Every time OpenAI launches something new, another 100 AI startups quietly die.
The reason is simple: too many founders are making the same mistakes, building products that lack defensibility or staying power.
Here are three common mistakes I keep seeing and what I would build instead.
Hard-Coded Prompts
Many products rely on hard-coded prompts like:
- “Take a deep breath and think step by step…”
- “Act like a helpful financial assistant who works in a bank…”
If your product’s magic depends on a specific prompt behaving a specific way, it is fragile. It will eventually break when the internal model weights change.
What to build instead: Prompt templates.
For example:
“Write a follow-up email to {{customer_name}} about {{product_feature}}”
This structure is far more stable because it is generic, reusable, and not dependent on quirks in the model’s behavior.
“Copilot for X” Wrappers
Another common pattern is the endless stream of “Copilot for X” products — wrapper apps that sit on top of a vector database to surface data.
The problem: foundational model providers are creeping into the application layer. They do not care about protecting your business or mine.
What to build instead: Agentic workflows.
Rather than sitting beside the work, sit inside the system doing the work with company-specific logic. For example:
When a finance manager submits a vendor contract, an AI agent could check if it is within budget, rewrite pricing and payment terms to match policy, attach required documents, and then send it to the first approver.
This type of workflow cannot be easily replaced by generic tools. It embeds directly into processes and creates lock-in.
Multi-Agent Reasoning Loops
You have probably seen the demos: one agent proposes a plan, another critiques it, a third rewrites it, and so on.
The issue is that, without grounding in actual results, this is just large language models arguing — or worse, hallucinating — on top of each other.
What to build instead: Self-learning agents.
A real system should track what actually happened:
- Did the email convert?
- Did the ticket close?
- Did the workflow succeed?
Then it should use those signals to refine its behavior.
For example, if the legal team repeatedly rejects certain clause structures, the agent learns the pattern, rewrites content based on who is reviewing, and adjusts over time to increase approval rates.
Conclusion
If your product is not embedded in the work, grounded in context, and learning from real outcomes, it is only one OpenAI announcement away from being replaced.
Here are the key takeaways from this article:
- Hard-coded prompts are fragile; prompt templates are more durable
- Wrappers like “Copilot for X” will fade; embedded agentic workflows create stickiness
- Multi-agent loops waste resources; self-learning agents grounded in outcomes create real value
- Defensibility in AI comes from context, integration, and continuous learning
The future belongs to startups that build systems tied to results, not gimmicks.