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You've tried ChatGPT. You've experimented with Claude. Maybe you've even paid for the premium versions.

Anthropic's Agent Skills changes that paradigm entirely.And yet... the outputs are underwhelming. Generic. Vague. Not quite what you needed. So you conclude: "AI isn't there yet."

But here's the thing: The AI isn't the problem. Your context is.

The Hidden Skill Nobody's Teaching

While everyone's talking about "prompt engineering" like it's some mystical art form, they're missing the real issue: Most people don't know how to provide context.

They type "write me a blog post about marketing" and wonder why they get a bland, 101-level article that could've been written by anyone, about anything, for no one in particular.

The AI isn't failing. It's doing exactly what you asked—with exactly the information you gave it.

Which is almost nothing.

What's Actually Happening

Here's what most people don't realize: AI models are context machines. They don't have access to your brain, your business, your audience, your goals, or your brand voice. They only know what you tell them.

When you give minimal context, you get minimal output. When you provide rich, specific context, the output transforms completely.

Think of it like this: Imagine hiring a copywriter, but instead of a proper brief, you just say "write something good." Then you're shocked when they don't nail your brand voice, understand your customer pain points, or match your business objectives.

That's what most people are doing with AI.

The Context Gap

Research from Anthropic's usage data shows that the average prompt length is under 50 words. Meanwhile, prompts over 500 words—those that include detailed context, examples, and constraints—produce outputs that users rate 3.7x higher in quality.

The pattern is clear: More context = better output.

But here's where it gets interesting: It's not just about quantity of context. It's about what kind of context you provide.

The Five Context Layers That Actually Matter

1. Objective Context

What are you actually trying to achieve? "Write a blog post" isn't an objective. "Create a 1,500-word SEO-optimized blog post targeting mid-market SaaS founders who are considering their first content hire" is an objective.

2. Audience Context

Who's going to read/see/use this? What do they know? What do they care about? What language do they use? Generic outputs come from generic audience assumptions.

3. Style Context

How should this sound? Formal or casual? Technical or accessible? Punchy or flowing? Don't just describe it—show examples of writing you love. Feed it your own content to learn from.

4. Background Context

What happened before this? What's the larger story? What constraints exist? What's been tried already? Context isn't just about the task—it's about everything surrounding the task.

5. Success Context

What does good look like? Provide examples. Link to competitors you admire. Describe the feeling you're going for. "You'll know it when you see it" doesn't work with AI.

How It Works in Practice

Let's say you're creating a product description.

Bad prompt (typical approach):

"Write a product description for my SaaS tool"

Good prompt (context-rich approach):

"Write a 150-word product description for TimeSync, a calendar coordination tool for remote teams. Our target customer is operations managers at 50-200 person companies who are frustrated by the endless 'when works for you?' email chains. Our brand voice is helpful but not cutesy—think Notion meets Superhuman. The description should emphasize time saved (users report 5 hours/week) and the feeling of finally having meetings that don't require 12 emails to schedule. End with a soft CTA to start a free trial. Here's our current homepage copy for voice reference: [paste copy]."

The second prompt gives the AI everything it needs to succeed. The first prompt guarantees mediocrity.

Why This Matters Now:

As AI tools become ubiquitous, the quality gap between good and bad AI users is widening dramatically.

Early adopters who understand context are producing work that's indistinguishable from (or better than) expensive agencies and freelancers. Meanwhile, people using AI poorly are flooding the internet with slop—and then blaming the technology.

The companies investing in "AI literacy" aren't teaching their teams how to use ChatGPT. They're teaching them how to think about context, information architecture, and communication.

This isn't a technical skill. It's a thinking skill.

The Real Opportunity

Here's what's fascinating: Most people have the context they need—it's in their heads, their Google Docs, their Notion databases, their previous work. They just don't think to provide it.

They treat AI like a search engine: Type a few words, get an answer.

But AI isn't a search engine. It's a collaborator. And collaborators need context to do their best work.

The breakthrough happens when you realize: The effort you put into providing context directly determines the quality of output you receive.

Want better blog posts? Feed the AI your best past posts, your brand guidelines, your target keyword research, and specific reader pain points.

Want better sales copy? Provide customer testimonials, objection lists, competitor positioning, and your unique value prop.

