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Get your head in the game: The rise of the 10× employee

11 Thursday Dec 2025

Posted by Ulysses Maclaren in Management

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Tags

ai, artificial-intelligence, chatgpt, technology

This is a message for employees who want to be more effective, more useful, and harder to ignore.

There is a growing mismatch in most organisations. Individual capability has expanded rapidly, while job descriptions have stayed mostly the same. AI is the reason.

For a long time, we talked about the idea of the “10× developer.” Someone who could produce ten times the output of their peers. This was usually attributed to rare talent, deep experience, or some hard-to-define brilliance. Something you either had or didn’t.

What’s changed is not the people. It’s the tools.

AI has significantly lowered the cost of thinking, drafting, exploring, and prototyping. Ordinary, capable people can now do things that previously required specialists, or at least permission. As a result, “10×” is no longer a property of a role. It’s a property of leverage.

Your role is no longer the boundary

AI allows you to contribute outside your formal job description. That’s the shift.

You don’t need a promotion. You don’t need approval. You don’t need to become an expert.

In practice, this already looks like sales people drafting marketing copy, HR staff sketching simple financial models, designers reasoning about technical constraints, and developers producing rough UX concepts themselves.

None of these people suddenly mastered a new discipline. They just got far enough to be useful.

The people who benefit most are not the most technical. They are the ones who default to a simple question:

“How could AI help me think about this first?”

Thinking AI-first by default, not as an afterthought, is what creates a 10× employee.

If you want to outperform others, you don’t start by working harder. You start by assuming that for most problems, AI can help you explore options, draft a first pass, or challenge your thinking before you do anything else. Ten-times performance now comes from leverage, not effort.

10× is about range, not speed

Most AI discussions focus on speed: faster emails, quicker reports, shorter cycles. That misses the bigger change.

The real gain is range.

AI collapses the cost of trying things outside your lane. You can explore sales, marketing, product, operations, and engineering at a basic level. When you do that, you start to see connections that siloed roles often miss.

That’s where disproportionate value comes from.

The mess is expected

AI-driven work often looks messy. Half-finished scripts. Rough prototypes. Tools that work just well enough. Experiments that never ship.

This is not inefficiency. It’s learning.

The cost of experimentation has dropped. The cost of waiting has increased. Even technical debt means less than it used to, because tools improve fast and cleanup is cheaper than it once was.

Not experimenting is now the riskier choice.

This isn’t about replacing you, it’s about expanding you

AI is not here to replace you. It’s here to expand the space you can operate in.

It fills in gaps just enough for you to step into adjacent problems. You don’t need to be a marketer to add marketing value. You don’t need to be an accountant to notice financial patterns.

Humans are still doing the important part: connecting ideas, applying judgement, and deciding what matters.

What to do next, if you want to be a 10× employee

You don’t need a strategy document or permission from leadership.

You can start now:

  1. Default to AI-first thinking for most problems.
  2. Use AI to explore adjacent areas, not just your core role.
  3. Bring half-formed but thoughtful ideas, not polished perfection.
  4. Treat experimentation as part of your job, not a distraction from it.
  5. Measure yourself by the value you create, not the box you sit in.

The people who will stand out over the next few years will not be the ones who stayed neatly inside their lane.

They will be the ones who expanded it.

Think AI-first.
Act broader than your job description.

Cognitive Offloading with AI: Clear Your Mind, Boost Your Productivity

07 Wednesday May 2025

Posted by Ulysses Maclaren in Management

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Tags

ai, artificial-intelligence, mental-health, philosophy, technology

Ever find yourself mentally juggling too many tasks? Meetings, deadlines, follow-ups – the list seems endless. Cognitive overload happens when you push your brain to handle more information than it comfortably can, leading to reduced effectiveness and unnecessary stress. Fortunately, there’s an elegant solution: Cognitive offloading with AI.

What is Cognitive Offloading?

Cognitive offloading is simply delegating mental tasks to external tools to free your mind from excessive cognitive load. Traditionally, we’ve done this with simple tools – calendars, to-do lists, and reminder apps. Now, powerful AI tools offer next-level cognitive offloading, managing complex mental tasks far beyond basic scheduling.

Why Cognitive Offloading Matters

  1. Improved Focus
    By handing off mundane or repetitive tasks to AI, your mind can zero in on higher-value work – like strategy or creative problem-solving.
  2. Reduced Mental Fatigue
    Decision fatigue accumulates throughout your day. Delegating small decisions (“What’s the best phrasing for this email?”) conserves mental energy for big-picture thinking.
  3. Better Quality Outputs
    AI handles routine work reliably. An AI assistant won’t overlook small but critical details, making sure your outputs are consistently high-quality.

