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The Full Ratchet

496. How Model Progress Shifts the Goalposts, Why The Death of Software Is Overstated, and How to Diligence Hypergrowth Without Getting Burned (Jacob Effron)

37 min episode · 2 min read
·
Jacob Effron

Episode

37 min

Read time

2 min

Topics

Productivity, Health & Wellness, Relationships

AI-Generated Summary

Key Takeaways

  • AI Growth Benchmarks: Traditional triple-triple-double-double-double growth is no longer exceptional for AI companies. Reaching 100 million ARR in under three years has become the new standard for top-tier AI product-market fit, given how rapidly AI solutions are being adopted across industries and enterprises today.
  • Model Progress Focus: Foundation models show strongest improvements in coding and math through reinforcement learning, but future progress will be domain-specific rather than universal. Application companies now develop their own evaluation benchmarks that matter more than standardized tests for measuring real-world model performance in specialized verticals.
  • Vertical AI Defensibility: Data moats are overestimated in AI. Fine-tuning and reinforcement learning require less proprietary data than expected. Defensibility comes from connecting ecosystem stakeholders, strategic partnerships, funding velocity enabling broader product development, and deeply understanding domain-specific evaluation metrics that drive product quality in healthcare, legal, and logistics.
  • Diligencing Hypergrowth: When evaluating AI companies growing at extreme rates, prioritize team velocity to adapt quickly, identify persistent customer needs that survive 10x model improvements, and focus on landing with customers today to evolve together rather than predicting future needs from an ivory tower without deployment learnings.

What It Covers

Jacob Effron from Redpoint discusses AI model progress, evaluating hypergrowth startups in the AI era, vertical AI applications in healthcare and legal, agentic AI development, and why traditional T2D3 growth metrics no longer indicate exceptional product-market fit.

Key Questions Answered

  • AI Growth Benchmarks: Traditional triple-triple-double-double-double growth is no longer exceptional for AI companies. Reaching 100 million ARR in under three years has become the new standard for top-tier AI product-market fit, given how rapidly AI solutions are being adopted across industries and enterprises today.
  • Model Progress Focus: Foundation models show strongest improvements in coding and math through reinforcement learning, but future progress will be domain-specific rather than universal. Application companies now develop their own evaluation benchmarks that matter more than standardized tests for measuring real-world model performance in specialized verticals.
  • Vertical AI Defensibility: Data moats are overestimated in AI. Fine-tuning and reinforcement learning require less proprietary data than expected. Defensibility comes from connecting ecosystem stakeholders, strategic partnerships, funding velocity enabling broader product development, and deeply understanding domain-specific evaluation metrics that drive product quality in healthcare, legal, and logistics.
  • Diligencing Hypergrowth: When evaluating AI companies growing at extreme rates, prioritize team velocity to adapt quickly, identify persistent customer needs that survive 10x model improvements, and focus on landing with customers today to evolve together rather than predicting future needs from an ivory tower without deployment learnings.

Notable Moment

Effron challenges the conventional wisdom that vertical software requires domain expertise, noting AI has made it dramatically easier for technical founders to access decision-makers at hospitals and law firms who previously only met with industry veterans, fundamentally changing the founder archetype equation.

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Episode Transcript

This episode of TFR is brought to you by Ramp, the spend management platform we use here at TFR. They're offering listeners a $150 just to take a demo. We've never had an offer quite like this. Claim your $150 before this offer is gone at our partner link, ramp.com/partner/tfr. And this episode of TFR is brought to you by the American Arbitration Association, where smart startups and investors turn to for fast, efficient, and cost effective dispute resolution. Visit adr.org/tfr to learn more. Welcome to the podcast about venture capital, where investors and founders alike can learn how VCs make decisions and reach conviction. Your host is Nick Moran, and this is the full ratchet. Jacob Efron joins us today from New York City. He's an MD at Redpoint, an early and mid stage firm focused on AI enterprise software and health care. Before Redpoint, Jacob spent time at Flatiron Health, OffGrid Electric, companies including Abridge, Logura, Augment, Physical Intelligence, Ramp, and Garner to mention a few. Jacob, welcome to the show. Thanks so much for having me, Nick. Really excited to be here. Yeah. Excited to have you. So tell us a bit about your backstory and your path to becoming a VC. Yeah. Well, I I think you got the the the kind of basics there. You know, essentially, started my career working at a a few startups post consulting. I was over at Flatiron Health, on the product side. Flatiron got acquired and I was thinking about what I wanted to do with my life post acquisition. Had always been curious about venture and had had exposure to a bunch of different VCs and and tech companies through that startup work and had the good fortune of meeting the Redpoint folks. Ended up joining five and a half years ago as a as a vice president initially, and it's been a pretty amazing ride since then. Awesome. And tell us more about the thesis at Redpoint. We've featured some folks in the past, but would love to hear the update. Yeah. So we have two funds at Redpoint, an early stage fund and then what we call our early growth fund. I sit on the early growth side. It sounds like an oxymoron, but what it basically means is series b is kind of the majority of what we do. And the way we think of it is we wanna be investing post product market fit, but at the earliest signs of inflection possible. So the goal is to to be a half step before something becomes obvious in the numbers. We're obviously looking for iconic independent companies that will be the big names of tomorrow. You know, over the years, we've led rounds in companies like Snowflake and Stripe and Twilio and HashiCorp, Abridge, Legora, some of the other ones you mentioned. But we're pretty broad in the in the mandate we have and and trying to find those n of one special companies before …

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