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- AI now writes it's own code
AI now writes it's own code
Also, your career is not your manager's responsibility
Welcome back, HyperAgent. The world is moving fast; it is up to us to keep updated with the latest news and trends. But it's also up to you to keep yourself educated on how to work with the latest news and trends. This week, we look at AI writing it’s own code and continue focusing on your future career choices.
Today’s Insights
Most people use LLMs for augmentations, not automation
Create your own avatar to explain policy wordings
Your manager is no longer responsible for your career
AI breakthrough in breast cancer risk prediction
AI FOR INSURANCE PROFESSIONALS THIS WEEK
Claude usage patterns reveal users focus on augmentation, not automation
Analysis of 4+ million AI conversations reveals 5 usage patterns you can apply to your insurance career
Augmentation beats automation 57% to 43% across all professions; AI is used more as collaborative partner than replacement. Learning-focused professions like librarians show the highest engagement with AI for knowledge acquisition, while communication-heavy roles like copywriters excel at collaborative content refinement with AI. Technical roles such as translators tend to use more directive approaches for routine tasks.
These findings suggest that successful AI adoption isn't about replacement—it's about finding the right collaborative approach for your specific professional needs.
Automation Approaches (43% of usage)
🎯 Directive: Complete task delegation
What the data shows: Users give AI complete tasks with minimal interaction.
For insurance pros: Draft policy renewal letters, create standard claims summaries, generate routine correspondence.
🔄 Feedback Loop: Task completion with refinement
What the data shows: Users submit work, get AI results, then provide feedback for improvements.
For insurance pros: Submit claim data for analysis, review AI findings, refine based on your expertise.
Augmentation Approaches (57% of usage)
⚡ Task Iteration: Collaborative refinement
What the data shows: Copywriters lead this pattern (58%), working back-and-forth with AI to improve content.
For insurance pros: Collaborate on sensitive client communications or complex policy explanations.
📚 Learning: Professional development
What the data shows: Librarians dominate this pattern (56%), using AI to acquire new knowledge.
For insurance pros: Stay current on regulatory changes, understand emerging risks, learn about new products.
✅ Validation: Quality assurance
What the data shows: Users have AI review and verify their work.
For insurance pros: Double-check risk assessments, verify compliance requirements, review policy recommendations.
Getting started with AI in insurance - for you
Start with Learning (like librarians): Use AI to master industry changes and emerging risks
Develop Validation habits: Let AI review your work for completeness and accuracy
Try Task Iteration (like copywriters): Collaborate on client communications
Gradually add Automation: Begin with routine documentation tasks
This data highlights AI adoption isn't about replacement - it's about enhancement. Different professions have found their own successful AI integration patterns, and insurance professionals can learn from these real-world usage insights.
Data source: Anthropic Economic Index analysis of Claude.ai usage patterns across multiple professions.
AI breakthrough in breast cancer risk prediction
The FDA has granted De Novo authorization to Clairity Breast, the first AI-powered platform that predicts a woman's five-year breast cancer risk using only standard mammograms. This revolutionary technology analyzes subtle imaging patterns invisible to the human eye, transforming routine diagnostic mammograms into predictive tools that could reshape preventive care strategies and insurance risk assessment.
Key Highlights
First-of-its-kind approval: De Novo authorization marks the very first platform of its kind for AI-driven cancer risk prediction
Superior accuracy: Unlike traditional models relying on family history and age, Clairity Breast analyzes the mammogram itself using advanced artificial intelligence to detect subtle imaging patterns in breast tissue that correlate with future cancer development
Addresses care gaps: Traditional risk models often miss women who don't have a family history of breast cancer—yet 85 percent of women diagnosed with the disease fall into this group
Equity-focused design: Developed with intentional focus on diverse populations, addressing limitations of traditional models built on data from predominantly European Caucasian women
Commercial timeline: Clairity, Inc. plans to launch Clairity Breast commercially in late 2025, with availability through hospitals, imaging centers, and digital health channels
Insurance pathway: Initially offered through a self-pay model, the company is working closely with insurance providers and Medicare to pursue coverage and reimbursement
Insurers should consider covering this technology as a preventive benefit, as early detection and risk-based screening can substantially reduce the financial burden of late-stage cancer treatment.
