Startup Project

Author: Nataraj Sindam

  • Modern Data Stack: Practical Strategies for Enterprise Product Management Leaders

    In the rapidly evolving landscape of cloud technology, it’s crucial for product management professionals to stay ahead of the curve, understanding not just the latest trends but also the foundational principles that shape the future of AI-driven products. In a recent episode of the podcast, Nataraj, host of the show, engaged in a fascinating conversation with Molham Aref, CEO of Relational AI, offering a treasure trove of insights for product leaders navigating the complexities of the modern data stack and enterprise AI.

    Molham, a seasoned veteran with over 30 years of experience in machine learning and AI, shared his journey from early computer vision projects at AT&T to leading Relational AI, a company revolutionizing how enterprises build intelligent applications. His career path, marked by stints at HNC Software (pioneers in neural networks), Brickstream (early computer vision in retail), PredictX, and Logicbox, provides a rich tapestry of lessons for product managers aiming to build impactful and scalable solutions.

    The Evolution of Enterprise AI and Product Management’s Role

    Molham’s journey underscores a critical evolution in enterprise AI. He began in an era where neural networks were nascent, focusing on specific problem domains like credit card fraud detection. “I started out working on computer vision problems at AT &T as a young engineer… and then I joined a company that was commercializing neural networks,” he recounted. This early phase highlighted the power of specialized AI models but also the challenges of broad applicability and integration within complex enterprise systems.

    For product managers, this historical context is vital. It reminds us that technological advancements are often iterative, building upon previous paradigms. Just as neural networks evolved, so too is the current wave of Gen AI. Understanding these historical cycles allows product teams to better anticipate future trends and avoid being swept away by hype cycles.

    A key product lesson Molham shared is the importance of speaking the customer’s language. Reflecting on his time at HNC, he noted, “When H &C started, they were just selling neural networks. And you go to a bank and say, buy my neural networks. And the bank goes, what’s a neural network and why would I buy it? And at some point, they realized, hey, that’s not really effective. Let’s go to a bank and tell them we’re solving a problem they have in their language.” This emphasizes a fundamental product principle: value proposition trumps feature fascination. Product managers must articulate how their AI solutions directly address business problems, focusing on tangible outcomes like cost reduction, revenue generation, or risk mitigation.

    Decoding the Modern Data Stack and Relational AI’s Solution

    Molham’s career narrative culminates in Relational AI, born from the frustration of building intelligent applications with fragmented technology stacks. “My whole career was spent working at companies focused on building one or two intelligent applications and in every situation it was a mess,” he confessed. He highlighted the pain of “gluing it all together” – the operational stack, BI stack, predictive, and prescriptive analytics – each with its own data management, programming model, and limitations.

    This pain point is highly relatable for product managers in the data-driven era. The “modern data stack,” as Molham explains, emerged as an “unbundling of data management.” While offering flexibility, it also introduces complexity. Relational AI addresses this head-on by offering a “co-processor” for data clouds like Snowflake, creating a “relational knowledge graph” that unifies graph analytics, rule-based reasoning, and predictive/prescriptive analytics.

    For product managers, Relational AI’s approach offers a valuable blueprint: focus on simplifying complexity. In a world of proliferating tools and technologies, solutions that streamline workflows and reduce integration headaches are immensely valuable. Molham’s platform choice – building on Snowflake – is also instructive. “For SQL, for data management, Snowflake is by far the leader,” he stated, emphasizing the importance of platform decisions in product strategy and go-to-market. Product managers must carefully consider platform ecosystems, choosing those that offer broad adoption and strong market traction.

    Gen AI in the Enterprise: Beyond the Hype and Towards Practical Application

    The conversation naturally shifted to Gen AI, the current buzzword in the AI space. Molham acknowledged its excitement but injected a dose of realism. “Gen.AI is super exciting. For the first time, we have models that can be trained in general, and then you have general applicability.” However, he cautioned against over-optimism in enterprise contexts. “In the enterprise, what people are finding out is having a model trained about the world doesn’t mean that it knows about your business.”

    This is a crucial insight for product managers exploring Gen AI applications. While Gen AI offers powerful capabilities, it’s not a silver bullet. Molham advocates for combining Gen AI with “more traditional AI technology, ontology, symbolic definitions of an enterprise, where you can talk about the core concepts of an enterprise.” This hybrid approach, leveraging knowledge graphs and structured data, is essential for building truly intelligent and context-aware enterprise applications.

    Product managers should heed this advice: Gen AI is a tool, not a strategy. Effective product strategies will involve thoughtfully integrating Gen AI with existing AI techniques and enterprise knowledge to deliver meaningful business value. Focus on use cases where Gen AI can augment, not replace, existing capabilities.

    B2B Sales and Founder Engagement: Lessons from the Trenches

    Molham shared invaluable insights on B2B sales, particularly for early-stage companies. He strongly believes in founder-led sales. “I really think it’s a mistake for the founders of the company not to take direct responsibility for sales,” he asserted. “You really have to go out there and do the really hard work of customer engagement and embarrassing yourself and doing all of those things to see what really works, what really resonates and where the pain is.”

