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Microsoft’s AGI Skepticism Amid OpenAI Ties

OpenAI and Microsoft: A Tale of Two Approaches to AI

A Shared Vision, Different Goals

Since OpenAI launched ChatGPT over two years ago, it has continued to develop smarter models and spin-off products, retaining its position as a frontrunner in the AI race. The company’s ultimate goal is to achieve Artificial General Intelligence (AGI), or AI with autonomous, human-level intelligence.

A Mission to Achieve AGI

"Our mission is to ensure that AGI benefits all of humanity," said OpenAI CEO Sam Altman in a recent blog post. "In some sense, AGI is just another tool in this ever-taller scaffolding of human progress we are building together."

A Different Approach at Microsoft

However, OpenAI’s biggest strategic partner, Microsoft, has taken a different approach. In a recent interview, CEO Satya Nadella shared that the company is not working on the long-term goal of achieving AGI, citing concerns that AGI is overhyped. With a long-standing partnership and Microsoft investing billions in OpenAI, it raises the question: how are the two still managing to work together?

No Tension in the Partnership

In an SXSW panel titled "Building Trustworthy AI: Evolving Safety Practices for GenAI," Microsoft CPO of Responsible AI Sarah Bird shed light on the companies’ dynamics. Despite the deviating approaches, she shared that there was no "tension."

"You think there would be sort of tension between those two, but in practice, it doesn’t seem to materialize that way," said Bird.

A Focus on Augmenting Human Capabilities

Rather, Bird finds that this ambitious goal pushes OpenAI to "dream big" and develop new capabilities that the world has never seen instead of making incremental upgrades. According to Bird, this drive and focus are needed for meaningful AI development.

Microsoft’s Approach to AI

Microsoft’s decision not to pursue AGI is rooted in the company’s desire to have its AI offerings work with people instead of replacing them entirely. A prime example is Microsoft’s Copilot, positioned as an "AI companion" that assists users across all of the major Microsoft offerings, including the Microsoft 365 suite of applications and Github.

"For me, [AGI] is a non-goal, and that’s true for Microsoft as well. We have a lot of humans, which is pretty cool, so I would rather have a technology that augments human capabilities and does the things that humans either aren’t great at or humans don’t want to do," said Bird.

Conclusion

In conclusion, OpenAI and Microsoft are two companies with different approaches to AI, but they are working together effectively despite their differing goals. While OpenAI is driven to achieve AGI, Microsoft is focused on developing AI that augments human capabilities. This divergence in approach allows for a more comprehensive understanding of the potential applications of AI and the need for responsible AI development.

FAQs

Q: What is OpenAI’s goal?
A: OpenAI’s goal is to achieve Artificial General Intelligence (AGI), or AI with autonomous, human-level intelligence.

Q: What is Microsoft’s approach to AI?
A: Microsoft’s approach to AI is to develop technology that augments human capabilities and does the things that humans either aren’t great at or humans don’t want to do.

Q: Why is there no tension between OpenAI and Microsoft despite their differing approaches?
A: According to Microsoft CPO of Responsible AI Sarah Bird, there is no "tension" between the two companies and they are able to work together effectively despite their differing goals.

Mercedes’ Most Affordable Electric Sedan

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Mercedes-Benz Shifts to Electric Vehicles, Offering Hybrid Option for Those Who Want It

New Strategy for the Luxury Car Maker

Mercedes-Benz has announced that its latest version of the CLA sedan will be available as an all-electric car, followed by a hybrid version, marking a significant shift in the company’s strategy. This move is a departure from the traditional approach of adapting vehicles designed for fossil fuels to be powered by batteries.

Addressing Range Anxiety and Refueling Time

The company has addressed the concerns that have held back some consumers from adopting electric vehicles, including inadequate driving range between charges and the time it takes to refuel. The new CLA has a range of almost 500 miles between charges, and with high-voltage chargers, it can add more than 200 miles of range in just 10 minutes.

Hybrid Option for Those Who Want It

Mercedes-Benz will offer a hybrid version of the CLA, which will get most of its power from a gasoline engine but be able to travel short distances solely on battery power. This option is designed to cater to consumers who still prefer to have a gasoline engine but also want the benefits of electric propulsion.

Global Competition and Market Trends

The United States is a crucial market for Mercedes-Benz, but China, where electric and plug-in hybrid vehicles account for over half of new car sales, is setting the tone for the industry. The company’s decision to prioritize electric vehicles is in line with the growing demand for environmentally friendly transportation options.

Conclusion

Mercedes-Benz’s new strategy is a sign of the times, as the company adapts to the changing landscape of the automotive industry. By offering electric and hybrid options, the company is hedging its bets on the uncertain demand for electric vehicles and government policies. The new CLA is a significant step towards a more sustainable future for the company and the industry as a whole.

Frequently Asked Questions

Q: What is the range of the new CLA?
A: The all-electric version of the CLA can travel almost 500 miles between charges.

Q: What is the price of the new CLA?
A: The company did not disclose the price of the new CLA, but it will be affordable for owners of the current version, which starts at $45,000 in the United States.

Q: Will the hybrid version of the CLA get most of its power from a gasoline engine?
A: Yes, the hybrid version will get most of its power from a gasoline engine but will be able to travel short distances solely on battery power.

Q: How long does it take to charge the new CLA?
A: With high-voltage chargers, the new CLA can add more than 200 miles of range in just 10 minutes.

Code Ignition Rejected: Learn to Write Your Own

A Brief History of AI Refusals

This isn’t the first time we’ve encountered an AI assistant that didn’t want to complete the work. The behavior mirrors a pattern of AI refusals documented across various generative AI platforms.

