From smartphones to supercomputers, Reduced Instruction Set Computing (RISC) architectures have risen to dominate many corners of the tech world. Once overshadowed by their Complex Instruction Set Computing (CISC) counterparts—most famously exemplified by Intel’s x86—RISC architectures are now the foundation of countless devices and systems. This article explores the historical context, the fundamental differences between RISC and CISC, how RISC managed to rise to prominence, the current state of the industry, and what the future might hold.

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1. Historical Context

The Early Days of CISC

In the 1970s and early 1980s, memory was extremely expensive and slow by today’s standards. Computers needed to be as efficient as possible in their use of memory. As a result, designers of mainframe and minicomputer CPUs packed in as many complex instructions as possible, hoping to enable programmers to perform tasks in fewer lines of assembly code. This approach birthed CISC architectures—where a single instruction could do a lot of work (like iterating through an array or manipulating memory).

Examples of CISC designs from this era include the DEC VAX series and, most influentially, the Intel x86 architecture. These chips flourished in the personal computer revolution, largely thanks to IBM PCs and compatibility concerns that locked in x86 for decades to come.

Emergence of the RISC Concept

Amid the rise of CISC, researchers at universities like the University of California, Berkeley (led by David Patterson) and IBM’s 801 project were experimenting with a novel idea: Reduced Instruction Set Computing (RISC). Their hypothesis was that simpler instructions that executed very quickly would ultimately produce higher performance, especially as compilers grew more sophisticated at translating high-level languages into efficient assembly code.

Early RISC designs, such as Berkeley’s RISC I (1980) and IBM’s 801 (1975), proved that smaller instruction sets could achieve better performance per transistor. By the mid-1980s, commercial RISC processors like the Sun SPARC, MIPS, and HP PA-RISC were on the market, introducing a new paradigm to CPU design.

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2. Key Differences Between RISC and CISC

1. Instruction Set Complexity
   • CISC: Contains a large number of instructions, some of which are highly specialized and can perform multi-step operations in one instruction.
   • RISC: Uses a smaller, simpler set of instructions, each designed to execute in one clock cycle (ideally), with the idea that simplicity allows for faster performance and easier pipelining.
2. Performance and Execution Model
   • CISC: Instructions can take multiple clock cycles to complete and require more complex decoding hardware.
   • RISC: Generally emphasizes pipelining—where different stages of instruction execution overlap—leading to higher instruction throughput.
3. Memory and Register Usage
   • CISC: Often allows memory operations within many instructions (e.g., loading from memory and adding in one instruction).
   • RISC: Typically enforces a load/store architecture, where all arithmetic operations happen in registers, and only load/store instructions access memory. This simplifies design and speeds execution.
4. Hardware Design Complexity
   • CISC: Requires more complex hardware to decode and execute the large variety of instructions, which can lead to larger chips and more power consumption.
   • RISC: Relies on simpler hardware design, which can reduce power usage and manufacturing complexity.
5. Compiler and Software Support
   • CISC: Historically was easier to program in assembly (fewer lines of code), but modern compilers make this advantage less relevant.
   • RISC: Heavily relies on effective compilers to generate optimal code for the streamlined instruction set.

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3. The Rise of RISC

Performance Meets Power Efficiency

By the 1990s, transistor budgets (the number of transistors designers can put on a chip) were increasing, but so was demand for energy efficiency—particularly for emerging mobile and embedded devices. RISC architectures, due to their simpler and more power-efficient designs, became popular in embedded systems like printers, routers, gaming consoles, and, most crucially, mobile devices.

ARM’s Mobile Revolution

Nowhere is the success of RISC clearer than in the dominance of ARM-based processors. ARM chips have powered the vast majority of smartphones for over a decade and have expanded to tablets, wearables, IoT devices, and more. ARM’s simple instruction set and focus on low power consumption gave it a decisive edge in the battery-powered realm where x86 chips struggled.

Leveraging Manufacturing Advancements

As manufacturing processes shrank transistors and allowed more complex designs, the simplicity and scalability of RISC became even more compelling. Designers could pack more cores, bigger caches, and advanced features (like deep pipelines and out-of-order execution) into RISC processors without ballooning power consumption or design complexity.

CISC Fights Back with Microarchitecture

Intel and AMD did not sit idly by. From the Pentium Pro onward, x86 chips introduced RISC-like micro-operations under the hood. They translate complex x86 instructions into simpler micro-ops for faster internal execution, effectively embedding a RISC core in a CISC wrapper. This hybrid approach allowed x86 to remain competitive and keep backward compatibility while reaping some benefits of RISC-style execution.

Still, ARM and other RISC-based designs continued to gain traction, especially outside the traditional PC server domain, in areas like embedded systems and mobile computing.

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4. The Current Stage

Desktop and Laptop Shift

Even in the consumer PC market, the landscape is evolving. Apple’s transition from Intel x86 chips to Apple Silicon—based on ARM architecture—has demonstrated the feasibility of RISC-based processors in high-performance desktop and laptop applications. Apple’s M-series chips offer significant performance-per-watt advantages, reinvigorating the “RISC vs. CISC” conversation in mainstream computing.

Server and Cloud Adoption

Companies like Amazon (with AWS Graviton) and Ampere are designing ARM-based server chips specifically tailored for cloud workloads. With energy efficiency becoming a top priority at datacenters, RISC-based servers are gaining steam, challenging Intel and AMD’s x86 dominance.

Open-Source Momentum: RISC-V

Another major development is RISC-V, an open-source RISC architecture. RISC-V provides a royalty-free instruction set, enabling startups, researchers, and hobbyists to design custom processors. Its openness, extensibility, and community-driven ethos have attracted investment from industry heavyweights, leading to ongoing innovation in both embedded and high-performance areas.

