What obsessing over team alignment taught me - Sahil Jain

In this episode of The Product Experience, Lily Smith speaks with Sahil Jain, co-founder and CEO of Samepage.ai, about one of product management’s hardest challenges: keeping teams aligned.

From his early career at Yahoo and AOL to founding multiple startups, Sahil shares lessons on building products that tackle “unsolvable” problems like communication and alignment. He explains why shared understanding matters more than speed, how product managers can become better storytellers, and why early-stage startups should obsess over just a handful of teams before chasing scale.

Chapters

• 0:00 – Why alignment is so hard
• 1:14 – Sahil’s unconventional career path
• 4:00 – First foray into startups at AOL and beyond
• 6:50 – Founding AdStage and lessons from raising early capital
• 9:00 – Moving into product leadership after acquisition
• 12:53 – On delusion, motivation, and tackling “unsolvable” problems
• 16:34 – Starting Samepage.ai and the problem of information asymmetry
• 22:43 – Validating the problem and testing prototypes
• 27:22 – Why product managers are the perfect early adopters
• 29:20 – The first 10 obsessed teams: startup focus
• 34:00 – Neurodivergence, communication, and shared understanding
• 36:43 – From Claude Shannon to storytelling: frameworks for better communication
• 39:59 – Lessons from Duolingo on multimodal learning
• 41:19 – Where to find Samepage.ai

Key Takeaways

1. Alignment is harder than communication: Efficient tools don’t guarantee shared understanding.
2. Storytelling is underrated: Stories activate more of the brain than facts, making them up to 22 times more memorable.
3. PMs as communication hubs: Product managers juggle multiple stakeholders, and great ones translate goals into team-specific language.
4. Founding lessons: Sahil argues that naivety and a “chip on your shoulder” can be startup superpowers.
5. Early-stage focus: Rather than chasing scale, Samepage is laser-focused on winning the obsession of just 5–10 teams.
6. AI reality check: Large language models are powerful but face the “trough of disillusionment”—the real challenge is extracting production value.