With nearly 20 years at the forefront of learning technology, I help L&D professionals harness technology to improve performance and skills. My mission is to simplify complex tech, making it accessible and actionable. I work with leading global Fortune 500 companies, and share weekly insights with 5,000 readers in my Steal These Thoughts newsletter.
Yet, I find myself in rare company these days as so many seek to use AI to ‘do the work’ instead of collaborating to do ‘your best work’.
I think that’s gonna be a big problem and I have some thoughts on that.
Before GPT
The first time I used a generative AI tool was back in early 2023 when a colleague introduced me to a platform called GPT 2.5 from, at the time, a little known company called OpenAI.
ChatGPT didn’t exist yet.
This was it’s basic form before we experienced life through a prompt bar. The only people who were playing around at this point were nerds like me. After I got around the not so friendly interface, I saw the impact of this tech’s early potential.
At that time, I kept thinking this would be a great way to collaborate with technology to do better work.
What I couldn’t see at that point, or perhaps didn’t want to recognise, is humanities desire for instant gratification and the obsession to outsource/delegate every piece of work. The current AI marketing from all corners of the industry leans on this sense of ‘work is bad, so let AI do it for you’. I know that sounds like some weird slogan from a commercial in the 60’s.
The purpose of work
I get a great deal of value from my AI tool stack.
Perhaps I’m the weird one but my focus with AI is to help me to my best work, not outsource it.
The work, very much like learning, is where the hard stuff happens.
The ‘aha’ moments you would never have conceived without the focused effort, the seemingly unrelated events that craft a connective bridge of ideas which lead to something incredible.
My industry of workplace learning has/had a saying “Learning is the work and the work is learning” – its something like that.
It seems like too many of us have fallen out of love with doing the work.
Again, the problem isn’t AI, it’s us.
Our intentions have become skewed in the promise of an era where an artificial intelligence will do anything and everything for you. Yet, we rarely sit back to ask “Just because we can, does it mean we should?”, and even if we can, do we really want to?
Doing ‘the work’ is a big part of purpose for many.
Purpose, meaning and fulfilment is a dumpster on fire that is quickly rolling across society as we race to delegate, automate and outsource everything in the pursuit of “reclaiming time” or “Being efficient”.
We may not see it now, but its coming.
A bit of effort, struggle and focus is not bad for you, so don’t discount “Doing the work”.
“If you don’t make mistakes, you’re not working on hard enough problems and that’s a big mistake.” – Frank Wilczek
Before you go… 👋
If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.
You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.
L&D teams trying to and/or being responsible for total company AI adoption is a fools errand.
We both know this is stupid, yet I see too many instances of companies trying to “train” their way into successful AI adoption.
Of course, L&D teams alone aren’t going to make any organisation achieve meaningful AI adoption (however you measure that). Yet, we do have a part to play, and recognising where we can best support is critical.
So, lets explore Where L&D Can Support Meaningful AI Adoption.
It’s odd because the validation of “adoption” has many definitions, dependent on the context and environment. The common pitfall is to measure adoption as ‘use of AI tools’ alone.
As we know, with previous technology, usage alone doesn’t mean meaningful adoption.
Setting what adoption looks like in your organisation is not a task for the L&D team.
Yet, we have an opportunity to contribute to long-term and meaningful adoption of AI across workforces as part of a wider collaboration in a community.
Let’s talk about that…
It takes more than access
Let’s go beyond the veil of bullshit we see online.
Access to an AI tool alone means nothing, and putting on one hour lunch and learns to “make people learn AI” is a comical up-skilling strategy.
If you’re a long-time reader, you’ve heard me become a broken record when I talk about what it takes to nurture meaningful and long-term change. We have much to consider with context, culture and constraints in each environment. No two workplaces are the same, that’s why the cookie-cutter “adoption frameworks” make me laugh.
They’re a good point of inspiration, but you shouldn’t follow them like a strict set of instructions.
Saying that, what is it we need to consider beyond tools?
People, Systems and Tools
As you’ve probably guessed, launching new technology and tools alone rarely leads to meaningful adoption.
There’s a bigger ecosystem at play.
We have to consider:
1/ People
Where are people at today, and how do we meet them?
Everyone will have a different understanding, maturity and receptiveness to something new and unknown. In AI’s case, we have a mix of emotions from “will this take my job” to “I want it to do all this stuff I hate doing”.
