Soft landing scenario for the AI bubble: the inputs a decision engine needs

What counts as an input for a soft landing in the AI bubble case?

An input is a measured value that the engine can read: a number, its source, and the date of the reading. Three groups feed a soft landing. The market inputs describe the enterprise AI market, the funding inputs describe who pays for AI spending, and the exposure inputs describe your own position.

The engine is deterministic, which means the same inputs return the same score on every run. That property makes input quality the whole game. A careful input sheet gives a score that two readers can recompute, and a loose sheet gives a number that nobody can defend.

Which 9 market dimensions feed the soft landing score?

The study groups the dimensions in 3 domains. Internal alignment holds Coherence at 0.425, Integrity at 0.458, and Resonance at 0.325. Relation and living holds Care at 0.338, Trust at 0.450, and Communication at 0.305. Action and matter holds Clarity at 0.487, Stability at 0.412, and Energy at 0.588 (EQOS Asia AI study, April 2026).

No dimension sits below the critical threshold of 0.3, and the study places the system in the fragile zone. A soft landing weighs the distance between each dimension and that line. Communication, 0.005 above it, receives the closest attention, and Energy, at 0.588, carries the system while the others improve.

How do you measure the explainability inputs of a soft landing?

Explainability of AI models deployed in companies reads 0.25 out of 1, and transparency of automated decisions reads 0.30. The study gives a concrete picture: when a model recommends cutting 200 jobs or investing $50M in a new market, nobody can say why. These 2 readings hold the Communication dimension down at 0.305.

To measure them in your own company, take a sample of recent automated decisions and ask whether a person can state the reason for each one. The study asks for explainability above 0.5 before critical decisions, using SHAP, LIME, or interpretable models. The share that passes becomes your input.

Which Care inputs matter for a soft landing in the AI bubble?

The study cites McKinsey for the estimate that 30% of hours worked could be automated by 2030, and reports that fewer than 15% of companies have a retraining plan for the people affected. Care reads 0.338. Inside it, protection of impacted people reads 0.25 and regeneration through retraining reads 0.20.

Your Care input is the share of automated roles that have a written skills plan, plus the budget behind it. The study suggests 2% to 5% of total AI investment. A rising share moves Care upward, and a soft landing needs that movement because a market that replaces workers without a plan loses support.

Which failure causes work as inputs for the soft landing score?

The study ties each cause of failed AI projects to a dimension: insufficient or biased data at 42%, no alignment with strategy at 31%, team resistance at 24%, ROI not demonstrated at 22%, and ethical issues at 18%. Each frequency tells you which dimension drives most failures and where an improvement pays the most.

Check which cause dominates in your own projects and enter it as an input. A company whose projects stall on data quality starts with Communication, and one that stalls on strategy starts with Coherence. The cause with the highest frequency gives the first lever to pull in a soft landing.

Which funding and exposure inputs complete the soft landing score?

Funding inputs show who repays AI spending. Market references from the study are about $200B a year invested and valuations up ×4 in 18 months. Add your own funding picture: the share of AI revenue paid by customers, the share financed by new capital, and the revenue per user you can document.

Exposure inputs describe your position. Enter the share of your portfolio or business plan tied to AI, the cash and debt that set your runway, and the horizon of the decision. The method page names runway as financial leeway including cash, debt, and operational resilience, so these three numbers describe it well.

How should you record and refresh soft landing inputs each quarter?

Record four fields for every input: the value, the source, the date, and the person who owns it. Mark any input older than 90 days as stale. A sheet in this format lets a colleague rerun the score without a call, and it shows which input changed when the score moves.

Rerun the inputs each quarter, and monthly when positions move fast. The EQOS Asia method page describes a monthly recompute through its Sentinel model and real-time alerts when a threshold is approached (EQOS Asia method page, October 2026). Keep a dated copy of each run to build the trend.

Where can you test soft landing inputs on your own AI bubble case?

The Λ simulator takes your ratings of the 9 dimensions and returns a structural score in about 10 minutes, free and anonymous (EQOS Asia study page, April 2026). It is the quickest way to see how your inputs change the soft landing reading, and it needs only the sheet described above.

For a decision with real money or headcount behind it, a free 30-minute pre-diagnostic with EQOS Asia reviews your input sheet and names the inputs that move the result most. This page is general information, not investment advice, as of October 2026.

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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