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Nataraj speaks with Mike Collins, founder of Alumni Ventures, about how his firm opened up venture capital to individual investors through pooled funds and co-investing. They also dig into the structural problems in private markets, why companies stay private longer, and how AI is reshaping venture without eliminating the need for patience, judgment, and diversification.Key topics Mike Collins’ background in ventureWhat Alumni Ventures is built to doHow the alumni fund model worksWhy diversification matters in ventureThe long-term nature of venture investingProblems in today’s venture and public marketsWhy companies stay private longerAI, hype, and real innovationWhy adoption is slower than demosHow new venture firms get startedLearning resources for retail investors Timestamps 00:00 - Introduction and why Alumni Ventures matters 00:24 - Mike Collins’ VC background and founding Alumni Ventures 01:24 - The mission: access and education for individual investors 03:59 - How an alumni fund is structured and invested 05:55 - Why pooled capital and network scale matter06:24 - Venture capital as a core engine of the economy07:28 - Why most individuals need private-company exposure08:51 - Why venture portfolios need diversification10:33 - Green D fund leadership and how team sourcing works11:52 - How deals get allocated across multiple funds13:11 - Venture as a long-term, slow-compounding asset class14:07 - Building a venture allocation over several years15:04 - Why checking investments too often looks like trading16:44 - The biggest accessibility problem in venture today17:42 - Why public-company incentives have weakened18:43 - Why companies delay going public20:16 - Late-stage private companies and who captures the upside21:14 - Why SpaceX illustrates the privatization of gains22:51 - Public versus private markets and the role of competition24:19 - AI as a real platform shift, not just hype26:15 - Big opportunities beyond AI: energy, defense tech, healthcare27:41 - The cultural bias toward doom and negative headlines29:37 - Why staying private can help companies like Stripe32:40 - How AI capital concentration affects the broader venture market34:53 - The Series A squeeze and how market corrections happen36:49 - The main paths to starting a new venture fund39:15 - Why great companies still take decades to build40:29 - Why AGI timelines are often faster in theory than in reality42:24 - Regulatory backlash and the slower pace of adoption43:18 - Self-driving cars as a cautionary example for AI timelines44:54 - Why the last 5 percent of product adoption is the hardest46:58 - AI as a tutor for learning venture capital47:53 - Books and frameworks for understanding startups and VC49:18 - How to filter noise and think in decades, not days50:44 - Why the best investors often do the least trading51:50 - Mike’s core investing rules and closing thoughts

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 testingReaching Ramen profitability with in-product upgradesWhy developer products are hard to monetizeTurning down acquisition and moving from India to the USSingle-player tool to enterprise API platformBuilding in India vs. the USAI agents as a new class of API consumersAstro, Passport, Fabric, and Fern — the agent-native stackThoughts on "token maxing" and AI spendTimestamps: 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

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

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 workflowsThe evolution of AI at Canva: from traditional ML to transformers and large language modelsThe impact of ChatGPT integration on Canva's user experience and business growthAgentic AI: Canva’s approach to AI that acts as a collaborative partner in designChallenges and misconceptions about AI generative models in creative industriesFuture plans: video content tools and more AI-powered features Timestamps: 00:00 - Introduction to Canva’s innovation in abstraction layers01:12 - Danny Wu’s background and journey at Canva02:46 - Transition from software engineer to Head of AI Products04:25 - How diffusion and large language models accelerate Canva’s AI capabilities06:50 - The dominance of transformer models in Canva’s AI strategy08:51 - Shift from pixel to object, then conceptual design with AI09:19 - Limitations of chat-based creativity vs direct manipulation11:01 - The future of design involving AI-generated, editable content13:30 - Launch of Canva AI and the platform's new architecture15:22 - Use cases and limitations of AI in visual and video content17:05 - Canva’s diverse user base and how AI personalization fits different needs18:48 - Challenges of AI aesthetics and user customizations20:32 - Amazing AI features like Magic Layers for editable image designs22:16 - Comparing models: diffusion, open source, and proprietary tech27:36 - Exploring agentic AI: Canva's vision of AI as a collaborative partner30:37 - How ChatGPT and similar tools boost Canva’s reach and usability34:26 - Personalizing AI output for users and reducing generated “cookie-cutter” content37:38 - Managing AI's creative style and avoiding homogenization42:39 - Canva’s feature development process and testing workflows46:36 - Surprising use cases, like self-grading quizzes in education48:59 - Overlap and differentiation between Canva and other design tools like Figma50:54 - Future video tools and content creation enhancements at Canva