The most difficult part of a change process is people, because we’re all so unpredictable.
2/ Systems
Quite simply, how we work today.
What are the tried, tested and trusted conscious and unconscious systems we have in place? This covers both how we execute tasks and how we think about executing those tasks (deep, I know).
We each follow different types of systems in our day to day.
Understanding what these are and how AI will impact those is key to this change.
3/ Tools
The part you’re most likely more familiar with.
Here, we should consider the tools in use today alongside new ones being deployed, and how to bridge the gap in both understanding and knowing when and where to deploy them.
Too many forget the ‘when and where’ part at their own peril.
For us to recognise where we can provide support and drive value, we must recognise what’s changing.
I think this framework from BCG can help recognise the moments where performance support is most needed with AI transformation.
They propose it for navigating AI transformation at scale, and through an L&D lens, I see this as a conversation point of what to map against when focusing on how best to support workforces.
It’s built on two key dimensions:
1️⃣ AI Maturity
It progresses from tool-based adoption by individuals to workflow transformation, to full, agent-led orchestration. Most organisations, and even teams within them, operate across multiple stages at once, not in a linear path.
2️⃣ Workforce Impact
This spans how tasks are executed, to what skills are needed, to how teams are structured, to how organisational culture must evolve to support new ways of working.
While this covers the wider transformation AI brings across businesses, it acts as a roadmap for L&D.
A roadmap is often what we need because it’s not uncommon for senior leaders to treat “training” (as they call it) as a boomerang that’s thrown at will when they decide people need to know stuff.
The framework above provides a view of where the friction/pain points/ problems exist in the cycle of change. That’s where we should focus.
Map it out
I mentioned before not to blindly follow frameworks, and that advice is the same here.
This view from BCG is a useful foundation for each of us to think about “where can we add value”, but it will look different for each environment.
So, I’d recommend you map out what your organisational journey looks like today.
Explore the 3 pillars of tasks, talent and teams across your business and how/where AI is starting to and might impact these. It’s here that you will uncover the friction and pain points where we can be of most service.
Some of that will be through tooling, no doubt. Yet, I feel pretty safe in saying you’ll be spending a good deal of your time navigating changes within people and systems.
Final thoughts
I’m going to leave it here for now, folks.
There’s much to say, of course, but only so much attention span I can ask you to give.
I’m thinking of expanding some of this thought into a long-form video. If that sounds like something you’d like to see, let me know.
In the meantime, some additional resources to explore on this include:
Yes, it is another report about AI, and about AI in L&D.
I know there have been lots over the years already, and I’m not here to try and replicate those because that would be boring for both of us.
I’m no KPMG, PWC, BCG or any other 3 word consultancy with an army of consultants and flowing cash. What I am armed with is nearly 20 years in learning tech as both a practitioner and consultant, a large audience of L&D professionals who share their stories with me and a curious mind that wants to craft that altogether.
My mission with this report is help our industry understand the habits, behaviours and choices that L&D teams are making with AI.
Specifically we’ll unpack:
The tried and trusted AI tools teams are using today
Why they choose to use these tools
How they’re using these tools to meaningfully drive value
Through all of this we’ll understand a little more about how AI is being used as a co-worker across global L&D teams.
TL;DR
A quick snapshot of everything you need to know:
85% of L&D professionals use AI daily
ChatGPT is the most popular tool among L&D teams in 2026
80% of AI tools used are paid or enterprise licenses, but practitioners fill the 20% with personal AI tools, aka the “shadow stack”
NotebookLM is part of many practitioners “Shadow AI Stack”
AI provides 5 big value drivers for most L&D teams
There are 3 levels of AI value in L&D: Efficiency, Quality and Strategy
Practitioners are ditching the content factory mentality in favour of thought partnering with AI
What L&D teams are actually doing with AI today
Ok, here’s what we’re going to do.
We have 3 main questions to explore:
Which AI tools do L&D pros use most often in their work?
Why do they find these tools useful vs others?
What value are these tools delivering for teams?
We’ll unpack each in more detail, of course, with some standout comments, use cases and surprising insights.
Then I’ll finish off our time together by exploring how all of this impacts our habits, behaviours and choices as we integrate AI as a co-worker.
1/ These are the AI tools L&D pros are using
So, who is actually using what behind both the walls of corporations and in their personal ecosystem? Perhaps few surprises here, but let’s see how we go.
