Ross G.

💥 Some startups build apps. Others build the infrastructure behind engineering, finance, and AI workflows. This US lineup just raised and teams are expanding. 🇺🇸 Axiomatic_AI (Cambridge, MA) #AI Engineering systems require provable reasoning. Axiomatic AI raised $18M Seed led by Engine Ventures, with Kleiner Perkins, Big Sur Ventures, Propagator Ventures, and others, bringing total funding to $25M. Founded by Marin Soljacic, Frank Koppens, Joyce Poon, led by CEO Jake Taylor, the company builds verification infrastructure combining AI with physics-based validation for semiconductors and advanced manufacturing. ❗ Hiring: 14 openings - https://bit.ly/4bqMxeu 🇺🇸 KAST (New York, NY) #FinTech Cross-border money movement remains slow and fragmented. KAST raised $80M Series A co-led by QED Investors and Left Lane Capital, with Peak XV Partners, and others. Founded by Raagulan Pathy, James Butland, and Sam Kerrins, the platform lets users hold, send, and spend digital dollars globally through stablecoin infrastructure. ❗ Hiring: 18 openings - https://bit.ly/4cP9gTU 🇺🇸 DiligenceSquared (YC F25) (New York, NY) #FinTech Commercial due diligence remains time-consuming for investment teams. DiligenceSquared raised $5M Seed led by Relentless, with Y Combinator. Founded by Frederik Kofoed Hansen, Søren Biltoft-Knudsen, and Harshil Rastogi, the platform uses AI to automate expert interviews, analysis, and reporting for private equity firms. ❗ Hiring: 4 openings - https://bit.ly/4uDlKV5 🇺🇸 Mega (New York, NY) #MarTech Marketing execution is shifting to autonomous systems. Mega raised $11.5M Series A led by Goodwater Capital, with Andreessen Horowitz, Atreides, SignalFire, and Kearny Jackson. Founded by Robbie Schneidman and Lucas Pellan, the platform deploys AI agents that continuously optimize search rankings, ads, and websites for SMBs. ❗ Hiring: 6 openings - https://bit.ly/4uLic38 🇺🇸 Dify (Menlo Park, CA) #DevTools Agentic workflows are becoming a core layer of enterprise AI infrastructure. Dify raised $30M Series Pre-A led by HSG, with Alt-Alpha Capital, 5Y Capital, and others. Founded by Luyu Zhang, John Wang, and Junchen Yan, the open-source platform lets teams build and run AI workflows with a visual builder and production infrastructure. ❗ Hiring: 16 openings - https://bit.ly/46YyF9Y 🇺🇸 Yourco (Chicago, IL) #HRTech Connecting frontline workers remains a blind spot for many organizations. Yourco raised $6M Series A led by High Alpha. Founded by Brodie Meyer and Benjamin Meyer, the SMS platform helps companies communicate with deskless employees across 135+ languages without apps or company email. ❗ Hiring: 6 openings - https://bit.ly/4dmBF3B Funding is the milestone. Execution starts the day after. If you’re facing post-raise people decisions around hiring, people planning, and execution risk, I open limited office hours for founders and People leaders on what comes next after a raise. DM me or comment below for a 30-min slot.

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AI is revolutionizing how software companies build & scale. Noah Lin & I have been codifying successful strategies in the market, some of which we shared on Crunchbase last week. Given the enthusiasm and questions we received, I'm excited to share a more in depth follow-up with tactical strategies for building early GTM in an AI first world. We unpack the strategies that AI-first companies are using to redefine value and growth. AI-first products are shifting from traditional subscription models to dynamic, usage-based, or hybrid pricing. At defy.vc, we’ve identified four key ways top companies are capitalizing on this trend in their GTM strategies: 💡 Pricing & ROI: Crafting compelling ROI narratives to align with budgets and deliver clear value. 🔍 Discovery & Qualification: Sharpening sales fundamentals to identify high-potential leads early. 🤝 Consultative Selling: Guiding buyers to reimagine workflows and build long-term trust. 📈 High-Leverage POCs: Proving transformative value with tightly scoped, metrics-driven proof-of-concepts. We share specific questions to ask and actionable tactics to navigate unfamiliar spend structures and turn POCs into lasting partnerships. Link to the full article below. At Defy, we’re passionate about partnering with founders who are pushing the boundaries of GTM, pricing, or post-sales strategies in the AI-first world. If you’re innovating in this space, drop a comment or DM us—we’d love to connect! #AI #SaaS #GoToMarket #Innovation #Tech

