Startup Project: Build the future

Nataraj

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/

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

    7H AGO

    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 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

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

    MAR 20

    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 LinkedinNataraj on LinkedinTry Yoodli

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

    MAR 8

    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 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.

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

    FEB 20

    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

    57 min
  5. How Klaviyo Built a $7B+ Public Company With Just $15M Raised | Andrew Bialecki, Co-founder and CEO of Klaviyo

    FEB 1

    How Klaviyo Built a $7B+ Public Company With Just $15M Raised | Andrew Bialecki, Co-founder and CEO of Klaviyo

    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/

    55 min
  6. How AI Is Unlocking Materials We’ve Never Been Able to Build | Radical AI

    JAN 4

    How AI Is Unlocking Materials We’ve Never Been Able to Build | Radical AI

    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

    38 min

Ratings & Reviews

5
out of 5
6 Ratings

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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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