# Why NOW is the Golden Era to build AI apps.

Source: https://www.youtube.com/watch?v=3XVDtPU8xKE
Recap page: https://rapidrecap.app/video/3XVDtPU8xKE
Generated: 2026-01-19T17:32:23.82+00:00

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## Quick Overview

The current AI era represents the fourth major product cycle, following the PC, Internet, and Mobile eras, and it is a golden era for building AI applications because it builds upon the existing infrastructure of smartphones and cloud computing, enabling unprecedented adoption and net new revenue generation in both application and infrastructure layers.

**Key Points:**
- The AI era builds upon the infrastructure established by the Mobile era, noting that the vast majority of humans now possess smartphones, which is crucial for adoption.
- The pace of innovation is remarkable, exemplified by the fact that 15% of adults on the planet now use ChatGPT every single week as part of their daily routine.
- Enterprises are moving past the 'magic trick' phase of AI (like GPT-3.5/4) into real enterprise deployment, saving time and money, as evidenced by a tick up in usage data from companies like Ramp.
- Foundational to investing in this space are three categories: traditional software going AI native, software eating labor by creating entirely new categories, and building proprietary data models (the 'walled garden').
- For applications replacing labor, the value proposition shifts from saving money to generating revenue, as seen with Saliant, which collects 50% more revenue in auto loan servicing.
- Defensibility relies on becoming a 'system of record' or creating a proprietary data moat, as demonstrated by Eve in legal AI, whose unique case outcome data compounds its competitive advantage.
- The best companies have 'hostages, not customers,' meaning they build sticky, end-to-end workflows or proprietary data models that incumbent providers cannot easily replicate.

**Context:** Alex Rampel of the Apps Fund frames the current technological moment as the fourth major product cycle, following the PC, Internet, and Mobile eras, by analyzing the NASDAQ performance over time. This presentation argues that the convergence of massive smartphone penetration and mature cloud infrastructure has created the perfect foundation for the AI era to unlock significant growth and value creation across software development, echoing historical patterns where product cycles drive economic expansion.

## Detailed Analysis

Alex Rampel asserts that the AI era is the golden era for building applications because it leverages prior technological foundations, specifically the global deployment of smartphones and cloud services, leading to explosive adoption rates where 15% of adults use ChatGPT weekly. He contrasts this with earlier technologies, noting that AI is generating significant net new revenue across both application and infrastructure layers. Rampel outlines three compelling investment themes: traditional software becoming AI native, software directly replacing labor (a much larger market), and building businesses around proprietary data models, or 'walled gardens.' He emphasizes that while incumbents will adopt AI, startups succeed by targeting 'greenfield' opportunities or inflection points where incumbents are weak, citing Mercury as a greenfield success against Silicon Valley Bank. For software replacing labor, the focus is on value generation, not just cost savings; Saliant grows explosively by collecting 50% more revenue in loan servicing, not just reducing call center costs. Defensibility is paramount, requiring companies to become systems of record or possess unique data moats, like Eve in legal tech using proprietary case outcome data to compound advantage, making them essential tools that cannot be easily displaced by generic AI tools like standard ChatGPT.

### Historical Product Cycles

- The four major cycles identified are the PC, Internet, Cloud, and Mobile eras, with the AI era currently emerging on top of the mobile and cloud infrastructure.

### AI Adoption and Value

- Adoption is taking off like never before, with 15% of adults using ChatGPT weekly; the magic trick phase is transitioning to enterprise deployment saving time and money.

### Investment Themes for Enduring Companies

- Three focus areas are traditional software going AI native (targeting greenfield opportunities over brownfield competition)
- software eating labor by creating new categories
- and building proprietary data models (walled gardens).

### Software Eating Labor Dynamics

- This new category targets jobs where software can do 90% of the human work; success hinges on becoming a sticky system of record rather than just offering a slight cost reduction.

### Defensibility and Moats

- Moats matter more than ever because software creation is faster; defensibility comes from owning the end-to-end workflow (like Eve) or possessing unique, non-public data (like Saliant's statute ingestion).

### The Walled Garden Strategy

- This involves building value around proprietary data sources, even if the raw data is free (e.g., FlightAware's ADS-B data, PitchBook's historical funding data), turning raw material into a finished, high-value product.