How to Use AI for Cognitive Offloading

Emails and Communication

  • Drafting Emails: Tools like ChatGPT can rapidly produce clear, effective drafts. Adjust the AI-generated content as needed, but you’ll start from a strong baseline.
  • Summarising Conversations: AI meeting assistants summarise lengthy conversations, capturing essential action items without taxing your memory.

Task Management

  • Prioritisation: AI-powered task managers automatically highlight what’s urgent and what’s important, freeing your mind from constantly reassessing priorities.
  • Reminder Automation: AI reminders prompt you proactively, reducing anxiety about forgetting tasks.

Decision Support

  • Information Synthesis: AI quickly analyses large data sets, delivering insights you can use to make informed decisions without drowning in details.
  • Creative Idea Generation: AI tools like Claude or ChatGPT can suggest multiple options quickly, kickstarting your creative thinking without heavy initial cognitive effort.

Practical Steps to Start Cognitive Offloading

  1. Identify Repetitive Tasks: Audit your weekly activities—flag tasks that don’t require your unique perspective or creativity.
  2. Choose AI Tools Wisely: Not every AI tool fits every situation. Select tools that integrate smoothly into your workflow.
  3. Test and Refine: Experiment with AI assistance in small tasks initially. Gradually increase reliance as trust grows.

The Bottom Line

Embracing cognitive offloading with AI doesn’t mean relinquishing control. Instead, it’s about intelligently leveraging technology to clear your cognitive runway. With AI taking care of routine tasks, your mind is free for strategic thinking and innovation.

Clear your mind, boost your productivity, and let AI handle the cognitive clutter.

The Evolution of Software Development from Command Lines to AI

05 Tuesday Dec 2023

Posted by Ulysses Maclaren in Management

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Tags

ai, chatgpt, Leadership, network, neural, software-development, technology

As someone deeply entrenched in the world of technology and software development, I’ve witnessed firsthand the remarkable evolution of this industry. It’s been a journey from the early days of bulky mainframes and cryptic command lines to the sleek, intelligent systems of today – and there’s no sign of slowing down. This post delves into the rich history of software development, tracing the pivotal moments and groundbreaking innovations that have brought us to the current state of digital sophistication. We’ll explore the past, assess the present, and take a speculative glance into the future, where AI, AR and quantum computing are poised to redefine what’s possible.

Timeline of Industry Trends in Custom Software Development

1950s-1960s: Mainframe Era

Figure: mainframes took up whole floors of buildings
  • Introduction of mainframe computers.
  • Command-line interface and batch processing.
  • Dominance of languages like COBOL and FORTRAN.

1960s-1970s: Rise of Minicomputers

Figure: Early Minicomputers were still the size of filing cabinets
  • Minicomputers offer a smaller, more affordable option.
  • Continued use of command-line interfaces.
  • Expansion into businesses and academic institutions.

1980s: Birth of Personal Computers and Windows Development

Figure: Early Windows applications
  • Personal computers bring computing to a wider audience.
  • Introduction of Graphical User Interfaces (GUIs): GUIs revolutionize user interaction with computers, making them more user-friendly and accessible to non-technical users.
  • Launch of Microsoft Windows: Windows becomes a significant player in the software development world, popularizing the use of GUIs in personal computing.
  • Development of software applications for Windows accelerates, marking a significant shift in how software is designed and interacted with.

1990s: Internet and Web Development

Figure: Dell’s original website
  • The rise of the internet and web browsers.
  • Shift towards web development: from static to dynamic web applications.
  • Emergence of web-based languages and technologies (HTML, JavaScript, PHP).

2000s: Business Intelligence (BI)

  • As businesses started to generate and collect more data, there was a growing need for tools and methodologies to analyze this data for strategic decision-making.
  • BI tools began to evolve from basic data reporting functions to more sophisticated analytics, including data mining, online analytical processing (OLAP), and later, predictive analytics.
  • The focus shifted towards providing business users with insights for performance measurement, identifying trends, and making informed decisions based on data.
  • This era also saw the integration of BI solutions with other business systems, improving accessibility and usability for non-technical users.

2010s: SaaS, Cloud Computing and Big Data

Figure: SaaS offerings like SalesForce
  • Cloud computing (Azure, AWS, Google Cloud) becomes mainstream.
  • SaaS (Software as a Service) models gain popularity.
  • Focus on big data analytics and processing.
  • BI tools further evolved. They began to handle larger data sets, offering more advanced analytics capabilities, and providing cloud-based, scalable solutions.

