🚀 AI Just Took 55,000 Jobs (Here's What Changed)

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Hey, it's Oliver, here's your AI update for this week!

This week: AI is taking jobs (55,000 layoffs citing AI in 2025), hiring is becoming a nightmare for both sides, and OpenAI just added teen protections after some serious pressure.

Here's what happened:

• AI-driven layoffs hit 55,000 – Amazon, Microsoft, Salesforce cut thousands citing AI
• AI hiring creates "doom loop" – both companies and job seekers miserable
• OpenAI updates teen safety – new U18 Principles for ChatGPT and Sora

Let’s break it down.

🛠️ Tool of the Week: Bearly AI (Not Sponsored)

The AI research assistant that works offline (because your data shouldn’t leave your computer)

Most AI tools send everything you type to their servers. Bearly doesn’t.

It’s a desktop app (Mac/Windows) that brings AI directly to your workflow — reading PDFs, summarizing articles, generating content, answering questions about documents — all while keeping your data local and encrypted.

What makes it different:

• Works offline – AI runs on your device, not the cloud
• Privacy-first – Your research, documents, and queries stay on your computer
• Works anywhere – Hotkey brings it up in any app
• Multi-model – Access GPT-4, Claude, and other models in one place

What it does:

• Summarize research papers – Upload PDFs, get instant summaries
• Chat with documents – Ask questions about any file
• Generate content – Writing, code, analysis without switching apps
• Web scraping – Grab content from articles and process it
• Templates – Pre-built prompts for common tasks

Real-world example:

You're researching a topic with 10 PDFs open. Instead of reading all 200 pages:

1. Upload PDFs to Bearly
2. Ask: "What are the main disagreements between these papers on [topic]?"
3. Bearly analyzes all 10 docs and gives you a synthesized answer
4. Follow up: "Which paper has the strongest evidence?"
5. Get a detailed breakdown with citations

All without your documents ever leaving your laptop.

Why people use it:

• Privacy – No data sent to cloud servers
• Researchers – Handle multiple papers/articles at once
• Speed – No tab-switching, hotkey brings AI to any app
• Control – Choose which AI model to use

The privacy angle:

If you work with sensitive docs (legal, medical, proprietary research), sending everything to ChatGPT’s servers is a risk. Bearly processes locally, so confidential information stays confidential.

Who it's for:

Researchers, academics, lawyers, anyone reading 10+ articles/papers per project, people who care about data privacy.

The catch:

• Desktop only (no mobile)
• Offline mode requires paid plan
• $20/month is pricier than some alternatives

Pricing:

Free: Limited queries, cloud-based
Pro: $20/month – unlimited, offline mode, all models

Tools:

1. AI Drove 55,000 Layoffs in 2025 (And Counting)

AI was cited as a factor in nearly 55,000 U.S. layoffs in 2025, according to Challenger, Gray & Christmas. Total job cuts topped 1.17 million — the highest since COVID in 2020.

The big names:

Amazon – Slashed 14,000 corporate roles in October (largest layoff in company history)

• Amazon's VP of People: "This generation of AI is the most transformative technology since the Internet...we need to be organized more leanly with fewer layers."

Microsoft – Cut ~15,000 jobs through 2025, including 9,000 in July

• CEO Satya Nadella: We need to "reimagine our mission for a new era" and shift "from a software factory to an intelligence engine."

Salesforce – CEO Marc Benioff confirmed 4,000 customer support workers were cut with AI's help

• Reduced headcount from 9,000 to 5,000: "I need less heads."
• Benioff revealed AI was already doing up to 50% of work at the company by summer 2025

IBM – CEO Arvind Krishna told WSJ that AI chatbots replaced "a few hundred" HR workers

What's actually happening:

Companies aren't just automating tasks — they’re restructuring entire departments around AI. Customer support, HR, and administrative roles are being hit hardest.

The pattern:

Announce AI investment → Cut jobs → Say "AI enables us to be leaner" → Repeat.

