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Everyone’s talking about AI “users” and simulators.
They’re missing the real risk.
Your AI may pass tests… and still fail customers.

Google built ConvApparel with 4,000+ real shopping chats.
People thought they were chatting normally.
They were secretly routed to a “Good” assistant or a “Bad” one.

Humans got annoyed fast when the bot was awful.
They pushed back.
They left.
They changed what they asked.

But many prompted user simulators stayed calm.
They kept being polite.
Even when the experience was clearly broken.

That gap matters.
Because polite simulators make your product look better than it is.
You ship.
Real customers churn.

The interesting part is this.
Data-trained simulators adapted more like humans.
But a detector still flagged them as fake.
So “more realistic” is not the same as “real.”

Here’s what to do if you test AI systems.
↓
↳ Use real conversation logs whenever you can.
↳ Measure frustration signals, not just task success.
↳ Add “bad assistant” scenarios on purpose.
↳ Track drop-off, re-asks, and sarcasm.
↳ Red-team your sim to be impatient.

If your evaluation never gets messy, it isn’t real.
What’s one moment your users stop being polite?

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Everyone’s tracking AI exposure like it predicts layoffs.

It doesn’t.

The real predictor is demand.

More specifically: price elasticity.

When AI makes a task cheaper and faster, two things can happen.

Demand explodes.

Or demand barely moves.

If demand explodes, companies often hire more people.

Because there’s suddenly more work worth doing.

If demand barely moves, automation turns into headcount cuts.

Same AI.

Opposite outcome.

Example.

If AI cuts the cost of writing product descriptions by 70%.

An e-commerce brand might go from 5,000 SKUs to 50,000.

That creates new needs: QA, brand voice, compliance, testing, localization.

Demand surged.

Now flip it.

If AI cuts the cost of basic meeting notes by 70%.

Most teams won’t run 10x more meetings.

Demand stays flat.

So the “savings” show up as fewer roles.

Here’s what smart leaders do ↓

↳ Map your work into elastic vs inelastic buckets.

↳ Ask: if this gets 50% cheaper, do we do 2x more?

↳ Invest where volume can scale, not where it can’t.

Economist Alex Imas argues we need a Manhattan Project for elasticity data.

Not just for groceries.

For every major job task we’re betting careers on.

Because guessing your future is not a strategy.

In your industry, where will demand surge when AI drops costs?

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Everyone's talking about AI in sourcing.
They're missing the real opportunity.
It’s not speed.
It’s leverage.

A solo online seller asked Alibaba’s AI, Accio, to remake his old best-selling flashlight.
In one chat, it suggested simple tweaks.
Smaller body.
A bit dimmer.
Battery-powered.

Then it surfaced a factory in Ningbo, China.
The estimated unit cost dropped from $17 to about $2.50.
Weeks of back-and-forth became days.

That shift changes the game for small operators.
If your product margin is thin, sourcing speed is survival.
If your volume is low, finding the right factory is the moat.

Here’s the part most people miss.
AI can compress the messy middle.
But it can also create new risk.

↓ A simple guardrail framework before you trust any sourcing AI.

↳ Ask for full supplier transparency.
• Who is the factory.
• Who is the broker.
• What data trained the recommendations.

↳ Lock down security.
• What product files get stored.
• Who can access your specs.

↳ Set clear data guardrails.
• What the tool can retain.
• What must be deleted.

AI won’t replace good judgment.
But it can give you negotiating power faster.

What would you automate first in your sourcing process?

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A gig worker in Lagos wears an iPhone on their forehead.
They film your chores for $15 an hour.
And the video might end up training a humanoid robot.

Most people hear “robot training data” and think factories.
But a lot of it is laundry.
Dishes.
Ironing for hours.

That’s the hidden shift.
AI isn’t learning only from the internet anymore.
It’s learning from real homes, in real time.

Here’s the part leaders keep skipping.
When you train robots on intimate home footage, you inherit the mess.
Privacy gaps.
Consent gaps.
Safety gaps.

One worker making $15/hour can be life changing locally.
But the downstream value can be massive for the buyer.
The footage becomes a product.
And the worker rarely owns the upside.

If you build, buy, or deploy AI, use this simple checklist ↓
• Who owns the raw footage, forever.
• Who can resell it, and to whom.
• What gets blurred, deleted, or retained.
• How “unsafe habits” are filtered out.
↳ If you can’t answer fast, you have a risk.

Robots will copy whatever we normalize at scale.
That includes the dangerous shortcuts.

If this were your home on camera, what rule would you demand first?