Startup Project: Build the future

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127

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Aug 2026

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Conversations with founders, operators and investors who are building the future. Listen to find the stories, ideas, tactics & investments behind the products that will define the future of technology. https://startupproject.substack.com/

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August 27, 202649 min

How Postman Became the World's Leading API Platform | Co-Founder & CEO of Postman Abhinav Asthana

Postman is the world's leading API platform, trusted by more than 40 million developers and 98% of the Fortune 500. In this episode, we trace how Postman went from a developer's side project into a global, AI-native enterprise platform. Abhinav Asthana shares how Postman reached profitability before raising venture capital, why developer products are so hard to monetize, why he refused to sell and moved from India to the US, and how AI agents are reshaping the company's next decade. Key topics: Building Postman as a side project to solve API testing Reaching Ramen profitability with in-product upgrades Why developer products are hard to monetize Turning down acquisition and moving from India to the US Single-player tool to enterprise API platformBuilding in India vs. the US AI agents as a new class of API consumers Astro, Passport, Fabric, and Fern — the agent-native stack Thoughts on "token maxing" and AI spend Timestamps: 00:00 - Introduction: Postman and Abhinav's journey 02:15 - Before Postman: becoming a developer in India 05:06 - BITS 360: the virtual campus tour that started it all 07:09 - Monetizing mobile and the rise of Instagram 08:32 - Parting ways and hacking on Postman on the side 09:08 - The original problem: testing APIs 11:05 - From side project to the main thing 11:58 - The first signals of demand (Chrome Web Store) 13:18 - Reaching Ramen profitability with upgrade packs 15:52 - Realizing Postman was a venture-scale business 17:46 - The enterprise journey and product validation 20:22 - Why he refused to sell Postman 22:10 - Moving to the US as an Indian founder 24:38 - What made Postman compound 27:01 - Why developers are hard to serve 28:32 - How vibe coding and agents change API consumption 35:02 - Building in India vs. the US 38:59 - How AI changed how Postman operates 41:22 - Thoughts on "token maxing" 43:55 - Inside Astro: an agentic operating system 45:17 - The agent identity Passport 47:08 - Where Postman goes in three to five years 48:27 - IPO plans? Resources & Links: Postman → https://www.postman.com/ Abhinav Asthana → https://www.linkedin.com/in/abhinavasthana/ Nataraj Sindam → https://www.linkedin.com/in/natarajsindam/ Startup Project Episodes → https://thestartupproject.io/episodes

August 13, 202648 min

Bringing Robotics for Electronics Manufacturing & AI Infrastructure | Bright Machines Founder

