The AI bubble charts to watch and how to read them

What can an AI bubble graph show, and what can it not show?

A chart shows a relationship over time. One line alone, such as a rising stock index, says little, because rising prices occur in booms and in bubbles. A bubble shows in the distance between 2 lines: price against earnings, spending against revenue, funding against results. When you open an AI bubble graph, first ask which 2 things it compares.

A chart also cannot give a date. Prices can stay high for years after the gap appears, and a falling line only confirms a bubble afterwards. Use charts to measure the size of the gap and its direction, and compare the same gap across several quarters. Use the direction to decide how much risk to carry, and leave the timing to scenarios.

Which AI bubble charts should you watch first?

6 charts cover most of the question. A valuation chart compares prices with earnings. A spending chart compares capital spent with revenue earned. A funding chart tracks venture rounds and their prices. A credit chart tracks borrowing for the build-out. A data center chart tracks power, vacancy, and chip life. A composite score chart combines the others.

Each chart answers one question and carries one weakness. Valuations can be high for good reasons. Spending can lead revenue by years. Funding can reflect a few large deals. Credit can hide off the main balance sheet. Data center figures lag construction. Composite scores depend on their method. Reading the 6 together limits the damage of each weakness, since a weak signal on one chart is checked by the others.

How do you read an AI valuation chart for bubble signs?

Plot the price of AI-exposed companies next to their earnings or revenue, using the same dates. In a boom the 2 lines climb together. In a bubble the price line climbs faster and the gap widens. The EQOS Asia AI study records valuations up ×4 in 18 months, which is the kind of slope this chart is designed to catch (April 2026).

Look at the ratio line, not only the 2 lines. A price-to-earnings or price-to-sales ratio shows the gap in one curve. Compare it with the ratio of an earlier technology wave and with the ratio for mature companies in the same sector. Write down the ratio at each quarter end so that the slope becomes visible. A ratio far above both, for several quarters, deserves a closer look at who is buying.

How do you read an AI spending-versus-revenue chart?

Put yearly capital spending on one line and the revenue that AI products earn on the other. A gap is normal at the start, since builders invest before customers pay. What matters is the slope. If revenue is closing the gap year after year, the boom reading gains weight. If the gap keeps widening, spending is running on expectations.

The EQOS Asia AI study puts spending at about $200 billion a year with 95% of deployed projects failing in production. Add a second revenue line for paid, recurring contracts only, since pilots and free trials inflate the first. The distance between the 2 revenue lines shows how much of the apparent demand is real. This chart holds the strongest evidence in the whole set.

How do you read AI funding, credit, and data center charts?

For funding, chart the median price step-up between rounds and the share of rounds that are down rounds. For credit, chart borrowing for data centers against the revenue those sites earn. A rising credit line beside flat revenue means lenders are carrying the risk. A funding chart that turns down first often leads the credit chart by a few quarters.

For data centers, chart announced power against energized power, vacancy by hub, and the depreciation period companies assign to chips. Keep each hub on its own line, since an average hides tight and loose markets. Singapore, Hong Kong, and ASEAN readers gain most from separate lines, because supply limits differ from hub to hub, and a hub with a long history gives the clearest baseline.

Which chart mistakes distort an AI bubble graph?

5 mistakes recur. A truncated axis makes small moves look huge. A log scale makes steep climbs look gentle. A start date picked at a low point exaggerates growth. Nominal prices ignore inflation. A chart of survivors leaves out the companies that failed. Check the axis, the scale, the start date, the adjustment, and the sample before you trust a picture.

Beware of overlaid lines with 2 vertical axes. The chart maker can pick scales that make any 2 lines look related. Prefer a ratio on a single axis, or 2 charts side by side with the same dates. When a graph arrives without its source and its data table, treat it as an illustration, not as evidence.

How can you build your own AI bubble dashboard each quarter?

Choose 6 charts, fix the sources, fix the dates, and update them every quarter. Write one line under each chart: green, amber, or red, with the reason. 3 or more reds in one quarter mean the gap is widening on several fronts at once. Keep last quarter’s version so that the direction stays visible.

The EQOS Asia AI study gives a composite view: a φ(S) score of 0.337 for the enterprise AI market, below the 0.5 viability threshold the study sets for a successful deployment. The EQOS Asia method explains the score, and the Λ simulator estimates yours. A 30-minute session on the contact page builds the dashboard for your own decision.

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About the author

Stéphane Jambu

President, EQOS Forecast Asia

Stéphane Jambu leads EQOS Forecast Asia and works as a consultant in e-commerce, search visibility, and AI. LinkedIn profile