Why this matters:

We're past the "AI will change work" phase. We're in the "AI is actively replacing workers right now" phase. If your role involves repetitive knowledge work, the pressure to "work with AI or be replaced by it" is real.

2. AI Hiring Is Making Everyone Miserable (The "Doom Loop")

Over half of companies (54% per SHRM) now use AI to recruit. About a third of ChatGPT users use it to write cover letters and resumes. The result? A hiring nightmare for both sides.

What's broken:

• AI-led interviews feel "cold" (54% of US job seekers had one)
• Cover letters all look the same now (everyone uses ChatGPT)
• Companies stopped valuing cover letters because they can't tell if a human wrote them
• Using AI to apply makes you less likely to get hired (per Dartmouth research)

• Flooded with applications (everyone uses AI to apply to hundreds of jobs)
• Can't distinguish good candidates (all resumes look polished)
• AI screening tools can "copy and magnify human biases" (researcher Djurre Holtrop)
• AFL-CIO president: "AI systems rob workers of opportunities based on criteria as arbitrary as zip codes or how often they smile"

Greenhouse CEO Daniel Chait: AI created a "doom loop" where both sides say "This is impossible, it's not working, it's getting worse."

The cycle:

1. Job seekers use AI to mass-apply to hundreds of jobs
2. Companies get flooded with AI-written applications
3. Companies deploy AI to screen applicants faster
4. Job seekers use more AI to beat the screening
5. Repeat until everyone is miserable

Real example: One job seeker hung up on an AI recruiter the first time because the experience felt too "cold." Now worries: "Some great people are going to be left behind."

State response: California, Colorado, and Illinois are enacting AI hiring regulations. Trump’s recent executive order threatens to undermine state-level AI rules.

Bottom line: The market for recruiting tech will hit $3.1 billion by end of 2025, but nobody — job seekers or employers — is happier for it.

3. OpenAI Adds Teen Protections After Pressure

On December 18, OpenAI updated its Model Spec with new "Under-18 (U18) Principles" for ChatGPT and Sora.

What changed:

• New behavioral guidelines for how ChatGPT interacts with teens (ages 13–17)
• Extended parental controls to group chats, ChatGPT Atlas browser, and Sora app
• Age prediction model rolling out to automatically detect minors and apply safeguards
• Partnership with ThroughLine to surface localized helplines in ChatGPT/Sora
• New resources: "Family Guide to Help Teens Use AI Responsibly"

Why this happened:

• Sora 2 launched in October with minimal content protections
• Japanese content group CODA (including Studio Ghibli) demanded OpenAI stop training on their content
• Disney deal announced Dec 11 — terms require "age-appropriate policies and reasonable controls"

The safeguards:

• If OpenAI can't confirm someone’s age, it defaults to U18 experience
• Adults can verify their age to access full features
• Grounded in "developmental science" per American Psychological Association guidance

What's NOT included: The announcement doesn’t detail what specifically changes in teen interactions — just that there are now "rules, values, and behavioral expectations" for minors.

The timing: This dropped 7 days after Disney invested $1B in OpenAI. Disney’s deal explicitly requires OpenAI to maintain "age-appropriate policies." Coincidence? Probably not.

Bottom line: After rushing Sora 2 to market with an "opt-out" content model that enraged creators, OpenAI is now backfilling safety features under pressure from partners and advocacy groups.

🔁 1. The "Explain It Back to Me" Technique

The technique: After AI generates something complex (code, strategy, analysis), ask it to explain what it just created back to you in simple terms. This catches errors and ensures you actually understand what you're using.

Why it works: AI makes mistakes. When it explains its own output, inconsistencies or flawed logic become obvious. Plus, you learn instead of just copy-pasting.

Copy-paste template:

[After AI generates something]

Now explain what you just created back to me:
1. What does this do?
2. What are the key decisions you made?
3. What could go wrong?
4. What would I need to change for [different scenario]?
  

Example - Code Review:

You: [AI writes a Python script]

You: Explain this code back to me like I'm a junior developer:
1. What does each function do?
2. Why did you structure it this way?
3. What would break if the API returns an error?
4. How would I modify this to handle 1000+ records?

