Frequency, Duration, and Intensity: From Sprinting to Endurance in Sports and Work

Posted on September 2, 2025 by admin

When we first dive into any athletic pursuit (be it running, cycling, or even a new fitness routine), we often start by playing around with three core variables:

  • Frequency: how often we do it
  • Duration: how long we do it each session
  • Intensity: how hard we push ourselves

Early on, especially when we’re younger, we might focus heavily on intensity: short, explosive bursts that test our limits and give us quick feedback. Think about revision cramming as text before exams.

But as we mature (both in sports and in our professional lives), there’s a shift. We start valuing frequency and duration more. We unlock endurance. Instead of going all out in a single sprint, we learn the importance of pacing ourselves. We focus on doing something consistently, for longer periods, and building  that endurance. This approach doesn’t just lead to safer, more sustainable progress in sports; it also mirrors how we might evolve in our careers.

In the workplace, the young professional might tackle projects with high intensity: pulling long hours and sprinting through tasks. But with experience, we understand that frequency (showing up reliably) and duration (putting in steady, sustained effort) matter more for long-term success. Just like a marathon, maturity in work and life is about endurance rather than raw speed.

Data. Operations. Talent. Software, or DOTS.  Their digital transformations within organizations and across enterprise require endurance programs over explosive projects.  Favoring horizontal change over vertical inertia. 

So, whether you’re lacing up your running shoes or stepping into a new role, think about shifting from that initial intense sprint to a more measured, frequent, and enduring pace. It’s not just about lasting longer; it’s about performing smarter in the long run. Slow and steady wins the race!

And yes, we’re all getting older…

Posted in transformation, change, work | Leave a comment

From GenAI to Agentic AI: Reconnecting the DOTS for Real ROI

Posted on August 31, 2025 by admin

One of the most pressing concerns with Generative AI (GenAI) today is the perceived lack of return on investment. Many organizations are struggling to translate AI hype into tangible value. But the problem isn’t with the technology, it’s with the readiness of the organizations attempting to adopt it.

Most enterprises simply aren’t AI ready. Their processes are fragmented, governance is inconsistent, and integration strategies are underdeveloped. Without these foundational elements in place, AI efforts stall, delivering limited business value.

To change this, we need to shift our focus from GenAI to Agentic AI.

Agentic AI: The Real Opportunity

GenAI was the warm-up. It showed us what’s possible with content creation and automation. But the main event is Agentic AI, autonomous, goal-oriented systems that can make decisions, take action, and collaborate effectively alongside human teams. Agentic AI isn’t just about generating content. It’s about augmenting human capability and driving business outcomes.

This evolution demands that we rethink how work gets done and redefine the role of the human workforce. We’re moving from humans using tools to humans working with agents.

Reconnecting the DOTS: A Holistic Approach

To realize the full potential of Agentic AI, organizations need to reconnect the DOTS: Data, Operations, Talent, and Software.

  • Data must be restructured to feed intelligent agents with quality, context-rich information.
  • Operations need to be reengineered to accommodate new workflows where human and agentic systems collaborate.
  • Talent must be reskilled to work with, manage, and supervise AI agents.
  • Software architecture must be rethought to enable interoperability, agility, and continuous learning.

This is not a quick fix. Reconnecting the DOTS is a long-term commitment. It’s hard, often thankless work that must span across lines of business, not just isolated digital functions. But it’s essential, both for immediate gains and sustainable transformation.

The ROI of AI Starts with Readiness

The business case for AI becomes clear when Agentic AI is in place and the DOTS are aligned. Until then, expecting ROI from GenAI alone is like expecting enterprise transformation from a chatbot. GenAI may have lit the spark, but Agentic AI is what will power the engine.

Now is the time to move beyond experiments and proofs of concept. It’s time to get serious about AI readiness. That means making the hard changes, starting with reconnecting the DOTS.

