Where's All The Fun AI Stuff for Consumers?

💡Why AI is B2B-First

💸 1. The Cost of Compute is High, and Consumers Won’t Pay

The upfront investment (before we can even prove a product-market fit is way higher than in web or mobile) - a $10/month subscription doesn’t nearly cover the GPU costs needed to train and run LLMs. Startups can’t cover OR pass on the cost of the full tech stack.

You might say, but they can just use OpenAI’s APIs. Yes, that might be true but please keep reading.

🏢 2. Enterprise Pain Points Are Obvious and Valuable

Business outcomes (cost savings, efficiencies etc.) are measurable. The investment is justifiable with clear ROI and not as much of a gamble.

Tasks that GenAI eliminates first:

• Repetitive workflows

• Document overload

• Customer support

• Internal data/document organization

It’s proven that GenAI eliminates these immediately. Legal teams use Harvey, and support teams use Intercom Fin. These are not experiments anymore; they are clear business cases.

🔀 3. The Cost To Value Gap Difference

Back In My Days…

Back in the Web and Mobile days, a scrappy team could build and ship something to test their product-market-fit for next to nothing. You get $100 of AWS credits and you are well on your way to an MVP.

If it works, you add some more credits and scale your infra or start building it to your liking. Investors love it.

With AI, it’s a different game.

You can still get a simple idea tested for very little upfront cost. I call these new AI startups API Consumer Startups. They are basically API wrappers. They can be very useful, but they completely rely on OpenAI, Anthropic, or other existing models.

How will they scale? How will they fine-tune the models? How can they differentiate? And thus, these startups have a hard time building a real moat.

So even if the product sticks, they need to train a model, which costs billions.

I compare it to an Amazon FBA seller who uses Amazon’s platform to sell products. They risk Amazon taking them offline, others copying their product, and being fully dependent on someone else’s service. It’s a huge risk that investors don’t want to take on.

🔥 4. Enterprise Is Where AI Gets Real

So, Enterprise might be the current sweet spot. AI starts with data. Big companies have (mostly) clean data. They have real workflows with process documentation. They understand ROI and business cases.

That makes them ideal for AI adoption today. You can also test in controlled environments and measure impact in isolation. And if everything looks good, they have the budget to invest in infrastructure to scale.

So while consumers wait for AI to change their world, it’s already rewriting the enterprise playbook. And that’s where startups are chasing glory right now.