Where Are The AI Startups? — With Rick Heitzmann - Big Technology Podcast Recap

Podcast: Big Technology Podcast

Published: 2025-10-15

Duration: 59 min

Summary

In this episode, Rick Heitzmann discusses the stagnation of AI startups despite the potential of generative AI technologies like ChatGPT. He explores the reasons behind this phenomenon and the challenges startups face in a landscape dominated by powerful players.

What Happened

The episode opens with host Alex interviewing Rick Heitzmann, the managing partner of FirstMark Capital, to discuss the apparent absence of new AI startups amidst the generative AI boom. While there are successful models like ChatGPT and Claude, Heitzmann notes that the surge of startups expected to build on generative AI has not materialized. He attributes this to the quality of existing products and the challenges of data differentiation, which are critical for startups aiming to create innovative solutions.

Heitzmann highlights that OpenAI has maintained a competitive edge by continuously improving its offerings, making it challenging for new entrants to carve out a niche. The conversation shifts to specific applications of AI in various industries, with examples like Harvey for legal services and Evolution IQ for insurance. These companies leverage unique datasets to enhance their models, a luxury not afforded to consumer-focused applications that rely on broad web data. Heitzmann expresses frustration over the lack of sustainable startups, noting that many emerging companies are merely add-ons to existing platforms rather than groundbreaking innovations.

Key Insights

• OpenAI's strong product development makes it hard for startups to compete.
• AI startups require unique datasets to build effective applications.
• There is a frustration with the lack of innovative consumer AI startups.
• Vertical search models in AI have not seen the same success as broad applications.

Key Questions Answered

Why haven't we seen a surge of AI startups recently?

Heitzmann points out that while generative AI is considered transformative, the expected wave of startups hasn't occurred due to the dominance of existing platforms like ChatGPT. These leading products have set a high bar for quality and functionality, making it difficult for new entrants to gain traction.

What role does data play in the success of AI startups?

Heitzmann emphasizes that the effectiveness of AI solutions largely depends on the quality of the underlying data. Startups with access to unique or private datasets, like those in the legal or insurance sectors, can build better models compared to those relying on general web data.

What examples of successful AI applications did Heitzmann mention?

Heitzmann references several specialized applications, such as Harvey in the legal field and Evolution IQ in insurance, which leverage discrete and sometimes proprietary data sets to deliver superior outcomes. These examples illustrate the potential for AI in specific sectors, contrasted with the broader consumer applications that struggle to differentiate.

Is there potential for new consumer AI startups?

Despite the potential for innovative consumer AI applications, Heitzmann expresses skepticism about the prospects of new startups emerging. He notes that many current offerings are merely enhancements to existing platforms, lacking the distinctiveness needed for sustainable growth.

How do vertical search models relate to AI startups?

Heitzmann draws parallels between traditional vertical search companies like Indeed and Kayak, suggesting that while these models achieved significant success, the landscape for AI startups looks different. The dominance of broad applications like ChatGPT complicates the emergence of new vertical solutions, making it challenging for startups to establish themselves.