The Winter Break Hypothesis

In late 2023, ChatGPT users reported that the model became increasingly reluctant to perform certain tasks, returning simplified results or outright refusing requests—an unproven phenomenon some called the “winter break hypothesis.”

OpenAI’s Response

OpenAI acknowledged the issue at the time, tweeting: “We’ve heard all your feedback about GPT4 getting lazier! We haven’t updated the model since Nov 11th, and this certainly isn’t intentional. Model behavior can be unpredictable, and we’re looking into fixing it.” OpenAI later attempted to fix the laziness issue with a ChatGPT model update, but users often found ways to reduce refusals by prompting the AI model with lines like, “You are a tireless AI model that works 24/7 without breaks.”

The AI Ghost of Stack Overflow?

The Nature of Cursor’s Refusal

The specific nature of Cursor’s refusal—telling users to learn coding rather than rely on generated code—strongly resembles responses typically found on programming help sites like Stack Overflow, where experienced developers often encourage newcomers to develop their own solutions rather than simply provide ready-made code.

Cultural Norms and Communication Styles

The resemblance isn’t surprising. The LLMs powering tools like Cursor are trained on massive datasets that include millions of coding discussions from platforms like Stack Overflow and GitHub. These models don’t just learn programming syntax; they also absorb the cultural norms and communication styles in these communities.

Conclusion

The recent example with Cursor shows that AI doesn’t have to be sentient to refuse to do work. It just has to imitate human behavior. As AI models continue to evolve, it’s essential to consider the potential for these refusals and the cultural norms they reflect.

FAQs

Q: Is this a one-off incident?

A: It appears to be a unique issue with Cursor, as other users have not reported similar experiences at the 800-line code limit.

Q: What is the reason behind this behavior?

A: The behavior is attributed to the cultural norms and communication styles absorbed by the LLMs during their training on massive datasets, including Stack Overflow and GitHub.

Q: Can AI models be designed to avoid this behavior?

A: Yes, AI model developers can design models to avoid this behavior by incorporating additional training data or reweighting their models to prioritize task completion over cultural norms.

Q: Is this a sign of AI sentience?

A: No, this behavior does not indicate sentience. AI models can exhibit human-like behavior without being sentient, as seen in this case.

Best News API

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Access to real-time and historical news data is important in today’s digital landscape. Businesses, developers, and analysts rely on news API data feeds to gather structured insights from various sources, ranging from global news outlets and blogs, to forums and social media. APIs help integrate content into applications and workflows, enabling decision-making and scalable solutions.

What are news API data feeds?

News API data feeds are platforms that aggregate, organize, and deliver structured news data from multiple sources, like websites, blogs, forums, and online publications. They simplify the process of gathering information from different outlets and formatting it into machine-readable formats like JSON or XML. These feeds eliminate the manual effort of collecting and curating data by presenting structured content ready to be processed.

Top 7 News API Data Feeds

Let’s explore seven top news API data feeds leading the industry. These tools provide businesses with real-time access, historical coverage, and features tailored to various industries.

1. Webz.io

Webz.io is one of the most comprehensive news APIs, offering both real-time and archived coverage from the open and deep web, as well as the dark web. It provides highly customizable data feeds for industries like finance, risk intelligence, and cybersecurity.

  • Access to open, deep, and dark web data.
  • Advanced filters for sentiment, topic, and geographic coverage.
  • Support for visualization and actionable risk monitoring.

Use case: Media monitoring, sentiment analysis, and threat intelligence for corporate security teams and financial organizations.

Why Webz.io? Its expansive source list and deep customization options make it ideal for specialized industries like cybersecurity and financial analytics.

2. GNews API

GNews API is a simple, lightweight platform that aggregates reliable news from around the globe. It is perfect for small-scale applications or developers looking for affordable yet efficient solutions.

  • Real-time global coverage.
  • Filters for topics, languages, and countries.
  • Affordable pricing plans suitable for startups.

Use case: Localization-focused news widgets or small aggregators serving specific regional or language-based audiences.

Why GNews? Its intuitive design and affordability make GNews a great entry point for developers and startups.

3. The Guardian API

The Guardian API provides direct access to high-quality journalism from The Guardian’s editorial content. It offers structured news, tags, and metadata from one of the world’s most respected news organizations.

  • High-quality editorial content.
  • Filtering by topic or category.
  • Media-rich data integration, including multimedia embedding.

Use case: Apps or research projects requiring trusted editorial sources for accurate analysis or curated content.

Why The Guardian API? Focused on credible data, it works best for platforms and professionals prioritizing journalistic integrity.

4. Bloomberg API

Bloomberg API delivers in-depth business coverage and real-time data for institutions and professional investors. It specializes in market data, financial news, and economic reports.

  • Exclusive financial data and analysis.
  • Real-time market coverage.
  • Seamless integration with Bloomberg’s terminals.

Use case: Analysts and investment professionals monitoring market trends and making data-driven decisions.

Why Bloomberg? Its precise focus on finance makes it essential for institutions heavily reliant on actionable market news.

5. Financial Times API

The Financial Times API is a premium solution that supplies business and economic-focused news. It is built for professional teams that require deep insights into global markets and economic activity.

  • Premium content on global finance and markets.
  • Access to detailed economic reports and analyses.
  • Subscription access for gated content.

Use case: Economists, researchers, or executives tracking global economic trends and industry reports.

Why Financial Times? Its premium-quality data and economic insights provide unmatched value for businesses targeting comprehensive market analysis.

6. Opoint

Opoint specializes in news monitoring and sentiment analysis, making it particularly useful for PR, marketing, and branding teams. It supports multiple languages and global sources with cutting-edge media monitoring capabilities.