Startup Project sits down with Sviat, CEO of Bright Machines, to unpack how the company is using software-first robotics to manufacture complex electronics closer to where they’re deployed. The conversation focuses on why AI infrastructure is a strategic category, how Bright Machines differs from traditional contract manufacturing, and what onshoring really means for speed, quality, and security. Key Topics: In this episode, Sviat explains that Bright Machines is focused on AI infrastructure, specifically the electronics that go inside modern data centers, including compute nodes, storage, and racks. He traces the company’s thesis back to a broader idea: use software and robotics to manufacture electronics anywhere, then narrow that focus to the data center market as demand became clearer. The discussion breaks down the market stack, from chip designers like NVIDIA and AMD, to ODMs, OEMs, hyperscalers, and contract manufacturers. Sviat shares why data center hardware became the right bet before ChatGPT accelerated the market: the products are expensive, strategically important, and driven by quality and throughput more than labor cost alone. The show compares traditional assembly lines with Bright Machines’ approach, which uses more robotics, sensors, cameras, traceability, and humans in the loop where automation does not make sense. Sviat explains how Bright Machines starts with design, using Bright Designer to simulate and improve manufacturability before lines are built, which helps reduce bottlenecks and improve automation over time. He says the company’s main differentiator is its software platform, which orchestrates the line, powers smart skills for navigation and inspection, collects data, and feeds insights back into design. The conversation covers line flexibility, including how much can be reused when switching between CPU, GPU, or different accelerator-based server designs, and when end-of-arm tooling must change. Sviat says Bright Machines is growing rapidly, expects more than 3x growth this year, and can produce high volumes from a small number of sites because of robotics efficiency. The episode closes on the broader case for onshoring AI infrastructure manufacturing in the US: security, time to market, quality, and a labor shortage that makes robotics necessary. Timestamps: 06:39 - The market stack: chip designers, ODMs, OEMs, hyperscalers, and CMs 09:07 - Why Foxconn, Jabil, and similar contract manufacturers matter 10:04 - Why large factories still rely on massive manual labor 12:20 - Why data centers are different from cheap consumer electronics 13:49 - Security, strategic sectors, and why AI infrastructure belongs onshore 16:26 - The first Bright Machines product: CPU compute servers for a hyperscaler 17:58 - How the line works: modular stations, yields, and automation levels 19:26 - Bright Designer and design-for-manufacturing feedback loops 21:20 - Robots, sensors, traceability, and humans in the loop 22:19 - Why time to market matters as much as cost 23:31 - Yield and throughput: 98% line-level yields and up to 2x throughput 25:25 - The Bright Robotic Cell and how the assembly line is structured 27:35 - Reusability across products and when tooling changes are needed 30:31 - Manufacturing as a service, not repair or field service 31:24 - Growth, gigawatt-scale capacity, and output from a single site 33:00 - Why current hyperscaler capex is not expected to slow near term 34:45 - The bottlenecks before deployment: chips, components, power, permits 36:54 - Bright Machines’ three pillars: platform, data layer, and Bright Designer 39:15 - Why humanoid robotics is exciting but not ready for industrial use 41:16 - Where LLMs and newer AI tools can help the robotics workflow 43:57 - The overlooked advantages of onshoring manufacturing in the US 45:59 - What Bright Machines could build next: more complex electronics and future AI devices

July 23, 202652 min

How Canva Is Building AI Into Design | Head of AI Products at Canva | Danny Wu

Dive into the evolving role of AI in design and collaboration as Danny Wu, Head of AI Products at Canva, shares insights on how the platform is transforming creative workflows, democratizing design, and leveraging large language models and diffusion techniques. In this episode: How Canva redefined abstraction layers in design, moving from pixel edits to object-based workflows The evolution of AI at Canva: from traditional ML to transformers and large language models The impact of ChatGPT integration on Canva's user experience and business growth Agentic AI: Canva’s approach to AI that acts as a collaborative partner in design Challenges and misconceptions about AI generative models in creative industries Future plans: video content tools and more AI-powered features Timestamps: 00:00 - Introduction to Canva’s innovation in abstraction layers 01:12 - Danny Wu’s background and journey at Canva 02:46 - Transition from software engineer to Head of AI Products 04:25 - How diffusion and large language models accelerate Canva’s AI capabilities 06:50 - The dominance of transformer models in Canva’s AI strategy 08:51 - Shift from pixel to object, then conceptual design with AI 09:19 - Limitations of chat-based creativity vs direct manipulation 11:01 - The future of design involving AI-generated, editable content 13:30 - Launch of Canva AI and the platform's new architecture 15:22 - Use cases and limitations of AI in visual and video content 17:05 - Canva’s diverse user base and how AI personalization fits different needs 18:48 - Challenges of AI aesthetics and user customizations 20:32 - Amazing AI features like Magic Layers for editable image designs 22:16 - Comparing models: diffusion, open source, and proprietary tech 27:36 - Exploring agentic AI: Canva's vision of AI as a collaborative partner 30:37 - How ChatGPT and similar tools boost Canva’s reach and usability 34:26 - Personalizing AI output for users and reducing generated “cookie-cutter” content 37:38 - Managing AI's creative style and avoiding homogenization 42:39 - Canva’s feature development process and testing workflows 46:36 - Surprising use cases, like self-grading quizzes in education 48:59 - Overlap and differentiation between Canva and other design tools like Figma 50:54 - Future video tools and content creation enhancements at Canva

