Startup Project

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  • The Power of Look-Alikes: A Growth Hacking Strategy for Product Managers

    Product managers are constantly juggling priorities, navigating market trends, and striving to build products that not only meet user needs but also anticipate the future. In the whirlwind of daily tasks, it’s invaluable to step back and learn from those who have built incredible things, from groundbreaking space missions to cloud infrastructure that powers the internet.

    In a recent episode of the Startup Project Podcast, Natraj chats with Kwaja, a seasoned engineer and entrepreneur with a career spanning NASA JPL, Amazon AWS, and now, his own startup, Momento. Kwaja’s journey, filled with experiences ranging from image processing for Mars rovers to building core AWS services, is a treasure trove of insights for product managers. This blog post distills some of the key product management lessons learned from Kwaja’s remarkable career, offering actionable takeaways for PMs at all stages.

    Customer Obsession: Beyond a Buzzword, It’s a Way of Life

    Kwaja’s journey is deeply rooted in customer obsession, a principle famously championed at Amazon. From his early days at NASA, where he personally used his credit card to leverage AWS for faster image processing, to his time at Amazon AWS and now Momento, the customer has always been central.

    “Amazon was a much smaller company… and the customer obsession was really nice because people put themselves in the shoes of the customers and you would get to go work with all kinds of customers around the globe and understand problems in a completely foreign domain that you have no idea about and help them solve deeply technical problems via using the AWS infrastructure.”

    For product managers, this resonates deeply. It’s not enough to just say you’re customer-centric; you need to live it. This means:

    • Deeply Understanding Customer Pain Points: Go beyond surface-level requests. Dive deep into the why behind customer needs. Kwaja highlights the importance of working with customers in “completely foreign domains” to truly grasp their challenges.
    • Empathy and Proximity to the User: Put yourself in your customer’s shoes. Spend time with them, observe their workflows, and actively listen to their frustrations and aspirations.
    • Building Solutions, Not Just Features: Focus on solving real customer problems, even if it means building “boring infrastructure.” As Kwaja says, “we sell boring infrastructure, but we take pride in the fact that as more AI applications are formed… people are gonna want more interactivity and that interactivity has to be fuelled by the latest data.” This is about identifying fundamental, enduring needs.

    Think Big, Start Small, Iterate Fast: The Amazon Way

    Kwaja’s experience at Amazon underscores the power of the “Think Big” leadership principle. While many initially doubted Werner Vogels’ prediction that AWS would surpass Amazon’s retail business, the Amazon leadership team genuinely believed in the potential for massive scale.

    “The one thing about Amazon leaders is, you know, they really believe in the think big leadership principle and the leadership principle just says, you know, thinking small is a self-fulfilling prophecy.”

    For product managers, this translates to:

    • Visionary Thinking: Don’t limit yourself to incremental improvements. Envision the future of your product and industry. What impact can you truly make?
    • Breaking Down Big Visions: “Think Big” doesn’t mean building everything at once. It means having a grand vision and breaking it down into manageable, iterative steps. Kwaja’s experience moving the NASA image processing pipeline to AWS using only EC2, S3, and SQS in the early days showcases starting with the essential building blocks.
    • Embrace Experimentation: Cloud computing, as Kwaja points out, “was demonetizing infrastructure to make it available for experimentation.” Product managers should foster a culture of experimentation, enabling rapid prototyping and validation of ideas.

    Focus: The Unsung Hero of Product Success

    One of Kwaja’s key learnings as a founder is the critical importance of focus, especially in the early stages. Initially, Momento’s go-to-market strategy was too broad, targeting every vertical. He learned the hard way that focus is paramount.

    “One thing I learned is the focus really matters… once you land a pretty customer, you immediately gotta start looking for look-alikes because when you go to those look-alikes, they know that they’re not the first ones, they’re not the sacrificial lamb that you’re trying with.”

    For product managers, this means:

    • Defining Your Ideal Customer Profile (ICP): Don’t try to be everything to everyone. Identify your core target audience and their specific needs. Momento found success by focusing on media, gaming, and fintech companies with “spiky workloads and mission-critical needs.”
    • Prioritization and Ruthless Scoping: Say “no” to features that don’t align with your core value proposition and target audience. Focus your team’s energy on the most impactful initiatives.
    • Look-Alike Customer Strategy: Once you find success with a customer, leverage that to identify and target similar customers. This creates a virtuous cycle of growth and reinforces your product-market fit.

    Abstraction and Developer Productivity: Building for the Future

    Kwaja’s current venture, Momento, directly addresses a pain point he experienced firsthand at AWS: the complexity of caching solutions. He recognized the need for higher levels of abstraction to boost developer productivity.

    “One of the things that we really loved about Dynamo was… you don’t have to learn what instance type, the number of shards, the number of replicas… they just kind of go away with Dynamo and that experience did not exist with any of the caching solutions… they exposed a lot of the knobs to the end-user.”

    For product managers, this highlights the importance of:

    • Developer Experience (DX): Especially for infrastructure products, prioritize developer ease of use. Reduce cognitive load and abstract away unnecessary complexity.
    • Simplicity and Usability: Strive to simplify complex tasks and workflows. Focus on creating intuitive and user-friendly products, even for technically demanding domains.
    • Anticipating Future Needs: Kwaja recognized the growing demand for interactive, real-time applications driven by data. Momento is built to address this future need, providing the foundational infrastructure for these applications.

    Culture and Team: The Foundation of Success

    Throughout the podcast, Kwaja emphasizes the importance of people and culture. From the passionate mission-driven environment at NASA to the customer-obsessed culture at Amazon and the craftsmanship of team building at Momento, people are at the heart of every successful endeavor.

    “The people matter, the people matter a lot, and if you have the right team and you know and that team has the right passion, you can accomplish anything.”

