19/11/2025
When I Solved the Wrong Problem for Thirty Minutes
I diagnosed myself wrong. Confidently wrong. For thirty minutes.
Evening. Service quarters in Vadodara. Black coffee going cold on my desk. I'm trying to draft newsletter content when everything stops. Comet freezes. ChatGPT won't load. Claude gives me nothing. Perplexity – dead.
I'd upgraded to MacOS Sequoia that morning.
Obviously, it's the upgrade.
My mobile apps work fine. ChatGPT on my phone responds. Perplexity pulls results. This confirms it – the problem is local. My system. My configuration. Something I broke.
I open terminal. Blue text on black screen. Start copying commands from my phone to my Mac.
sudo dscacheutil -flushcache
Nothing.
Okay. DNS issue. Simple fix.
DNS changes. Test. Still nothing.
Proxy settings then.
Proxy configuration. Test. Nothing.
The coffee goes from warm to lukewarm.
Network diagnostics. Has to be network.
Ten minutes in. Still methodical. This is what I do – isolate variables, test hypotheses, eliminate possibilities. Good medicine. Good troubleshooting.
Fifteen minutes.
Why isn't this working?
Twenty minutes. The confidence starts cracking.
I know computers. I've built apps. This should be straightforward.
But there's another voice now. Quieter.
What if it's not your system?
I dismiss it. Mobile works. Desktop doesn't. Obviously local.
Twenty-five minutes. Coffee is cold. Irritation building.
Come on. What am I missing?
The quieter voice again, more insistent:
You're solving the wrong problem.
No. I'm being systematic. This is thorough troubleshooting. This is—
Thirty minutes.
I finally search: "Cloudflare issue today."
Global infrastructure failure. Mumbai nodes down. Warsaw. Ashburn. Multiple regions.
The realization doesn't arrive gently.
You ****** idiot.
Thirty minutes. Gone. Solving a problem that didn't exist. Following AI troubleshooting guides that were diagnosing MY system when the problem was THE WORLD'S system.
You gave yourself the wrong diagnosis and committed to the wrong treatment.
The exact error I lecture residents about. Anchoring bias. Confirmation bias. Treating your first hypothesis like a conclusion.
I try VPN to verify. Switch to US servers. Then Poland. Nothing works because the problem isn't WHERE I am.
The backbone itself is cracking.
And I spent thirty minutes adjusting MY posture.
Here's what bothers me about this: I should know better.
The meta-realization hits harder than the wasted time.
I gave the AI the wrong diagnostic information. "I upgraded my system and nothing works" – accurate statement, wrong causal link. The AI troubleshot exactly what I told it to troubleshoot. Helped me solve MY system problem with precision and care.
Garbage in, garbage out.
Even when you're the one feeding the garbage to yourself.
In medicine, we call this anchoring bias. Patient says "chest pain after eating spicy food" and you anchor on GERD. Order antacids. Except it's actually cardiac. The history was accurate – chest pain DID happen after eating – but the causal story was wrong.
The AI is only as good as the context you provide. The diagnosis you suggest. The problem you frame.
I framed it wrong. Committed early. Followed that path with confidence.
Exactly what I warn residents not to do.
I learned Python during COVID. Lockdown. No surgeries. Just time and tutorials and the satisfaction of making something work.
Built small scripts. Felt competent. Felt like I was building a new muscle.
This is it. This is the skill that matters now.
Then LLMs exploded. ChatGPT. Claude. Copilot writing entire functions from natural language prompts.
The whisper started:
Why spend hours learning syntax when AI generates it in seconds?
Valid question, right? Efficiency. Leverage. Working smarter.
You're a surgeon. Not a programmer. Stay in your lane.
I let the practice slip. Told myself it was strategic. Told myself I was using tools effectively.
You're being lazy.
No. I'm being realistic about time allocation. I have patients. Research. Content creation. I can't learn everything.
You're scared it's hard.
It's not that. It's just—
You're scared you won't be good at it.
Maybe.
The skill atrophied. Syntax faded. The confidence I'd built dissolved back into dependence.
And tonight, when everything broke, I couldn't diagnose infrastructure from configuration. Couldn't separate local from global failure. Couldn't tell the difference between my system and the world's system.
I felt foolish for a moment.
That's the truth I don't want to admit.
I didn't save time by letting AI handle the code. I traded understanding for convenience. Traded diagnostic capability for surface-level functionality.
And that trade made me helpless when things broke in ways I didn't expect.
You can't afford this ignorance. Not anymore.
So I'm relearning. Not when I have time. Not casually. Now.
Because here's what I finally understand:
As a surgical oncologist, I do difficult things every day. Complex anatomy. High-stakes decisions. Life and death calls.