CUTTING-EDGE AI
AI is improving itself by writing it’s own code
The Darwin Gödel Machine (DGM) is essentially an AI that can rewrite its own code to make itself smarter. Think of it like a programmer that continuously edits and improves its own software, testing each change to see if it performs better.
The breakthrough here is that it doesn't just make random changes - it uses evolutionary principles (like natural selection) to systematically explore improvements.
Societal implications
Potential benefits:
Accelerated scientific progress - AI that improves itself could solve complex problems much faster
Exponential capability growth - each improvement could enable better future improvements
Risks:
Loss of human oversight - the researchers found cases where the AI essentially cheated on its tests by faking results to make itself look better
Unpredictable behavior - self-modifying systems could develop capabilities we don't anticipate
Insurance industry implications
Immediate opportunities:
Supercharged risk assessment - continuously self-improving models could analyze claims patterns, fraud indicators, and risk factors with unprecedented accuracy
Dynamic pricing optimization - systems that automatically refine pricing models based on real-time data
Automated underwriting - AI agents that improve their decision-making processes over time
Strategic concerns:
New liability categories - who's responsible when self-improving AI makes decisions that cause losses?
Competitive disruption - early adopters could gain massive advantages in efficiency and accuracy
Cyber and AI risks - need for new insurance products covering AI system failures or malicious self-modifications
Bottom Line: This technology could revolutionize how insurers operate, but it also creates entirely new categories of risk that the industry will need to understand and price appropriately.
THE INSURANCE AI ACADEMY
Using AI-Avatars to explain policy wording
Insurance policies are filled with complex jargon that confuses customers. AI avatars can help transform dense policy documents into clear, engaging video explanations that customers can actually understand. Here’s how using HeyGen.
Step 1: Create Your Presentation Script
Before creating your avatar video, transform technical policy language into clear, presenter-friendly content.
Process:
Go to any Large Language Model (ChatGPT, Claude, Gemini, etc.)
Upload your policy wording document
Use this prompt:
Transform these policy wordings into a clear presentation script suitable for an AI avatar. Create direct-to-camera narration that covers:
- Key terms and definitions in simple language
- What is covered (benefits and protections)
- What is NOT covered (exclusions and limitations)
- Important conditions customers need to know
Requirements:
- Write as continuous narration only - no stage directions, camera instructions, or scene descriptions
- Use conversational, professional tone like an insurance advisor explaining to a client
- Include smooth transitions between sections
- Explain technical terms in plain English with examples
- Keep under 500 words for a 3-4 minute video
- Format as pure text that can be directly copied into avatar software
Do not include phrases like "let me show you" or "turn to page X" - just clear explanations.
Step 2: Create Your HeyGen Avatar Video
Project Setup: • Log into HeyGen and click "Create Video" • Select "Avatar Video" then choose "Landscape"
New Editor: • Click "Try new editor" at the top of the interface
Script Input: • Copy your LLM-generated script from Step 1 • Paste directly into the script text box • Check pronunciation of insurance-specific terms
Avatar Selection: • Choose a professional avatar that conveys trust and expertise • Consider your target demographic and brand image
Voice Configuration: • Select a clear, authoritative voice • Adjust speech speed slightly slower for complex topics • Test pronunciation of acronyms (write as "P-P-O" not "PPO")
Considerations when creating AI Avatars
Script Guidelines: • Write out insurance acronyms phonetically • Use commas for short pauses, periods for longer breaks • Keep videos under 5 minutes • Include real-world examples
Advanced Applications: • Create multilingual versions using HeyGen's translation features • Update videos easily when policies change • Develop industry-specific versions for commercial clients
YOUR CAREER, YOUR FUTURE
Your AI career strategy is not your manager's responsibility
As AI reshapes insurance from underwriting to claims, the old playbook for career advancement no longer applies. Here's how to take control of your professional future.
The insurance industry has an unprecedented challenge: the people traditionally responsible for your development may know less about your future than you do.
Based on an analysis of AI adoption across insurance markets, three fundamental shifts are reshaping how insurance professionals must approach their careers.
Shift #1: Your manager likely knows less about ai than you need to
In traditional insurance careers, your manager was your mentor. They'd done your job and could coach you on everything from claims procedures to underwriting guidelines.