For the sake of simplicity, I’ve broken the tools down into these 4 categories:
Large Language Models (LLMs)
Research & Knowledge Management
Content Creation
Specialised Tools & Integrations
👑 Who is the King/Queen of LLMs for L&D?
Ok, no surprises… it’s ChatGPT.
I mean, were you expecting anything else?
This OG tool from OpenAI took the top spot with both its enterprise and free plans for teams and practitioners. Alongside this, we had the usual competitors in Copilot from Microsoft, Gemini from Google and Claude from Anthropic.
Over 80% of responses specified using enterprise or paid versions of these tools, so read into that what you will.
🧐 Research & Knowledge Management
While LLMs are cool, they’re also a very jack of all trades or all in one solution, which isn’t bad but can sometimes mean they don’t perform as well for your niche use cases.
This came through a lot in the data.
When it comes to research, analysis and crafting a place to store all of that for evergreen access, two tools kept coming up.
They were NotebookLM and Perplexity.
Again, no surprise given they’re built specifically for these use cases, and as long-time readers of this newsletter will know, I must talk about NotebookLM every other week.
📝 Content Creation
While I loathe to focus on this use, I can’t deny it is still the number one use case for the industry in its current state.
I have nothing against that as we stand, because context is everything, and until the system is rebuilt, you can’t blame teams for trying to do more with less. That’s a much wider discussion to have outside of today.
So, outside of the LLMs we’ve covered, the tools that kept being mentioned on the content front were:
Synthesia
Elevenlabs
HeyGen
Canva
Seems like you’re all loving those AI avatars and voiceovers, and who can blame you.
They can be powerful in the right hands.
📼 Specialised Tools & Integrations
This is the area for tools that didn’t quite fit into one category or could span them all.
There was a real mix here, so I’m not going to list everything.
What I can share is on the image generation front, it seems a lot of you are loving Midjourney, and I can see a lot of use with bolted on AI-powered features in Articulate Rise, Adobe Creative Cloud and, of course, the mighty Microsoft 365.
Curious Insight: Shadow AI Stacks
While most respondents have access to a suite of AI tools at work, they’re not huge fans of them.
Many respondents reported poor performance due to instances of approved company AI tools not being on the level of the widely available paid models that many use personally. It might be no surprise that Microsoft Copilot took most of this hate.
It seems many have access to Microsofts flagship AI product, yet they’d rather not.
This has created a lot of friction, and I’m sure its not exclusive to L&D. What I found in the data is that teams will use their mandated company AI tools for very little, and instead, engage with external tools as they provide much better quality.
These external tools can be classed as “shadow stacks”, aka tools being used in secret to complete work.
Look, I’m not the police, so its not for me to tell you what to do.
Its just fascinating that some people are willing to take risks with company data with these tools in the pursuit of doing stuff faster. so, if you’re doing this, it seems you’re not alone.
2/ Why L&D Pros choose these AI tools
I’m sure you can imagine that the most obvious answer here will be: “Because these are my company-approved tools”, which is mostly spot on.
You know, I hope common sense screams don’t go leaking data to AI tools that aren’t approved by your company. Besides this main factor, we see a few more variables that affect both our purchase of tools and their use.
Ease of Use & Accessibility
Many of you use these tools because they are easy to use, accessible, and often (but not always) the only tools approved or available on work devices due to company IT/security restrictions.
Speed, Efficiency, and Time Savings
These chosen tools are highly valued for their ability to generate content, ideas, and complete tasks faster, leading to quicker work and significant time savings in summarising, analysing, and content creation.
Quality of Output and Niche Functionality
Many of you mentioned the preference for tools that provide high-quality, precise, and relevant outputs.
Specific tools were highlighted for their distinct strengths, such as Claude for high-quality writing, NotebookLM for deep content analysis and knowledge base creation, Midjourney for consistent image generation, and ElevenLabs for natural-sounding voiceovers.
Easy Integration
Integration with existing ecosystems (like MS365 or Google Workspace) and the ability to maintain context across conversations (e.g., ChatGPT’s continuous context or custom GPTs connected to company data/SharePoint) make them more effective and relevant to personal/organisational needs.
Yes, that’s a no brainer, but still good to see in writing.
