First, Clarify the Definitions/Scope
This is not investment advice. The stock price data uses an approximate retrospective based on public market quotes and historical closing prices, focusing on stages and logic rather than aiming for decimal precision for every trading day.
I set the starting point to 2022-11-30, which is near the date ChatGPT was released. The endpoint is based on an understanding of public market conditions around the writing time, 2026-05-08.
The gain can be roughly understood as:
\[
\text{Increase Rate}=\frac{\text{Period-end Price}-\text{Period-start Price}}{\text{Period-start Price}}
\]
There are two sources of potential error here: First, different websites do not handle adjusted pricing, stock splits, and intraday prices consistently; second, the US stock market on May 8, 2026, has not yet closed, so real-time prices will continue to change. Therefore, the main body focuses more on “multiple levels” and “relative strength,” rather than presenting it as a trading system.
Timeline: Where AI Goes, Stocks Rise
| Phase | AI Development Status | Top Gainers | What the Market is Buying |
|---|---|---|---|
| Nov 2022 to Mar 2023 | ChatGPT goes mainstream |
May 2023 was the most critical turning point in the first phase.
When ChatGPT gained mainstream attention, the market could still question: Is this a chatbot bubble? However, Nvidia’s earnings guidance in May 2023 directly shattered that doubt. The data center revenue and next quarter’s revenue guidance were clearly higher than market expectations, marking the first time the market saw “model capability” translating into actual “GPU orders.”
This is why Nvidia did not begin its rise in 2024, but rather entered the major uptrend phase as early as 2023.
As of 2024, the market trend has shifted from merely “buying GPUs” to requiring the acquisition of entire “AI factories.” Training large models is not just about buying several graphics cards; the true expense lies in the complete cluster: GPU, HBM, networking equipment, servers, liquid cooling, power supply, data center space, and software stack. If any single component is missing, the system cannot function.
Therefore, Super Micro Computer will surge, Broadcom will surge, TSMC is expected to rise, and Oracle will also increase. They are not the same companies, but they all stand on the value chain of AI infrastructure.
After 2025, the market began searching for a second level of certainty: whether models could actually be integrated into enterprise workflows. Palantir’s AIP is representative of this phase; the market isn’t buying merely a software company, but rather the imaginative potential of “AI entering enterprise decision-making and operational systems.”
The speculative potential here is significantly higher. GPU companies are already generating revenue, while enterprise AI software is still proving its capacity for sustained revenue generation.
Approximate Gains of Major Companies
Judging by the trend since the release of ChatGPT, the stocks experiencing the most dramatic gains are not large-cap tech giants like Microsoft, Google, and Amazon, but rather companies with relatively small market capitalizations whose earnings potential has been amplified by AI.
What is most noteworthy in this table is that price appreciation is determined not only by the “degree of AI relevance,” but also jointly by the original market capitalization, profit elasticity, stock holding pattern, and the industry cycle.
Microsoft is certainly important, but it is simply too large. For Microsoft to rise 50%, the required capital and resulting increase in market capitalization are quite exaggerated. A small or medium-cap company, if suddenly perceived by the market as being on the main AI track, will find its stock price much easier to multiply several times.
This is also one of the core rationales behind the massive surge in flash memory.
Why Did Silicon Industry Surge So Much?
Solidigm is not a stock that has risen along with the AI trend since 2022. Its specialty is that, as of 2025, it spun off from Western Data to become a purer NAND and flash memory stock.
It skyrocketed, not just because “AI requires storage.” More accurately, it is due to several overlapping factors:
| Factor | Impact |
|---|---|
| AI data centers require more high-performance storage | Training data, inference cache, vector search, data lakes, logs, and checkpoints all increase storage requirements |
| The NAND industry itself is undergoing a cyclical reversal | The storage industry has gone through a trough; after supply contraction, price recovery will be significant, leading to high profit elasticity |
| The listed target company becomes purer after going public independently | After being spun out from Western Digital, the market is easier to price based on the NAND/SSD cycle |
| Original market capitalization is not high | Compared to giants like Nvidia or Microsoft, less absolute capital is needed to boost the stock price |
| Short selling or low expectations are easily counterattacked | Once the earnings and guidance of a cyclical stock exceed expectations, valuation recovery can be very aggressive |
I agree with the user’s assessment that the company has a low market capitalization and low capital expenditure, but I must add one point: Low market cap only provides potential elasticity, it is not the inherent catalyst for growth itself.
