Ash Ashutosh has just been appointed CEO at Pinecone, the leading vector database, where he will focus on growing the company and further expanding Pinecone’s position at the forefront of the category it created.

He is a three-time founder of storage and data infrastructure companies Serano Systems, AppIQ, and Actifio, leading those to hundreds of millions in annual revenues and eventual acquisitions by Vitesse, HP and Google, respectively. His 40-year journey in technology also includes time spent as the CTO of HP’s storage division, and as a partner at VC firm Greylock.

The market is moving quickly, but our strategy is simple. We aren’t chasing trends; we’re building the foundational infrastructure that every knowledgeable AI application will rely on for years to come.

Most recently, Ashutosh served as Global Director of Solution Sales at Google, where he was responsible for incubating and scaling new businesses and managing a portfolio exceeding $1B in revenue. He holds a degree in electrical engineering and a master’s degree in computer science from Penn State University.

You’ve mentioned that ‘the AI hype cycle is ending and the business cycle is starting.’ What specific shifts are you seeing in how enterprises approach vector database adoption, and how will you position Pinecone to capture this transition?

The market is maturing right before our eyes. The conversation has shifted from “What’s possible with AI?” to “What’s practical and profitable?” Companies are moving past building proof-of-concept chatbots and are now asking much more specific, ROI-driven questions, like, “How can we use Retrieval-Augmented Generation (RAG) to make our supply chain forecasts 30% more accurate?”

The shift from the experimental phase to the era of production demands scale, reliability, and security. Pinecone was engineered for production-grade workloads from day one. We spent years obsessing over the performance, accuracy, and scale required for mission-critical AI applications. That’s our advantage, and it’s why the most demanding enterprises are building on our platform.

With your experience building three successful data infrastructure companies to hundreds of millions in revenue, what proven growth strategies will you bring to Pinecone? How will your approach differ from typical Silicon Valley hypergrowth playbooks?

I’ve learned that sustainable, capital-efficient growth is far more powerful than the “growth-at-all-costs” mindset. The vision is essential, but it must be built on strong business fundamentals. My approach is centered on three core principles that have served me well.

First, solve a real, painful problem so effectively that your customers become your best evangelists. Second, build for the enterprise from the very beginning. This means focusing on security, reliability, and scale—the things that matter to the world’s largest and most complex organizations. Pinecone has been doing this for years, and we will only deepen that commitment. Third, prioritize capital efficiency. Burning cash isn’t a prerequisite for building a great, enduring company. We will continue to invest resources where they matter most: solving hard technical problems and delivering immense value to our customers.

You’ve drawn a clear distinction between ‘good enough’ bundled platforms and Pinecone’s best-of-breed approach. Can you elaborate on why enterprises should choose a specialized vector database over integrated AI platforms?

The distinction is simple: it’s the difference between a demo and a business. A bundled solution might be “good enough” to power a small-scale chatbot. But when you are processing millions of customer queries with sub-second latency and a 99.99% uptime SLA, “good enough” is a recipe for failure.

Platforms that bolt-on vector search as a feature inevitably make compromises. They optimize for breadth, not depth. We optimize for one thing and one thing only: being the most performant, scalable, and reliable vector database on the planet. When your AI application has a direct impact on revenue, customer experience, or operational efficiency, that specialization isn’t a luxury; it’s a requirement.

How do you plan to expand Pinecone’s market leadership as more cloud providers and database companies add vector capabilities? What’s your competitive moat?

Our moat is both technical and organizational. Our founder, Edo Liberty, one of the world’s foremost experts in this domain, is now our full-time Chief Scientist. We also have years of production deployments under our belt, which has taught us invaluable lessons about what truly matters at scale. You can’t buy that kind of experience.

Cloud providers and database companies add vector capabilities because it’s a feature they feel they have to offer. But their business model optimizes for platform lock-in, not for pushing the boundaries of search and retrieval technology. For them, it’s a feature on a checklist; for us, it’s our entire business. Our obsessive focus on solving this one problem better than anyone else is our ultimate competitive advantage.

With founder Edo Liberty transitioning to Chief Scientist, how will you ensure that Pinecone maintains its technical edge while scaling the business? What’s the balance between innovation and commercialization?”