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5. The Future of RISC Architectures

Growing Ubiquity

RISC architectures are expected to continue their forward march, particularly as computing diversifies beyond traditional PCs and servers. IoT endpoints, edge computing devices, automotive systems, and specialized AI accelerators are all domains where the efficiency of RISC shines.

Dominance in Mobile and Embedded

ARM’s foothold in mobile and embedded computing is unlikely to loosen anytime soon. With 5G, autonomous systems, and a continued explosion of smart devices, ARM and potentially RISC-V are well-positioned to capture even greater market share.

Shifting Market for PCs and Servers

While x86 chips remain extremely important—and are still widely used for legacy software compatibility, gaming, and enterprise solutions—the rapid improvements in ARM-based and RISC-V server offerings could chip away at Intel and AMD’s market share. Enterprises that prioritize power efficiency and can recompile or containerize their workloads for ARM or RISC-V might find compelling cost savings.

Innovation in AI and Specialized Processing

AI accelerators and specialized co-processors for machine learning, cryptography, and high-performance computing are often RISC-based or RISC-inspired, as these accelerators benefit from streamlined instruction sets and can incorporate custom instructions easily. This opens the door for continued innovation around heterogeneous computing, where traditional CPUs and specialized accelerators work together efficiently.

Software Ecosystem Maturity

For years, software support—particularly operating systems, development tools, and commercial applications—was a barrier to broader RISC adoption in the desktop/server world. But with the rise of Linux and cloud-native containerization, porting applications between architectures has become much easier. Apple’s macOS, Microsoft Windows on ARM, and widespread Linux support for ARM and RISC-V all illustrate how the software ecosystem has matured.

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6. Conclusion

The shift from CISC to RISC architectures over the past few decades is a testament to the power of simpler, more efficient instruction sets. While CISC architectures dominated the computing scene in the early PC era, RISC-based designs gained the upper hand in mobile, embedded, and now increasingly in desktop and server environments thanks to superior power efficiency and a growing software ecosystem.

Looking ahead, RISC architectures are poised to continue their ascent. Whether it’s ARM’s ongoing success in smartphones and servers, the growing popularity of the open-source RISC-V, or specialized AI accelerators built on RISC principles, the trend toward reduced instruction sets is clear. As computing demands evolve—in terms of power efficiency, heterogeneous designs, and specialized workloads—the simplicity, flexibility, and scalability of RISC are likely to keep pushing the frontier of innovation for years to come.

Artificial Intelligence (AI) models, especially large-scale machine learning and deep learning systems, are fueled by data. These systems comb through vast amounts of information—text documents, images, audio, sensor data—to learn patterns and make predictions. But what happens when we reach a point where the supply of new, unconsumed training data effectively runs dry? This scenario is often referred to as peak data: the stage at which AI has already been trained on virtually all relevant and accessible data.

In this post, we’ll explore why peak data is becoming an increasingly relevant concept, why it poses a real challenge for the AI community, and how researchers and businesses are planning to adapt and overcome it.

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Understanding Peak Data

What Does Peak Data Mean?

“Peak data” in the context of AI refers to the point where we’ve exhausted all the large, high-quality datasets that are publicly (or privately) available or can be economically created. Simply put, we’ve hoovered up everything from Wikipedia articles to social media posts, news archives, and public domain books, and fed them into AI models. After this point, finding new data that significantly improves model performance becomes far more difficult, costly, or both.

Why Now?

• Rapid Growth of Large Language Models (LLMs): Models like GPT, PaLM, and other large-scale neural networks have used massive corpora comprising nearly the entire accessible internet. These approaches assume more data always leads to better performance—but eventually, we start running out of “new” text to feed them.
• Data Overlap and Diminishing Returns: Even when new data appears, it often overlaps heavily with what has already been consumed. Models may not see a dramatic improvement from re-feeding essentially the same information.
• Quality vs. Quantity: While the internet is vast, not all of it is high-quality or even relevant. Curating large, high-quality datasets has become a bottleneck.

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Why Is Peak Data a Problem?

1. Stalled Improvement in AI Models: When data is the engine that powers AI, a shortage of genuinely new data can lead to stagnation in model performance. Even if the hardware and architectures continue to improve, the lack of fresh, diverse information undermines the potential gains.
2. Biases and Blind Spots: If the same data is cycled through training processes, models risk re-ingesting and reinforcing existing biases. Without access to novel or more balanced datasets, efforts to correct these biases become more difficult.
3. Economic and Competitive Challenges: Tech companies have spent billions on computing resources and data acquisition. Hitting peak data introduces a barrier to entry for newcomers and a plateau for incumbents—companies can no longer rely on simple “scale up your data” strategies to stay ahead.
4. Privacy and Ethical Concerns: As researchers look for new data sources, the temptation might be to scrape more personal and sensitive information. But in a world with increasing data privacy regulations and rising user awareness, this can lead to serious legal and ethical dilemmas.

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How We Are Planning to Overcome Peak Data

Despite the alarming notion that we’re running out of new data for AI, several strategies and emerging fields offer potential ways forward.

1. Synthetic Data Generation

• AI-Created Datasets: One of the most promising solutions is using AI itself to generate synthetic data. By learning underlying patterns from real data, generative models (like GANs or diffusion models) can create new, high-fidelity samples (e.g., text, images). These synthetic datasets can help models explore data “variations” that don’t exist in the real world, injecting novelty into the training process.
• Domain-Specific Simulation: In industries like autonomous driving, simulated environments can produce endless scenarios for training AI models. This allows for the creation of edge cases—rare but critical situations—without waiting for them to occur naturally on roads.

2. Curating Underutilized or Specialized Data Sources

• Niche Domains: Vast troves of data exist in specialized repositories (e.g., scientific journals, technical documentation, or lesser-known archives) that haven’t yet been fully tapped. By carefully curating and converting these sources into AI-ready formats, we can uncover new training material.
• Collaborative Data Sharing: Companies and organizations can pool data that might otherwise sit unused. Secure data-sharing platforms and federated learning frameworks allow multiple parties to train models collaboratively without exposing proprietary data to competitors.

