The standard advice now is “let the AI write the boilerplate,” and in IoT work it is quietly wrecking system stability. Fourteen years of untangling WordPress and WooCommerce ecosystems taught me that projects rarely die from bad code. They die from code that looks correct and knows nothing about the environment it runs in. That is what Technical Debt in IoT looks like now.
I keep coming back to the Ariane 5 failure in 1996. A software module from the Ariane 4 was reused without anyone checking whether its constraints still held in the new vehicle, and a data overflow took the rocket apart 40 seconds after liftoff. AI code generation repeats that mistake at volume. It echoes back “acceptable” code with no idea what the hardware or the scale actually allows.
An army of juniors, and the debt it leaves behind
A report from Ox Security calls AI coding tools an “army of juniors.” Fast, willing, functional, with no architectural judgment behind any of it. Point that at device firmware or gateway services and you are scaling your existing bad habits faster than your team can refactor them.
I have written before about how AI code breaks in WordPress environments. In IoT the consequences are physical. Retry logic that ignores battery budget and network latency does not hand you a slow site. It hands you thousands of dead devices in the field.
1. It copies the patterns already there
Copilot and its relatives work inside the context of your current repository. If the project is already full of workarounds and redundant transients, that is the house style as far as the model can tell, so it reproduces the hack instead of proposing something better. A 2025 GitClear report found code duplication up eightfold since AI tooling went mainstream. Duplicate a bug across gateway services and device firmware and the fix becomes a synchronized update across the whole fleet.
2. It has no sense of hardware limits
These models learned mostly from cloud and server code, where memory is cheap and the network might as well be infinite. IoT devices have neither. Ask one to handle a data stream and you will probably get a fat JSON payload for a job that needed a compact binary protocol like Protobuf or MessagePack.
Here is the mistake in its usual form, PHP for a cloud-side telemetry handler:
// NAIVE APPROACH: The AI-generated 'Wait and Retry' loop
function bbioon_process_telemetry($device_id, $data) {
$attempt = 0;
while ($attempt < 5) {
$response = wp_remote_post('https://api.internal/v1/store', ['body' => $data]);
if (!is_wp_error($response) && 200 === wp_remote_retrieve_response_code($response)) {
return true;
}
$attempt++;
sleep(2); // This blocks the process and kills scalability at 10k devices
}
return false;
}The version a senior engineer writes uses an asynchronous queue, or a non-blocking transient lock to keep race conditions out during a high-volume telemetry burst. Blocking on sleep() is Technical Debt in IoT in its purest form: it costs nothing in testing and surfaces the day you hit real scale.
Nobody told the model about your architecture
AI does not see the whole system. It has no idea that your time-series data belongs in InfluxDB while your logs go to Elasticsearch. It will cheerfully write everything into a standard MySQL table, and that table will lock up once writes start arriving every 100ms. Missing the topology is why AI-heavy projects keep drifting back into monolithic anti-patterns.
How to push back
- Review every AI commit yourself. Only 48% of developers read AI code before committing it, so do not join the other 52%. Read for architectural fit, not just whether it compiles.
- Declare some paths off limits. Interrupt handling, authentication logic, and packet parsing should be written by a person, or supervised closely by one.
- Watch the devices with custom WP-CLI commands for state and latency. A spike in memory use at the edge is usually an unoptimized AI loop.
If Technical Debt in IoT is eating your dev hours, hand it over. I have been working with WordPress since the 4.x days, and I know where the bodies are buried in architectures like these.
What to take from this
AI will speed up your CRUD work. It should not be deciding a device’s battery budget or how data moves through the system, because it cannot reason about either. Treat it as an autonomous developer and what you are building is the next legacy system, with an Ariane 5 style failure somewhere in its future. Slow down on the parts that are expensive to get wrong.