Trusted and Reliable
Trust is a complicated word in the workplace AI game.
A lot of you chose tools because they are company-sanctioned, allowing for the safe use of confidential data, or because you trust the accuracy and reliability of the sources they pull from.
3/ The value these AI tools really deliver
This is the killer question, and in my eyes, the more important one than “How much money did this make us?”
Without value, we have very little, if nothing to show for all these investments. Safe to say this is the part of the data I spent most of my time scrolling through.
⏰ Reclaiming time
The most valued benefit mentioned is saving significant time by speeding up content creation (first drafts, outlines, storyboards, copy), administration, analysis, and summarising large volumes of content, freeing up time for higher-value activities.
That makes sense. After all, time is our most precious non-renewable resource; just don’t sacrifice quality for speed!
🤔 Developing ideas and structuring thoughts
AI tools serve many of you as a valuable ‘thought partner’, ‘sounding board’, and ‘sparring partner’ for brainstorming, generating new ideas, challenging assumptions, validating concepts, and looking at topics from different angles.
📈 Improving quality
The important one, if I may say so.
Many of you highly value your trusted tools for increasing the quality of work through better writing/copy, editing, adapting content for specific audiences, restructuring, and simplifying complex topics into understandable snippets.
🔎 Better Research & Analysis
Everyone goes on about being data savvy in L&D, but it ain’t easy.
AI excels here, and it seems many of you agreed. I had so many comments on the quality of support with research, data analysis, synthesising information, extracting key themes, and summarising content from multiple sources.
⚒️ Crafting New Skills
So many examples here, including creating websites, learning to code (HTML, Python, APIs), building Q&A bots, developing specialised agents, and being coached through difficult conversations with AI.
More on this, but you don’t need them right now. I’ll be sharing more as the weeks and months go on, so fret not.
“These tools save me significant time.
They help me quickly summarise content from multiple sources, recall and organise information, and search back through large transcripts, websites, and white papers. They also allow me to iterate on ideas and refine wording multiple times until the message is clear and impactful. Call note-taking and action-item extraction have been game changers, enabling me to capture details I’d never have been able to track manually.
Overall, the ability to pull together diverse perspectives, distill them, and adapt content for specific audiences has elevated the quality and effectiveness of my work vs. the level of effort and time spent.”
Survey Respondent
What this tells us about L&D teams habits, choices and behaviours to derive value from AI
Ok, so what can we learn from all this data?
This is where I see many research reports die.
They share lots of valuable data, yet provide no simplified insights on what we can take action on. Fret not, friend, I’m not going to leave you hanging.
To answer the obvious, what we do know is that most teams/pros are heavily LLM-based when it comes to AI tool usage, and the tool of choice is dominated by what’s approved in the workplace. That makes sense.
The bigger piece to talk about is what we can note from the way teams and individual pros get value from these tools.
What’s most revealing is that understanding the value proposition also provides a framework for adoption (stick with me). I see the value of AI for L&D teams as 3 levers. Some sit in one level, while others move freely across all 3 as new tools emerge. There is no one right way, and you might make a first point of entry into any of these.
From a high level, the value AI can bring to your work as of 2026 is with efficiency, quality and strategy.
Let’s unpack each of these.
Level 1: The Efficiency Engine
From an adoption standpoint, this is what I class as the gateway drug to AI for L&D teams.
Its the most common and immediate value driver for most.
It’s about speed, not necessarily quality (see next level). The tantalising prospect of saving time on the most mundane of tasks is so incredibly alluring that even the biggest AI haters will struggle not to turn their head.
This is where we sit in what I class as “The Efficiency Engine”.
Here we see the benefits of freeing up time and automating our routine tasks. Once people experience this, they often want to know what else these tools can do.
It’s both a value driver and the entry point for creating behaviour change.
And I should point out that this was not with content creation alone. Many of you mentioned the speed to summarise, analyse and find niche research.
Level 2: The Quality Amplifier
I’ve always believed that any tool is only useful in the hands of a competent user, and this is no different with AI.
AI helping you to make your best work even better is highly admirable.
I know so many are obsessed with delegating work to sit on some mythical beach somewhere. Those people aren’t going to do much in life. Instead, those who use AI to amplify what they do today will be the winners.
This is why I’ve become rather obsessed with tools like NotebookLM.
What’s clear is that quality counts when working with AI, and knowing which niche tools can provide that is going to be your strategic advantage.