3. Quality Over Quantity

• Data Cleaning and Enrichment: Instead of simply adding more data, AI teams are focusing on improving the quality of what they already have. Enhanced labeling, eliminating duplicates, and ensuring data accuracy can yield substantial performance gains.
• Active Learning: In active learning setups, the model “asks” a human annotator for help only when it encounters particularly challenging or ambiguous examples. This targeted approach maximizes the impact of each new data point, making the most of limited labeling resources.

4. Model and Algorithmic Innovations

• Few-Shot and Zero-Shot Learning: Recent breakthroughs in AI enable models to understand new tasks with only a handful of examples—or, in some cases, no examples at all. These techniques reduce the dependence on massive labeled datasets by leveraging existing, general-purpose representations.
• Transfer Learning and Multitask Learning: Instead of training a model from scratch for every new task, transfer learning uses a model trained on one domain and adapts it to another. This strategy helps break the direct reliance on large amounts of fresh data each time.

5. Continuous Data Generation from Real-World Interactions

• Reinforcement Learning from Human Feedback: Models can refine themselves by interacting with humans—e.g., chatbots that learn from user input over time (taken into consideration the privacy, GDPR, etc concerns adding another layer of complexity), or recommendation systems that adapt based on user choices. These ongoing interactions produce fresh data, albeit in smaller batches.
• IoT and Sensor Data Streams: As more devices become connected, real-time sensor data (e.g., from wearables, industrial machinery, or city infrastructure) can feed AI models with continuously updated information. This can keep models relevant and mitigate data stagnation.

6. Leveraging Test-Time and Inference-Time Compute

While most AI development has historically emphasized training-time data, a growing trend focuses on harnessing compute at test-time (or inference-time) to reduce the need for massive new training sets. By dynamically adapting to real-world inputs during inference—such as retrieving additional context on the fly or updating certain parameters in response to user interactions—models can “learn” or refine their outputs in real time. Techniques like meta-learning, few-shot inference, or retrieval-based approaches (some of these mentioned above too) enable the system to handle unseen tasks using minimal, context-specific information gathered at runtime. This not only mitigates the reliance on endless streams of new data but also keeps AI applications responsive and up-to-date long after they’ve consumed the bulk of what is already available, thereby extending their utility even beyond the apparent limits of peak data.

The Road Ahead

While hitting peak data can feel like a looming crisis—especially for a field that has thrived on scaling dataset sizes—ongoing innovations provide strong reasons for optimism. Researchers are finding new ways to generate, share, and improve data. Simultaneously, advanced modeling techniques reduce our dependence on endless data streams.

Balancing Innovation with Responsibility

As we push the boundaries to circumvent peak data, privacy, ethics, and sustainability must remain at the forefront of AI development. Whether generating synthetic data or sharing real data among partners, responsible data governance and transparent practices will determine the long-term viability of these solutions.

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Conclusion

Peak data, understood as the point where AI has consumed all readily available, high-quality information, highlights the challenges of our data-intensive AI approach. Yet it also sparks creativity and drives innovation. From synthetic data generation to new learning paradigms, the AI community is exploring numerous pathways to ensure that innovation doesn’t stall once we have combed through every last corner of the internet (and beyond).

The next frontier for AI may well lie in how we handle the quality and generation of data, rather than just the quantity. By focusing on more efficient algorithms, responsible data sharing, and novel data creation techniques, we can continue to build intelligent systems that grow in capability—even in a world where we’ve seemingly run out of “new” data to train them on.

Mark your calendars! The Microsoft AI Tour is making its highly anticipated stop in New York City on January 30, 2025, at the renowned North Javits Center. This one-day event is set to bring together a dynamic community of senior business leaders, developers, IT professionals, and AI enthusiasts to explore the groundbreaking possibilities of artificial intelligence.

What to Expect at the Microsoft AI Tour NYC

This exclusive event promises to be a hub of knowledge, innovation, and connection, offering:

1. Keynote Sessions 🌐
Hear from Microsoft’s top industry leaders and AI visionaries as they unveil the latest advancements in AI technologies and share insights on how organizations can leverage AI to drive innovation and achieve strategic goals.

2. Technical Sessions and Workshops 🔧
Whether you’re an experienced developer or new to AI, these expert-led sessions are designed to enhance your understanding of Microsoft’s AI solutions. Explore topics such as building AI-powered applications, integrating AI Copilots, and implementing data science and machine learning strategies.

3. The Connection Hub 💬
Network with peers, engage with thought leaders, and participate in interactive sessions. The Connection Hub is your opportunity to collaborate and exchange ideas with professionals who are shaping the future of AI.

4. Featured Partner Insights 🔍
Discover real-world applications of AI from leading Microsoft partners, including NetApp, Celonis, and Quisitive. Learn how organizations are transforming their businesses with Microsoft’s AI-powered solutions.

5. Expert Discussions Featuring – Me 🤓
I’m thrilled to announce that I will be among the experts featured in the Connection Hub. I’ll be leading a discussion on the exciting new features in Azure and how they can empower organizations to harness the full potential of AI and cloud technologies.

Why Attend?

In a world increasingly driven by data and AI, staying ahead of the curve is essential. The Microsoft AI Tour NYC provides attendees with actionable insights, hands-on experiences, and the chance to engage with the latest AI innovations from one of the world’s most forward-thinking tech companies. Registration is complimentary, making it accessible for all who are eager to explore the future of AI.

Event Details:

• Date: January 30, 2025
• Location: North Javits Center, 445 11th Avenue, New York, NY 10001
• Registration: Register here

Secure Your Spot Today

Seats are limited for this incredible opportunity to learn, network, and grow with the AI community. Don’t miss out on the chance to be part of the conversation that is shaping the future of technology.

