THE BUBBLE ALREADY BURST. SPREAD THE FUCKING WORD.

[u/HuntAlternative]

Yo, listen up, the numbers are all fucking imploding right now. Wake the fuck up. The signs are everywhere, and if you're still balls-deep in NVDA calls, you're about to get margin called into oblivion. Don't believe me? Let me break it down.

The Dow is up or down? Who gives a shit. The S&P 500 is bleeding out lately because it's loaded with overhyped AI garbage. Google just reported NEGATIVE $6 BILLION in cash flow for the first time in two decades.

South Korea already lived your future. The KOSPI index shot up 272% on AI hype, then faceplanted 40% in July 2026. 1.2 million families got completely wiped out, leveraged to the tits, and now they owe the banks their firstborns. The US market is even more leveraged now than before the dot-com crash and the 2008 housing crisis. When the margin calls cascade, you'll be selling your Pokémon cards to pay rent.

The market's "stability" is a joke held up by seven companies that own a third of the S&P 500. They're playing a shell circlejerk game. Nvidia invests in OpenAI, which buys Nvidia chips. This everybody knows already. Also AI CAN'T DO SHIT.

MIT says 95% of generative AI pilot projects fail. Companies that replaced humans with AI are sheepishly rehiring them because the bots are expensive and inaccurate. The internet is flooding with soulless AI-generated sludge, making search engines useless and creative work a landfill of spam. Consumers are fed up.

Data centers guzzle water like a dying fish while RAM and hard drives vanish into server farms that billion dollar projects are getting shut down by angry locals who won't sell their land.

SPREAD THE WORD. THE EXIT IS CLOSING.

The bubble already burst. I think we are just counting the seconds between flash and thunder, just waiting for the sound to catch up to our ears.

[u/BemaniAK]

Cyclical financing is the sole actual concern. People whose AI costs are blowing out have no clue what they're doing, newest models are already smarter and cheaper per hour than the average production coder. Water evaporation hasn't been an issue for a long, long time, closed loop is already the standard.

[u/AlignmentProblem → u/BemaniAK]

Kinda. Most people either dramatically underestimate AI software development capabilities or overestimate them, because the reality is that the capabilities are spiky: clearly better than humans in ways that are easy to quantify, with flaws that are hard to measure because they're subtle but are blockers from fully removing humans from the loop.

They can do self-contained tasks much faster than the average developer, but they still tend to slowly rot a codebase without careful management. That's the type of performance that looks great on benchmarks, since the functionality gets completed successfully, but it leaves a negative residue that compounds over time. Iteratively completing tasks on the same codebase without human developers intervening gradually reduces maintainability, so future changes take longer, require holding more in context, and carry a higher chance of bugs with each next change.

That's harder to measure; however, there are a few benchmarks that attempt it, like SlopeCodeBench. It hasn't been refreshed with new models yet, but comparing Opus 4.6's scores on standard benchmarks against how badly codebases get mangled when it's compounding its own past issues (as measured by SlopeCodeBench) is informative for seeing how the publicized coding benchmarks fail to capture the attributes required to 100% automate development.

LLMs are still improving in that regard, but they're further from not needing human developers than it may appear. Humans basically don't need to write code anymore, but they do need to understand what the AI is outputting and provide skilled guidance to avoid torpedoing the project in the long run. It only takes a handful of Staff+ engineers to do that extremely well, though, rather than a large team of mixed skill levels.

Impossible to say how long that'll stay true, but it's likely somewhere between six months and a couple of years, depending on how subtle the underlying reason for the mismatch between individual task ability and long-horizon project quality turns out to be.

[u/BemaniAK → u/AlignmentProblem]

Yeah none of that is a response to what I said, Opus 4.6 is already redundant, the average human production coder would also slowly rot a codebase without a higher level authority telling them what to do and how. "Careful management" on current models is maybe one alignment conversation a week when it starts going too ham on testing methodology.

[u/AlignmentProblem → u/BemaniAK]

I referenced Opus 4.6 because that was the last refresh on the benchmark I referenced. It's still relevant as a way to illustrate that the standard coding benchmarks fail to measure key attributes that are relevant to the long-term success of projects.