    For product managers, especially in B2B tech, this underscores the importance of direct customer engagement. Product roadmaps should be informed by firsthand customer feedback, not just market research or analyst reports. Founder-led sales, as Molham suggests, provides invaluable raw data and customer intimacy that shapes product direction and market positioning.

    He also debunked the stereotype of the “slick talker” salesperson, emphasizing the value of “content rich folks who are also able to study and learn the problems of the prospect… teaching and tailoring.” This resonates deeply with product management – successful B2B sales, like successful product management, is about understanding and solving customer problems with expertise and tailored solutions.

    Mentorship, Hard Truths, and the Human Element

    Molham concluded with reflections on mentorship and the challenges of being a founder/CEO. He highlighted the immense value of mentors like Cam Lanier and Bob Muglia, emphasizing their integrity, long-term thinking, and win-win approach. He also candidly shared the difficulty of the founder journey. “It’s hard. It’s very difficult. This will probably be the last time I do this,” he joked, before quickly adding his passion for the mission and the quality of his team keeps him going.

    For product managers, these reflections are a reminder of the human element in building products and companies. Mentorship is crucial for navigating career challenges and gaining wisdom from experienced leaders. And the journey of product development, like entrepreneurship, is inherently challenging, requiring resilience, passion, and a strong team.

    Key Takeaways for Product Managers

    Molham Aref’s insights offer a powerful framework for product managers in the AI era:

    • Understand the historical context of AI: Technological evolution is iterative. Learn from the past to anticipate the future.

    • Focus on customer value proposition: Speak the customer’s language and solve real business problems.

    • Simplify complexity in the data stack: Prioritize solutions that streamline workflows and reduce integration burdens.

    • Gen AI is a tool, not a strategy: Integrate Gen AI thoughtfully with traditional AI and enterprise knowledge.

    • Engage directly with customers: Founder-led sales and direct customer feedback are invaluable for product direction.

    • Embrace mentorship and the human element: Learn from experienced leaders and build resilient, passionate teams.

    By internalizing these lessons, product management professionals can navigate the complexities of the modern data stack, harness the power of AI, and build truly impactful products for the enterprise of tomorrow.

    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.

  • Navigating Hype Cycles for Sustainable AI Innovation

    The buzz around Artificial Intelligence, particularly Large Language Models (LLMs), is deafening. From boardrooms to break rooms, the promise of AI is touted as transformative, revolutionary, even existential. Yet, beneath the surface of breathless headlines and billion-dollar valuations, a more nuanced reality is emerging. To effectively leverage AI for business advantage, leaders must move beyond the hype and adopt a strategic, long-term perspective.

    Pedro Domingos, Professor Emeritus of Computer Science and Engineering at the University of Washington and author of The Master Algorithm, offers a critical yet constructive lens through which to view the current AI landscape. In a recent podcast interview, Domingos, a pioneer in machine learning, cautioned against the inflated expectations surrounding LLMs and urged a more balanced understanding of AI’s true potential – and limitations.

    1. Acknowledge Progress, Temper Expectations:

    It’s undeniable that AI has made “tremendous progress,” as Domingos acknowledges. Transformers, the architecture underpinning LLMs like GPT, represent a significant leap forward. These models demonstrate impressive abilities in language processing, generation, and even creative tasks. Dismissing them as mere “stochastic parrots,” as some critics do, is inaccurate. LLMs are learning systems exhibiting genuine generalization capabilities.

    However, the current hype cycle has outpaced the underlying reality. The narrative that we are on the cusp of Artificial General Intelligence (AGI) thanks to LLMs is, according to Domingos, “farsal.” He warns of a potential “stock market bubble” fueled by unrealistic expectations, echoing historical patterns of technological exuberance followed by inevitable corrections.

    For business leaders, this means celebrating genuine AI advancements while avoiding the trap of over-promising and under-delivering. Focus on practical applications and incremental improvements rather than chasing the mirage of near-term AGI. Remember, as Domingos points out, we’ve traveled “a thousand miles in AI,” but there are still “a million miles more to go.”

    2. Look Beyond the LLM Hype to Broader AI Capabilities:

    The intense focus on LLMs risks obscuring the broader landscape of AI and its diverse applications. Domingos emphasizes that LLMs, despite their current prominence, are “nothing compared to the major applications of AI today.” He points to recommendation systems as a prime example – the algorithms that curate our online experiences, from e-commerce suggestions to social media feeds. These systems, often built on different machine learning techniques, have already profoundly shaped consumer behavior and business strategies for years.

    Furthermore, the assumption that deep learning, and specifically Transformers, is the singular path forward is limiting. Domingos argues that “tweaks on Transformers will not get us to human-level intelligence.” He advocates for exploring diverse AI approaches beyond the current deep learning paradigm, emphasizing that true innovation may lie in uncharted territories.

    For businesses, this means diversifying AI investments beyond LLMs. Explore applications in areas like computer vision, robotics, optimization, and traditional machine learning techniques. Consider how AI can enhance existing processes and create new value streams across various functions, not just in language-centric tasks. The most impactful AI strategy is likely to be multifaceted and tailored to specific business needs, not solely reliant on the latest LLM breakthrough.