June 19, 202655 min

How Inference Layer Innovations Are Changing AI Efficiency and Costs | Sudip Roy Cofounder & CTO of Adaption Labs

Explore how the latest advancements in AI are shifting from traditional training to inference-focused efficiencies, and how companies like Adaptation Labs are pioneering adaptive, full-stack AI solutions that democratize control across industries. Key topics: The evolution from compute-heavy training models to efficient inference layers How inference costs are changing despite increasing AI demand The role of adaptive, gradient-free learning in democratizing AI customization Challenges with the last 5% reliability gap and continuous learning The importance of full-stack optimization—from data to interfaces in AI systems Future trends: decentralized AI, edge computing, and ongoing innovation Timestamps: 00:00 - Introduction to AI trends: scaling vs inference efficiencies 01:01 - Sudip’s background: Google Brain, DeepMind, and inference infrastructure 01:34 - The rapid growth of foundation and large language models 02:36 - Comparing traditional ML project timelines to large foundation models 04:20 - The transformative potential of foundation models in enterprise and underserved communities 05:33 - The shift from task-specific models to general-purpose foundation models 07:07 - How inference costs have evolved: the rising demand vs falling per-token costs 08:37 - The challenge of inference in trillion-parameter models and the move towards smaller, verticalized models 10:14 - Factors driving high inference costs: model size, reasoning, agentic workloads 12:13 - The probabilistic nature of inference and API pricing complexities 13:07 - Variability in inference costs and demand in real-world scenarios 14:14 - The autoregressive, sequential nature of LLM inference and system challenges 16:45 - Cost implications of autoregressive inference and the move to more efficient, localized models 18:18 - The motivation behind Adaptation Labs: democratizing AI control and customization 19:47 - Adaptive, gradient-free continual learning and environment interaction 21:26 - Co-optimizing full-stack AI: systems, interfaces, and models 22:34 - How interface design impacts AI adoption and continuous learning 23:55 - The evolution of techniques: from foundational training to open-source innovations 26:18 - Handling the ‘last 5%’ reliability challenge in enterprise AI deployments 28:02 - The importance of system feedback and adaptive learning in coding and decision-making 31:12 - Adaptive Data and AutoScientist: seamless data transformation and model co-optimization 32:55 - Use cases: finance, low-resource languages, long context data 34:13 - The role of inference techniques and creating high-quality data for customization 36:10 - Future of adaptive, task-specific interfaces and continuous, real-time learning 38:49 - Full-stack AI: data, models, interfaces, and their iterative feedback loops 41:18 - The competition between fine-tuning and adaptive inference techniques 43:29 - The origin of new inference techniques: industry labs, open source, and innovation hubs 45:27 - The “last 5%” reliability gap: why it’s critical and how dynamic learning can help 48:27 - Hardware vs software optimization in AI systems and the future of systemic efficiency 51:25 - Growing AI demand, hardware constraints, and the opportunity for systemic innovation 52:48 - The shift from training to inference and decentralized AI models at the edge 54:12 - Final thoughts: the evolving landscape and long-term AI innovation Connect with Sudip: LinkedIn Connect with Nataraj: ⁠LinkedIn⁠

May 31, 202643 min

The Story of Vast Data’s Disruptive Storage Tech | Co-Founder Vast Data Jeff Denworth