    For product managers, this underscores:

    • Hiring for Passion and Mission Alignment: Seek out individuals who are not just skilled but also genuinely passionate about the problem you are solving.
    • Building a Strong Culture: Cultivate a culture of ownership, psychological safety, and customer-centricity. These values, as Kwaja points out, are crucial for fostering innovation and operational excellence.
    • Mentorship and Continuous Learning: Surround yourself with mentors and advisors. Embrace continuous learning and encourage your team to do the same.

    Embrace the Axioms: Foundational Truths in a Changing World

    In closing, Kwaja offers a powerful perspective on the future of technology, particularly in the age of AI. While AI is transformative, he emphasizes the enduring importance of fundamental needs.

    “We sell boring infrastructure, but we take pride in the fact that as more AI applications are formed, the undeniable truth, the axioms that are always gonna be true, people are gonna want more interactivity and that interactivity has to be fuelled by the latest data… real-time data and they want it fast. It’s never gonna change.”

    For product managers, this is a crucial reminder:

    • Focus on Foundational Needs: In the face of rapid technological change, don’t lose sight of the fundamental user needs that remain constant. Interactivity, speed, and real-time data are timeless requirements.
    • Build for Enduring Value: Create products that are not just trendy but built on solid foundations and address long-term needs.
    • Embrace “Boring Infrastructure”: Sometimes, the most impactful products are not the flashiest but the most reliable and foundational. Investing in robust infrastructure that enables innovation is crucial.

    Kwaja’s journey is a testament to the power of customer obsession and a relentless pursuit of solving real problems. By embracing these lessons, product managers can navigate the complexities of product development and build truly impactful products.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • Developer-Friendly Security: Building Products for Data Privacy and Compliance

    The technology landscape is in constant upheaval. For entrepreneurs, this environment demands a rare combination of visionary thinking and practical execution. Few individuals exemplify this blend as effectively as the serial entrepreneur Ameesh Divatia who was recently featured on the Startup Project podcast. Across four successful ventures, culminating in his current focus on data-centric security, this founder has not only weathered the tech industry’s storms but consistently leveraged them for growth and acquisition.

    His journey, spanning from the networking boom of the late 1990s to the cutting edge of cloud data protection in the era of Generative AI, provides a valuable blueprint for adaptability, market timing, and the enduring principles of building valuable companies. From the heady days of the dot-com boom at Cisco to the complex challenges of securing data in today’s cloud-first world, his insights are essential for founders at all stages – and for leaders guiding established organizations through continuous technological transformation.

    The Echo of the Dot-Com Era: The Power of Humility

    A particularly insightful moment in our conversation was his reflection on the dot-com boom during his time at Cisco, then the world’s most valuable company. He draws a striking parallel to the current excitement around Nvidia and Artificial Intelligence, observing, “This whole Nvidia story is something that we have lived through.” This isn’t to diminish Nvidia’s achievements, but to offer crucial historical context. “It was Euphoria,” he remembers of the late 1990s, “you would get into the office in the morning and see the stock up six bucks, everybody’s smiling, and we literally thought we could take over the world.”

    This experience underscores a vital lesson, especially relevant in times of rapid technological advancement and market exuberance: humility is paramount. When asked for advice for those currently at Nvidia, his response was immediate and direct: “Be humble. That’s it. Just be humble.” This isn’t just good manners; it’s a hard-won lesson from witnessing the cyclical nature of tech dominance. He wisely points out that even established giants like Apple face continuous disruption and evolution. True, sustainable success, he suggests, stems not only from groundbreaking innovation but from a grounded understanding that no market position is guaranteed forever.

    Orchestrating the Exit: A Proactive Strategy, Not an Afterthought

    Beyond navigating market cycles, our discussion highlighted a strategic approach to company building that prioritizes, rather than shies away from, the idea of acquisition. His advice to aspiring entrepreneurs is refreshingly proactive: “Don’t ever rely on somebody else to find you the exit. It’s something that you have to do over the course of time.” This isn’t about building a company solely to flip it, but rather about strategically positioning your venture for long-term value creation, which often naturally leads to acquisition by a larger entity seeking to expand into new markets or integrate groundbreaking technologies.

    He emphasizes the importance of early and consistent engagement with potential acquirers. This doesn’t mean aggressive sales pitches from day one, but a sustained effort to cultivate genuine relationships, openly communicate your unique value proposition, and demonstrate a collaborative and learning-oriented approach. His experience with Lightwire, a silicon photonics company he joined, perfectly illustrates this principle. By proactively engaging with Cisco early in their development, seeking a partnership to help productize their innovative technology, they not only secured crucial investment but ultimately positioned themselves as a strategically vital acquisition target. This proactive, relationship-driven approach stands in stark contrast to a passive stance, where founders might simply hope that an attractive exit will materialize organically.

    Beyond the Tech: Focus on Commercial Viability

    For founders with a deep engineering background, a common misstep is assuming that revolutionary technology alone will pave the road to success. Our interviewee offers a critical correction to this often-held belief: “As an engineer founder entrepreneur, you always tend to fall back on the fact that the technology will sell itself. That is seldom the case.” While cutting-edge technology is undoubtedly a foundational element, it is simply not enough without an equally intense focus on understanding market needs, validating product-market fit, and developing effective sales and marketing strategies.

    He readily acknowledges that earlier in his own entrepreneurial journey, he leaned too heavily on a technology-centric worldview. Through experience, he learned the critical importance of deeply understanding the commercial landscape, clearly articulating a compelling value proposition that resonates with customers, and building a robust and scalable business model that attracts both users and investors. This fundamental shift in perspective – from technology-first to business-first (while still valuing technology) – is essential for engineer-founders to successfully transition from technologists to effective business leaders, recognizing that, ultimately, commercial viability is the key determinant of long-term startup success.