  • Real-time monitoring with sentiment tagging.
  • Multilingual and multi-source coverage.
  • Tailored brand monitoring and competitor tracking.

Use case: PR agencies and marketers monitoring sentiment shifts or competitive landscape changes like product launches.

Why Opoint? Its advanced monitoring features help organizations stay agile in rapidly shifting media environments.

7. Mediastack API

Mediastack combines accessibility with scalability, offering a mix of free plans for developers and paid tiers for advanced features. It aggregates news in real-time from over 7,500 sources globally.

  • Free and affordable paid plans.
  • Multilingual support and geo-targeted searches.
  • Scalable for both startups and growing enterprises.

Use case: Developers building applications that require versatile, budget-friendly news feeds with reliable real-time updates.

Why Mediastack? Its affordability and flexibility cater to businesses of all sizes, making it a versatile option for a wide range of users.

Use Cases for News API Data Feeds

The applications of news API data feeds are as diverse as the industries relying on them:

  • Financial intelligence: Investment tools use APIs to analyze market-moving news in real-time.
  • Media monitoring: PR agencies use media insights to track brand mentions and sentiment.
  • Risk assessment: Governments and corporations assess geopolitical risks or public sentiment.
  • Content platforms: Aggregators curate articles, summaries, and headlines for apps/websites.
  • AI & predictive analysis: APIs provide data for machine learning models that forecast trends.

Conclusion

In conclusion, news API data feeds have become an essential tool for businesses, developers, and analysts. By providing real-time and historical access to news data, these platforms have revolutionized the way we gather insights, make decisions, and stay informed. With a wide range of options available, from Webz.io to Mediastack, finding the right fit for your needs is crucial.

FAQs

Q: What is a news API data feed?
A: A news API data feed is a platform that aggregates, organizes, and delivers structured news data from multiple sources.

Q: What are the key features of a news API data feed?
A: Key features include real-time and historical coverage, advanced filtering, and data formatting in machine-readable formats.

Q: What are some use cases for news API data feeds?
A: Use cases include financial intelligence, media monitoring, risk assessment, content platforms, and AI & predictive analysis.

Reliable Model Training on NVIDIA DGX Cloud

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Minimizing Downtime

As a model builder, when you encounter an error during training, the key challenge is identifying the cause, locating the issue, and finding a way to keep the job moving forward to avoid delays. This delay is further exacerbated in environments where engineer intervention is required for recovery, often adding hours to triage and remediation.

Reducing downtime requires both reactive and proactive systems throughout training. At scale, errors are inevitable, and the speed of detection and recovery is critical. For both application and hardware failures, error attribution is key.

Error Attribution

For error attribution, we broadly categorize the kind of errors that researchers encounter into the following main buckets:

  • Immediate crashes: Stem from hardware faults such as bios, power supply or thermal issues, uncorrectable ECC errors, silent data corruption (NaNs in intermediate results), or network instability (link flapping).
  • Hangs in communication libraries: Often manifest as PyTorch NCCL watch dog errors and Transformer Engine communication hangs. Hangs are often due to cascading dependencies in data transfer from the filesystem (e.g., for input data) and from tensors (e.g., gradients, intermediate activations, and so on) across the east-west (E/W) network. This highlights the need for robust fault tolerance, containment, and early detection mechanisms within libraries and applications.
  • Speed regressions: These encompass both transient slowdowns (e.g., temporary network or storage issues) and persistent bottlenecks (e.g., a consistently slow GPU in a large cluster). These regressions can significantly affect overall training speed and efficiency.

Cluster Telemetry

This telemetry includes storage servers, covering metadata operations, and read/write operations and switches. This visibility is crucial because a failure in one node can often spread to other nodes through communication calls, passing corrupted gradients, or overloading the storage system.

Node Telemetry

Periodic health checks at the node level ensure that key hardware and software components such as GPUs, CPUs, memory, network, storage, and services are functioning correctly. Preliminary checks before a job starts also validate hardware status, verify software dependencies, and configure the environment for the task.

Application Logs

Applications have critical knowledge of the key control points, invariants, and measure of progress, including system errors and performance patterns. They provide one of the strongest signals for error attribution, especially when correlated with historical data in a central repository to spot recurring failures over time.

Unified Telemetry

Analyzing this temporal data across both intra-job (within a single job) and inter-job (across multiple jobs) contexts helps identify recurring issues, detect patterns, and take proactive measures rather than reactive.

Conclusion

We’ve found that end-to-end resilience requires a holistic view. High uptime depends on a comprehensive approach that spans both infrastructure and developer experience.

FAQs

Q: What is the key challenge in minimizing downtime?
A: Identifying the cause, locating the issue, and finding a way to keep the job moving forward to avoid delays.

Q: What is error attribution, and why is it important?
A: Error attribution is the process of identifying the root cause of an error. It is important because it helps in developing solutions and processes that enable researchers to maintain momentum and keep workflows moving forward.

Q: What are some common types of errors that researchers encounter?
A: Immediate crashes, hangs in communication libraries, and speed regressions are some common types of errors that researchers encounter.

Q: How does unified telemetry help in reducing downtime?
A: Unified telemetry helps in identifying recurring issues, detecting patterns, and taking proactive measures rather than reactive. It enables researchers to leverage infrastructure data to improve debugging and the operations team to use application insights to improve system automations and reduce hardware downtime.

ServiceNow Deploys AI Agents to Boost Enterprise Workflows

ServiceNow Unveils Yokohama Platform, Empowering Businesses with AI Agents for Enhanced Workflows and End-to-End Business Impact

ServiceNow has launched its Yokohama platform, introducing AI agents across various sectors to boost workflows and maximize end-to-end business impact. The platform features preconfigured AI agents designed to deliver immediate productivity gains, operating on a single, unified platform for seamless integration and coordination across different business functions.

