Good morning ☀️, leader of the next gen.

The future belongs to you. Let’s make conscious leadership the norm and embrace innovation as the driving force for positive change 🌍✨

WHAT’S AT STAKE TODAY ⚡

• 📊😇 Meta exec denies artificially boosting Llama 4's benchmark scores. AI performance numbers drama heats up!
• 🎬⏱️ Amazon says its AI video model can now generate minutes-long clips. Digital filmmaking gets extended cut!
• 🔍✨ Andi enters the AI tool spotlight. Another helper wants your search attention!
• 💻🧠 OpenAI reportedly mulls buying Jony Ive and Sam Altman's AI hardware startup. AI software giant eyes hardware territory!
• 🛒💼 IBM acquires consultancy Hakkoda as it continues its AI investment push. Big Blue's shopping spree continues!
• 👑💰 Krea's founders snubbed royal grants to build AI startup now valued at $500M. Rejecting kings pays off!
• 🌙💸 AI video startup Moonvalley raised a fresh $43M, SEC filing shows. Movie magic gets more funding!
• 🤖👥 Shopify CEO tells teams to consider using AI before growing headcount. Robots first, humans second!
• 🖼️🔍 Google's AI mode now lets users ask complex questions about images. Picture detective gets smarter!

⚡ Latest in AI

Meta exec denies the company artificially boosted Llama 4’s benchmark scores

Meta VP Ahmad Al-Dahle denies Llama 4 benchmark manipulation as evaluation integrity takes center stage

Meta has denied allegations that it trained Llama 4 models on benchmark datasets, a move that would compromise evaluation fairness. VP Ahmad Al-Dahle responded to rumors—sparked by an unverified claim from a supposed ex-employee—calling them "simply not true."

The controversy erupted after social media users noted inconsistent Llama 4 performance and discrepancies between publicly released models and those used in LM Arena tests. Al-Dahle attributed the issues to deployment variations across platforms.

Critics argue that AI benchmarks like MMLU and GSM8K are increasingly susceptible to overfitting. As models grow larger, keeping training data clean from test content is harder—raising questions for the entire industry, not just Meta.

The incident puts pressure on Meta to boost transparency in its benchmarking practices as it promotes Llama 4 as an open-source challenger to closed models from OpenAI and Anthropic. The larger challenge: keeping evaluations fair in an AI arms race.

⚡ The companies of the future

Amazon says its AI video model can now generate minutes-long clips

Amazon Nova Reel AI Video

Amazon has upgraded its AI video model Nova Reel to version 1.1, which can now generate videos up to two minutes long.

The enhanced model can create "multi-shot" videos with consistent style across shots and accepts prompts up to 4,000 characters to produce videos composed of six-second segments. Nova Reel 1.1 also introduces a "Multishot Manual" mode that allows users to reference an image alongside a prompt for greater control over video composition, supporting up to 20 shots per video when using a 1280 x 720 resolution image.

The model is available through AWS platforms including Bedrock, though users must request access (which is automatically approved). While Amazon hasn't disclosed the source of Nova Reel's training data or provided creators a way to opt out, the company has committed to protecting AWS customers accused of copyright violations with model-generated media.

Why it matters: Amazon’s improvements to Nova Reel push it further into competition with OpenAI’s Sora and other generative video tools—turning AI-generated film from novelty into serious creative infrastructure.

⚡ A Private, Conversational Alternative to Search

Andi is a generative AI-powered search assistant that replaces cluttered result pages with direct, conversational answers. Using semantic search and a clean, ad-free interface, it delivers fast, context-aware responses—without tracking your data.

Whether you're a student, professional, or casual browser, Andi offers a smarter and more private way to find what you need.

⚡ More AI Bites

1. 🛠️🤖 OpenAI reportedly mulls buying Jony Ive and Sam Altman's AI hardware startup. AI software giant eyes hardware territory!
2. 🧠💼 IBM acquires consultancy Hakkoda as it continues its AI investment push. Big Blue's shopping spree continues!
3. 👑🚀 Krea's founders snubbed royal grants to build AI startup now valued at $500M. Rejecting kings pays off!
4. 🎥💸 AI video startup Moonvalley raised a fresh $43M, SEC filing shows. Movie magic gets more funding!
5. 🛒🤖 Shopify CEO tells teams to consider using AI before growing headcount. Robots first, humans second!
6. 🖼️🧠 Google's AI mode now lets users ask complex questions about images. Picture detective gets smarter!

⚡ Trends for the Future

IBM Launches z17 Mainframe to Power the AI Workloads of Tomorrow

IBM’s AI-optimized z17 Mainframe

The details:

IBM has unveiled the IBM z17, its latest mainframe computer hardware designed to accelerate AI adoption across enterprises. The system, announced Monday, builds upon IBM's mainframe legacy while specifically addressing the growing demand for AI processing capabilities.

Despite seeming old-fashioned, mainframes remain critical infrastructure for 71% of Fortune 500 companies, with the market valued at approximately $5.3 billion in 2024 according to Market Research Future. The z17 represents a significant advancement in this space, featuring full encryption and powered by IBM's new Telum II processor.

