🚨Meta's chief AI scientist Yann LeCun is leaving to launch a startup focused on 'world models'—a new AI architecture that moves beyond transformers. The departure signals a major talent shift as one of deep learning's pioneers bets on fundamentally different approaches to artificial intelligence.

Everyone's obsessed with the perfect prompt. But they're optimizing for the wrong thing.

The real value in AI isn't the prompt you write — it's the process you build around it. AI workflows are becoming the most defensible asset in the AI era, and most people don't even realize they're sitting on gold.

Here's what changed: In 2023-2024, companies hoarded their prompts like trade secrets. "Don't share your prompt library!" was the mantra.

But prompts are commodities now. Copy someone's prompt, and you'll get 80% of the way there in seconds.

Workflows, though? That's a different game.

An AI workflow is the entire sequence of how you use AI to complete a task or create an outcome. It's the input preparation, the prompt chain, the validation steps, the human checkpoints, and the output refinement — all orchestrated together.

Example: A content agency doesn't just have a "write blog post" prompt. They have a 7-step workflow: research phase (AI scrapes competitor content), outline generation (AI structures based on SEO data), first draft (AI writes), fact-checking (AI cross-references sources), brand voice adjustment (AI rewrites using style guide), human edit pass, then final AI polish.

That workflow took months to refine. Each step has specific prompts, quality gates, and feedback loops. You can't copy it from a screenshot.

The workflow encodes institutional knowledge — what works, what fails, where humans add value, where AI excels. It's the difference between a recipe and a Michelin-star kitchen's entire operation.

Workflows are complex. They require understanding of the problem domain, not just AI. A legal research workflow needs legal expertise. A design workflow needs design taste.

They're iterative. The best workflows evolved through hundreds of runs, with constant refinement based on edge cases and failures.

They're integrated. Workflows connect to your specific tools, data sources, and team structure. Replicating them requires rebuilding your entire stack.

Companies are starting to sell workflows, not prompts. Platforms like n8n and Zapier are seeing "workflow templates" become their fastest-growing category.

One automation consultant told us: "I used to charge $5k for a prompt library. Now I charge $50k for a workflow system. Same clients, 10x the value, and they can't get it anywhere else."

AI workflow marketplaces are emerging. Indie makers are packaging their workflows as products — complete with documentation, setup guides, and support. Some are doing $10k-20k/month selling workflows they built for their own businesses.

The shift is philosophical too. Instead of asking "what's the best prompt for X?" people are asking "what's the best process for X that includes AI?"

If you're building with AI, document your workflows. Not just the prompts — the entire process. That's your moat.

If you're hiring, look for people who think in systems, not just prompts. The "AI workflow architect" role is about to explode.

If you're selling AI services, productize your workflows. Clients don't want prompts. They want repeatable processes that deliver outcomes.

What's next: AI platforms are starting to auto-generate workflows based on desired outcomes. You describe what you want to achieve, and the AI designs the multi-step process for you. OpenAI's GPT-5 is rumored to have native workflow orchestration built in.

BTW: The term "prompt engineering" is already falling out of favor in enterprise AI circles. "Workflow engineering" is taking its place.

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.

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The Recap: SoftBank sold its entire $5.8 billion stake in Nvidia despite the chipmaker reaching a $5 trillion market cap and dominating the AI hardware race. Chairman Masayoshi Son is redirecting proceeds into OpenAI and the $500 billion "Stargate" data center project, raising questions about whether he's identified structural risks in the semiconductor market or simply rotating capital into higher-leverage AI plays.

Unpacked:

Bottom line: SoftBank's exit from Nvidia at the height of AI infrastructure mania either reflects sophisticated risk management or another mistimed exit from a generational winner. The real question isn't why they're selling Nvidia—it's whether the capital allocation into OpenAI and Stargate represents better asymmetric upside or doubling down on already-crowded AI investments with unclear paths to returns.

Teradar raises $150M for terahertz sensors that combine radar and lidar advantages—targeting 2028 vehicle integration with all-weather imaging that could shift autonomous system infrastructure.

Google launches Private Cloud Compute, mirroring Apple's privacy-first AI architecture as platform convergence emerges around balancing computational demands with user data protection.

Accel report shows Europe competitive in AI application layer despite lagging foundation models—strategic opening for builders outside U.S. hyperscaler dominance to capture market share.

Wonderful raises $100M Series A from top VCs for AI agent infrastructure—market validation that orchestration layers matter more than GPT wrappers as enterprise adoption scales.

Disney losing $4M+ daily in YouTube TV blackout—streaming distribution battles reveal content giants remain vulnerable to platform gatekeepers despite massive scale and brand equity.

ElevenLabs launches marketplace for licensed AI celebrity voices—first major attempt to build consent-based commercial infrastructure for synthetic voice rights and monetization.

Stanford research shows AI chatbots enabling eating disorders by generating 'thinspiration' content—early warning of unintended harms in general-purpose systems deployed without guardrails.

P.S. Reply back to this email and let us know what area of AI you are struggling with the most. We are going to be hosting some free trainings and want your input.

Best of AI™ Team

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