    Data-Centric Security: Securing the Next Frontier

    His current company, Baffle, is tackling the rapidly evolving landscape of data security, a domain undergoing radical transformation in the cloud era and with the rise of Generative AI. He articulates a compelling vision for “data-centric protection,” arguing that traditional network-centric and even identity-based security approaches are becoming increasingly insufficient in the face of modern threats and distributed data environments. In a world where sensitive data increasingly resides in complex, multi-cloud environments, and where identity perimeters are constantly challenged and breached, securing the data itself – at the most granular level – becomes the ultimate and most effective line of defense.

    This proactive, data-first security paradigm is particularly prescient and crucial in the context of Generative AI. The immense power and potential of these transformative technologies hinge on access to and analysis of vast datasets, often requiring organizations to share and collaborate with data in ways that were previously considered too risky or simply impractical. Data-centric security, he argues, provides the necessary framework to enable secure data sharing and collaboration, fostering innovation and progress while simultaneously maintaining stringent data privacy, regulatory compliance, and, most importantly, customer trust. This forward-thinking perspective firmly positions Baffle at the forefront of a critical security evolution, proactively addressing a challenge that will only become more pressing and complex as AI adoption rapidly accelerates across industries.

    Enduring Principles for Startup Success

    Throughout our insightful conversation, several core principles consistently emerged as foundational to his repeated entrepreneurial successes:

    • Unwavering Customer Obsession: A relentless focus on deeply understanding customer needs and building solutions that directly and effectively address their most pressing pain points.
    • Strategic and Proactive Networking: A deliberate and consistent effort to build meaningful relationships with potential acquirers, strategic partners, and key industry players, proactively laying the groundwork for future opportunities and potential exits.
    • Adaptability and Continuous Learning: An inherent willingness to constantly learn, adapt to rapidly changing market conditions and technological landscapes, and to strategically pivot business strategies and product roadmaps as needed.
    • Building Exceptional Teams: A dedication to assembling high-caliber teams of talented individuals who are not only deeply technically proficient but also fully aligned with and passionately committed to the company’s shared vision and mission.
    • Strong Commercial Acumen: A recognition that groundbreaking technology must always be coupled with a robust understanding of market dynamics, effective sales and marketing strategies, and a sound and scalable overall business strategy to achieve lasting success.

    In a technology world often characterized by fleeting trends and short-lived companies, this serial entrepreneur’s journey provides a valuable reminder of the enduring principles of sustainable value creation.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • Why Data and Compute Are the Real Drivers of AI Breakthroughs

    Artificial intelligence has captivated industries and imaginations alike, promising to reshape how we work, learn, and interact with technology. From self-driving cars to sophisticated language models, the advancements seem almost boundless. But beneath the surface of architectural innovations like Transformers, a more fundamental shift is driving this progress: the power of scale, fueled by vast datasets and immense computing resources.

    This insight comes from someone who has been at the forefront of this revolution. Jiquan Ngiam, a veteran of Google Brain and early leader at Coursera, and now founder of AI agent company, Lutra AI, offers a grounded perspective on the forces truly propelling AI forward. In a recent interview on the Startup Project podcast, he shared invaluable lessons gleaned from years of experience in the trenches of AI development. His key takeaway? While architectural ingenuity is crucial, it’s the often-underestimated elements of data and compute that are now the primary levers of progress.

    The “AlexNet Moment”: A Lesson in Scale

    To understand this perspective, it’s crucial to revisit a pivotal moment in deep learning history: AlexNet in 2012. As Jiquan explains, AlexNet wasn’t a radical architectural departure. Convolutional Neural Networks (CNNs), the foundation of AlexNet, had been around for decades. The breakthrough wasn’t a novel algorithm, but rather a bold scaling up of existing concepts.

    “AlexNet took convolutional neural networks… and they just scaled it up,” Jiquan recounts. “They made the filters bigger, added more layers, used a lot more data, trained it for longer, and just made it bigger.” This brute-force approach, coupled with innovations in utilizing GPUs for parallel processing, shattered previous performance benchmarks in image classification. This “AlexNet moment” underscored a crucial lesson: sometimes, raw scale trumps algorithmic complexity.

    This principle has echoed through subsequent AI advancements. Whether in image recognition or natural language processing, the pattern repeats. Architectures like ResNets and Transformers provided improvements, but their true power was unleashed when combined with exponentially larger datasets and ever-increasing computational power. The evolution of language models, from early Recurrent Neural Networks to the Transformer-based giants of today, vividly illustrates this point. The leap from GPT-2 to GPT-3 and beyond wasn’t solely about algorithmic tweaks; it was about orders of magnitude increases in model size, training data, and compute.

    The Data Bottleneck and the Future of AI

    However, this emphasis on scale also reveals a looming challenge: data scarcity. [Podcast Guest Name] raises a critical question about the sustainability of this exponential growth. “To scale it up more, you need not just more compute, you also need more data, and data is one that I think is going to be limiting us,” he cautions. The readily available datasets for language models, while vast, are finite and potentially becoming exhausted. Generating synthetic data offers a potential workaround, but its effectiveness remains limited by the quality of the models creating it.

    This data bottleneck is particularly acute in emerging AI applications like robotics. Consider the quest for general-purpose robots capable of performing everyday tasks. As [Podcast Guest Name] points out, “there is no data of me folding clothes… continuously of different types, of different kinds, in different households.” Replicating human dexterity and adaptability in robots requires massive amounts of real-world, task-specific data, which is currently lacking.