In this episode, we explore how Vast Data is revolutionizing storage solutions to support the exponential growth in AI workloads. Jeff Denworth shares insights into their innovative architecture, market strategy, competitive differentiation, and how they’re shaping enterprise data management in the era of AI. Main topics: The origin and evolution of Vast Data’s innovative storage architecture since 2016 How Vast’s solutions support large-scale AI and deep learning workloads The strategic focus on enterprise features, multi-tenancy, and integration with hyperscalers The impact of data reduction and cost efficiency on global flash supply New opportunities unlocked by Vast’s platform for analytics, vector search, and long context inference Business model nuances for cloud and on-premise deployments Vast’s profitability, market traction, and future growth prospects Timestamps: 00:00 - The AI super cycle and storage bottlenecks creating new opportunities02:20 - Understanding Vast Data's origin story and core architecture04:15 - How Google’s distributed systems influenced new storage innovations06:10 - Addressing scalability limitations of traditional storage systems08:00 - The shift from hard drives to flash and its market implications10:05 - Supporting AI workloads through scalable, enterprise-grade storage solutions12:00 - Customer sectors: life sciences, finance, and AI cloud providers14:15 - On-premise focus versus cloud deployment and hyperscaler strategies16:05 - Vast’s competitive differentiation: features, performance, and new data modalities18:15 - Integration with vector databases, analytics, and real-time AI inference20:30 - Business models: capacity-based, subscription, and partner collaborations22:50 - Addressing flash supply chain constraints and global market impact26:10 - The role of data reduction, federated data management, and long context storage30:50 - Unlocking enterprise data monetization and AI agent scalability34:15 - Impact of advanced storage on inference, context windows, and model efficiency36:50 - The current hardware procurement landscape and Vast’s software-led approach40:05 - Profitability metrics, growth, and the valuation of Vast Data42:25 - Final thoughts: the evolving data infrastructure landscape driving AI innovationResources & Links:Connect with Jeff Denworth:

May 1, 202636 min

Why AI Runs on Object Storage & How MinIO is Competing with AWS S3

In this episode, Garima Kapoor, co-founder and co-CEO of Min.io, shares insights into how storage infrastructure is evolving in response to AI, cloud, and enterprise needs. She offers a clear view of the market dynamics, innovative trends, and the strategic role of open-source technology in shaping the future. Key topics: The origins and motivation behind Min.io’s development How data growth influences storage strategies and the shift toward hybrid and private clouds The impact of AI on storage infrastructure and workloads Competitive landscape with giants like AWS, Azure, GCP, and the rise of Neo Clouds The importance of open standards for application portability and data gravity Evolving customer adoption: from open source developer community to enterprise sales The role of AI in accelerating product development, coding, and organizational decision-making How AI’s rapid evolution is shifting the fundamentals of skills and fundamentals for engineers Future market opportunities: exponential growth in storage needs driven by AI and IoT Timestamps: 00:00 - Introduction to Garima Kapoor and Min.io 00:31 - Motivation behind starting Min.io & market needs for object storage 01:07 - The founding story and personal drivers for creating Min.io 02:13 - Data growth drivers and the importance of data proximity over cloud location 03:05 - Business landscape: cloud vs. on-premises and hybrid environments 04:01 - Data migration challenges and promoting application portability 06:10 - Early product-market fit through open source and developer community growth 07:19 - Enterprise adoption journey from open source to cloud-native architecture 08:17 - Customer acquisition strategies blending bottom-up developer growth and enterprise sales 09:27 - Competing with Amazon, Microsoft, Google in the cloud storage space 11:33 - Impact of AI on storage: demand, infrastructure evolution, and market timing 12:51 - Min.io’s advantage in AI workloads due to cloud-native architecture 13:21 - Penetration of AI in storage: training, inferencing, and data utilization 15:01 - AI for enterprise applications: storage, models, and data lakes 16:26 - Neo Clouds and their role in GPU-optimized storage architectures 18:58 - The increasing demand for object storage driven by AI and data creation 21:02 - The effect of AI coding tools on product development speed and engineering skills 23:36 - Internal AI-driven solutions for operational efficiency 24:44 - The role of AI in reducing reliance on SaaS tools and infrastructure security 27:22 - Managing costs and building for the future in AI investment and storage 29:01 - The opportunity cost of tokens and AI-driven productivity gains 31:00 - Skills for early engineers in an AI-enabled future 33:32 - Min.io’s next steps and market expansion plans 34:36 - The paradigm shift: every business becoming AI and data-driven by 2026 Resources & Links: Connect with Garima Kapoor: ⁠Min.io Official Website⁠ ⁠Garima Kapoor - LinkedIn⁠ ⁠OpenAI⁠ ⁠NVIDIA GDC Announcements on Object Storage⁠ ⁠Nataraj's previous interview on startup infrastructure⁠ ⁠LinkedIn⁠ ⁠Twitter⁠

