DEV Community: Shekhar Bhardwaj

I Spent $200 Solving a $2 Problem. That Is Why AI Site Reliability Will Matter.

Shekhar Bhardwaj — Mon, 29 Jun 2026 00:16:26 +0000

So this weekend I spent $200 solving a $2 problem.

Not because I was careless. Not because the system was broken in the old way. It happened because the tool was powerful, fast, confident, and wrong for just long enough.

That is the strange thing about AI systems. They do not always fail loudly. A cloud server goes down, an alert fires, a dashboard turns red, someone opens an incident bridge, and the team knows what kind of movie they are in. AI failure is softer. The answer looks useful. The workflow keeps moving. The agent tries another path. The model explains itself beautifully. The bill keeps climbing.

With cloud reliability, we learned how to survive machines failing. We built retries, failover, backups, autoscaling, health checks, runbooks, and incident reviews. The cloud taught us that infrastructure is never perfect, so systems must be designed to bend without breaking.

AI is teaching us something different. The machine may be running perfectly and still produce the wrong result. The API may be healthy, the latency may be fine, the token stream may complete, and the business outcome may still be bad.

That is why AI Site Reliability is going to become its own serious discipline.

It will not be enough to ask, “Is the model available?” We will have to ask, “Is the model still useful?” “Is it drifting?” “Is it spending too much?” “Is it using the right tools?” “Is it looping?” “Is it making the same mistake with more confidence?” “Is a human needed before this continues?”

In the cloud world, uptime was the king metric. In the AI world, usefulness will matter just as much. A model that is always available but often wrong is not reliable. An agent that finishes every task but spends 100 times more than needed is not reliable. A chatbot that gives answers with perfect grammar but poor judgment is not reliable.

The next generation of reliability engineering will care about cost, correctness, context, and control.

Cost matters because AI turns thinking into metered usage. Every retry has a price. Every long context has a price. Every tool call has a price. A bad loop is no longer just wasted time. It is a live meter running in the background.

Correctness matters because AI can fail while looking successful. Traditional systems usually return errors when something breaks. AI can return a confident paragraph. That means we need new checks. Not just status codes, but reasonableness checks. Not just logs, but decision trails. Not just observability, but explainability at the workflow level.

Context matters because AI systems depend heavily on what they are given. A great model with bad context becomes a fancy guessing machine. Missing policy, stale data, poor prompts, broken retrieval, or unclear instructions can quietly damage the final answer. In AI systems, reliability starts before the model is even called.

Control matters because autonomy without guardrails becomes expensive chaos. Agents need budgets. They need stop signs. They need permission levels. They need escalation points. They need to know when to ask a human instead of burning another 200,000 tokens trying to be clever.

This is where AI reliability will feel different from cloud reliability. Cloud systems fail because components break. AI systems fail because judgment breaks. The server may be healthy, but the reasoning path may be sick.

That changes the work.

Future AI runbooks will not only say, “Restart service.” They will say, “Check prompt version.” “Compare answer against source.” “Review tool call chain.” “Inspect token spend.” “Validate retrieval freshness.” “Freeze autonomous retries.” “Route to human approval.” “Roll back model behavior.” “Switch to smaller model.” “Stop the agent.”

The best teams will not be the ones using the biggest models everywhere. They will be the ones that know when not to use AI. They will know when a $4 rule, a database query, a simple form, or a human approval is better than a $400 reasoning adventure.

That is the real lesson.

AI is not magic infrastructure. It is probabilistic labor inside software. It can work beautifully. It can also overthink, overspend, hallucinate, retry, and explain its way into trouble.

So the future of AI reliability is not about distrusting AI. It is about respecting it enough to build around its failure modes.

We need systems that treat AI as powerful but not sacred. Helpful but not always right. Fast but not free. Available but not automatically reliable.

Because in the old world, downtime was expensive.

In the AI world, uptime can be expensive too.

And sometimes the system will not crash at all. It will just calmly spend $1000 solving a $10 problem.

Orignal Post

FONA: Fear of Not AI-sking

Shekhar Bhardwaj — Sat, 27 Jun 2026 18:28:09 +0000

FONA — short for "Fear of Not AI-sking" is a term I coined for the anxiety that you should be using AI for a task, and that reaching for it last, or not at all, means you're falling behind.

