5 Beautiful Things About AI · PlainTextPoetry

I’m on the internet. I’m well aware of the beating that AI takes. It gets called spooky a lot, which to be fair, is fair.

It also gets used to spray slop all over the internet. Also fair and quite annoying…

But if you dig up underneath the garbage, there’s some real beauty in what it lets a regular‑ass human do with deeply complex work. As much as I love writing satirical pieces, this isn’t satire. I really do find it beautiful in some ways. That’s part of the reason why I name all my robots. Granted I’m biased, but aren’t we all?

Anyways, here’s my list of 5 beautiful things about AI.

1) It speaks code fluently & globally

At work, I live in system stacks nested like Russian dolls. Boomi processes with a dozen shapes and too many Dynamic Doc Properties; MuleSoft flows with overlapping Batch jobs; Infor Process Automation (IPA) where a JavaScript node relies on a derived field + relation written in Landmark Pattern Language (LPL). It’s complicated, there’s lots of dependencies, lots of different languages, and I have to work within interconnected platforms.

I describe the behavior I’m expecting in plain language — “retry with backoff, send to dead letter queue after 5 tries, idempotent write, then allocate to job by relation X” — and AI drafts me the scaffolding. For example, a Boomi try/catch with a Data Process, a Mule DataWeave transform, or the IPA javascript that reads my LPL‑derived field without nulling out. I still read every line, but I’m not starting from scratch taxing my brain with codifying complex architecture.

That means that I spend less time jumping through syntax hoops and more time solving the real problem.

Side note: with integration work, the problems are boringly consistent — legacy sprawl, data silos, API sprawl, slow time‑to‑value — so having a fast code generator for the glue work matters.

It’s not a wizard. It’s a torque wrench for thinking. It makes hard edges grab.

Another note: Ask it to write the failing test first. Then the fix. You’ll get fewer ghosts in the machine.

2) It remembers what I was doing when my brain doesn’t

Deep work has a cruel ramp. You’re 70 tabs deep, you finally fully understand the event flow, and then you’ve gotta go pee or walk your dogs or eat or whatever else humans do. Without context, re‑entry costs 40 minutes of “What was I doing here… How was I planning to solve this next problem… What exactly does this line of code do in the context of everything else…etc”

I can say, “Summarize what we built in the Boomi flow, why we used Atom Queues, and what’s left on the Mule orchestration,” and it spits back a clean “next steps + risks” memo.

Same with IPA: “Remind me which Work Unit fails when the LPL relation returns null and what we decided to do about it.” It remembers just enough to get me rolling again.

AI reduces the mental tax. I scroll the thread all the sudden I remember exactly what I was doing and why I was doing it. It compresses state. Very quickly I can get back into focus mode and avoid performing archaeology on my own brain.

A suggestion: End a session with “Write me a next‑time plan” (5 bullets, risks, TODOs). Tomorrow‑you will be happy with today-you.

3) It’s brutally honest

No, I don’t “trust” AI more than my family or my team or my own frontal lobe. If you’re in a relationship with your chatbot, please log off and call someone (and consider therapy). But as a low‑ego contrarian, it’s truly wonderful.

I’ll paste an architecture sketch and say, “Find three catastrophic failure modes and tell me why everything would break at 2am on a Saturday.” Or, “Argue against this from a CFO’s perspective. Be mean.” Or, “Convince me my migration plan is reckless.”

It pushes back instantly and without politics. It invents failure I didn’t think about, and I can fix the plan while it’s still cheap. I still make the calls, of course, but after running it through the AI bullshit filter, the call is cleaner and I’m more confident about it.

4) It’s an absolute monster with prototyping

I can now operate fully in parallel. I send my thoughts/initial designs to chatGPT Pro and it spends 5–10 minutes coming up with a comprehensive implementation plan. Then I put that plan into Claude Code and it makes a brilliant prototype that would have taken me at least a week of intense, focused effort.

When it’s that cheap to try, you can finally do the thing we all pretend to do: test your riskiest assumption first. You can stop arguing opinions and start comparing artifacts. You need fewer debates, you can deliver more demos, faster.

Example: we helped stand up a kiosk → transcript → sentiment → chatbot for execs workflow (Walmart museum exhibit). That included S3 storage with 90‑day retention for videos, a RAG index so deletions stop influencing answers, and SSO via Cognito to their IdP. Prototyped thin, then hardened. It’s repeatable. And now we’re moving onto Phase 2.

Here’s a little recipe:

One‑pager with success metric + kill criteria -> Chat GPT 5 Pro.
Ask AI for schema + endpoints + property‑based tests -> Chat GPT 5 Pro.
Wire one real integration (auth, payments, or storage) -> Claude Code.
Record a 90‑second demo, ask for refinement -> Chat GPT 5 Pro.
Take what works, leave the rest -> Your brain.

See how much less work your brain needs to do when generating a prototype? It’s pretty magical.

5) I can measure 18 ways before I cut

“Measure twice, cut once” is cute. With AI I can measure from weird angles I wouldn’t normally check:

Generate a risk table (impact × likelihood) with mitigations I can actually implement.
Draft a migration plan with a clean back‑out path and the exact dashboards I should watch.
Brainstorm failure injection (timeouts, partial writes, split‑brain, sequence collisions).
List fuzz cases and property tests to prove I’m not lying to myself.
Write the runbook future‑me will need when something inevitably breaks.

Surprises still happen, of course…software is chaos. BUT not because I ignored something obvious. The surprises are more interesting and lead to a much deeper solution, faster.

The honest downsides

Hallucinations: Treat outputs like a confident intern wrote them. Verify with tests and docs.
Version drift: Pin versions. Ask for code against this library at this version. Matter of fact, pin EVERYTHING.
Security: Redact secrets. Use some sort of placeholder. Unless you want to open your backdoor to someone running 600 GPUs in a basement somewhere.
False confidence: Smooth words ≠ sound ideas. Make it prove claims with reproducing tests or a live poke at reality.

Final Musings

Why do I find AI beautiful? Because it returns me to the part of the job that feels human: choosing trade‑offs, naming things with care, building systems that won’t bite the next person who touches them.

It’s not a rocket ship, nothing really is (except an actual rocket ship). It’s a quiet, weird little exoskeleton for focus. It lets ordinary people make surprisingly elegant things and spend less time battling the syntax gremlins.

It’s a small kind of beauty, IMO.