Speaking of strategic…
Level 3: The Strategic Partner
This is where I’ve always seen the real value of AI since day 1.
I do bash on teams using AI solely for content creation a lot, yet that’s only because I know how powerful LLMs can be as strategic partners.
Even 3 years removed from the launch of ChatGPT, I still see many LLMs vastly underutilised by L&D teams in this way.
The ability to pull together diverse perspectives, distill them, and adapt content for specific audiences has elevated the quality and effectiveness of my work vs. the level of effort and time spent.
Respondent
I see strategic partnering as bringing human thought/intelligence together with AI to uncover insights, points of view and develop ideas to do our best work. This comes through clearly from the survey responses.
Many of you referenced looking beyond AI for content creation and enhancing your cognitive processes by unpacking collaborative and critical thinking tasks with AI.
Use cases that surfaced included:
Acting as a sounding board, especially for solo pros
Challenging assumptions and expanding current perspectives
Refining and sense-making of thoughts
Facilitating critical analysis of data and scenarios
So, when we talk about value and helping teams recognise this, and supporting adoption journeys, this is a useful framing to consider.
The Most Interesting Case Study: Building a New Workshop Booking Engine
We’ve covered a lot of ground with AI assisting many of you as a thought partner and automation machine, but not so much as a builder.
In the past 18 months, the market has been flooded with more AI-powered coding apps than any one person can keep up with. They come with mixed results, and as always, heavily rely on the expertise of the user.
Perhaps one of the most remarkable stories shared in the survey came from a manager who needed to build a new booking engine for their company but lacked expertise in the required coding language.
Using Cursor, an AI-powered coding tool, they were able to accomplish this task, which would have otherwise been impossible for them. This wasn’t the only mention of coding-based tools, either.
What this tells me is that more L&D pros are experimenting firsthand to uncover “How does this thing work?” They might not always achieve their goal, yet there is a lot we can each learn from these experiences.
“Cursor has aided me in coding in a language I didn’t know beforehand, in doing so developing a new booking engine for the company from scratch.”
Respondent
Final thoughts
That’s your highlights of the top insights on how high-performing L&D teams are crafting value with AI today.
Here’s a few more ways for you to get into this data:
A customised AI assistant trained on all the survey data to guide you through the insightsand ask all of your questions. I thought there’s no better way to unpack L&D teams’ behaviours with AI than by using AI (be gentle with it, as it’s still in a testing phase, so it will do odd things at times). It’s built on Google’s Gemini Pro LLM, so it has all those sexy thinking capabilities too.
I wrote 156,000 words across 52 editions to 5,000 people in my newsletter across 2025.
These thoughts might not be the most popular according to my stats, but they’re the ones I believe are the most meaningful and that I enjoyed writing.
Ready?
Here we go…
1/ The Anatomy of A Modern L&D Team
Now, I update this article every year.
I call it an article but its more like a playbook for the modern L&D leader. I’ve been publishing a new edition of this every year to help leaders craft a team, skills and tech stack to navigate today’s world.
In 2025, it had its biggest update.
And yes, AI had a lot to do with that, yet it goes beyond technology.
In the almost unstoppable AI takeover this year one thing became clear to me, the human element is more crucial than ever.
2/ Everything L&D Teams Need To Know About AI Agents
2025 was supposed to be the year of AI agents – but was it?
I’m not so sure.
This time last year, every tech and AI CEO preached that 2025 would be the year AI agents hit the big time. While I’m not convinced the hype delivered, I do believe these will become important parts of the work ecosystem in the years ahead.
Yet, something that grinds my gears is when a lot of social media gurus try to confuse and deceive the every day human on what exactly AI agents are.
So, that led to me creating this mini-guide for L&D to understand AI agents with out the BS, and explore how they can impact and amplify work as we know it.
This isn’t exclusively an L&D thing, yet I really wanted to say something about the state of what I see (and I’m sure you do) online.
The tipping point for me came when I saw one too many so called ‘L&D influencers’ continually spread misinformation through clickbaity headlines about research they didn’t actually read.
There’s a reason you should read beyond the headline.
And with the 2025 word of the year being claimed by “Rage bait”, I believe we need to look deeper into what we see, hear and read in online spaces.
If you want to discover why being a Skeptical hippo could improve your mind, ability to learn and your emotions, then this one is for you, dear reader.