Join us at the Microsoft AI Tour NYC and let’s redefine innovation together.

As I’m pondering over the Thanksgiving sales sponsored refresh of my family’s aging laptop set, could not escape the thought – why I ended up only buying ARM machines? Funny thought does not leave my mind, how much I loved the Motorola 68xxx series’s CISC model – and where did that world ended? But let’s stay with ARM for now.

In the evolving world of computing, the dominance of x86 processors from Intel and AMD is being challenged by a new contender: ARM-based PCs. ARM (Advanced RISC Machine) processors, long associated with mobile devices, are now making significant inroads into the PC market, offering a range of benefits that make them an attractive option for both consumers and businesses. Let’s explore why ARM PCs are becoming a game changer.

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1. Energy Efficiency: Longer Battery Life

One of the most significant advantages of ARM PCs is their energy efficiency. ARM processors are designed to consume less power compared to traditional x86 processors. This makes them ideal for laptops and portable devices, where battery life is a critical factor.

• Benefit for Users: ARM PCs, such as those powered by Apple’s M-series chips or Qualcomm’s Snapdragon processors, often boast all-day battery life, eliminating the need to carry chargers or hunt for power outlets during the day.

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2. Cooler and Quieter Operation

The low power consumption of ARM processors translates to less heat generation. Unlike traditional Intel or AMD PCs that often require bulky cooling systems and fans, ARM PCs can operate with minimal or no active cooling.

• Benefit for Users: ARM PCs are quieter, slimmer, and lighter, offering a sleek and modern design without the distraction of fan noise or overheating issues.

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3. Performance Innovations

ARM processors, particularly Apple’s M-series chips, have proven that power efficiency doesn’t mean sacrificing performance. These chips combine high-performance and high-efficiency cores in a hybrid architecture, delivering excellent performance for both single-threaded and multi-threaded tasks.

• Benefit for Users: ARM PCs handle intensive workloads such as video editing, 3D modeling, and software development while maintaining superior responsiveness for everyday tasks.

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4. Built-In AI and Neural Processing

Many ARM processors come with dedicated AI and neural processing units (NPUs), allowing them to handle machine learning tasks more efficiently than traditional CPUs.

• Benefit for Users: Features like real-time video enhancement, voice recognition, and advanced photo editing are faster and more accessible, enhancing productivity and creativity.

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5. Seamless Integration with Mobile Ecosystems

ARM processors share the same architecture as most smartphones and tablets, enabling a seamless integration between devices.

• Benefit for Users: ARM-based PCs, like Apple’s MacBooks, allow users to run mobile apps directly on their computers, offering a unified experience across platforms.

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6. Enhanced Portability

ARM PCs are designed with portability in mind, often boasting thinner and lighter builds due to reduced thermal and battery constraints.

• Benefit for Users: These devices are perfect for on-the-go professionals, students, and anyone who values mobility without compromising on power.

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7. Improved Security

ARM’s architecture includes advanced security features, such as hardware-enforced memory isolation and sandboxing, which make it harder for malicious software to exploit vulnerabilities.

• Benefit for Users: ARM PCs provide a more secure computing environment, protecting sensitive data and reducing the risk of malware attacks.

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8. Compatibility with the Future

ARM PCs align with the industry trend toward cloud computing and software optimized for ARM architecture. Companies like Microsoft and Adobe are actively developing native ARM applications, ensuring that users won’t be left behind.

• Benefit for Users: Early adopters of ARM PCs are investing in future-proof devices that will only become more capable as the ecosystem expands.

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Challenges to Consider

While ARM PCs offer many benefits, it’s essential to acknowledge some of the current limitations:

• Software Compatibility: Legacy x86 applications may require emulation, potentially impacting performance.
• Upgradability: ARM PCs are often less customizable compared to traditional PCs.
• Price Point: Some ARM PCs, such as Apple’s MacBook lineup, may have a higher initial cost.

However, these challenges are diminishing as the ecosystem matures and developers increasingly embrace ARM architecture.

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Conclusion

ARM PCs represent a significant shift in computing, offering unparalleled energy efficiency, performance, and integration with modern workflows. As software ecosystems adapt and hardware capabilities improve, ARM PCs are poised to become the standard for personal and professional computing.

For those looking to future-proof their tech investment, now is the time to consider making the switch to an ARM-based PC. It’s not just a choice of processor; it’s a choice of innovation, efficiency, and adaptability.

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By embracing ARM, you’re not just buying a computer—you’re stepping into the future of personal computing.

Life often feels like a race against time. Whether you’re 20, 40, or 60, there’s a nagging voice that sometimes whispers, “You’re late.” But what if you’re not late at all? What if you’re simply in your Season 1?

Many beloved TV series begin with characters who are clueless about their goals or how to achieve them. Think of classics like Friends, The Office, or Breaking Bad. These series didn’t start with their protagonists at the peak of their journeys. Instead, they started with awkward first steps, mistakes, and moments of doubt. Season 1 is rarely glamorous, yet it lays the foundation for the greatness to come.

Season 1: The Setup, Not the Summary

In life, as in TV, the first season is about exploration. It’s when you figure out what works, what doesn’t, and who you are becoming. The characters we grow to love don’t know their full potential in Season 1. They stumble through odd jobs, complicated relationships, and missteps that, in hindsight, were pivotal to their growth.

Your life might be following a similar arc. Perhaps you’re juggling different interests, questioning your choices, or feeling unsure about the future. That’s normal. Great stories are messy in the beginning.

Embracing the Unknown

Most successful series don’t reveal their overarching narrative from the start. Game of Thrones begins with politics and intrigue before unveiling dragons and destiny. Parks and Recreation started as a quirky local government sitcom before evolving into a heartfelt exploration of ambition and community.