Want better code? Include your coding standards, architecture decisions, relevant documentation, and specific use cases.

What's Next?

The next generation of AI tools will get better at asking for context—prompting users to provide the missing pieces before generating output.

But the real competitive advantage will belong to people and teams who develop "context fluency"—the ability to quickly identify and articulate the information AI needs to produce exceptional work.

Some companies are already building internal "context libraries"—repositories of brand voice examples, audience research, style guides, and past work that can be quickly referenced when prompting AI.

Others are training "prompt specialists" whose job isn't to write prompts, but to extract context from stakeholders and translate it into AI-ready briefs.

The skill of the future isn't "knowing how to prompt AI." It's knowing what information matters and how to communicate it clearly.

BTW: The Irony of Context

Here's the twist: This entire piece about the importance of context was created using AI—but only after providing it with detailed context about Big Idea story structure, target audience, narrative voice, and specific examples to learn from.

The context made the difference.

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The Recap: Meta acquired Singapore-based AI startup Manus for over $2 billion in a deal struck in just 10 days, bringing general-purpose AI agents that hit $100 million ARR within eight months of launch directly into Facebook, Instagram, and WhatsApp's 3+ billion user ecosystem. The acquisition—Meta's third-largest ever—positions Zuckerberg to convert social platforms into distribution channels for AI agents while shedding Manus's Chinese origins (the startup will cut all ties to Chinese investors and cease China operations post-deal) to avoid regulatory scrutiny as Senator John Cornyn already raised flags about American capital funding Chinese AI.

Unpacked:

Bottom line: Meta's $2B Manus acquisition isn't about the technology—it's about distribution arbitrage. By embedding general AI agents into platforms with 3 billion users, Meta transforms Facebook, Instagram, and WhatsApp from social networks into agent marketplaces, positioning itself as the infrastructure layer where AI agents reach consumers at scale. The 10-day deal timeline and aggressive China-decoupling reveal Meta's panic about OpenAI's integration moat: if ChatGPT becomes the transaction layer across DoorDash, Spotify, and Uber, Meta's platforms risk becoming dumb pipes that users bypass for agentic workflows.

Electrical grid software startups like Gridcare and Yottar are gaining traction by finding spare capacity that already exists on stressed infrastructure, as AI data centers push electricity rates up 13% in 2025 and utilities scramble to avoid building expensive new plants—making grid optimization the critical bottleneck constraining AI scaling, not just chips or models.

ChatGPT integrations with DoorDash, Spotify, Uber, Target, and others let 800 million weekly users order food, book rides, and shop without leaving chat—positioning OpenAI as a transaction intermediary that captures value across verticals by embedding itself between consumers and every service they use.

Social media follower counts have never mattered less as algorithms prioritize engagement over audience size, fundamentally restructuring influence models and forcing creators to optimize for viral distribution rather than building loyal followings—the shift from follower-based to algorithm-based metrics is reshaping competitive advantage in content platforms.

HSBC blocks its banking app for users with F-Droid-installed Bitwarden, weaponizing security policies to lock customers into proprietary ecosystems and revealing how platform control over security infrastructure can override user autonomy—financial institutions are using "security" as justification for walled-garden enforcement.

Google's search dominance is genuinely eroding as AI chatbots and alternative discovery methods fragment information consumption, with Adobe reporting AI search referrals surged 1,300% during 2024 holidays—forcing enterprises to rethink search-dependent customer acquisition strategies as conversational AI becomes the new interface layer.

GOG's separation from CD Projekt while staying DRM-free signals that platform alternatives can sustain competitive positions against walled gardens when they offer genuine user-side value—consolidating independence from corporate owners is rare but demonstrates open approaches can work at scale.

Windows on Arm achieved viability in 2025 with Snapdragon X finally delivering compatibility and performance, potentially disrupting Intel/AMD's desktop monopoly as businesses face multi-architecture futures that reshape chip strategy, software dependencies, and supply chain decisions across enterprise and consumer markets.

A developer migrated to an almost all-EU tech stack and saved €500 annually, proving European alternatives are becoming economically competitive with US big tech—geographic decentralization and regulatory divergence are enabling viable platform alternatives that weren't possible five years ago.

Best of AI™ Team

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