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Can AI Startups Outrun Dot-Com Bubble Comparisons? Investors Aren’t So Sure.

🌈 Abstract

The article discusses the concerns and caution among investors regarding the current artificial intelligence (AI) startup boom, drawing parallels to the dot-com bubble of the late 1990s. It highlights the growing number of AI-focused startups exhibiting at the Collision conference, and the investors' shifting focus towards startups with sustainable business models, corporate problem-solving products, and access to unique data sources to train AI models.

🙋 Q&A

[01] Fears of the AI Startup Boom

1. What are the key concerns that investors have regarding the current AI startup boom?

  • Investors are wary of repeating the mistakes of the dot-com bubble, where every venture capital firm needed to have a bet in the space, leading to inflated costs and a "messy" situation.
  • There is a sense of déjà vu, as investors see similarities between the current AI boom and the dot-com boom, where every startup claimed to be "useful" but many ultimately failed.
  • Investors are increasingly looking for startups with long-term viability, products that solve corporate business problems, and access to unique data sources to train AI models.

2. How does the current AI startup landscape compare to the dot-com era?

  • The dot-com boom got "messy" because every venture capital firm needed to have a bet in the space, leading to inflation in costs such as hiring and office space.
  • A similar dynamic is playing out now with the AI boom, where a rush to fund AI startups has created a lot of "noise" in the market.
  • Training large language models at the scale of OpenAI requires significant investment in computing and AI chips, making it difficult for new startups to be competitive in this area.

[02] Trends in AI Startup Funding

1. What are some examples of the significant funding and investments in AI startups?

  • Mistral AI raised $650 million earlier this month at a roughly $6 billion valuation.
  • Amazon invested $2.75 billion in Anthropic in March, bringing its total investment in the AI company to $4 billion.
  • CoreWeave, an AI computing startup, raised $7.5 billion in private-debt financing in May.
  • Investors put $21.8 billion into generative AI deals last year, up fivefold from the prior year, with an average round size of $51 million, compared to the industry average of $8 million.

2. What are the key trends observed in the AI startup landscape at the Collision conference?

  • A large number of startups are working on products that do the same thing as existing AI models, offering little differentiation.
  • Some startups are just "a pretty interface on top of a large language model," such as those offering products to help companies analyze their invoices.
  • There is a need for startups to build "autonomous agents" or virtual AI workers that can perform specific tasks on behalf of humans, as this technology is developing quickly.
  • Investors are more interested in startups that can offer the ability to search across databases using AI and help companies use multiple AI models, rather than those with general AI capabilities.
Shared by Daniel Chen ·
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