Likewise, your life’s story may have layers yet to unfold. It’s okay to not have all the answers right now. The process of discovery—those “pilot episodes” of trial and error—adds depth to your journey.

Late Starts Are Not Failures

Some of the most iconic characters achieve their breakthroughs later in their arcs. Walter White doesn’t break bad until his mid-life crisis. Leslie Knope doesn’t achieve her ambitions overnight. Their journeys remind us that being “late” often just means arriving on time for your own story.

Similarly, countless real-world figures began their Season 1 long after society told them they were “too old.” Vera Wang didn’t design her first dress until her 40s. Colonel Sanders franchised KFC in his 60s. Their success wasn’t diminished by the timing—it was enhanced by the richness of their experiences.

Writing Your Script

If you’re in your Season 1, take heart. Focus on building your character, learning from challenges, and exploring your passions. Remember, the best series build anticipation by letting the plot unfold naturally. Your story is no different.

Every great show starts somewhere, and every person’s journey begins with a Season 1. Don’t rush the process. Give yourself permission to explore, fail, and grow. After all, the best is yet to come.

As we look forward to the opportunities and challenges of 2025, the world continues to evolve at a rapid pace. New technologies, changing work environments, and novel ways to connect with others mean that the bar for success is constantly shifting—and we must shift along with it. Whether you’re launching a startup, adapting to remote work, or pursuing a personal passion, the key to standing out in this ever-changing landscape is the willingness to do more than just “try.” It’s about finding that extra grit, that extra determination, that extra “umph” to turn mere attempts into genuine triumphs. By harnessing the power of persistence and resilience, you can make 2025 your most impactful year yet.

The Power of “Umph”

Have you ever noticed how the word “triumph” is just “try” with a little extra something added? That extra something—spelled “umph”—embodies the special push that distinguishes success from mere attempt. It’s more than just a playful twist of the English language. It’s a reminder that true achievement often comes down to the willingness to add a bit more grit, determination, and perseverance.

Going Beyond Comfort

No great achievement ever occurred in the realm of comfort. Whether you’re learning an advanced skill, aiming for career growth, or launching a new project, there will come a time when your initial enthusiasm starts to fade. That’s precisely the point at which “umph” becomes essential. By stepping outside your comfort zone—by being willing to push past fatigue, doubt, and discouragement—you turn a mere “try” into a genuine stride toward “triumph.”

Embracing Setbacks

“Umph” also involves resilience—learning to treat failures and setbacks not as reasons to quit, but as feedback that shapes and strengthens your approach. A failed proposal can become a motivational springboard to refine your craft. A misstep in your strategy can lead you to revise your process until it resonates more effectively. Every stumble or detour is an opportunity to learn and gather the extra “umph” needed to move forward.

The Ripple Effect of Persistence

When you decide to go the extra mile, you don’t just change yourself—you often inspire those around you. People who witness your determination can be motivated to pursue their own goals more courageously. This collective atmosphere of persistence and positive energy sets the stage for broader transformations, where one person’s unwavering resolve becomes the catalyst for a community of doers.

Cultivating “Umph” in Everyday Life

• Set Clear Goals: Be specific about what success looks like. Clarity makes it easier to persist when the going gets tough.
• Break It Down: Turn your big dreams into small, achievable steps, building momentum along the way.
• Reward Progress: Celebrate small wins. Recognizing each forward step keeps your motivation strong.
• Stay Accountable: Whether it’s a friend, mentor, or team, sharing your goals fosters commitment.
• Learn from Failure: Reframe any setback as an opportunity to build resilience and sharpen your methods.

Conclusion

The difference between “try” and “triumph” truly is found in that extra “umph.” It’s the extra energy we invest, the resilience we build through setbacks, and the determination we summon when it would be easier to quit. By embracing the “umph”—by committing yourself wholeheartedly—you turn a mere attempt into a remarkable achievement. So, as 2025 unfolds and presents new challenges and opportunities, remember: all you need is a little more “umph” to transform your story into one of triumph.

Measuring performance is one of the most critical aspects of running a successful organization. It provides the data and feedback necessary to refine strategies, optimize operations, and adapt to changing market conditions. Two of the most popular frameworks for performance measurement are OKRs (Objectives and Key Results) and KPIs (Key Performance Indicators). Although they both play a role in assessing progress, they do so in slightly different ways—and choosing the right approach can have a significant impact on an organization’s success.

In this article, we will explore the differences between OKRs and KPIs, the benefits of each, practical tips for creating and implementing them, and real-world examples to illustrate their use.

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1. What Are OKRs?

OKRs (Objectives and Key Results) is a goal-setting framework popularized by tech giants such as Intel, Google, and LinkedIn. It helps organizations, teams, and individuals articulate what they want to achieve (Objectives) and how they know if they have achieved it (Key Results).

• Objectives: Broad, inspirational goals that you want to reach. They should be ambitious, clear, and direction-setting.
• Key Results: Specific, measurable metrics that indicate progress toward the objective. They outline the criteria for success—if you meet or exceed the Key Results, you have achieved the objective.

An OKR cycle usually spans a set time frame—often quarterly—with frequent check-ins to track progress. The key idea is transparency, alignment, and a focus on outcomes rather than tasks.

1.1 Benefits of Using OKRs

1. Alignment and Focus: OKRs help ensure that everyone is working toward the same overarching goals. By defining clear Objectives and Key Results, teams and individuals can align their efforts in a focused manner.
2. Transparency: In many companies, OKRs are shared publicly within the organization, so everyone knows what others are working on and how it connects to the bigger picture.
3. Ambition and Stretch Goals: Objectives are meant to be inspirational and challenging. This drives teams to push beyond their comfort zone and pursue significant achievements.
4. Agility and Iteration: Because OKRs are often reviewed frequently (weekly or bi-weekly check-ins), teams can make quick course corrections if they see they are off track.