Without fundamental catalysts such as NAND price recovery, AI data center SSD demand, or improvement in financial performance following the company’s independence, a low market cap can only make it easier to speculate on, and also easier to fall back from. A truly significant market rally typically occurs when “low market cap + low expectations + marginal improvement in fundamentals” appear simultaneously.
Micron’s current movement appears to be driven by the powerful AI tailwind hitting the bottom of the storage cycle. The wind itself is enormous, and the market ground is perfectly dry.
Does this market cycle resemble the internet bubble?
Like, but not like.
The key point is that valuation precedes profit realization. The rise in many companies’ stock prices reflects expectations for the next 5 or even 10 years, as the market preemptively prices in companies with the potential to become infrastructure.
The difference is that: upstream companies in this round are already earning real money. Nvidia, TSMC, Broadcom, memory manufacturers, and server manufacturers are not selling PPTs; they are delivering hardware and services.
So, I do not quite agree with summarizing it in one sentence as “all bubbles.” It is more like:
| Layer | Current Status | Risk |
|---|---|---|
| Compute Hardware | The profit loop is clearest, orders are real | If capex slows down, valuation and inventory will recoil/be hit by it |
| Cloud Infrastructure | Revenue is real, but depreciation and electricity cost pressure are high | Whether customers are willing to pay long-term for AI computing power |
| Enterprise Software | Has cases and growth, but ROI has not been widely proven | Many pilots, little scaling; easily shifts from enthusiasm to budget scrutiny |
| Consumer Applications | Many users, clear monetization divergence | The balance between customer acquisition, retention, inference costs, and subscription pricing may not be achieved |
The biggest contradiction currently is that the AI upstream segment has formed a profitable closed loop, but the downstream applications have not.
Nvidia profits from cloud vendors, which spend capital expenditures (CapEx). This CapEx ultimately gets paid for by enterprise customers and consumers. If the end-users do not pay enough, or if AI fails to deliver sufficient cost reduction and efficiency gains to enterprises, someone in the chain will ultimately bear the depreciation and valuation pressure.
The stock price might not crash immediately, but the market will begin to ask a harder question: Where is the return on investment (ROI) for these GPUs?
How Do Research Reports and Institutions View the Risk of a Collapse?
The institutional views I found are not consistent, but they can be grouped into three categories.
The first camp is the cautious one. The title of the Goldman Sachs 2024 report on generative AI was very direct: it suggests that investment is too high and returns are too low. Its core argument is not that AI is useless, but rather questioning whether massive short-term capital expenditure can generate sufficient returns.
The second type is the moderate view. Sequoia has raised the issue of an “AI revenue gap”: to support GPU investments, the entire ecosystem needs to generate very large end-user revenues, but current application layer revenue has not yet caught up with infrastructure investment. This is not a bearish take on AI; it is a reminder that the commercial loop has not been closed yet.
The third category represents the optimists. They believe that AI will follow a path similar to cloud computing: first requiring years of infrastructure investment, followed by a gradual release of software and service revenues. This assessment also has merit; after all, cloud computing itself was questioned for its high costs in its early days.
The problem is that the stock market won’t wait 10 years to price it. It will buy early, and it will also kill early.
My judgment is:
The market may not face a sharp downturn in the short term because capital expenditure remains high, orders are still flowing, and the AI race is ongoing. As long as major companies like Microsoft, Google, Amazon, Meta, and Oracle continue to expand their data centers, the revenue of upstream hardware companies will remain supported.