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 layersHow inference costs are changing despite increasing AI demandThe role of adaptive, gradient-free learning in democratizing AI customizationChallenges with the last 5% reliability gap and continuous learningThe importance of full-stack optimization—from data to interfaces in AI systemsFuture trends: decentralized AI, edge computing, and ongoing innovationTimestamps: 00:00 - Introduction to AI trends: scaling vs inference efficiencies01:01 - Sudip’s background: Google Brain, DeepMind, and inference infrastructure01:34 - The rapid growth of foundation and large language models02:36 - Comparing traditional ML project timelines to large foundation models04:20 - The transformative potential of foundation models in enterprise and underserved communities05:33 - The shift from task-specific models to general-purpose foundation models07:07 - How inference costs have evolved: the rising demand vs falling per-token costs08:37 - The challenge of inference in trillion-parameter models and the move towards smaller, verticalized models10:14 - Factors driving high inference costs: model size, reasoning, agentic workloads12:13 - The probabilistic nature of inference and API pricing complexities13:07 - Variability in inference costs and demand in real-world scenarios14:14 - The autoregressive, sequential nature of LLM inference and system challenges16:45 - Cost implications of autoregressive inference and the move to more efficient, localized models18:18 - The motivation behind Adaptation Labs: democratizing AI control and customization19:47 - Adaptive, gradient-free continual learning and environment interaction21:26 - Co-optimizing full-stack AI: systems, interfaces, and models22:34 - How interface design impacts AI adoption and continuous learning23:55 - The evolution of techniques: from foundational training to open-source innovations26:18 - Handling the ‘last 5%’ reliability challenge in enterprise AI deployments28:02 - The importance of system feedback and adaptive learning in coding and decision-making31:12 - Adaptive Data and AutoScientist: seamless data transformation and model co-optimization32:55 - Use cases: finance, low-resource languages, long context data34:13 - The role of inference techniques and creating high-quality data for customization36:10 - Future of adaptive, task-specific interfaces and continuous, real-time learning38:49 - Full-stack AI: data, models, interfaces, and their iterative feedback loops41:18 - The competition between fine-tuning and adaptive inference techniques43:29 - The origin of new inference techniques: industry labs, open source, and innovation hubs45:27 - The “last 5%” reliability gap: why it’s critical and how dynamic learning can help48:27 - Hardware vs software optimization in AI systems and the future of systemic efficiency51:25 - Growing AI demand, hardware constraints, and the opportunity for systemic innovation52:48 - The shift from training to inference and decentralized AI models at the edge54:12 - Final thoughts: the evolving landscape and long-term AI innovationConnect with Sudip: LinkedInConnect with Nataraj: ⁠LinkedIn⁠

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 2016How Vast’s solutions support large-scale AI and deep learning workloadsThe strategic focus on enterprise features, multi-tenancy, and integration with hyperscalersThe impact of data reduction and cost efficiency on global flash supplyNew opportunities unlocked by Vast’s platform for analytics, vector search, and long context inferenceBusiness model nuances for cloud and on-premise deploymentsVast’s profitability, market traction, and future growth prospectsTimestamps: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:

