Past the "Wow" Phase
We're no longer in the phase where seeing an AI write something feels like magic. It's just a tool now, which is better. Now we can ask: What's it actually good for?
The buzz around AI content generation created unrealistic expectations. People bought tools expecting to generate perfect content that requires zero editing. That's not how it works. AI generates first drafts, not finished pieces.
Understanding this distinction changes how you use the tool. A first draft that's 40% done and requires 2 hours of editing is genuinely useful. A first draft that's 10% done and requires 6 hours of rework is a waste of your time.
What AI Actually Generates Well
Outlines and structure. Give Claude a topic and 3 minutes of notes, and it will generate a solid outline with H2s, H3s, and a logical flow. You'll probably edit 20% of it, but 80% is there. This saves serious time.
First drafts at scale. If you need 10 variations on a topic or angle, AI is faster than thinking through 10 options yourself. Variation work—emails with different value props, social posts targeting different angles, headline iterations—this is where AI shines.
Summaries and reformatting. "Summarize this 2,000-word article in 200 words" or "Turn this outline into a podcast script"—AI handles format conversion cleanly. The structure is already there, so it's just translation.
Social media snippets. Email-to-social, blog-to-social, article-to-tweet conversions. AI is good at compression and tone adjustment. Takes a 1,500-word blog post and generates 6 social posts in seconds.
Boilerplate and filler. "Write an introduction explaining why this matters" or "Write a conclusion that calls the reader to action." These are sections where there's less risk of losing your voice because boilerplate is boilerplate.
What AI Generates Poorly (Still)
Genuine insight. AI can't access your lived experience, your specific data, your real failures. If your content's value comes from original analysis or personal story, AI can't generate that. You have to.
Research-backed claims. AI hallucinates. It generates plausible-sounding statistics, dates, and citations that don't exist. I've caught Claude confidently stating that a feature was released in 2019 when it was released in 2021. You need to fact-check everything.
Sustained narrative voice. AI can do voice for 100 words. Can it maintain your voice for 2,000 words? Harder. It drifts. You'll notice the tone shift midway through if you're paying attention.
Humor and timing. Funny writing requires understanding context, audience, and what makes something surprising. AI is better at formulaic humor (puns, wordplay) than genuine comedic timing.
Controversial or nuanced takes. When there's actual debate, AI tends to hedge. It won't fully commit to an opinion. If your content's power comes from a bold stance, AI will water it down.
Prompt Engineering: The Skill That Matters
Prompt engineering isn't magic. It's specification. The better you specify what you want, the better the output.
Here's my template for content prompts:
- Context: "I'm a Make.com developer writing for other Make.com developers."
- Goal: "Write a guide that teaches workflow automation, not product promotion."
- Format: "5 sections, 400-500 words each, H2 titles with no numbers."
- Voice: "Conversational, Chicago-accent friendliness, assume intermediate technical knowledge."
- Reference material: [Paste 2-3 existing pieces you've written as style examples].
That specificity is what separates a usable draft from garbage output. Most people skip this and wonder why the AI output is generic. You have to be specific.
Workflow Integration: The Real Power
Standalone AI tools are less useful than AI in a workflow.
My process: Write outline (manual) → Feed outline to Claude → Rough draft (AI) → Edit for voice and accuracy (manual) → Copy edit (Grammarly) → Publish.
The AI does steps 2 and 3. I do the heavy lifting on steps 1, 4, and 5. Total time: 2 hours instead of 4 hours. That's a real win, but it's not "set it and forget it."
Quality Control: The Non-Negotiable Step
You can't publish AI drafts unedited. Not if you care about your reputation. You need a QC process.
My QC checklist:
- Fact-check any statistics, dates, or claims. (AI hallucinates.)
- Read for voice consistency. Did the tone shift? Kill those paragraphs.
- Check for vague statements. AI loves weasel words. ("Generally," "typically," "often.")
- Verify any code examples or technical instructions work exactly as described.
- Trim repetition. AI repeats ideas because it's pattern-matching, not thinking.
This takes 30-45 minutes for a 2,000-word post. That's the cost of using AI responsibly.
The Volume vs. Quality Trade-Off
You can use AI to generate a lot of mediocre content fast, or less content at high quality. Both are valid strategies, depending on your goal.
High volume, lower QC: Generate 4 blog posts per week. Light editing, ship fast. Works for news-cycle content, trend-responsive writing where timing matters more than depth.
Lower volume, higher QC: Generate 1-2 posts per week. Deep editing, fact-check everything, ensure voice is perfect. Works for evergreen content, where reputation matters more than speed.
I choose #2 because my audience comes back for voice and depth, not speed. But if you're running a content news site, #1 is smarter.
Realistic Expectations for 2025
AI content generation will get better at reasoning, fact-checking, and voice consistency. But it won't get better at original insight. That requires lived experience. AI can't have that.
Expect AI to be genuinely useful for: Outlines, first drafts, format conversion, social snippets, boilerplate sections. The "AI writes your entire blog" dream isn't here yet, and I don't think it's coming.
The realistic future: Every writer uses AI tools. Quality writers use them for speed. Bad writers use them as a crutch and the output is obvious. The differential isn't whether you use AI. It's how well you use it.