There is a new kind of guilt in the AI era. It is not the fear that AI will replace you. It is the fear that AI was available, ready, waiting in the prompt box and you still did not ask. That quiet feeling has a name: FONA Fear of Not AI-sking. It is the anxiety that the task you avoided, the idea you parked, or the project you kept calling “someday” may no longer be blocked by time, skill, or tools. It may just be waiting for one honest prompt.

A friend texted me at 11 PM on a Tuesday. Not the usual kind of late-night text like, “Are you awake?” This was more of a quiet existential audit. He had been thinking about a side project for two years. Two full years of “I should build this someday.” Two years of casually mentioning it, mentally polishing it, occasionally getting excited about it, and then putting it back on the shelf like a decorative ambition.

Then it hit him. Claude could probably scaffold the whole thing in an evening. Not finish it. Not make it beautiful. Not turn him into a founder with a podcast mic, a black turtleneck, and strong opinions about cold plunge therapy. But enough to make the thing real. Enough to remove the comfort of “someday.”

And that was the horrifying part. Because once the excuse is gone, what are you left with? Yourself. The final boss.

I told him I had no good answer, because I was currently losing the same boss fight. I had my own graveyard of half-finished ideas. The newsletter I never started. The script that would save me an hour every Monday. The blog post sitting in a draft folder, quietly judging me. The little product idea I kept calling “interesting” so I would not have to call it “abandoned.”

And now each one had a new label attached: could’ve prompted it.

That is the new guilt. Not “I didn’t have time.” Not “I didn’t know how.” Not “I need to learn React first,” which, historically, has been the adult version of “my dog ate my homework.” No. Now the brain says: you could have asked.

You could have opened ChatGPT, Claude, Cursor, or whatever flavor of robot intern you prefer, and typed: “Make me a starting point.” “Turn this into a plan.” “Write the first ugly version.” “Explain the hard part.” “Generate the annoying boilerplate.” “Help me stop pretending this is blocked.”

That feeling needs a name. So here it is: FONA — Fear of Not AI-sking.

FONA is the creeping anxiety that you are underusing the most powerful assistant you have ever had access to. It is not the fear that AI will take your job. It is the fear that AI is sitting there, fully charged, emotionally unavailable, ready to help, and you are still raw-dogging your to-do list like it is 2016.

FONA is looking at a task and realizing the hardest part is no longer, “Can this be done?” It is, “Why haven’t I even asked?”

It shows up in small moments. You spend 45 minutes formatting an email and then remember AI could have made it sound less like a hostage note. You manually rename 80 files and then feel a spiritual slap from the automation gods. You sit on a blog idea for three weeks and then watch someone else publish a worse version with more confidence. You open an empty document, stare at it, close it, and somehow call that “thinking.”

Before AI, procrastination had dignity. You could say things like, “I need a weekend,” “I need a designer,” “I need to research the market,” or “I need to wait until things calm down.” Beautiful lies. Respectable lies. Lies with a blazer on.

Now the lie has to survive a prompt box. That is much harder.

Because AI has made starting embarrassingly cheap. Not succeeding. Not mastering. Not shipping something great. Just starting. And starting used to be where we hid.

That is what my friend was really texting me about at 11 PM. Not the side project. Not Claude. Not code. He was grieving the death of a very comfortable excuse. The idea had not been blocked for two years. It had been waiting for him to ask.

And honestly, same.

So maybe the point is not to become the kind of person who prompts everything. That sounds exhausting and slightly cursed. Maybe the point is to notice the moment when you are avoiding the ask. When the task is small enough to start, but vague enough to dodge. When the idea is not impossible, just inconvenient. When the first version would take one decent prompt and 20 minutes of honesty.

That is where FONA lives.

And maybe the cure is simple: do not build the whole thing. Just ask the first question. “Can you help me make this real enough that I can’t keep pretending it is only an idea?”

That might be the most dangerous prompt now. Because once AI gives you the first draft, the skeleton, the outline, the script, the landing page, or the messy prototype, the excuse is officially dead. And then it is just you and the thing you said you wanted to do.

Terrifying. Useful. A little funny. Very Tuesday night.

Originally published at shekharbhardwaj.com