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The fastest way to make bad decisions is to trust AI summaries that do not show where the information came from. AI for research and analysis is most helpful when it turns messy inputs into clear and proven evidence. A product manager can scan product updates, forum threads, and support tickets to find three real user problems and attach the original posts. A strategy lead can check five competitor websites, recent interviews, and pricing details, then make a one-page brief with side by side claims and links to proof. A compliance analyst can track new policy changes, highlight what affects a workflow, and keep a record so audits are easier later. As of September 2025, most serious teams want grounded answers as a default. This means every insight needs a source, a date, and a short note on how the model reached it. Many tools now give a notebook mode that shows step by step reasoning. They also include simple checks to avoid reading the same claim twice and tracking features that keep names, companies, and numbers consistent. The change is clear: people now prefer explainable research instead of fast but shallow summaries. If you are choosing a tool, start with integration and access. Make sure it connects to the platforms you already use such as Docs, Drive, Notion, Slack, or Confluence. Check that it respects row level permissions when searching. Look for tools that show sources, give confidence notes, and allow you to compare two or more answers side by side. Review how they handle data, including retention, personal information, hosting regions, and audit logs. Think about cost in the way your team works. Per seat pricing is simple, but research often comes in spikes, so check usage limits and the real cost per page or minute analyzed. Finally, test accuracy on your own tasks by running three repeatable jobs and checking for precision, recall, and time to useful output. The main point is this: choose tools that make research clear, repeatable, and easy to share, not only fast. That is why we are building AI Review Insider, a place where professionals can find clear reviews and choose the right tools with confidence.

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AI product risk is not just about model accuracy. From experience, that’s usually the smallest part of the problem. What breaks AI products in the real world isn’t just hallucinations or drift—it’s trust erosion, hidden costs, weak feedback loops, and second-order behavioral effects that no metric dashboard shows upfront. After working on large-scale data and AI-driven platforms, I’ve learned that an AI Product Manager is really a risk orchestrator: • Managing user trust, not just outputs • Managing data quality and bias • Managing model uncertainty • Managing cost volatility • Managing compliance, explainability, and auditability, not just features • Managing human behavior changes triggered by automation The hardest part? Many of these risks only emerge after partial success. That’s why strong AI product leadership isn’t about shipping smarter models—it’s about continuously sensing, surfacing, and sequencing risk as the system learns and scales. I’ve seen that teams who call out these risks early build systems that are easier to run, easier to trust, and don’t slowly bleed value while dashboards still look green. Curious to hear from others building AI products— which risk has surprised you the most after launch? #AIProductManagement #ProductLeadership #ResponsibleAI #SystemsThinking #RiskManagement #TrustInAI #AIGovernance #MLOps #ProductStrategy

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If Netanyahu videos are Ai generated then I must say military grade Ai is far superior than what they are selling to us masses (VEO3,Sora)