    3. Strategic Investment: Prioritizing Diverse Research and Foundational Capabilities:

    The influx of capital into AI is unprecedented, yet Domingos questions whether these investments are being directed optimally. He argues that there’s “too much funding of the same narrow kinds of things” and a concerning lack of diversity in AI research compared to previous decades. He advocates for a “thousand flowers bloom” approach, encouraging exploration across a wider spectrum of AI methodologies.

    Domingos also highlights the importance of investing in foundational AI research and hardware. He suggests that the next wave of AI innovation may require specialized hardware beyond GPUs, potentially focusing on “primitives for any possible next thing,” like sparse tensors or relational operations. This forward-looking perspective contrasts with the current industry fixation on optimizing existing deep learning infrastructure.

    For investors and business leaders allocating capital to AI, Domingos’ insights are crucial. Avoid the allure of chasing the latest hype cycle and instead consider a more diversified investment strategy. Support research into diverse AI approaches, explore hardware innovations that can unlock new capabilities, and prioritize long-term strategic goals over short-term gains driven by hype.

    Moving Forward with Realistic Optimism:

    Pedro Domingos’ perspective offers a valuable corrective to the current AI exuberance. It’s not a call for pessimism, but for realism and strategic foresight. AI holds immense potential to transform businesses and society, but realizing this potential requires navigating the hype cycles with a clear understanding of both the current capabilities and the long road ahead.

    By acknowledging genuine progress while tempering expectations, diversifying AI strategies beyond LLMs, and strategically investing in diverse research and foundational capabilities, businesses can position themselves to harness the true, long-term power of AI. The future of AI is not about chasing fleeting hype, but about building robust, sustainable value through informed and strategic innovation.

  • 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.

  • Silicon Valley Secrets: 3x Exit Founder Ameesh Divatia on Data Security

    With three successful exits and a career spanning the core of Silicon Valley’s technological evolution, Ameesh Divatia offers a rare and valuable perspective on building transformative companies. From his early days in networking silicon and navigating the dot-com boom at Cisco to founding Baffle, a pioneering data protection company, Ameesh has consistently focused on creating new market categories. In this conversation, he shares the critical lessons learned from his entrepreneurial journey, including the importance of a customer-centric view, building strategic relationships with potential acquirers, and adapting to industry-wide shifts. Ameesh provides deep insights into the evolving landscape of data security, the challenges and opportunities presented by GenAI, and the timeless principles of finding the right idea and the right team to bring it to life. This discussion is a masterclass for any founder or tech leader looking to understand the mechanics of long-term success in a fast-changing world.

    → Enjoy this conversation with Ameesh Divatia, on Spotify or Apple.

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    Nataraj: We like to feature founders solving interesting problems, and you’re tackling data protection. Before getting into what Baffle does, can you give a little introduction to yourself and your journey so far?

    Ameesh Divatia: Absolutely. I have a traditional engineering background. I grew up in India, got my bachelor’s degree there, and came here for graduate school. After grad school, I got into the computer networking industry, which was a very hot space at the time. I was fascinated with how computers could be connected and evolve into a larger IT paradigm. My first job was as an application engineer supporting networking silicon, but I quickly moved into an architectural role because I was always interested in the big picture and understanding how the customer was using the product. That has stayed with me forever. I moved into a system architect role and eventually went to work for 3Com, one of the big three along with Cisco. After about three years there, I decided I wanted to do something on my own. I quit my job, came up with an interesting angle on using data networking concepts for optical networking, and that was my first startup in the late 90s. My second startup was in storage networking, the third was a turnaround in silicon photonics, and now Baffle is in data protection. My journey has taken me through very different spaces because I like to learn new things and go after complex technical challenges. I’ve also been extraordinarily lucky; all three of my previous startups were acquired. In every case, we built a completely new market for the acquirer or the space in general.

    Nataraj: Looking at your career, you were at Cisco during one of its most interesting times, the dot-com bubble. At one point, wasn’t Cisco the highest-valued company?

    Ameesh Divatia: It absolutely was. It was sort of like the NVIDIA of the dot-com bubble. This whole NVIDIA story is something we have lived through. I don’t want to say it doesn’t end well, but it doesn’t stay like this forever. But it was euphoria. You would get into the office and see the stock up six bucks, and we literally thought we could take over the world. We were newly acquired into Cisco, and two things about Cisco were amazing. First, they always reinvented themselves. John Chambers had an edict that companies have to reinvent themselves every four years. We were acquired and built a completely new business on the optical networking side. Second, when you get acquired at Cisco, you are treated very well. I remember we had a record of 33 EBCs in a quarter. It was insane; every day, the who’s who of the networking and telecom world—AT&T, Verizon, Deutsche Telekom, France Telecom—would be there, and we would be presenting to them. It was a magical time.

    Nataraj: Any advice for whoever is inside NVIDIA that you did or didn’t do when you were at Cisco?

    Ameesh Divatia: Be humble, that’s it. NVIDIA is a different story; it’s an overnight success that took 30 years. They completely deserve what they have, but it will be disrupted. Every company has to fight to be the incumbent and drive the industry. Some are more successful than others, but nothing lasts forever.

    Nataraj: Your company before Baffle was also acquired by Cisco.