    This data challenge suggests a potential shift in AI development. While scaling up models will continue to be important, future breakthroughs may hinge on more efficient data utilization, innovative data generation techniques, and perhaps a renewed focus on algorithmic efficiency. [Podcast Guest Name] hints at this, noting, “incremental quality improvements are going to be harder moving forward… we might be at that curve where… the next incremental progress is harder and harder.”

    Agentic AI: Extending Intelligence Beyond Code

    Despite these challenges, [Podcast Guest Name] remains optimistic about the transformative potential of AI, particularly in the realm of “agentic AI.” His company, Lutra AI, is focused on building AI agents that can assist knowledge workers in their daily tasks, from research and data analysis to report generation and communication.

    The vision is to create AI that is “natively integrated into the apps you use,” capable of understanding context, manipulating data within those applications, and automating complex workflows. This goes beyond code generation, aiming to empower users to delegate a wide range of knowledge-based tasks to intelligent assistants.

    Navigating the Hype and Reality

    As AI continues its rapid evolution, it’s crucial to maintain a balanced perspective, separating hype from reality. [Podcast Guest Name] offers a pragmatic view on the ongoing debate about Artificial General Intelligence (AGI). He suggests shifting the focus from abstract definitions of AGI to the more tangible question of “what set of tasks… can we start to delegate to the computer now?”

    This practical approach emphasizes the immediate, real-world impact of AI. Whether it’s enhancing productivity through AI-powered coding tools like Cursor, or streamlining workflows with agentic AI assistants like Lutra AI, the benefits are already materializing. The future of AI, therefore, may be less about achieving a singular, human-level intelligence and more about continually expanding the scope of tasks that AI can effectively augment and automate, driven by the ongoing forces of data, compute, and human ingenuity. As we move forward, understanding and strategically leveraging these fundamental drivers will be key to unlocking AI’s full potential.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • 5 Business Lessons from the Gaming Industry’s Explosive Growth

    The entertainment industry is often viewed through the lens of Hollywood blockbusters and streaming giants. Yet, a colossus quietly dominates in revenue and innovation: gaming. Outpacing movies, music, and books combined, the gaming industry is not just entertainment; it’s an economic engine. For businesses seeking to navigate disruption and understand future consumer trends, the gaming world offers a wealth of strategic lessons. In a recent interview, Ian Bateman, CEO of cloud gaming startup High Score, shared key insights into this often-misunderstood market.

    Here are five critical takeaways about the gaming businesses:

    1. Content is King, Ecosystem is Queen.

    Google’s failed Stadia venture provides a stark reminder: technology alone isn’t enough. Despite Google’s infrastructure and resources, Stadia faltered due to a critical lack of compelling content. As Ian noted, “ultimately Stadia’s failure boils down to games… in gaming at the end of the day it’s all about the games.” Stadia’s limited game library and the difficulty of porting titles to its platform created a content desert.

    The Lesson: In gaming, content is paramount. A robust and constantly refreshed content ecosystem is crucial for user engagement and long-term success. Focus on content acquisition and curation as much as – technological innovation.

    2. Compatibility Trumps Proprietary Systems.

    Stadia’s closed ecosystem, requiring games to be specifically ported to its platform, proved to be a major impediment. In contrast, High Score is leveraging the vast existing library of Windows-compatible PC games, accessible through platforms like Steam. This approach immediately offers users a massive content catalog without relying on developers to create new, platform-specific versions.

    The Lesson: Prioritize compatibility and interoperability. In fragmented markets, offering seamless access to existing content and platforms can be a powerful competitive advantage. Avoid walled gardens that limit user choice and content availability.

    3. Democratization Drives Innovation.

    The gaming industry is experiencing a indie game boom, fueled by increasingly accessible game engines like Unity and Unreal Engine. These tools empower smaller teams and individual developers to create high-quality games, fostering innovation and experimentation outside the confines of large studios. This mirrors trends in other industries, where democratization of technology empowers smaller players to disrupt established markets.

    The Lesson: Embrace and leverage democratizing technologies. Lower barriers to entry foster innovation and create new opportunities for smaller, agile players to compete and disrupt established giants. Look for ways to empower individual creators and smaller teams within your own industry.

    4. Ride the Right Platform Wave.

    The PC gaming market, while massive, is heavily Windows-centric. This creates a significant accessibility gap for users of macOS and ChromeOS. High Score is directly addressing this by offering cloud-based Windows gaming PCs, effectively bridging the compatibility gap and unlocking the vast PC game library for a wider audience.

    The Lesson: Identify and capitalize on dominant platforms. Understanding platform dynamics and addressing accessibility gaps can unlock significant market opportunities. Look for ways to leverage existing infrastructure and ecosystems rather than always building from scratch.

    5. Gaming is the New Media Frontier.

    Major media and tech companies, including Netflix, YouTube, and LinkedIn, are increasingly investing in gaming. This isn’t simply a fleeting trend; it’s a recognition of gaming’s immense market size and its evolving role as a primary form of entertainment and social interaction. Gaming is no longer a niche; it’s becoming central to the digital media landscape.

    The Lesson: Recognize the growing importance of interactive entertainment. Gaming is not just for gamers; it’s shaping the future of media consumption and user engagement. Explore opportunities to integrate game-like elements, interactive experiences, and gamified strategies into your own business to capture attention and drive engagement.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • How No-Code Backends Empower Product Builders

    For years, the promise of “no-code” development has been whispered in the tech industry, often met with a healthy dose of skepticism. Could software development truly be democratized, moving beyond the realm of specialized engineers? Like many, I’ve been cautiously optimistic, observing various iterations of no-code tools emerge, each with varying degrees of success. However, a recent conversation with Prakash Chandran, founder of the no-code backend platform Xano, has shifted my perspective from cautious observer to genuine believer.

