Everyone Is Shipping AI SaaS. Here’s Why Most Will Fail.
Everyone is racing to slap “AI” on a SaaS and ship it. Open X, Product Hunt, or any launch Discord and it feels like the same product is being released ten different times with a new logo and price tag. And yet, if you check back six months later, most of those “AI-powered” startups are either dormant or gone.
Everyone is shipping AI SaaS. Most will fail.
Not because AI is a fad, it isn’t - but because the way many founders are building on AI is fundamentally fragile.
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The AI wrapper trap
The biggest reason most AI SaaS dies: they’re not really products. They’re wrappers.
A wrapper is a thin UI or workflow sitting directly on top of a commodity model like GPT‑5 or Claude. Same underlying intelligence, slightly different packaging. Investors and founders have started calling these “LLM wrappers,” and there’s already fatigue around them.
The problem is simple:
OpenAI ships a new feature → your differentiation vanishes overnight
Prices drop or APIs change → your margins evaporate
Platform bakes your core feature in natively → users abandon you for the default
If the only thing between your user and GPT‑5 is your website, you don’t own a business. You own an integration.
Wrappers disappear. Products with real workflows and moats survive.
Zero moat in a world of infinite builders
Traditional SaaS at least had some friction: you needed engineers, capital, time.
In 2025, a solo developer can ship a basic AI SaaS in a weekend. That’s great for experimentation - but brutal for defensibility. Whole categories are already overcrowded:
Generic chatbots
“AI note takers”
Simple text summarizers and caption generators
When 500 tools do the same thing with the same model, customers default to:
The cheapest option
The one with the best brand
Or the one bundled into something they already pay for
If you’re #497 in the category, you’re not competing. You’re decorating the long tail.
Bad economics hiding behind “AI”
Another unsexy reason many AI SaaS startups will fail: unit economics.
Calling it “AI” doesn’t change the math:
You’re paying per token, per image, or per minute to your model provider
You’re adding infra, support, and marketing on top
You’re often underpricing because “it’s just an app” and everyone else is racing to the bottom
Many “AI tools” quietly run with SaaS pricing but services‑like gross margins - or worse. When OpenAI or Anthropic raise prices, or when users start hammering your app more than expected, the economics break.
A lot of founders are subsidizing their users without realizing it.
No real workflow = no real value
The best AI products don’t just “generate content” or “answer questions.” They sit inside a workflow and remove friction at a specific point.
Most failing AI SaaS does something like:
“Paste your text, click generate, copy the result.”
That’s a toy, not a product.
The tools that are winning embed themselves where work already happens:
Inside CRMs, ticketing tools, or IDEs
Inside support or sales workflows, not next to them
Triggered by real events (new lead, new ticket, new deploy), not by “user feels like trying AI today”
If your user has to stop their work, open your app, paste things in, and then figure out what to do with the output, you’ve increased friction - not reduced it.
Chasing hype instead of pain
A pattern you see in AI launches:
Founder plays with a new model
Sees a cool demo
Builds a product around the demo
What’s missing? A painful, expensive problem.
In 2025, founders are drowning in “cool AI demos” but buyers are still thinking in boring terms:
“Does this reduce my cost?”
“Does this increase my revenue?”
“Does this lower my risk?”
Investors are already pushing back on AI wrappers that don’t solve hard problems or integrate deeply into existing systems. Users will be even harsher.
So what actually works?
This isn’t a “don’t build AI” rant. It’s the opposite.
AI is the new electricity. But electricity didn’t win because people made “electric wrappers.” It won because people reinvented industries with it.
Here’s what the more resilient AI SaaS companies tend to have in common:
1) A narrow, painful problem
They don’t try to be “AI for everything.” They pick a vertical or role (dentists, recruiters, QA engineers) and solve a problem that already hurts and already has a budget.
2) Deep workflow integration
They plug into the tools customers already use - CRMs, ERPs, ticketing systems and automate steps that were previously human bottlenecks. They don’t ask users to change behavior; they remove steps from existing behavior.
3) Proprietary data or process
They add something the base model doesn’t have:
Domain‑specific data
A unique process or methodology
A feedback loop that improves the product over time
Even if the underlying model changes, the system remains valuable.
4) Healthy economics
They understand their true cost per active user and price for value, not vibes. They design features that are cheap to run and expensive to replace, not the other way around.
7. How to avoid being “just another AI SaaS”
If you’re building (or about to build) something in this space, here’s a quick filter:
If OpenAI shipped your core feature tomorrow, would you still have a business?
If not, rethink.Can you describe your product without saying “AI” or “GPT”?
“We reduce support resolution time by 40% for Shopify stores” is better than “AI for support.”Does it live where your users already work?
If it’s yet another separate tab, expect drop‑off.Could a smart intern rebuild your MVP in a weekend?
If yes, your moat isn’t the code.Do customers care if the AI is there, or do they care about the outcome?
Build for the outcome. AI is just the engine under the hood.
If you’re building in this space, build something that would still matter even if the letters “AI” disappeared from your landing page tomorrow.
See you next time,
Better Every Day