    Ameesh Divatia: That’s right. One thing I’ve done a lot is get involved with prospective acquirers very early. I always tell entrepreneurs not to rely on someone else to find you the exit. You have to build relationships over time. You have to be open about sharing your value proposition, but not your crown jewels. With Lightwire, it was a very capital-intensive project in silicon photonics. You need a big brother. We went out and shortlisted companies, saying, “Look, we have this amazing technology, but we need somebody to productize it.” We got a few interested, and eventually, Cisco not only got interested but invested $20 million in a $40 million round and started working with us. They needed to pack a terabit of throughput in a router blade, which wasn’t possible with existing technologies. They gave us a spec, and our team did it in less than two years. At that point, Cisco couldn’t let that technology be out there because it was critical for their success, so they decided to buy it. It was the first time they had ever bought a component company.

    Nataraj: Is this technology actively used today?

    Ameesh Divatia: Absolutely, it’s a billion-dollar business line right now for just this product, and they’ve expanded beyond that.

    Nataraj: And you served as a partner at an investment fund, Carta. What was that experience like?

    Ameesh Divatia: After that exit, I was done with operating roles and wanted a break. I thought I could take my expertise to help others at a very early stage. My CFO, one of my mentors, and I started Carta as a seed-stage fund to invest in companies. We invested in eight different companies, and six of them have been acquired by now. It was an interesting experience to be on the other side, but I quickly got bored. It’s not like running the place; you’re watching the entrepreneur run it. Then a great idea came along, which is how Baffle really started, and I jumped back in.

    Nataraj: You mentioned you’ve always created new categories. How was Baffle a new category at that point?

    Ameesh Divatia: We had the vision to create a new category. If you look at security, it started with protecting the pipe—the network—which led to the device wave with firewalls. Today, we are in the identity boom because we don’t own the infrastructure anymore; the data center is in the cloud. We think this is setting it up nicely for the next big thing, which is actually protecting the data itself. Identity can only go so far. If you protect the data at the record level with encryption, there’s no way to look at the data unless you have access to the keys. You’re making the hacker’s job much harder. Security has always been a race. We feel data is the next big frontier. If you protect data at the record level, you don’t care if a hack happens, because whatever is stolen is ciphertext. That’s the category we are creating: data-centric protection. The industry is starting to gravitate towards that, especially with the GenAI explosion, because you have to share your data to get good outcomes. The fundamental problem we solve is that once data moves to the cloud, you don’t want the cloud vendor to see it.

    Nataraj: At what abstraction level does Baffle come into the picture, and who’s your typical customer?

    Ameesh Divatia: We operate at the application tier. It’s a pure application layer solution with no dependence on any processor, operating system, or programming language. We intercept the packet that goes between the application and the database as a network-level proxy. A typical customer is the one responsible for infrastructure in the cloud. But it starts with compliance regulations. We started in 2015, and GDPR came into effect in 2018. Security sets the rules. Data analysts or prompt engineers want to move data to the cloud, but security says no unless it’s compliant. That’s when the infrastructure provider, responsible for the database, has to adopt Baffle.

    Nataraj: There’s criticism that regulations like GDPR benefit big companies and make it harder for startups. What are your thoughts?

    Ameesh Divatia: If you ask me, regulation is not going far enough. If regulation was great and everyone was compliant, we wouldn’t have hacks. But regulation should be easy to adopt. With data-centric protection, you’re taking a proactive measure to protect your data and ensure your customers’ data won’t be stolen. That enhances your reputation and builds trust. I don’t think it’s worth fighting regulation; it’s important to embrace it. GDPR has now been taken to a new level with things like CCPA and CPRA, so it’s proliferating.

    Nataraj: When GDPR first came out, did things improve, and did the tooling evolve so a small company could also comply?

    Ameesh Divatia: Absolutely. One of the big things about GDPR is the right to be forgotten and the bring-your-own-key model. That’s something we have enabled for any SaaS vendor. It used to be very complicated; you had to buy an HSM, know crypto, encrypt your data, and manage keys. We’ve completely abstracted that away from the developer. You just tell us who the tenant is, associate a key with them, and our tool handles the rest. If a tenant wants to trash their data, they just take the key away, and the data becomes invisible.

    Nataraj: Talk to me about your initial go-to-market strategy. How did you pitch your early customers?

    Ameesh Divatia: An entrepreneurial suggestion is you don’t want a channel on day one. You want to go direct to know what the end customer is doing. The key is to find an early adopter with a pain point that only you can solve. We found a gap in the industry with Postgres databases, which are fast-growing but have no native encryption capabilities. Our first customer tried a DIY strategy and failed miserably. They found us. Digital marketing, SEO, and SEM are absolutely critical for any business these days. People don’t like to be cold-called; when they need something, they search. You have to advertise and write lots of content. We started with a direct strategy, and to scale, we’ve gravitated towards using the cloud vendors themselves as our channel. It’s counterintuitive, but they have a shared responsibility model. We help them implement it, allowing their customers to take control of their data. This helps the cloud vendor migrate more customers and make 100x what they would pay for us.

    Nataraj: Security is a cat-and-mouse game. What are the current trends in the security industry?