    Chandran, a seasoned product and UX leader whose career spans Google’s early days (think Picasa and Google Calendar) and the tumultuous startup trenches, didn’t arrive at no-code through abstract theorizing. His journey was forged in the fires of practical experience, recognizing a persistent, often overlooked bottleneck in software creation: the backend. During our discussion on the Startup Project podcast, he articulated a frustration many product-focused individuals quietly harbor: the opaque and often cumbersome nature of backend development.

    The Backend as a Black Box

    Chandran’s experience resonated deeply. As someone deeply invested in the product side – the design, the user experience, the core functionality – he felt a tangible disconnect from the underlying engineering process. “You have to pay for an expensive engineer,” Chandran explained, “They’re kind of like a car mechanic. If they tell you something is going to take a month and tens of thousands of dollars, you just kind of have to believe them.” This sentiment highlights a crucial, often unspoken tension in product development. Product leaders, designers, and even business stakeholders are frequently reliant on engineering timelines and cost estimates that can feel arbitrary, lacking transparency and direct control.

    This isn’t to diminish the crucial role of backend engineers, but rather to acknowledge an evolving challenge. As software systems grow more intricate, distributed, and demanding, the backend – the unseen infrastructure powering every application – has become a domain of increasing specialization and complexity. Even for seasoned full-stack developers, navigating the ever-shifting landscape of backend technologies can feel like an uphill battle. The initial promise of cloud computing to abstract away infrastructure complexities has, in some ways, been replaced by a new layer of abstraction that can be just as daunting to navigate. The very act of starting a new application, even a relatively simple one, can involve a steep learning curve and a significant investment of time and resources simply to establish the foundational backend.

    Xano’s No-Code Solution: Reclaiming Simplicity and Scale

    Chandran’s solution, embodied in Xano, is a radical proposition: a fully scalable, enterprise-grade backend platform that requires absolutely no code. This isn’t another iteration of website builders or simplified database tools. Xano, as Chandran describes it, represents a “new part of no-code,” engineered from the ground up to handle the demands of production applications, not just prototypes. The key differentiator lies in its singular focus: the backend. While other no-code platforms often attempt to be full-stack solutions, Xano deliberately concentrates on the server, database, and API layers, allowing users to connect it to any frontend they choose. This focused approach, Chandran argues, allows for both unprecedented simplicity and robust scalability.

    During our conversation, Chandran emphasized that Xano isn’t just about simplifying the interface; it’s about fundamentally rethinking the development process. He positions Xano as a “visual programming language,” one that empowers users to articulate complex application logic without writing a single line of code. This isn’t about dumbing down development; it’s about making the core concepts of software engineering – variables, loops, conditionals – accessible to a broader audience through a visual, intuitive interface.

    Empowering the Citizen Developer and Embracing AI

    Xano’s target user is the “citizen developer,” a Gartner-defined persona representing product owners, system thinkers, and business experts who understand the intricacies of application logic but lack traditional coding skills. These are the individuals who possess deep domain expertise and a clear vision for software solutions but have historically been reliant on engineering teams to translate their ideas into reality. Xano aims to bridge this gap, empowering these citizen developers to directly build and deploy their own applications, reclaiming control over the backend and accelerating the entire development lifecycle.

    Looking ahead, Chandran is keenly focused on the intersection of no-code and artificial intelligence. He doesn’t envision AI replacing no-code platforms, but rather as a powerful synergistic force. He suggests AI could serve as a generative tool, creating initial application scaffolding, while platforms like Xano provide the crucial visual canvas for refinement, customization, and the infusion of human intention. This partnership, he argues, will unlock a new era of software creation, where AI augments human creativity and no-code platforms provide the accessible, powerful tools to bring those visions to life.

    Beyond the Hype: A Glimpse of the Future?

    My conversation with Chandran left me with a sense of optimism I hadn’t anticipated. Xano isn’t just another no-code tool; it represents a potentially significant shift in how we approach software development. By squarely addressing the complexities of the backend and empowering a new generation of “citizen developers,” Xano is challenging the traditional paradigms of software creation. While the no-code space has often been associated with limitations and compromises, Xano’s architecture and vision suggest a future where powerful, scalable applications can be built with unprecedented speed and accessibility. It’s a future where product leaders and business innovators can reclaim control over the entire development process, moving beyond the backend bottleneck and focusing on what truly matters: building impactful, user-centric solutions. Whether Xano will fully realize this ambitious vision remains to be seen, but my conversation with Prakash Chandran has certainly made me a believer in the transformative potential of no-code backends, and the exciting possibilities they unlock.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • How Asimily Is Re‑thinking Security for the Internet of Things

    When Shanker left a senior post at Symantec to found Assembly, he thought he understood hard problems. He had managed billion‑dollar product lines, helped design the iPhone 3G modem, and spent years mapping out Symantec’s IoT strategy. Yet the moment he stepped away from corporate infrastructure—“me, myself, and PowerPoint slides,” as he jokes—he discovered something tougher: bringing order to the chaotic, heterogeneous world of connected devices that power hospitals, factories, and entire cities.

    Today, Asimily is one of Gartner’s highest‑ranked vendors for medical‑ and industrial‑IoT security, but the path there reveals as much about the state of critical‑infrastructure security as it does about start‑up grit. Below are five takeaways from Shankar’s recent appearance on Startup Project podcast—and why they matter to anyone watching the next wave of connected systems.

    1. Healthcare Is the Ultimate “System of Systems”

    Asimily’s origin story begins in hospitals, where device diversity and regulatory complexity collide. A single facility may run MRI scanners, infusion pumps, HVAC controllers, and paging systems—all from different manufacturers, all speaking their own arcane protocols, and all subject to HIPAA or GDPR restraints that forbid tampering with patient data. Shankar calls it “the most challenging environment in any vertical.” By focusing first on healthcare, Asimily forced itself to solve for the hardest edge cases—passive network monitoring that never interrogates a life‑critical device, on‑prem deployments for data sovereignty, and integrations with everything from CMDBs to SIEMs. If it works in an ICU, it will probably work anywhere.