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 developmentHow data growth influences storage strategies and the shift toward hybrid and private cloudsThe impact of AI on storage infrastructure and workloadsCompetitive landscape with giants like AWS, Azure, GCP, and the rise of Neo CloudsThe importance of open standards for application portability and data gravityEvolving customer adoption: from open source developer community to enterprise salesThe role of AI in accelerating product development, coding, and organizational decision-makingHow AI’s rapid evolution is shifting the fundamentals of skills and fundamentals for engineersFuture market opportunities: exponential growth in storage needs driven by AI and IoTTimestamps: 00:00 - Introduction to Garima Kapoor and Min.io00:31 - Motivation behind starting Min.io & market needs for object storage01:07 - The founding story and personal drivers for creating Min.io02:13 - Data growth drivers and the importance of data proximity over cloud location03:05 - Business landscape: cloud vs. on-premises and hybrid environments04:01 - Data migration challenges and promoting application portability06:10 - Early product-market fit through open source and developer community growth07:19 - Enterprise adoption journey from open source to cloud-native architecture08:17 - Customer acquisition strategies blending bottom-up developer growth and enterprise sales09:27 - Competing with Amazon, Microsoft, Google in the cloud storage space11:33 - Impact of AI on storage: demand, infrastructure evolution, and market timing12:51 - Min.io’s advantage in AI workloads due to cloud-native architecture13:21 - Penetration of AI in storage: training, inferencing, and data utilization15:01 - AI for enterprise applications: storage, models, and data lakes16:26 - Neo Clouds and their role in GPU-optimized storage architectures18:58 - The increasing demand for object storage driven by AI and data creation21:02 - The effect of AI coding tools on product development speed and engineering skills23:36 - Internal AI-driven solutions for operational efficiency24:44 - The role of AI in reducing reliance on SaaS tools and infrastructure security27:22 - Managing costs and building for the future in AI investment and storage29:01 - The opportunity cost of tokens and AI-driven productivity gains31:00 - Skills for early engineers in an AI-enabled future33:32 - Min.io’s next steps and market expansion plans34:36 - The paradigm shift: every business becoming AI and data-driven by 2026Resources & Links: Connect with Garima Kapoor: ⁠Min.io Official Website⁠⁠Garima Kapoor - LinkedIn⁠⁠OpenAI⁠⁠NVIDIA GDC Announcements on Object Storage⁠⁠Nataraj's previous interview on startup infrastructure⁠⁠LinkedIn⁠⁠Twitter⁠

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 Scout02:01 - Motivation for building autonomous AI agents03:25 - The technical evolution from simulation to physical robots04:44 - Origins of the Scout idea and focus on productivity tools07:11 - The vision for web automation and agent-driven interactions09:38 - Push vs Pull content systems and control over web consumption10:38 - Demo of Scout setup and operation14:00 - Technology foundation: web crawling, in-house navigation, and orchestration16:28 - Data indexing and real-time monitoring approaches18:20 - Subagents' distinct roles: navigator, researcher, social media scout20:37 - Reporting, alerting, and workflows with Scout outputs22:07 - Practical examples: monitoring market trends, personal tasks, and competitive intelligence23:42 - Extending Scout functionality to actions and integrations24:24 - The future vision: integrating Scout results into broader workflows25:53 - Developer flexibility with subagents and API controls27:03 - Cost considerations and architecture efficiencies28:50 - The move towards proactive, autonomous agent behaviors30:33 - Challenges of consumer adoption and simplifying interfaces32:23 - Incentives for content creation and web ecosystem evolution37:33 - Building trust and reliability in agent systems39:18 - The web’s evolution and the rise of self-hosted content41:24 - Impact of agent-based systems on content quality and SEO43:32 - Measuring product-market fit and collecting user feedback44:51 - Strategies for user acquisition and word-of-mouth growth45:35 - Meta’s AI investments and industry trends47:04 - Business models: subscription vs usage-based pricing49:55 - Robotics advancements and synthetic data generation52:22 - Final thoughts and opportunities for developersResources & Links: Yutori API → https://yutori.com/apiAbhishek Das → https://abhishekdas.com/Nataraj Sindam → https://www.linkedin.com/in/natarajsindam/Startup Project Episodes → https://thestartupproject.io/episodes

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 LinkedinNataraj on LinkedinTry Yoodli

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 computeTensorWave’s unique strategy of deploying AMD GPUs in custom data centersLessons learned from FPGA cloud business and transitioning into GPU infrastructureThe technical challenges and solutions in scaling data centers quickly amidst power and supply chain constraintsThe importance of software ecosystems, interoperability, and supporting AMD’s software stackHow TensorWave differentiates itself from purely financial arbitrage models and pure Nvidia-centric cloudsAMD’s advantages in memory capacity, chiplet architecture, and software supportThe technical intricacies of CUDA versus ROCm, and efforts to build an open ecosystemFuture 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: ⁠TensorWave⁠⁠Beyond CUDA Summit⁠⁠Scalar LM by Greg De Almos⁠⁠AMD MI300X Data Center Chip⁠⁠Nvidia H100⁠⁠RoCM Software Stack⁠⁠LinkedIn⁠⁠Twitter⁠ 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.