April 2, 202652 min

Autonomous AI Agents Are Changing How We Interact with the Web | Abhishek Das - Co-founder and Co-CEO of Yutori

Discover how Yutori is revolutionizing web interactions through autonomous AI agents designed for digital and web-based tasks. In this episode, Abhishek shares insights into building agentic AI, the technical challenges, and the evolving landscape of AI-powered automation. Main insights: Yutori's founders come from Meta’s AI division, bringing top-tier expertise in AI and ML. The motivation behind Yutori's product stems from a long-standing interest in productivity tools and autonomous agents. Scouts by Yutori are AI agents monitoring web for specific signals, reducing manual browsing and keeping users up-to-date. The architecture relies heavily on specialized subagents, optimizing costs and relevance in web navigation. Abhishek emphasizes the transition from reactive to proactive AI, enabling agents to oversee tasks without constant prompts. The importance of user-centric design is reflected in a simplified UI, API integrations, and customizable workflows. Cost-effective strategies, like subagent architecture, help balance performance with scalability. The web is shrinking in terms of contribution and content creation; autonomous agents could change the landscape by managing and synthesizing information. Future product directions include deeper integrations, multi-task workflows, and enhanced proactivity in AI agents. Abhishek predicts a shift towards outcome-based pricing for AI tools, aligning value with costs. The conversation also explores implications for robotics, data generation, and the potential disruption of traditional content ecosystems. Timestamps: 00:00 - Introduction to Yutori and its core product Scout 02:01 - Motivation for building autonomous AI agents 03:25 - The technical evolution from simulation to physical robots 04:44 - Origins of the Scout idea and focus on productivity tools 07:11 - The vision for web automation and agent-driven interactions 09:38 - Push vs Pull content systems and control over web consumption 10:38 - Demo of Scout setup and operation 14:00 - Technology foundation: web crawling, in-house navigation, and orchestration 16:28 - Data indexing and real-time monitoring approaches 18:20 - Subagents' distinct roles: navigator, researcher, social media scout 20:37 - Reporting, alerting, and workflows with Scout outputs 22:07 - Practical examples: monitoring market trends, personal tasks, and competitive intelligence 23:42 - Extending Scout functionality to actions and integrations 24:24 - The future vision: integrating Scout results into broader workflows 25:53 - Developer flexibility with subagents and API controls 27:03 - Cost considerations and architecture efficiencies 28:50 - The move towards proactive, autonomous agent behaviors 30:33 - Challenges of consumer adoption and simplifying interfaces 32:23 - Incentives for content creation and web ecosystem evolution 37:33 - Building trust and reliability in agent systems 39:18 - The web’s evolution and the rise of self-hosted content 41:24 - Impact of agent-based systems on content quality and SEO 43:32 - Measuring product-market fit and collecting user feedback 44:51 - Strategies for user acquisition and word-of-mouth growth 45:35 - Meta’s AI investments and industry trends 47:04 - Business models: subscription vs usage-based pricing 49:55 - Robotics advancements and synthetic data generation 52:22 - Final thoughts and opportunities for developers Resources & Links: Yutori API → https://yutori.com/api Abhishek Das → https://abhishekdas.com/ Nataraj Sindam → https://www.linkedin.com/in/natarajsindam/ Startup Project Episodes → https://thestartupproject.io/episodes

