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Invest Like the Best with Patrick O'Shaughnessy

Jesse Zhang - Building Decagon - [Invest Like the Best, EP.443]

81 min episode · 2 min read
·

Episode

81 min

Read time

2 min

Topics

Career Growth, Productivity, Startups

AI-Generated Summary

Key Takeaways

  • Finding Product-Market Fit: Ask potential customers exact pricing they would pay for solutions during discovery calls, then probe deeper on approval processes, ROI calculations, and budget allocation. This systematic qualification process revealed customer service had 10x higher willingness to pay than other AI use cases explored.
  • Enterprise AI Deployment Strategy: Start with 5% of conversation volume, monitor resolution rates and customer satisfaction metrics for one week, then rapidly scale to full deployment within weeks. The built-in escalation path to human agents reduces risk and enables fast enterprise adoption compared to other AI use cases.
  • Voice-to-Voice Model Challenges: Direct voice-to-voice AI models have 8x higher hallucination rates than text-based approaches but offer superior latency and emotional understanding. Current enterprise solutions convert voice to text for accuracy checks, sacrificing some naturalness for reliability until voice models improve substantially.
  • Forward-Deployed Engineer Economics: The forward-deployed engineering model only works economically with clients paying minimum $1 million annually, not the $50,000 deals many startups pursue. Scaling requires building products that don't need dedicated engineers per customer, as hiring constraints prevent rapid growth otherwise.
  • AI Cost Margin Philosophy: Run zero or slightly positive gross margins on AI inference costs today rather than optimizing prematurely, because exponential improvements in model efficiency and declining compute costs will naturally improve economics. Focus engineering time on customer acquisition and product quality instead of margin optimization.

What It Covers

Jesse Zhang, CEO of Decagon, explains building AI customer service agents that automate support conversations, achieving product-market fit through systematic customer discovery, competing in intense AI talent wars, and scaling enterprise deployments with 500-person call centers.

Key Questions Answered

  • Finding Product-Market Fit: Ask potential customers exact pricing they would pay for solutions during discovery calls, then probe deeper on approval processes, ROI calculations, and budget allocation. This systematic qualification process revealed customer service had 10x higher willingness to pay than other AI use cases explored.
  • Enterprise AI Deployment Strategy: Start with 5% of conversation volume, monitor resolution rates and customer satisfaction metrics for one week, then rapidly scale to full deployment within weeks. The built-in escalation path to human agents reduces risk and enables fast enterprise adoption compared to other AI use cases.
  • Voice-to-Voice Model Challenges: Direct voice-to-voice AI models have 8x higher hallucination rates than text-based approaches but offer superior latency and emotional understanding. Current enterprise solutions convert voice to text for accuracy checks, sacrificing some naturalness for reliability until voice models improve substantially.
  • Forward-Deployed Engineer Economics: The forward-deployed engineering model only works economically with clients paying minimum $1 million annually, not the $50,000 deals many startups pursue. Scaling requires building products that don't need dedicated engineers per customer, as hiring constraints prevent rapid growth otherwise.
  • AI Cost Margin Philosophy: Run zero or slightly positive gross margins on AI inference costs today rather than optimizing prematurely, because exponential improvements in model efficiency and declining compute costs will naturally improve economics. Focus engineering time on customer acquisition and product quality instead of margin optimization.

Notable Moment

Zhang reveals that during customer discovery calls, some prospects claimed to use Decagon when the company had never heard of them, demonstrating how investor due diligence through expert networks generates unreliable signal despite being the primary method VCs use to underwrite AI companies.

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Episode Transcript

Here's an interesting question to think about. If your finance team suddenly had an extra week every month, what would you have them work on? Most CFOs don't know because their finance teams are grinding it out on lost expense reports, invoice coding, and tracking down receipts until the last possible minute. That's exactly the problem that Ramp set out to solve. Looking at the parts of finance everyone quietly hates and asking why are humans doing any of this? Turns out they don't need to. Ramp's AI handles 85% of expense reviews automatically with 99% accuracy, which means your finance team stops being the department that processes stuff and starts being the team that thinks about stuff. Here's the real shift. Companies using Ramp aren't just saving time, they're reallocating it. While competitors spend two weeks closing their books, you're already planning next quarter. While they're cleaning up spreadsheets, you're thinking about new pricing strategy, new markets, and where the next dollar of ROI comes from. That difference compounds. Go to ramp.com/invest to try Ramp and see how much leverage your team gains when the work you have to do stops getting in the way of the work that you want to do. To me, Ridgeline isn't just a software provider. It's a true partner in innovation. They're redefining what's possible in asset management technology, helping firms scale faster, operate smarter, and stay ahead of the curve. I wanna share a real world example of how they're making a difference. Let me introduce you to Brian. Brian, please introduce yourself and tell us a bit about your role. My name is Brian Strang. I'm the technical operations lead, and I work at Congress Asset Management. How would you describe your experience working with Ridgeline? Ridgeline is a technology partner, not a software vendor, and the people really care. I get sales calls all the time, and I ignore them. Ridgeline sold me very quickly. We went from 7,000,000,000 to 23,000,000,000, and the goal is 50,000,000,000. Ridgeline was the clear front runner to help us scale. In your view, what most distinguishes Ridgeline? They reimagined how this industry should work because obviously they were operating on another level. It's worth reaching out to Ridgeline to see what the unlock can be for your firm. Visit ridgelineapps.com to schedule a demo. One of the hardest parts of investing is seeing what's shifting before everyone else does. AlphaSense is helping investors do exactly that. You may already know AlphaSense as the market intelligence platform trusted by 75% of the world's top hedge funds, providing access to over 500,000,000 premium sources from company filings and broker research to news, trade journals, and over 200,000 expert transcript calls. What you might not know is that they've recently launched something game changing, AI powered channel checks. Channel checks give you a real time expert driven perspective on public companies, weeks before they show in earnings or consensus revisions. AlphaSense uses an AI interviewer to run …

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