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MCP Servers: Giving LLMs Hands to Act

Posted on July 3, 2025 by admin

Dinner Talk & The Talent Crunch  

My wife and I had dinner last night with a Seattle‑based engineering couple.
The husband was laid off in January; the wife voluntarily left her role soon after.
Both landed new jobs—but only because they could prove they’d create value on Day One. The market is flooded with strong résumés; what companies crave is demonstrable expertise.

Fast‑forward to an Anthropic conference in San Francisco. When a room of 275 engineers was asked, “Who here has built an MCP Server?” every hand went up. “A remote MCP Server?”—almost every hand stayed raised. Clearly, knowing MCP is fast becoming table‑stakes.

So… what is an MCP Server, and why does it matter?

Why LLMs Need Hands

The Lonely LLM

Large Language Models are wonderfully chatty—but all mouth, no action.

“Send an email.” —Sure, they’ll draft it, but they can’t actually press Send.
“Book me a doctor’s appointment.”—They’ll explain how, yet never pick up the phone.

The Handy LLM

Early fixes bolted one‑off “hands” onto the model: custom code to hit Gmail’s API, Instagram’s API, DoorDash’s API, and so on. Every new action meant wiring up another bespoke integration.

The result? A brittle monster that’s impossible to maintain.

Enter MCP: One Language, Endless Actions

MCP (Machine Control Protocol) normalises the chaos in two key ways:

  1. Shared language – All hands speak the same, JSON‑based dialect (think of it as everyone agreeing to conduct business in English).
  2. Provider‑owned hands – Each service vendor ships its own MCP Server, so you don’t own the integration burden.
MCP TermReal‑World Analogy
MCP ClientYour LLM‑powered app (the “brain”)
MCP ServerA detachable hand built by, say, Gmail or Notion
MCP ProtocolThe common language that brain ↔︎ hand both understand
The MagicGlove LLM

With that glove‑and‑hand abstraction, an LLM can now:

  • Send an email (Gmail MCP Server)
  • Post to Instagram (Meta MCP Server)
  • Order pizza (DoorDash MCP Server)

All by speaking plain English.

2025 Outlook: A Cambrian Explosion of Servers

Expect 2025 to feel like the early days of mobile app stores—but for actions instead of apps. Every SaaS vendor wants to be “MCP‑enabled” so the next generation of agentic products can wield its service with zero friction.

  • Acceleration loops – As more servers appear, building agentic apps gets simpler, which attracts more builders, which motivates more servers…
  • Commoditised integrations – Hand‑rolling APIs will quickly feel as dated as dial‑up modems.

Why Non‑Techies Should Care

If you can describe a task in English, you can now automate it:

“Email the team my weekly update, post the highlights to LinkedIn, and text me when it’s done.”

MCP shifts the bottleneck from coding skills to clear intent. Business builders—from product managers to HR partners—gain superpowers without touching a line of imperative code.

Key Takeaways

  1. Expertise beats experience. Hiring managers want people who already know today’s critical tools—MCP is rapidly joining that list.
  2. LLMs + MCP = action. Generative models become operative models when equipped with MCP “hands.”
  3. The next platform play. Just as HTTP unlocked the Web, MCP aims to unlock autonomous agents.

Ready to slip on the magic glove? 2025 is your year.

Posted in AI, automation, agentic | 1 Comment

The Agentic Slider: Reflections from Karpathy’s Keynote

Posted on June 19, 2025 by cleve

I really enjoyed Andrej Karpathy’s keynote at AI Startup School in San Francisco. As the former Director of AI at Tesla, Andrej is both smart and articulate. His talk got me thinking—again—about agentic modes of work, especially the idea of the agentic slider, which I’ll unpack here.

Agentic Modes of Work

We are shifting—slowly but surely—from the left column to the right. And contrary to popular opinion, I agree with Andrej: this isn’t the year of agents. It’s the decade of agents. It’s going to take longer than most people think, because people are involved.

The Agentic Slider

Let’s use Andrej’s Iron Man analogy. Right now, we’re just starting to suit up. We’re augmented—with a few nifty gadgets at our disposal—but still firmly in control. We’re the human boss.