1.2 How to Create Effective OKRs

1. Define Objectives: Start by asking, “What do we want to accomplish?” Objectives should be ambitious yet realistic, and they should provide a clear direction.
2. Establish Key Results: For each Objective, define 2–5 Key Results that are quantifiable and measurable. Ask, “How will we know if we’re successful?”
3. Set Time Frames: Most organizations use quarterly OKRs to keep them relevant and urgent.
4. Ensure Alignment: Each team or individual’s OKRs should roll up into the broader organizational goals.
5. Regular Check-Ins: Schedule regular (often weekly) check-ins to review progress, discuss challenges, and pivot if needed.
6. Evaluate and Reflect: At the end of the OKR cycle, reflect on what worked, what didn’t, and how to improve in the next round.

1.3 OKR Examples

• Company-Level Objective: Expand the company’s global footprint in the next quarter.
  • Key Result 1: Increase international website traffic by 50%.
  • Key Result 2: Set up operations in two new countries.
  • Key Result 3: Achieve 10% of total revenue from new international markets.
• Team-Level Objective: Improve user satisfaction for our mobile app.
  • Key Result 1: Increase average app rating from 4.0 to 4.5 on the app store.
  • Key Result 2: Reduce support tickets related to app performance by 30%.
  • Key Result 3: Achieve a 20% increase in daily active users (DAU).

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2. What Are KPIs?

KPIs (Key Performance Indicators) are quantifiable measures used to evaluate the success of an organization, team, project, or individual in meeting objectives over time. Unlike OKRs, which are often used to drive change and push boundaries, KPIs are typically used to monitor and maintain performance against established targets.

KPIs can be found in every aspect of a business—from sales figures, financial metrics, customer satisfaction scores, and product performance statistics, to human resources metrics, such as employee turnover and engagement rates.

2.1 Benefits of Using KPIs

1. Monitoring Stability: KPIs are excellent for tracking ongoing performance and detecting issues early. If a KPI starts trending downward, it’s a signal to investigate and correct course.
2. Decision-Making Tool: Because KPIs are typically numerical, they can serve as benchmarks for data-driven decision-making.
3. Clarity and Consistency: By measuring the same KPIs consistently, organizations can maintain clarity on their progress over time.
4. Performance Benchmarking: KPIs can be compared across time, departments, or even against industry standards, offering valuable insights into how you stack up.

2.2 How to Develop Effective KPIs

1. Identify Key Areas: Pinpoint the areas that are most critical to your business or team success. This might be sales, customer satisfaction, growth metrics, etc.
2. Define Clear Metrics: A KPI should be quantifiable, actionable, and relevant. For instance, instead of “improve sales,” define something like “achieve monthly recurring revenue (MRR) of $200,000.”
3. Set Targets: Determine a target or threshold for each KPI. Are you aiming for a certain percentage increase? A specific revenue figure?
4. Choose Time Frames: Decide how frequently you will measure each KPI—daily, weekly, monthly, or quarterly.
5. Assign Ownership: Each KPI should have an owner who is accountable for tracking progress and reporting results.
6. Review and Refine: Regularly review KPI performance and refine metrics or targets as needed.

2.3 KPI Examples

• Sales and Marketing KPIs
  • Monthly Recurring Revenue (MRR)
  • Conversion Rate (from leads to customers)
  • Customer Acquisition Cost (CAC)
  • Customer Lifetime Value (CLV)
• Customer Success and Support KPIs
  • Net Promoter Score (NPS)
  • Average Resolution Time (in customer support)
  • First-Contact Resolution Rate
  • Customer Retention Rate
• Operations and Engineering KPIs
  • System Uptime (e.g., 99.9% availability)
  • Average Deploy Frequency (e.g., continuous deployment rate)
  • Mean Time to Recovery (MTTR) after an incident
  • Defect Density (issues found per 1,000 lines of code)

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3. Comparing OKRs and KPIs

OKRs and KPIs serve different purposes, although sometimes they can overlap. Here are the main differences:

1. Focus vs. Monitoring
   • OKRs focus on growth, stretch goals, and transformation.
   • KPIs focus on monitoring and maintaining specific performance levels.
2. Ambition vs. Stability
   • OKRs encourage ambitious, sometimes aspirational goals designed to drive significant change.
   • KPIs often revolve around stable or incremental improvements to existing processes.
3. Time Frame
   • OKRs are often set for a specific, relatively short time frame (e.g., quarterly) with frequent check-ins and an emphasis on continuous improvement.
   • KPIs tend to be measured consistently over longer periods (monthly, quarterly, yearly) to track trends and ensure stability.
4. Flexibility
   • OKRs may be adjusted mid-cycle if circumstances change, reflecting an agile mindset.
   • KPIs are relatively stable because they measure the ongoing health of the business.
5. Transparency and Alignment
   • OKRs are generally shared publicly within the organization, promoting cross-functional alignment and collaboration.
   • KPIs may be shared at different levels (team-level, managerial-level, or executive-level), but are often seen as performance benchmarks tied to specific roles or departments.

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4. Choosing the Right Approach

Many organizations find value in combining both frameworks. OKRs drive innovation and stretch goals, while KPIs provide a strong foundation for keeping the business healthy. In simpler terms, consider KPIs as the vital signs of your ongoing operations, while OKRs offer a roadmap for future growth and innovation.

4.1 When to Use OKRs

• When you need to drive significant change or transformation in a short time frame.
• When you want to align teams around ambitious goals and encourage collaboration.
• When you prefer transparency and frequent iteration to keep everyone focused.
• When your main aim is to push beyond incremental improvements and try new strategies.