The z17 delivers impressive performance metrics, capable of processing 450 billion inference operations daily—a 50% increase over its predecessor, the z16, which was released in 2022. IBM has engineered the system for compatibility with various hardware, software, and open-source tools, optimizing it for more than 250 AI use cases including AI agents and generative AI applications.

Tina Tarquinio, VP of product management and design for IBM Z, revealed that development of the z17 began five years ago, well before the current AI boom triggered by ChatGPT's release in late 2022. The company invested over 2,000 research hours gathering feedback from more than 100 customers during the development process.

"It has been wild knowing that we're introducing an AI accelerator, and then seeing, especially in the later half of 2022, all of the changes in the industry regarding AI," Tarquinio told TechCrunch. "The possibilities are really unlimited in terms of what AI can help us do."

The z17 is designed with future adaptability in mind. Initially supporting 48 IBM Spyre AI accelerator chips, IBM plans to increase this to 96 within a year. "We are purposely building in headroom," Tarquinio explained. "We're purposely building in AI agility. So as new models are introduced, [we're] making sure that we've built in the headroom for bigger, larger models."

Energy efficiency stands as another highlight of the new mainframe. According to Tarquinio, the z17 increases on-chip AI acceleration by 7.5 times while using 5.5 times less energy than competitive solutions for multi-model processing.

What makes this crucial: With the z17, IBM is betting big on AI's future by bringing cutting-edge performance and energy efficiency to mission-critical enterprise infrastructure—positioning itself as a key player in the next generation of intelligent computing.

⚡ Women Powerhouse Event

Women in AI: Redefining the Future of Technology

Women powerhouse all under one single event 🤩

About the event:

Women in AI is a dynamic panel showcasing how women have successfully created efficiency in their processes by leveraging technology in their business and personal lives.

Be there and support women with a great cause

Event Details:

📅 Date: April 14, 2025

📍 Location: The Moore, Miami

🔗 Register here:Sign up now

Do you have a business problem keeping you up at night?

Here’s your chance to get it solved! Share your most staggering challenges with us, and I’ll use the power of AI to find solutions tailored just for you. I’ll feature the answers in one of our upcoming Supercharged issues—let’s tackle it together!

📈 AI Playground: Elevate Your Data Analytics 📈

🔧 This Week’s Tool: SAS Viya 🔧

Overview: SAS Viya is an advanced AI, analytics, and data management platform developed by SAS Institute. It offers a comprehensive suite of tools designed to facilitate data analysis, machine learning, and model deployment across various environments. By integrating open-source compatibility and cloud-native architecture, SAS Viya empowers organizations to make data-driven decisions efficiently. 🚀

Why Is It Better Than Other Tools? ✨

• ⚡ Open-Source Integration: Seamlessly integrates with popular open-source tools like Python, R, and Jupyter, allowing data scientists to leverage existing code and methodologies.
• ☁️ Cloud-Native Architecture: Designed for cloud deployment, offering scalability and flexibility to meet the evolving needs of businesses.
• 🛡️ Comprehensive Governance: Provides robust features for detecting algorithmic bias, auditing decisions, and monitoring models, ensuring ethical and responsible AI deployment.
• 📊 Advanced Analytics: Utilizes econometrics and machine learning to create predictive models, enabling organizations to forecast scenarios based on complex data.

What Does It Do Best? 🌟

• 📈 Predictive Modeling: Develops accurate forecasts and simulations to support strategic planning and decision-making.
• 🔍 Fraud Detection: Identifies unusual patterns and anomalies in data to detect and prevent fraudulent activities.
• 🛠️ Data Management: Offers tools for data cleansing, transformation, and integration, ensuring high-quality data for analysis.
• 📉 Risk Assessment: Evaluates potential risks by analyzing historical data, aiding in proactive risk management strategies.

Applications 💼:

• 🏦 Financial Services: Enhances credit scoring models, detects fraudulent transactions, and manages financial risks.
• 🏥 Healthcare: Assists in patient data analysis, disease prediction, and healthcare resource optimization.
• 🛒 Retail: Analyzes customer behavior to optimize inventory, pricing strategies, and personalized marketing.
• 🚚 Manufacturing: Predicts equipment failures and optimizes maintenance schedules to reduce downtime.

Follow This Simple Guide to Get Started with SAS Viya:

1. 🌐 Visit the Website: Go to SAS Viya to learn more about the platform.
2. 🔗 Explore Documentation: Access comprehensive resources and tutorials to understand the platform's capabilities.
3. 🛠️ Set Up Environment: Utilize cloud providers like AWS, Google Cloud, or Microsoft Azure to deploy SAS Viya.
4. 📊 Integrate Data Sources: Connect your existing data repositories to SAS Viya for seamless data analysis.
5. 🤖 Develop Models: Use the integrated tools to build, train, and validate machine learning models.
6. 🚀 Deploy and Monitor: Deploy models into production and monitor their performance to ensure accuracy and reliability.

SAS Viya empowers organizations to harness the power of AI and analytics, driving innovation and informed decision-making. 🌟

💡 Challenge: Utilize SAS Viya to analyze a dataset relevant to your industry and share the insights gained. Discover how it can transform your data analytics processes and enhance business outcomes! 🚀