    Ameesh Divatia: The most important trend is the evolution away from just monitoring. Things like SIM logs are being reimagined with GenAI for automated monitoring and alerting. But monitoring is great for identifying problems; what about remediation? Discovery is a big space, but just classifying data doesn’t make the problem go away. Remediation is the big trend. How do you remediate data so it becomes invisible? Encryption, masking, tokenization are techniques that can be used. We allow field-based control to transform data so it’s safe in environments you don’t control.

    Nataraj: You brought up GenAI. How are you thinking about it as an entrepreneur who has seen multiple technology cycles?

    Ameesh Divatia: This is going to be a multi-year cycle and a massive productivity improvement. We’re seeing it in multiple places. Our engineers use co-pilots all the time, which helps remediate things like CVEs. We use it in our customer success environments as well. A new engineer can troubleshoot a problem without needing to know who the actual customer is. We tokenize the customer name, put the data in a repository, and run GenAI models on it. From a product perspective, it will completely transform data discovery, which has never been a fully solved problem. We expect to integrate it across all our functions—sales, marketing, product blogs, all of it.

    Nataraj: If you were starting a new company right now, where would you look for opportunities?

    Ameesh Divatia: I have lots of ideas, but implementation and commercial fit are key. I strongly feel we are in a phase where ease of use and adoption are critical. One of the biggest problems with GenAI is that it lies with authority. Figuring out ways to detect that is a massive opportunity. In general, anything in the data realm—getting the right dataset into the hands of the right people—is always going to be a lucrative area.

    Nataraj: Are your customers doing data deals with other companies to build GenAI solutions?

    Ameesh Divatia: Yes. Data sharing was always selective, but GenAI is changing that. You have to share your data, but it’s hard to do without compromising customer trust. That’s where data-centric security and privacy-enhancing computation will be critical. There’s still a tremendous problem in making it easy to use, and I see a lot of innovation coming where you can share data securely.

    Nataraj: This is your fourth company. Do you have a checklist or mental model you follow when evaluating a new idea?

    Ameesh Divatia: I wish. All four happened for very different reasons. The most important thing is you have to get excited about an idea. Second, and sometimes more important, is finding the right team to work with. It’s like a movie: you need the right plot and the right team of actors. You want people who are subject matter experts in their areas. After that, there’s a whole slew of commercial things: find the right market and make sure it’s a critical solution, not just a nice-to-have. But those first two are the most important to start.

    Nataraj: What do you consume for information? Any books or podcasts?

    Ameesh Divatia: LinkedIn is a big source of my information. I also like reading business books, though I often get the gist through something like Blinkist. I like to read about entrepreneurs and innovators, so I’m a big fan of Walter Isaacson. He writes really well when it comes to capturing a person’s history.

    Nataraj: Who are the entrepreneurs you most admire?

    Ameesh Divatia: I’ll start with the founding fathers of this country. I think they were the ultimate entrepreneurs. They completely wrote the model from scratch, which gives me the most confidence in America’s future. Nearer term, of course, Steve Jobs was an amazing entrepreneur, as is Elon Musk.

    Nataraj: Who are your mentors?

    Ameesh Divatia: I’ve had a lot of them. It starts with my family—a supportive spouse who gives the right feedback. My father was a big influence. And I’ve had many other mentors in Silicon Valley who helped me along the way and continue to help me today.

    Nataraj: What do you know about being a founder now that you wish you knew when you were starting your first company?

    Ameesh Divatia: This is a tough one. As an engineer-founder, you tend to fall back on the idea that the technology will sell itself. That is seldom the case. You have to constantly adapt the story based on what the customer perceives. The technology is a cornerstone, but you have to constantly make sure the commercial value is there. If I knew that, I would have looked at the business side of it way earlier in a lot of cases.

    Nataraj: Ameesh, thanks for coming on the show. This has been a very fun and insightful conversation.

    Ameesh Divatia’s journey offers a masterclass in navigating Silicon Valley’s dynamic landscape. His insights on creating new markets, adapting to technological shifts like GenAI, and the foundational importance of data-centric security provide a valuable roadmap for entrepreneurs and innovators.

    → If you enjoyed this conversation with Ameesh Divatia, listen to the full episode here on Spotify or Apple.

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  • 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.

  • Prakash Chandran on Building Xano, a Scalable No-Code Backend

    In the evolving landscape of software development, the demand for powerful, scalable applications often outpaces the supply of engineers. This gap has fueled the rise of no-code and low-code platforms, but many come with limitations in scalability and security. Enter Xano, a platform dedicated to providing a robust, enterprise-grade no-code backend. We sat down with Prakash Chandran, co-founder of Xano, to explore his journey from leading design at Google to tackling one of the biggest challenges in modern development. Prakash shares how his experiences, including a self-described “failed startup,” shaped his vision for Xano. He discusses the platform’s unique approach of focusing solely on the backend, empowering citizen developers to build complex, scalable applications without writing a single line of code, and moving beyond the prototyping phase to full-scale production. This conversation offers deep insights into the future of software creation and the power of abstracting complexity.

    → Enjoy this conversation with Prakash Chandran, on Spotify or Apple.

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    Nataraj: On the Startup Project, I like to feature interesting products solving interesting problems. As I explored Xano, I found it’s at the intersection of a couple of very interesting trends in how we develop and scale applications. I thought it would be useful for our audience to discuss what you’re building at Xano. Before we get into that, can you give a little introduction about yourself and your career before starting Xano?

