What Happens After the AI Bubble Bursts?

Lately, everyone I talk to carries an undercurrent of anxiety, as if being swept along by the times. Scroll through Twitter: another big-tech model shaking the internet; another AI tool replacing jobs.

In this orgy of compute, I want to pour cold water instead — on a counterintuitive claim: the AI bubble will almost certainly burst, and its bursting is precisely when ordinary people like us start to actually make money and reap the rewards.

Most people hear "bubble bursts" and assume apocalypse. Stretch the timeline out, though, and you see the real pattern: a bubble bursting is almost always the watershed moment when hardcore technology transitions from "a capital game" to "genuinely universal infrastructure."

But before we project what comes after, we have to understand why today's AI giants are backing themselves into a dead end.

Squeezing the Last Drop of Toothpaste Out of the Transformer

Every "jaw-dropping" large model on the market right now (GPT, Claude, Gemini) sits on top of the same architecture: the Transformer.

Quick explainer on its core "attention mechanism":

Imagine asking an AI to read a 100,000-character book. Older models read sentence by sentence, moving forward. The Transformer is brutally different — for every new character, it loops back and re-compares against every previous character, calculating pairwise associations across the entire context.

This brute-force aesthetic is what made it smart, but it also buried a fatal flaw: exponential explosion in compute cost.

In computer science, this is called O(N²) complexity. As the context window grows, memory and compute consumption grow geometrically. Response latency balloons, compute bills become terrifying, and the model starts producing "hallucinations" — confidently fabricated nonsense that's a congenital defect of generative probabilistic models, with no way to fix it at the root.

When the giants discovered that ten-x-ing parameter counts with hundreds of billions in spend still left AI writing buggy code and inventing fake data, the tube of toothpaste labeled "Transformer" was effectively squeezed dry.

New Architectures Are Actually a Top Signal

The tech world never lacks smart people. Since the Transformer is too expensive and too heavy, a new wave of architectures — RWKV, SSM (e.g., Mamba) — has erupted. They elegantly solve the long-context compute problem, slashing inference costs.

Plenty of people interpret this as: "Look, new architecture, AGI's right around the corner, the bubble won't pop."

That's a massive blind spot. Technical breakthroughs and financial bubbles are two completely different things.

Capital is extremely impatient. Bubbles don't burst because the technology failed to deliver — they burst because the commercial ROI doesn't pencil out.

The 2000 dot-com bubble is the perfect control group.

Back then, all you needed was a domain name and a traffic story to score a nine-figure valuation. The world laid fiber-optic cable and built server farms like crazy to back those stories. The result? E-commerce and ad-monetization paths weren't mature yet, burn rates were astronomical, the bubble imploded, and 90% of vaporware companies went to zero.

Today's AI is history rhyming:

All you need is GPU compute and a benchmark leaderboard to rake in massive funding. Hyperscalers are spending hundreds of billions per year on foundational model capex, but real revenue at the application layer in B2C/B2B doesn't even cover electricity and depreciation costs.

So whether or not a new architecture saves the day, the moment capital realizes the "god-making movement" can't form a commercial closed loop in the short term, the great reshuffle is inevitable.

What Happens After the AI Bubble Bursts?

This round's AI bubble will wash out speculators without moats — and hand engineers who actually understand production deployment and business re-architecture an enormous asymmetric-opportunity runway.

Drawing the historical parallel with the post-dot-com era, I project three major evolutions:

1. "Avalanche-Style Deflation" in Compute and Token Prices

After the dot-com bust, the glut of "dark fiber" caused network bandwidth costs to crash by over 90%. This dirt-cheap bandwidth became the literal soil in which Web 2.0 (YouTube, Netflix, social platforms) exploded.

Same logic: when the AI bubble squeezes valuation down, the previously frenzied buildout of data centers and GPU capacity will be massively over-supplied. The result: the inference cost of foundation models (i.e., token prices) will collapse toward zero. Expensive compute stops being a barrier, and genuinely creative developers can run unlimited long-context, deeply nested multi-agent workflows without flinching at the bill.

2. From "Foundation-Model Centralism" to "Full-Stack Execution Frameworkism"

During the bubble phase, everyone's racing to see whose parameters are biggest, whose model is the "all-knowing brain." But a brain, however brilliant, produces no real-world output without hands and feet.

After the bubble bursts, the industry will sober up from "AGI is coming any day now" and pivot hard. The center of gravity will shift decisively to the upper-layer agentic workflow and execution layer.

Since monolithic models have flaws and hallucinate, stop expecting them to be omniscient. We take already-commoditized foundation models, stitch them together with external APIs, databases, and automation scripts. We use engineering rigor to compensate for the model's intelligence gaps — hands, feet, and steady, bug-free execution that replaces humans on long, complex work chains. The one-person companies or small teams that close this loop are the ones that survive.

3. The Silent "Deep-Water Digital Transformation" (DX)

When the internet was at peak hype, everyone thought it would disrupt everything — then the bubble burst. But by 2010, from ride-hailing to food delivery to banking, no one was spouting "internet thinking" anymore — yet no industry could function without it.

Same story for AI. Once the fireworks fade, AI will degrade into mundane, ubiquitous infrastructure on par with electricity or databases. It will silently weave itself into your company's legacy codebases, into quant-trading strategy routers, into self-media content pipelines. Nobody will talk about it every day, but the operational logic of every industry will be thoroughly restructured.

Closing Thoughts

History doesn't repeat, but it does rhyme.

While everyone is panicking and partying over sub-percent-point improvements on foundation-model leaderboards, the bubble is quietly topping out. True AGI may still be far away — and that doesn't matter at all.

For ordinary people like us, we don't need to build rockets. Use this cycle to take the existing — even slightly broken — AI tools and grind through the muddy "last mile," building your own automation moat. When compute turns into dirt-cheap cabbage, the people with system-level building capability will be the real winners of the next era.

No blind following, no submission. Build your cognitive lead time now — see you next time.