4.2 When to Use KPIs

• When you want to monitor the long-term health of your operations.
• When you require consistent benchmarks to guide everyday decisions and measure stable processes.
• When you need to quickly spot issues or deviations from expected performance.
• When your focus is on incremental improvements or maintaining certain standards.

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5. Implementation Guides

5.1 Implementation Guide for OKRs

1. Set the Stage: Communicate the purpose of OKRs throughout the organization. Provide training or workshops so everyone understands how to set and track OKRs.
2. Align with Company Goals: Start with the organization’s overarching mission or vision. Define a few top-level Objectives and Key Results for the entire company.
3. Cascade OKRs: Encourage teams and individuals to create their own OKRs that align with the company-level OKRs.
4. Transparency and Ownership: Store OKRs in a shared platform or document, so everyone can see them. Assign owners for each Key Result.
5. Regular Check-Ins: Hold weekly or bi-weekly meetings to review progress, discuss roadblocks, and celebrate wins. Adjust targets if necessary.
6. End-of-Cycle Review: At the end of the quarter (or chosen cycle), assess how well you performed against the Key Results. Document learnings and use these insights to set new OKRs.

Tips for Success:

• Keep the number of Objectives small (3–5 at most) to maintain focus.
• Make sure Key Results are quantifiable—this is the only way to objectively measure success.
• Don’t be afraid to set ambitious goals. Even if you only achieve 70–80% of the target, you may still outperform prior periods.

5.2 Implementation Guide for KPIs

1. Identify Core Functions: Determine the key aspects of your business that need consistent monitoring (e.g., sales, operations, marketing, customer support).
2. Select Relevant Metrics: Choose KPIs that truly reflect the health of each function. Involve stakeholders to gain insights and buy-in.
3. Set Targets and Thresholds: Define what success looks like. Is it 95% customer satisfaction? 10,000 new sign-ups per month? Clarify these targets.
4. Establish Reporting Cadence: Decide how often you will measure and review each KPI—daily, weekly, monthly, or quarterly.
5. Data Collection and Tools: Ensure you have the systems in place to track and measure your KPIs accurately. This could involve CRM systems, analytics software, or custom dashboards.
6. Regular Reporting: Build a culture of data-driven decision-making by sharing KPI reports during team meetings and management reviews.
7. Adjust and Improve: If a KPI isn’t providing useful insights or if it stops being relevant, refine or replace it.

Tips for Success:

• Limit the number of KPIs to avoid “analysis paralysis.” Focus on the metrics that truly matter.
• Make sure KPIs are used as insights, not just numbers. Discuss them openly to drive improvements.
• Celebrate achievements when teams meet or exceed their KPI targets.

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6. Real-World Examples

Example 1: A SaaS Startup

• OKR: Expand user base in a new market within the quarter.
  • Key Results:
    1. Acquire 1,000 new users in the European market.
    2. Implement local payment methods in 3 new countries.
    3. Raise brand awareness by collaborating with 2 industry influencers.
• KPI: Monitor monthly user churn rate and user engagement.
  • Target churn rate: < 5% per month.
  • Target DAU (Daily Active Users): 20% of total user base.

Here, the OKR drives a transformational goal (expanding into a new market), while the KPI ensures the ongoing health of the existing user base.

Example 2: An E-commerce Company

• OKR: Increase customer lifetime value (CLV) by improving the loyalty program.
  • Key Results:
    1. Increase average purchase frequency by 20%.
    2. Boost repeat purchase rate from 30% to 40%.
    3. Launch a new premium loyalty program tier with 5,000 sign-ups.
• KPI: Track Net Promoter Score (NPS) and conversion rate on the website.
  • Target NPS: 60 or higher.
  • Conversion rate goal: 5% during promotional campaigns.

Here, the OKR aims to transform how customers engage with the brand, and the KPIs keep track of the regular performance metrics—NPS and conversion rate.

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7. Conclusion

Both OKRs and KPIs are invaluable tools for measuring and improving organizational performance. While KPIs serve as the stable, ongoing indicators of success, OKRs drive you to aim higher and push boundaries. In many cases, companies use both to maintain a balanced approach: use KPIs to gauge overall health and consistency, and use OKRs to ignite ambition and focus on short-term breakthroughs.

When properly implemented, these frameworks foster clarity, alignment, and accountability across all levels of an organization. By understanding when and how to use each—choosing the right tool for the right job—you can keep your teams motivated, your operations running smoothly, and your business on a path to continuous growth and success.

Here’s my 2024 LinkedIn Rewind, by Coauthor:

Engineering is about building bridges – between systems, communities, and people. 2024 taught me that the strongest bridges are built not just with code, but with collaboration and trust.

Like a well-designed distributed system, impact scales through connection points. This year brought new opportunities to strengthen those connections:

Technical Architecture 🏗️
• Elevated to Distinguished Engineer at Morgan Stanley
• Led quantum computing initiatives through FINOS Zenith SIG
• Contributed to ComposeUI achieving FDC3 2.0 conformance
• Advanced XR adoption in financial services

Community Foundation 🌉
• Appointed FINOS Ambassador and Vice Chair of Technical Oversight Committee
• Continued Microsoft MVP journey in .NET Technologies
• Expanded DEI initiatives through FINOS
• Mentored next generation through KOMP program

Three posts that resonated deeply with our community:

“Starting a new position as Distinguished Engineer”
On how success is built through collective wisdom and support
https://lnkd.in/ei7ixDSY

“I’m happy to share that I’m starting a new role as Vice Chair”
On shaping the future of open source in finance
https://lnkd.in/eFaDZGFS

“Microsoft MVP Recognition”
On how community contribution leads to unexpected rewards
https://lnkd.in/eRN_aK6t

Looking toward 2025, we’re expanding these bridges:
• Advancing quantum computing applications in finance
• Growing open source adoption through FINOS initiatives
• Mentoring more developers through KOMP
• Completing my book on high-performance .NET