    Prakash Chandran: Absolutely. After I graduated from Cal Poly Pomona, I joined a small startup called Picasa. Google ended up buying Picasa, and it became Google Photos. My coworkers called me very lucky because I had no idea; I just joined, and then that happened. I spent the next eight and a half years at Google. I had the privilege of leading the design on Google Calendar for a bit and led the design and research team for Google Enterprise, which became G Suite and Google Workspace. I had an amazing career there, learning a lot and working with brilliant people. After Google, I left for the startup world. After a short break, I did a startup, which I call three and a half years of getting the crap kicked out of myself. Then I went into consulting, as one does after a failed startup, for a couple of years before starting Xano. That’s been the trajectory. At Google, I evolved from a UX person to a product person, and after doing a startup, you become more horizontal across the business.

    Nataraj: I have this thesis about software products: cracking the right software product is about finding the right abstraction layer at which people want to work. Exploring Xano, your background makes sense. Someone like you starting Xano makes sense; you need an eye for what level the product should be designed at.

    Prakash Chandran: That’s a keen observation. One of the main pains I felt during my startup was my lack of control as a design and product person over the engineering process. You have to pay for an expensive engineer. They’re kind of like a car mechanic. If they tell you something is going to take a month to make and tens of thousands of dollars, you just have to believe them and hope that it works out.

    Nataraj: With that, explain what Xano is.

    Prakash Chandran: I always knew that was a painful process. Then I learned that 80% of the time and resources in software development are spent on the backend. So, Xano is a scalable no-code backend. For those who don’t know the no-code space, it’s about helping people create software without knowing how to code. No-code has been around for a long time; a tool like Squarespace is considered no-code for website building. It’s gone through a couple of different iterations. We’re part of a new wave of no-code where you can build anything and scale without limits. We 100% focus on the backend—the server, the database, the API layer—and we connect to any front end you want.

    Nataraj: What kind of applications does Xano enable people to build?

    Prakash Chandran: Xano is really meant to be a visual programming language. It’s Turing complete. Anything you can articulate in a software programming language, you can do in Xano without code. In terms of the types of applications we see built, we see everything from a dog-walking application all the way up to a customer advocacy platform at a big company like Qualtrics. It’s kind of like asking what you can build with JavaScript; the use cases are endless. In the same way, we see our customers building many types of things on Xano.

    Nataraj: One of the assumptions people often have when they hear a product is categorized as no-code is that it might be good for experimentation, but not for running production applications. Can you really go from a demo or a hobby project to actually running something that makes money or building a company on top of it? How do you see Xano fitting in?

    Prakash Chandran: I always say that no-code and low-code have some baggage associated with them. This is because the no-code vendors and tools of the past have had limitations, generally around scalability, security, reliability, and compliance. The hesitation is that no-code is something you should just prototype and tinker with, not take seriously at scale. But as I mentioned, these next-generation tools are architected fundamentally differently. For example, as a no-code backend, we are architected differently than some of our predecessors that might look like a spreadsheet on steroids, where scalability and compliance are layered in as an afterthought. We were architected from the very beginning to be enterprise-grade, portable, allowing you to move it to your own infrastructure and control the resources yourself. The flexibility in what you can build was also addressed very early on. The concern is definitely valid, but one of the things we hope to do is break that stereotype and show that you can start with no-code tools like Xano and scale without limits.

    Nataraj: While exploring Xano, it almost felt like… because I was a backend and frontend engineer, I’ve played around with different backend stacks. One of the problems I see is that building a small application is getting really complicated, even for seasoned developers, because things are so rapidly changing. It’s becoming more complex even to get started. What is your thought process on this? Am I just a bad developer, or is this really happening?

    Prakash Chandran: No, not at all. You call out something that’s pretty important. As we’ve introduced new technologies to handle different use cases, especially on the DevOps or site reliability side, it gives the consumer more choices, which leads to confusion. The most important thing when building software as a business is validating it in the market. You want to get it out the door. We believe vendors should be responsible for making good long-term decisions regarding infrastructure and DevOps, so you can focus on the business logic and your relationship with customers. The more quickly you can do that, the better. You’re either going to fail and learn and then iterate, or you’re going to get it right and scale your business on something you can trust. A general rule of thumb we tell everyone is you’re always going to rebuild. No matter what stack you build on, you’re going to rebuild it at some point. So the stack you can use to iterate the quickest is probably the best one. We wanted that to be true to our spirit, but on a trusted infrastructure, so if they did find product-market fit, they could rely on it to scale with them.

    Nataraj: Whenever I talk to early-stage founders, what they start with and where they end up is slightly different. Talk to me about the process of finding product-market fit for Xano.