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

In this episode of Startup Project, host Nataraj sits down with Andrew Bialecki, Co-founder and CEO of Klaviyo, to unpack one of the most capital-efficient growth stories in modern SaaS history. Klaviyo grew into a $7B+ public company while raising just $15M in total funding—a sharp contrast to today’s venture-backed growth playbooks. Klaviyo now powers the marketing and growth of thousands of ecommerce brands worldwide, using data, automation, and AI to help businesses build deeper relationships with their customers. This conversation goes deep into the real work behind that outcome: the long road to product-market fit, the tradeoffs the team made early on, and why Klaviyo intentionally avoided blitzscaling in favor of building a durable, data-driven platform. Andrew shares candid insights into Klaviyo’s early years, when growth was slow, uncertainty was high, and the company looked nothing like a future public-market success story. We discuss why finding product-market fit took longer than expected, how early customer feedback shaped the product, and why patience turned out to be one of Klaviyo’s biggest competitive advantages. Rather than chasing short-term growth metrics, Klaviyo focused on deeply understanding ecommerce customers and building infrastructure that could scale for the long term. That discipline ultimately shaped the company’s culture, product roadmap, and go-to-market strategy. A major theme of the episode is how AI and customer data are reshaping the future of ecommerce marketing. Andrew explains why data—not features—is the real moat in modern marketing platforms, and how Klaviyo’s architecture allowed the company to benefit from AI as the technology matured. We explore how AI is changing personalization, segmentation, and automation for ecommerce brands, and why many AI marketing tools miss the point by focusing on surface-level automation instead of foundational data infrastructure. This is a thoughtful, grounded discussion about where AI actually creates leverage in marketing—and where hype often distracts teams from doing the hard work. At a time when many startups raise hundreds of millions of dollars to fuel growth, Klaviyo’s story stands out as a case study in capital efficiency. Andrew walks through why the company chose to raise so little capital, how constraints shaped better decision-making, and what founders today can learn from building with discipline. We also discuss the tradeoffs of avoiding aggressive fundraising, the pressure that comes with slower growth, and why capital efficiency can be a long-term strategic advantage rather than a limitation. This episode is especially valuable for: Startup founders building SaaS or ecommerce companies Marketers and ecommerce leaders navigating AI-driven change Product and growth leaders thinking about data as a moat Investors and operators interested in capital-efficient businesses Anyone curious about how enduring companies are actually built How Klaviyo became a $7B+ public company with only $15M raised The long path to product-market fit Why capital efficiency beat hypergrowth Building an AI-powered marketing platform for ecommerce Turning customer data into a durable competitive moat How AI is reshaping ecommerce and digital marketing Lessons for founders building long-term SaaS businesses Nataraj is the host of Startup Project, an investor and product leader who explores how real companies are built—from early product decisions to scaling with discipline. Startup Project features in-depth conversations with founders who’ve achieved genuine product-market fit, focusing on the thinking, tradeoffs, and execution behind enduring businesses. 🌐 Startup Project: https://startupproject.substack.com/🔗 Klaviyo: https://www.klaviyo.com🔗 Follow Nataraj (Host): https://www.linkedin.com/in/natarajsindam/

In this episode of Startup Project, host Nataraj speaks with Ilya Levtov, Founder and CEO of CraftCo, an AI-powered platform designed to help organizations better understand and organize supplier information. The conversation focuses on how enterprises and public-sector organizations manage large supplier networks, bring together data from multiple systems, and use AI-driven tools to improve visibility and operational efficiency. Ilya shares insights from building an enterprise software company, working with complex customers, and applying data and automation to support better decision-making across procurement and supply chain teams. This episode is intended for listeners interested in enterprise technology, AI platforms, procurement software, and the practical challenges of building and scaling data-driven companies. Topics discussed AI-powered supplier intelligence Supply chain data organization and visibility Procurement software and enterprise workflows Building scalable enterprise platforms Lessons from founding and growing a technology company 🎙 Guest: Ilya Levtov, Founder & CEO, CraftCo 🎧 Host: Nataraj 📌 Podcast: Startup Project Follow Startup Project for more conversations with founders building modern enterprise and AI-driven technology companies.