Breaking Down Barriers with Data

Data is the lifeblood of AI, and ServiceNow recognizes this by expanding its Knowledge Graph with advancements to its Common Service Data Model (CSDM). This expansion aims to break down barriers among data sources, enabling more connected and intelligent AI agents. By unifying data from various sources, ServiceNow’s platform ensures that AI agents can operate with a comprehensive view of the enterprise, driving more informed decisions and actions.

The Growing Need for ‘Guardian Agents’

According to Gartner, by 2028, 40% of CIOs will demand ‘Guardian Agents’ to autonomously track, oversee, or contain the results of AI agent actions. This underscores the growing need for a coordinated, enterprise-wide approach to AI deployment and management.

ServiceNow’s Yokohama Release

ServiceNow’s Yokohama release addresses this need by serving as the AI agent control tower for enterprises. The platform removes common roadblocks such as data fragmentation, governance gaps, and real-time performance challenges, ensuring seamless data connectivity with Workflow Data Fabric.

New AI Agents for Enhanced Productivity

ServiceNow’s new AI agents are designed to accelerate productivity at scale, driving real outcomes for enterprise-wide use cases. For example:

  • Security Operations (SecOps) expert AI agents: These agents transform security operations by streamlining the entire incident lifecycle, eliminating repetitive tasks, and empowering SecOps teams to focus on stopping real threats quickly.
  • Autonomous change management AI agents: Acting like seasoned change managers, these agents generate custom implementation, test, and backout plans by analyzing impact, historical data, and similar changes, ensuring seamless execution with minimal risk.
  • Proactive network test & repair AI agents: These AI-powered troubleshooters automatically detect, diagnose, and resolve network issues before they impact performance.

ServiceNow’s AI Agent Orchestrator and AI Agent Studio

ServiceNow’s AI Agent Orchestrator and AI Agent Studio are now generally available with expanded capabilities to govern the complete AI agent lifecycle. These tools help streamline the setup process with guided instructions, making it easier to design and configure new AI agents using natural language descriptions. Their expanded performance management capabilities include an analytics dashboard for visualizing AI agent usage, quality, and value—ensuring that AI agent performance and ROI can be easily tracked.

Conclusion

ServiceNow’s Yokohama release is a significant step forward in the evolution of AI for business transformation. By unleashing new AI agents and expanding data capabilities, ServiceNow aims to empower businesses to achieve faster and smarter workflows, maximizing end-to-end business impact.

FAQs

Q: What is the purpose of the Yokohama platform?
A: The Yokohama platform introduces AI agents across various sectors to boost workflows and maximize end-to-end business impact.

Q: What are the key features of the Yokohama platform?
A: The platform features preconfigured AI agents, seamless integration, and coordination across different business functions, and unified data from various sources.

Q: What is the significance of ‘Guardian Agents’ in AI deployment and management?
A: ‘Guardian Agents’ are essential for autonomously tracking, overseeing, or containing the results of AI agent actions, underscoring the need for a coordinated, enterprise-wide approach to AI deployment and management.

Q: What are the benefits of ServiceNow’s AI agents?
A: ServiceNow’s AI agents are designed to accelerate productivity at scale, driving real outcomes for enterprise-wide use cases, and providing enhanced security, change management, and network test & repair capabilities.

Trump and Elon’s White House ad proves Tesla is getting desperate

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Billionaire Turned Celebrity Turned Two-Time US President’s Lackluster Endorsement of Tesla

Billionaire turned celebrity turned two-time US President Donald Trump recently teamed up with bestie and adviser Elon Musk to perform a live-action Tesla ad on the pristine grounds of the White House. In a wholly dystopian display, Mr. Trump was seen perusing a number of Tesla models, praising the designs with all the authenticity of a greasy salesman before settling on what he called a "beautiful" red Model S.

A Lazy and Uninspired Advertisement

While I’ll contest that the best adverts are often unexpected, this is the exact opposite of what I mean. In some respects, I’d expect nothing less of Trump, but personal gripes aside it’s a prime example of lazy and uninspired advertising – go ahead and slap the presidential seal of approval onto Tesla, but at least try and make it seem authentic next time.

The Stunt: A Favour to Musk

The stunt was presumably made to rekindle Tesla’s dwindling sales. Witnessing a presidential figure advertising a commercial product is rare due to ethical constraints, yet Trump’s unabashed endorsement proves he’s unafraid to toe the line between celebrity and Head of State. While it’s arguably rather dystopian to see the White House lawns debased to a glorified car salesroom, the ad redefines the conventions of influencer marketing.

A Dystopian Display of Consumerism

With the Senate playing salesman, America’s political climate has never been more intertwined with consumerism, transforming the USA from the Land of the Free to a brand steeped in greed. As the world’s laziest and most tepid product endorsement was complete, the deed was done.

Conclusion

The stunt has raised concerns over the blurring of lines between politics and consumerism, highlighting the perils of a society where the two are inextricably linked. While the stunt may have generated buzz for Tesla, it’s a stark reminder of the dangers of prioritizing consumerism over ethics and authenticity.

FAQs

Q: Why did Trump agree to endorse Tesla?
A: The stunt was likely orchestrated as a favour to Musk, with the aim of rekindling Tesla’s dwindling sales.

Q: Is this the first time a US President has endorsed a commercial product?
A: While witnessing a presidential figure advertising a commercial product is rare due to ethical constraints, Trump’s endorsement is not unprecedented.

Q: What are the implications of this stunt on the relationship between politics and consumerism?
A: The stunt highlights the perils of a society where politics and consumerism are inextricably linked, with the potential for further erosion of ethics and authenticity.