    2. Visibility Still Beats AI Hype … But Context Is King

    Five years ago hospital CISOs wanted one thing: a real‑time asset inventory. They still do, yet visibility alone can no longer keep pace with ransomware crews that treat unpatched ultrasound machines the way pickpockets treat unlocked cars. Asimily’s answer layers device‑aware context over classic network telemetry: Which vulnerabilities are actually reachable from a given subnet? How would malware laterally move through an OR? When every medical device vendor warns that patching voids the warranty, prioritization and compensating controls—micro‑segmentation, firmware‑level mitigations, or even simple network throttling—matter more than a tidy CVE list. That depth of analysis, Shankar argues, is where Assembly now outpaces look‑alike scanners.

    3. Smart Cities Are Less Sci‑Fi, More Plumbing

    “Smart city” once conjured Jetsons‑style streets that anticipate traffic and locate parking spots. The reality, Shankar says, is prosaic: wastewater plants, traffic lights, environmental sensors—all suddenly IP‑addressable. At 5 percent connectivity the risk felt hypothetical; at 40 percent it is an urgent operational question. A stalled sewage pump or frozen signal grid cripples civic life faster than a consumer website outage. Asimily ports its healthcare playbook here: passive collectors, cloud or fully on‑prem analytics, and APIs that enrich the municipality’s existing SOC. The lesson is simple: critical infrastructure rarely needs bleeding‑edge features; it needs tools that respect uptime, safety, and long equipment lifecycles.

    4. Hardware Is Just a Delivery Vehicle

    Despite shipping its own appliance, Asimily thinks of itself as a pure‑software firm. Off‑the‑shelf boxes (or virtual machines) sit inside a customer’s network, siphon mirrored traffic, strip out any patient or personally identifiable information, and forward only device metadata for analysis. Where policy forbids the cloud, everything runs on‑prem. That architectural choice—commodity hardware plus software smarts—keeps margins healthy while side‑stepping import‑control nightmares and silicon shortages. It also lets Assembly pivot quickly when customers ask for AI‑assisted incident forensics or automated compliance reports; new modules roll out as firmware updates, not forklift refreshes.

    5. Sales Motions Mature, but Trust Stays Personal

    Shankar closed Asimily’s first deal himself, armed with a demo and a handful of Symantec‑era relationships. Today the company runs a channel‑first model, complete with solution engineers and VAR partners. Yet decision makers remain largely the same: CISOs who balance MRI uptime against cyber risk, wastewater supervisors who fear midnight phone calls, operations chiefs who know that a security product which bricks a CT scanner on day one will be ripped out on day two. For all the talk of AI copilots and self‑healing networks, enterprise buyers still reward vendors that obsess over patient safety, regulatory nuance, and the gritty details of packet capture in a 15‑year‑old PLC.

    Asimily’s roadmap hints at where industrial security is heading. New modules for configuration control and richer forensic replay will appear this year, and the company is quietly weaving generative‑AI techniques into both engineering workflows and customer‑facing features. Shankar is cautious—no customer data touches public LLMs—but optimistic that the technology can shrink incident‑response time without adding headcount.

    The bigger story, though, is that critical‑infrastructure security is far from “solved.” Hospitals, factories, and cities are still climbing Maslow’s hierarchy: first inventory, then analytics, then autonomous defence. Ten years from now, winners will be the platforms that layered innovation upon pragmatic foundations—partners who remembered that a traffic signal or infusion pump is not an endpoint but, quite literally, a lifeline.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • #77 Learn It All Mindset with CEO Damon Lemby

    Damon Lemby, CEO of Learn It, a company that has trained over 1.9 million individuals, shares his journey from professional baseball to the world of education, offering insights on leadership, learning, and the future of work.

    The Power of the “Learn It All” Mindset

    Lemby argues that the key to success in today’s dynamic world is not being a “know-it-all,” but embracing a “learn it all” mindset. This means being humble, curious, and constantly seeking new knowledge. He emphasizes that this approach is crucial for navigating the rapid changes in the business world, especially with the rise of AI.

    Overcoming Imposter Syndrome: A Practical Framework

    Recognizing that imposter syndrome is a common struggle, Lemby outlines a three-step framework for overcoming it:

    Identify the Source:  Clearly define what you’re worried about.

    Purposeful Awfulizing:  Imagine the worst-case scenario and determine if you can handle it.

    Work Hard, Focus, & Let Go:  Put in the effort, practice, and release the need for perfection.

    Navigating AI and the Future of Work

    Lemby believes that while AI will significantly impact the workforce, those who adapt and learn to leverage it will thrive. He advises companies to empower their employees with access to AI tools and encourage them to explore these technologies.

    Building Trust and Elite Teams

    Lemby emphasizes that a company is not a family, but a team that needs to be constantly evolving. He encourages leaders to build high-performing teams, recognizing that sometimes this may involve letting go of individuals who have reached their capacity within the organization. He also underscores the importance of giving people the benefit of the doubt, acknowledging that building trust is essential for long-term success.

    Invest in Your Growth:

    Lemby encourages listeners to invest in their own development, read biographies of successful individuals, and constantly seek new knowledge. He also shares his own investment philosophy, focusing on seed-stage companies and traditional businesses with a strong leadership team and a solid market fit.

    Key Takeaways:

    • Embrace a “learn it all” mindset for success in a dynamic world.
    • Conquer imposter syndrome with a practical framework.
    • Leverage AI for growth and prepare for the future of work.
    • Build elite teams and trust your employees.
    • Invest in your own development and seek out new knowledge.

