The AI Startup Graveyard

It’s never been more exciting to start an AI startup. But the graveyard is vast. Here’s what not to do:

Spencer Shulem and I studied dozens of AI startup failures and successes.

This is what we learned:

1. Falling for shiny object syndrome

When a shiny new model or tech drops, it’s tempting to pursue it. For example, Argo AI raised billions of dollars to build self-driving tech. But after 6 years, the company realized the tech wasn’t ready for public roads. Now, it’s gone.

Successful startups stay laser-focused on their target user and use case. For example, Anthropic has been working on its constitutional AI technology for years, despite many flashy new approaches emerging. That focus allowed them to make (one of) the best LLM(s) out there.

2. “It works in the lab”

Turning prototypes into products takes massive investments. Don’t make the Rabbit/Humane mistake: they had good demos and commercials, but the AI devices didn’t live up to the hype in the real-world. Now, both are headed to the graveyard.

Successful AI startups make demos replicable in reality. For instance, Cohere spent two years building a robust serving platform. This foundational work enabled their self-serve API to reliably handle billions of requests from day 1.

3. Irresponsible deployment

In the rush to market, many AI product teams fail to put adequate safeguards in place. Take Clearview AI. They scraped hundreds of millions of social media photos without consent. When the NYT exposed it, they got banned from selling to companies and folded.

On the other hand, teams like those at Perplexity AI pay especially close attention to Red Teaming. Their vigilance has allowed them to take share from Google, whose AI search has myriad examples of irresponsible outputs (like recommending the depressed to jump off a bridge).

4. Prioritizing flash over function

Many failed AI startups churn out flashy demos that generate reams of press, but don’t solve real problems. Remember Quixey? Their demos touted a deep learning-powered “search engine for apps.” Now, they don’t exist.

Successful startups like video AI tool Runway laser-focused on their users’ gnarliest problems. They went deep on discovery with video creators to find the workflows that burn hours and dollars. Then, they cut the time & cost by 10x.

5. Raising too much, too fast

VC can seem necessary as an AI founder. But have you heard the stories of Olive AI or Inflection? Each raised a billion or more without achieving product-market fit. Now, they barely exist.

On the other hand, successful startups like Cohere bootstrapped for 2 years before raising a $40M Series A. This allowed them to deeply validate their self-serve model and hit $1M ARR before taking on VC. With strong fundamentals in place, they could then scale with confidence.

So what SHOULD you do to build an AI startup? Our 5K word deep dive covers the key pillars.