As the suit gets smarter and more capable, the dynamic shifts. We move from being the driver to the passenger. The system becomes orchestrated. We’re still in the loop, but now we’re more contributor than controller.

Eventually, the Iron Man suit flies on its own—automated. That’s the far end of the agentic slider. But we’re not there yet. Today, we’ve got employees walking corporate corridors trying to right-size themselves into Iron Man suits.

This is the agentic slider in action: from augmented → orchestrated → automated.

But here’s the catch: this shift doesn’t happen in neat, sequential steps. It’s not a smooth handover of control. Why? Because humans still need to verify everything agents generate. And that makes us the bottleneck.

It’s a necessary bottleneck—for now. Which is why we need better GUIs. Interfaces that help us audit AI. Tools that keep agents on a tight leash. Otherwise, it’s garbage in… and even more garbage out.

Moving the agentic slider to the right requires maturity—earned through real-world experience. That maturity comes from:

  • Building GUIs that reduce human bottlenecks,
  • Shortening the distance between generated and verified,
  • And crafting agent experiences that are AI-ready and optimized for high-automation environments.

We’re not flying yet. But the suits are getting smarter. It’s time to design the systems—technical and human—that will get us off the ground.

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Reshaping the Digital Workforce

Posted on June 3, 2025 by cleve

AI is reshaping the digital workforce and our relationship with work. They are interconnected. We are witnessing and experiencing a significant upheaval in both the nature of work and the composition of the workforce. Not knowing what or how you’ll work a year from now injects fear, uncertainty, and doubt into the system.  However, consider these three dimensions that directly impact the reshaping of the digital workforce: the expertise gap, the demand for digital skills, and the unstoppable advancement of automation.

Widening Human Expertise Gap

As employees retire, the workforce loses valuable experience. As technology advances, it becomes harder for less experienced individuals to acquire essential skills. Routine tasks, which traditionally served as a training ground for new employees, are increasingly automated. Put another way, AI is replacing entry-level work. As a result, businesses are rethinking how they teach and train employees to keep their workforce relevant.

For example, marketing and advertising require a balance of seasoned strategic thinking and fresh creative ideas. However, a lack of knowledge transfer between experienced professionals and emerging talent impedes innovation and empowered execution. We must therefore devise smarter ways to educate new entrants and upskill existing employees.

Demand for Advanced Digital Skills

The demand for advanced digital skills in the tech sector is unprecedented. Big tech companies continue to integrate increasing levels of AI-generated software into their products.   

However, the need for digital skills extends beyond the tech sector. Across the entire workforce, 92% of jobs require digital skills, yet one-third of workers possess little or none. In 2023, IBM laid off 8,000 employees while rolling out its proprietary AI platform, AskHR, to digitise routine tasks. IBM then quietly rehired talent with strategic and critical thinking skills to support work requiring human judgment and empathy. Although IBM’s workforce size has returned to previous levels, its composition has changed, with demand surging for advanced digital skills. Its digital workforce has been reshaped.

Bridging the digital divide offers significant economic benefits for individuals (higher pay) and businesses (lower turnover when training is prioritised). Employees can remain relevant today by adopting digital tools and emerging AI technologies and becoming more AI-enabled.

Automation Continues to Reshape Jobs

As tasks are automated and jobs augmented, employees must develop new competencies. Automation isn’t eliminating jobs—it’s transforming them. Companies are redesigning job roles across several dimensions:

  • Separating job titles (what we think we do) from job roles (what we actually do)
  • Focusing on human strengths augmented by AI
  • Surfacing skills for effective human-AI interaction
  • Creating levels for and the interconnectedness between job roles
  • Introducing AI-only roles

Soft skills are the new hard skills. Emotional intelligence, teamwork, critical thinking, and complex problem-solving are becoming increasingly valuable as routine cognitive and manual tasks are automated. Not all jobs require digital skills. The need for ‘intrinsically human’ skills that machines cannot replicate will be considered a luxury service. Individuals will also specialise in these areas.

