Teardown Of LinkedIn's AI Features: What Helps Professionals And What Is Noise
Nasr Khan · Oct 5, 2025 · 9 min read
I spend a lot of time on LinkedIn. Not for the content — most of that is noise. But for hiring, recruiting, and keeping tabs on what's happening in the product world. So when LinkedIn started rolling out AI features over the past year, I paid attention.
As someone who's shipped AI features into production (at SimSim AI, we built an enterprise generative AI platform; at HexaHealth, we added AI-powered search), I have opinions about what makes AI useful versus what's just marketing theater. LinkedIn has done some of both.
Here's my honest teardown of LinkedIn's AI features. What actually helps professionals, what's noise, and what product lessons we can learn from their approach.
The Context: LinkedIn's AI Bet
According to Microsoft and LinkedIn's 2024 Work Trend Index, there's been a 142x increase in members adding AI skills to their profiles and a 160% increase in non-technical professionals taking AI courses on LinkedIn Learning. Job posts that mention AI get a 17% bump in applications.
LinkedIn saw this and went all-in on AI features across the platform. Some targeted job seekers. Some targeted content consumers. Some targeted... well, I'm still not sure who.
Feature-by-Feature Breakdown
Why It Works
This solves a real problem. LinkedIn's job search filters are clunky. You have to select location, then remote options, then salary range, then industry. Natural language lets you express complex requirements in one sentence. When I tested it, the results were surprisingly good. It understood "fintech" as an industry filter without me having to specify.
The Product Lesson
AI is best when it replaces a tedious multi-step process with a single action. This is the pattern. Find the workflow where users click 5+ times to accomplish something, and let AI do it in one step.
Why It Works
Resume tailoring is a known pain point. Most job seekers know they should customize their resume for each application, but few actually do it because it's tedious. LinkedIn's AI makes this fast enough to actually happen. The suggestions I saw were specific ("add quantified achievements in your project management section") rather than generic.
The Product Lesson
AI works when it turns "I know I should do this but won't" into "I'll do this because it's easy now." The behavior change requires removing friction, not just adding capability.
Why It Works
This addresses a real anxiety: "Should I even bother applying?" Job seekers often self-select out of roles they could get or waste time on roles they won't. The AI match percentage (while not perfect) gives people a data point to inform their decision. LinkedIn claims 90% of subscribers find these tools helpful in job search.
The Product Lesson
Reducing decision anxiety is valuable. When users face uncertainty ("is this worth my time?"), AI that provides a confidence signal helps them act instead of hesitating.
Why It's Mixed
The problem here is that everyone now sounds the same. When I receive LinkedIn messages, I can spot the AI-generated ones instantly. They're too polished, too structured, too... LinkedIn. The irony: the tool designed to help you stand out makes you blend in. That said, for people who genuinely struggle to start a message, it's better than nothing. Just don't send the draft without significant editing.
The Product Lesson
AI that generates content for personal communication needs a "make it weird" button. Generic perfection is a negative signal when authenticity matters.
Why It's Mixed
In theory, great. LinkedIn posts are often padded with fluff to hit some imaginary engagement algorithm threshold. A summary could save time. In practice, most LinkedIn posts don't need a summary because they could've been a single sentence to begin with. The feature is most useful for actual articles and long-form content, which is a small percentage of the feed.
The Product Lesson
Summaries are valuable when the source content is information-dense. For low-density content (most social media), you're just summarizing noise into smaller noise.
Why It's Noise
I genuinely don't understand who asked for this. If I want to learn about negotiation, I'll take a course or read a book. I don't need to "chat" with an AI simulation of a negotiation expert. It feels like LinkedIn saw Meta's celebrity chatbots and thought they needed something similar. The result is a solution looking for a problem.
The Product Lesson
Not every AI application needs a persona. Sometimes AI should be invisible infrastructure, not a character you interact with. The persona adds friction without adding value.
What LinkedIn Got Right
Let me give credit where it's due. LinkedIn's AI strategy shows some smart thinking:
1. They focused on their core use case first
Job search is LinkedIn's bread and butter. The best AI features (natural language search, resume review, job fit) all serve this core use case. They didn't try to make AI improve everything at once. They picked the highest-value workflow and went deep.
When we built search at HexaHealth, we took the same approach. Search-to-booking was the critical path. We made AI improve that first, then expanded to other features.
2. They gated features behind Premium
This is controversial, but I think it's smart product strategy. By making AI features Premium-only, LinkedIn can:
- Monetize the AI investment directly
- Target the most engaged users who'll give useful feedback
- Avoid the "AI for everyone" problem where casual users generate expensive API calls for low value
3. They kept humans in the loop
The resume suggestions don't automatically rewrite your resume. The writing assistant doesn't send messages without your approval. Everything is positioned as "assistance" not "automation." This is the right call for a product where authenticity and personal brand matter.
What LinkedIn Got Wrong
1. The AI personas feel like a checkbox
Someone on the LinkedIn product team clearly said "we need AI chatbots like everyone else." The result is a feature that exists because AI chatbots exist, not because users needed it. I'd be surprised if the engagement metrics justify the development cost.
2. The writing assistant makes everyone sound identical
This is the trap every AI writing tool falls into. The same model, trained on the same data, produces similar outputs. On a platform where millions of people are trying to stand out, making everyone's outreach sound the same is counterproductive.
3. Too many features, too fast
LinkedIn rolled out AI features across multiple surfaces in a short period. Some are excellent, some are mediocre, some are noise. A more disciplined approach would have been to ship fewer features at higher quality.
The Scorecard
Here's my summary rating of LinkedIn's AI features:
| Feature | Rating | Worth Premium? |
|---|---|---|
| Natural Language Job Search | Useful | Yes |
| Resume Review & Suggestions | Useful | Yes |
| Job Fit Assessment | Useful | Yes |
| AI Writing Assistant | Mixed | Only if you edit heavily |
| Feed Summaries | Mixed | Marginal |
| AI Expert Personas | Noise | No |
Lessons for Product Teams
If you're building AI features into your product, here's what you can learn from LinkedIn's approach:
Start with your core workflow. LinkedIn's job search features are the best because job search is LinkedIn's primary use case. Don't sprinkle AI across everything. Go deep on what matters most.
Reduce steps, don't add features. The natural language search works because it eliminates filter clicking. The best AI compresses effort.
Keep humans in control. For anything involving personal communication or professional reputation, AI should suggest, not act. LinkedIn got this right.
Resist the persona trap. Not every AI needs to be a chatbot with a personality. Sometimes the best AI is invisible.
Differentiation is hard when AI homogenizes. If your AI writing tool makes everyone sound the same, you've created a new problem while solving the old one.
Should You Pay for LinkedIn Premium for AI?
If you're actively job searching, probably yes. The natural language search, resume review, and job fit features are genuinely useful and could save you hours per week.
If you're a casual LinkedIn user or not job hunting, probably no. The AI features for content consumption (summaries, personalized takeaways) don't justify the cost on their own.
The AI expert personas? Skip them entirely regardless of your use case.
The measure of an AI feature isn't whether it uses AI. It's whether it makes users measurably better at the thing they came to do. LinkedIn's job search AI passes that test. Their chatbot personas fail it.— Nasr Khan