Your customers used to find you on Google. Now they ask ChatGPT, Claude, or Perplexity who to buy from, and the answer decides whether you exist. Harrison Painter sits down with Krisztián Király, who runs international partnerships at OptiMonk, a Hungarian conversion optimization platform used by more than 30,000 websites across 150+ countries.

Recorded days after the EU AI Act's transparency rules took effect, the conversation covers what those rules could mean for AI-generated product photos, the pushback against AI imagery in advertising, ads arriving inside ChatGPT, and how product copy now gets written for the AI that answers your customer, not just the customer alone.

The surprising stretch comes when Király names the situations where AI is not needed at all, including his own company's rule that its AI features are not worth switching on until a store sees about 15,000 visitors a month. A software vendor naming the floor where his product stops working is rare, and it makes the advice on either side of it easier to trust.

Whether you run a store, advise companies that do, or just want to understand how buying decisions moved inside the AI assistant, this one gives you the operator's view from someone watching 30,000 storefronts adjust in real time.

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OptiMonk: https://www.optimonk.com

Krisztián Király on LinkedIn: https://www.linkedin.com/in/christian-kiraly

Getting good at AI does not take a degree, a tech background, or a year you do not have.

Kathleen deLaski has spent a decade proving it. She founded the Education Design Lab, she advises the Harvard Project on the Workforce, she teaches AI at George Mason, and she wrote the book *Who Needs College Anymore?*

She joined me on the AI Ready Podcast, and her message to anyone who feels behind was simple: if you know how to use the internet, you can figure this out.

A few things she said worth holding onto:

AI lets you isolate the exact skill a job needs, then hands you the shortest path to learn it.

For the solo entrepreneur, AI is becoming the great equalizer. In her words, it gives them a staff, in effect.

And to the professional over 50 who thinks the wave already passed: you do not need a degree, you need a push.

If you have been waiting for permission or a prerequisite to start, this episode removes both.

A new AI model out of Japan, Sakana Fugu, does something we have not really seen before. Instead of answering you itself, it hires a team of the best AI models, gives each one a piece of the job, and merges their work into one answer. Harrison calls it a manager, or a conductor: you ask one question, and behind the scenes it quietly builds a team for you.

In this episode, Harrison explains what model orchestration actually is in plain language, why he thinks this is where AI is heading, and then puts it to the test. He sends the same 8 questions to Fugu, to Claude Opus 4.8, and to GPT-5.5, and grades every answer. The result is honest, and the cost is the part that should give every builder pause.

What you'll learn:

- What "orchestration" means, explained simply

- Why the future may be teams of models, not one genius model

- What happened when a team of models went head to head with single models

- The real speed and cost tradeoff, with actual numbers

- The hidden tokens you pay for but never see

- When an orchestrator is worth it, and when one good model is plenty

- A heads-up on AI pricing and subsidies most people are not thinking about

CHAPTERS

0:00 A glimpse into the future

0:19 What is Sakana Fugu?

1:55 Not a smarter model, a manager

3:30 The test: 8 questions, three models

4:50 Speed: about 10x slower

5:30 Cost: about 49x more expensive

6:07 The hidden tokens you pay for

7:20 Inside the console

8:30 The questions, and why they're tricky

9:06 Is a team of models worth it?

9:27 When a team earns its place

10:09 The verdict

10:57 The subsidy nobody is talking about

11:18 Where this goes next

12:04 Wrap up

Mentioned: Sakana Fugu — https://sakana.ai/fugu/

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