This transition is a strategic multiplication of force. It allows us to have the best of both worlds: a world-class researcher laser-focused on pioneering the future of AI infrastructure, and a seasoned operator focused on scaling the business and delivering that innovation to customers.

Innovation without commercialization is just expensive research, and commercialization without innovation is a race to commoditization. This new structure creates a powerful feedback loop where the needs of our most demanding customers drive research priorities, and breakthrough research creates new market opportunities. With Edo leading research and myself leading the business, we are structured to master both.

You have previously discussed focusing on ‘people quietly building real things that change how their business runs.’ Can you share examples of transformative use cases you’re seeing that go beyond chatbots?

The most sensational claims often get the headlines, but the real revolution is happening quietly inside businesses. That’s my focus. The use cases that excite me most are the ones delivering tangible business value today. For example:

• A major retailer is reducing supply chain costs by 15% by using vector search for more accurate demand prediction.
• A pharmaceutical company is accelerating drug discovery by identifying similar molecular structures orders of magnitude faster than before.
• Financial services firms are detecting sophisticated fraud patterns that traditional rules-based engines would have missed entirely.

These applications may not be flashy, but they are fundamentally changing how these businesses operate. That is the quiet, transformative power of knowledgeable AI.

Coming from solution sales at Google, how will you evolve Pinecone’s go-to-market strategy? What lessons from selling enterprise infrastructure at scale will you apply?

My time at Google reinforced a crucial lesson: enterprises don’t buy technology; they buy outcomes. They make infrastructure decisions based on how a platform will solve their specific business problems, whether that’s reducing risk, increasing revenue, or improving efficiency.

The key lesson: meet customers where they are. Some want a fully-managed service, others need on-premises deployment, many want hybrid. Some need hand-holding, others want APIs and documentation. You can’t have a one-size-fits-all GTM strategy when you’re serving everyone from startups to Fortune 10 companies.

What does ‘making AI knowledgeable’ mean in practical terms for your customers? How will you measure success in this mission?

Edo will drive forward the mission he envisioned for the company: to make AI knowledgeable.

“Making AI knowledgeable” means empowering AI applications to deliver accurate, context-aware, and up-to-date answers by grounding them in real, proprietary data rather than relying on generic or outdated information. In practical terms for our customers, this translates to unlocking business value from previously untapped unstructured data – like emails, conversations, images, contracts, and technical documents – which represents over 80% of the world’s data.

It also means enabling AI-powered applications to provide relevant insights in real time, drawn from semantically understood and organized data and ensuring that AI systems are not just guessing, but are able to retrieve and synthesize knowledge on demand, resulting in more accurate, well-informed, and up-to-date outputs for end users.

Success isn’t just measured in benchmarks, it’s measured in business outcomes. When a support AI can instantly access every previous customer interaction and resolve issues without escalation, that’s knowledgeable AI. When a research assistant can surface relevant findings from millions of documents in milliseconds, that’s knowledgeable AI. We’ll measure success by how much value our customers extract from their AI investments.

In his blog article announcing the move, Edo mentioned that it was critical to find someone who ‘fits our culture’. How would you describe Pinecone’s culture, and how will you preserve it while scaling the company?

Pinecone’s culture is built on intellectual acumen, honesty, and technical excellence. This team doesn’t just build features, they question assumptions, push boundaries, and obsess over customer outcomes. It reminds me of the early days at Actifio when we were reimagining data management from first principles.

Preserving culture is about hiring decisions and operating principles. We’ll maintain our high bar for technical talent, our bias toward action, and our commitment to solving real problems.

With technologies shifting as quickly as they are, how would you outline Pinecone’s strategic direction at this point? What should we expect to see in the coming year?

Our focus for the next year is threefold.

• First, production excellence. We will double down on the features that matter for enterprise-scale deployments: advanced security, deeper observability, and even greater performance and cost-efficiency.
• Second, ecosystem expansion. We will deepen our integrations across the AI and data landscape. Our goal is to ensure that developers can seamlessly use Pinecone as the best-of-breed knowledge source for any application, on any platform.
• Third, pioneering what’s next. With Edo leading research, we will continue to push the boundaries of AI infrastructure, introducing capabilities that solve problems our customers don’t even know they have yet.

The market is moving quickly, but our strategy is simple. We aren’t chasing trends; we’re building the foundational infrastructure that every knowledgeable AI application will rely on for years to come.

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