Pascal BORNET recently shared something that caught my attention: "Robert Kiyosaki’s warning caught my attention this week." Pascal added that Kiyosaki is not focused on inflation or debt this time, but on AI, and he predicts widespread automation-driven job losses could trigger "the largest financial collapse in history."

That framing is intentionally provocative. But it also forces a useful question: what happens to an economy built on consumption if productivity rises faster than paychecks? Pascal put it plainly: fewer jobs can mean falling incomes, crashing demand, and eventually a breakdown of markets that rely on people buying things.

In this post, I want to expand on Pascal’s point, stress-test the logic, and explore what being "ready" might actually look like for leaders, workers, and policymakers.

The core logic: jobs fund demand

At the heart of the warning is a simple macroeconomic loop:

Most households rely on wages or salaries.
Wages fund consumption.
Consumption drives revenues.
Revenues justify investment and employment.

If AI meaningfully reduces the number of paid hours humans work, the loop can break unless something else replaces lost income. Pascal captured the essence when he wrote that economies could face a world where "productivity grows, but employment doesn’t."

"Every wave of automation has displaced some and empowered others. The difference now is speed."

That last word, speed, is doing a lot of work. In previous transitions, whole industries changed over decades. With AI, adoption can happen in quarters, not generations. That changes who can adapt, how fast institutions respond, and whether the adjustment is gradual or chaotic.

What is different about AI compared to past automation

Automation is not new. We have seen mechanization in agriculture, robots in manufacturing, and software in offices. Historically, many people moved into new roles as old ones faded. So why does this wave feel different?

1) AI automates tasks that look like "knowledge work"

Generative AI and agentic workflows can draft text, analyze documents, write code, handle customer interactions, and coordinate multi-step processes. That means displacement is not limited to routine physical jobs. It hits the middle of the wage distribution, where spending power is concentrated.

2) Software scales almost instantly

A factory robot takes capital, installation, and time. A model update can be deployed globally overnight. This is the speed Pascal highlighted, and it matters because fast displacement creates short-term demand shocks even if long-term outcomes are positive.

3) Winner-take-most dynamics can concentrate gains

If a small number of firms capture a large share of AI profits, the economy can become more productive on paper while households see little income growth. When the gains are concentrated, aggregate demand can weaken even as corporate margins rise.

Could AI still make us richer? Yes, but the transition is the risk

Pascal asked the right closing question: will AI make us richer, or destabilize the system that creates wealth? The honest answer is: both are plausible, depending on how benefits and costs are distributed during the transition.

The optimistic pathway: abundance and new categories of work

AI can boost output per worker, lower costs, and create new markets. If prices fall meaningfully (for example, cheaper services, faster healthcare triage, lower administrative costs), real living standards can rise even without matching wage growth.

We have seen versions of this in the past: computing reduced the cost of many tasks and spawned entire industries. If AI enables new products and services people value, demand can shift rather than collapse.

The destabilizing pathway: a demand gap and a confidence shock

Kiyosaki’s collapse thesis is essentially a demand-gap thesis. If many people lose income quickly, they spend less. Businesses then see falling revenues, cut more costs, and a negative spiral can begin.

The deeper risk is confidence. Markets are forward-looking. If investors believe consumption will structurally weaken, valuations can reset abruptly. That is how "job losses" becomes "financial collapse" in the story: not through a single mechanism, but through feedback loops across households, firms, credit, and sentiment.

A more practical question: what does "ready" mean?

Pascal suggested the real debate is not whether the warning is right or wrong, but whether our economies are prepared. Readiness is not a vibe. It is a set of concrete choices.

What policymakers can do (without pretending there is one silver bullet)

1) Build income bridges, not just training slogans

Reskilling matters, but reskilling without income support is often unrealistic. Consider tools like wage insurance, expanded unemployment support tied to training, and portable benefits for contract and gig work.

2) Encourage broader ownership of AI-driven productivity

If capital earns more as labor earns less, inequality rises and demand weakens. Policies that widen ownership can counter this, for example employee stock ownership plans, profit-sharing incentives, or retirement vehicles that capture productivity gains.

3) Modernize competition policy

If a small set of platforms controls models, distribution, and data, gains concentrate. Competitive markets are not just about fairness, they are about sustaining a broad income base that keeps demand healthy.

4) Treat work-hour reduction as an option, not a taboo

If output per hour rises, societies can choose to convert some productivity into leisure rather than layoffs, through shorter workweeks, job sharing, or phased retirement. This requires negotiation and redesign, but it is a legitimate macroeconomic stabilizer.

What business leaders should do now

If you are deploying AI inside a company, the question is not "Can we automate this?" It is "What happens to our customers and our talent base if everyone automates this?"

Shift from replacement to redesign

Identify tasks to automate, but also identify new higher-value tasks humans will own.
Rebuild workflows so humans supervise, handle edge cases, and innovate.
Measure outcomes beyond cost: cycle time, quality, customer retention, and employee capability growth.

Invest in AI literacy like it is infrastructure

Training should be role-based: sales, finance, legal, HR, engineering, operations. The goal is not everyone becoming a prompt engineer. The goal is reducing fear, improving judgment, and preventing brittle automation that breaks trust.

Prepare for second-order effects

If AI reduces headcount, what happens to your own demand? If your customer base also faces job disruption, revenue forecasts change. Scenario planning should include labor market shocks, not just "AI efficiency" targets.

What workers can do: build leverage, not panic

A realistic posture is neither denial nor doom. Focus on building skills that complement automation and travel across industries.

AI fluency: know how to use tools, evaluate outputs, and spot failure modes.
Domain depth: expertise in a field remains valuable when paired with AI.
Human strengths: relationship-building, persuasion, leadership, creativity, and ethical judgment.
Portfolio resilience: side projects, certifications, and networks that reduce dependence on a single role.

The goal is to stay in the part of the workflow where accountability sits, not in the part that is easiest to turn into a button.

Why Pascal’s post resonated (and what it teaches about LinkedIn content)

Part of the virality is the structure. Pascal anchored the idea in a recognizable name (Kiyosaki), stated a high-stakes claim, then invited debate with a clear question. This is strong LinkedIn content because it is:

Timely: AI anxiety is current and personal.
Specific: it is not "AI changes everything," it is "AI could collapse demand."
Balanced: "bold statement - but not without logic."
Conversational: it ends with an open question that encourages comments.

If you study viral posts and content strategy, this is a reminder that the best engagement often comes from tension plus nuance, not from certainty.

So, will AI make us richer or destabilize markets?

Pascal BORNET’s framing is useful because it keeps both outcomes on the table. AI can increase prosperity, but the transition could be rough if income distribution and labor institutions lag behind technology deployment.

The most actionable takeaway for me is this: treat AI adoption as an economic redesign challenge, not just a productivity project. The faster the technology scales, the more intentionally we need to manage who benefits, how quickly workers can move, and how demand stays healthy.