March 20, 202639 min

How Yoodli is Replacing Boring Sales Training with AI Roleplays | Varun Puri, Co-Founder & CEO of Yoodli

In this episode, Varun, co-founder of Yoodli, shares insights into how his startup leverages AI to enhance communication skills, from public speaking to enterprise sales training. Tune in to understand how AI can empower humans rather than replace them, and the strategic evolution from consumer to enterprise products. Key Topics: The origin story of Yoodli and its focus on helping people find their voice Transition from B2C to B2B: What was learned along the way The role of storytelling as a meta-skill in a world dominated by AI Using AI to make communication more authentic and human How large organizations like Google and Snowflake are integrating Yoodli The evolution of AI capabilities, from role plays to experiential learning Building modular, customizable AI products that adapt to customer needs The importance of deep integrations and the challenge of SaaS vendor proliferation Real-world growth stats: 900% revenue increase and millions of users Insights into leadership, authenticity on social media, and the value of vulnerability Personal stories from Sergey Brin’s projects and leadership lessons learned Timestamps: 00:00 – Introduction to Varun and Yoodli’s journey 02:01 – Early days of Yoodli: Founding thesis and initial challenges 04:19 – Key lessons about public speaking skills 05:45 – The importance of recording and reviewing oneself 06:25 – Describing Yoodli as “Duolingo for public speaking” 07:25 – The role of storytelling in high-performance communication 08:21 – Building AI to enhance, not replace, human authenticity 09:07 – Judgment as a differentiator in AI-enabled work 10:01 – How Yoodli expanded into enterprise with Google & others 11:24 – Social media as a branding tool for founders 12:38 – The impact of authenticity on LinkedIn and lead generation 14:09 – The Google GTM training case study: How it started 15:07 – Product features for enterprise sales training 16:05 – Impact on sales onboarding and role play automation 17:32 – The future of experiential learning and AI role plays 20:17 – The broader vision for AI in education and training 21:26 – Impressive growth stats and customer insights 22:01 – The technological foundation: Modular AI architectures 23:52 – The influence of LLM improvements on product features 24:46 – The commoditization of AI role plays and experiential learning 25:12 – Building deep, customizable, scalable AI solutions 26:36 – The importance of scale and deep integrations 30:03 – Product differentiation through vertical focus and deep specialization 33:07 – Market challenges: Demand, consolidation, and customer expectations 34:42 – How to find and connect with Varun 35:30 – Sergey Brin’s projects, leadership lessons, and human insights 37:36 – Overcoming imposter syndrome: Everyone’s learning curve 39:01 – Final reflections and looking ahead Resources & Links: Varun on Linkedin Nataraj on Linkedin Try Yoodli

March 8, 202642 min

Inside the Battle for AI Cloud Dominance — Why Cloud Builders like TensorWave are Rethinking NVIDIA’s Monopoly | Jeff Tatarchuk, Co-Founder of TensorWave