Late 2010s: AI and Machine Learning

Figure: Google’s Tensor Flow showing a machine learning model being trained
  • Integration of AI and machine learning into applications.
  • Rise of data-driven decision-making and predictive analytics.
  • Advancements in natural language processing and automation.

2020s: DevOps, IoT, and Cybersecurity

  • Widespread adoption of Agile and DevOps methodologies.
  • Growth of the Internet of Things (IoT) and connected devices.
  • Increased focus on cybersecurity in software development.

2020s: Remote Work and Distributed Teams

  • The COVID-19 pandemic accelerates remote work trends.
  • Emphasis on tools and practices for remote software development.

Ok. That brings us to now. We’re building web and mobile applications with good DevOps, while prioritising remote access, and starting to incorporate more and more AI features… so let’s look at what’s coming next:

Future Trends (2020s and Beyond)

  • Continued advancement in AI and machine learning.

The realm of AI and machine learning isn’t just advancing; it’s evolving at a breakneck pace. What was once the domain of theoretical research is now driving the core of many modern applications. From predictive analytics that power business decisions to AI-driven personal assistants in our smartphones, the practical applications of these technologies are becoming increasingly sophisticated. In the coming years, we’re likely to see even more personalized and intelligent AI solutions, pushing the boundaries of automation, decision-making, and user experience. The integration of AI in various industries isn’t just an add-on anymore; it’s becoming a fundamental aspect of digital innovation.

  • Emergence of quantum computing and its potential impact.

Quantum computing, often regarded as the next frontier in computational power, is poised to redefine the limits of data processing. Unlike traditional computing, which relies on bits to process information, quantum computing uses quantum bits or qubits. This allows it to process complex datasets much more efficiently. The potential impact of quantum computing is enormous, particularly in fields requiring enormous computational power like cryptography, material science, and complex system modeling. While still in its early stages, the progress in this field could revolutionize how we approach problem-solving in sectors where current computing power hits its limits.

  • Growth in edge computing and serverless architectures.

Edge computing and serverless architectures are reshaping how we handle data and run applications. Edge computing brings computation and data storage closer to the location where it’s needed, improving response times and saving bandwidth. This is crucial in a world increasingly reliant on IoT devices and mobile computing. Meanwhile, serverless architectures allow developers to build and run applications without managing servers, significantly simplifying operations and reducing costs. This paradigm shift in computing not only enhances efficiency but also allows organizations to focus more on development and innovation rather than infrastructure management.

Projection: 2030s and Beyond

  • Anticipated integration of virtual reality (VR) and augmented reality (AR) in applications.

The integration of Virtual Reality (VR) and Augmented Reality (AR) into various applications is anticipated to transform our interaction with the digital world. VR, with its immersive environments, is poised to revolutionize industries like gaming, training, and virtual tours, offering experiences that are as close to reality as possible. AR, on the other hand, overlays digital information onto the real world, enhancing everyday activities with interactive and context-rich information. From AR-assisted surgeries to interactive educational experiences and improved retail shopping, these technologies are bridging the gap between the physical and digital realms, creating opportunities for innovation that were once the stuff of science fiction.

  • Possible advancements in brain-computer interfaces.

Brain-computer interfaces (BCIs) represent a frontier in the convergence of neuroscience and technology. The potential advancements in this field are groundbreaking, offering the promise of directly translating brain activity into computer commands. Imagine controlling devices, communicating, or even navigating digital spaces with just your thoughts. BCIs could revolutionize the way individuals with mobility or speech impairments interact with technology, offering newfound independence. Beyond assistive technologies, the implications in gaming, virtual reality, and even medical diagnostics are profound, potentially leading to a future where the line between thought and action, biology and technology, becomes seamlessly integrated.

  • Evolution of AI towards more autonomous and intelligent systems.

The evolution of AI is trending towards the creation of more autonomous and intelligent systems, capable of complex decision-making with minimal human intervention. This future wave of AI will likely see systems that not only learn and adapt but also understand context and exhibit a form of ‘intuitive reasoning.’ The goal is to develop AI that can tackle nuanced and sophisticated tasks, from advanced medical diagnostics to real-time, complex problem-solving in unpredictable environments. As these systems become more capable, the emphasis will also shift towards ensuring they operate within ethical and responsible frameworks, balancing autonomy with accountability. This evolution represents not just a technological leap, but a paradigm shift in how we conceive of and interact with intelligent systems.

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