AI: This script does three things:
1. fetch_data() calls the API and returns JSON
2. process_data() filters records where status='active'
3. save_to_csv() writes results to a file

I structured it this way because...

If the API returns an error, the script would crash at line 12 because there's no try/catch...

To handle 1000+ records, you'd need to add pagination at line 8...
  

Now you understand why the code works (or doesn't), not just that it exists.

When to use: Any time AI generates something you'll rely on — code, strategy docs, analysis, technical explanations.

Tools:ChatGPT | Claude | Gemini

🎯 2. Role-Based Prompting

The technique: Tell AI to assume a specific expert role before answering. "Act as a [role]" dramatically improves response quality for specialized topics.

Why it works: AI models were trained on expert content. When you invoke a role, you're essentially filtering for that expert's perspective and vocabulary.

Copy-paste template:

Act as a [specific expert role with experience].

Context: [Your situation]

Task: [What you need]

Constraints: [Any limitations]
  

Example - Marketing Strategy:

Act as a B2B SaaS marketing director with 10 years of experience in dev tools.

Context: I'm launching a code review tool for engineering teams. Budget is $50K for 6 months. No brand awareness yet.

Task: Create a go-to-market strategy focusing on developer communities, not paid ads.

Constraints:
- Can't sponsor conferences (too expensive)
- Team of 2 (me + one contractor)
- Must see traction in 90 days
  

AI will respond with tactics specific to dev tool marketing — writing for Hacker News, contributing to open source, developer documentation strategies — instead of generic "create social media posts" advice.

Role examples:

• "Act as a senior SQL developer reviewing a junior's code"
• "Act as a UX researcher conducting usability testing"
• "Act as a CFO explaining financial metrics to non-finance teams"
• "Act as a technical writer simplifying complex concepts"

When to use: Specialized topics where generic advice won't cut it.

Tools:ChatGPT | Claude | Gemini

⚡ 3. The "Progressive Detail" Method

The technique: Start with a high-level request, then drill down into specifics through follow-up prompts. Think of it like zooming in on a map.

Why it works: AI gives better answers when it understands the big picture first, then adds detail. One massive prompt with 10 requirements often produces generic results.

Copy-paste template:

LEVEL 1 (Overview):
"Give me a high-level approach to [task]"

LEVEL 2 (Structure):
"Expand on [specific part from Level 1]"

LEVEL 3 (Details):
"For [specific element], give me step-by-step instructions"

LEVEL 4 (Examples):
"Show me a concrete example of [step from Level 3]"
  

Example - Content Strategy:

LEVEL 1:
You: "Give me a high-level content strategy for a productivity app targeting remote workers"

AI: Focus on 3 pillars: async communication, focus/deep work, and work-life boundaries. Create content that...

LEVEL 2:
You: "Expand on the 'async communication' pillar. What specific content types would work?"

AI: For async communication:
- How-to guides on tools (Slack, Loom, Notion)
- Case studies from remote-first companies
- Templates for async standup updates...

LEVEL 3:
You: "For the 'how-to guides,' give me a step-by-step process to create one"

AI: Step 1: Identify the specific async tool...
Step 2: Outline common pain points...
Step 3: Create screenshots showing...

LEVEL 4:
You: "Show me an outline for a how-to guide on 'Async Standups in Slack'"

AI: Title: "How to Run Async Standups in Slack (Without Meetings)"
1. Why async standups work...
2. Setting up a dedicated channel...
3. Template for daily updates...
  

Each level gives AI more context about what you actually want.

When NOT to use: Simple, straightforward requests where one prompt is enough.

Tools:ChatGPT | Claude | Gemini

A quick note before you go

Thanks for reading this week’s Brain Bytes — I hope something here helped you move faster or think better.

How’d this one land?

• A ✨ Super Helpful!
• B 🤔 Some Good Stuff!
• C 😕 Could be better.

See you next week, — Oliver