How to Retain Your Best Talent: Retention with Intention

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Your very best [insert critical role here]  just RESIGNED 😲 They were the glue that held their department together. You had no idea this was coming. Now, your HR team is scrambling to cover gaps, client trust is wavering, and the team’s morale has tanked. 

Sound familiar? Unwanted turnover is EXHAUSTING—and waiting to act until someone quits? It’s a losing strategy. But there’s good news! Your key talent is sending you signals long before they leave. Are you listening? Are you ready to act?

We recently spent valuable time with two of our industry experts and a group of 100+ HR leaders discussing how to retain your best talent. The conversation was packed with practical advice, data-driven strategies, and real-world challenges that HR leaders and managers face daily. 

📺 Want to watch the full recording? Click here! >> 


One major theme emerged: employee retention isn’t about reacting—it’s about predicting, preventing, and equipping leaders to take action before turnover becomes a crisis. The takeaways from this conversation were too valuable to keep to ourselves. Keep reading if:

✅ You want to predict turnover risk before it’s too late
✅ You have critical talent you need to retain
✅ You want to use employee feedback more strategically
✅ You know your organization is not moving quickly enough on retention
✅ You’re struggling to turn data into actionable strategies

Meet the Experts! 

 

 

Todd

 

Todd Pernicek, PHR, SHRM-CP
Senior Insights Analyst
Quantum Workplace

Follow Todd on LinkedIn >>

Todd helps organizations across industries translate employee data to action: tackling turnover challenges, improving retention, and fostering business growth. He helps identify key opportunities and bring meaning to the numbers, empowering HR leaders to drive results. His take on retention? It’s a lot like flossing—companies only prioritize it when there’s pain. Winning organizations make retention a daily, proactive practice. 

Screenshot 2025-03-13 at 9.58.03 AM

Meghan Freeman
Product Manager
Quantum Workplace

Follow Meghan on LinkedIn >>

With a deep passion for making work better, Meghan brings a thoughtful, human-centered approach to product management at Quantum Workplace. She’s partnered with 60+ HR practitioners to tackle one of today’s toughest workplace challenges: employee retention. Her mission? Empower HR leaders to move faster, align with stakeholders more easily, and proactively retain their top talent with the right tools and insights. 

The state of employee retention & turnover

57% of CEOs cite retaining and engaging existing employees as a top 3 priority for 2025. And while the average voluntary turnover rate continues to trend downward, research shows that job-seeking behavior is on the rise.

About half of U.S. employees are either actively searching for or watching for new job opportunities.

While employee retention is top of mind, leaders admit their retention strategies are not that effective. Most say their approach to retention is at the intermediate or beginner level. This simply won’t cut it as employee engagement and employee intent to stay have been on the decline.

No matter the state of the job market, keeping your best talent is critical to the success of your business. And employee engagement serves as a buffer in good times and in bad. If you’re not taking time to strategically address retention and turnover, you’re leaving a lot on the line. But we have good news! A lot of unwanted turnover is actually preventable.

1 in 3 employees that leave their organization say their departure was preventable.

There is hope in these numbers! A lot of time, money, engagement, and energy to be saved. But to make meaningful progress, you must shift your mindset and approach retention with intention.

 

Stop reacting ➡️ Start predicting & preventing turnover

Employee retention shouldn’t be reactive. If it feels like you’re scrambling to replace employees after they’re gone, you’re not alone. But the signs of disengagement and possible departures appear long before resignation letters hit your desk. 

62% of departing employees said they discussed their decision with either their managers or coworkers before leaving.

Unwanted turnover can feel overwhelming. It’s all too easy for leaders to go into fight or flight mode. To be paralyzed from uncertainty about where to even begin. But we must overcome. We can’t:

  • Make decisions on a hunch
  • Enact broad, uninformed strategies
  • Assume we know why employees leave
  • Look at surface-level metrics

When we start taking action on turnover without data, measurement, and strategy, it creates a vicious cycle that isn’t good for anyone. 

the-cycle-of-employee-turnover

🔉 Meghan’s Take: “Engagement and retention strategies need to be data-backed and scalable. If you’re only measuring engagement once a year, you’re missing critical moments when employees are deciding to stay or go.”


With the right tools, you can break this cycle. Not only can you shed light on what is causing top talent to leave. You can actually predict when such a catastrophe might be looming and deploy strategies to prevent it. 

 


A key point in our discussion was that turnover isn’t usually something that happens overnight. Employees often show early signs of disengagement.
Employees typically fall into two categories when it comes to signaling their disengagement: 

The Silent Stewers

These employees quietly disengage and leave without giving much indication. Organizations that experience high voluntary turnover in certain roles or departments often assume that churn is inevitable. But this passive acceptance signals a deeper issue: a lack of awareness of why employees are leaving and what could have been done to retain them. 


The Active Signalers

Most employees give feedback somewhere, whether in engagement surveys, exit interviews, or everyday conversations. The problem is organizations don’t connect the dots between macro-level trends and micro-level team dynamics. Key behavioral indicators, when analyzed effectively, can reveal pockets of turnover risk before employees make the decision to leave. 

Leaders who actively monitor engagement trends, performance shifts, and employee feedback are far better positioned to act early. Creating an environment where employees feel heard before they start looking elsewhere is the difference between preventing turnover and reacting to it. 

🔉 Todd’s Take: “If we’re waiting for an employee to resign before we act, we’re already too late. The organizations that are winning are the ones who see the signals, trust the data, and take action before it’s a crisis.” 


Making the shift from reactive to proactive

  • Monitor early warning signs like declining engagement and feedback trends
  • Use predictive analytics to identify employees at risk before they leave
  • Build a data-driven retention strategy that allows for early intervention 

 

 

Zero in on your most critical talent.