Rethinking AI Compute Infrastructure: The TensorWave ApproachIn this episode, Jeff Tatarchuk, co-founder of TensorWave, shares how his deep industry experience and innovative mindset are transforming AI compute infrastructure. We explore how building specialized data centers, focusing on AMD GPUs, and creating flexible ecosystems are shaping the future of scalable AI. In this episode: The evolution of cloud companies and the rise of Neo clouds focused on AI compute TensorWave’s unique strategy of deploying AMD GPUs in custom data centers Lessons learned from FPGA cloud business and transitioning into GPU infrastructure The technical challenges and solutions in scaling data centers quickly amidst power and supply chain constraints The importance of software ecosystems, interoperability, and supporting AMD’s software stack How TensorWave differentiates itself from purely financial arbitrage models and pure Nvidia-centric clouds AMD’s advantages in memory capacity, chiplet architecture, and software support The technical intricacies of CUDA versus ROCm, and efforts to build an open ecosystem Future vision: democratized, reliable, and flexible AI compute options for enterprise and labs Timestamps:00:00 – Introduction to TensorWave and the AI compute landscape 02:30 – The rise of Neo clouds and innovation waves in cloud infrastructure 06:00 – How TensorWave’s FPGA cloud background shaped its GPU strategy 10:00 – Challenges in deploying large data centers: power, supply chain, and permitting 14:00 – Building and scaling AMD GPU data centers quickly and efficiently 19:00 – Software ecosystems: the CUDA moat and TensorWave’s ‘Beyond CUDA’ summit 23:00 – Market differentiation: technical and operational challenges in the Neo cloud space 27:00 – Supporting enterprise fine tuning and large-scale training demands 32:00 – AMD’s technical advantages: VRAM, chiplet architecture, and software support 36:00 – Building an open, heterogeneous AI ecosystem beyond CUDA 40:00 – What success looks like: a resilient, accessible AI compute future Resources & Links: ⁠TensorWave⁠ ⁠Beyond CUDA Summit⁠ ⁠Scalar LM by Greg De Almos⁠ ⁠AMD MI300X Data Center Chip⁠ ⁠Nvidia H100⁠ ⁠RoCM Software Stack⁠ ⁠LinkedIn⁠ ⁠Twitter⁠ This conversation offers a strategic look at how focused infrastructure development, software ecosystem support, and hardware differentiation are critical in shaping the future of accessible, scalable AI compute. Whether you're building data centers, developing AI hardware, or just interested in industry shifts, this episode provides valuable insights into how companies like TensorWave are reshaping the landscape.

February 20, 202656 min

Building the AI Operating System for Revenue: How Gong scales to 5,000+ customers | Eilon Reshef (CPO, Gong)

How Gong Built a $7B AI Category: From "Conversation Intelligence" to the Revenue Operating System Most sales teams fly blind. They rely on "gut feel" and "art" rather than data and science. Eilon Reshef (Co-founder & CPO of Gong) realized this in 2015 and built a platform that captures the reality of every customer interaction to drive predictable growth. In this episode of Startup Project , Eilon breaks down the evolution of Gong, how they achieved 57% higher win rates for companies like PayPal and DocuSign, and why the "Revenue Graph" is the next frontier of enterprise AI. If you are a founder, a product leader, or a sales professional looking to understand how AI is actually transforming the enterprise, this deep dive is for you. What you’ll learn in this episode: The Genesis of Gong: Why Eilon moved from a successful exit at WebCollage to solving the "black box" of sales conversations. The "Science" of Sales: How to move away from subjective CRM updates to hard data captured from video, email, and phone calls. The Revenue Graph: Why Gong’s proprietary data model is more valuable than a generic LLM. Scaling to 5,000+ Customers: The tactical steps Gong took to achieve product-market fit in a crowded SaaS landscape. The Future of AI Agents: Why "Vibe Coding" and prosumer AI are just the beginning, and how the enterprise shift is happening now. Timestamps: 0:00 - Intro: Meeting Eilon Reshef2:15 - The "Aha!" moment that led to Gong10:45 - Moving from transcription to "Revenue Intelligence"18:30 - How Gong achieves 57% higher win rates for customers25:50 - Building a proprietary AI layer on top of LLMs34:10 - The "Revenue Graph" explained42:15 - Why most enterprise AI implementations fail50:00 - Advice for founders building in the AI era54:14 - Closing thoughts Connect with Eilon & Gong: Website: https://www.gong.io/ Eilon’s LinkedIn: https://www.linkedin.com/in/eilonreshef #Gong #AI #SalesTech #StartupGrowth #Entrepreneurship #RevenueIntelligence #SaaS #ProductMarketFit #EilonReshef #StartupProject