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Here's how Clay used "Reverse Demos" to find product-market fit and build a self-serve motion from scratch: When we first launched Clay on Product Hunt in February 2022, we got hundreds of signups overnight. We demo’d our product to them like we knew best: by running 30-minute presentations like college professors on Zoom. Unfortunately, few retained -- most likely because no one was actually learning during the demo. Potential customers were usually impressed when they saw me use Clay, but as soon as they tried to use it themselves, they got stuck and churned. Enter: the reverse demo. Instead of running canned demo meetings, we started emailing potential customers before meetings, asking them to come prepared with a specific data enrichment problem to solve. If they didn’t come with an idea, we’d use the first 5 minutes to come up with a personalized use case for them. Once they were in the meeting, we’d ask them to share their screen, give them a Clay signup link, and use Zoom annotations to show them exactly where to click to complete their workflow. Our goal on every reverse demo was simple: solve the customer’s stated problem within the 30 minutes -- and try to blow their minds in the process. Initially, we had to walk people through multiple calls before they had their "a-ha" moments. Through reverse demos, we were able to get it in under one call. Our goal was to build product habits: by solving any customer’s unique business problem within 30 minutes, we helped them build confidence with Clay and made it more likely for them to use the product to solve their future problems. The obvious benefit of the reverse demo was better customer conversion and retention. After 100+ reverse demos, we were getting people to “a-ha” moments within just a few minutes and most would convert after that call alone. But—much more importantly—the reverse demo helped us quickly improve Clay’s product. There’s no tighter feedback loop than getting your ideal customers on Zoom and watching them use the product for the first time. There were dozens of times that I noticed something on a reverse demo, whether bad UX, bad copy, or a bug, and asked Eric Engoron on our engineering team to ship a change the same day. Each reverse demo was a UX masterclass. That helped us go from 7 calls to convert a customer to just 1 in less than a year. Eventually this improvement gave us the confidence to remove our waitlist and launch a successful self-serve motion. This feedback loop was foundational for our growth as we navigated product-market fit and evolved our GTM motion.

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I talked to this partner of Andreessen Horowitz about how AI Consumer Apps are charging $200 per month & people are paying it 🤯 I sat down with Anish Acharya, General Partner at a16z, to break down what’s really happening in consumer AI. Anish has built and sold two consumer startups (one to Google, one to Credit Karma where he scaled Cards to 100M+ members and nearly $1B revenue), and now invests in companies like Deel, HappyRobot, Mosaic, and Titan. Here are the takeaways founders need to know: 1. No marketing problems, only product problems. If your app isn’t growing while competitors raise prices, the value isn’t there. 2. Foundation models aren’t winner-take-all. OpenAI, Claude, Gemini, and Mistral are all thriving, specialization wins. 3. Voice AI is moving up the value chain. From low-level support calls to high-stakes revenue conversations. 4. App generation is just getting started. Think Shopify, but for software. 5. The biggest opportunities are AI-native products. Therapy, social, accounting, and companionship — reimagined from first principles. 6. SF is still the epicenter. The serendipity, conversations, and mindset are unmatched. Want to listen to the whole conversation?

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If you sell enterprise, the game isn’t hype - it’s controllable revenue motion. In Quantum Neuron we bootstrapped to $600k #ARR (US, UK, EU, MENA) by treating GTM like an execution system, not a series of campaigns. Enterprise growth isn’t a “moment”. It’s a machine. And the machine is only as good as a few inputs you can measure and control: 1. Cost per Discovery (CPD), not CPL We measure CPD as the moment the funnel truly opens. CPL can look good while pipeline quality collapses. 2. Discovery show-up rate Fastest signal of targeting and messaging drift. 3. Time-to-first-value (TTV) Short TTV turns interest into internal momentum and makes expansion easier. 4. Sales velocity by ICP segment Track cycle time separately by segment, region, and deal type. 5. Follow-up reliability Most enterprise deals are won in the follow-up layer. A practical unlock for us has been cross-platform signal validation: pixels and trackers across Meta, LinkedIn, and TikTok validate each other and continuously feed multi-platform retargeting. Even in the US, we’ve kept a stable $30-40 cost per discovery over months, with an average deal size around $25k. We’re currently in an active #seedround and speaking with a small group of investors who value disciplined execution and predictable GTM. If that’s your lens, feel free to reach out. In enterprise, repeatability is the product. Everything else is noise.

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Sales velocity > product velocity. With AI, everyone can ship products fast. Product velocity matters, but sales velocity matters even more. The startups that win will be the ones that reply instantly to customers. They will schedule demos on the spot instead of a week out. They will work with customers to prove the ROI quickly and collaboratively. And they will move contracts from draft → signed without any delay or hassle. Responsiveness is the differentiator. When every product can look the same, the company that moves fastest with customers will stand out. Because of this, there will be a new wave of startups created specifically to help increase sales velocity. Supersonik is one of them. By having AI run your demo and interact seamlessly with customers, it creates a new level of responsiveness. If you are not using something like Supersonik, you are already behind your competitors. Sales velocity is still underappreciated v. product velocity, and the founders who understand this have a significant advantage.

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