4/ How AI Is Redefining the Way We Assess Learning
Ok, I’m big on the future of learning not focusing on recall with stupid end of course tests and quizzes, but shifting to human reasoning.
The catalyst for this? Yes, you guessed it…AI.
In this one, I propose that now is the time to ditch the memory games in place of true activities that nurture human intelligence through the use of modern tech solutions.
If you fancy shaking things up in 2026, the come join me in this one.
Somehow, it’s been a year since I hit publish on that one.
The message of that piece was to think deeply about the over-reliance we will easily slip into with AI, and how easy it will be to convince ourselves we’re learning how to do something, when in reality, AI is doing it for us.
A year later, I only see more activity, which has amplified both.
That’s not to say there are not those who are rejecting total delegation to AI and those finding the balance between artificial and human intelligence.
As society obsesses over what it gains from AI, perhaps we should be asking what we lose, too.
I love learning, and I’ve loved my L&D career. We add so much value in many situations and that’s what keeps me writing more words every week.
While AI is changing the world, I don’t see it replacing human learning and those who work in organisations working to amplify that. It will look different but it won’t die (fyi, human learning will never die).
There’s been a ton of talk about AI adoption the last two years
It’s odd because the validation of “adoption” has many definitions dependent on the context and environment. The common pitfall is to measure adoption as ‘use of AI tools’ alone.
As we know with previous technology, usage alone doesn’t mean meaningful adoption.
Setting what adoption looks like in your organisation is not a task for the L&D team.
Yet, we have an opportunity to contribute to long term and meaningful adoption of AI across workforces as part of a wide collaboration in a community.
Choosing your best work is always hard, and I’m sure if you asked me to pick again a week from now, I might have a different combination
But for now, this is it.
Hopefully, I can keep talking about these topics in more detail with you across the next year, both here and in the weekly Steal These Thoughts newsletter.
Before you go… 👋
If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.
You can join me every Tuesday morning for more tools, templates and insights for the modern L&D pro in my weekly newsletter.
Are you working on building your 100-million-dollar skills?
I’m not saying these skills will magically deposit a cool $100 million in your bank account tomorrow (but wouldn’t that be something?).
The term “100-million dollar skills” isn’t about actual dollar value (sorry).
It’s a metaphor borrowed from Alex Hormozi to describe the ultra-valuable skills that can elevate your career opportunities and overall wealth. It’s about focusing on the quality of skills, not just quantity.
Historically, everyone used to craft better opportunities with fancy job titles.
Thankfully, the world has changed.
Niche skills make you stand out, consider these your career currency.
They’re the skills that separate you from the pack. I’m talking about the way you communicate, your ability to think critically, make sound judgments, and how you work intelligently with AI.
Why focus on high-value skills?
I’d hope the answer to this is obvious but I’ll play ball anyway.
Like money, you can compound these skills to unlock better opportunities further down the road. We’re playing an infinite game in a finite space after all. Think of it as investing in a high-yield stock that keeps giving back yearly. Building skills in high demand ensures that you are a key player in your field with an advantage very few possess.
As Tim Ferriss said: “Don’t try to be one of, be the only”.
The 100 million dollar skills principle in action
Speaking of Tim Ferriss, he’s a good example to explore on this.
For those who don’t know, Tim is the popular author and I suppose ‘productivity/self-help consultant’ of the 4-hr books, and his podcast.
Tim’s career began in technology startups in Silicon Valley, a highly competitive environment where efficiency and rapid learning are crucial.
Although he found early success, Ferriss was overwhelmed by overwork and stress, pushing him to seek more efficient ways to manage his time and productivity.
Recognising the need for a change, Ferriss started focusing on what he terms “meta-learning”, a skill of learning how to learn efficiently and effectively.
He explored various techniques for time management, productivity, and personal optimisation, aiming to work smarter, not harder. This exploration led to the development of the “4-Hour” concept, which he first applied to his personal health and fitness routines.
I’ve always struggled to clearly define what Tim actually does.
His skill has always felt like ‘Tim Ferris’ because he’s the only one doing the many things he does in the way he does them. Meta-learning sounds much better, though.
How Tim unlocked unique opportunities with niche skills
Ferriss’s breakthrough came with the publication of “The 4-Hour Workweek”.
