Dan Haiem is the founder and CEO of AppMakers USA, helping business leaders design, build and scale apps that deliver real-world impact.

Like many companies, when AI began making noise in the app dev world, we started experimenting with it to streamline internal work. As we grew more comfortable, we began offering AI‑powered integrations to our clients. It felt like a natural evolution for us until we hit our first real roadblock.

We were in the middle of developing an AI-powered feature for a client when our model provider suddenly throttled our access during peak hours. Our entire sprint stalled for days because we couldn’t secure enough computing power to finish the job.

The experience was like shutting off the water when you’re trying to cook.

So, it became clear to me that AI infrastructure behaves like a supply chain. Modern app companies rely on GPUs, model capacity and inference availability the same way manufacturers rely on raw materials. And that system is showing strain because apps now depend on infrastructure outside our control.

The Real Reason AI Features Stall

Here’s what is happening now. Evidently, AI usage has grown fast across every industry, but infrastructure has not kept pace.

In fact, a report from Reuters states that the AI boom is causing shortages not only for high-end GPUs but also for memory chips that weren’t even considered "hot" before. This is true for DRAM chips, for example, where their inventory fell significantly in 2025.

Needless to say, the entire semiconductor chain is tightening because AI workloads pull in more hardware than expected, and traditional systems were not designed for this scale. Deloitte’s research backs this up. In a recent study, leaders said they expect AI workloads to jump by around 20% over the next year, and because the survey was done in March and April 2025, we’re already in that wave now.

Yes, traditional software once scaled without friction, but AI has changed that. Every AI feature introduces new operational dependencies, similar to the hardware supply chains manufacturers have managed for decades.

Strengthening The Build Process

As a CEO working with clients who request AI features more and more, I see three issues show up repeatedly: cost volatility, development delays and vendor dependence and risk. This is when I decided to rethink how we approached AI features, and it led us to make several changes inside our company.

What follows is the game plan we built. These are practical steps any tech team can adapt to stay steady when the AI supply chain wobbles:

1. Adding Multi‑Model Fallback Paths For New AI Features

Instead of relying on a single provider, we built our systems so we can switch between models when one becomes unstable, overloaded or too costly. This gives you flexibility and prevents your entire feature set from stalling.

2. Building Cost Buffers Into Budgets And Estimates

AI compute pricing changes often and sometimes without notice. Adding buffers allows you to absorb unexpected spikes and avoid awkward client conversations about sudden overruns.

3. Creating Dashboards To Monitor API Limits, Outages And Model Availability

Real‑time visibility helps us respond before a slowdown becomes a full blockage. It also gives our engineering team data to decide when to switch models or trigger fallback options.

4. Adding Onboarding Materials To Help Clients Understand AI Infrastructure Dependencies

We learned that many clients assume AI features are as stable as any other part of the app. Clear onboarding helps set expectations and reduces confusion when external providers face issues.

5. Encouraging Teams To Use AI With Intention Instead Of Letting Hype Drive Decisions

We shifted the mindset from "add AI because it’s trending" to "add AI where it solves a real problem." This reduces unnecessary dependencies and keeps our builds resilient.

These steps are not dramatic strategic moves. They are practical actions that can help you deliver reliably in a world where AI infrastructure remains unpredictable.

The New Reality For App Builders

It is important to understand that AI is not only a capability. It is an ecosystem with supply and demand pressures.

If you are building AI‑powered products, your success depends on the infrastructure behind them. The companies that recognize this and prepare for it will produce stronger and more resilient products. The ones who overlook it could face delays, outages and rising costs.

In a world where AI infrastructure is strained, leaders should build with one mindset:

Plan for the future and prepare for unexpected disruption.

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