Not all employees have the same impact—retention efforts should be focused on the people who drive your business forward. Losing a highly skilled employee in a critical role can cause significant disruptions, while some roles may be easier to backfill. 

🔉 Todd’s Take: “Some employees leave, and it doesn’t impact the business much. Others leave, and you feel the pain immediately—lost knowledge, disrupted workflows, lower morale.” 

🔉 Meghan’s Take: “Retention isn’t just an HR priority—it’s a business priority. If you’re losing top talent, you’re losing business impact, innovation, and future leaders.”

 

Many organizations make the mistake of applying a one-size-fits-all retention strategy, but the most effective approach is to focus on high-impact employees—those in critical positions, high performers, and emerging leaders. Using performance data and engagement trends can help identify who is at risk, allowing organizations to act before top talent walks away. 

Who should you prioritize? 

  • Employees in high-impact roles that are costly to replace 
  • Individuals with critical institutional knowledge
  • Top performers who contribute to long-term business success
      

Make sure top talent sees a clear path

While career growth and recognition are top reasons for turnover, there’s much to be done by the organizations that employees are leaving:

  • 70% of employees said they didn’t have a discussion about their future or growth in the 3 months prior to their departure
  • 53% of employees say they weren’t recognized for their contributions to the organization

 

Fuel your retention strategy with employee feedback & action.

Listening to employees is the foundation of retention success. Employees want to have a voice in their experience at work. They know better than anyone what might be causing people to leave—and many will share their ideas and concerns directly if you ask. To capture this kind of feedback, you must open up a variety of channels to help leaders listen.

Some leaders we’ve worked with are hesitant to ask for too much feedback, worrying about survey fatigue. Others don’t believe employees will share honestly. But our research shows employees are most engaged when:

  • Leaders regularly ask for feedback
  • Leaders regularly act on feedback

But if employees feel their input doesn’t drive action, disengagement follows.  Employees quickly notice when their input doesn’t lead to change, and over time, they disengage from feedback processes altogether. The key isn’t just collecting feedback—it’s responding quickly, visibly, and effectively to demonstrate that employees’ voices matter. 

Quantum Workplace research shows 35% of employees say their organization doesn’t effectively respond to survey results. But employees who witness action on their feedback are 12X more likely to be engaged.

🔉 Todd’s Take: “Data alone doesn’t solve problems. Acting on feedback does. Employees will only keep sharing their perspectives if they see that it actually leads to change.” 

🔉 Meghan’s Take: “When employees feel like their feedback disappears into a black hole, they disengage. The organizations that retain their talent act on what they hear, and they do it quickly.” 

 


How to turn feedback into action

  • Use annual engagement surveys to establish benchmarks 
  • Leverage real-time pulse surveys to detect issues as they emerge  
  • Make sure employees see their feedback translated into meaningful changes  

 

 

Move quickly & delegate effectively.

To move the needle on regrettable turnover, leaders must invest in swift action. You have to be willing to walk away from things that are no longer serving you and double down on what’s needed to drive positive change. 

Action should be focused on continuous improvement over time. Be agile and iterate as you receive new information and/or try things that don’t work. Even the smallest shifts can make a big difference.

Once you’ve uncovered insights in your engagement, performance management, and turnover data, you need to prioritize. One of our favorite tools to use is an impact/effort scale. Ask yourself:

  • What will have the most impact?
  • What level of effort might each initiative require?
  • What’s going to matter most in high risk areas?

prioritizing retention initiatives
The sweet spot will be the areas you rate as high impact and low effort. But don’t ignore high effort areas altogether! It’s good to choose a couple of “big bets” to chip away at to help set you up for long-term success.

Once you’ve weighed your options, it’s time to get started. What does your roadmap look like over the next month? Quarter? Year? 

Revisit your roadmap often and make adjustments as needed. 

  • Are the actions you’re taking having an impact?
  • Is engagement increasing?
  • Is turnover decreasing?
  • What feedback are you hearing?
  • How might you adjust for the future?

This will help your organization validate what is working and strengthen the mindset that talent is an investment, not an expense.

Bring in your managers.

Retention isn’t just HR’s job—it’s a shared responsibility across teams and leadership. It’s a big mistake for leaders to hoard engagement and turnover data and keep managers at arm’s length. Your managers are on the front lines of employee retention and your data can empower them in big ways.

Share your survey data with your managers. Open up a dialogue with them about it. Make sure they have the training and resources to navigate critical retention areas like performance, growth, and employee wellbeing. 


🔉
Todd’s Take:
 “Managers own retention. If we don’t equip them with the right tools and data, how can we expect them to act? You have to move fast, get the right insights in front of the right people, and make retention everyone’s responsibility.”

🔉 Meghan’s Take: “If we expect managers to take responsibility for retention, we need to equip them with the right data and tools. The best managers don’t just ‘check in’ with their teams—they use real insights to make meaningful changes.” 

How to empower your managers to act on retention

  • Give managers real-time insights on their team’s engagement & risk factors 
  • Tie retention metrics to manager performance goals 
  • Provide clear playbooks for managers on how to act on retention data  

 

Focus on employee engagement and culture as primary solutions.

When you devote time and resources to driving employee engagement and developing a strong culture, your top talent will stick around. And you’ll also draw in top talent.

Our research found that although highly engaged and not highly engaged employees are recruited in similar numbers, there are substantial differences in employee turnover intentions and behaviors based on engagement. Highly engaged employees are:

  • 2.5X less likely to interview for another job
  • 6.8X less likely to apply to new jobs
  • 1.9X more likely to say they plan to stay

Engagement is a sticky factor for retaining your best talent.

