🙋‍♂️ The AI product market is overcrowded—not with effective tools, but with promises.

If you have an idea to solve a problem with AI and see there are already 10 companies addressing it, chances are most of them haven’t gotten it right.

Many people talk about implementing AI, but few address the specific, tough problems you encounter in production—like reliably testing flows that include LLMs, systematizing prompt engineering, or preventing drift when underlying models change.

Popular frameworks are often used for proofs of concept but rarely make it to production because they have overly complicated abstractions or very little documentation on deployment.

There are endless debates on POCs level topics while very few address the real challenges of scaling and maintaining AI in production.

It’s also common to see startups receiving significant funding for ideas that have already been promised as solved by 10+ other seemingly serious companies.

The AI market isn’t as saturated as it seems. It’s crowded with ideas, not effective solutions. The winners will be those who focus on practical, reliable tools that can tackle these real-world challenges.

We’ve seen this pattern before across other industries. ERP and CRM systems were once hyped as the ultimate solutions for business automation. By 2001, 51% of companies viewed their ERP implementations as unsuccessful. Failure rates were estimated between 40% and 60%, and by 2013, only 31% of organizations achieved 50% or more of the anticipated benefits.

The wearable tech industry was initially expected to revolutionize healthcare and fitness tracking, but devices struggled with accuracy, battery life, and user comfort. The market, didn’t explode as anticipated.

Same for low/no code platforms: Gartner predicted that by 2025, 70% of new apps would use low/no code platforms. Though, 74% of IT leaders report that these platforms are too limited for most enterprise needs, and 79% of enterprises say that citizen developers lack the necessary skills to use these tools effectively.

The same story played out in the smart home market during the 2010s.

“If you have competitors it means there is a market”, yes. But in AI, because of the low barrier to entry, where most products are just API calls to LLMs, many competitors don’t even have a working solution. It’s not about refining; it’s about whether the product actually works or is just another overhyped promise.