As we compile our achievements for 2024, let’s remember – the best code isn’t just efficient, it’s inclusive. Here’s to building more bridges in 2025. 🌉

After coauthor.studio, I tried Cleve.AI‘s similar service too. It made some interesting mistakes, let me know if you spot them 😀

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📊 My 2024 LinkedIn stats (from Cleve.ai):
– 542 posts
– 9,401 total reactions
– 1,434 comments

Here’s 10 lessons I learned in 2024:
🙏 Gratitude is essential; acknowledge everyone who supports your journey.
🌟 Mentorship can propel your career; cherish every mentor’s advice.
🤝 Collaboration and teamwork pave the way for innovation and success.
🔗 Building strong relationships in the tech community amplifies your impact.
📈 Active participation in community activities fosters growth and learning.
🔄 Paying it forward creates a cycle of support and encouragement.
🤲 Trust is the cornerstone of strong partnerships and community engagement.
🌈 Diversity and inclusion must be at the forefront of every initiative.
📚 Pursue your passions—writing a book can share your expertise with others.
🏆 Recognition, like the MVP award, reflects the collective effort of many.

My 3 favourite personal highlights in 2024:

1️⃣ Traveling to Davos and reflecting on personal growth for my birthday.
2️⃣ Awarded Microsoft MVP for contributions to .NET community!
3️⃣ Started as Distinguished Engineer at Morgan Stanley! 🎉

Looking forward to more amazing experiences ahead! 🌟

Get your free LinkedIn unwrapped from cleve.ai/unwrapped.

I wrote about 3D printing before, multiple times – and WAAM is taking this industry like a storm. Additive manufacturing has revolutionized how industries approach prototyping, production, and even large-scale fabrication. Among the growing arsenal of 3D printing techniques, Wire Arc Additive Manufacturing (WAAM) has emerged as a robust and transformative method, particularly for creating large metal components. By marrying the principles of 3D printing with the proven capabilities of arc welding, WAAM offers an innovative approach to producing durable, cost-effective, and highly customizable structures.

What is WAAM Welding?

WAAM welding leverages arc welding technology to deposit layers of molten metal onto a substrate, which cool and solidify to form a three-dimensional object. Unlike traditional 3D printing, which typically uses powder-based materials, WAAM uses metal wire as its feedstock. This makes it especially attractive for industries requiring strength, scalability, and the ability to use a wide range of metals, such as:

• Steel
• Titanium
• Aluminum
• Inconel (nickel-based superalloys)

The process is guided by CAD (Computer-Aided Design) models, ensuring precision while minimizing material waste. Robotic arms or CNC-controlled systems usually direct the welding process, making WAAM suitable for producing intricate geometries and complex shapes.

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Key Advantages of WAAM Welding

1. Material Efficiency
   WAAM’s reliance on wire feedstock significantly reduces waste compared to subtractive manufacturing processes. The ability to build components layer by layer ensures that only the required material is used.
2. Cost-Effectiveness
   Using affordable and widely available wire feedstock makes WAAM an economical alternative to powder-based metal 3D printing methods. Additionally, its scalability allows for the production of large parts without the need for expensive molds or tooling.
3. Scalability
   One of WAAM’s standout features is its ability to produce large-scale components, making it a game-changer for industries like aerospace, automotive, and maritime manufacturing.
4. Customization and Flexibility
   WAAM is well-suited for creating bespoke parts or repairing existing components. Its ability to work with a range of materials means manufacturers can optimize properties like strength, heat resistance, and corrosion resistance.

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Applications of WAAM Welding

1. Aerospace
WAAM is being used to fabricate lightweight, high-strength components, such as aircraft wings, landing gear, and engine parts. The precision and scalability of WAAM align perfectly with the aerospace industry’s demand for performance and durability.

2. Automotive
In the automotive sector, WAAM supports rapid prototyping and production of custom parts, particularly for high-performance vehicles and motorsport applications.

3. Maritime and Offshore
WAAM’s ability to produce large metal structures has found applications in shipbuilding and offshore oil platforms. Components like propellers and structural reinforcements can be fabricated or repaired efficiently.

4. Construction and Architecture
WAAM’s scalability opens possibilities for innovative architectural designs and structural elements. Intricate, artistic, and load-bearing components can be produced cost-effectively.

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Challenges and Innovations in WAAM Welding

Despite its advantages, WAAM is not without challenges. Controlling the microstructure and mechanical properties of the deposited material requires careful process monitoring. Surface finish quality also often necessitates post-processing to meet industry standards.

Emerging innovations, however, are addressing these challenges. Advanced sensors, machine learning, and real-time monitoring systems are being integrated into WAAM setups to improve precision, ensure consistency, and reduce human intervention. Hybrid approaches, combining WAAM with traditional machining or polishing techniques, are also becoming common to achieve superior surface finishes.

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The Future of WAAM Welding

WAAM is rapidly gaining traction as industries recognize its potential to lower costs, enhance sustainability, and enable innovative designs. As technology continues to advance, we can expect to see:

• Increased adoption in sustainable manufacturing due to reduced material waste.
• Broader use of WAAM in space exploration, such as fabricating parts for spacecraft or extraterrestrial structures.
• Enhanced compatibility with emerging alloys and composites for specialized applications.

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Conclusion

3D printing with WAAM welding represents a significant leap forward in manufacturing technology. By combining the strengths of arc welding with the flexibility of additive manufacturing, WAAM offers unparalleled opportunities for industries that demand large-scale, robust, and custom solutions. While challenges remain, the ongoing innovations and growing adoption of WAAM suggest a bright future for this transformative technology.

Industries poised to embrace WAAM will undoubtedly gain a competitive edge, not just in terms of cost-efficiency but also in delivering high-quality, innovative, and sustainable solutions. It’s not just about printing parts—it’s about printing the future.