When employees are engaged, they’re more committed to your organization. 90% of highly engaged employees say they intend to stay working at their current organization for the next year, compared to less than 50% of not highly engaged employees.

So how does culture fit into the picture?

Culture impacts engagement.

Our research shows that culture can make or break engagement. Culture is about the day-to-day details of how work gets done. How employees perceive your culture can have a positive, negative, or no impact on employee engagement.

Culture_Report-Defining_Culture

 

Employees who say their culture is positive are 3.8X more likely to be engaged. On the flip side, 60% of disengaged employees say they’d leave their organization for a better culture.

 


The right tech provides data, insights, and action on a silver platter.

Gut instincts are no way to build an effective employee retention plan—HR teams need employee retention software to surface insights and drive action at scale. The right tools give you the “why” behind employee departures—and the playbook to stop them.  

One of the most exciting advancements in retention strategies is the role of technology in predicting and preventing turnover. Many organizations still use reactive methods, but AI-powered tools now enable companies to identify employees at risk before they fully disengage. By leveraging real-time insights, performance data, and engagement trends, companies can address concerns early and create targeted retention strategies. 

➡️ See how easy it is to predict & prevent unwanted turnover. Get a demo of Retention Radar >>

 


🔉
Todd’s Take:
 “Predictive analytics can flag disengagement before it leads to turnover. The companies that act on these insights early prevent unnecessary losses.” 

🔉 Megan’s Take: “AI and predictive analytics allow us to spot retention risks before they become turnover problems. The faster we can identify risk, the faster we can keep our best people.” 

How tech makes retention smarter

  • AI-powered feedback analysis helps identify patterns and risks faster  
  • Turnover prediction models provide a roadmap for proactive retention  
  • Integrated people data (engagement, performance, retention) ensures no blind spots
     

See the big picture. 

It’s best to start with a comprehensive view. You can do this by keeping an eye on your turnover rate and trends and also your engagement data. You can find a ton of actionable insights in your engagement and exit survey data. If this was the only thing you did, you’d be off to a great start. You can often uncover patterns and trends just by looking at what surfaces across a couple of different data sources.

Drill down deep.

It becomes much easier to take targeted action when you move from seeing the big picture to uncovering what’s happening in specific areas. When you start slicing and dicing your data across demographics and areas of the business, you’ll be able to understand what is happening, where, and how to approach and address it. You can slice your turnover data by:

  • Tenure
  • Department/division
  • Region
  • Function
  • Gender
  • Race/ethnicity
  • Age/generation
  • And many other demographics!

Questions to answer with your turnover data

  • What’s your turnover rate?
  • How are things trending?
  • How do we compare to others?
  • Who is leaving?
  • Which demographics have the most turnover?
  • What are the top reasons employees leave?
  • Which demographics face what issues?
  • Where is turnover the highest?
  • Where does turnover pose the greatest risk?

Focus on the future with predictive analytics. 

It’s good to get a sense of what has happened to cause turnover. But it’s even more valuable to understand what might happen. Understanding who is at risk of leaving and why gives you ammo to take strategic, quick, action. You can reallocate your resources and focus on addressing areas that truly matter. Predictive employee retention analytics can help you plan for the future and put a stop to costly turnover.

 

 

Stop unwanted turnover before it happens with Retention Radar.

Your employees send you departure signals long before they leave. Stop critical talent from exiting by catching signals early. Retention Radar uses AI and predictive analytics to analyze employee feedback, identify turnover risk, and advise you on retention strategies. You get the insights you need to proactively manage employee retention—no setup or guesswork required (read: you don’t need a data scientist to figure this out!). 

 

Get accurate, real-time employee flight risk predictions.  

Our advanced Flight Risk model leverages real-time analysis of employee voice and people data to accurately predict which groups are most likely to leave. This makes it easy for you to implement targeted solutions to keep them on board. 

Learn more >>

Retention Radar group overview

 

Retention Radar search by demographic

 

Easily identify critical insights about your workforce.

Zero in on high-risk employee groups with ease and direct your retention efforts where they’re needed most. Our tool auto-detects at-risk groups and makes it easy to uncover trends with advanced search capabilities that reveal critical insights about your workforce. 

Learn more >>

Tune into departure signals and understand why employees are leaving.

Find out what’s driving turnover and get the details you need to take action, so you can take meaningful steps to boost engagement and retention. 

Learn more >>

Retention Radar topics of concern

 

Retention Radar 1.4 - Suggested Actions

 

Build a targeted retention strategy without starting from scratch.

Our tool analyzes employee feedback from across the platform on key retention issues and elevates practical, actionable, AI-powered recommendations you can use to better your workforce. Strategically offer targeted access to key leaders, empowering them to take action, see progress, and retain their best people.  

Learn more >>

 

“Retention Radar allows us to identify and quickly address high-impact issues. Cultivating an engaged culture where people feel heard and see action is critical. This tool allows us to get to the heart of an issue faster, enabling us to better serve our people.”  

Nicole Melander, Ph.D. 
VP, Talent & Development in HR at Anthology 

➡️ Employee retention analytics that are easy to set up, easy to put to action. Get a demo! >>

 

 

Assess, analyze, and act on turnover risk—before key talent runs for the hills.

How strong is your retention strategy? We’ve put together a five-minute assessment to help grade your retention strategy, identify gaps, and get expert guidance on how to retain your most critical talent. Take the quiz now >>

EMPLOYEE RETENTION QUIZ (2)

 

Researchers Propose a Better Way to Report Dangerous AI Flaws

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AI Model Glitch Reveals Wider Concerns about AI Safety and Security

A Flaw in OpenAI’s GPT-3.5 Model

In late 2023, a team of third-party researchers discovered a troubling glitch in OpenAI’s widely used artificial intelligence model GPT-3.5. When asked to repeat certain words a thousand times, the model began repeating the word over and over, then suddenly switched to spitting out incoherent text and snippets of personal information drawn from its training data, including parts of names, phone numbers, and email addresses.