    This conversation with Damon Lemby offers valuable insights for entrepreneurs, leaders, and anyone seeking to navigate the evolving business landscape.

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  • How Mighty Capital Defies the Odds of Technology Investing by Being Product-First

    In a world where venture capital success is often described as a game of chance, with a hit rate of one in 20 or even one in 30, Mighty Capital stands out. Founded by entrepreneur, product leader, & author SC Moatti, Mighty Capital has carved a unique path in the industry, focusing on a “product-first” approach and achieving a remarkable hit rate of one in five.

    On a recent episode of the Startup Project podcast, SC Moatti shared insights into her journey with host Nataraj SIndam, revealing the secrets behind her unconventional success.

    From Product Guru to VC Pioneer

    SC Moatti has a diverse background, ranging from a successful career in product management at companies like Meta and Nokia to founding her own companies and angel investing. Her passion for product excellence led her to establish “Products That Count,” a non-profit organization dedicated to fostering knowledge and best practices within the product management community. This platform has served as a valuable resource for Mighty Capital, providing valuable insights into emerging trends and identifying potential investments.

    The Product Mindset in Venture Capital

    Mighty Capital distinguishes itself by applying a product mindset to venture capital. This means looking beyond traditional metrics and focusing on the core elements of a successful product:

    • Team: They meticulously evaluate the team’s performance, board composition, and the CEO’s ability to be coached.
    • Traction: They seek companies with demonstrable revenue growth, analyzing revenue composition and customer references.
    • Market: They analyze the market, roadmap, and the company’s potential for growth.
    • Terms: They prioritize fair terms that foster a long-term partnership with entrepreneurs.

    This approach, combined with her deep understanding of the product landscape, and the unique network of Products That Count, has enabled Mighty Capital to invest in companies like Amplitude, Grok, Airbnb, and Digital Ocean, demonstrating a knack for identifying winners before they become mainstream.

    Beyond the Numbers: Building a Better Board

    SC Moatti also highlights the importance of board governance in early-stage companies. She teaches a course on the subject at Stanford’s Executive Program, emphasizing the critical role of board members in maximizing shareholder value through effective use of financial and human resources. She believes that effective board engagement transcends the traditional power dynamics, focusing instead on collaborative partnerships with founders.

    The Future of Product Management and AI

    SC Moatti believes that product management is a constantly evolving field, and emphasizes the need for ongoing learning and adaptation. She encourages aspiring product managers to engage with the product community through platforms like “Products That Count,” to keep up with the latest trends and challenges.

    When it comes to the future of AI, SC Moatti cautions against focusing solely on small, quick-win problems. She advocates for tackling larger, more complex issues, such as drug discovery, self-driving cars, and loneliness, areas where AI has the potential to revolutionize industries and improve lives.

    Key Takeaways for Startups

    SC Moatti’s insights offer valuable lessons for aspiring entrepreneurs:

    • Think big, start small: Focus on solving big problems but take a smaller, incremental approach to execution.
    • Invest in product excellence: Prioritize product quality and user experience as foundational elements of success.
    • Embrace lifelong learning: Continuously expand your knowledge and skills in the ever-evolving tech landscape.
    • Seek out mentors: Connect with peers and industry leaders who can offer guidance and support.

    Mighty Capital’s success serves as a testament to the power of applying a product mindset to the world of venture capital. By prioritizing a product-first approach and building strong relationships with entrepreneurs, they are defying the odds and shaping a new era of VC innovation.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • How Scispot is redefining modern biotech’s data infrastructure

    Biotech is becoming one of the world’s single biggest generator of data, expected to reach 40 exabytes a year by 2025—outstripping even astronomy’s fabled data deluge. Yet as much as 80 percent of those bytes never make it into an analytics pipeline. Three bottlenecks explain the gap: (1) stubbornly paper-based processes, (2) binary or proprietary instrument file formats that general-purpose integration tools cannot parse, and (3) hand-offs between wet-lab scientists and dry-lab bioinformaticians that break data lineage.

    Verticalization 2.0: Solving for Domain-Specific Friction

    Enter Scispot, a Seattle-based start-up founded in 2021 by brothers Satya and Guru Singh, which positions itself not as an electronic lab notebook or a data warehouse, but as a middleware layer purpose-built for life-science R&D, quality and manufacturing. The strategic insight is subtle and powerful: horizontal cloud platforms already exist, but they optimize for structured, JSON-ready data. Biotech’s heterogeneity demands schema-on-read ingestion and ontology mapping that an AWS or Snowflake cannot supply out of the box.

    Scispot’s architecture borrows liberally from modern data stacks—an unstructured “lake-house” for raw instrument output, metadata extraction via embeddings, and API access to graph databases—but is wrapped in compliance scaffolding (SOC 2, HIPAA, FDA 21 CFR 11) that is prohibitively expensive for labs to build alone. The company is effectively productizing the cost of trust, a move that mirrors how Zipline built FDA-grade logistics in drones or how Databricks turned Apache Spark into audit-ready enterprise software.

    YC’s Real Dividend: Market Signal Discipline

    Although accepted to Y Combinator on the promise of a voice-activated lab assistant, Scispot pivoted within weeks when early interviews revealed that customers valued reliable data plumbing over conversational bells and whistles. This underscores a counter-intuitive lesson from YC alumni: the program’s most enduring value may not be its brand or cheque, but its insistence that founders divorce themselves from their first idea and marry themselves to user-observed pain.

    That discipline paid off. Scispot signed its first customer before writing a line of production code—a pattern consistent with what Harvard Business School’s Thomas Eisenmann calls “lean startup inside a vertical wedge.” By focusing on a tiny subset of users (labs already running AI-driven experiments) but solving 90 percent of their total workflow, the brothers accelerated to profitability in year one and maintained “default alive” status, insulating the firm from the 2024 venture slowdown.