We may also see the rise of gig work as companies adopt alternative employment models to maintain a fluid workforce amid growing digital demands, widening expertise gaps, and sweeping automation-driven changes.

So what? Why should you care? Honestly, be vigilant. If you want to thrive in the future digital workforce, start by filling the expertise gap—build your digital skills.s and touching up your softer skills to remain human in the age of Automation and AI.  

Posted in digital, AI, automation, workforce | Leave a comment

Work Intelligence

Posted on May 27, 2025 by cleve

Work intelligence combines Automation and AI to deliver better business outcomes within a future-ready digital workforce. Work Intelligence is coming. Let me explain.

In the late nineties, as a newly minted computer science postgraduate, I built an application to help undergraduates design better software systems. It took three years to build.  Today, I could deliver a better version with agent assistance in three weeks. 

As a fintech architect in the early 2000s, I developed straight-through processing systems for the major investment banks, managing the life cycle of trades through the front, middle, and back office systems. We worked in high-performing teams across multiple disciplines. Today, I could achieve a similar throughput with a single squad supported by agents and get more done.

I joined Cognifide as their CTO in 2006. We built digital experience platforms for Fortune 500 companies to enable multiple brands, with multiple products, in multiple markets, to better engage with their consumers globally, at speed and at scale. Five hundred platform deployments later, I could do the same job with a fraction of the engineering horsepower. So if Cognifide started today, I would not have had the pleasure of meeting the hundreds of people who became proud Cogs. Instead, agents would build over 50% of the code base that powers these global brands today.  

A couple of years ago, the amount of code generated by machines surpassed that crafted by humans.  And now companies are using AI to write more and more of their code.  We are seeing the software engineering landscape being disrupted daily, and the other sectors – health, marketing, automotive, travel, and CPG – will be fast followers.  So it’s about the work, and work intelligence is coming for us all.

Work Intelligence

Agents are redefining work across all industries. Work includes coding, research, diagnosis, scheduling, production, analyzing medical scans, editing copy, and reflowing video. Work comes in many different shapes and sizes. As a result, there are many other combinations of ‘work modes’ with a keen emphasis on who directs (leads) versus who does (follows).  

Work Modes

These work modes range from manual to fully automated, with hybrid models in between. Today, we are embracing augmentation where humans lead. This is a big space, ranging from using it for yourself to effectively delegating to virtual workers as part of larger global teams. Orchestration looks at horizontal workflows led by agents for end-to-end services with a mixture of agent and human workers—a bigger space than the augmented.

Many questions still require answers.  What kinds of discrete work tasks can/should we entrust with agents? In what scenarios should agents delegate work items to humans? How much human oversight is required for fully orchestrated agent workflows? Can agents replace entire roles? What new human roles are needed?  How does an agent mentor operate in tomorrow’s digital workforce? 

Intersection of Work, Artificial and Automation Intelligence

So, I’ve updated my previous Venn diagram to include Work that keeps intelligence at the centre of everything. Work Intelligence is coming.

Posted in AI, automation, intelligent, work | Leave a comment

AI Enabled

Posted on May 14, 2025 by admin

If you haven’t read the note below from Micha Kaufman, CEO of Fiverr, to his team about AI coming for their jobs, you should. So do that, then come back here.

Done? Good. I agree with Micha. I’m a technology executive going through massive personal and professional change, and loving it. Personally, I’ve adapted to working with a team of machines that equip me with adjacent capabilities that were once inaccessible, too expensive, or unavailable. Professionally, they are a part of my everyday. So, what’s changed?

  1. I subscribe to OpenAI, Google, and Perplexity.
  2. I use Google Search less, and perplexity.ai more.
  3. I work in Google Docs for fast writing, cross-referencing to OpenAI via Canvas.
  4. I search in perplexity.ai, delegating to other models such as Claude, Gemini, and ChatGPT.
  5. I generate images through all the models.
  6. I draft plans, execute strategies, build hypotheses, analyze work, and summarize research.
  7. I develop code, launch applications, test systems, and validate thinking.
  8. I have shifted from information pulled from outside to insights pushed to me inside.
  9. I create a lot, but curate even more.
  10. I am always learning how to do things smarter.