But it will definitely undergo a rigorous ROI review in the mid-term. The triggers might be:
- Cloud vendors slowing down AI Capex;
- The large model price war results in insufficient inference revenue coverage for costs;
- The failure rate of enterprise AI projects transitioning from pilot phase to production is too high;
- High inventory levels in certain upstream links;
- Interest rates or the macroeconomic environment make the market reluctant to assign high valuations to long-term narratives.
This does not mean AI technology has failed. After the dot-com bubble burst, the internet did not disappear. What truly disappeared was the portion that had been prematurely overvalued in the valuations.
Which metrics should I focus on
If I continue observing this round of market trends, I won’t just look at model announcement conferences/releases.
Several more useful indicators are:
| Metric | Why It Is Important |
|---|---|
| CAPEX growth of four major cloud providers | Determines the sustainability of upstream hardware orders. |
| NVIDIA data center revenue and gross margin | Indicates whether compute power demand is genuinely strong or if prices are starting to ease. |
| HBM / NAND / SSD pricing | Shows whether storage market recovery is occurring, or if it |
Especially the last one. AI cannot only look at revenue, but it must also account for depreciation.
Buying GPUs is not free, nor is building a data center. If AI service revenue growth is very impressive, but free cash flow becomes increasingly concerning, the market will eventually reprice it.
Conclusion
The surge in AI stocks this round started with the technological shock brought by ChatGPT, then moved to Nvidia’s orders, then to the entire AI factory, and finally spread to storage, memory, power, and enterprise software.
Cymbet’s surge is not an isolated event. It stands at the intersection of AI storage demand, NAND cycle reversal, independent listing, and low market cap elasticity; therefore, its gains will be more exaggerated than those of many mega-cap companies.
However, the more such market conditions arise, the more it is unwise to only look at “infinite AI demand.” Capital markets prefer to incorporate long-term trends into stock prices all at once, and they are also adept at correcting in reverse when the rate of realization is insufficient.
AI is probably not fake. The problem is that current stock prices have already assumed that AI will soon become a very profitable, very stable, and very large-scale business.
This default value is where problems are most likely to occur later.
References
- OpenAI: Introducing ChatGPT, November 30, 2022.
- OpenAI: GPT-4 research, March 14, 2023.
- NVIDIA: FY2024 Q1 Earnings Report, May 24, 2023.
- NVIDIA: FY2025 Q1 Earnings Report, May 22, 2024.
- Western Digital: Completes SanDisk Spin-Off, February 24, 2025.
- SanDisk: FY2026 Q3 Earnings Report, April 30, 2026.
- Goldman Sachs: Gen AI: Too Much Spend, Too Little Benefit?, 2024.
- Sequoia Capital: AI’s $600B Question, 2024.
- Gartner: 30% of GenAI Projects Will Be Abandoned After Proof of Concept, July 29, 2024.
- MIT NANDA:
The GenAI Divide: State of AI in Business 2025.
Author’s Notes
Original Prompts
The AI boom has caused many companies' stock prices to soar. We need to organize the stock gains of related companies since ChatGPT was released according to a timeline, marking periods of steepest increases, what state of AI corresponded to those times, and why the corresponding stocks surged. Why did [Shandili/Company Name] surge so much? Besides the influence of AI, there are other factors. [Shandili]'s original market capitalization was not high; lifting it requires capital, and if the market cap is low, less capital needs to be expended. Search through research reports: Will this wave of AI eventually collapse? Currently, everything is burning cash, and there is no complete profit cycle established yet.
Writing Outline Summary
- Instead of presenting all AI stocks as a market data list in the main body, it writes according to categories such as “model capability, order fulfillment, infrastructure diffusion, enterprise implementation, and storage cycle.”
- The section on Flash Memory deliberately did not only focus on AI demand, but also included NAND cycles, independent listing status, and low market cap elasticity.
- Regarding the bubble issue, it was not presented directly as collapsing or stable; instead, it was broken down into upstream profit cycles and downstream ROI cycles.
- The article minimized details of many individual companies (e.g., AMD, TSMC, and power stocks), otherwise it would become a mere stack of data/information dumping.
- Stock price metrics are limited to the multiples level, used for explanatory purposes, not for trading judgment.