LLM based AI isn't just pattern matching any more.
Reasoning AI is achieving things like solving novel Defcon problems (Defon conference issues complex issues for hackers to solve in a competition).
If you think the current state of the art is still simply basic next token prediction, you're about a year behind.
I agree that 'stochastic parrot' is an outdated beatdown, but let's be precise about what 'Reasoning' means here.
Solving Defcon Capture the Flag challenges is impressive, but it’s a
bounded problem. It has a clear rule set, a defined success state, and a logical path to a solution. That is exactly what 'Chain of Thought' (CoT) compute excels at.
However, enterprise software is an
Unbounded Problem. It involves ambiguous stakeholder requirements, unwritten business rules, and shifting infrastructure constraints. AI models like Claude 4.5 '
reason' by
simulating logic paths (probabilistic pathfinding), not by understanding intent. When you say the code is '
clean and well structured,' you are judging the syntax (the
how), not the semantic validity (the
why).
AI is great at making code
look clean (e.g. PEP-8 compliant, commented etc...). But 'clean' code can still contain race conditions, insecure deserialization, or logic bombs that only trigger in edge cases. A visual inspection of 'cleanliness' is not a security audit. To that end, 'Reasoning' in a sandbox is not the same as 'Engineering' in production.
Also... maintainability: that code was something that works to solve a real world problem before my morning coffee, with 2026 state of the art coding LLM.
If we need to revisit in 6 months or whenever I've left the company, just get the much more advanced LLM in mid 2026 to audit/fix it.
Relying on a future, unreleased model to maintain today's code is a gamble, not a strategy. What if the cost of inference for that 'smarter' model is 10x higher? You've just locked yourself into a high-rent maintenance plan.
A strategy of '
just get the more advanced LLM in mid-2026 audit/fix it later' represents huge business risk and is literally the definition of technical debt: borrowing efficiency today against a future resource you
hope will exist or will be more capable.
We have entered the era of '
opaqueware' - software that works, but nobody at the company fully quite understands how or why!! I'm not trying to be funny, I'm actually being serious!! If a companies plan for maintenance is '
ask the next version of the AI,' then you aren't building a toolset; you are building a dependency chain and risk removing the institutional knowledge (understanding your own stack) and replacing it with a subscription to a model provider.
If that 'mid-2026 AI' hallucinates a fix on a deprecated library that the 2025 AI chose, and no human has sufficient knowledge to spot it because they've been 'one-shotting' everything for six months, then you don't have an efficiency gain. Instead you have a critical failure waiting to happen.
What I AM saying is that getting usable tools cranked out in 15 minutes and actually using them to solve real world problems TODAY instead of a week of developer time is a revolution.
The issue(s) discovered in my code were without any basic instructions outside of (this was the entire prompt for v1.0):
And then it just one shotted it... with a better prompt, to tell it about those additional constraints via a system prompt for code style, other requirements, etc... it would have done better in the first place.
The planning steps, etc. below aren't basic "next token prediction" ... and if you claim they are then I'd argue that its doing a better job than most mid-tier developers do, and that most devs can't even do that.
I've read through the code, it's pretty clean and well structured in my opinion.
The fact that it 'one-shotted' the code with a basic prompt actually proves that it was a generic problem. The harder the problem, the less likely a 'one-shot' works or the more time involved providing a prompt that suitably describes . Solving common boilerplate problems quickly - while very useful - should not be confused with solving novel engineering challenges.
I have to push back on the timeline comparison too. If a disposable tool takes a human developer a full week to write, but an AI gets the job done in 15 minutes, that says more about the developer's toolkit than the AI's brilliance.
You're right, the efficiency gain
can be real, no doubt about it, but let's not exaggerate it. The risk here is the
commoditization of quality argument
.
Because it only took 15 minutes to generate, we tend to treat the code as disposable. But 'disposable' code has a really nasty habit of quickly becoming 'production' code with a bunch of downstream TODO's and supporting critical business workflows.
When we lower the barrier to entry for creating tools to zero (or next to zero), there can be a tendency to flood our ecosystem with unmanaged scripts. The 'efficiency' of the 15-minute build is quickly eroded by the 'inefficiency' of managing 50 different 'one-shot' shadow IT tools that all break simultaneously when an API updates.
I think we actually agree on the potential when suitably applied as a job aid, just not the risk profile.
I’m not saying don’t use the tools - I use them too. I’m just saying that 'it works' and 'it’s maintainable' are two very different bars to clear. If we stop understanding our own technical stack because it’s cheaper to let an AI 'one-shot' it, we aren’t becoming super-developers, we’re becoming passengers. That worries future me! Let’s enjoy the speed boost, but keep our hands on the wheel.
Just my 0.02
Have a great day