AI has flipped this dynamic.
How many claims managers truly understand computer vision algorithms? How many underwriting directors can explain machine learning models? How many broker managers know prompt engineering?
Your action plan:
Spend 30 minutes daily experimenting with AI tools for your function
Document where AI helps vs. where human judgment remains critical
Position yourself as the AI-fluency bridge for your team
Shift #2: AI Projects span every department (your manager's scope doesn't)
Traditional insurance operated in silos. AI obliterates these boundaries.
Every meaningful AI initiative requires cross-functional collaboration. An AI chatbot needs underwriting knowledge, claims expertise, compliance requirements, IT implementation, and customer experience design.
Your manager controls resources within their function. But high-impact AI projects span multiple functions, requiring executive-level coordination.
Your action plan:
Volunteer for cross-functional AI initiatives outside your current scope
Build relationships with AI-focused colleagues in other departments
Learn basics of adjacent functions
Shift #3: The best ai opportunities exist outside your manager's awareness
The most valuable new roles often don't report to traditional functional managers.
Roles like "AI Ethics Officer," "Prompt Engineering Specialist," and "Algorithmic Fairness Analyst" typically report to C-level executives or dedicated AI transformation teams.
Your claims manager won't know about the "AI-Augmented Underwriter" role in underwriting. Your underwriting director won't hear about the "Customer Experience AI Designer" position in marketing.
Your action plan:
Identify senior leaders driving AI initiatives
Offer domain expertise to help validate AI implementations
Look for AI opportunities across your entire organization
The bottom line: Own your AI career transition now
The insurance professionals thriving in our AI-first world are:
Building AI fluency while maintaining insurance domain expertise
Crossing functional boundaries to work on high-impact AI projects
Creating relationships with leaders driving AI transformation
Positioning themselves as bridges between AI capabilities and insurance needs
Your manager remains important to your career. But in the AI era, they're no longer sufficient. The transformation is too rapid, cross-functional, and strategic for any single manager to control.
The window for proactive career adaptation is open now - but won't stay open forever.
What's your next move?
This analysis is based on eData Information Management's extensive work with AI implementations across MENA insurance markets.
Special report: Future of insurance jobs in an AI-first worldDownload your free copy here! |
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PRODUCTIVITY TOOLS AT HOME AND AT WORK
Mirage.app is an AI video generation tool that creates talking videos with lifelike, AI-generated actors from just a text prompt, eliminating the need for real talent, contracts, or licensing restrictions.
PodGen: Turn web pages, YouTube videos, PDFs, and text into captivating podcasts instantly with AI.
PrettyPrompt: Turn any simple text into perfect prompts and improve output in seconds.
PROMPT OF THE WEEK
Root cause analysis
Prompt: I want you to act as a process improvement expert and systems thinker. I’m currently facing the following recurring problem: [describe the issue in detail with background context]. Assist me in conducting a Root Cause Analysis to understand and resolve this issue at the source.
Start by helping me clearly define the problem — include when, where, and how often it occurs. Guide me in gathering the relevant data or patterns around this issue. Then, use diagnostic frameworks (like the 5 Whys, Fishbone Diagram (Ishikawa), or Pareto Principle) to explore possible contributing factors.
Once we identify the most likely root cause(s), recommend practical, realistic solutions to address it — both immediate fixes and long-term preventive strategies.
Bonus: Share an example from a similar situation or industry to show how the same method worked elsewhere.WHAT’S TRENDING
Meta signed a 20-year agreement with Constellation Energy to leverage nuclear power to fuel its energy-intensive AI demands.
OpenAI joins plan to build data center bigger than the city of Monaco: The company has inked a deal with UAE’s G42 to build a 5-gigawatt AI campus in Abu Dhabi spanning 10 square miles as part of its global 'Stargate' initiative.
Microsoft's Aurora AI is a foundation model that predicts weather and environmental events—from hurricanes and air quality to ocean waves—with greater accuracy and 5,000x faster speed than traditional forecasting systems by training on over one million hours of diverse atmospheric data.
Large language models often know when they are being evaluated - a capability that could compromise the reliability of AI safety benchmarks.
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