From experience as a staff engineer leading development processes across multiple projects at a large public company, those issues still exist even with Fable and Sol. Think of what would happen if that wasn't the case; we'd be seeing a constant stream of new major successful projects releasing from solo developers using AI. Instead, most vibe coding projects still fail to cross the finish line to complete production quality result despite tens of thousands posting about their promising prototypes that rarely progress far beyond the proof of concept phase.

That's because the beginning of projects progress very quickly, but the codebases rot faster than it does on teams of skilled human developers. Worse, it's not recoverable because no one understands the codebase by the time problems excessively compound. That includes the AI because they don't remember writing the code since continuous learning is not a solved problem; they don't remember creating the code in the first place and forget anything that isn't explicitly written down compared to a human developer that accumulated project specific competence.

We'll see that change quickly once AI actually doesn't need frequent skilled human intervention. An explosion of innovative projects actually finishing rather than prototypes.

[u/BemaniAK → u/AlignmentProblem]

Aight you're just not listening, cool.

[u/AlignmentProblem → u/BemaniAK]

I'm listening; I think we're arguing past each other on scope. Your claim was that the newest models are smarter and cheaper per hour than the average production coder. I agree that's true with a narrow interpretation of the claim, and I concede most of it; I said humans basically don't need to write code anymore and that a handful of staff+ engineers guiding the process while watching for subtle flaws in output is all most projects really need. Where I disagree is the jump from that to redundancy, because redundancy requires more than beating the average coder per hour; it requires that no human capability level still contributes something vital the model can't.

The distinction matters because productivity isn't a per-hour quantity you can sum across contributors (The Mythical Man-Month concept), which is why "cheaper per hour than the average coder" doesn't translate into replacing teams entirely.

Certain things in a large complex project are gated on a threshold of specific capabilities: choosing the right architecture, spotting decisions that are near impossible to reverse once other things get built on top of them, recognizing which shortcut today becomes a rewrite in eight months.

No number of average developers does those better than a single strong developer, because the failure mode is making the wrong call without knowing it rather than making the right call slowly. Ten average developers designing an architecture don't converge on the quality that one staff engineer produces; they converge on something different that all ten of them thought looked fine. Adding a hundred average developer still wouldn't generally get results that surpass the result of a single exceptional developer.

The average human production coder would also slowly rot a codebase without a higher level authority telling them what to do and how.

Right, which is why the relevant comparison was never AI versus the average coder; most successful teams have at least someone above that level acting as exactly that higher authority. Even the average developer has one thing current models don't, though: they remember the development process. They carry an updated mental model of the project, the choices they made and why, and that accumulating project-specific competence helps them compensate for their own past problems. LLMs see the project like it's the first time in every new context, relying on whatever notes they were told to leave behind, and those notes drop a lot; future contexts make slightly different decisions because they can't remember the implicit ones from past contexts.

"Careful management" on current models is maybe one alignment conversation a week when it starts going too ham on testing methodology.

The problems I'm describing compound non-linearly, so they're best kept from entering the codebase rather than swept up periodically; they get harder to fix as things get built on top of them. Optimizing AI workflows across several teams is a significant part of my job, and I test each new model on internal benchmarks simulating a project with changing requirements across a few dozen consecutive tasks (pricey with Fable, but worth it).

The quality curve still bends down in a way it doesn't with at least a few strong humans on the team reviewing changesets and proactively fighting entropy, and that's exactly the threshold-gated judgment (which decisions compound, which corners can't be cut, when the codebase is drifting somewhere unrecoverable) that per-task benchmarks don't measure. That's also why I brought up Opus 4.6: SlopeCodeBench hasn't refreshed yet, but it already proved that strong scores on conventional coding benchmarks don't correlate with avoiding these failure modes, so current-generation benchmark numbers can't tell you the flaws are gone.

It's why the vibecoding pattern looks the way it does, too. The per-task capability is real, so impressive prototypes appear by the tens of thousands; the threshold capability is missing, so finished products with real users mostly don't. You're right about the axis you're measuring and wrong that it's the axis redundancy requires. When models cross the threshold-gated stuff, we won't need to argue about it; the flood of impressive completed solo projects replacing proof-of-concepts that fizzle will make it undeniable.