Why am I avoiding difficulty in THIS domain?
My brain needs genuine uncertainty. New territory where I don't have answers. Where I have to think from first principles.
De novo discovery. That's what keeps you sharp.
Python isn't just about building tools. It's about maintaining cognitive flexibility. About proving to myself I can still learn hard things.
Economics. Management. Agentic AI. I'm learning all of it not because it's directly useful in the OR, but because different domains give different perspectives.
Different ways of seeing problems I've looked at the same way for years.
This isn't optional anymore. This is survival.
There's a bigger issue here. One that keeps me up at night.
No-code tools are phenomenal for prototyping. I built a functional web app without writing much code. Magic. Democratization. Access.
Everyone can build now. That's good, right?
Yes. Until it's not.
Picture this: Junior resident. Night shift. Thirty-year-old woman, abdominal pain. The AI diagnostic support tool suggests appendicitis based on symptom pattern matching.
Resident trusts it. Why wouldn't they? The tool has 94% accuracy. Peer-reviewed. Hospital-approved.
Orders surgery prep.
But she's six weeks pregnant.
The AI didn't know. Lab values didn't flow into the prompt. Integration missed the recent hCG. The resident didn't know to verify the data pipeline.
Nobody built wrong. Nobody coded maliciously. The no-code tool worked exactly as designed.
But nobody understood the architecture well enough to know what data wasn't flowing.
The appendicitis diagnosis might even be correct. But the surgical protocol is catastrophically different for pregnancy.
How many near-misses happen because we don't understand our own tools?
Here's what scares me: We're building healthcare AI on abstractions we can't troubleshoot. On cloud dependencies we don't control. On data flows we don't verify.
Healthcare AI needs three non-negotiables:
Ethical practice: Not checkbox consent. Real informed consent about what the model sees and what it can't.
Can you explain what data your AI is using? Can you? Really?
Data security: Not HIPAA compliance theatre. Actual security. Knowing where data lives, who accesses it, what happens during system handshakes.
When the abstraction breaks, can you trace the breach?
Uninterrupted flow: You cannot have diagnostic support fail mid-shift because Cloudflare is down. Cannot have critical protocols unreachable because cloud connectivity dropped.
Patient safety cannot depend on infrastructure you don't understand.
Tonight proved it. Global backbone cracks. Everything stops. And I couldn't even diagnose whether it was my problem or the world's problem.
If I can't diagnose infrastructure failure, how can I trust myself to deploy clinical AI?
That's the question that won't let me sleep.
You can't build safety guarantees on abstraction layers you don't understand. On no-code magic you can't troubleshoot when it inevitably breaks.
And it will break.
Today proved that too.
This is what I mean by co-elevation.
It's not clinicians learning just enough tech to be dangerous. It's not engineers deploying healthcare tools without clinical context.
It's both sides becoming literate enough in each other's domains to have productive disagreement. To catch each other's blind spots. To know when the other is solving the wrong problem.
I can't fake technical understanding anymore. The "I'm just a doctor" excuse doesn't work when I'm building AI-assisted clinical tools. When I'm advising on hospital digital infrastructure. When I'm training residents who'll work in AI-augmented environments.
And engineers can't fake clinical literacy. Can't deploy diagnostic algorithms without understanding diagnostic reasoning. Can't build patient-facing tools without understanding the power dynamics in healthcare encounters.
Neither can succeed alone. Both must be uncomfortable enough to learn the other's language.
I'm still relearning Python. Still making rookie mistakes. Still Googling syntax I used to know.
But now I'm asking different questions.
Not "How do I make this work?" but "What's the actual problem I'm solving?"
Not "What command do I need?" but "Why did this fail?"
Not "Can AI do this for me?" but "Do I understand enough to verify what AI generates?"
Here's what I'm wondering about you:
What technical domain are you avoiding because "someone else handles that" or "AI can do it now"?
Where's your Cloudflare moment waiting – the infrastructure you depend on but don't understand?
What expertise do you claim while relying on abstractions you couldn't troubleshoot if they failed?
I'm not suggesting everyone learn to code. I'm suggesting we stop pretending abstraction is the same as understanding.
That we recognize technical literacy as diagnostic literacy.
That we get uncomfortable in domains where we're currently just... trusting the system.
Co-elevation isn't optional anymore. It's the baseline for building anything that matters.
Especially in healthcare. Especially with AI.
Especially when the backbone can crack without warning, and you need to know: Is it me, or is it the world?
Usually, it's the world.
But you need to understand enough to tell the difference.
PS: What I'm still learning: Python (again), network architecture, agentic AI frameworks, when to trust my diagnostic instincts versus when I'm anchoring on the wrong problem. Some days, I get it right. Some days, I spend thirty minutes solving problems that don't exist.