    Prakash Chandran: We generally knew that this no-code, low-code space was growing. More people were turning to it despite its limitations because there’s more demand than there are engineers. We saw this limitation on the backend regarding security and scalability. So we made two decisions. One, we decided to 100% focus just on the backend and not do the full stack. This was the non-obvious thing everyone said was probably not a good idea. The second piece is we decided not to abstract away the core principles of a software development language. For example, we chose to call it an API instead of a workflow or a Zap. We chose to call it a webhook. We believe you should teach the next generation of software developers by abstracting away the code but making those concepts more accessible. Those two decisions were non-obvious. Getting product-market fit started with a landing page as we were building, saying, “Hey, this is a no-code backend. If you’re interested, sign up for early access.” Then once you have people using the product, you see how quickly they start relying on it. The next step is, will they pay for it? And over time, you see that word of mouth grow and how sticky it becomes. We got lucky in that we had this non-obvious approach and we thought it was a need in the market.

    Nataraj: Because the backend has so many components, was there a specific customer use case that gave you the confidence that you had really found something?

    Prakash Chandran: It wasn’t a specific customer or use case, but Xano was actually born out of a development agency. It started as a command-line tool to make backend creation easier without having to grow the team. When you see hundreds of different use cases, you realize most software is the same. They have the same kind of infrastructure setup, the same database. There are the same motions you’re doing over and over. For us, it was less about the specific use case and more about the procedure and those steps you had to go through just to start validating your idea. You could do this on other tools, but you couldn’t rely on it once you started to get usage and scale, or if security was a prerequisite. So that was the approach we took, and we found that being pretty horizontal has worked for us so far.

    Nataraj: You mentioned it came out of a studio. Is it from your consulting, or were your co-founders running a studio that gave you that insight?

    Prakash Chandran: My co-founders were running the development agency. I had done some consulting work as an individual for that agency. It was in doing some of that joint client work where I had seen the evolution of Xano from the very beginning as a command-line tool to a decade later, having served hundreds of use cases. I told my two co-founders, “If we took this and we productized this, I think we could serve a pretty big need in the market and we might have something special.” That’s how it came about.

    Nataraj: We’ve evolved products based on the cloud, right? We had infrastructure as a service, platform as a service, and now probably with AI, intelligence as a service. We are moving higher in that abstraction level. I was thinking at some point someone has to abstract away why everyone has to build a scalable website. You’re sort of an evolution of abstracting away that complexity from a backend perspective.

    Prakash Chandran: 100%. We could serve that use case pretty well. A lot of companies in the no-code space are very good at building building blocks, these connectors that connect one service to another. We took the other approach and built the engine and the foundation, this visual development layer on top of an engine that abstracts away the DevOps component—the infrastructure, Kubernetes, Docker, Postgres. You don’t have to worry about those things. So if someone wants to build, which you can do in Xano today, an Amazon.com template with the right database schema, and you want to be in multiple geographic regions, you can start with that business logic layer and the database. And then from the infrastructure side, we’re able to deploy in whatever region you want. It’s really just building the foundation where people agree this is the right layer of abstraction for business logic in the backend, and then they’re able to move it as they grow.

    Nataraj: Who is the ideal user today for Xano?

    Prakash Chandran: We service primarily the citizen developer. If you haven’t heard of that persona before, it’s a Gartner-defined persona. You can think of them as a product owner type. They’re a systems thinker, they don’t know how to code, but they need to build software leveraging low-code tools. There are also developers on Xano as well, but primarily we serve the citizen developer. Now, there’s a wide spectrum of experience. People more on the citizen side will use tools like Airtable or Google Sheets because it’s very easy to pick up. That serves simple use cases very well. We serve an advanced citizen developer who needs to graduate out of tools like Airtable when their needs require scalability, security, and reliability. That’s who we serve. And we’re constantly working to make ourselves more accessible to the broader citizen developer.

    Nataraj: Let’s talk about how you’re doing as a company. I think you raised your Series A. What is your revenue split between enterprise and SMB or citizen developers?

    Prakash Chandran: We launched in January of 2021. Since then, we’ve seen some pretty exponential growth, largely due to word of mouth. We’re just a backend, so we’re front-end agnostic. All the different front-end forums, whether it’s a JavaScript forum or a no-code front-end forum, mention us. We have over 70,000 backends that we’ve deployed. In terms of the enterprise versus self-serve split, it’s about 20% enterprise revenue, and the other 80% is self-serve. We’re obviously working right now to develop our muscle in the enterprise go-to-market motion, but for right now, we are mostly known in the small to medium-sized business space. In the enterprise, no-code is still very early in adoption, and we’re trying to prove ourselves there.

    Nataraj: How is the competition in the space, specifically at the abstraction layer you are operating at, focused on the backend?

    Prakash Chandran: There’s a spectrum between citizen and developer, and then a spectrum from developer to engineer. We service the upper end of the citizen developer into the midway point of becoming an engineer. In that space, there are a number of different tools. When you think about the backend specifically, if I’m going to pick the number one competitor, I think we serve different markets, but we definitely see them, is Supabase. Supabase is basically Postgres in the cloud. They’ve done an amazing job. Great founders, they execute very well. But I think there are tools that believe making a developer’s life easier is the future, and then there are tools like us that believe the next generation of software creators are going to look more like citizen developers. We serve these two markets, but because we serve the upper end, we tend to see each other there.

    Nataraj: You mentioned you almost thought of Xano as a design language. That gave me a thought that AI could use this design language to make things happen on Xano. How are you thinking about AI intersecting with Xano?