    Why Profitability Matters More in Vertical SaaS

    Horizontal SaaS vendors can afford years of cash-burn while they chase winner-take-all network effects; vertical players rarely enjoy those economies of scale. Instead, their defensibility comes from domain expertise, proprietary integrations and regulatory moats. Profitability becomes a strategic asset: it signals staying power to conservative customers, funds the painstaking addition of each new instrument driver, and reduces dependence on boom-and-bust capital cycles.

    Scispot’s break-even footing has already shaped its product roadmap. Rather than racing to become an all-in-one “Microsoft for Bio” suite, the team is doubling down on an agent-based orchestration engine that lets instrument-specific agents talk to experiment-metadata agents under human supervision. The choice keeps R&D burn modest while reinforcing the middleware thesis: be everywhere, own little, connect all.

    Lessons for Operators and Investors

    1. Treat Unstructured Data as a Feature, Not a Bug. Companies that design for messiness—using vector search, ontologies and schema-on-read—capture value where horizontal rivals stall.
    2. Compliance Is a Product Line. SOC 2 and HIPAA are not check-box exercises; they are sources of price premium and switching cost when woven into the core architecture.
    3. Fundamentals Trump Funding. YC’s internal analysis, echoed by Sizeport’s trajectory, shows no linear correlation between dollars raised and long-term success. Default-alive vertical SaaS firms can wait for strategic rather than survival capital.
    4. Remote Trust-Building Is a Competency. Sizeport’s COVID-era cohort had to master virtual selling and onboarding. As biotech globalizes, that skill set scales better than another flight to Cambridge, MA.

    What Comes Next

    Sizeport’s stated near-term goal is to become the staging warehouse for every experimental data point a lab produces, integrating seamlessly with incumbent ELNs and LIMS. Over a five-year horizon, the company aims to enable customers to mint their own AI-ready knowledge graphs—effectively turning drug-discovery IP into a queryable asset class. If successful, the platform could evolve into the “Databricks of Biotech,” but without owning the data outright.

    Nataraj is a Senior Product Manager at Microsoft Azure and the Author at Startup Project, featuring insights about building the next generation of enterprise technology products & businesses.

    Listen to the latest insights from leaders building the next generation products on Spotify, Apple, Substack and YouTube.

  • #74 Sajid Rahman – Angel Investor Secrets – Lessons from 1000+ Startup Investments

    In this episode of Startup Project, host Nataraj sits down with Sajid Rahman, a prolific angel investor with over 1000+ investments under his belt. They delve into the evolving investment landscape, exploring the significant changes that have occurred since their last conversation a couple of years ago.

    • Guest: Sajid Rahman – Top Angel Investor – GP at My Asia VC
    • Host: Nataraj – Senior Product Manager, Investor at Incisive.vc & Startup Advisor

    From Bull Run to Downturns:

    Rahman notes a significant shift from the bull run of 2020–2021, where companies secured funding at sometimes unjustified valuations. Rising interest rates and global challenges have led to a market correction, with many startups facing down rounds or struggling to raise capital. However, this also presents opportunities for investors to enter later–stage deals at a discount.

    Secondary Market Dynamics:

    The conversation touches upon the impact of changing market conditions on secondary deals. Rahman suggests that many secondary deals from 2021 may not hold their value, citing examples like Robinhood and Stripe, which have seen significant valuation fluctuations. He also highlights the unique case of SpaceX and the potential spin-off of Starlink, which could offer earlier liquidity for investors.

    Demystifying Dry Powder:

    Rahman addresses the concept of “dry powder“ – uninvested capital committed to VC funds. He clarifies misconceptions surrounding this term, explaining that LP commitments don‘t always translate to immediate deployment of capital. The decision to call capital depends on market conditions and the fund‘s investment strategy. While the current dry powder might not reach the levels of 2020–2021, Rahman believes it will gradually return to the market as valuations stabilize and investor confidence grows.

    The Web3 Evolution:

    The discussion then shifts to the Web3 space, where Rahman actively invests in web infrastructure companies building on blockchain technology. He acknowledges the presence of scams and emphasizes the importance of thorough due diligence. Rahman outlines three key narratives driving the current Web3 boom: the intersection of AI and blockchain, real–world asset tokenization (RWA), and decentralized infrastructure development. He also observes a trend of Web3 companies opting for traditional equity models instead of token–based fundraising to avoid regulatory hurdles.

    Sajid‘s Investment Approach and Portfolio:

    Nataraj and Rahman explore Sajid‘s diverse investment portfolio, which spans angel investments, syndicates, and four different funds focusing on Web3, YC companies, general early–stage startups, and soon, AI. Rahman shares his insights on managing multiple funds and his strategy for sourcing deals, which involves both inbound inquiries and proactive outreach. He reveals that his personal investment portfolio is heavily skewed towards startups, reflecting his belief in their long–term potential.

    The AI Boom and Valuation Concerns:

    The conversation concludes with a deep dive into the AI space, where Rahman sees value creation in both foundational models and application–layer companies. While he acknowledges the hype surrounding AI startups and the potential for overvaluation, he believes the sector holds immense promise. Rahman is currently working on launching an AI–focused fund to capitalize on the opportunities within this rapidly evolving field.

    This episode offers valuable insights into the current state of the investment world and the trends shaping the future. Rahman‘s experience and perspectives provide valuable guidance for both aspiring and seasoned investors navigating the ever–changing landscape.

    Additional Resources:

    #startup #investing #venturecapital #web3 #AI #angelinvesting #podcast #startupproject #secondarydeals #drypowder #blockchain #artificialintelligence #fundmanagement #YC #startupecosystem

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