The era of basic AI is over. Product Managers, it’s time to level up. We’ve seen the demos, played with the chatbots, and scratched the surface of what AI can do. But the real game-changer is building AI that proactively assists, optimizes, and anticipates user needs across every aspect of their work. Want to know how to build that kind of next-gen AI product? Listen closely to David Shim, CEO of Read.ai. In a recent Startup Project interview, Shim laid out the roadmap, not just for better meeting summaries, but for a future where AI is a true “co-pilot for everyone, everywhere.” This isn’t just a vision; it’s a $50 million Series B-backed reality. Product Managers, the future of productivity is being built now – are you ready to lead the charge?
Read.ai, initially known for its AI meeting summarizer, harbors a much grander vision: to be a “co-pilot for everyone, everywhere.” This ambition, backed by a recent $50 million Series B raise, isn’t just about better meeting notes; it’s about fundamentally rethinking how AI can augment human productivity across all facets of work and life. For product managers eager to build truly impactful AI products, Shim’s journey and insights are invaluable.
Start with the Problem, Not Just the Technology:
Shim’s story of Read.ai’s inception is a powerful reminder for product managers. It didn’t begin with a fascination with large language models (LLMs) or the latest AI breakthroughs. It started with a personal pain point: the agonizing realization of being stuck in unproductive meetings. “Within two or three minutes of a call, you know if you should be there or not… but now I turned off my camera. I cannot leave this meeting,” Shim recounts.
This relatable frustration became the seed for Read.ai. For product managers, this underscores a crucial principle: innovation begins with identifying a genuine problem. Don’t get swept away by the hype of new technologies. Instead, deeply understand user needs, frustrations, and inefficiencies. What are the “meetings” – metaphorical or literal – where your users are feeling stuck and unproductive?
Unlocking Unconventional Data for Deeper Insights:
Most AI products today heavily leverage text data. Read.ai, however, took a different path, recognizing the untapped potential of video and metadata. Shim’s “aha!” moment came from observing reflections in someone’s glasses during a virtual meeting, sparking the idea to analyze video for sentiment and engagement.
This highlights a critical lesson for product managers: look beyond the obvious data sources. While text transcripts are valuable, they are just one layer of the story. Consider the rich data exhaust often overlooked – video cues, metadata like speaking speed, interruption patterns, response times to emails and messages. As Shim points out, “large language models don’t pick up” on the crucial reactions and non-verbal cues that humans instinctively understand.
By incorporating this “reaction layer,” Read.ai’s summaries became materially different and more human-centric, highlighting what truly resonated with participants based on their engagement, not just the words spoken. For product managers, this means thinking creatively about data. What unconventional data sources can you leverage to build richer, more insightful AI experiences?
Hybrid Intelligence: Marrying Traditional and Modern AI:
Read.ai’s architecture is not solely reliant on LLMs. In fact, Shim reveals that “90% of our processing was our own proprietary models” last month. They strategically use LLMs for the “last mile” – for generating readable sentences and paragraphs – after their proprietary NLP and computer vision models have already done the heavy lifting of topic identification, sentiment analysis, and metadata extraction.
This hybrid approach is a powerful strategy for product managers. It emphasizes the importance of building core intellectual property rather than solely relying on wrapping existing foundation models. While LLMs are powerful tools, defensibility often lies in unique data processing, specialized models for specific tasks, and innovative feature combinations.
Product-Led Growth and Horizontal Market Vision:
Read.ai’s explosive growth, adding “25,000 to 30,000 net new users every single day without spending a dollar on media,” is a testament to the power of product-led growth (PLG). This PLG engine is fueled by the inherently multiplayer nature of meetings. When one person uses Read.ai in a meeting, everyone present experiences its value, organically driving adoption.
Furthermore, Read.ai consciously chose a horizontal market approach, resisting the pressure to niche down initially. Shim’s belief that “this is more mainstream… from an engineer at Google leads it to a teacher to an auto mechanic” proved prescient. Their user base spans diverse industries and geographies, highlighting the broad applicability of their co-pilot vision.
For product managers, this demonstrates the power of designing for virality and considering broad market appeal, especially when building truly transformative products. Sometimes, focusing too narrowly early on can limit your potential impact.
The Co-pilot Everywhere Vision and the Future of Optimization:
Read.ai’s evolution from meeting notes to a “co-pilot everywhere” reflects a profound shift in AI’s role in productivity. It’s not just about generating content; it’s about optimization, action, and seamless integration into existing workflows. Shim envisions a future where Read.ai “pushes” insights to tools like Jira, Confluence, Notion, and Salesforce, and also “pulls” data from various sources to provide a unified, intelligent work assistant.
This vision aligns with the emerging trend of AI agents. However, Shim emphasizes that the real power lies in practical integrations and seamless data flow between different work platforms, rather than just standalone agents. “You want your JIRA to talk with your Notion, to talk with your Microsoft, to talk with your Google, and talk with your Zoom,” he explains.
For product managers, this means thinking beyond single-feature AI products. The next wave of innovation will be in building interconnected, optimized AI systems that proactively assist users across their entire workflow. It’s about moving from “draft AI” – generating content – to “optimization AI” – driving action and improving outcomes.
Key Takeaways for Product Managers Building Next-Gen AI Products:
- Focus on Real Problems: Start with genuine user pain points, not just technological possibilities.
- Explore Unconventional Data: Look beyond text for richer, more nuanced insights.
- Embrace Hybrid AI Architectures: Combine proprietary models with LLMs for defensibility and specialization.
- Design for Product-Led Growth: Leverage inherent network effects and broad market appeal.
- Vision Beyond Content Generation: Aim for optimization, action, and seamless integration into workflows.
- Prioritize Value over Hype: Build solutions that deliver tangible ROI and improve user lives.
- Iterate and Adapt: Constantly learn from user feedback and market dynamics to evolve your product.
David Shim and Read.ai’s journey offer a compelling blueprint for product managers aiming to build the next generation of AI products. By focusing on genuine user needs, leveraging unconventional data, and envisioning a future of optimized, interconnected AI, product leaders can unlock the true potential of AI to transform the way we work and live.
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.
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