Up-and-coming product managers - September 2026

The product managers and builders on our September 2026 watchlist are David Spiegel, Jess Lu, KrishnaS Hemanth, Monish Nallagondalla, Salil Harit, and Sanjit Kangovi. Each gives readers something concrete to examine: public code, a product demonstration, or a detailed account of a product decision. Their work is useful to founders and hiring managers looking beyond a familiar employer name.

What this watchlist means

We use “up-and-coming” to describe practitioners with emerging work worth following. The list spans people moving into product ownership and people extending an existing product career through independent building. It is an editorial selection, not an award, an age category, or a forecast of career success.

Selection centers on public work that makes a candidate’s contribution understandable. We looked for a named user problem, a concrete product or case study, and reasoning about what to build. First-person career and commercial-project accounts remain the authors’ reports. A synthetic demonstration, a TestFlight release, and an enterprise deployment answer different questions, so we identify those distinctions below.

Compare the product talent

David Spiegel: product growth and independent building

David Spiegel works across product, growth, and design, with a psychology background from NYU focused on human cognition. His interest in how people perceive an experience and decide what to do next connects that background with his product work. His portfolio includes Product Lead experience at 5 Axis Health and independent work on Obeagle, a video-chat product for pets. David Spiegel’s background.

His career account reports 30–50% higher revenue per session from his Content IQ work. That publisher-monetization experience gives context to his AdTech Eval Lab, which turns advertising-operations problems into two synthetic evaluations with defined tasks and deterministic checks. Readers can examine what the exercises ask a system to do and how success is judged. Career background and outcomes.

He belongs on this watchlist as an experienced operator extending his practice through new projects. For a team hiring across growth and AI product work, the interesting connection is between a business problem and a build that makes it testable. Ask David to walk through how he chooses an outcome, scopes the first version, and decides whether the result is useful enough to keep developing.

Jess Lu: consumer discovery and AI product design

Jess Lu’s PictureCook case study describes product-management work on a children’s AI companion. The most revealing detail is the discovery approach: a physical postcard pilot before the full digital feature. Her account then connects interface simplification with observations about how children used the experience.

Her independent Nibble project, a private video-sharing app, is explicitly labeled a TestFlight MVP. The case study traces research into communication habits through decisions about recording, sharing, and small-group interaction. That gives a hiring manager a tangible consumer-product discussion without treating an early release as a mature business.

Lu is worth following for the connection between research and interface choices. In an interview, explore which observations changed her original idea, which findings were uncertain, and what she would measure after the next release. Her published work provides enough specificity to make those questions productive.

KrishnaS Hemanth: AI product judgment under constraints

KrishnaS Hemanth’s portfolio focuses on enterprise software in regulated settings. His AI Quote Automation case study describes narrowing an automation request to extracting information for analysts while preserving human decisions and sign-off. It explains discovery, scope, and his contribution rather than stopping at a screenshot.

A separate case study about a canceled initiative describes recommending that work stop after examining data restrictions and economics. These are his accounts of the projects. The useful hiring signal is the reasoning available for discussion, especially how constraints changed the recommendation.

His work suits a conversation about enterprise AI ownership: where automation ends, who handles an exception, and when a promising feature should be shelved. Ask how the team checked errors after launch and what evidence would have justified revisiting the canceled proposal.

Monish Nallagondalla: technical depth with public artifacts

Monish Nallagondalla describes a move from data and AI work into product management. His portfolio covers application workflows, requirements, and delivery alongside technical builds. A useful next step is his public code, which lets an engineering partner inspect more than a list of tools.

The Nirixa repository documents an AI assistant system with persistent memory, multiple interfaces, and evaluation tooling. Readers can inspect the architecture and setup instructions. Repository availability supports a discussion of implementation choices; it does not independently establish reliability or customer adoption.

For a technical product role, ask him to explain one boundary between application logic and model behavior. Which action should require human approval? What happens when context is incomplete? The combination of a product narrative and accessible code makes it possible to examine those decisions together.

Salil Harit: requirements and delivery in business software

Salil Harit’s portfolio describes work on FAMS, a fixed-asset management system, and AstroVista, an education app. His accounts focus on requirements, stakeholder communication, scope decisions, and revenue-model design. Those details make the work relevant to teams with operational complexity and several people influencing what gets built.

The AstroVista account explains a prototype-first approach and decisions about how creators would sell content. FAMS provides a different discussion: translating an existing business process into software while coordinating acceptance with the client. Both are self-reported case studies; their value here is the specificity of the product responsibilities described.

Ask Harit to reconstruct a requirement that changed during delivery. What triggered the change, who needed to agree, and what did he cut to accommodate it? That conversation will reveal more about role fit than accepting a headline result without its context.

Sanjit Kangovi: AI product ideas you can explore

Sanjit Kangovi identifies as an AI product builder. His Verdict demonstration turns fictional advertising data into a marketing brief and clearly labels the dataset as synthetic. It includes different weekly scenarios, giving readers a concrete way to discuss how an AI application should respond when the underlying situation changes.

Another project, Redline, presents a review workflow for AI transcripts through engineering, risk, and business perspectives. Together, the projects make his product ideas accessible through interfaces, rather than requiring a hiring manager to imagine the experience from a presentation.

Kangovi is a relevant person to follow when a team needs someone who can build an exploratory version of an AI workflow. Evaluate him as a product builder. For a PM opening, also examine customer discovery, prioritization, and collaboration; an interactive portfolio alone cannot answer those questions.

How to turn a portfolio into a useful interview

Start with the problem your team needs solved. Choose one relevant project and ask the practitioner to explain their actual contribution. Separate what they proposed, what they built, what other people owned, and what reached users.

Then examine a difficult decision. A productive discussion covers an alternative that was rejected, the evidence available at the time, and the cost of being wrong. For AI work, include an example where the system should refuse, ask for clarification, or hand control to a person.

Finally, agree on what any claimed result measures. A prototype demonstrates an experience; a live release establishes availability; an outcome claim needs a baseline and measurement context. Use the portfolios above to choose the right conversation, then assess the person against the responsibilities of your role.

Public sources

• David Spiegel: portfolio and AdTech Eval Lab.
• David Spiegel: career background and selected outcomes.
• Jess Lu: PictureCook and Nibble.
• KrishnaS Hemanth: AI Quote Automation and canceled-project case study.
• Monish Nallagondalla: portfolio and Nirixa.
• Salil Harit: product portfolio.
• Sanjit Kangovi: Verdict and Redline.

Sources reviewed September 8, 2026.