A book that encapsulated his principles of lifestyle design and productivity. The book, which details how to outsource life tasks, automate business processes, and design an ideal lifestyle, struck a chord with a global audience tired of the traditional 9-to-5 grind.
Including me, back in 2015.
The success of “The 4-Hour Workweek” transformed Tim from a stressed entrepreneur into a leading voice in life hacking (do people still use this word?) and personal productivity.
His ability to distill complex subjects into actionable advice proved to be a high-value skill, setting him apart from other self-help authors.
Especially at the time because many self-help authors acted like gurus, where as Tim adopted a professor approach of showing, not just telling.
Building on his success, Tim continued to expand his niche skills into other areas, including cooking (“The 4-Hour Chef”) and fitness (“The 4-Hour Body”). Each project leveraged his meta-learning skills, showing others how to master complex skills quickly and efficiently.
He also launched a popular podcast, “The Tim Ferriss Show”where he interviews world-class performers from diverse areas to share their experience.
This podcast has run for over a decade with millions of listeners.
Today, Tim Ferriss is recognised not just for his books and podcast but for his unique approach to learning and productivity.
Like I said, he’s kinda known for doing Tim Ferris stuff which no one else is even attempting.
His mastery of meta-learning, combined with his skill in communicating these concepts to a broad audience, has not only built his career but showcases the power of niche skills in creating a successful and influential career.
The 9-5 example
You might read the above example and think “That’s cool but I’m not going to be able to do that”.
I totally get that. Tim is in the 1% of that category.
So, what could this look like for us in the 9-5 game?
Let me tell you the story of my pal, Dave. He’s a great guy and works a 9-5 (probably a few more hours here and there) like most of us.
On the surface, you might not think Dave is killing it in the Career Game.
But in reality, he’s crafted a set of skills which has turned him into a in-demand consultant able to command an annual salary of up to $150k. How is he doing this you ask? AI, quantum physics, world class heart surgeon??
No – his skills are niche in Excel and data visualisation.
Were you expecting something sexier? Most people do. Dave is not doing anything revolutionary. He discovered early in his career that people are terrified of excel.
They love the data output and beautiful visualisations, yet seeing rows induced a sense of doom.
Dave didn’t see doom here, he saw opportunity.
He told me “I saw an opportunity to scale something I could do well and tolerate what others couldn’t”. He quickly found his skills in-demand in his first organisation because no one else wanted to tame the beast of excel.
Dave became the Excel and data king 👑.
It turns out, that people will pay kings very well. You could do this too, as could I. Everyone has access to and uses Excel. We all produce data in many apps, yet most of us suck at it. Dave understood that and built his 100-million dollar skillset around that.
You can do this in any job and industry with the millions of apps we each use.
Let’s unpack the blueprint to do that together ↓
How to identify your High-Value Skills
Identifying which skills can catapult your career into that $100-million valuation starts with a good look at your current job and industry.
Ask yourself:
What skills are most admired and rewarded in my field?
Which abilities do top performers in my sector possess that I can develop?
How do my unique insights and capabilities stand out?
The idea is to zero in on skills that add significant value to your work and enhance your unique selling proposition.
Whether it’s exceptional project management, innovative problem-solving, or cutting-edge tech proficiency, these are the skills that can define a high-value career.
4 ways to compound these High-Value Skills
Focused Learning: Pick one skill at a time to develop. Trying to master multiple skills simultaneously often leads to mediocrity. If critical thinking is your target, dedicate time to courses, books, and activities that enhance that skill specifically.
Practical Application: Apply what you learn in real-world scenarios. If you’re improving your tech skills, work on projects that allow you to use new tools. Real-world application cements learning far more effectively than theory alone.
Feedback and Iteration: Seek feedback from peers and mentors. Understand how your skills are perceived and where you can improve further.
Network and Collaborate: Engage with others who excel in areas you aspire to master. Networking isn’t about swapping business cards anymore. It’s about exchanging ideas and strategies that can help refine your own skills.
Final Thoughts
Building your 100-million dollar skills isn’t about adding more to your plate.
Be brutally specific on the 3 – 5 skills that can make the difference in your industry or even cross-industry. Start today, focus deeply, and create your own opportunities.
Oh, and be like Dave.
Before you go… 👋
If you like my writing and think “Hey, I’d like to hear more of what this guy has to say” then you’re in luck.
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