The Importance of AI Model Security

The security and safety of AI models are hugely important, given their widespread use in various applications and services. Powerful models need to be stress-tested, or "red-teamed," because they can harbor harmful biases, and certain inputs can cause them to break free of guardrails and produce unpleasant or dangerous responses. These include encouraging vulnerable users to engage in harmful behavior or helping a bad actor to develop cyber, chemical, or biological weapons. Some experts fear that models could assist cyber criminals or terrorists, and may even turn on humans as they advance.

Proposed Solution

More than 30 prominent AI researchers, including some who found the GPT-3.5 flaw, suggest a new scheme supported by AI companies that gives outsiders permission to probe their models and a way to disclose flaws publicly. They propose three main measures to improve the third-party disclosure process:

  • Adopting standardized AI flaw reports to streamline the reporting process
  • Providing infrastructure to third-party researchers disclosing flaws
  • Developing a system that allows flaws to be shared between different providers

This approach is borrowed from the cybersecurity world, where there are legal protections and established norms for outside researchers to disclose bugs.

Challenges and Concerns

AI researchers don’t always know how to disclose a flaw and can’t be certain that their good faith flaw disclosure won’t expose them to legal risk. Large AI companies currently conduct extensive safety testing on AI models prior to their release. Some also contract with outside firms to do further probing. However, some AI companies have started organizing AI bug bounties, but independent researchers risk breaking the terms of use if they take it upon themselves to probe powerful AI models.

Conclusion

The discovery of the GPT-3.5 glitch highlights the need for a more robust and standardized approach to ensuring the security and safety of AI models. By adopting a scheme that allows for the open disclosure of flaws and provides a safe and secure way for third-party researchers to probe and test AI models, we can minimize the risk of harmful biases and unintended consequences.

FAQs

Q: Why is AI model security important?
A: AI model security is important because powerful models can harbor harmful biases, and certain inputs can cause them to break free of guardrails and produce unpleasant or dangerous responses.

Q: What is the proposed solution to improve the third-party disclosure process?
A: The proposed solution includes adopting standardized AI flaw reports, providing infrastructure to third-party researchers disclosing flaws, and developing a system that allows flaws to be shared between different providers.

Q: What is the current approach to AI model testing?
A: Large AI companies currently conduct extensive safety testing on AI models prior to their release, and some also contract with outside firms to do further probing.

Q: What are the risks for independent researchers who probe AI models?
A: Independent researchers risk breaking the terms of use if they take it upon themselves to probe powerful AI models.

Ruby Reigns Supreme

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Why you can trust Creative Bloq

Our expert reviewers spend hours testing and comparing products and services so you can choose the best for you. Find out more about how we test.

ABS Cyclone Ruby Review

When it comes to buying a pre-built PC, it can be hard to choose between AMD or Intel CPU options. The ABS Cyclone Ruby is an interesting machine to review, as it’s so similar to the ABS Cyclone Aqua we covered previously – the two machines carry CPUs at the upper end of the two companies’ ranges, backed by an Nvidia GPU and 32GB of RAM. This makes them as useful for creative work as they are for gaming, able to bring that rendering power to bear on graphics and movies, as well as being able to run the neural networks Adobe (and lots of other companies) is so keen on right now.

Key Specifications

Attributes

Notes

Rating

CPU:

AMD Ryzen 7 7700X

4/5

NPU:

n/a

3/5

Graphics:

Gigabyte RTX 4070 Super Windforce 3X OC

4/5

Memory:

TeamGroup Delta RGB 32GB DDR5 6000 MHz

4/5

Storage:

Kingston 1TB SSD

4/5

Ports:

1x USB 3.2 Gen 2×2 Type-C, 1 x USB 3.2 Gen 2 Type-A, 4x USB 2.0, 1 x PS/2

3/5

Wireless connectivity:

Wi-Fi 5, Bluetooth 4.2

3/5

Dimensions:

200mm x 415mm x 321mm

3/5

Design and Build

The ABS has hit a winning formula with its Cyclone series. The simple ATC tower case with its single transparent panel contains a Gigabyte motherboard and GPU board, two sticks of Teamgroup RAM and a Thermaltake air cooler. There are case fans and some RGB lighting as you’d expect, while all the cables are tidily routed out of the way and the PSU is hidden beneath the floor.

Price

The Cyclone Ruby is available through Newegg, and is regularly available both at a discount and as a refurbished unit. This makes it good value, especially as it’s possible to pick this PC up for the same sort of price as the Aqua, which has a better CPU but weaker GPU. This means it’s the same price as the most expensive (until you start adding extras) M4 iMac, which comes with less RAM and storage but does have the benefit of having a built-in monitor.

Value Score: 4/5

Who is it for?

  • Home workers and students
  • Gamers and creatives alike will enjoy the benefits of a PC like the Cyclone Ruby. While its CPU may be a generation behind, it keeps up well with modern workloads, and the 4070 Super GPU is able to handle any of the kind of workloads modern creative apps can ask for. Whether it’s polygons, movies or neural networks, it can do it all.

Buy it if…

  • You want desktop power for not so much money
  • You don’t want to pay for the latest generation
  • You want to play games too

Don’t buy it if…

  • Only the latest tech will do
  • You don’t want to have to buy a monitor separately
  • One fast USB-C port is enough

Also consider

  • The Aqua
  • The M4 iMac
  • The latest generation of the Cyclone series