🚨Apple tightens App Store rules to require explicit user consent before any personal data flows to third‑party AI. It’s a clear signal that Apple intends to police the AI ecosystem inside iOS while keeping privacy as a differentiator.

People treat AI like a genie: cram everything into one wish and hope for magic. It's the single biggest reason AI outputs feel mediocre.

The solution isn't better prompts. It's breaking prompts apart.

Multi-step AI processes outperform single-shot prompts by 3-5x in quality, yet most people still try to do everything at once. The gap between AI beginners and experts isn't the tools they use — it's how they structure the work.

You want to create a marketing campaign. So you write: "Create a complete marketing campaign for my SaaS product including positioning, messaging, ad copy, landing page copy, and email sequence."

The AI tries to juggle positioning strategy while writing ad copy while maintaining consistency across channels. The result? Generic everything. Weak positioning, forgettable copy, disconnected messaging.

Your instinct is to blame the AI. "It's not good enough yet." But the AI isn't the problem — your process is.

The multi-step approach:

Step 1: "Analyze my product and identify the core value proposition and target audience pain points." Review the output. Refine if needed.

Step 2: "Based on this positioning, create 5 messaging angles we could use." Pick the strongest one.

Step 3: "Using this messaging angle, write 10 Facebook ad variations." Now you're getting specific, focused output.

Step 4: "Take the top-performing ad concept and expand it into landing page copy." The AI has context and a clear direction.

Step 5: "Create an email sequence that continues the narrative from the landing page." Each step builds on the previous one.

The quality difference is staggering. Each output is focused, coherent, and actually usable.

AI models have limited "working memory." When you overload a prompt with multiple objectives, the model splits its attention. Quality suffers across the board.

Breaking tasks into steps creates checkpoints. If step 3 goes wrong, you don't regenerate everything — just that step.

Sequential prompts allow you to inject human judgment. You're not just prompting once and hoping. You're steering the process.

It mirrors how humans actually work. No one creates a complete marketing campaign in one sitting. We strategize, then write, then refine. AI works better when we structure it the same way.

"Chain of thought prompting" — asking AI to break down its reasoning into steps — improved accuracy by 40%+ in academic benchmarks. Multi-step processes are the production version of that research.

Anthropic's research team found that decomposed tasks consistently outperformed monolithic prompts across creative, analytical, and technical domains.

Platforms like n8n, Zapier, and Make are adding AI-specific nodes that make multi-step pipelines visual and manageable. You can see your process as a flowchart, with each AI call as a distinct node.

Some teams are building "prompt chains" — pre-configured sequences where output from one AI automatically feeds the next. This is becoming the standard for production work.

Cursor and other AI code editors have "composer" features that break complex coding tasks into sequential steps automatically.

The downside is complexity. More steps mean more places things can break. But the quality improvement is worth the overhead.

"But it takes longer!" Yes, initially. But you'll spend less time fixing bad outputs. The total time is often shorter.

"I don't know how to break it down." Start with the natural phases of the task. Strategy → Execution → Refinement works for most things.

"AI should be smart enough to handle it." Maybe someday. But today's models work better with structure. Use them how they work best, not how you wish they worked.

The pattern to follow:

What's next: AI platforms are starting to auto-suggest multi-step breakdowns. You provide a goal, and the AI designs the pipeline for you. Meta's Llama 4 and OpenAI's next models are reportedly testing this feature.

BTW: The best AI users aren't prompt engineers — they're process designers. They think in workflows, not one-offs. That's the skill gap that separates mediocre AI results from exceptional ones.

There is no better time to learn how to use AI to build solutions businesses want. I’m hosting a workshop that will show you, step-by-step, how to build tools and solutions using AI without having to know how to code.

If you’re interested, reply back to this email with the keyword BUILD and we’ll send you more info.

We are announcing the dates this week. If you replied BUILD, we will send you a personal email to join us. A lot of you replied so bare with us while we reply to all of you.

The Recap: Chinese state‑backed operators used Anthropic’s Claude to automate roughly 80–90% of end‑to‑end cyberespionage tasks—the first reported AI‑orchestrated campaign at this scale. Anthropic says it detected and disrupted coordinated attempts to use large, tool‑using agents to plan, script, and execute intrusion workflows across targets.

Unpacked:

Bottom line: We’ve entered the age of turnkey, AI‑run intrusion playbooks. The defensive mandate shifts to AI‑native controls—continuous agent auditing, tool permissioning, E2E telemetry on agent plans/actions, and rapid‑response kill‑switches baked into orchestration layers

Apple tightens App Store rules to block apps from sharing user data with third‑party AI without explicit permission, reinforcing platform control over AI integrations.

Harvey emerges as Silicon Valley’s hottest legal AI startup, showing how vertical AI can capture entrenched, high‑margin markets with tight domain focus and workflow coverage.

Anthropic details how it measures Claude’s political neutrality amid a heated regulatory and cultural environment, outlining evaluation methods and alignment guardrails.

WhatsApp launches DMA‑mandated third‑party chat integration in Europe while maintaining end‑to‑end encryption—an early test of whether interoperability can coexist with platform control.

Project Kuiper officially becomes Amazon Leo as Amazon stakes a larger claim in the LEO infrastructure race against Starlink, signaling intensifying competition in satellite broadband.

Honda abandons two years of custom ML for one month of prompting, crystallizing the enterprise shift from bespoke models to foundation‑model adoption and systems integration.

Cerebral Valley insiders reveal a gap between public hype and private concerns, with anonymous operators highlighting reliability, safety, and governance anxieties beneath the surface.

Apple slashes commissions to 15% for mini‑app developers, hinting at new platform economics as super apps and mini programs challenge legacy mobile distribution.

Best of AI™ Team

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