In short, assume you can always get the answer. The challenge I’m finding is how do you ask better questions to shorten the distance between request and response. The ten things above help me ask the right question, within the right context, for the right outcome.

–

Fivrr @ Micha Kaufman 

Hey team,

I’ve always believed in radical candor and despise those who sugar-coat reality to avoid stating the unpleasant truth. The very basis for radical candor is care. You care enough about your friends and colleagues to tell them the truth because you want them to be able to understand it, grow, and succeed.

So here is the unpleasant truth: AI is coming for your jobs. Heck, it’s coming for my job too. This is a wake-up call.

It does not matter if you are a programmer, designer, product manager, data scientist, lawyer, customer support rep, salesperson, or a finance person – AI is coming for you.

You must understand that what was once considered ‘easy tasks’ will no longer exist; what was considered ‘hard tasks’ will be the new easy, and what was considered ‘impossible tasks’ will be the new hard. If you do not become an exceptional talent at what you do, a master, you will face the need for a career change in a matter of months. I am not trying to scare you. I am not talking about your job at Fiverr. I am talking about your ability to stay in your profession in the industry.

Are we all doomed? Not all of us, but those who will not wake up and understand the new reality fast, are, unfortunately, doomed.

What can we do? First of all, take a moment and let this sink in. Drink a glass of water. Scream hard in front of the mirror if it helps you. Now relax. Panic hasn’t solved problems for anyone. Let’s talk about what would help you become an exceptional talent in your field:

  1. Study, research, and master the latest AI solutions in your field. Try multiple solutions and figure out what gives you super-powers. By superpowers, I mean the ability to generate more outcomes per unit of time with better quality per delivery. Programmers: code (Cursor…). Customer support: tickets (Intercom Fin, SentiSum…), Lawyers: contracts (Lexis+ AI, Legora…), etc.
  2. Find the most knowledgeable people on our team who can help you become more familiar with the latest and greatest in AI.
  3. Time is the most valuable asset we have—if you’re working like it’s 2024, you’re doing it wrong! You are expected and needed to do more, faster, and more efficiently now.
  4. Become a prompt engineer. Google is dead. LLM and GenAI are the new basics, and if you’re not using them as experts, your value will decrease before you know what hit you.
  5. Get involved in making the organization more efficient using AI tools and technologies. It does not make sense to hire more people before we learn how to do more with what we have.
  6. Understand the company strategy well and contribute to helping it achieve its goals. Don’t wait to be invited to a meeting where we ask each participant for ideas – there will be no such meeting. Instead, pitch your ideas proactively.
  7. Stop waiting for the world or your place of work to hand you opportunities to learn and grow—create those opportunities yourself. I vow to help anyone who wants to help themselves.

If you don’t like what I wrote; If you think I’m full of shit, or just an asshole who’s trying to scare you – be my guest and disregard this message. I love all of you and wish you nothing but good things, but I honestly don’t think that a promising professional future awaits you if you disregard reality.

If, on the other hand, you understand deep inside that I’m right and want all of us to be on the winning side of history, join me in a conversation about where we go from here as a company and as individual professionals. We have a magnificent company and a bright future ahead of us. We just need to wake up and understand that it won’t be pretty or easy. It will be hard and demanding, but damn well worth it.

This message is food for thought. I have asked Shelly to free up time on my calendar in the next few weeks so that those of you who wish to sit with me and discuss our future can do so. I look forward to seeing you.

Yours,

Micha

GO BACK

Posted in AI | 1 Comment

Technology Storytelling

Posted on July 22, 2024 by cleve

Pablo Picasso once said:

“The meaning of life is to find your gift. The purpose of life is to give it away.”