Discover how Cerebras is challenging NVIDIA with a fundamentally different approach to AI hardware and large-scale inference. In this episode of Startup Project, Nataraj sits down with Andrew Feldman, co-founder and CEO of Cerebras Systems, to discuss how the company built a wafer-scale AI chip from first principles. Andrew shares the origin story of Cerebras, why they chose to rethink chip architecture entirely, and how system-level design decisions unlock new performance for modern AI workloads. The conversation explores: Why inference is becoming the dominant cost and performance bottleneck in AI How Cerebras’ wafer-scale architecture overcomes GPU memory and communication limits What it takes to compete with incumbents like NVIDIA and AMD as a new chip company The tradeoffs between training and inference at scale Cerebras’ product strategy across systems, cloud offerings, and enterprise deployments This episode is a deep dive into AI infrastructure, semiconductor architecture, and system-level design, and is especially relevant for builders, engineers, and leaders thinking about the future of AI compute. 🎧 Listen to the full episode of Startup Project on YouTube or your favorite podcast platform.

Discover how Radical AI is revolutionizing material science using self-driving labs. About the episode: Nataraj hosts Joseph Krause, CEO of Radical AI, to explore how they're speeding up material R&D by combining AI, engineering, and robotics. Joseph shares his journey from material science to venture capital, highlighting Radical AI's mission to create a self-driving lab that autonomously designs tests and discovers new materials. The episode dives into Radical AI's materials flywheel concept, their open-source engine, and how they're attracting funding to drive innovation in material science. Discover how Radical AI is set to revolutionize industries from aerospace to energy with cutting-edge material discovery. What you’ll learn Understand the traditional challenges hindering the commercialization of new materials and how Radical AI is overcoming them.Discover the materials flywheel concept and how it accelerates the speed of material discovery.Learn about the types of customers who are seeking new materials and the diverse applications across various industries.Explore the role of AI in simulating and experimenting with materials, and the importance of experimental validation.Understand the types of AI models Radical AI uses, including machine learning, generative AI, and computer vision.Identify Radical AI’s hiring strategy to build an interdisciplinary team across machine learning, software engineering, robotics, and material science.Comprehend the importance of experimental data in materials science and how self-driving labs capture and utilize this data.Learn about Radical AI’s stepwise approach to focus on customer-driven problems and enabling technologies.About the Guest and Host: Guest Name: Joseph Krause, Co-founder and CEO of Radical AI, aiming to revolutionize material science with AI, engineering, and robotics. Connect with Guest:  → LinkedIn: https://www.linkedin.com/in/josephfkrause → Website: https://www.radical-ai.com/ Nataraj: Host of the Startup Project podcast, Senior PM at Azure & Investor.  → LinkedIn: https://www.linkedin.com/in/natarajsindam/   → Substack: ⁠https://startupproject.substack.com/⁠ In this episode, we cover    (00:01) Introduction to Radical AI and Joseph Krause(01:15) Joseph’s diverse background and how it led to Radical AI(05:01) Traditional ways preventing commercialization of new materials (09:06) Radical AI’s product: novel materials for aerospace, defense, and energy(11:36) Customers seeking new materials and the advantage of speed in the materials flywheel(13:39) Challenges in digital research and the importance of physical experimentation(16:18) How Radical AI picks directions for new material discovery(23:48) The AI part of Radical AI: hiring and AI models used(27:13) Predicting crystal structures with AI(31:57) Why New York is the best place for Radical AI(33:37) Joseph’s best AI use case for personal research(37:35) Material research happening at Apple Don’t forget to subscribe and leave us a review/comment on YouTube Apple Spotify or wherever you listen to podcasts. #RadicalAI #AI #MaterialScience #Robotics #DeepTech #Innovation #VentureCapital #Aerospace #Defense #Energy #NewMaterials #SelfDrivingLabs #MachineLearning #GenerativeAI #OpenSource #Podcast #Startup #Technology #Research #NVIDIA