    Prakash Chandran: I mentioned visual development because we use all core foundational software engineering principles—variables, loops, conditionals—things that every programmer knows, and we visualize that. When it comes to AI, AI understands these concepts. AI has been such a beautiful thing because it has opened up so much more opportunity and built awareness around creating software and making it more accessible. People can ask AI to generate simple applications for them. I love AI for that reason. That being said, something else is needed to pick up where AI leaves off. If I say I want to build an Uber-type application and you give the same prompt, we’re probably going to receive two different code sets. Even if we don’t, what I mean in my mind with an Uber application is very different than what you mean. In that case, we’re going to keep re-prompting until it spits out obfuscated code. What’s better is you need to pick up from that scaffolding and take a visual canvas where you can infuse your intention. We believe Xano will be the visual canvas that can pick up where AI leaves off. I think a lot of people make the mistake of saying AI is going to replace coders or replace no-code. I don’t think that’s going to be the case. They’re going to work very well together.

    Nataraj: I’m curious, what are some of the techniques you guys use to acquire new customers?

    Prakash Chandran: One thing that we’ve done very well is create lots of valuable YouTube content. If you go to our YouTube channel, you will see videos starting from, “How is software made? What is a backend?” all the way to, “How do you merge two JSON arrays?” It is very important to continually put out content because this is how people consume it these days. It’s probably our largest source of high-quality traffic. The beautiful thing about content is it just keeps giving; some of it is just evergreen. We also started running office hours, which was pretty unique at the time. Every user, even a non-paid user, has the opportunity every week to come and meet with the team and get their questions answered. If they ask something unique that can help other people, we clip that and put it on YouTube. The final thing is having a community tool that can be indexed on the questions that are answered and searched for. The chances are the problems your customers are having today is a problem a customer is going to have three months from now. It’s a mistake to have a community on Discord or Slack because all of that knowledge evaporates.

    Nataraj: I’ve seen this in other tools which became ecosystems, where you have marketplaces of people who can develop things. Are you seeing a marketplace evolving in Xano?

    Prakash Chandran: Absolutely. We already have a marketplace of developers, agencies, and coaches. We’re currently working on a marketplace where people will be able to release and soon be able to sell capability, all using the Xano platform. The ecosystem and this partner marketplace are extraordinarily important. We’re even working on a certification process because our agency partners need to separate themselves from everyone else that says they build on Xano. So we have certification programs that we’re actively working on as well.

    Nataraj: It looks like you should almost rebrand yourself to calling as a ‘backend cloud’ instead of a no-code backend. We’re almost at the end of our conversation. What are you consuming right now that’s influencing your thinking?

    Prakash Chandran: I listen to a lot of podcasts. I subscribe to podcasts like the All-In podcast, Revenue Builders, The Logan Bartlett Show, and Invest Like the Best with Patrick O’Shaughnessy. I learn something every single week. Some people will work out to music; I’m working out to podcasts. I’m listening on my commute. I’m one of those types that listen at 2x and skip around because I’m at the point where I kind of know the nuggets of wisdom I’m looking for. I’m just so grateful for the community of podcasters that have amazing guests I can learn from about management style, growing a business, or customer acquisition.

    Nataraj: Who are your mentors that have helped your career?

    Prakash Chandran: There are moments of wisdom you pull from all of these podcasts and individuals that influence you. Two people I’ve never met but who have really influenced my way of thinking are Chamath Palihapitiya, who has a wonderful framework of intellectual honesty and seeking the truth, and Dev Ittycheria from MongoDB, for his philosophy around management. In terms of in-person people, I used to work with Adrian Graham and Carl Showalter. They were at Google with me, sold their company to Facebook, and then created Seesaw. Working with them directly, they’re just brilliant individuals. They work really hard and always have this healthy dose of skeptical optimism that has taught me to build in a very measured way, where you have your head in the clouds but your feet on the ground.

    Nataraj: One final question, what do you know about being a founder that you wish you knew before starting Xano?

    Prakash Chandran: I’m very happy with all of the mistakes that I’ve made. Obviously, it’s shaped where we are today. But if there was one thing, it’s that the things that matter take time. I think when we’re younger, we’re always in a hurry. We think there might be a silver bullet shortcut and we’re always looking for it. But when you’re trying to do something impactful that will have a long-lasting impact, there’s no shortcut. It just takes day-in, day-out work, trying, and learning. It’s important to align yourself with your zone of excellence and what gets you excited because it’s going to be a very long journey. In this case, Xano is it for me. I really feel like I’m doing what I’m meant to do. But when I was younger, especially before my first startup, I didn’t really have that mindset.

    Nataraj: That’s a good note to stop the conversation. Again, thanks for coming on the show, Prakash. Really looking forward to what Xano will do in the future.

    Prakash Chandran: I really appreciate it. Thank you so much for having me.

    Conclusion

    Prakash’s journey with Xano highlights a critical shift in software development towards more accessible, yet powerful, tools. By focusing on an enterprise-grade, scalable backend, Xano empowers a new generation of builders to bring their ideas to life without being limited by traditional coding barriers.

    → If you enjoyed this conversation with Prakash Chandran, listen to the full episode here on Spotify or Apple.

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  • 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.