It can be difficult to give our gifts away in capitalist societies where we struggle to achieve economic security for our families. Regardless, we need to discover our gifts in order to decide how to provide for ourselves and others, whether that means selling our talents or giving them away.

When I was young, I thought my gift was long-distance running. This evolved into a passion for athletics, and then football. But then I discovered technology. As I learned more about myself, I found that I’m an extrovert in public settings and an introvert in private ones. In other words, I love to research independently and share my findings broadly in person. My ideal platform for sharing is on a stage or in a meeting. I dislike using other people’s material; I need to develop the story and tell it my way. My gift is storytelling—specifically, how technology evolves and impacts people and their environments. And those people could be employees, consumers, friends, family, or you!

Technology Storytelling

Technology storytelling

Technology storytelling is crucial. Our technological canvas is expanding, with more and more ways to color in between blurred lines. Storytelling gives technology purpose, establishes boundaries, and guides how we begin our journey with it. It clearly articulates where you are today, why things matter to you, where you could be, and your best next steps to get there.

I didn’t know it at the time, but my first career as a university lecturer revolved around storytelling. I taught computer science students about the application of technology within industry. My subsequent careers in the private sector allowed me to gain experience and expertise in doing just that. And guess what? 30 years later, technology storytelling remains essential—a gift that keeps on giving. There are so many ways to share it, even beyond the realm of technology. So I’m super excited about what’s to come.

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From Obligation to Opportunity

Posted on July 3, 2024 by cleve
Glass Half Full

We all know the oft-cited analogy of a glass half full versus half empty. In his book Atomic Habits, James Clear expands on this topic and explains how to put it into action.

When building a new habit, Clear discusses the subtle but important mindset shift of moving from obligation to opportunity. Here are a few everyday habits I’m building in 2024:

  • Do 100 push-ups before my morning coffee
  • Listen to a podcast
  • Help my wife with a chore
  • Read for 30 minutes
  • Write for 30 minutes
  • Practice Spanish for 5 minutes

But here’s the twist. Instead of feeling like I have to do these things, I tell myself I get to do them. I consider myself lucky to have the opportunity. And that’s the shift from obligation to opportunity—or seeing the glass as half full.

Oh, and by the way, I’m working up towards 100 push-ups…that’s a process.

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Knowledge and Allocation Economies

Posted on June 14, 2024 by cleve

We are using AI to enhance performance and improve decision-making.  The allocation economy suggests that knowledge workers will shift to managing AI tools that do more of the work rather than doing it themselves.  These AI tools are agents that have large language model (LLM) front ends to facilitate scaled interactions using plain language.  What does this mean for our knowledge and allocation economies?

knowledge and allocation

Many hands, light work

A recent study by Cornell researchers found that increasing the number of AI agents collaborating can significantly improve performance. Imagine a world where you’re compensated more for how well you can allocate intelligence, rather than having it.  People who can effectively allocate intelligent agents to improve outcomes will arguably perform better than those who can’t. 

In Knowledge lies power

Knowledge workers are rewarded for what they know. Thanks to internet-powered tools like Google, access to world knowledge is fully democratized.  Now AI is making sense of all this accessible knowledge to put accessible intelligence into the hands of the many.   

Think of AI as having your own personal research assistants to make sense of all the world’s accessible knowledge.  So if everyone has access to intelligence, we are all smarter. However, delivering real value is the result of good execution on a good idea.  

Allocation is key for good execution

The allocation economy is largely about execution.  How well can you assemble an execution pipeline with the intelligence and means to deliver on a good idea?  Imagine a world where you don’t need to find smart people.  Instead, you are manager of accessible intelligent resources, where successful outcomes rely upon your ability to right-size allocations. What kind of skills do you need to thrive in a world like this? 

Summary

An agentic workforce is a digital workforce infused with AI agents. Research highlights that organizations are getting smarter through sheer strength in numbers, and those numbers are through the addition of intelligence, or AI agents, to the digital workforce.  As compute power and abilities continue to scale, automating unlimited amounts of agents to complete tasks could lead to mind-blowing increases in capabilities. 

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