can do it predictably and unlock generational returns. Most are aware they need to act. Very few have.
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Neal Ghosh
We've designed, launched, or operated within 15+ venture studios and codified 40+ operational frameworks from that work. The same structural questions come up in every engagement. Entity design, governance, sequencing, capital strategy — the patterns are remarkably consistent regardless of sector or operating model. So much so that more often than not, we do our best work by slowing down and working through fundamentals before getting into complexities. So we made the pattern recognition available. Studio GPT is trained on what we've learned across our collective experience. The Readiness Assessment tells you where you actually are in the studio lifecycle — not where you think you are. Both are free. No account needed.
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Scott MacDonald
😧It appears the public markets fear of AI has done a 180 - from fear of excessive investment to AI's ability to obliterate long incumbent software companies. I have a feeling we are going to hear the new riff "software is eating software" more times than the famous 2011 op-ed statement from Andreessen that "software is eating the world". Another 180 - long hardware, short (trad) software 🤯. AI infrastructure is literally eating memory. The global memory-ship shortage is expected to last another few years! 💽 Shout out to our pals at Inovia Capital for their State of Canadian Software Report. Thanks for the Miovision mention regarding the massive partnership potential for Canadian companies in the Middle East. 👉 https://lnkd.in/dyB8JkEh
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Nick Durham
I’ve been working on the theory of why small, shippable factories are the perfect inversion to the centralized prefab factory model. It all collapsed into one line of math (credit to Gilles Retsin): Factory Efficiency = Usage × (Throughput × Product Value ) / ( CapEx + Deployment OpEx) The idea is that the closer you can drive Usage to 100 % while keeping the denominator tiny, the faster the flywheel spins. A shippable microfactory tackles this by primarily focusing on a high Usage factor. Because the microfactory can be packed up afterwards and reused, its CapEx gets amortized over multiple projects, effectively raising its overall Usage across the year. If one project alone doesn’t fully occupy the factory, it can simply roll over to another job to maintain high Usage. Its CapEx is often an order of magnitude lower than a centralized factory (< $1M vs. $10-50M min), so the breakeven throughput is more achievable on a small pipeline of work. The real breakthrough here is portability. There is near-zero stranded capital and almost no idle time. To flesh this theory out, I wrote 3000 words on the topic for Brad Hargreaves and Thesis Driven. The article breaks down: - why “fixed factories, shipped goods” is being inverted to shipped factories, fixed goods - what a sub‑$1 M robotic cell does to CapEx per home versus a £45 M off‑site plant - early production/cost data on a shippable microfactory from Mollie Claypool, Gilles Retsin, Sam Baker and the Automated Architecture (AUAR) team - the hybrid reality of robots tackling the heavy structural components and human crews assembling faster and cheaper - technical constraints to pull this model off and future considerations on the optimal business model (own vs. rent) Major shoutout to Gilles Retsin for helping me pull this together. https://lnkd.in/eRTnbZPa
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DC Palter
“Hey DC — I’m raising a pre-seed at a $50M cap. Interested???” I ought to ignore the cold outreach like everyone else, but I can't help myself from replying, “Not at a $50M valuation!” “But we did a discounted cash flow analysis and calculated the valuation at $50,874,392.66.” DCF is the perfect answer to the question: tell me you know nothing about startups without telling me how you’re going to fail. But how should this founder determine the valuation of his startup? There’s a lot of methods to value startups. DCF. Berkus Method. Discount off next round. Cost to duplicate. Scorecard. Comparable transactions. Blah blah blah. None of them work. Valuation is not a calculation. It’s a negotiation. The only method that works is what I'll call the Palter Valuation Process. It's a process rather than a formula, with the following simple steps: 1. Determine your stage. Honestly. Based on customer traction rather than wishful thinking. Is this raise a friends & family & network (no traction yet), pre-seed (initial sales), seed ($100K+ ARR), or Series A ($4M+ ARR)? 2. Find typical valuations for your stage, sector, and geography. Look at both the median and ranges. Use the incredibly helpful data from Carta (thank you Peter Walker and Carta) plus whatever you can find from Pitchbook, Crunchbase, investment banks, and VCs that follow your sector. 3. Adjust within the ranges based on your traction and risk factors. 4. Check with a few trusted advisors who invest regularly at your stage to see if they think your proposed valuation will be attractive to investors in their network. 5. Begin pitching to potential lead investors. Negotiate the valuation and other investment terms. 6. See if the valuation attracts follow-on investors to fill the round. If not, renegotiate with the lead investor and other investors to set the final valuation. Keep in mind that valuation is just one piece the investment puzzle. Liquidation preference frequently determines how much (if anything) founders and employees get in an exit and is arguably more important than valuation. Investment vehicle (preferred shares, common shares, SAFE, convertible note) make a big difference to investors and will affect the valuation we find attractive. Full details on the Palter Valuation Process in the following article: http://bit.ly/3UAIwMw
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Paige Soya
The art and science of diligence in early-stage VC goes far beyond scorecards and rubrics. Sure, frameworks can provide structure - but they’re rigid, easy to game, and often miss the nuances of what makes a startup truly investable. That’s why in this week’s episode of High Stakes, I sat down with early-stage investor Matt Gittleman who shares his “levers and hurdles” approach to evaluating deals and why disciplined judgment matters more than box-checking. Watch the trailer then catch the full episode for a behind-the-scenes look at how investors really make decisions.
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